Meteorological disaster early warning device based on multi-source data and early warning method thereof

Abstract

The invention discloses a multi-source data meteorological disaster early warning device, and relates to the technical field of meteorological early warning. The system comprises a meteorological monitoring station, a radar monitoring unit, an Internet of Things platform and a database. The invention also discloses a multi-source data meteorological disaster early warning method. The method comprises the following steps: step 1, data acquisition; 2, data processing; step 3, weather early warning; step 4, early warning and reminding; and step 5, data backup. The meteorological monitoring station is used for collecting various environmental parameters and road surface parameters, and the radar monitoring unit is used for collecting meteorological data, so that multi-source data collection can be carried out, organic integration of regional information and disaster information is realized, meteorological conditions in a set area can be visually seen in a GIS system, and short-time forecast information of the corresponding conditions is obtained; moreover, a processing strategy can be quickly made according to the short-time forecast information, and protection and early warning can be carried out on regions possibly affected by meteorological disasters.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological early warning, and in particular to a meteorological disaster early warning device and an early warning method thereof with multi-source data. Background Art

[0002] Geological disasters refer to geological actions or phenomena that are caused by natural or human factors and cause loss of human life and property and damage to the environment. Geological disasters often cause significant loss of life and property, so the study of geological disaster prediction and forecasting has important practical significance. In addition to the special topography and geological conditions, rainfall is an important factor in inducing geological disasters. However, the current meteorological risk warning method for geological disasters relies on expert experience and cannot quantitatively consider the coupling effect of geological environment background and rainfall, resulting in a large impact of human factors, low accuracy, and inability to be effectively popularized and promoted.

[0003] The invention with publication number CN115019474A discloses a method for early warning of meteorological risks of geological disasters, comprising: obtaining the potential of a geological unit; constructing a training set based on the cumulative rainfall of the geological unit in a first time period, the actual rainfall, the geological disaster category, and the potential of the geological unit; training a geological disaster meteorological risk early warning model based on the training set; calling the trained early warning model for prediction processing based on the cumulative rainfall of the geological unit in a second time period, the forecast rainfall and the potential of the geological unit to obtain the early warning level of the geological unit.

[0004] As shown in the above invention, the existing meteorological risk warning methods generally predict and forecast geological disaster meteorological risk warnings by considering the geological environment background and actual rainfall in combination with future rainfall conditions, thereby reducing manual dependence. However, this method only issues meteorological disaster warnings based on one or two environmental factors, and the alarm accuracy is low. In addition, the lack of meteorological simulation and analysis for specific areas will lead to low forecast accuracy. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a meteorological disaster warning device and a warning method based on multi-source data, which solve the existing problems.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A meteorological disaster early warning device with multi-source data, comprising:

[0007] Meteorological monitoring station, used to collect environmental parameters and road surface parameters;

[0008] Radar monitoring unit, used to collect regional meteorological data;

[0009] An Internet of Things platform, which is connected to a meteorological monitoring station and a radar monitoring unit through communication, and is used to process and model the data uploaded by the meteorological monitoring station and the radar monitoring unit, and issue an early warning;

[0010] The database is used to store the data collected by the meteorological monitoring station and radar monitoring unit and the corresponding time, and also stores the data analyzed by the IoT platform.

[0011] Preferably, the meteorological monitoring station includes a meteorological environment sensor, a remote sensing road surface sensor and a data transmission module;

[0012] The meteorological environment sensor is used to collect environmental parameters;

[0013] The remote sensing road surface sensor is used to collect road surface parameters;

[0014] The data transmission module is used to upload the collected environmental parameters and road surface parameters to the Internet of Things platform.

[0015] Preferably, the environmental parameters include air temperature, air humidity, atmospheric pressure, light intensity, wind speed, wind direction, rainfall, PM2.5 content, and PM10 content;

[0016] The road surface parameters include road surface temperature, road surface humidity, road surface water film height, road surface friction coefficient, road surface icing conditions and road surface freezing point temperature.

