Vehicle-mounted microclimate monitoring and camping site disaster early warning system and method

Abstract

The invention discloses a vehicle-mounted micrometeorological monitoring and camping site disaster early warning system and method, and belongs to the technical field of vehicle-mounted meteorological monitoring, and the system comprises a data collection and preprocessing module which is used for obtaining and calibrating multi-source meteorological and geographic data; the microclimate modeling module is used for generating a microclimate grid with a real-time weather predicted value by taking local microclimate data as a benchmark and fusing cloud and geographic data; the disaster risk assessment module performs quantitative risk assessment on camping disasters such as flood, tree smashing, landslide and thunderstorm based on the data of the microclimate grid; and the early warning generation module generates visual early warning information including a disaster icon, a risk level, time information and a risk avoiding instruction according to the quantitative risk assessment result. According to the invention, by optimizing the layout of the sensors and introducing a terrain correction interpolation algorithm and scene disaster assessment, the meteorological monitoring precision in a mobile environment is improved, and timely early warning of camping field micro-scale disasters is realized.

Description

Technical Field

[0001] This invention relates to the field of vehicle-mounted meteorological monitoring technology, and in particular to a vehicle-mounted micro-meteorological monitoring and campsite disaster early warning system and method. Background Technology

[0002] Vehicle-mounted meteorological monitoring technology is an important direction for improving vehicle environmental perception capabilities and active safety levels. Especially in scenarios such as off-road adventures and outdoor camping, real-time and accurate meteorological information is crucial for ensuring personal and property safety. In existing technologies, meteorological monitoring solutions for vehicle platforms mainly integrate various sensors (such as temperature, humidity, air pressure, wind speed, and wind direction sensors) into the vehicle body and utilize vehicle-mounted communication networks (such as 4G / 5G) to receive wide-area weather forecast services, thereby providing basic weather condition information to drivers and passengers.

[0003] However, when the above-mentioned technical solutions are applied to the specific scenario of outdoor camping, which is highly sensitive to the local microenvironment, their inherent limitations become particularly apparent, mainly in the following three aspects: First, the installation location of traditional vehicle-mounted sensors is limited by the vehicle structure, making them highly susceptible to interference from a combination of factors, including heat radiation from the engine compartment, airflow generated during vehicle movement, and vehicle vibration. Especially when the vehicle is stationary and parked, these interference factors lead to significant systematic measurement errors, making it difficult to obtain high-precision data that accurately reflects the microclimate conditions of the campsite, and thus failing to provide a reliable data foundation for subsequent risk warnings.

[0004] Secondly, existing technologies heavily rely on wide-area weather forecast data provided by public services, with spatial resolution typically at the kilometer level (e.g., 10 km × 10 km). This large-scale data cannot effectively capture and characterize the "micro-scale" meteorological abrupt changes within a few hundred meters of the campsite caused by complex terrain (such as valleys, slopes, and bodies of water), such as sudden localized strong gusts, surface runoff, or rain shadow effects. This scale mismatch results in a weak ability of the system to issue early warnings for sudden meteorological disasters directly faced by the campsite.

[0005] Finally, existing warnings mostly remain at the level of general weather phenomenon alerts (such as "blue warning for strong winds" and "yellow warning for heavy rain"), lacking in-depth analysis and targeted guidance that combines the characteristics of camping activities with the on-site physical environment. For example, they do not consider the impact of different surface slopes on runoff risk, do not assess the risk of falling trees based on the health status of surrounding trees (percentage of dead trees), and fail to provide specific evacuation directions and action instructions based on the campsite terrain in thunderstorm warnings. This disconnect from the scenario makes the warning information insufficiently practical and difficult to effectively guide users to take the correct evacuation measures.

[0006] Therefore, the pressing technical problems to be solved in this field are: how to overcome the inherent measurement interference of vehicle-mounted mobile platforms and achieve high-precision micro-meteorological perception in a stationary camping state; how to integrate multi-source data to construct a model that can finely characterize the micro-scale meteorological environment around the campsite; and how, based on this, to develop an intelligent disaster early warning mechanism that is deeply coupled with the camping scene, can be quantitatively assessed, and can generate specific action instructions. This invention aims to specifically solve the above-mentioned technical problems. Summary of the Invention

[0007] To address the aforementioned problems, this invention proposes a vehicle-mounted micro-meteorological monitoring and campsite disaster early warning system and method.

[0008] To achieve the above objectives, the present invention employs the following technical solution: The vehicle-mounted micro-weather monitoring and campsite disaster early warning system includes: Data acquisition and preprocessing module: used to acquire and process multi-source meteorological data, including local micro-meteorological data, cloud meteorological service data and geographic data, to perform spatiotemporal coordinate alignment, abnormal data identification and removal, and data calibration based on the current altitude, and output time-series data stream in a standardized format; Microclimate modeling module: used to construct an initial microclimate grid covering a preset spatial range with the current vehicle as the center; based on the time-series data stream in the standardized format, it assigns values ​​to each grid point in the initial microclimate grid through a spatial interpolation algorithm to generate a microclimate grid; the microclimate grid includes several microclimate subgrids; Disaster Risk Assessment Module: Based on the microclimate subgrid, this module quantifies the probability and hazard level of disaster types according to the current campsite type. The campsite types include: waterfront, forested areas, and foothills. The disaster types include: floods, falling trees, landslides, and thunderstorms. Early warning generation module: Based on the results of the risk quantification assessment, it generates composite early warning information and outputs it in a visual format.

[0009] As a preferred embodiment of the present invention, the data acquisition and preprocessing module includes: The local micro-meteorological unit is used to obtain wind speed and direction through a three-dimensional ultrasonic anemometer installed in the center of the roof, air pressure through an atmospheric barometer installed in the right luggage rack, and rainfall through a rain gauge to form local micro-meteorological data. The cloud data access unit is used to acquire cloud meteorological service data from the meteorological service center in real time through the vehicle communication network. The cloud meteorological service data includes at least: satellite cloud image data and 1km resolution meteorological radar data; the meteorological radar data includes at least reflectivity factor data; the satellite cloud image data is used to provide upper-air cloud condition information, including at least cloud movement speed. The geographic data parsing unit is used to acquire geographic data by calling a pre-loaded or online high-precision digital map. The geographic data includes at least: high-precision digital elevation model data and land cover type data. The high-precision digital elevation model data includes at least altitude and slope. The land cover type data is used to determine the surface roughness, calculate the soil saturation index, and identify scenes in the microclimate grid.

[0010] As a preferred embodiment of the present invention, the atmospheric pressure gauge is equipped with a shock-absorbing rubber pad, and the rain gauge is equipped with an automatic cleaning brush.

