CN112305641B - Expressway traffic safety meteorological internet of things monitoring and early warning system - Google Patents
Expressway traffic safety meteorological internet of things monitoring and early warning system Download PDFInfo
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Abstract
The invention discloses a monitoring and early warning system for a highway traffic safety meteorological internet of things, which comprises a front-end distributed integrated multi-element meteorological monitoring node and a rear-end cloud platform, wherein the front-end distributed integrated multi-element meteorological monitoring node and the rear-end cloud platform establish network communication; the front-end distributed integrated multi-element meteorological monitoring node is in communication connection with a sensing module, a network transmission module, a video monitoring module and a power supply module; the sensing module collects original meteorological data and uploads the data to the network transmission module, and the network transmission module carries out data preprocessing and then sends the data to the rear-end cloud platform. The back-end cloud platform comprises a monitoring data receiving/processing module, a database, a monitoring system configuration and management module, a data visualization module, an early warning and forecasting module, a multi-user configuration and authority management module, a GIS module and a video monitoring module; the back-end cloud platform realizes interactive management of the monitoring and early warning system and users through network communication, can realize timely decision making and timely response of multiple departments and multiple parties, and reduces traffic accidents caused by meteorological problems of highways.
Description
Technical Field
The invention relates to the technical field of monitoring and early warning systems, in particular to a monitoring and early warning system for a highway traffic safety meteorological internet of things.
Background
The highway traffic safety problem under the influence of severe weather is increasingly prominent while the construction of the highway in China is rapidly developed. Reports of accidents in transportation safety production (2019) indicate that: the influence of extreme weather on traffic safety cannot be ignored. The highway traffic safety is closely related to meteorological conditions, wherein strong wind, heavy fog and rainfall are main meteorological factors influencing highway driving safety, and a stable and reliable highway traffic meteorological information real-time monitoring and early warning system is necessary to be established in order to reduce adverse effects of meteorological disasters on highway traffic to the maximum extent, reduce the natural disaster damage degree of a highway and achieve the purpose of achieving the purpose. From the actual use condition of the current highway meteorological monitoring and early warning system in China, the following problems mainly exist at present: 1) The qualitative monitoring is more, and the quantitative detection is insufficient. 2) The method is limited by the traditional network architecture technology, and the density of the weather monitoring stations is too low. 3) The monitoring mode is single, the information is dispersed, and the system practicability is not strong. 4) Monitoring result analysis and information sharing capacity are insufficient, information issuing means is single, and decision transmission is delayed. 5) The current monitoring system lacks comprehensive analysis and comprehensive judgment of monitoring data of a plurality of meteorological elements. 6) Most of sensors used by the monitoring system have mechanical structures, so that the long-term operation reliability is low, and the defects of large measurement value error, large maintenance workload and the like caused by mechanical faults are easy to occur.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and the invention aims to form a set of highway meteorological monitoring and early warning system with advanced, stable, reliable, high efficiency and practicability by fully utilizing new technologies such as Internet of things, cloud computing and the like according to the requirement of highway meteorological monitoring and combining with the standard.