[0017] Preferably, the radar monitoring unit includes a Doppler radar, a radar data processing module, a radar data identification module and a latitude and longitude sampling module;

[0018] The Doppler radar is used to collect real-time data, and the real-time data is a radar weather map;

[0019] The radar data processing module is used to pre-process the data provided by all Doppler radars, remove noise and clutter, and perform filtering processing;

[0020] The radar data identification module is used to extract the reflection factor of the radar echo in the processed radar data, compare it with the reference table of weather attributes after calculation, identify the meteorological attributes of each area in the meteorological map, and provide the meteorological attributes, meteorological data and location information of each meteorological area;

[0021] The latitude and longitude sampling module is used to sample the location information of the Doppler radar and transmit it to the Internet of Things platform.

[0022] Preferably, the Internet of Things platform includes a data processing module, a model building module, a data analysis module, a data sending module and an early warning module;

[0023] The data processing module is used to process and analyze uploaded environmental parameters, road surface parameters and real-time weather maps;

[0024] The model building module is used to build an assessment model of meteorological elements based on the analyzed data;

[0025] The data analysis module analyzes the meteorological disaster risk in the current area based on the established meteorological element assessment model, and establishes meteorological data graphs at different time nodes according to the analyzed data;

[0026] The warning module is used to determine whether to issue a warning based on the results of data analysis and a preset threshold;

[0027] The data sending module is used to send the analyzed meteorological data of different areas to the staff, and send the warning information to the personnel in the relevant areas in the form of SMS or push.

[0028] Preferably, the data analysis module can also compare the meteorological data with the data threshold of the corresponding type to determine whether the current meteorological data has data exceeding the standard. When it is detected that the data exceeds the standard, it outputs a data exceeding standard abnormal prompt, queries the meteorological disaster type reference directory according to the type of meteorological data with data exceeding the standard, determines the matching meteorological disaster type in the meteorological disaster type reference directory, and verifies the micro-meteorological disaster prediction results according to the matching meteorological disaster type. The meteorological disaster type reference directory is pre-constructed according to the types of meteorological data with data exceeding the standard for different meteorological disaster types.

[0029] Preferably, the Internet of Things platform also performs statistical analysis on the analyzed data, generates operation analysis reports regularly, and sends them to a database for storage to form historical data for later use.

[0030] The present invention also discloses a meteorological disaster early warning method based on multi-source data, comprising the following steps:

[0031] Step 1: Data collection: Collect environmental data, road surface data and meteorological data of relevant areas through meteorological monitoring stations and radar monitoring units;

[0032] Step 2: Data processing: The uploaded environmental parameters, road surface parameters and real-time weather maps are processed and analyzed by the data processing module on the IoT platform. Then, a meteorological element assessment model is established based on the analyzed data. Then, the meteorological disaster risk in the current area is analyzed based on the established meteorological element assessment model. The meteorological data maps at different time nodes are established based on the analyzed data.

[0033] Step 3: Weather warning: Based on the results of data analysis, determine whether to issue a warning based on the preset threshold;

[0034] Step 4: Early warning reminder: Send the analyzed meteorological data of different regions to the staff, and send the early warning information to the personnel in the relevant areas by SMS or push;

[0035] Step 5: Data backup: Perform statistical analysis on the analyzed data, generate operation analysis reports regularly, and send them to the database to store them as historical data for future use.

[0036] Beneficial Effects

[0037] The present invention provides a meteorological disaster early warning device and early warning method based on multi-source data. Compared with the prior art, it has the following beneficial effects:

[0038] 1. The multi-source data meteorological disaster warning device and its warning method collect multiple environmental parameters and road parameters through meteorological monitoring stations and collect meteorological data through radar monitoring units, so that the present invention can perform multi-source data collection and realize the organic integration of regional information and disaster information. The meteorological conditions in the set area can be intuitively seen in the GIS system, and the short-term (generally 30 minutes to 60 minutes) forecast information of the corresponding conditions can be obtained. Moreover, processing strategies can be quickly made according to the short-term forecast information, and protective warnings can be given to areas that may be affected by meteorological disasters, playing an important role in early warning.

