A method and apparatus for predicting omnidirectional slant range visibility based on lidar

Abstract

This invention relates to the field of lidar technology, and particularly to a method and apparatus for predicting omnidirectional slope visibility based on lidar. The method comprises the following steps: determining the laser emission path based on the measurement area; acquiring scattered echo signal data, slope visibility data, and wind data at the same distance threshold along the laser emission path using a micro-pulse lidar and a coherent Doppler wind lidar, respectively; storing and processing the acquired data; and inputting the processed data into a vector autoregressive exogenous variable prediction model to obtain the predicted slope visibility value for future times. An apparatus for executing the above method is also disclosed. Using this lidar-based omnidirectional slope visibility prediction method and apparatus, slope visibility along the laser emission path can be predicted, providing technical support for future applications.

Description

Technical Field

[0001] This invention relates to the field of lidar technology, and in particular to a method and apparatus for predicting omnidirectional slant range visibility based on lidar. Background Technology

[0002] Lidar, as an active optical remote sensing device, boasts advantages such as high spatiotemporal resolution, high precision, and long-range measurement. Micropulse lidar serves as an effective means of acquiring slant-path visibility, while coherent Doppler wind lidar is an advanced atmospheric wind speed and direction monitoring technology, offering significant advantages in the precise measurement of wind speed and direction. Atmospheric visibility, as an important meteorological parameter, mainly includes horizontal visibility and slant-path visibility. Slant-path visibility is one of the important meteorological factors affecting traffic. For example, the visibility requirement for aircraft takeoff is at least 600 meters, and the requirement for landing is even higher. Low visibility can easily lead to accidents during takeoff and landing. The prediction of high-speed fog also directly affects traffic safety. Existing visibility prediction methods have low accuracy and cannot provide real-time, high-precision measurement and prediction of slant-path visibility along traffic planning routes, thus limiting the decision support provided to personnel. Summary of the Invention

[0003] The purpose of this invention is to provide an omnidirectional slant range visibility prediction method and device based on lidar, thereby solving the above-mentioned technical problems.

[0004] To achieve the above objectives, this invention provides an omnidirectional slant-path visibility prediction method based on lidar, the specific steps of which are as follows:

[0005] Step S1: Determine the laser emission path based on the measurement area, and acquire the scattered echo signal data, slant path visibility data, and wind data at the same distance along the laser emission path using micropulse lidar and coherent Doppler wind lidar, respectively.

[0006] Step S2: Store and process the data obtained in step S1;

[0007] Step S3: Input the processed data into the vector autoregressive exogenous variable prediction model after introducing the distance gate spatial correlation term and dynamically adjusted exogenous variable weights to obtain the predicted slant visibility value for the future time.

[0008] Preferably, in step S1, the micropulse lidar and the coherent Doppler wind lidar are powered on and start working simultaneously, and the spatiotemporal resolution is set uniformly.

[0009] Micropulse lidar measures the backscattered echo signal at each distance gate along the laser emission path and calculates the slant path visibility in real time based on the backscattered echo signal;

[0010] The wind data acquired by the coherent Doppler wind lidar includes wind speed and wind direction.

[0011] Preferably, in step S2, the data processing of the scattered echo signal includes photodetector nonlinearity correction, geometric factor correction, distance squared correction, and normalization processing;

[0012] The processed scattered echo signal and wind data were subjected to spatiotemporal resolution unification.

[0013] Preferably, in step S3, the scattered echo signal with unified spatiotemporal resolution and wind data are input into the vector autoregressive exogenous variable prediction model;

[0014] The endogenous variable is the scattered echo signal at each range gate of the micropulse lidar, and the exogenous variable is the wind data at each range gate of the coherent Doppler wind lidar; the stationarity of the input time series is tested using the sample autocorrelation function test.

[0015] Preferably, the vector of endogenous variables in the vector autoregressive exogenous variable prediction model as follows:

[0016] ;

[0017] in, For micropulse lidar at time Distance Gate The distance squared correction signal obtained at the location, ; The dimension of the endogenous variable vector represents the distance squared correction signal corresponding to each distance gate;

[0018] Exogenous variable vector as follows:

[0019] ;

[0020] in, This indicates that the coherent Doppler wind lidar is at time... Distance Gate The eastward wind speed and air volume obtained at the location; This indicates that the coherent Doppler wind lidar is at time... Distance Gate Northward wind speed and volume obtained at [location], exogenous variable vector The dimension is 2 The corresponding eastward and northward wind speed components for each distance gate;

[0021] The basic vector autoregressive exogenous variable prediction model expression is as follows:

[0022] ;

[0023] in, The lag order of the model is determined using the Akaike information criterion. for dimensional endogenous variable coefficient matrix, This characterizes the effect of lagged terms of endogenous variables on current terms; for The exogenous variable coefficient matrix represents the influence of exogenous variables and their lagged terms on endogenous variables. for A dimensional random error vector, satisfying , Here is the error covariance matrix;

[0024] Visibility and scattered echo signal exhibit a monotonically decreasing relationship. The mapping model between visibility and scattered echo signal is as follows:

[0025] ;

[0026] in, For at any time Distance Gate Visibility, This is the mapping function between visibility and the scattered echo signal;

[0027] Predicting endogenous variables as follows:

[0028] ;

[0029] in, For micropulse lidar at time Distance Gate The predicted distance squared correction signal obtained at the location, To predict the step size;

[0030] The predicted visibility is as follows:

[0031] ;

[0032] in, For at any time Distance Gate Predicted visibility.