The purpose of the invention is realized by the following technical scheme: on one hand, the monitoring and early warning system comprises a front-end distributed integrated multi-element meteorological monitoring node and a rear-end cloud platform, wherein the front-end distributed integrated multi-element meteorological monitoring node and the rear-end cloud platform establish network communication; the front-end distributed integrated multi-element meteorological monitoring nodes are provided with a plurality of monitoring nodes according to the complex situation of a high-speed road section, the monitoring nodes are connected through a network, each communication front-end distributed integrated multi-element meteorological monitoring node is connected with a sensing module, a network transmission module, a video monitoring module and a power supply module, the sensing module is formed by integrating sensors and comprises a wind speed sensor, a wind direction sensor, a visibility sensor, a rainfall sensor, a temperature sensor, an air pressure sensor and a relative humidity sensor, the sensing module is respectively used for acquiring seven element raw data of wind speed, wind direction, visibility, rainfall, temperature, air pressure and relative humidity and uploading the seven element raw data to the network transmission module, the network transmission module carries out data preprocessing and then sends the seven element raw data to a rear-end cloud platform, the video monitoring module synchronously acquires field video and uploads the field video to the rear-end cloud platform, and the power supply module provides a stable power supply for the front-end distributed integrated multi-element meteorological monitoring nodes; the back-end cloud platform comprises a monitoring data receiving/processing module, a database, a monitoring system configuration and management module, a data visualization module, an early warning and forecasting module, a multi-user configuration and authority management module, a GIS module and a video monitoring module;
the monitoring data receiving/processing module is used for receiving information uploaded to the database by the front-end distributed integrated multi-element meteorological monitoring nodes, processing original data to form monitoring result values of visibility, wind speed, rainfall and crosswind pressure, and storing the calculated monitoring result values into the database; the database is used for receiving original data of the front-end distributed integrated multi-element meteorological monitoring node, storing and processing data in the monitoring data receiving/processing module, the monitoring system configuration and management module, the data visualization module, the early warning and forecasting module, the multi-user configuration and authority management module, the GIS module and the video monitoring module, and realizing data communication and processing; the monitoring system configuration and management module is used for interaction between a user and the system and storing various input configuration parameters in a database in a configuration file form; the data visualization module is used for visually presenting the processed monitoring data; the early warning forecasting module is used for comparing a monitoring result value in the database with a multi-stage early warning threshold value and distributing corresponding early warning information again; the multi-user configuration and authority management module is used for setting the operation authority and configuration of a plurality of users; the GIS module is used for loading a GIS layer three-dimensional model along a road to realize the fusion of the road GIS and the meteorological monitoring information; the video monitoring module is used for calling monitoring videos from the streaming media server to the terminal and is also used for controlling the field monitoring equipment through an API (application program interface).
Preferably, the sensing module is integrated by a sensor with a non-mechanical structure.
Preferably, the network transmission module comprises 4G/5G communication, loRa networking communication and/or wired network link transmission established along the highway.
Preferably, the power supply mode of the power supply module includes mains supply access power supply or solar charging and storage access power supply.
Preferably, the early warning forecasting module is in communication connection with a vehicle-mounted GPS navigation system, and the vehicle-mounted GPS navigation system receives early warning information sent by the early warning forecasting module.
Preferably, the monitoring system configuration and management module comprises a monitoring item parameter configuration unit, an early warning and forecasting configuration unit, a meteorological monitoring node acquisition mode configuration unit, a video monitoring unit and a three-dimensional model configuration unit.
On the other hand, the monitoring and early warning system for the highway traffic safety meteorological internet of things comprises two parts of front-end distributed integrated multi-element meteorological monitoring nodes and back-end cloud platform network communication, and the method comprises the following steps:
in the first step (S1), a front-end distributed integrated multi-element meteorological monitoring node collects seven elements of wind speed, wind direction, visibility, rainfall, temperature, air pressure and relative humidity and sends the seven elements of original data to a database of a rear-end cloud platform;
in the second step (S2), the monitoring data receiving/processing module reads data in the database, calculates visibility, wind speed, rainfall and crosswind pressure monitoring result values, compares the visibility, the wind speed, the rainfall and the crosswind pressure monitoring result values with respective preset thresholds to calculate single-element early warning grade data, then performs integrated calculation on the single-element early warning grade data to obtain multiple-element result values and performs comprehensive comparison evaluation on the multiple-element result values and the preset multi-element grading thresholds, and sends the data to the database after obtaining comprehensive early warning grades;
and in the third step (S3), the database sends the data to an early warning and forecasting module, the early warning and forecasting module judges whether the data exceeds an alarm threshold value, if the data exceeds the alarm threshold value, early warning is triggered, and the early warning and forecasting module pushes early warning information to an expressway comprehensive management department.
