Vehicle-mounted mobile meteorological detection system and method

Abstract

The invention relates to the technical field of vehicle-mounted mobile meteorological detection, in particular to a vehicle-mounted mobile meteorological detection system and method. The method comprises the following steps: activating a laser radar system of a vehicle-mounted end to collect back scattering signals of a target air mass corresponding to each collection point on a voyage route, performing inversion based on the back scattering signals to obtain an extinction coefficient of the target air mass, and determining the boundary of the target air mass based on the variation of the extinction coefficient of the target air mass on the voyage route; determining a wind field vertical profile of the target air mass based on the heterodyne spectrum; and obtaining a posterior failure probability of the moving trend of the target air mass based on Bayesian inversion analysis, and predicting the moving trend of the target air mass based on the posterior failure probability. According to the method, the air mass boundary can be accurately determined, the height of the vertical profile of the wind field is calculated, the air mass moving trend is analyzed and predicted through Bayesian inversion, and the accuracy of air mass moving trend prediction is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle-mounted mobile meteorological detection, and particularly relates to a vehicle-mounted mobile meteorological detection system and method. BACKGROUND

[0002] In the field of meteorological detection, accurate grasp of the movement trend of air mass is of great significance for weather forecasting, disaster weather warning, etc. Traditional meteorological detection methods, such as fixed site meteorological observation, have the problem of insufficient spatial coverage, and it is difficult to continuously, dynamically and finely monitor the moving air mass.

[0003] The existing vehicle-mounted system still has many deficiencies in air mass detection. On the one hand, in the determination of the air mass boundary, there is a lack of efficient and accurate method, and it is difficult to quickly determine the range of the air mass according to the detection data; on the other hand, there is a phenomenon that the accuracy is not enough for obtaining the vertical profile of the air mass wind field, and it is difficult to effectively observe in combination with the characteristics of vehicle-mounted movement; in addition, in the analysis of the movement trend of the air mass, it is mostly dependent on experience model or simple statistical analysis, and there is a lack of scientific method based on advanced probability analysis and reliability evaluation, resulting in great uncertainty in the prediction of the movement trend of the air mass, and the demand for fine meteorological service cannot be met.

[0004] Therefore, there is an urgent need for a vehicle-mounted mobile meteorological detection system and method to solve the above problems. SUMMARY

[0005] The purpose of the present application is to provide a vehicle-mounted mobile meteorological detection system and method, which aims to solve the problems of inaccurate air mass boundary detection and inaccurate air mass movement trend judgment in traditional meteorological detection methods.

[0006] The purpose of the present application can be achieved by the following technical solutions: On the one hand, a vehicle-mounted mobile meteorological detection system, the system comprising: an air mass boundary determination module for determining the sailing route of the vehicle-mounted end, and activating the laser radar system of the vehicle-mounted end to collect the backscattering signals of the target air mass corresponding to each collection point on the sailing route, and based on the backscattering signals, the extinction coefficient of the target air mass is obtained by inversion, and the boundary of the target air mass is determined based on the change of the extinction coefficient of the target air mass on the sailing route; a wind field vertical profile determination module for activating the laser heterodyne radiometer of the vehicle-mounted end to observe the heterodyne spectrum containing the wind field information of the target air mass within the boundary of the target air mass, and determining the wind field vertical profile of the target air mass based on the heterodyne spectrum; The air mass movement trend analysis module is configured to analyze the vertical profile of the wind field of the target air mass by Bayesian inversion to obtain posterior samples of the random field model parameters, perform reliability analysis based on the posterior samples to calculate the posterior failure probability of the movement trend of the target air mass, and predict the movement trend of the target air mass based on the posterior failure probability.

[0007] Further, the determination of the underway route of the vehicle-mounted end specifically includes the following process: An image of the target air mass is collected, the image of the target air mass is processed based on an algorithm of digital image processing to obtain a contour line of the target air mass, and a collection point passed by the vehicle-mounted end is determined based on the contour line, with the position of the vehicle-mounted end as an initial collection point, the collection points being sequentially connected in order of distance from the initial collection point to form the underway route.

[0008] Further, the inversion of the extinction coefficient of the target air mass based on the backscattering signal specifically includes the following process: The backscattering signal is denoised by using a time-domain filtering algorithm, the position and attitude data of the vehicle-mounted end obtained by the Beidou high-precision positioning and timing unit are combined, and the stray light interference signal generated when the vehicle-mounted end passes through high-rise buildings and tunnels is removed by spatial filtering to obtain the effective backscattering signal of the target air mass; the signal interval corresponding to the target air mass is segmented based on the intensity mutation characteristics of the effective backscattering signal, and the starting height and the ending height of the signal interval are determined by the detection height data of the laser radar system; Inversion model selection and parameter configuration: the inversion model is selected according to the uniformity characteristics of the target air mass, if the target air mass is a single-source uniform air mass, the Klett two-point boundary method is selected, if the target air mass is a multi-source mixed non-uniform air mass, the Fernald single-boundary method is selected, and the core parameter laser radar ratio S of the inversion model is configured, wherein if the target air mass is a pollution air mass, the corresponding S value is configured as 40-60sr -1 , and if the target air mass is a dust air mass, the corresponding S value is configured as 20-30sr -1 ; Boundary condition calibration: when the target air mass is concentrated in the boundary layer within 1km from the ground, the near-ground extinction coefficient measured by the turbidity meter in the ground meteorological element measurement assembly is taken as the near-ground boundary point of the Klett two-point boundary method, , and the measurement accuracy is not less than ±5%; when the target air mass is located above 1km in the upper air, the height at which the backscattering signal monitored by the laser radar system decays to the clean atmospheric background value is taken as the top height of the air mass , and is taken as the high-altitude boundary point of the Fernald single-boundary method; The extinction coefficient inversion and verification correction: input the effective backscattering signal, the configured inversion model parameters and the calibrated boundary condition into the data processing unit, solve the extinction coefficient of the target air mass at different height layers of the target air mass through the atmospheric radiation transfer equation ; through internal consistency verification, judge the spatial position coincidence degree of the high value area of the extinction coefficient and the high value area of the backscattering signal, if the coincidence degree is lower than 85%, re-adjust the laser radar ratio S value; combine the laser radar extinction coefficient data of the fixed ground station along the line or the aerosol optical depth data of satellite remote sensing for external cross verification, if the deviation between the vehicle-mounted inversion result and the ground station data is more than 25% or the deviation between the vehicle-mounted inversion result and the satellite data is more than 20%, correct the boundary condition and re-execute the inversion, and finally output the extinction coefficient profile of the target air mass.