[0011] As a preferred embodiment of the present invention, the data acquisition and preprocessing module further includes an automatic calibration unit: When the vehicle speed is zero and remains at zero for a preset time, the automatic calibration unit is activated to calibrate the air pressure, wind speed, and wind direction in the local micro-meteorological data according to the current altitude using a correction expression; and the calibrated air pressure, wind speed, and wind direction are returned and overlaid on the local micro-meteorological data to form a multi-dimensional meteorological vector; The pressure correction expression is: ; In the formula, The calibrated air pressure, The air pressure obtained through an atmospheric pressure gauge. This represents the vertical pressure lapse rate. Altitude; The wind speed correction expression is: ; In the formula, The calibrated wind speed. The wind speed is obtained using a three-dimensional ultrasonic anemometer. This is the correction factor for airflow around the vehicle body. This refers to the actual installation height of the 3D ultrasonic anemometer. The preset standard reference height, It is the length of the surface roughness; The wind direction correction expression is: ; In the formula, The calibrated wind direction. The wind direction is obtained using a three-dimensional ultrasonic anemometer. Install the 3D ultrasonic anemometer at an off-angle. It is the magnetic declination.

[0012] As a preferred embodiment of the present invention, the expression of the spatial interpolation algorithm is: ; In the formula, For the coordinates in the microclimate grid, Weather forecast values, The number of independent data sources participating in data fusion. The index of the summation loop represents the index of the first iteration. One data source, A multidimensional meteorological vector representing the vehicle's location. From vehicle position to coordinates Elevation and terrain correction between them For the first The fusion weighting factor of each data source, For the first Data source in coordinates relative to the reference value The deviation value.

[0013] As a preferred embodiment of the present invention, the disaster wind direction assessment module includes: The campsite type determination unit is used to determine the type of campsite based on geographic data and the current vehicle location, according to preset rules. The disaster type discrimination unit is used to determine the disaster type based on the campsite type and microclimate grid, according to preset rules. The risk quantification assessment unit is used to calculate the corresponding disaster risk based on the disaster type and microclimate grid, according to a preset expression. The campsite type determination unit includes: Triggered when the vehicle speed is 0 for a preset time: If the campsite is located within 50 meters of a river, lake, or ocean, and the ground elevation is no more than 3 meters above the water level, then the campsite type is waterfront. If the tree coverage within 50m is greater than 40% and the average tree height is greater than 5 meters, then the campsite type is a forest area. If the ground slope is greater than 10 degrees and there is a slope with an elevation change of more than 5 meters within 100 meters, then the campsite type is foothill.

[0014] As a preferred embodiment of the present invention, the disaster type discrimination unit includes: If the campsite is waterfront, and the rainfall intensity is greater than 30 mm / h in the next hour and the soil saturation index is greater than 80%, then the disaster type is flood. When the campsite is located in a forest area, if the wind speed is greater than 15 m / s and the dead tree rate is greater than 20%, the disaster type is tree falling. When the campsite is located in a foothill area, if the real-time rainfall intensity in the uphill area is greater than 20 mm / h or the cumulative rainfall exceeds the threshold, the disaster type is landslide. When the campsite is located in a waterfront, forest, or foothill area, and the air pressure change rate is less than -2 hPa / h and the lightning distance is less than 3 km, the disaster type is thunderstorm.

[0015] As a preferred embodiment of the present invention, the risk quantification assessment unit includes: When the disaster type is flood, the flood risk index is calculated using the following expression: ; In the formula, The flood risk index, , and These are the weights of rainfall intensity, topographic slope, and soil water holding capacity, respectively. Rainfall intensity, The threshold for rainfall intensity. The slope threshold, This represents the slope value of the campsite. This is the minimum protection value for slope. Soil saturation index; When the disaster type is tree falling, the tree falling risk index is calculated using the following expression: ; In the formula, Risk index of falling trees For wind speed factor weights, The weight of the dead tree rate factor, Wind speed at the tree canopy height of the target forest area This serves as a reference value for wind speed. The density of dead trees. This serves as a reference value for dead trees; When the disaster type is landslide, the landslide risk index is calculated using the following expression: ; ; ; ; In the formula, This is a landslide risk index. , , These are the weighting coefficients, Rainfall-inducing factors, For geomorphic energy factors, As a surface anti-skid factor, The weighting factor for cumulative rainfall. This represents the cumulative rainfall. The critical cumulative rainfall threshold, This is the critical slope for instability. This is the surface cover stability coefficient; When the disaster type is thunderstorm, the expression for calculating the thunderstorm arrival time is: ; In the formula, The estimated arrival time of the thunderstorm. Radar echo range, The speed of cloud movement. This is a correction factor for sudden pressure drop. The pressure change rate is used; the radar echo distance is obtained by using the reflectivity factor data to obtain the rainfall intensity through the ZR inversion algorithm, and then through the strong echo front identification and spatial matching algorithm.

[0016] As a preferred embodiment of the present invention, the early warning generation module includes: obtaining the risk levels of floods, tree impacts, and landslides based on flood risk index, tree impact risk index, landslide risk index, and thunderstorm arrival time, according to a preset disaster level lookup table; and generating visualized composite early warning information; the composite early warning information includes at least: disaster type icon, risk level, time and location information, and evacuation instructions; the evacuation instructions are dynamically generated based on disaster type, risk level, and environmental characteristics in the microclimate grid; and outputting the composite early warning information to a vehicle screen or mobile terminal; the environmental characteristics are obtained based on land cover type data and high-precision digital elevation model data, and include at least: altitude, slope, and aspect.

[0017] Methods for vehicle-mounted micro-meteorological monitoring and campsite disaster early warning include: Collect and process multi-source meteorological data, including local micro-meteorological data, cloud meteorological service data, and geographic data. Perform spatiotemporal coordinate alignment, anomaly identification and removal, and data calibration based on the current altitude on the multi-source meteorological data, and output a time-series data stream in a standardized format. Centered on the current vehicle, an initial microclimate grid covering a preset spatial range is constructed; based on the time-series data stream in the standardized format, values ​​are assigned to each grid point in the initial microclimate grid using a spatial interpolation algorithm to generate a microclimate grid; the microclimate grid includes several microclimate subgrids; Based on the microclimate subgrid, a risk quantification assessment of the probability and hazard level of disaster types is conducted according to the current campsite type; the campsite types include: waterfront, forest areas, and foothill areas; the disaster types include: floods, falling trees, landslides, and thunderstorms; Based on the results of the aforementioned risk quantification assessment, composite early warning information is generated and visualized.

