Intelligent weather warning method based on light traffic weather station

Abstract

The application relates to the technical field of meteorological early warning, and particularly discloses an intelligent meteorological early warning method based on a portable traffic meteorological station, which comprises the following steps: collecting meteorological data and rainfall data in real time through the traffic meteorological station and storing the data into a database; predicting rainfall risk in future time periods according to the real-time collected meteorological data and satellite cloud picture information to obtain predicted rainfall data; obtaining risk waterlogging points in a region, obtaining corresponding regions of each risk waterlogging point as risk regions, and establishing a waterlogging risk model of each risk region according to historical rainfall data in the database; and early warning each waterlogging point through the real-time collected rainfall data, the predicted rainfall data and the waterlogging risk model. The application improves the accuracy of the early warning time point, facilitates the traffic department to control according to the judgment result, avoids the influence of road waterlogging problems on people's life and property safety, and reduces the influence on traffic.

Description

Technical Field

[0001] This invention relates to the field of meteorological early warning technology, specifically to an intelligent meteorological early warning method based on a portable transportation meteorological station. Background Technology

[0002] A portable transportation weather station is a device integrating multiple sensors to monitor meteorological parameters such as temperature, humidity, air pressure, wind speed, wind direction, and precipitation at transportation hubs. Traditional weather warning methods mainly rely on large weather station data, satellite cloud images, and radar echoes. While these technologies are effective, they suffer from latency issues, potentially issuing warnings only minutes before extreme weather events occur. This may not be timely enough for disaster preparedness. However, by setting up a portable transportation weather station, not only can the weather conditions at the current location be monitored in real time, but the monitored meteorological parameters can also be combined with real-time satellite cloud images and radar echoes to make a more accurate judgment of the weather conditions. This allows for timely warnings in the event of extreme weather. In particular, for the most frequent problem of water accumulation in urban traffic, timely judgment of rainfall amounts can enable timely warnings when severe water accumulation occurs on roads, reducing the damage caused by extreme weather.

[0003] Existing technologies for early warning of waterlogging risks mainly rely on the amount of rainfall and the locations where waterlogging frequently occurs in urban road networks to make a rough judgment. Since the drainage capacity of different areas is difficult to measure directly, the accuracy of waterlogging risk assessment is low. During extreme weather events, when rainfall is significantly higher, existing technologies can provide timely warnings. However, for non-extreme weather events, if the accuracy of the warning time cannot be guaranteed, it will have an adverse impact on traffic control of the road network. Therefore, how to accurately and timely assess the risk of road waterlogging is the fundamental problem that this invention aims to solve. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent weather warning method based on a portable transportation weather station, solving the following technical problems:

[0005] How to accurately and promptly assess the risk of road flooding.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] A smart weather warning method based on a portable transportation weather station, the method comprising:

[0008] Meteorological and rainfall data are collected in real time through traffic weather stations and stored in a database;

[0009] Based on real-time collected meteorological data and satellite cloud image information, predictions are made regarding the risk of rainfall in future periods to obtain predicted rainfall data;

[0010] Identify the risk points of water accumulation within the region, and designate the corresponding area of ​​each risk point as a risk area. Establish a water accumulation risk model for each risk area based on historical rainfall data in the database.

[0011] Early warnings are issued for each waterlogging point by using real-time collected rainfall data, predicted rainfall data, and waterlogging risk models.

[0012] By analyzing each waterlogging point and establishing a waterlogging risk model, different standards can be used to judge different waterlogging points, ensuring the accuracy of the judgment. This facilitates the traffic department to manage the situation based on the judgment results, avoiding the impact of road waterlogging on people's lives and property safety while reducing the impact on traffic.

[0013] Furthermore, the process of establishing a flood risk model includes:

[0014] Obtain rainfall data when waterlogging occurs in historical risk areas, fit the rainfall data into a rainfall curve, and obtain the rainfall value corresponding to the time point when waterlogging occurs in each waterlogging problem.

[0015] Calculate the surface permeability of the risk area each time a waterlogging problem occurs;

[0016] Determine the time required for full surface infiltration for each water accumulation problem based on the rainfall curve and surface permeability value.