[0009] Further, the boundary of the target air mass is determined based on the change of the extinction coefficient of the target air mass on the sailing route, and specifically includes the following processes: obtain the extinction coefficient of each collection point i obtain the geographic coordinates (L(i), B(i)) and time information t(i) obtained by the Beidou high-precision positioning and timing unit; perform sliding window analysis on the extinction coefficients of the continuous N collection points on the sailing route, calculate the mean value μ(k), the standard deviation σ(k) and the spatial gradient G(k) of the extinction coefficients in the window, wherein k is the window number, N is 5-20 and is positively correlated with the driving speed of the vehicle-mounted end; wherein the spatial gradient G(k) is calculated by the formula: G(k)=| (k+1)- (k)| / D(k), D(k) is the straight line distance between the kth and k+1th collection points, which is calculated from the geographic coordinates of the two points; preset the extinction coefficient threshold value and the gradient threshold value G0 according to the type of the target air mass; when the extinction coefficients of the continuous 3 collection points suddenly increase from less than the extinction coefficient threshold value to greater than , and the corresponding spatial gradient G(i) is greater than or equal to G0, it is determined that the region is the entering boundary of the air mass, and the entering boundary point coordinates (L_in, B_in) are recorded; when the extinction coefficients of the continuous 3 collection points suddenly decrease from greater than to less than , and the corresponding spatial gradient G(i) is greater than or equal to G0, it is determined that the region is the leaving boundary of the air mass, and the leaving boundary point coordinates (L_out, B_out) are recorded, and the boundary of the target air mass is determined based on the position information of the collection points on the sailing route.

[0010] Further, before determining the wind field vertical profile of the target air mass based on the heterodyne spectrum, the working process of the system specifically includes the following processes:​ A laser heterodyne radiometer emits a laser signal into the airspace containing the target air mass and receives the echo signal scattered by the target air mass. This echo signal is heterodyne-mixed with a local oscillating laser to generate a heterodyne spectrum containing wind field information of the target air mass. Based on the elevation and roll angles of the vehicle-mounted antenna obtained by the BeiDou high-precision positioning and timing unit, the attitude of the radiometer receiving antenna is adjusted in real time to counteract beam shift caused by vehicle bumps. By using temporal and spatial filtering to remove spectral segments corresponding to stray light from tall buildings and tunnels, the effective heterodyne spectrum of the target air mass is obtained.

[0011] Furthermore, determining the vertical profile of the wind field of the target air mass based on heterodyne spectroscopy specifically includes the following process: Spectral analysis was performed on the effective heterodyne spectrum. The spectrum was transformed from the time domain to the frequency domain using a fast Fourier transform to identify the characteristic spectral peaks corresponding to the scattering of the target air mass. The difference between the center frequency of the characteristic spectral peak and the inherent emission frequency of the laser heterodyne radiometer was calculated to obtain the Doppler frequency shift at different altitudes of the target air mass. ,in To detect altitude; Based on the quantitative relationship between Doppler frequency shift and wind speed, the target air mass at different altitudes was obtained through inversion. Corresponding radial wind speed The quantitative relationship formula is: ,in, The laser wavelength of the laser heterodyne radiometer. The angle between the laser detection beam and the direction of movement of the target air mass is calculated from the heading angle of the vehicle-mounted end and the beam pointing angle of the radiometer obtained by the Beidou high-precision positioning and timing unit; the radial wind speed of each altitude layer is integrated at height intervals of 50-200m to generate the vertical profile of the wind field of the target air mass.

[0012] Furthermore, obtaining posterior samples of random field model parameters from the vertical profile of the target air mass's wind field through Bayesian inversion analysis specifically includes the following process: make This is a sample of the vertical profile of the wind field for the target air mass, where... Indicates height layer radial wind speed; To determine the number of height layers, Bayesian theory is used to update the random variable. : ; in, This represents information about the wind field vertical profile sample provided by the target air mass. The posterior probability density function; To be independent The proportionality coefficient, the value of which is set by the system based on historical measurement experience; The prior probability density function is used for quantization. Prior information; Let be the likelihood function, representing the given... Occasionally The possibility; ;in, To fix the measurement error, it is set based on historical measurement experience. express The corresponding random variable representation value, The value is .

[0013] Furthermore, the process of performing reliability analysis based on posterior samples to calculate the posterior failure probability of the target air mass movement trend specifically includes the following steps: Perform reliability analysis to calculate the posterior failure probability of the target air mass movement trend. : ;in, To evaluate the function of the target air mass movement trend, Indicates when hour, Take 1, otherwise Set to 0; in, The following steps were taken: Calculate the radial wind speed at each height level. Compared with the expected radial wind speed The deviation, and obtain .