[0039] 2. The meteorological disaster warning device and warning method based on multi-source data predicts meteorological data, constructs meteorological data curves of various types based on different types of meteorological data and corresponding predicted data, and classifies and warns meteorological curves based on predicted data, so as to accurately predict potential meteorological disasters and prevent meteorological disasters in time. It integrates multiple environmental factors and greatly improves the accuracy of meteorological disaster alarms. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of system connection of the present invention;

[0041] Figure 2 It is a connection diagram of the meteorological monitoring station of the present invention;

[0042] Figure 3 It is a connection diagram of the radar monitoring unit of the present invention;

[0043] Figure 4 This is a schematic diagram of the connection of the Internet of Things platform of the present invention;

[0044] Figure 5 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0045] 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.

[0046] See Figure 1-5 , the present invention provides the following three technical solutions:

[0047] The first implementation method: a meteorological disaster early warning device with multi-source data, comprising:

[0048] A meteorological monitoring station is used to collect environmental parameters and road surface parameters. The meteorological monitoring station includes a meteorological environment sensor, a remote sensing road surface sensor and a data transmission module;

[0049] Meteorological environment sensors are used to collect environmental parameters, including air temperature, air humidity, atmospheric pressure, light intensity, wind speed, wind direction, rainfall, PM2.5 content, and PM10 content; road surface parameters include road surface temperature, road surface humidity, road surface water film height, road surface friction coefficient, road surface icing conditions, and road surface freezing point temperature.

[0050] Remote sensing road surface sensors are used to collect road surface parameters;

[0051] The data transmission module is used to upload the collected environmental parameters and road surface parameters to the Internet of Things platform.

[0052] The weather monitoring station also includes a status monitoring module, which is used to monitor the working status parameters of the weather station and issue an alarm when the working status parameters of the weather station exceed the preset threshold. The working status parameters of the weather station include: solar power supply status, battery working voltage, temperature inside the weather station and sensor working status parameters.

[0053] The weather station status monitoring module includes a working status parameter monitoring module and a weather station abnormality alarm module. The working status parameter monitoring module is used to collect the working status parameters of the weather station; the weather station abnormality alarm module is used to upload the working status parameters of the weather station that exceed the preset threshold to the meteorological disaster warning platform.

[0054] A radar monitoring unit is used to collect meteorological data within the region. The radar monitoring unit includes a Doppler radar, a radar data processing module, a radar data recognition module, and a latitude and longitude sampling module;

[0055] Doppler radar is used to collect real-time data, which is the radar weather map;

[0056] The radar data processing module is used to pre-process the data provided by all Doppler radars, remove noise and clutter, and perform filtering processing. The radar data processing module includes a connected denoising processing module and a filtering processing module. The data receiving end of the denoising processing module is connected to the data transmission end of the Doppler radar, and the output end is connected to the filtering processing module. The output interface of the filtering processing module is connected to the radar data identification module.

[0057] The denoising processing module first scans the real-time radar data and pre-sets the size of the connected block and the temporary threshold. When the scanned area is smaller than the set data, the area is removed, which can remove most of the isolated noise.

[0058] The filtering processing module includes a median filtering module and an opening and closing operation filtering module.

[0059] Median filtering is a nonlinear digital filtering technique that is often used to remove noise from images. The design idea is to check the samples in the input signal and determine whether it represents a signal. This function is achieved using an observation window composed of an odd number of samples. The values ​​in the observation window are sorted, and the median in the middle of the observation window is used as the output. Then the earliest value is discarded, a new sample is obtained, and the above calculation process is repeated. This can preserve the edge characteristics of each area in the weather map, making the radar data edge smooth without losing the characteristics of the radar echo.