[0017] Excluding historical waterlogging issues whose time to full ground infiltration occurred later than the time of water accumulation, the critical rainfall amount for the risk area is obtained based on the remaining historical waterlogging issues data.

[0018] Early warnings of waterlogging risk are issued based on the current ground permeability value, rainfall data, predicted rainfall data, and critical rainfall levels in risk areas.

[0019] By using the above technical solution, historical waterlogging data corresponding to the time when the ground infiltration was fully loaded was excluded, which was later than the time when water accumulation occurred. Therefore, the remaining data all correspond to water accumulation that occurred after the ground infiltration was fully loaded, thus avoiding the influence of rainwater infiltration capacity on the judgment of drainage capacity. Based on the remaining historical waterlogging data, the critical rainfall amount for risk areas can be obtained, thereby accurately obtaining the critical rainfall amount for evaluating different risk areas, accurately determining the time point when water accumulation occurred and the time points corresponding to different water accumulation amounts, which facilitates timely early warning of road conditions.

[0020] Furthermore, the process of calculating the surface permeability value includes:

[0021] Through formula Calculate the surface infiltration capacity value at the start time of rainfall corresponding to the waterlogging problem. ;

[0022] in, This represents the surface permeability at the point where the last rainfall ended, and T(t) is the temperature change curve. Here, H(t) is the reference temperature value, H(t) is the humidity change curve, and f is the soil moisture evaporation model function. This refers to the time when the last rainfall ended. This represents the starting time of rainfall corresponding to the current waterlogging problem.

[0023] By using the above technical solution and calculating the ground permeability value, the ground permeability at the current point in time can be accurately determined.

[0024] Furthermore, the calculation process for the time required for full ground infiltration includes:

[0025] By establishing equations Determine the time required for full-load ground infiltration ;

[0026] in, Let r(t) be the average soil infiltration rate, and r(t) be the rainfall curve. Indicates selection The smaller value in r(t).

[0027] The above technical solution can accurately determine the time required for the ground to fully infiltrate.

[0028] Furthermore, the process of obtaining the critical rainfall amount for the risk area based on the remaining historical waterlogging data includes:

[0029] Obtain the rainfall values ​​corresponding to the time points when the waterlogging occurred in the remaining historical data on waterlogging issues;

[0030] Calculate the variance of all rainfall values ​​and compare it with a preset error value:

[0031] When the variance is less than or equal to the preset error value, the critical rainfall amount for the risk area is the mean of all rainfall values.

[0032] When the variance is greater than the preset error value, the rainfall values ​​are eliminated one by one in descending order of the difference between the rainfall value and the mean of all rainfall values, until the variance is less than or equal to the preset error value.

[0033] The above technical solution can exclude data that affects the natural drainage capacity of the risk area, and use the average of the remaining data as the critical rainfall amount of the risk area to obtain an accurate critical rainfall amount for evaluating the natural drainage capacity of the risk area. By using the critical rainfall amount of the risk area, we can more accurately provide early warning of water accumulation in the risk area.

[0034] Furthermore, the process of issuing early warnings for each waterlogged area includes:

[0035] A rainfall variation curve is obtained by fitting real-time rainfall data and predicted rainfall data. ;

[0036] Establish a plane coordinate system with the rainfall start time as the origin, time as the x-axis, and rainfall as the y-axis;

[0037] Through formula Calculate the water accumulation G(t) in the risk area and issue a weather warning based on the magnitude of the water accumulation G(t);

[0038] in, To determine the function, when x < 0, When x≥0, r(t) is the rainfall variation curve. The percentage of water-permeable land in the risk area. Indicates selection and The smaller of the two values, Q, represents the critical rainfall amount for the risk area, and S represents the total area of ​​the risk area.

[0039] The above technical solution enables weather warnings to be issued based on the calculated amount of water accumulation, allowing for more accurate determination of the time of water accumulation and the timing of warnings. This facilitates traffic management based on the assessment results, preventing road flooding from impacting people's lives and property while minimizing traffic disruptions.