[0014] Furthermore, predicting the movement trend of the target air mass based on the posterior failure probability specifically includes the following process: Establish an inverse proportional function between the posterior failure probability and the target air mass movement trend characterization value BZ: ;when When the value exceeds the preset threshold for representing the movement trend, it indicates that the movement trend of the target air mass is consistent with the direction of radial wind flow. When the value is less than or equal to the preset moving trend characterization threshold, Bayesian inversion analysis needs to be performed again. The preset moving trend characterization threshold is set by the system based on historical measurement experience.

[0015] On the other hand, a vehicle-mounted mobile weather detection method includes: The vehicle-mounted terminal's navigation route is determined, and the vehicle-mounted terminal's lidar system is activated to collect the backscattered signals of the target air mass corresponding to each collection point on the navigation route. The extinction coefficient of the target air mass is obtained based on the backscattered signals, and the boundary of the target air mass is determined based on the change of the extinction coefficient of the target air mass on the navigation route. The on-board laser heterodyne radiometer is activated to observe the heterodyne spectrum containing wind field information of the target air mass within the boundary of the target air mass, and the vertical profile of the wind field of the target air mass is determined based on the heterodyne spectrum. The vertical profile of the wind field of the target air mass is obtained by Bayesian inversion analysis to obtain posterior samples of the random field model parameters. Reliability analysis is performed based on the posterior samples to calculate the posterior failure probability of the target air mass's movement trend, and the movement trend of the target air mass is predicted based on the posterior failure probability.

[0016] Compared to existing solutions, the beneficial effects achieved by this invention are: By collecting backscattered signals and retrieving the extinction coefficient using a lidar system, the boundary of the target air mass can be determined based on the change in the extinction coefficient. This method can accurately and efficiently delineate the range of the air mass, providing an accurate research object for subsequent air mass-related analyses. It solves the problems of inaccurate and inefficient air mass boundary determination by traditional methods.

[0017] By using a laser heterodyne radiometer to observe heterodyne spectra to determine the vertical profile of the wind field, the high resolution and high sensitivity of laser heterodyne technology can be fully utilized to obtain wind field information at different heights of the target air mass with high precision, thus making up for the lack of accuracy in wind field observation of existing vehicle-mounted systems.

[0018] By employing Bayesian inversion analysis to obtain posterior samples of random field model parameters, and then combining this with reliability analysis to calculate the posterior failure probability to predict air mass movement trends, advanced probability analysis and reliability assessment methods are introduced. This significantly improves the scientific rigor and accuracy of air mass movement trend prediction, reduces prediction uncertainty, and better meets the needs of refined meteorological services, such as weather forecasts and severe weather warnings. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0020] Figure 1 This is a system block diagram of a vehicle-mounted mobile weather detection system according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the operation of a vehicle-mounted mobile weather detection system according to an embodiment of the present invention. Figure 3 This is a flowchart illustrating the workflow of a vehicle-mounted mobile weather detection method according to an embodiment of the present invention. Figure 4 This is a structural block diagram of an electronic device according to an embodiment of the present invention; Figure 5This is a schematic diagram illustrating the application of a vehicle-mounted mobile weather detection system according to an embodiment of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more exemplary embodiments. Numerous specific details are provided in the following description to give a full understanding of exemplary embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of the specific details omitted, or other methods, components, steps, etc., can be employed. In other instances, well-known structures, methods, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0023] This embodiment provides a vehicle-mounted mobile weather detection system. Figure 1 This is a system block diagram of a vehicle-mounted mobile weather detection system according to an embodiment of the present invention, such as... Figure 1 As shown, the system includes: The target air mass boundary determination module is used to determine the driving route of the vehicle-mounted terminal, activate the lidar system of the vehicle-mounted terminal to collect the backscattered signals of the target air mass corresponding to each collection point on the driving route, and obtain the extinction coefficient of the target air mass based on the backscattered signals, and determine the boundary of the target air mass based on the change of the extinction coefficient of the target air mass on the driving route. The wind field vertical profile determination module is used to activate the laser heterodyne radiometer on the vehicle to observe the heterodyne spectrum containing wind field information of the target air mass within the boundary of the target air mass, and determine the wind field vertical profile of the target air mass based on the heterodyne spectrum. The air mass movement trend analysis module is used to obtain posterior samples of random field model parameters by performing Bayesian inversion analysis on the vertical profile of the wind field of the target air mass. Based on the posterior samples, reliability analysis is performed to calculate the posterior failure probability of the target air mass movement trend, and the movement trend of the target air mass is predicted based on the posterior failure probability.

[0024] In summary, this invention determines the vehicle-mounted navigation route and activates the vehicle-mounted lidar system to collect backscattered signals of the target air mass corresponding to each collection point along the route. Based on the backscattered signals, the extinction coefficient of the target air mass is obtained through inversion, and the boundary of the target air mass is determined based on the change in the extinction coefficient along the navigation route. The vehicle-mounted laser heterodyne radiometer is activated to observe the heterodyne spectrum containing wind field information of the target air mass within its boundary, and the vertical profile of the target air mass's wind field is determined based on the heterodyne spectrum. The vertical profile of the target air mass's wind field is then analyzed using Bayesian inversion to obtain posterior samples of random field model parameters. Based on these posterior samples, reliability analysis is performed to calculate the posterior failure probability of the target air mass's movement trend, and the movement trend of the target air mass is predicted based on the posterior failure probability. This invention can accurately determine the boundary of an air mass and calculate the height of the vertical profile of the wind field. It uses Bayesian inversion analysis to obtain posterior samples of random field model parameters, and then combines reliability analysis to calculate the posterior failure probability to predict the movement trend of the air mass. By introducing advanced probability analysis and reliability assessment methods, it significantly improves the scientificity and accuracy of air mass movement trend prediction.