Preferably, the calculation method of the crosswind pressure is as follows:
1) Inputting: azimuth angle theta of highway 1 Mounting height (elevation) z of meteorological monitoring node sensor and latitude of highway meteorological monitoring station
2) Reading from the database: wind speed (V) and wind direction (theta) 2 ) Monitoring result values of temperature (t) and air pressure (P).
3) Calculating the corrected air density rho z ：
ρ 0 Atmospheric density (in kg/m) corrected for temperature and pressure based on the equation of state for ideal gas 3 )；
ρ z Atmospheric density (in kg/m) at meteorological monitoring node sensor mounting height (elevation) z 3 )；
P-air pressure monitoring value (unit: hPa)
T-temperature monitoring value converted to thermodynamic temperature, T = T +273.15 (unit: K),
z-Meteorological monitoring node sensor installation height (elevation) z, (unit: m)
z 0 -homogeneous atmospheric thickness, taking 8000m
4) Calculating crosswind component values
V 0 *sin(|θ 2 -θ 1 |)
V 0 -component values of wind speed perpendicular to the road direction, in units: m/s
V-wind speed monitoring result value, unit: m/s
θ 2 Wind direction azimuth monitoring result value, unit: degree (C)
θ 1 -highway strike azimuth, unit: degree (C)
5) Calculating cross wind pressure
W p The unit of horizontal wind pressure at the monitoring station is kN/square meter, the unit conversion is N/square meter, and the multiplication coefficient is 1000, and the above steps are carried out
The formula is simplified as follows:
preferably, the early warning method is a multi-element comprehensive early warning method, the early warning forecasting module is provided with I-V-level multi-level early warning threshold values, different early warning levels are respectively and correspondingly represented by green (safety), blue (attention), yellow (warning), orange (warning) and red (warning), wherein the wind speed early warning level W, the visibility early warning level L, the rainfall early warning level R and the crosswind pressure early warning level W p are all {0,1,2,3 and 4} in the value ranges, the comprehensive early warning level S is calculated, and S = {0,1,2,3 and 4}, and when the comprehensive early warning level is matched with the corresponding threshold value, the corresponding early warning level is triggered.
The invention has the following advantages:
(1) The distributed integrated multi-element meteorological monitoring node is formed by integrating sensors with non-mechanical structural formulas, has various network transmission modes and power supply modes, and can be independent of a wired network and mains supply access. And the power supply problem under the condition of no mains supply access is realized by utilizing communication modes such as 4G/5G communication, loRa networking communication or a wired network link established along a highway and the like and a solar-storage battery system. The technical difficulty of deployment and maintenance of the meteorological monitoring nodes along the highway is reduced.
(2) The invention introduces a crosswind concept into an early warning system, calculates crosswind pressure based on multiple monitoring parameters, and carries out early warning judgment according to the crosswind pressure. The invention fuses the original independent multi-element meteorological monitoring information with road trend (driving direction), geographic information (station longitude and latitude, elevation, road mile post number) and other elements to obtain the comprehensive early warning parameter, namely crosswind pressure (wind pressure component vertical to the side surface of the automobile), which has definite physical significance and can express directly. The early warning parameters have definite guiding significance in real application.
(3) According to the cross wind partial pressure calculation result and the index of the weather data statistic value of the expressway over the years, the wind pressure grading early warning threshold value is set, and early warning and forecasting of wind pressure can be realized more accurately.
(4) The invention carries out comprehensive early warning according to 4 elements of visibility, wind speed, rainfall and wind pressure, and compared with the prior art, the method for evaluating the early warning level by comprehensively evaluating multiple monitoring factors is more accurate and reliable in the mode of simply comparing monitoring data with a preset early warning threshold value to carry out early warning triggering, and is quicker and safer in early warning judgment.