[0060] The opening and closing operation mainly uses mathematical morphology, and it has four basic operations. The opening operation can remove isolated small points, burrs and small bridges (i.e. small points connecting two areas) by corrosion first, while the overall position and shape remain unchanged. The closing operation can fill in the broken places and fill in small cracks, while the overall position and shape remain unchanged, which can retain the original detection information of the radar to the greatest extent and facilitate calculation.

[0061] The radar data recognition module is used to extract the reflection factor of the radar echo in the processed radar data, and after calculation, compare it with the reference table of weather attributes to identify the meteorological attributes of each area in the meteorological map, and provide the meteorological attributes, meteorological data and location information of each meteorological area;

[0062] The latitude and longitude sampling module is used to sample the location information of the Doppler radar and transmit it to the Internet of Things platform.

[0063] The Internet of Things platform is connected to the meteorological monitoring station and the radar monitoring unit through communication, and is used to process and model the data uploaded by the meteorological monitoring station and the radar monitoring unit and issue warnings: the Internet of Things platform includes a data processing module, a model building module, a data analysis module, a data sending module and a warning module;

[0064] The data processing module is used to process and analyze uploaded environmental parameters, road surface parameters and real-time weather maps;

[0065] The model building module is used to build an assessment model of meteorological elements based on the analyzed data;

[0066] The data analysis module analyzes the meteorological disaster risk in the current area based on the established meteorological element assessment model, and establishes meteorological data graphs at different time nodes based on the analyzed data;

[0067] The early warning module is used to determine whether to issue an early warning based on the results of data analysis and the preset threshold;

[0068] The data sending module is used to send the analyzed meteorological data of different areas to the staff, and send the warning information to the personnel in the relevant areas by SMS or push.

[0069] The data analysis module can also compare the meteorological data with the data threshold of the corresponding type to determine whether the current meteorological data has data exceeding the standard. If it is detected that the data exceeds the standard, it will output a data exceeding standard abnormal prompt, and query the meteorological disaster type reference directory according to the type of meteorological data with data exceeding the standard to determine the matching meteorological disaster type in the meteorological disaster type reference directory, so as to verify the micro-meteorological disaster prediction results according to the matching meteorological disaster type. The meteorological disaster type reference directory is pre-constructed according to the types of meteorological data with data exceeding the standard for different meteorological disaster types.

[0070] The IoT platform also performs statistical analysis on the analyzed data, generates regular operation analysis reports, and sends them to the database to store them as historical data for future use.

[0071] The database is used to store the data collected by the meteorological monitoring station and radar monitoring unit and the corresponding time, and also stores the data analyzed by the IoT platform.

[0072] The device of the present invention can analyze meteorological environment monitoring data, monitor weather conditions such as strong winds, high temperatures, and heavy rains that affect the safe driving of vehicles on highways in real time, and issue graded warnings for severe weather conditions on highways. After the platform issues a warning for severe weather, the relevant warning information will be displayed on the platform page, and the color of the corresponding weather station icon on the GIS map will change synchronously according to the severe weather classification. Clicking the weather station icon can view the meteorological environment data of the road section at that point.

[0073] High wind warning classification:

[0074] Blue warning: The minimum temperature will drop by more than 8°C within 48 hours, the minimum temperature will be less than or equal to 4°C, and the average wind speed on land can reach above Level 5; or it has dropped by more than 8°C, the minimum temperature will be less than or equal to 4°C, the average wind speed will reach above Level 5, and it may continue.

[0075] Yellow warning: The minimum temperature will drop by more than 10℃ within 24 hours, the minimum temperature will be less than or equal to 4℃, and the average wind speed on land can reach above Level 6; or it has dropped by more than 10℃, the minimum temperature will be less than or equal to 4℃, the average wind speed will reach above Level 6, and it may continue.