[0025] In some embodiments, an image of the target air mass is acquired, and the image of the target air mass is processed based on a digital image processing algorithm to obtain the outline of the target air mass. Based on the outline, the acquisition points passed by the vehicle-mounted terminal are determined. The location of the vehicle-mounted terminal is taken as the initial acquisition point, and the acquisition points are connected in sequence according to their distance from the initial acquisition point to form a navigation route.

[0026] Specifically, the first step is to acquire images of the target air mass. The vehicle is equipped with a high-definition image acquisition device (such as an industrial camera) to capture images of the surrounding atmospheric environment in real time during the movement. The focus is on the area where the target air mass is located, ensuring that the acquired images can clearly show the morphological characteristics of the target air mass and avoid affecting subsequent processing due to image blurring or missing information.

[0027] Next, digital image processing is performed on the acquired target air mass image. First, image preprocessing is carried out, using denoising algorithms (such as Gaussian filtering) to eliminate interference factors such as random noise and salt-and-pepper noise in the image. Then, image enhancement techniques (such as histogram equalization) are used to improve the contrast between the air mass and the background area, making the air mass outline more prominent. Subsequently, edge detection algorithms (such as the Canny edge detection algorithm) are used to extract the edge pixels of the target air mass. These edge pixels together constitute the preliminary outline of the target air mass. Finally, the preliminary outline is smoothed using contour optimization algorithms (such as morphological closing operation) to remove burrs and breaks in the outline, resulting in a continuous, complete, and accurate target air mass outline.

[0028] Based on the obtained target air mass contour line, the sampling points passed by the vehicle-mounted terminal are determined. The spatial distribution characteristics of the contour line are analyzed. Combined with the movement range and detection requirements of the vehicle-mounted terminal, points that can fully reflect the characteristics of the target air mass are selected as sampling points within the area covered by the contour line. This ensures that each sampling point is within the influence range of the target air mass, and that the spacing between adjacent sampling points is reasonable. This avoids data redundancy caused by overly dense sampling points, and also prevents information loss caused by sparse sampling points.

[0029] Plan the navigation route, using the current location of the vehicle terminal as the initial data collection point, and obtain the straight-line distance between each data collection point and the initial data collection point through a positioning module (such as GPS positioning); sort all data collection points in order of distance from near to far, and then connect the sorted data collection points in sequence with a smooth curve or straight line to form a continuous navigation route.

[0030] In some embodiments, Figure 2 This is a flowchart illustrating the operation of a vehicle-mounted mobile weather detection system according to an embodiment of the present invention. Figure 2 As shown, the extinction coefficient of the target air mass is obtained by inverting the backscattered signal through the following process: Step 1: Use a time-domain filtering algorithm to perform moving average noise reduction on the backscattered signal. Combine the vehicle position and attitude data obtained by the Beidou high-precision positioning and timing unit, and use spatial filtering to remove stray light interference signals generated when the vehicle passes through tall buildings and tunnels to obtain the effective backscattered signal of the target air mass. The moving average window length of the time-domain filtering algorithm is determined based on the pulse frequency of the lidar system. When the pulse frequency is 1-10Hz, the window length is set to 10-20 pulse cycles. During the spatial filtering process, the spatiotemporal coordinates of the vehicle-mounted terminal in densely built-up areas or tunnels are marked by the Beidou high-precision positioning and timing unit, and the backscattered signal corresponding to the spatiotemporal coordinates is directly deleted.

[0031] Step 2: Based on the intensity abrupt change characteristics of the effective backscattered signal, the signal interval corresponding to the target air mass is segmented. The starting and ending heights of the signal interval are determined by the detection height data of the lidar system. Step 3: Inversion model selection and parameter configuration; Specifically, the inversion model is selected based on the homogeneity characteristics of the target air mass. If the target air mass is a homogeneous air mass from a single source, the Klett two-point boundary method is selected; if the target air mass is a non-homogeneous air mass from multiple sources, the Fernald single-boundary method is selected. The core parameter of the inversion model, the lidar ratio S, is configured. If the target air mass is a polluted air mass, the corresponding S value is configured to be 40-60 sr. -1 If the target air mass is a dust storm, the corresponding S value should be configured as 20-30 sr. -1 ; The homogeneity of the target air mass is determined by the standard deviation of the effective backscattered signal. If the standard deviation is less than 15% of the signal mean, it is considered a homogeneous air mass; if the standard deviation is greater than or equal to 15% of the signal mean, it is considered a non-homogeneous air mass. The Klett two-point boundary method and the Fernald single-boundary method are existing technologies and will not be elaborated upon here.

[0032] Step 4: Boundary condition calibration; When the target air mass is concentrated in the boundary layer within 1 km of the ground, the near-surface extinction coefficient is measured in real time by the turbidity meter in the surface meteorological element measurement component. As the near-ground boundary point of Klett's two-point boundary method, The measurement accuracy is no less than ±5%; when the target air mass is located at an altitude of more than 1 km, the height at which the backscattered signal attenuates to the clean atmospheric background value is used as the height of the air mass top by monitoring the backscattered signal through the lidar system. ,Will As the high-altitude boundary point of Fernald's single boundary method; The installation height of the turbidimeter and the laser emission port of the lidar system are not more than 1 meter apart to ensure the spatial correlation between the extinction coefficient at the near-ground boundary point and the extinction coefficient at the bottom of the target air mass; the height of the top of the air mass... In the process of determining the altitude, the altitude of the lidar detection is calibrated by combining the altitude data of the Beidou high-precision positioning and timing unit to eliminate the altitude error caused by terrain undulations. Step 5: Extinction coefficient inversion and verification correction.