(5) The cloud platform provides real-time monitoring and data analysis of the highway, visualization of three-dimensional information of key road sections for users, multi-party linkage of the early warning channel, timely decision making and timely response of multiple parties including a driver, a highway operation management department and a rescue department, and reduction of traffic accidents caused by meteorological problems of the highway.
Drawings
FIG. 1 is an overall architecture diagram of the present invention;
FIG. 2 is a schematic diagram of a cloud platform architecture according to the present invention;
FIG. 3 is a schematic diagram of a distributed integrated multi-element meteorological monitoring node structure according to the present invention;
FIG. 4 is a flow chart of data processing and early warning of the present invention;
FIG. 5 is a flow chart of cross wind pressure early warning level WP calculation according to the invention;
FIG. 6 is a flow chart of the calculation of the wind speed early warning level W according to the present invention;
FIG. 7 is a flow chart of visibility warning level L calculation according to the present invention;
FIG. 8 is a flowchart illustrating the calculation of the rainfall early warning level R according to the present invention;
FIG. 9 is a flow chart of the comprehensive early warning level determination of the present invention;
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
As shown in fig. 1 to 3, on one hand, the monitoring and early warning system includes two parts, namely a front-end distributed integrated multi-element meteorological monitoring node and a back-end cloud platform, wherein the front-end distributed integrated multi-element meteorological monitoring node establishes network communication with the back-end cloud platform; the front-end distributed integrated multi-element meteorological monitoring node is in communication connection with a sensing module, a network transmission module, a video monitoring module and a power supply module, wherein the sensing module is formed by integrating sensors and comprises a wind speed sensor, a wind direction sensor, a visibility sensor, a rainfall sensor, a temperature sensor, an air pressure sensor and a relative humidity sensor which are respectively used for acquiring original data of seven elements including wind speed, wind direction, visibility, rainfall, temperature, air pressure and relative humidity and uploading the original data to the network transmission module; the back-end cloud platform comprises a monitoring data receiving/processing module, a database, a monitoring system configuration and management module, a data visualization module, an early warning and forecasting module, a multi-user configuration and authority management module, a GIS module and a video monitoring module; the monitoring data receiving/processing module is used for receiving information uploaded to the database by the front-end distributed integrated multi-element meteorological monitoring nodes, processing original data into result values including visibility, wind speed, rainfall and crosswind pressure, and calculating and storing early warning information results of the monitoring data into the database; the database is used for receiving original data of the front-end distributed integrated multi-element meteorological monitoring node, storing and processing data in the monitoring data receiving/processing module, the monitoring system configuration and management module, the data visualization module, the early warning and forecasting module, the multi-user configuration and authority management module, the GIS module and the video monitoring module, and realizing data communication and processing; the monitoring system configuration and management module is used for interaction between a user and the system and storing various input configuration parameters in a database in a configuration file form; the data visualization module is used for visually presenting the processed monitoring data; the early warning forecasting module is used for comparing the monitoring information and the early warning information in the database with a multi-stage early warning threshold value and distributing the corresponding early warning information; the multi-user configuration and authority management module is used for the operation authority and configuration setting of a plurality of users; the GIS module is used for loading a GIS layer three-dimensional model along the highway to realize fusion of the GIS and meteorological monitoring information of the highway; the video monitoring module is used for calling monitoring videos from the streaming media server to the terminal and is also used for realizing the control of the field monitoring equipment through the API interface.
The front-end distributed integrated multi-element meteorological monitoring nodes are a combination of a plurality of meteorological monitoring nodes, network communication is established between every two meteorological monitoring nodes and between the meteorological monitoring nodes and a rear-end cloud platform, and a sensing module of each front-end distributed integrated multi-element meteorological monitoring node is formed by integrating sensors of a non-mechanical structural type, comprises a wind speed sensor, a wind direction sensor, a visibility sensor, a rainfall sensor, a temperature sensor, an air pressure sensor and a relative humidity sensor and is respectively used for collecting original data of seven elements of wind speed, wind direction, visibility, rainfall, temperature, air pressure and relative humidity; in addition, the front-end distributed integrated multi-element meteorological monitoring node also has various network transmission modes and power supply modes, can be independent of a wired network and mains supply access, and can realize the power supply problem under the condition of no mains supply access by utilizing communication modes such as 4G/5G communication, loRa networking communication or wired network links established along a highway and the like and a solar-storage battery system. The technical difficulty of deployment and maintenance of the meteorological monitoring nodes along the highway is reduced.