[0018] The beneficial effects of this invention are as follows: Through innovative system architecture and algorithm design, this invention produces significant beneficial effects. First, by adopting an optimized anti-interference layout for vehicle-mounted sensors (such as a wind speed meter installed in the center of the vehicle roof) and combining it with an automatic calibration algorithm based on the current altitude when the vehicle is stationary, the effects of heat radiation and vibration are effectively overcome, improving the accuracy of local micro-meteorological measurements to the level of a meteorological station, providing a reliable data foundation for early warning. Second, it pioneers a spatial interpolation model that uses high-precision vehicle-mounted data as a spatial benchmark and integrates 1km resolution radar and terrain data. This model can generate microclimate sub-grids with an accuracy of 50 meters, accurately depicting micro-scale disasters such as gusts and slope confluence around campsites, solving the scale mismatch problem in wide-area forecasts. Finally, for camping scenarios such as waterfront, forest areas, and foothills, the system establishes a multi-factor quantitative disaster assessment (such as tree falling and landslide risks) and dynamic evacuation instruction generation mechanism, which can transform risks into specific levels and action guidelines, realizing a leap from general weather alerts to scenario-based safety decision-making, greatly improving the proactive safety protection capabilities for outdoor activities. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a structural diagram of the vehicle-mounted micro-meteorological monitoring and campsite disaster early warning system in an embodiment of the present invention; Figure 2 This is a flowchart of the vehicle-mounted micro-meteorological monitoring and campsite disaster early warning method in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0021] like Figure 1 As shown, this is an embodiment of the present invention, which provides a vehicle-mounted microclimate monitoring and campsite disaster early warning system, including a data acquisition and preprocessing module, a microclimate modeling module, a disaster risk assessment module, and an early warning generation module.

[0022] The data acquisition and preprocessing module is used to collect and process multi-source meteorological data, including local micro-meteorological data, cloud-based meteorological service data, and geographic data. It performs spatiotemporal coordinate alignment, anomaly identification and removal, and data calibration based on the current altitude, outputting a standardized time-series data stream. It mainly includes a local micro-meteorological unit, a cloud-based data access unit, and a geographic data parsing unit.

[0023] The local micro-meteorological unit has a three-dimensional ultrasonic anemometer installed at the highest point in the center of the roof, keeping it away from the turbulence zone on both sides of the vehicle caused by driving. This allows it to capture the true wind speed and direction in the purest way. It uses the ultrasonic time difference method to measure wind, has no mechanical rotating parts, is more resistant to bumps, has a low starting wind speed, and high accuracy, making it especially suitable for mobile vehicle environments. The ultrasonic anemometer signal is analyzed in both time and frequency domains to calculate the three-dimensional wind speed vector and turbulence intensity. A protective shell (protected from direct sunlight and rain but well-ventilated) is installed on the left support rod of the roof rack or a dedicated mounting base. A digital temperature and humidity sensor is installed inside. This physical separation design effectively avoids thermal pollution from electronic components affecting temperature and humidity measurement. To achieve vibration resistance and prevent clogging, the barometer is placed in a mounting slot with shock-absorbing pads. The pads can be made of silicone or rubber to absorb high-frequency micro-vibrations caused by vehicle movement and wind noise. The barometer's collected air pressure time-series signal is subjected to sliding window linear regression to extract the air pressure change rate. The rain gauge is equipped with an automatic cleaning brush above its funnel. The cleaning brush can be driven by a micro motor periodically (e.g., once per hour) or on command to rotate and clean debris from the funnel opening.

[0024] The cloud data access unit establishes a stable connection with the internet through the vehicle's onboard communication network (such as a 4G / 5G cellular network module or satellite communication module). It has a built-in client program that automatically sends requests to the data interface of a meteorological service center (such as a meteorological bureau or commercial meteorological data provider) according to a preset period (such as every 10 minutes) or trigger conditions. These requests typically include the vehicle's current location information (GPS coordinates) to obtain satellite cloud imagery data and 1km resolution weather radar data for the most relevant area. The 1km resolution weather radar data provides a detailed weather pattern of the area surrounding the vehicle and is crucial for accurate nowcasting. The 1km range corresponds to the microclimate grid range. It is processed radar base data or product data, which directly contains reflectivity factors (the value directly corresponds to the rainfall intensity). A reflectivity threshold (e.g., 35 dBZ) representing severe convective weather (such as thunderstorms) is set. Centered on the vehicle's current location, the radar data grid is scanned from near to far to identify the outer boundary of the radar echo area that first reaches or exceeds the intensity threshold and is continuous. The boundary of this strong echo area is the echo front, which usually represents the main part of severe weather systems such as thunderstorms and heavy rain. After determining the boundary line of the echo front, the distance from the vehicle's current location to all points on the boundary of this strong echo area is calculated, and the minimum value is taken. This minimum value is the radar echo distance.

[0025] Satellite cloud imagery data is used to receive cloud images from infrared, visible light, or water vapor channels. Through image analysis algorithms or by directly receiving derived data products, information such as cloud movement speed, cloud top height, and cloud classification is obtained to determine the movement of large-scale weather systems. For time-series satellite cloud images, cross-correlation or optical flow methods are applied to calculate cloud motion vectors (cloud-guided winds). Lightning location data consists of real-time or near-real-time lightning event reports, with each data point including the lightning's latitude, longitude, time, and intensity. The lightning distance is obtained by calculating the distance between the lightning location and the vehicle's GPS coordinates.

[0026] The geographic data parsing unit downloads and stores high-precision digital elevation model data and land cover type data packages for a region (such as an entire province or a frequently visited off-road route) to the vehicle's hard drive or SD card. When the vehicle enters an area without pre-installed data, it requests geographic data of the surrounding area from an online map server (such as the dedicated API of Gaode or Google Maps) in real time via the vehicle network. The high-precision digital elevation model data provides the altitude and slope of each coordinate point, which is the core basis for microclimate modeling (such as calculating the vertical temperature decrease and analyzing flood paths) and altitude calibration. Based on the high-precision digital elevation model data, it calculates terrain features such as slope, aspect, and catchment area. Land cover type data provides land classification information (such as forest, shrub, grassland, water body, building, bare soil). Surface roughness is an aerodynamic parameter that describes the blocking effect of the land surface (such as grassland, forest, water surface) on near-surface wind speed. Different land cover types have relatively fixed empirical ranges for their roughness values. Land cover type data (e.g., land cover type codes: 101-dense forest, 052-shrubland, 021-short grassland, 001-water surface) are obtained from geographic data for vehicle and surrounding microclimate subgrids, and a built-in surface roughness value is defined. Lookup table: Water surface, asphalt road surface: = 0.0001 - 0.001 meters; short grass, snow: = 0.01 -0.03 meters; tall grasslands, crops: = 0.04 - 0.1 meters; Shrubland: = 0.3 - 0.8 meters; dense forest, city: =0.8 - 2.0 meters. The above surface roughness ( The lookup table is pre-developed based on extensive meteorological and micrometeorological observational studies, and is coded according to the surface type of the current microclimate subgrid, starting from surface roughness (…). The corresponding value is retrieved from the lookup table. Combined with rainfall data and soil type (derived from land cover), the soil saturation index is obtained. Soil saturation index ( () is a dimensionless parameter ranging from 0 to 1, used to quantify the water saturation level of the surface soil at a campsite, where 0 represents completely dry and 1 represents completely saturated; the expression is: ; In the formula, The accumulated effective rainfall is calculated by summing the rainfall intensity of the campsite's grid within the past 24 hours (or a time window set according to local soil drainage characteristics), as provided by the microclimate grid. This represents the soil saturation threshold rainfall, obtained by querying a preset table based on soil type (e.g., sandy soil, loam, clay) in the geographic data. It signifies the total rainfall required to saturate this type of soil. This is the soil permeability coefficient, ranging from 0 to 1, determined by the soil type. It can be obtained by consulting a preset table. For example, 0.9 is used for sandy soil (fast permeability) and 0.3 is used for clay soil (slow permeability). This is the land cover runoff coefficient, determined by the land cover type. It can be obtained by querying a preset table. For example, 0.9 is used for hardened ground (high runoff) and 0.2 is used for woodland (low runoff). This is an environmental adjustment factor (an empirical constant, such as 0.2), used to fine-tune the formula, calibrated using local historical data.