[0040] Furthermore, the process of issuing weather warnings based on the amount of water accumulation G(t) includes:

[0041] When G(t) > 0, it is determined that water accumulation has occurred at the water accumulation point, and a first-level warning is issued;

[0042] exist If the water level at a given location exceeds the warning line, a Level II warning will be issued.

[0043] Where A is the water accumulation threshold, This is the starting time point of the time interval G(t) > 0. , This is a preset fixed time period.

[0044] The above technical solutions can provide relatively accurate and timely warnings based on different water accumulation conditions, making it easier for traffic authorities to manage and control the situation based on their assessments.

[0045] Furthermore, the process of issuing early warnings for each water accumulation point also includes:

[0046] Calculate the time from the start of rainfall to Time difference ;

[0047] exist A level-two warning will be issued in a timely manner;

[0048] in, The threshold time difference.

[0049] The above technical solution calculates the rate of water accumulation based on the time difference and issues a level-two warning when the water accumulation is rapid. This allows for early traffic control at the waterlogged area, preventing road flooding from impacting people's lives and property while minimizing traffic disruptions.

[0050] The beneficial effects of this invention are:

[0051] (1) This invention analyzes each water accumulation point and establishes a water accumulation risk model, which can make different judgments on different water accumulation points according to different standards, ensuring the accuracy of the judgment, making it easier for the traffic department to manage according to the judgment results, avoiding the impact of road water accumulation on people's lives and property safety, while reducing the impact on traffic.

[0052] (2) By excluding the historical waterlogging problem data corresponding to the time when the ground infiltration is fully loaded and the waterlogging occurs later than the time when the waterlogging occurs, the present invention avoids the judgment of drainage capacity by the rainwater infiltration capacity factor. Based on the remaining historical waterlogging problem data, the critical rainfall amount of the risk area is obtained, and thus the critical rainfall amount used to evaluate different risk areas can be accurately obtained, and the time point when waterlogging occurs and the time point corresponding to different waterlogging amounts can be accurately determined, which facilitates timely early warning of road conditions. Attached Figure Description

[0053] The invention will now be further described with reference to the accompanying drawings.

[0054] Figure 1 This is a flowchart of the steps of the intelligent weather early warning method based on a portable transportation weather station in this invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] In one embodiment, a smart weather warning method based on a portable transportation weather station is provided. Please refer to [link to relevant documentation]. Figure 1As shown, the method includes: collecting meteorological data and rainfall data in real time through traffic meteorological stations and storing them in a database. The traffic meteorological stations can monitor the weather conditions at their location in real time, such as real-time rainfall, and also obtain meteorological data for the location, such as temperature, humidity, air pressure, wind speed, and wind direction. By combining meteorological data with satellite cloud image information, future weather conditions can be predicted more accurately. Therefore, in the analysis of waterlogging risk, predictions are made based on the real-time collected meteorological data and satellite cloud image information according to the rainfall risk in future periods to obtain predicted rainfall data. The prediction process can be obtained through analysis using existing meteorological models. Because localized data is involved, the judgment results are more accurate. Furthermore, this embodiment also obtains regional... Within the region, risky waterlogging points are identified, and the corresponding area for each risky waterlogging point is designated as a risk zone. A waterlogging risk model is established for each risk zone based on historical rainfall data from the database. The selection of risky waterlogging points is based on the frequency of waterlogging in different areas over historical periods. By analyzing each waterlogging point and establishing a waterlogging risk model, different standards can be applied to judge different waterlogging points, ensuring the accuracy of the judgment. Finally, by using real-time collected meteorological data, predicted rainfall data, and the waterlogging risk model, early warnings are issued for each waterlogging point. This allows for more accurate identification of the time of waterlogging occurrence and the timing of warnings, facilitating traffic management based on the judgment results. This helps to avoid the impact of road waterlogging on people's lives and property safety while minimizing traffic disruptions.