[0033] The effective backscattered signal, configured inversion model parameters, and calibrated boundary conditions are input into the data processing unit, and the atmospheric radiative transfer equation is used to solve for the target air mass at different altitudes. extinction coefficient Through internal consistency verification, the spatial overlap between the high extinction coefficient region and the high backscattering signal region is determined. If the overlap is less than 85%, the lidar ratio S value is readjusted. External cross-validation is performed by combining lidar extinction coefficient data from fixed ground stations along the route or aerosol optical thickness data from satellite remote sensing. If the vehicle-mounted inversion result deviates from the ground station data by more than 25% or from the satellite data by more than 20%, the boundary conditions are corrected and the inversion is re-executed. Finally, the extinction coefficient profile of the target air mass is output.

[0034] The atmospheric radiative transfer equation is as follows: ;in, For effective backscattered signal strength, The laser emission power of the lidar system. is the system constant of the lidar system. Different lidar systems correspond to different system constants. is the detection height of the target air mass. is the backscattering coefficient of the target air mass at the height ; is the integral value corresponding to the extinction coefficient from the ground to the height . For a certain height of the extinction coefficient , it is equal to the "vertical change rate" of the integral value at this height. After obtaining the expression of the integral value and taking the derivative of the integral value expression, the extinction coefficient at a certain height is obtained.

[0035] In some embodiments, determining the boundary of the target air mass based on the change of the extinction coefficient of the target air mass on the driving route specifically includes the following process: Obtain the extinction coefficient at each acquisition point i, and associate the geographical coordinates (L(i), B(i)) and time information t(i) obtained by the Beidou high-precision positioning and timing unit; Perform a sliding window analysis on the extinction coefficients of N consecutive acquisition points on the driving route, and calculate the mean value μ(k), standard deviation σ(k) and spatial gradient G(k) of the extinction coefficients within the window, where k is the window number, and N ranges from 5 to 20 and is positively correlated with the driving speed of the vehicle-mounted terminal; among them, the spatial gradient G(k) is calculated by the formula: G(k)=| (k + 1)- (k)| / D(k), where D(k) is the straight-line distance between the kth and k + 1th acquisition points, calculated from the geographical coordinates of the two points; Among them, the value of N of the sliding window is dynamically adjusted as follows: when the driving speed v of the vehicle-mounted terminal ≤ 30 km / h, N = 20; when 30 km / h < v ≤ 60 km / h, N = 10; when v > 60 km / h, N = 5.

[0036] Preset the extinction coefficient threshold and the gradient threshold G0 according to the type of the target air mass; Among them, for the polluted air mass, is set to 15 - 20 , G0 is set to 0.05 - 0.1 m, and for the dust air mass, is set to 10 - 15 , G0 is set to 0.03 - 0.08 m; When the extinction coefficients of three consecutive acquisition points suddenly increase from less than the extinction coefficient threshold to greater than When the corresponding spatial gradient G(i)≥G0, the region is determined to be the air mass entry boundary, and the coordinates of the entry boundary point are recorded as (L_in, B_in). When the extinction coefficient of three consecutive sampling points increases from greater than Sudden drop to less than When the corresponding spatial gradient G(i)≥G0, the region is determined to be the departure boundary of the air mass, and the coordinates of the departure boundary point are recorded as (L_out, B_out). The boundary of the target air mass is determined based on the location information of the collection points on the navigation route.

[0037] In some embodiments, before determining the vertical profile of the wind field of the target air mass based on heterodyne spectroscopy, the system workflow specifically includes the following processes: A laser heterodyne radiometer emits a laser signal into the airspace containing the target air mass and receives the echo signal scattered by the target air mass. This echo signal is heterodyne-mixed with a local oscillating laser to generate a heterodyne spectrum containing wind field information of the target air mass. Based on the elevation and roll angles of the vehicle-mounted antenna obtained by the BeiDou high-precision positioning and timing unit, the attitude of the radiometer receiving antenna is adjusted in real time to counteract beam shift caused by vehicle bumps. By using temporal and spatial filtering to remove spectral segments corresponding to stray light from tall buildings and tunnels, the effective heterodyne spectrum of the target air mass is obtained.

[0038] In some embodiments, determining the vertical profile of the wind field of a target air mass based on heterodyne spectroscopy specifically includes the following process: Spectral analysis was performed on the effective heterodyne spectrum. The spectrum was transformed from the time domain to the frequency domain using a fast Fourier transform to identify the characteristic spectral peaks corresponding to the scattering of the target air mass. The difference between the center frequency of the characteristic spectral peak and the inherent emission frequency of the laser heterodyne radiometer was calculated to obtain the Doppler frequency shift at different altitudes of the target air mass. ,in To detect altitude; Based on the quantitative relationship between Doppler frequency shift and wind speed, the target air mass at different altitudes was obtained through inversion. Corresponding radial wind speed The quantitative relationship formula is: ,in, The laser wavelength of the laser heterodyne radiometer. The angle between the laser detection beam and the direction of movement of the target air mass is calculated from the heading angle of the vehicle-mounted end and the beam pointing angle of the radiometer obtained by the Beidou high-precision positioning and timing unit; the radial wind speed of each altitude layer is integrated at height intervals of 50-200m to generate the vertical profile of the wind field of the target air mass.