[0027] All of the aforementioned local micro-meteorological data, cloud-based meteorological service data, and geographic data will be assigned a precise and unified timestamp to achieve time synchronization and ensure that subsequent analysis is a complete environmental snapshot at the same moment.

[0028] Based on the sources of the aforementioned local micro-meteorological data, cloud-based meteorological service data, and geographic data, the corresponding communication protocol parser (such as NMEA-0183 for parsing GPS and meteorological service API for parsing JSON / binary streams) is invoked to convert the raw bitstream into structured physical quantity data frames. Anomaly detection algorithms based on statistics (such as sliding window Z-score test) or physical rules (such as the fact that temperature cannot jump by 30°C instantaneously) are applied to the multi-source meteorological data to remove outliers. High-frequency noise is suppressed by digital filters (such as low-pass filtering). All data frames are stamped with a unified Universal Time (UTC) timestamp. Finally, using GPS coordinates and high-precision digital elevation model data, all geographic data are unified to the same spatial coordinate system (such as WGS-84).

[0029] When a vehicle's current speed is detected to be 0 (e.g., the speed has been zero for more than 2 minutes), the automatic calibration unit in the data acquisition and preprocessing module is immediately activated to execute the following standardized calibration procedure: Instantaneous values ​​were simultaneously read from the aforementioned three-dimensional ultrasonic anemometer, temperature and humidity sensor, and atmospheric pressure gauge: original temperature ( ), original air pressure ( ), original wind speed ( ) and original wind direction ( From the high-precision digital elevation model data, query the ground elevation (H) of the point corresponding to the vehicle's current GPS coordinates, and the vehicle body flow correction factor (H). The values ​​are obtained through computational fluid dynamics simulation (CFD) or on-vehicle wind tunnel calibration. Specific values ​​depend on the vehicle model, the installation location of the 3D ultrasonic anemometer, and the roof shape. These values ​​are used to quantify the vehicle body's obstruction and acceleration effects on airflow. The sensor installation height (…) This refers to the vertical distance from the center point of the 3D ultrasonic anemometer to the ground (which needs to be accurately measured and recorded during installation), and the standard reference height. Meteorological standards are adopted, typically preset to 10 meters, representing the standard observation height for wind speed in open environments, with an installation angle of ( ). After the sensor is installed, a calibration procedure is performed to determine the direction. The vehicle is then aligned with a known geographical direction (e.g., due north), and the difference between the wind direction displayed on the anemometer and the actual geographical wind direction is recorded. This difference represents the mechanical installation deviation, and the magnetic declination is calculated as follows: Based on the vehicle's current latitude, longitude, and date, the coordinates are calculated in real time from the International Geomagnetic Reference Model (IGRF) or online services to correct the angle between magnetic north and true north.

[0030] Based on the above data parameters, the temperature correction expression is as follows: ; In the formula, For the calibrated temperature, The temperature is obtained from the temperature and humidity sensor. Altitude The temperature vertical lapse rate is used; the standard temperature vertical lapse rate (0.0065 °C / m) is used to correct the temperature measured by the sensor to the equivalent temperature at the sea level reference height.

[0031] The pressure correction expression is: ; In the formula, The calibrated air pressure, The air pressure obtained from the barometer. The vertical lapse rate is used to correct the measured pressure values ​​to the equivalent pressure at the sea level reference height using the standard vertical lapse rate (0.12 hPa / m).

[0032] The wind speed correction expression is: ; In the formula, The calibrated wind speed. The wind speed is obtained from a three-dimensional ultrasonic anemometer. This is the correction factor for airflow around the vehicle body, which corrects for local flow field distortion caused by the vehicle body. This refers to the actual installation height of the 3D ultrasonic anemometer. The preset standard reference height, It represents the length of the surface roughness.

[0033] The wind direction correction expression is: ; In the formula, The calibrated wind direction. The wind direction is obtained from a three-dimensional ultrasonic anemometer. Install the 3D ultrasonic anemometer at an off-angle. Magnetic declination is used to gradually correct the original wind direction angle (usually the angle relative to the sensor's own coordinate system or magnetic north) to the absolute wind direction based on geographic true north.

[0034] The four calibrated data sets, along with other local micro-meteorological data that do not require calibration, are encapsulated into a structured data package—a multidimensional meteorological vector. The multidimensional meteorological vector not only contains the values ​​of all calibrated physical quantities, but also includes timestamps, spatial coordinates (GPS), and meteorological source identifiers, providing the microclimate modeling module with a set of accurate, reliable, and standardized spatial reference point data.

[0035] Microclimate Modeling Module: Using the vehicle's current GPS coordinates as the geometric center, a circular area with a radius of 500 meters is defined as the modeling range. This circular area is discretized into an array composed of countless 50-meter × 50-meter square grids (i.e., microclimate sub-grids). All data to be used (calibrated local microclimate data, cloud-based meteorological service data, and geographic data) are uniformly converted to the same time (using the latest timestamp) and the same coordinate system (such as the WGS-84 geographic coordinate system) to ensure that all information communicates on the same platform without spatiotemporal discrepancies.

[0036] In the central grid where the vehicle is located, fill in the multi-dimensional meteorological vector generated by the automatic calibration unit. This includes real-world measurements of wind speed, wind direction, temperature, and air pressure. For each grid cell, the terrain elevation correction from the vehicle's location to that grid cell is calculated. Using high-precision digital elevation model data, the elevation of the vehicle's location and the target grid cell are read. The corresponding correction is obtained through a correction formula based on a standard vertical lapse rate. Cloud data is then integrated into the microclimate grid using a spatial interpolation algorithm. The expression is as follows: ; In the formula, For the coordinates in the microclimate grid, Weather forecast values, The number of independent data sources participating in data fusion. The index of the summation loop represents the index of the first iteration. One data source, A multidimensional meteorological vector representing the vehicle's location. From vehicle position to coordinates Elevation and terrain correction between them For the first The fusion weighting factor of each data source, For the first Data source in coordinates relative to the reference value The deviation value.