[0076] Orange warning: The minimum temperature will drop by more than 12℃ within 24 hours, the minimum temperature will be less than or equal to 0℃, and the average wind speed on land can reach above Level 6; or it has dropped by more than 12℃, the minimum temperature will be less than or equal to 0℃, the average wind speed will reach above Level 6, and it may continue.

[0077] Red alert: The minimum temperature will drop by more than 16°C within 24 hours, the minimum temperature will be less than or equal to 0°C, and the average wind speed on land can reach above Level 6; or it has dropped by more than 16°C, the minimum temperature will be less than or equal to 0°C, the average wind speed will reach above Level 6, and it may continue.

[0078] High temperature warning classification:

[0079] Yellow warning: The maximum daily temperature will be above 35℃ for three consecutive days.

[0080] Orange warning: The maximum temperature will rise above 37°C within 24 hours.

[0081] Red alert: The maximum temperature will rise above 40℃ within 24 hours.

[0082] Heavy rain warning classification:

[0083] Blue warning: Rainfall will reach more than 50 mm within 12 hours, or has reached more than 50 mm and rainfall is likely to continue.

[0084] Yellow warning: Rainfall will reach more than 50 mm within 6 hours, or has reached more than 50 mm and rainfall is likely to continue.

[0085] Orange warning: Rainfall will reach more than 50 mm within 3 hours, or has reached more than 50 mm and rainfall is likely to continue.

[0086] Red alert: Rainfall will reach more than 100 mm within 3 hours, or has reached more than 100 mm and rainfall is likely to continue.

[0087] Second implementation mode: The present invention also discloses a meteorological disaster early warning method based on multi-source data, comprising the following steps:

[0088] Step 1: Data collection: Collect environmental data, road surface data and meteorological data of relevant areas through meteorological monitoring stations and radar monitoring units;

[0089] Step 2: Data processing: The uploaded environmental parameters, road surface parameters and real-time weather maps are processed and analyzed by the data processing module on the IoT platform. Then, a meteorological element assessment model is established based on the analyzed data. Then, the meteorological disaster risk in the current area is analyzed based on the established meteorological element assessment model. The meteorological data maps at different time nodes are established based on the analyzed data.

[0090] Step 3: Weather warning: Based on the results of data analysis, determine whether to issue a warning based on the preset threshold;

[0091] Step 4: Early warning reminder: Send the analyzed meteorological data of different regions to the staff, and send the early warning information to the personnel in the relevant areas by SMS or push;

[0092] Step 5: Data backup: Perform statistical analysis on the analyzed data, generate operation analysis reports regularly, and send them to the database to store them as historical data for future use.

[0093] The third embodiment: The present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the processor implements the steps of a meteorological disaster early warning method based on multi-source data.

[0094] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art, and the model parameters of each electrical appliance are not specifically limited, and conventional equipment can be used.

[0095] 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.

[0096] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A meteorological disaster early warning device based on multi-source data, characterized in that: include: Meteorological monitoring station, used to collect environmental parameters and road surface parameters; Radar monitoring unit, used to collect regional meteorological data; An Internet of Things platform, which is connected to a meteorological monitoring station and a radar monitoring unit through communication, and is used to process and model the data uploaded by the meteorological monitoring station and the radar monitoring unit, and issue an early warning; The database is used to store the data collected by the meteorological monitoring station and radar monitoring unit and the corresponding time, and also stores the data analyzed by the IoT platform.

2. The meteorological disaster warning device based on multi-source data according to claim 1, characterized in that: The meteorological monitoring station includes a meteorological environment sensor, a remote sensing road surface sensor and a data transmission module; The meteorological environment sensor is used to collect environmental parameters; The remote sensing road surface sensor is used to collect road surface parameters; The data transmission module is used to upload the collected environmental parameters and road surface parameters to the Internet of Things platform.