A highway traffic safety meteorological monitoring and early warning system cloud platform is established based on a B/S framework, and comprises a monitoring data receiving/processing module, a single-element monitoring result calculation module, a comprehensive factor (such as wind pressure) calculation module and a data analysis processing module, wherein the single-element monitoring result calculation module, the comprehensive factor (such as wind pressure) calculation module and the like are used for calculating, calculating results, storing the calculation results into a database, calling a preset early warning distinguishing method in the database according to the monitoring data calculation results to perform early warning grade division calculation, and storing early warning grade calculation results into the database.
The database comprises a monitoring data storage unit, a system configuration storage unit, an early warning record storage unit, a personnel authority control management and registration information storage unit and a three-dimensional model engineering file and configuration file storage unit, and is used for monitoring data, early warning records and system configuration storage so as to realize interaction between a user and a system.
The monitoring system configuration and management module comprises a monitoring item parameter configuration unit, an early warning and forecasting configuration unit, a meteorological monitoring node acquisition mode configuration unit, a video monitoring unit, a three-dimensional model configuration unit, a monitoring system and a monitoring node configuration unit, and realizes the functions of managing and configuring the distributed meteorological data acquisition station of the Internet of things deployed on site, such as configuring the acquisition mode, changing the data acquisition time interval and the like.
The data visualization module calls processed monitoring result data from the database, and displays the monitoring data in the form of curves, charts and the like by using time series curves (line graphs), multi-factor association curves (scatter diagrams), meteorological monitoring data distribution curves along the highway (line graphs with highway mileage as abscissa and monitoring results as ordinate), wind rose diagrams, wind pressure distribution diagrams and other professional curves. And calling the corresponding data field value in the database to fill in the specified position in the template according to a data report template prestored in the database, and generating a monitoring report. Through secondary analysis and calculation of monitoring results, mathematical statistics (average value, maximum value, minimum value and the like) of monitoring data in different time periods are displayed, or the mathematical statistics of the monitoring data of each road section in the same time period is displayed, and the processed monitoring information is visually displayed according to the requirements of industrial specifications.
The early warning forecasting module is provided with I-V level multi-level early warning threshold values, and green (safety), blue (attention), yellow (warning), orange (warning) and red (warning) are respectively and correspondingly used for representing different early warning levels. When the monitoring index exceeds a specific threshold value, triggering early warning, and after the monitoring index such as visibility, wind speed, rainfall and the like enters an early warning state, pushing a triggering early warning short message to a mobile phone of a highway related manager by the system, wherein the system is provided with an early warning data interface and can push early warning information to a comprehensive management system of a highway management department to issue in various forms (including highway LED screen pushing, highway driver mobile phone meteorological short message pushing, vehicle-mounted GPS, navigation map voice broadcasting, broadcasting station broadcasting early warning and the like); after the early warning is sent, the module can jump to a data visualization module under the operation of a user and display data information (curves and reports) in the early warning period. In addition, after the user knows the warning condition, the warning condition can be processed in the warning module according to a preset flow, and the method comprises the following steps: judging the authenticity of the alarm condition; false alarm, or true alarm; filling in a processing suggestion; closing the alarm condition; and transmitting the alarm processing flow to other user accounts and the like.
The multi-user configuration and authority management module comprises configurations such as registered user management, user role distribution, user authority distribution and the like, different users and corresponding operation authorities are created according to different departments and management ranges, and a management interface is effectively divided.