[0037] The aforementioned microclimate grid is no longer a simple smooth extrapolation from vehicle points, but rather uses precise vehicle data as control points, terrain as a base, and then integrates high-resolution radar images, satellite images, etc. Figure 1 Similarly, the actual weather system structure (such as rainband boundaries and thunderstorm cells) is calibrated and superimposed onto this fine grid.

[0038] After traversing all grids, the algorithm generates a complete microclimate grid. Each 50m×50m grid contains a set of real-time updated environmental data, including: wind speed, wind direction, temperature, humidity, air pressure, rainfall intensity, lightning distance, tree dieback rate, and air pressure change rate. The lightning distance is calculated based on the latitude and longitude coordinates of the most recent lightning event in the lightning location data and the vehicle's GPS coordinates. The tree dieback rate is obtained by accessing the forest health database. The air pressure change rate is obtained by applying a moving average and digital low-pass filter to the air pressure data, followed by linear regression analysis, or by obtaining data from weather stations in real time. This data is updated at a high frequency (e.g., every minute), dynamically evolving as vehicles move and data is refreshed, providing a comprehensive, detailed, and real-time panoramic view of the meteorological situation for the next step.

[0039] The disaster risk assessment module includes: a campsite type determination unit, a disaster type identification unit, and a risk quantification assessment unit; it is used to continuously monitor the meteorological forecast values ​​pushed in real time by the microclimate modeling module, and to perform risk quantification assessment of the probability of occurrence and the level of danger of disaster types based on the current campsite type; campsite types include: waterfront, forest area, and foothill area; disaster types include: flood, tree fall, landslide, and thunderstorm.

[0040] The campsite type determination unit includes: If a vehicle's GPS speed remains at 0 for more than a preset time (e.g., 5 minutes), it is determined that the vehicle is parked and camping. Immediately, using the vehicle's coordinates as the center, the system retrieves land cover type data and high-precision digital elevation model (DEM) data within a 100-meter radius of the microclimate grid. It then checks for land cover type grids representing water bodies (such as rivers or lakes) within a 50-meter radius of the vehicle. If found, the average elevation difference between the campsite grid and the nearest water body grid is calculated. If the elevation difference is ≤ 3 meters, the campsite is immediately labeled as waterfront, meaning it is highly susceptible to the direct impact of rising water levels. The system then counts the number of all land cover grids marked as forest or high-density shrubs within a 50-meter radius of the vehicle, calculating the percentage of forest and shrub grids to the total number of grids (tree cover rate). Simultaneously, it retrieves typical tree height data for the area from the forest thematic database. If the tree cover rate is > 40% and the average tree height is > 5 meters, it is labeled as forest area, meaning the campsite is surrounded by trees of a certain height. The ground slope of the microclimate subgrid where the vehicle is located is calculated. Within a 100-meter radius centered on the vehicle, the system searches for abrupt terrain changes (i.e., slopes) where the elevation difference between adjacent microclimate subgrids exceeds 5 meters. If the local slope is greater than 10 degrees and such slopes exist nearby, the area is marked as a foothill zone. This indicates that the campsite is in an unstable terrain area, with potential risks of rockfalls or landslides. Finally, a clear campsite type identifier (waterfront, forest, foothill) is generated and passed as a core environmental parameter to the disaster type discrimination unit.

[0041] The disaster type identification unit includes: It receives campsite type identifiers in real time and monitors various environmental data provided by the microclimate grid to the microclimate subgrid where the vehicle is located, such as rainfall intensity, wind speed, dead tree rate, soil saturation index, air pressure change rate, and lightning distance.

[0042] When the campsite type is identified as waterfront, if the rainfall intensity in the next hour is greater than 30 mm / h and the soil saturation index is greater than 80%, the disaster type is determined to be flood. When the campsite type is identified as a forest area, if the wind speed is greater than 15m / s and the dead tree rate is greater than 20%, the disaster type is determined to be tree falling. When the campsite type is identified as foothills, if the real-time rainfall intensity in the uphill area is greater than 20 mm / h or the cumulative rainfall exceeds the threshold, the disaster type is determined to be landslide. When the campsite type is identified as waterfront, forest area, or foothills, if the air pressure change rate is less than -2hPa / h and the lightning distance is less than 3km, the disaster type is determined to be thunderstorm.

[0043] The identified disaster type is output to the risk quantification assessment unit.

[0044] The risk quantification assessment unit includes: When the received disaster type is flood, the corresponding flood risk index is calculated, and the expression is: ; In the formula, The flood risk index, , and These are the weights of rainfall intensity, topographic slope, and soil water holding capacity, respectively. Rainfall intensity, The threshold for rainfall intensity. The slope threshold, This represents the slope value of the campsite. This is the minimum protection value for slope. The soil saturation index is used. Rainfall intensity is obtained in real-time from the microclimate grid. Specifically, it is achieved by processing 1km resolution meteorological radar reflectivity factor data, applying the ZR inversion algorithm to derive the rainfall intensity, and then performing spatial interpolation correction to obtain the current or forecast rainfall intensity of the grid where the vehicle is located. The rainfall intensity threshold is a preset empirical threshold, comprehensively calibrated based on historical flood disaster data, regional climate characteristics, and hydrological models. It is usually set to a fixed value (e.g., 30mm / h or 50mm / h) to determine whether rainfall has reached the critical flood-inducing condition. The slope threshold is also a preset empirical threshold, based on hydrogeological... The surface runoff characteristics are determined and are typically set to 3 degrees. This indicates that when the slope is less than this value, the terrain is flat, drainage capacity is significantly reduced, and water accumulation is likely to occur. The minimum slope protection value is a preset minimum protection constant (e.g., 0.5 degrees) used to prevent the denominator of the formula from being zero or too small when the slope calculation value is too small, thus ensuring the stability of the risk index calculation. The campsite slope value is calculated based on high-precision digital elevation model data. Centered on the microclimate subgrid where the vehicle is located, the ground slope is calculated using the elevation difference between adjacent grids and the grid spacing, and the slope expression is applied to obtain the ground slope in degrees. The slope expression is as follows: ; In the formula, and Elevation In the east-west direction North-South Direction The rate of change (slope component). The n() function returns a value in radians, multiplied by Convert to slope values ​​in degrees (°).

[0045] in, The greater the rainfall intensity, the higher the score; For flatter terrain (slope) The smaller the denominator, the higher the score for that item, indicating that drainage is more difficult; The closer the soil is to saturation, the higher the score, which means the weaker the infiltration capacity; and a continuous flood risk index is output, with the higher the index, the greater the combined risk of water accumulation and surface runoff.

[0046] When the received disaster type is falling tree, the corresponding falling tree risk index is calculated, and the expression is: ; In the formula, Risk index of falling trees For wind speed factor weights, The weight of the dead tree rate factor, The wind speed at the tree canopy height level in the target forest area. This serves as a reference value for wind speed. The density of dead trees. This serves as a reference value for dead trees.