[0057] In one embodiment, a process for establishing a waterlogging risk model is provided, which includes: firstly, acquiring rainfall data when waterlogging occurred in historical risk areas. Obviously, there are multiple sets of rainfall data. A planar coordinate system is established with time as the x-axis and rainfall as the y-axis. The points representing rainfall at different times in each set of rainfall data are connected in the planar coordinate system to fit a rainfall curve. The rainfall value corresponding to the time point of waterlogging occurrence in each waterlogging problem is obtained. This data can be obtained from historical data from traffic weather stations or from data from the transportation department. Next, the surface infiltration capacity value of the risk area is calculated for each waterlogging problem. Based on the rainfall curve and surface infiltration capacity value, the time required for full surface infiltration for each waterlogging problem is determined. Since the surface infiltration capacity is crucial when assessing the natural drainage capacity of a risk area, the infiltration capacity of the ground... Different data can affect the assessment of drainage capacity. Therefore, to avoid the influence of this data on the assessment results, this embodiment excludes historical waterlogging data corresponding to the time when the ground infiltration reaches full capacity later than the time when water accumulation occurs. Thus, the remaining data all represent water accumulation that occurred after the ground infiltration reaches full capacity, avoiding the influence of rainwater infiltration capacity on the assessment of drainage capacity. Based on the remaining historical waterlogging data, the critical rainfall amount for risk areas is obtained, thereby accurately obtaining the critical rainfall amount for evaluating different risk areas. Subsequently, during the warning process, the waterlogging risk is warned based on the current ground infiltration capacity value, rainfall data, predicted rainfall data, and critical rainfall amount for risk areas. This allows for accurate determination of the time when water accumulation occurs and the time points corresponding to different water accumulation amounts, facilitating timely warnings of road conditions.

[0058] It should be noted that the surface infiltration capacity is related to the dryness of the soil. If the dryness is high, the infiltration capacity is high. In high-risk areas with a high proportion of green space, the amount of rainwater infiltrating through the soil accounts for a large proportion. Since soil infiltration takes a certain amount of time, different rainfall conditions may result in the time it takes for the surface to fully infiltrate before the water accumulates. For example, in the case of sudden heavy rain, the rainfall exceeds the soil infiltration rate, so water may accumulate in waterlogged areas before the surface infiltration capacity is fully utilized.

[0059] In one embodiment, a process for calculating a ground permeability value is provided, including: using the formula Calculate the surface infiltration capacity value at the start time of rainfall corresponding to the waterlogging problem. ;in, This is the surface infiltration capacity value at the time the last rainfall ended. The magnitude of the surface infiltration capacity value is judged based on the amount of water accumulation per unit area (per square meter). T(t) is the temperature change curve. In this embodiment, the reference temperature is set to 10 degrees Celsius. H(t) represents the humidity variation curve, and f is the soil moisture evaporation model function. The soil moisture evaporation model function is established by compiling test data under different temperature and humidity gradient distributions to create a control relationship, which is then used as the soil moisture evaporation model function. This refers to the time when the last rainfall ended. The starting time of rainfall corresponds to the current waterlogging problem. Obviously, the higher the temperature, the longer the interval between the end time of the last rainfall and the starting time of the current rainfall, and the lower the humidity, the higher the ground permeability value. Therefore, by calculating the ground permeability value in this embodiment, the ground permeability at the current time point can be accurately judged.

[0060] In one embodiment, a calculation process for the time required for full ground infiltration is provided, including: establishing an equation Determine the time required for full-load ground infiltration ;in, Let r(t) be the average soil infiltration rate, and r(t) be the rainfall curve. Indicates selection The smaller value in r(t) is used because there are cases where the real-time rainfall is higher than or lower than the land infiltration rate. Therefore, when the real-time rainfall is not higher than the land infiltration rate, by selecting the real-time rainfall as the infiltration rate, the time required for the ground to fully infiltrate can be accurately determined.

[0061] It should be noted that the soil infiltration rate varies in actual processes and may slow down, but it generally tends to be a downward-sloping straight line. Therefore, the analysis is performed by obtaining the average value of the overall infiltration rate, and the error is negligible.