3. The meteorological disaster early warning device based on multi-source data according to claim 2, characterized in that: The environmental parameters include air temperature, air humidity, atmospheric pressure, light intensity, wind speed, wind direction, rainfall, PM2.5 content, and PM10 content; The road surface parameters include road surface temperature, road surface humidity, road surface water film height, road surface friction coefficient, road surface icing conditions and road surface freezing point temperature.

4. The meteorological disaster warning device based on multi-source data according to claim 1, characterized in that: The radar monitoring unit includes a Doppler radar, a radar data processing module, a radar data identification module and a latitude and longitude sampling module; The Doppler radar is used to collect real-time data, and the real-time data is a radar weather map; The radar data processing module is used to pre-process the data provided by all Doppler radars, remove noise and clutter, and perform filtering processing; The radar data identification module is used to extract the reflection factor of the radar echo in the processed radar data, compare it with the reference table of weather attributes after calculation, identify the meteorological attributes of each area in the meteorological map, and provide the meteorological attributes, meteorological data and location information of each meteorological area; The latitude and longitude sampling module is used to sample the location information of the Doppler radar and transmit it to the Internet of Things platform.

5. The meteorological disaster warning device based on multi-source data according to claim 1, characterized in that: The Internet of Things platform includes a data processing module, a model building module, a data analysis module, a data sending module and an early warning module; The data processing module is used to process and analyze uploaded environmental parameters, road surface parameters and real-time weather maps; The model building module is used to build an assessment model of meteorological elements based on the analyzed data; The data analysis module analyzes the meteorological disaster risk in the current area based on the established meteorological element assessment model, and establishes meteorological data graphs at different time nodes according to the analyzed data; The warning module is used to determine whether to issue a warning based on the results of data analysis and a preset threshold; The data sending module is used to send the analyzed meteorological data of different areas to the staff, and send the warning information to the personnel in the relevant areas in the form of SMS or push.

6. The meteorological disaster warning device based on multi-source data according to claim 5, characterized in that: The data analysis module can also compare the meteorological data with the data threshold of the corresponding type to determine whether the current meteorological data has data exceeding the standard. When it is detected that the data exceeds the standard, it outputs a data exceeding standard abnormal prompt, queries the meteorological disaster type reference directory according to the type of meteorological data with data exceeding the standard, determines the matching meteorological disaster type in the meteorological disaster type reference directory, and verifies the micro-meteorological disaster prediction results according to the matching meteorological disaster type. The meteorological disaster type reference directory is pre-constructed according to the types of meteorological data with data exceeding the standard for different meteorological disaster types.

7. The meteorological disaster warning device of multi-source data according to claim 5, characterized in that: The IoT platform also performs statistical analysis on the analyzed data, generates operation analysis reports regularly, and sends them to the database for storage to form historical data for future use.

8. A meteorological disaster early warning method based on multi-source data, implemented based on a meteorological disaster early warning device based on multi-source data according to claims 1-7, characterized in that: The following steps are involved: Step 1: Data collection: Collect environmental data, road surface data and meteorological data of relevant areas through meteorological monitoring stations and radar monitoring units; Step 2: Data processing: The uploaded environmental parameters, road surface parameters and real-time weather maps are processed and analyzed by the data processing module on the IoT platform. Then, a meteorological element assessment model is established based on the analyzed data. Then, the meteorological disaster risk in the current area is analyzed based on the established meteorological element assessment model. The meteorological data maps at different time nodes are established based on the analyzed data. Step 3: Weather warning: Based on the results of data analysis, determine whether to issue a warning based on the preset threshold; Step 4: Early warning reminder: Send the analyzed meteorological data of different regions to the staff, and send the early warning information to the personnel in the relevant areas by SMS or push; Step 5: Data backup: Perform statistical analysis on the analyzed data, generate operation analysis reports regularly, and send them to the database to store them as historical data for future use.

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