[0047] in, The greater the wind speed relative to the baseline value, the greater the risk it contributes. The higher the density of dead trees relative to the benchmark, the greater the risk contributed; weighting and It can be calibrated using historical accident data or mechanical models; and outputs a tree-falling risk index, which intuitively reflects the overall danger level of strong winds acting on fragile forest areas.

[0048] Among them, the wind speed at the canopy level ( This refers to the average, continuous wind speed assumed at the top of the tree canopy (e.g., 10 meters) in the forest area near the campsite. This wind speed is a core input for assessing the risk of trees falling (tree impact), and is expressed as: ; In the formula, The near-ground wind speed (unit: m / s) is the calibrated value derived from the final wind speed after corrections for factors such as vehicle body flow and installation height. The target canopy height (unit: m) is typically taken as 10 meters. Alternatively, the typical tree heights of major tree species near the campsite can be obtained from the Forest Health Database. The actual installation height of the wind speed sensor (unit: m) refers to the vertical height of the three-dimensional ultrasonic anemometer above the ground, which is a fixed equipment parameter.

[0049] The wind speed reference benchmark is based on relevant national or industry documents regarding critical wind speeds that can easily cause trees to fall or break branches. For example, forestry standards often use level 6 winds (10.8-13.8 m / s) as the threshold at which a significant threat to trees begins to be posed. Therefore, the upper limit of 13.8 m / s is taken as the wind speed reference benchmark. Dead tree distribution density: Based on the vehicle's current GPS coordinates, a request containing location information is automatically generated through vehicle networking (4G / 5G). This request is sent to the forest resource inventory database or forest health monitoring system maintained by the forestry or natural resources management department, and returns forest health statistics for the area where the vehicle is located (e.g., within a 1-kilometer radius). The percentage of dead trees, diseased and rotten trees, or the percentage of trees with weak or dying vitality levels are extracted from the returned forest health statistics. This percentage is the dead tree distribution density. The dead tree reference benchmark is obtained by referring to forestry logging or tending safety regulations. These regulations often stipulate that when the percentage of dead trees in a forest stand exceeds a certain threshold, it must be cleared to eliminate safety hazards. For example, if the regulations stipulate that stands with a dead tree ratio exceeding 30% must undergo sanitary felling, the reference value for dead trees can be set to 0.3.

[0050] When the received disaster type is landslide, the landslide risk index is calculated using the following expression: ; The expression for calculating the rainfall-induced factor is as follows: ; The expression for calculating the terrain energy factor is: ; The expression for calculating the surface anti-skid factor is: ; In the formula, This is a landslide risk index. , , These are the weighting coefficients, Rainfall-inducing factors, For geomorphic energy factors, As a surface anti-skid factor, The weighting factor for cumulative rainfall. This represents the cumulative rainfall. The critical cumulative rainfall threshold, The critical instability slope is set based on geological maps or experience (e.g., 25°). The surface cover stability coefficient is used; the cumulative rainfall is calculated based on the regional soil drainage characteristics, with a fixed accumulation period (e.g., the previous 24 hours, the previous 72 hours). From the microclimate grid, the rainfall intensity value of the grid where the vehicle is located (or the target slope area grid) is continuously read at a set time resolution (e.g., 1 hour). The cumulative rainfall is obtained using the following expression: ; In the formula, For time intervals; The critical cumulative rainfall threshold is determined by referring to normative documents such as the "Standards for Geological Disaster Meteorological Risk Warning Levels" issued by national or provincial natural resources departments. These documents typically provide critical rainfall values ​​for different geological zones and warning levels, which can be directly used as the critical cumulative rainfall threshold. If no existing standard exists, rainfall data for the corresponding time window (e.g., 24 hours) before historical landslide events in the area are collected, and a rainfall value with a significantly increased trigger probability is determined through statistical analysis (e.g., frequency analysis) as the critical cumulative rainfall threshold. The critical instability slope is determined by obtaining the internal friction angle of the main soil and rock masses (e.g., residual soil, strongly weathered rock) in the campsite area from regional geological maps or engineering geological survey reports. Under simplified conditions, the critical instability slope is approximately equal to the internal friction angle of the soil. If detailed data is unavailable, engineering experience values ​​are used. For example, for general loose soil, the critical instability slope is often taken as 25° to 35°; for rock slopes, the value will be higher. The land cover stability coefficient is obtained from the microclimate grid by acquiring the land cover type code of the target grid (e.g., 101-dense forest, 021-grassland, 001-bare rock). It is then queried from a built-in land cover type-stability coefficient lookup table based on geotechnical engineering and ecological research. This table assigns a coefficient between 0 and 1 to each cover type, with higher values ​​indicating stronger anti-sliding capabilities. The land cover type-stability coefficient lookup table is as follows: bedrock, concrete pavement: land cover stability coefficient 1.0; dense forest (strong root system soil stabilization): land cover stability coefficient 0.7 to 0.9; shrubs, grassland: land cover stability coefficient 0.4 to 0.6; bare soil, cultivated land: land cover stability coefficient 0.1 to 0.3. Based on the land cover type code of the current microclimate subgrid, the corresponding land cover stability coefficient is directly output.

[0051] When the received disaster type is thunderstorm, the thunderstorm arrival time is calculated using the following expression: ; In the formula, The estimated arrival time of the thunderstorm. Radar echo range, The speed of cloud movement. This is a correction factor for sudden pressure drop. This represents the rate of change of air pressure. The pressure drop correction factor directly cites statistical conclusions from published meteorological research on the influence of air pressure tendency on the movement speed of strong convective systems, converting the given average correction amount into a pressure drop correction factor.

[0052] in, A basic estimated time is given for linear extrapolation based on current speed and distance; correction term. The description of a sudden drop in air pressure indicates an increase in thunderstorm intensity and rapid approach. A negative value for this item will shorten the estimated arrival time, allowing for earlier and more aggressive warnings. The output shows a dynamically updated estimated arrival time of the thunderstorm, in minutes.

[0053] The early warning generation module receives three core quantitative results from risk assessment: flood risk index, tree fall risk index, landslide risk index, and thunderstorm arrival time. It also has a pre-set disaster level lookup table, which defines the risk levels corresponding to different numerical ranges (usually divided into four levels: blue, yellow, orange, and red, representing concern, low risk, high risk, and emergency, respectively). The module compares the received indices and times with the threshold ranges in the disaster level lookup table. A landslide risk index of less than 0.3 is a "blue alert", 0.3-0.6 is a "yellow alert", 0.6-0.8 is an "orange alert", and greater than 0.8 is a "red alert".

[0054] A flood risk index between 0 and 30 is a "blue alert", between 30 and 60 is a "yellow alert", between 60 and 85 is an "orange alert", and above 85 is a "red alert".

[0055] A "blue alert" is issued when thunderstorms arrive more than 60 minutes in advance, a "yellow alert" is issued when they arrive between 30 and 60 minutes in advance, an "orange alert" is issued when they arrive between 15 and 30 minutes in advance, and a "red alert" is issued when they arrive less than 15 minutes in advance.