The GIS module can load GIS layer information along the highway, including information such as topographic map, highway alignment, element marking, meteorological monitoring site location. In the key road section, a three-dimensional model of the road section can be loaded, and the road GIS and the meteorological monitoring information (including but not limited to information in a national meteorological database and the data of the system) can be displayed in a map.
The video monitoring module can call the live real-time monitoring video or the historical video from the streaming media server to a web browser or a mobile phone end for playing; the control (rotation, focusing, night light illumination, windshield wiper and other operations) of the on-site monitoring equipment is realized through the API interface, and when meteorological early warning occurs, the monitoring camera on the road section can be called quickly to verify the on-site situation, so that the related departments can make decisions quickly.
On the other hand, as shown in fig. 4, an early warning method of the monitoring and early warning system for the highway traffic safety meteorological internet of things is further provided, and the early warning method is applied to the monitoring and early warning system for the highway traffic safety meteorological internet of things, wherein a front-end distributed integrated multi-element meteorological monitoring node collects seven elements of wind speed, wind direction, visibility, rainfall, temperature, air pressure and relative humidity and sends the seven elements of original data to a database of a rear-end cloud platform; the monitoring data receiving/processing module receives the database data, calculates the monitoring result values of wind speed, wind direction, visibility, rainfall, temperature, air pressure, relative humidity and crosswind pressure and sends the monitoring result values to the early warning and forecasting module; the early warning forecasting module receives the data of the monitoring data receiving/processing module, judges an early warning threshold value, and if the data exceeds the threshold value, the early warning forecasting module pushes early warning information to an expressway comprehensive management department.
The main influence of the highway wind disaster is that the wind (strong cross wind) acting on the side surface of the automobile causes vehicle deviation and transverse instability, the parameters are related to wind speed and wind direction, and are also related to driving direction (road trend), and the like, and the wind speed and the wind direction are independently judged, so that the influence of the lateral stress of the automobile under the combination of different wind speeds, wind directions and road trends is difficult to be intuitively understood (for example, the wind direction is parallel to the driving direction, thrust cannot be generated on the side surface of the automobile even if the wind speed grade is higher, and when the wind direction is vertical to the driving direction, larger lateral thrust can be easily formed on the side surface of the automobile even if the wind speed grade is not high). When the wind direction is perpendicular to the direction of the expressway (when the wind direction and the direction of the expressway are 90 degrees or 270 degrees), the crosswind effect is most obvious, in order to solve the hidden danger of strong crosswind, particularly crosswind, on the driving safety of high-speed kilometers, as a more preferable scheme, a crosswind pressure calculation method is used, crosswind speed, wind direction, temperature, air pressure, road direction azimuth angle, weather monitoring station longitude and latitude, instrument installation elevation and other parameters are calculated and obtained through fusion of crosswind pressure calculation parameters, wind pressure intensity of the side face of a vehicle caused by wind force of different road sections along the expressway can be calculated, grading evaluation and early warning are carried out according to the wind pressure intensity, the method is more visual, accurate and practical, and has stronger pertinence on the early warning of different road sections. The calculation flow is shown in fig. 6, and is described in detail as follows:
1) Inputting: azimuth theta of highway 1 Mounting height (elevation) z of meteorological monitoring node sensor and latitude of highway meteorological monitoring station
2) Reading from the database: wind speed (V) and wind direction (theta) 2 ) Monitoring result values of temperature (t) and air pressure (P).