[0039] Among them, the characteristic spectral peak identification adopts the "dual threshold determination method": the first threshold is the spectral intensity threshold (set to 30% of the maximum spectral intensity), which filters out spectral peaks higher than this threshold; the second threshold is the spectral peak half-width threshold (≤10kHz), which eliminates the broad peak interference corresponding to non-target air masses such as atmospheric molecular scattering, and ensures that the Doppler frequency shift extraction error does not exceed 1kHz.

[0040] Among them, the real-time heading angle α_veh of the vehicle is obtained through the Beidou high-precision positioning and timing unit, and the beam pointing angle β of the laser heterodyne radiometer is obtained (β=90° when the direction is perpendicular to the zenith). Angle = |β - α_veh|, updated every 0.1 seconds when vehicle steering causes a change in α_veh. The angle calculation results ensure the real-time nature of wind speed inversion.

[0041] In some embodiments, obtaining posterior samples of random field model parameters from the vertical profile of the wind field of the target air mass through Bayesian inversion analysis specifically includes the following process: make This is a sample of the vertical profile of the wind field for the target air mass, where... Indicates height layer radial wind speed; To determine the number of height layers, Bayesian theory is used to update the random variable. : ; in, This represents information about the wind field vertical profile sample provided by the target air mass. The posterior probability density function; To be independent The proportionality coefficient, the value of which is set by the system based on historical measurement experience; The prior probability density function is used for quantization. Prior information; Let be the likelihood function, representing the given... Occasionally The possibility; ;in, To fix the measurement error, it is set based on historical measurement experience. express The corresponding random variable representation value, The value is The acquisition of random variable representation values ​​may include: collecting a large amount of vertical profile sample data of the target air mass at different times and locations, and determining the radial wind speed for each height level j. It is possible to calculate its mean, variance, and other statistics. If we assume that the radial wind speed follows a certain probability distribution (such as a normal distribution), we can estimate the parameters of the distribution through historical data, and then determine the value of the random variable. For example, by calculating the mean and variance of each height layer j in the historical data, if we assume that the wind speed of that layer follows a normal distribution, we can determine the value of the random variable corresponding to the current observation based on the characteristics of the normal distribution.

[0042] In some embodiments, performing reliability analysis based on posterior samples to calculate the posterior failure probability of the target air mass movement trend specifically includes the following process: Perform reliability analysis to calculate the posterior failure probability of the target air mass movement trend. : ;in, To evaluate the function of the target air mass movement trend, Indicates when hour, Take 1, otherwise Set to 0; in, The following steps were taken: Calculate the radial wind speed at each height level. Compared with the expected radial wind speed The deviation, and obtain .

[0043] Furthermore, predicting the movement trend of the target air mass based on the posterior failure probability specifically includes the following process: Establish an inverse proportional function between the posterior failure probability and the target air mass movement trend characterization value BZ: ;when When the value exceeds the preset threshold for representing the movement trend, it indicates that the movement trend of the target air mass is consistent with the direction of radial wind flow. When the value is less than or equal to the preset moving trend characterization threshold, the Bayesian inversion analysis needs to be performed again. The preset moving trend characterization threshold is set by the system based on historical measurement experience: collecting a large amount of relevant data on the target air mass in history, including the posterior failure probability. The BZ value represents the movement trend, and the actual movement trend (whether it aligns with the radial wind direction, etc.). Calculate the statistical measures of the BZ value for both cases where the target air mass's movement trend aligns with and does not align with the radial wind direction, such as the mean or median. Determine the threshold: The median of the BZ value when the movement trend aligns with the radial wind direction, or the mean minus a certain multiple of the standard deviation (e.g., subtracting 1 standard deviation), can be used as the preset threshold for the movement trend representation value. For example, statistical analysis shows that when the target air mass's movement trend aligns with the radial wind direction, the mean BZ value is 50 and the standard deviation is 5; therefore, the threshold can be set to 45.

[0044] In some embodiments, the present invention also provides a vehicle-mounted mobile weather detection method. Figure 3 This is a flowchart illustrating the workflow of a vehicle-mounted mobile weather detection method according to an embodiment of the present invention. Figure 3 As shown, the method includes the following steps: Step S301: Determine the driving route of the vehicle-mounted terminal, and activate the lidar system of the vehicle-mounted terminal to collect the backscattered signals of the target air mass corresponding to each collection point on the driving route, and obtain the extinction coefficient of the target air mass based on the backscattered signals, and determine the boundary of the target air mass based on the change of the extinction coefficient of the target air mass on the driving route. Step S302: Activate the laser heterodyne radiometer on the vehicle to observe the heterodyne spectrum containing wind field information of the target air mass within the boundary of the target air mass, and determine the vertical profile of the wind field of the target air mass based on the heterodyne spectrum. Step S303: Obtain posterior samples of random field model parameters by performing Bayesian inversion analysis on the vertical profile of the wind field of the target air mass. Perform reliability analysis based on the posterior samples to calculate the posterior failure probability of the target air mass's movement trend, and predict the movement trend of the target air mass based on the posterior failure probability.

[0045] This embodiment also provides an electronic device. Figure 4 This is a structural block diagram of an electronic device according to an embodiment of the present invention, such as... Figure 4 As shown, the electronic device includes a memory 401 and a processor 402. The memory 401 stores a computer program. When the computer program is executed by the processor 402, the processor 402 executes a vehicle-mounted mobile weather detection system as described in any of the above embodiments.

[0046] The memory 401 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. The memory 401 has storage space 403 for program code 413 for performing any of the method steps described above. For example, the storage space 403 for program code may include various program codes 413 for implementing the various steps in the methods described above. This program code can be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When run by a computing processing device, this code causes the computing processing device to perform the various steps in the methods described above. This program code can be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, CDs, memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When this code is run by a computing device, it causes the computing device to perform the various steps in a vehicle-mounted mobile weather detection system as described above.