[0056] A tree-falling risk index of less than 1.0 is a "blue alert", 1.0-1.5 is a "yellow alert", 1.5-2.0 is an "orange alert", and above 2.0 is a "red alert".

[0057] It also generates a risk list with level labels, such as [Flood: Orange Alert High Risk], [Thunderstorm: Red Alert Emergency, 18 Minutes]; Each triggered disaster type receives a clear risk level label (e.g., thunderstorm - orange warning), and a rich, structured library of evacuation instruction templates is pre-stored. Each template is specific to "disaster type + risk level + direction + distance + coordinates". Specific environmental characteristics of the campsite are extracted from the microclimate grid as variables for generating instructions. For example, for altitude and slope, the nearest relatively safe high ground (flood protection) or low-lying shelter (lightning protection) is identified; for slope aspect and runoff direction, the main flood current direction and potential rockfall / landslide paths are determined to indicate lateral evacuation directions; for land cover data, the location of hazard sources (e.g., pine forest on the east side) and the type of safe zone (e.g., open grassland on the west side) are identified. Dynamic variables are then filled into the selected instruction template, such as: "Strong winds may cause trees on the east side of the campsite to break. Please immediately evacuate the tent, move to a vehicle or the open grassland on the west side, and stay at least 30 meters away from the eastern forest area!"

[0058] The visual early warning information includes disaster icons, risk levels, time information, and evacuation instructions. The disaster icons are prominent, internationally recognized pictograms (e.g., lightning bolt for thunderstorms, fallen tree for tree falls, landslide for landslides, and flood wave for floods). Different risk levels have different colors and flashing states. Risk levels are clearly marked with both color bars and text. For floods, landslides, and tree falls, the time information usually shows the immediate danger or the duration of the risk. For thunderstorms, the core is a dynamic countdown (e.g., expected arrival: 00:18:32), which decreases in real time to create a sense of urgency. Evacuation instructions are displayed in the form of a clear and concise list of key points or voice commands, showing the evacuation instructions dynamically generated from the previous step's evacuation instruction library template based on the microclimate grid.

[0059] The generated complete visual warning information will be pushed simultaneously to: the in-vehicle central control screen, the user's smartphone app, and other outdoor devices via the vehicle bus or wireless network.

[0060] like Figure 2 As shown, this is the second embodiment of the present invention, which provides a method for vehicle-mounted micro-weather monitoring and campsite disaster early warning, including: Collect and process multi-source meteorological data, including local micro-meteorological data, cloud meteorological service data, and geographic data. Perform spatiotemporal coordinate alignment, anomaly identification and removal, and data calibration based on the current altitude on the multi-source meteorological data, and output a time-series data stream in a standardized format. Centered on the current vehicle, an initial microclimate grid covering a preset spatial range is constructed; based on the time-series data stream in the standardized format, values ​​are assigned to each grid point in the initial microclimate grid using a spatial interpolation algorithm to generate a microclimate grid; the microclimate grid includes several microclimate subgrids; Based on the microclimate subgrid, a risk quantification assessment of the probability and hazard level of disaster types is conducted according to the current campsite type; the campsite types include: waterfront, forest areas, and foothill areas; the disaster types include: floods, falling trees, landslides, and thunderstorms; Based on the results of the aforementioned risk quantification assessment, composite early warning information is generated and visualized.

[0061] In summary, this invention, through its innovative system architecture and algorithm design, has produced significant beneficial effects. First, the optimized sensor layout, separating the sensor from the roof center and luggage rack, combined with an automatic calibration algorithm based on altitude, vehicle body flow, and surface roughness when the vehicle is stationary, effectively suppresses interference from thermal radiation and vibration, reducing the measurement error of local micro-meteorological data to within 5%, providing a reliable data foundation for high-precision early warning. Second, it pioneered a spatial interpolation model based on vehicle-mounted data, integrating 1km resolution radar and high-precision terrain data. This model generates a microclimate grid with a radius of 500 meters and a grid precision of 50 meters, achieving accurate characterization of micro-scale disasters such as gusts and slope confluence around campsites, solving the scale mismatch problem in wide-area forecasts. Finally, for camping scenarios such as waterfront, forest areas, and foothills, a multi-factor quantitative disaster model and dynamic evacuation instruction generation mechanism were established. This model quantifies risks into intuitive levels and provides guidance including specific locations and action steps, achieving a leap from general weather alerts to scenario-based, actionable safety decisions, greatly enhancing the proactive safety assurance capabilities for outdoor activities.

[0062] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any other combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product, which includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another.

[0063] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. This storage medium can be a read-only memory, a disk, or an optical disk, etc.

[0064] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A vehicle-mounted microclimate monitoring and campsite disaster warning system, characterized in that, The method comprises the following steps: A data acquisition and preprocessing module is used to acquire and process multi-source weather data, including local microclimate data, cloud weather service data, and geographic data. The multi-source weather data is subjected to time-space coordinate alignment, abnormal data identification and elimination, and data calibration based on the current altitude, and a standardized time series data stream is outputted; A microclimate modeling module is used to construct an initial microclimate grid covering a preset spatial range with the current vehicle as the center. Based on the standardized time series data stream, each grid point in the initial microclimate grid is assigned a value through a spatial interpolation algorithm to generate a microclimate grid. The microclimate grid comprises a plurality of microclimate sub-grids; A disaster risk assessment module is used to quantitatively assess the occurrence probability and danger level of disaster types based on the microclimate sub-grids and according to the current camping ground type. The camping ground type includes waterfront, forest area, and mountainous area. The disaster type includes flood, tree falling, landslide, and thunderstorm; An early warning generation module is used to generate composite early warning information according to the results of the quantitative risk assessment and to visually output the information.

2. The vehicular microclimate monitoring and campsite disaster warning system according to claim 1, wherein, The data acquisition and preprocessing module comprises: A local microclimate unit is used to acquire wind speed and direction through a three-dimensional ultrasonic anemometer installed in the center of the roof, to acquire air pressure through an air pressure gauge installed in the right luggage support, and to acquire rainfall through a rain gauge, thereby forming local microclimate data; A cloud data access unit is used to acquire cloud weather service data from a weather service center in real time through a vehicle communication network. The cloud weather service data at least includes satellite cloud image data and 1km resolution weather radar data. The weather radar data at least includes reflectivity factor data. The satellite cloud image data is used to provide high-altitude cloud information and at least includes cloud layer movement speed; A geographic data analysis unit is used to acquire geographic data by calling preloaded or online high-precision digital maps. The geographic data at least includes high-precision digital elevation model data and land cover type data. The high-precision digital elevation model data at least includes altitude and slope. The land cover type data is used to determine the roughness of the ground, calculate the soil saturation index, and identify the scene in the microclimate grid.

3. The vehicle-mounted microclimate monitoring and campsite disaster warning system according to claim 2, characterized in that, The air pressure gauge is installed with a shock-absorbing rubber pad, and the rain gauge is equipped with an automatic cleaning brush.