3) Calculating the corrected air density rho z ：
ρ 0 Atmospheric density (in kg/m) corrected for temperature and pressure based on the equation of state for ideal gas 3 )；
ρ z Atmospheric density (in kg/m) at meteorological monitoring node sensor mounting height (elevation) z 3 )；
P-air pressure monitoring value (unit: hPa)
T-temperature monitoring value converted to thermodynamic temperature, T = T +273.15 (unit: K),
z-Meteorological monitoring node sensor installation height (elevation) z, (unit: m)
z 0 -homogeneous atmospheric thickness, taking 8000m
4) Calculating crosswind component values
V 0 *sin(|θ 2 -θ 1 |)
V 0 -component values of wind speed perpendicular to the road direction, in units: m/s
V-wind speed monitoring result value, unit: m/s
θ 2 -wind direction azimuth monitoring result value, unit: degree (C)
θ 1 -highway heading azimuth, unit: degree (C)
5) Calculating the cross wind pressure
W p The horizontal wind pressure at a monitoring station is converted into N/square meter in kN/square meter unit and the coefficient is required to be multiplied by 1000, and the formula is simplified as follows:
as a more preferable option, the early warning is carried out on a single element, the condition that multiple meteorological disasters occur simultaneously cannot be reflected, and traffic accidents are easily caused due to low early warning level. According to the invention, comprehensive early warning is carried out according to 4 elements of wind speed, visibility, rainfall and wind pressure, firstly, according to the preset early warning levels of the wind speed, visibility, rainfall and crosswind pressure, the early warning levels (a wind speed early warning level W, visibility early warning level L, rainfall early warning level R and crosswind pressure early warning level W p) of the 4 elements are respectively calculated, in order to better understand the early warning setting of the invention, the 4 element levels are displayed by using tables 1-4, wherein the wind speed early warning level W is shown in the table 1, and the calculation process is shown in the figure 6; visibility early warning level L is shown in Table 2, and the calculation flow is shown in FIG. 7; the rainfall early warning level R is shown in table 3, and the calculation flow is shown in fig. 8; the crosswind wind pressure early warning level wp is shown in table 4, and the calculation flow is shown in fig. 5;
table 1: wind speed early warning grade meter
Table 2: visibility early warning grade table
| Grade | Division criteria | Influence on highway |
| Level | ||
| 1 | 200m＜L≤500m | Have slight influence on |
| Stage 2 | 100m＜L≤200m | Has a certain influence |
| Grade 3 | 50m＜L≤100m | Has a large influence on |
| Grade 4 | L≤50m | Has a serious influence on |
Table 3: rainfall early warning grade meter
Table 4: crosswind pressure early warning grade
As shown in fig. 9, the wind speed warning level W, the visibility warning level L, the rainfall warning level R, and the crosswind wind pressure warning level wp are read from the database, and the ranges of values thereof are all {0,1,2,3,4}, and then the comprehensive warning level S, S = {0,1,2,3,4} is calculated. And judging according to the multi-stage early warning threshold corresponding to the S, and triggering corresponding early warning when the monitoring index exceeds a specific threshold.
Wind speed early warning grade: w = {0,1,2,3,4}
Visibility early warning level: l = {0,1,2,3,4}
Rainfall early warning grade: r = {0,1,2,3,4}
Crosswind wind pressure early warning grade: w p ＝{0,1,2,3,4}
And (3) judging:
if Min (W, L, R, W) p ) =0 and Max (W, L, R, W) p ) Not less than 0, then S = Max (W, L, R, W) p )；
If Max (W, L, R, W) p ) =4, then S =4;
otherwise S = Max (W, L, R, W) p )+1
Outputting S: s =0, normal, green;
s =1, level I warning, blue
S =2, level ii warning, yellow
S =3, level iii warning, orange
S =4, class iv early warning, red
The foregoing is illustrative of the preferred embodiments of this invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the concept as disclosed herein, either as described above or as apparent to those skilled in the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (2)
1. An early warning method of a highway traffic safety meteorological internet of things monitoring and early warning system is characterized by comprising the following steps: the monitoring and early warning system for the internet of things for the traffic safety meteorology of the expressway is applied, and comprises a front-end distributed integrated multi-element meteorology monitoring node and a rear-end cloud platform, wherein the front-end distributed integrated multi-element meteorology monitoring node and the rear-end cloud platform establish network communication; the back-end cloud platform comprises a monitoring data receiving/processing module, a database, a monitoring system configuration and management module, a data visualization module, an early warning and forecasting module, a multi-user configuration and authority management module, a GIS module and a video monitoring module; the video monitoring module is used for calling monitoring videos from the streaming media server to the terminal and is also used for realizing the control of the field monitoring equipment through an API (application program interface); the early warning method of the highway traffic safety meteorological Internet of things monitoring and early warning system comprises the following steps:
in the first step S1, a front-end distributed integrated multi-element meteorological monitoring node collects seven elements of wind speed, wind direction, visibility, rainfall, temperature, air pressure and relative humidity and sends the seven elements of original data to a database of a rear-end cloud platform;
in a second step S2, the monitoring data receiving/processing module reads data in the database, calculates visibility, wind speed, rainfall and crosswind pressure monitoring result values, compares the visibility, the wind speed, the rainfall and the crosswind pressure monitoring result values with respective preset threshold values to calculate single-element early warning grade data, integrates and calculates the single-element early warning grade data to obtain multiple-element result values, comprehensively compares and evaluates the multiple-element result values with the preset multi-element grading threshold values to obtain comprehensive early warning grades, and sends the data to the database;
in a third step S3, the database sends the data to an early warning and forecasting module, the early warning and forecasting module judges whether an alarm threshold value is exceeded or not, if the alarm threshold value is exceeded, early warning is triggered, and the early warning and forecasting module pushes early warning information to an expressway comprehensive management department; wherein the calculation method of the crosswind pressure monitoring result value in the second step S2 is
1) Inputting: azimuth theta of highway 1 Mounting height z of meteorological monitoring node sensor and latitude of highway meteorological monitoring station
2) Reading from the database: wind speed V and wind direction theta 2 The temperature t and the air pressure P are monitored to obtain result values,
3) Calculating a corrected air density ρ z ：
ρ 0 Atmospheric density in kg/m corrected for temperature and pressure based on the equation of state for ideal gas 3 ；
ρ z Atmospheric density at meteorological monitoring node sensor mounting height z, in kg/m 3 ；
P-air pressure monitoring value, unit: hPa
T — temperature monitoring value is converted to thermodynamic temperature, T = T +273.15, unit: k;
z-meteorological monitoring node sensor mounting height z, unit: m;
z 0 -8000 m for a homogeneous atmospheric thickness;
4) Calculating crosswind component values
V 0 ＝V*sin(|θ 2 -θ 1 |)
V 0 -component values of wind speed perpendicular to the road direction, in units: m/s
V-wind speed monitoring result value, unit: m/s
θ 2 Wind direction azimuth monitoring result value, unit: degree (C)
θ 1 -highway heading azimuth, unit: degree (C)
5) Calculating the cross wind pressure
W p The cross wind pressure at the monitoring station is expressed in kN/square meter, and the formula is simplified as follows:
2. the early warning method of the highway traffic safety meteorological internet of things monitoring and early warning system according to claim 1, characterized by comprising the following steps: the early warning method is a multi-element comprehensive early warning method, the early warning forecasting module is provided with I-V level multi-level early warning threshold values, green represents safety, blue represents attention, yellow represents warning, orange represents warning, and red represents warning; the method comprises the steps of respectively and correspondingly representing different early warning levels, wherein the value ranges of a wind speed early warning level W, a visibility early warning level L, a rainfall early warning level R and a crosswind and wind pressure early warning level Wp are {0,1,2,3 and 4}, calculating a comprehensive early warning level S, and S = {0,1,2,3 and 4}, and triggering the corresponding early warning levels when the comprehensive early warning levels are matched with corresponding threshold values.
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| CN114397714A (en) * | 2021-12-16 | 2022-04-26 | 福建省气象科学研究所 | Vehicle-mounted road traffic meteorological disaster monitoring and early warning device |
| CN115223367B (en) * | 2022-07-28 | 2023-11-03 | 福建工程学院 | Road crosswind early warning system based on Internet of things |
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