[0062] In one embodiment, the process of obtaining the critical rainfall amount for a risk area based on the remaining historical waterlogging problem data includes: obtaining the rainfall values ​​corresponding to the time points when waterlogging occurred in the remaining historical waterlogging problem data; calculating the variance of all rainfall values; comparing the variance with a preset error value, where the preset error value is set based on the acceptable error range of empirical data. Since historical data includes instances of waterlogging caused by drainage pipe blockages, and these issues can affect the assessment of the natural drainage capacity of the risk area, the variance of all rainfall values ​​is calculated and compared with the preset error value. When the variance ≤ the preset error value, it indicates that there is no impact on the natural drainage capacity of the risk area. Since the data on water capacity is limited, the critical rainfall amount for the risk area is selected as the mean of all rainfall values. When the variance is greater than the preset error value, it indicates that there is data that affects the natural drainage capacity of the risk area. Therefore, the rainfall values ​​are eliminated one by one in descending order of the difference between the rainfall value and the mean of all rainfall values ​​until the variance is less than or equal to the preset error value. This eliminates data that affects the natural drainage capacity of the risk area. The mean of the remaining data is taken as the critical rainfall amount for the risk area. Through the above process, the critical rainfall amount for the risk area that accurately evaluates the natural drainage capacity can be calculated. The critical rainfall amount for the risk area can be used to more accurately warn of water accumulation in the risk area.

[0063] In one embodiment, the process of issuing an early warning for each waterlogging point includes: fitting a rainfall change curve using real-time collected rainfall data and predicted rainfall data. The fitting process involves combining real-time rainfall data and predicted rainfall data, connecting the data points, and then establishing a planar coordinate system with the rainfall start time as the origin, time as the x-axis, and rainfall as the y-axis. The fitting is then performed using the formula... The water accumulation G(t) of the risk area is calculated by subtracting the land infiltration water and natural drainage from the rainfall per unit area to obtain the real-time water accumulation per unit area. This real-time water accumulation G(t) is then multiplied by the total area of ​​the risk area to obtain the water accumulation G(t) of the risk area. To determine the function, when x < 0, When x≥0, r(t) is the rainfall variation curve. The percentage of water-permeable land in the risk area. Indicates selection and The smaller value in the equation is Q, where Q is the critical rainfall amount for the risk area and S is the total area of ​​the risk area. By judging the amount of water accumulation in the risk area per unit time, meteorological warnings can be issued based on the amount of water accumulation G(t). This allows for more accurate acquisition of the time point when water accumulation occurs and the time point for issuing warnings. It also facilitates traffic departments to manage the situation based on the judgment results, thereby avoiding the impact of road water accumulation on people's lives and property safety while reducing the impact on traffic.

[0064] In one embodiment, the process of issuing a weather warning based on the amount of water accumulation G(t) includes: determining that water accumulation has occurred at the water accumulation point when G(t) > 0, and issuing a Level 1 warning. In this embodiment, the Level 1 warning includes issuing a water accumulation alert. If the water level at a given location exceeds the warning threshold, a level-two warning is issued; where A is the water level threshold. This is the starting time point of the time interval G(t) > 0. , For a preset fixed time period, it is based on user settings, such as 10 minutes, to calculate the water accumulation within 10 minutes for judgment. The water accumulation threshold is selected and set according to the size of the preset fixed time period and experience data of different water accumulation points. Therefore, the water accumulation threshold is different for different water accumulation points. Through the above technical solution, it is possible to issue corresponding warnings more accurately and in a timely manner according to different water accumulation states, which is convenient for traffic departments to manage according to the judgment results.

[0065] In one embodiment, the process of issuing early warnings for each water accumulation point also involves calculating the rate at which water accumulates, by calculating the time from the start of rainfall to... Time difference , The threshold time difference is set based on empirical data, due to the time difference. This reflects the rate at which water accumulates, therefore in The timing indicates that the water accumulation is increasing rapidly, therefore a level-two warning is issued to implement traffic control measures at the waterlogged area in advance. This aims to prevent the waterlogging from affecting people's lives and property while minimizing the impact on traffic.