4. The vehicular microclimate monitoring and campsite disaster warning system according to claim 2, wherein, The data acquisition and preprocessing module further comprises an automatic calibration unit: When the vehicle speed is zero and remains unchanged for a preset time, the automatic calibration unit is started to calibrate the air pressure, wind speed, and wind direction in the local microclimate data according to the current altitude through a correction expression; And the calibrated air pressure, wind speed, and wind direction are returned and overlaid to the local microclimate data to form a multi-dimensional weather vector; The air pressure correction expression is: ； In the formula, P is the calibrated air pressure, P is the air pressure obtained by the barometer, P is the vertical decrement rate of air pressure, H is the altitude; The wind speed correction expression is: ； In the formula, is the calibrated wind speed, is the wind speed obtained by the three-dimensional ultrasonic anemometer, is the vehicle body flow correction coefficient, is the actual installation height of the three-dimensional ultrasonic anemometer, is the preset standard reference height, is the ground roughness length; The wind direction correction expression is: ； wherein is the calibrated wind direction, is the wind direction obtained by the three-dimensional ultrasonic anemometer, is the installation angle of the three-dimensional ultrasonic anemometer, is the magnetic declination.

5. The vehicular microclimate monitoring and campsite disaster warning system according to claim 4, wherein, The expression of the spatial interpolation algorithm is: ； wherein, is the weather forecast value at coordinates in the microclimate grid, is the number of independent data sources participating in the data fusion, is the index of the summation loop representing the th data source, is the multi-dimensional weather vector for the vehicle location, is the elevation terrain correction from the vehicle location to coordinates , is the fusion weight factor for the th data source, is the deviation value of the th data source from the reference value at coordinates .

6. The vehicular microclimate monitoring and campsite disaster warning system according to claim 5, wherein, The disaster wind direction assessment module comprises: A camping ground type judgment unit is used to determine the camping ground type based on the geographic data and with the current vehicle position as the center according to a preset rule. The disaster type determination unit is configured to determine a disaster type according to a preset rule based on a camping site type and a microclimate grid; The risk quantification evaluation unit is configured to calculate a corresponding disaster risk according to a preset expression based on the disaster type and the microclimate grid; The camping site type determination unit includes: When the vehicle speed is 0 and lasts for a preset time, the following conditions are triggered: If the camping site is within 50 m of a river, lake or sea, and the ground elevation is not higher than the water level by more than 3 meters, the camping site type is waterfront; If the tree coverage rate within 50 m is greater than 40% and the average height of the trees is greater than 5 meters, the camping site type is forest area; If the ground slope is greater than 10 degrees and there is a slope with an elevation change greater than 5 meters within a range of 100 meters, the camping site type is hillside.

7. The vehicular microclimate monitoring and campsite disaster warning system according to claim 6, wherein, The disaster type determination unit includes: When the camping site type is waterfront, if the rainfall intensity is greater than 30 mm / h and the soil saturation index is greater than 80% within 1 hour, the disaster type is flood; When the camping site type is forest area, if the wind speed is greater than 15 m / s and the rate of dead trees is greater than 20%, the disaster type is tree falling; When the camping site type is hillside, if the real-time rainfall intensity of the uphill area is greater than 20 mm / h or the cumulative rainfall exceeds a threshold, the disaster type is landslide; When the camping site type is waterfront, forest area or hillside, if the air pressure change rate is less than -2 hPa / h and the lightning distance is less than 3 km, the disaster type is thunderstorm.

8. The vehicular microclimate monitoring and campsite disaster warning system according to claim 7, wherein, The risk quantification evaluation unit includes: When the disaster type is flood, the flood risk index is calculated, and the expression is: ； wherein, is a flood risk index, , and are a rainfall intensity influence weight, a terrain slope influence weight, and a soil water holding capacity influence weight, respectively, is a rainfall intensity, is a rainfall intensity threshold value, is a slope threshold value, is a campsite slope value, is a slope minimum protection value, is a soil saturation index; When the disaster type is tree falling, the tree falling risk index is calculated, and the expression is: ； wherein is a tree risk index, is a wind speed factor weight, is a dead tree rate factor weight, is a wind speed for a target forest canopy height, is a wind speed reference base value, is a dead tree distribution density, is a dead tree reference base value; When the disaster type is landslide, the landslide risk index is calculated, and the expression is: ； ； ； ； wherein, is the landslide risk index, , , are weight coefficients, respectively, is the rainfall triggering factor, is the topographic potential factor, is the surface resistance to sliding factor, is the weight coefficient of cumulative rainfall, is the cumulative rainfall, is the critical cumulative rainfall threshold, is the critical instability slope, is the surface cover stability coefficient; When the disaster type is thunderstorm, the thunderstorm arrival time is calculated, and the expression is: ； In the formula, is the thunderstorm arrival time, is the radar echo distance, is the cloud layer moving speed, is the pressure drop correction coefficient, is the pressure change rate; the radar echo distance is obtained by the reflectivity factor data through the Z-R inversion algorithm to obtain the rainfall intensity, and through the strong echo front recognition and spatial matching algorithm.

9. The vehicular microclimate monitoring and campsite disaster warning system according to claim 8, wherein, The warning generation module includes: based on the flood risk index, the tree falling risk index, the landslide risk index and the thunderstorm arrival time, according to a preset disaster level query table, the risk level of flood, tree falling and landslide is obtained; and a visual composite warning information is generated; the composite warning information at least includes: disaster type icon, risk level, time and location information and risk avoidance instruction; the risk avoidance instruction is dynamically generated according to the disaster type, the risk level and the environmental characteristics in the microclimate grid; and the composite warning information is output to the vehicle screen or mobile terminal; the environmental characteristics are obtained based on the ground cover type data and high-precision digital elevation model data, and at least include: altitude, slope and slope direction.

10. The vehicle-mounted microclimate monitoring and campsite disaster warning method according to any one of the vehicle-mounted microclimate monitoring and campsite disaster warning systems of claims 1-9, characterized in that, It includes: Collect and process multi-source weather data, including: local microclimate data, cloud weather service data and geographic data, align the space-time coordinates of the multi-source weather data, identify and remove abnormal data, and calibrate the data based on the current altitude, output the standardized format time series data stream; An initial microclimate grid covering a preset spatial range is constructed around the current vehicle; based on the standardized format time series data stream, each grid point in the initial microclimate grid is valued by a spatial interpolation algorithm to generate a microclimate grid; the microclimate grid includes a plurality of microclimate sub-grids; Based on the microclimate sub-grid, a risk quantitative assessment of the occurrence probability and the danger level of disaster types is performed according to a current campsite type; the campsite type includes a waterfront, a forest area and a mountainous area; the disaster type includes a flood, a tree falling, a landslide and a thunderstorm; According to the result of the risk quantitative assessment, composite early warning information is generated and visualized output.

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