[0047] at last, Figure 5 This is a schematic diagram illustrating the application of a vehicle-mounted mobile weather detection system according to an embodiment of the present invention, such as... Figure 5 As shown, the vehicle-mounted terminal is equipped with a vehicle-mounted mobile platform, satellite antenna, miniature weather station, lidar, microwave radiometer (laser heterodyne radiometer) and cabinet.

[0048] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of 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 transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0049] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0050] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0051] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0052] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0053] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should 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 mobile weather detection system, characterized in that, The system includes: The target air mass boundary determination module is used to determine the driving route of the vehicle-mounted terminal, activate the lidar system of the vehicle-mounted terminal to collect the backscattered signals of the target air mass corresponding to each collection point on the driving route, and obtain the extinction coefficient of the target air mass based on the backscattered signals, and determine the boundary of the target air mass based on the change of the extinction coefficient of the target air mass on the driving route. The wind field vertical profile determination module is used to activate the laser heterodyne radiometer on the vehicle to observe the heterodyne spectrum containing wind field information of the target air mass within the boundary of the target air mass, and determine the wind field vertical profile of the target air mass based on the heterodyne spectrum. The air mass movement trend analysis module is used to obtain posterior samples of random field model parameters by performing Bayesian inversion analysis on the vertical profile of the wind field of the target air mass. Based on the posterior samples, reliability analysis is performed to calculate the posterior failure probability of the target air mass movement trend, and the movement trend of the target air mass is predicted based on the posterior failure probability.

2. The vehicle-mounted mobile weather detection system according to claim 1, characterized in that, Determining the navigation route for the vehicle-mounted terminal specifically includes the following processes: Images of the target air mass are acquired, and the images are processed using digital image processing algorithms to obtain the outline of the target air mass. Based on the outline, the acquisition points passed by the vehicle-mounted terminal are determined. The location of the vehicle-mounted terminal is used as the initial acquisition point, and the acquisition points are connected in order of their distance from the initial acquisition point to form a navigation route.

3. The vehicle-mounted mobile weather detection system according to claim 1, characterized in that, The process of obtaining the extinction coefficient of the target air mass based on the backscattering signal inversion includes the following steps: The backscattered signal is denoised by moving average using a time-domain filtering algorithm. Combined with the vehicle-mounted position and attitude data obtained by the BeiDou high-precision positioning and timing unit, stray light interference signals generated when the vehicle-mounted terminal passes through tall buildings and tunnels are removed by spatial filtering to obtain the effective backscattered signal of the target air mass. Based on the intensity change characteristics of the effective backscattered signal, the signal interval corresponding to the target air mass is segmented. The starting and ending heights of the signal intervals are determined by the detection height data of the lidar system. Inversion model selection and parameter configuration: The inversion model is selected based on the homogeneity characteristics of the target air mass. If the target air mass is a homogeneous air mass with a single source, the Klett two-point boundary method is selected. If the target air mass is a non-uniform air mass mixed from multiple sources, the Fernald single boundary method should be selected. The core parameter for configuring the inversion model is the lidar ratio S. If the target air mass is a polluted air mass, the corresponding S value should be configured to 40-60 sr. -1 If the target air mass is a dust storm, the corresponding S value should be configured as 20-30 sr. -1 ; Boundary condition calibration: When the target air mass is concentrated in the boundary layer within 1 km of the ground, the near-surface extinction coefficient is measured in real time by the turbidity meter in the surface meteorological element measurement component. As the near-ground boundary point of Klett's two-point boundary method, The measurement accuracy is no less than ±5%; when the target air mass is located at an altitude of more than 1 km, the height at which the backscattered signal attenuates to the clean atmospheric background value is used as the height of the air mass top by monitoring the backscattered signal through the lidar system. ,Will As the high-altitude boundary point of Fernald's single boundary method; Extinction coefficient inversion and verification correction: The effective backscattering signal, configured inversion model parameters, and calibrated boundary conditions are input into the data processing unit, and the atmospheric radiative transfer equation is used to solve for different altitude layers of the target air mass. extinction coefficient Through internal consistency verification, the spatial overlap between the high extinction coefficient region and the high backscattering signal region is determined. If the overlap is less than 85%, the lidar ratio S value is readjusted. External cross-validation is performed by combining lidar extinction coefficient data from fixed ground stations along the route or aerosol optical thickness data from satellite remote sensing. If the vehicle-mounted inversion result deviates from the ground station data by more than 25% or from the satellite data by more than 20%, the boundary conditions are corrected and the inversion is re-executed. Finally, the extinction coefficient profile of the target air mass is output.

4. The vehicle-mounted mobile weather detection system according to claim 1, characterized in that, Determining the boundary of a target air mass based on the change in its extinction coefficient along the flight path specifically includes the following process: Obtain the extinction coefficient of each sampling point i. The geographic coordinates (L(i), B(i)) and time information t(i) obtained by the BeiDou high-precision positioning and timing unit are correlated. A sliding window analysis was performed on the extinction coefficients of N consecutive sampling points along the flight route. The mean μ(k), standard deviation σ(k), and spatial gradient G(k) of the extinction coefficients within the window were calculated, where k is the window number, and N is a value between 5 and 20 and positively correlated with the vehicle speed. The spatial gradient G(k) is calculated using the formula: G(k) = | (k+1)- The calculation is performed using (k)| / D(k), where D(k) is the straight-line distance between the k-th and k+1-th collection points, which is calculated from the geographic coordinates of the two points. Preset extinction coefficient threshold based on target air mass type and gradient threshold G0; When the extinction coefficients of three consecutive sampling points are less than the extinction coefficient threshold Increased to greater than When the corresponding spatial gradient G(i)≥G0, the region is determined to be the air mass entry boundary, and the coordinates of the entry boundary point are recorded as (L_in, B_in). When the extinction coefficient of three consecutive sampling points increases from greater than Sudden drop to less than When the corresponding spatial gradient G(i)≥G0, the region is determined to be the departure boundary of the air mass, and the coordinates of the departure boundary point are recorded as (L_out, B_out). The boundary of the target air mass is determined based on the location information of the collection points on the navigation route.