[0066] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A smart weather early warning method based on a portable transportation weather station, characterized in that, The method includes: Meteorological and rainfall data are collected in real time through traffic weather stations and stored in a database; Based on real-time collected meteorological data and satellite cloud image information, predictions are made regarding the risk of rainfall in future periods to obtain predicted rainfall data; Identify the risk points of water accumulation within the region, and designate the corresponding area of ​​each risk point as a risk area. Establish a water accumulation risk model for each risk area based on historical rainfall data in the database. Early warnings are issued for each waterlogging point by using real-time collected rainfall data, predicted rainfall data, and waterlogging risk models. The process of establishing a flood risk model includes: Obtain rainfall data when waterlogging occurs in historical risk areas, fit the rainfall data into a rainfall curve, and obtain the rainfall value corresponding to the time point when waterlogging occurs in each waterlogging problem. Calculate the surface permeability of the risk area each time a waterlogging problem occurs; Determine the time required for full surface infiltration for each water accumulation problem based on the rainfall curve and surface permeability value. Excluding historical waterlogging issues whose time to full ground infiltration occurred later than the time of water accumulation, the critical rainfall amount for the risk area is obtained based on the remaining historical waterlogging issues data. Early warning of waterlogging risk is issued based on the current ground permeability value, rainfall data, predicted rainfall data, and critical rainfall amount in the risk area. The process of calculating the surface permeability value includes: Through formula Calculate the surface infiltration capacity value at the start time of rainfall corresponding to the waterlogging problem. ; in, This represents the surface permeability at the point where the last rainfall ended, and T(t) is the temperature change curve. Here, H(t) is the reference temperature value, H(t) is the humidity change curve, and f is the soil moisture evaporation model function. This refers to the time when the last rainfall ended. This corresponds to the start time of rainfall for the current waterlogging problem; The calculation process for the time required for full-load ground infiltration includes: By establishing equations Determine the time required for full-load ground infiltration ; in, Let r(t) be the average soil infiltration rate, and r(t) be the rainfall curve. Indicates selection The smaller value in r(t); The process of determining the critical rainfall threshold for risk areas based on the remaining historical waterlogging data includes: Obtain the rainfall values ​​corresponding to the time points when the waterlogging occurred in the remaining historical data on waterlogging issues; Calculate the variance of all rainfall values ​​and compare it with a preset error value: When the variance is less than or equal to the preset error value, the critical rainfall amount for the risk area is the mean of all rainfall values. When the variance is greater than the preset error value, the rainfall values ​​are eliminated one by one in descending order of the difference between the rainfall value and the mean of all rainfall values, until the variance is less than or equal to the preset error value. The process of issuing early warnings for each water accumulation point includes: A rainfall variation curve is obtained by fitting real-time rainfall data and predicted rainfall data. ; Establish a plane coordinate system with the rainfall start time as the origin, time as the x-axis, and rainfall as the y-axis; Through formula Calculate the water accumulation G(t) in the risk area and issue a weather warning based on the magnitude of the water accumulation G(t); in, To determine the function, when x < 0, When x≥0, r(t) is the rainfall variation curve. The percentage of water-permeable land in the risk area. Indicates selection and The smaller of the two values, Q, represents the critical rainfall amount for the risk area, and S represents the total area of ​​the risk area.

2. The intelligent weather early warning method based on a portable transportation weather station according to claim 1, characterized in that, The process of issuing weather warnings based on the amount of water accumulation G(t) includes: When G(t) > 0, it is determined that water accumulation has occurred at the water accumulation point, and a first-level warning is issued; exist If the water level at a given location exceeds the warning line, a Level II warning will be issued. Where A is the water accumulation threshold, This is the starting time point of the time interval G(t) >

0. , This is a preset fixed time period.

3. The intelligent weather early warning method based on a portable transportation weather station according to claim 2, characterized in that, The process of issuing early warnings for each water accumulation point also includes: Calculate the time from the start of rainfall to Time difference ; exist A level-two warning will be issued in a timely manner; in, The threshold time difference.

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