5. The vehicle-mounted mobile weather detection system according to claim 1, characterized in that, Before determining the vertical profile of the wind field of the target air mass based on heterodyne spectroscopy, the system's workflow is as follows: Includes the following processes: A laser heterodyne radiometer emits a laser signal into the airspace containing the target air mass and receives the echo signal scattered by the target air mass. This echo signal is heterodyne-mixed with a local oscillating laser to generate a heterodyne spectrum containing wind field information of the target air mass. Based on the elevation and roll angles of the vehicle-mounted antenna obtained by the BeiDou high-precision positioning and timing unit, the attitude of the radiometer receiving antenna is adjusted in real time to counteract beam shift caused by vehicle bumps. By using temporal and spatial filtering to remove spectral segments corresponding to stray light from tall buildings and tunnels, the effective heterodyne spectrum of the target air mass is obtained.

6. The vehicle-mounted mobile weather detection system according to claim 1, characterized in that, Determining the vertical profile of the wind field of a target air mass based on heterodyne spectroscopy specifically includes the following process: Spectral analysis was performed on the effective heterodyne spectrum. The spectrum was transformed from the time domain to the frequency domain using a fast Fourier transform to identify the characteristic spectral peaks corresponding to the scattering of the target air mass. The difference between the center frequency of the characteristic spectral peak and the inherent emission frequency of the laser heterodyne radiometer was calculated to obtain the Doppler frequency shift at different altitudes of the target air mass. ,in To detect altitude; Based on the quantitative relationship between Doppler frequency shift and wind speed, the target air mass at different altitudes was obtained through inversion. Corresponding radial wind speed The quantitative relationship formula is: ,in, The laser wavelength of the laser heterodyne radiometer. The angle between the laser detection beam and the direction of movement of the target air mass is calculated from the heading angle of the vehicle-mounted end and the beam pointing angle of the radiometer obtained by the Beidou high-precision positioning and timing unit; the radial wind speed of each altitude layer is integrated at height intervals of 50-200m to generate the vertical profile of the wind field of the target air mass.

7. A vehicle-mounted mobile weather detection system according to claim 1, characterized in that, Obtaining posterior samples of random field model parameters from the vertical profile of the wind field of the target air mass through Bayesian inversion analysis specifically includes the following process: make This is a sample of the vertical profile of the wind field for the target air mass, where... Indicates height layer radial wind speed; To determine the number of height layers, Bayesian theory is used to update the random variable. : ; in, This represents information about the wind field vertical profile sample provided by the target air mass. The posterior probability density function; To be independent of The proportionality coefficient, the value of which is set by the system based on historical measurement experience; The prior probability density function is used for quantization. Prior information; Let be the likelihood function, representing the given... Occasionally The possibility; ;in, To fix the measurement error, it is set based on historical measurement experience. express The corresponding random variable representation value, The value is .

8. The vehicle-mounted mobile weather detection system according to claim 1, characterized in that, Reliability analysis is performed based on posterior samples to calculate the posterior failure probability of the target air mass movement trend. Includes the following processes: Perform reliability analysis to calculate the posterior failure probability of the target air mass movement trend. : ;in, To evaluate the function of the target air mass movement trend, Indicates when hour, Take 1, otherwise Set to 0; in, The following steps were taken: Calculate the radial wind speed at each height level. Compared with the expected radial wind speed The deviation, and obtain .

9. A vehicle-mounted mobile weather detection system according to claim 1, characterized in that, Predicting the movement trend of target air masses based on posterior failure probability. Includes the following processes: Establish an inverse proportional function between the posterior failure probability and the target air mass movement trend characterization value BZ: ;when When the value exceeds the preset threshold for representing the movement trend, it indicates that the movement trend of the target air mass is consistent with the direction of radial wind flow. When the value is less than or equal to the preset moving trend characterization threshold, Bayesian inversion analysis needs to be performed again. The preset moving trend characterization threshold is set by the system based on historical measurement experience.

10. A vehicle-mounted mobile weather detection method, characterized in that, A vehicle-mounted mobile weather detection system applicable to any one of claims 1 to 9, the method comprising: The vehicle-mounted terminal's navigation route is determined, and the vehicle-mounted terminal's lidar system is activated to collect the backscattered signals of the target air mass corresponding to each collection point on the navigation route. The extinction coefficient of the target air mass is obtained based on the backscattered signals, and the boundary of the target air mass is determined based on the change of the extinction coefficient of the target air mass on the navigation route. The on-board laser heterodyne radiometer is activated to observe the heterodyne spectrum containing wind field information of the target air mass within the boundary of the target air mass, and the vertical profile of the wind field of the target air mass is determined based on the heterodyne spectrum. The vertical profile of the wind field of the target air mass is obtained by Bayesian inversion analysis to obtain posterior samples of the random field model parameters. Reliability analysis is performed based on the posterior samples to calculate the posterior failure probability of the target air mass's movement trend, and the movement trend of the target air mass is predicted based on the posterior failure probability.

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