[0033] Preferably, the vector of endogenous variables is corrected by introducing spatial correlation terms. as follows:

[0034] ;

[0035] in, The spatial correlation coefficient is determined through cross-validation. For The dimensional weight matrix is ​​obtained using distance decay weights, and the expression for distance decay weights is as follows:

[0036] ;

[0037] in, For the first The distance between the gates is the first The spatial influence weight of each distance gate For spatial correlation threshold, Represented as distance gate and distance door The distance between them.

[0038] Preferably, a dynamic weight matrix is ​​introduced. The exogenous variables at each time point are weighted and adjusted, and the adjusted exogenous variable vector is as follows:

[0039] ;

[0040] in, for 3D diagonal matrix, diagonal elements The expression is as follows:

[0041] ;

[0042] in, The first exogenous variable vector Each element corresponds to a wind component at distance from the door. The weights are obtained by normalizing the absolute values ​​of the covariances of endogenous and exogenous variables in real time, thereby automatically weighting and enhancing exogenous variables with significant influence. Let covariance function be used. For traversal The maximum absolute value of the covariance obtained from each exogenous variable;

[0043] The covariance of endogenous variables and exogenous variables is normalized by real-time calculation, thereby achieving automatic weighting and enhancement of exogenous variables.

[0044] Preferably, the expression for the vector autoregressive exogenous variable prediction model after incorporating the distance gate spatial correlation term and dynamically adjusted exogenous variable weights is as follows:

[0045] ;

[0046] in, For lag Time-dependent endogenous variables are adjusted. For lag Time-dependent correction of exogenous variables.

[0047] An apparatus for the aforementioned omnidirectional slant-path visibility prediction method based on lidar includes:

[0048] Micropulse lidar is used to acquire scattered echo signal data and calculate slant range visibility data;

[0049] Coherent Doppler wind lidar is used to acquire wind data;

[0050] The data acquisition and processing module is used for the acquisition and processing of scattered echo signal data, slant path visibility data, and wind data.

[0051] The prediction module based on the vector autoregressive exogenous variable model is used to predict the slant visibility value at future times based on the scattered echo signal data, slant visibility data and wind data.

[0052] Both the micropulse lidar and the coherent Doppler wind lidar are connected to the data acquisition and processing module, which in turn is connected to the prediction module based on a vector autoregression exogenous variable model.

[0053] Therefore, the present invention employs the above-mentioned omnidirectional slant-path visibility prediction method and device based on lidar, which has the following beneficial effects: by acquiring scattered echo signal data, slant-path visibility data, and wind data under the same distance gate on the laser emission path through micropulse lidar and coherent Doppler wind lidar respectively, and obtaining the predicted slant-path visibility value at the future time through a vector autoregression exogenous variable prediction model, and by introducing a distance gate spatial correlation term and dynamic exogenous variable weights, high-precision prediction of visibility on the laser emission path is achieved. Real-time measurement and prediction of slant-path visibility on traffic planning paths can effectively improve travel safety and provide decision support for staff.

[0054] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0055] Figure 1 This is a flowchart of an omnidirectional slant path visibility prediction method based on lidar according to the present invention;

[0056] Figure 2 This is a schematic diagram of the device of the present invention;

[0057] Figure 3 This is a pseudo-color image of the echo energy of a micropulse lidar.

[0058] Figure 4 A pseudo-color image of visibility data measured by micropulse lidar;

[0059] Figure 5 This is a pseudo-color image of the predicted echo energy of the technical solution in this embodiment;

[0060] Figure 6 This embodiment provides a pseudo-color image of the predicted future visibility.

[0061] Figure 7 This is a comparison chart of the visibility accuracy of micropulse lidar measurement and the technical solution in this embodiment. Detailed Implementation

[0062] In the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product is in use. They are used only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," and "connect" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0063] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0064] like Figure 1 As shown, an omnidirectional slant-path visibility prediction method based on lidar is described, with the following specific steps:

[0065] Step S1: Determine the laser emission path based on the measurement area, and acquire the scattered echo signal data, slant path visibility data, and wind data at the same distance along the laser emission path using micropulse lidar and coherent Doppler wind lidar, respectively.

[0066] The micropulse lidar and the coherent Doppler wind lidar are powered on and start working simultaneously, and their spatiotemporal resolution is set uniformly.

[0067] Micropulse lidar measures the backscattered echo signal at each distance gate along the laser emission path and calculates the slant path visibility in real time based on the backscattered echo signal;

[0068] The wind data acquired by the coherent Doppler wind lidar includes wind speed and wind direction.

[0069] Step S2: Store and process the data acquired in step S1. In step S2, the data processing of the scattered echo signal includes photodetector nonlinearity correction, geometric factor correction, distance squared correction, and normalization.

[0070] The processed scattered echo signal and wind data were subjected to spatiotemporal resolution unification.

[0071] Step S3: Input the processed data into the vector autoregressive exogenous variable prediction model after introducing the distance gate spatial correlation term and dynamically adjusted exogenous variable weights.

[0072] The vector autoregressive exogenous variable prediction model obtains the predicted slant visibility values ​​for future times. The spatiotemporally unified scattered echo signals and wind data are then input into the vector autoregressive exogenous variable prediction model.

[0073] The endogenous variable is the scattered echo signal at each range gate of the micropulse lidar, and the exogenous variable is the wind data at each range gate of the coherent Doppler wind lidar. The stationarity of the input time series is tested using the sample autocorrelation function test. The lag order of the model is determined to be 5 using the Akaike information criterion.

[0074] Vector of endogenous variables in a vector autoregressive model of exogenous variables as follows:

[0075] ;

[0076] in, For micropulse lidar at time Distance Gate The distance squared correction signal obtained at the location, ; The dimension of the endogenous variable vector represents the distance squared correction signal corresponding to each distance gate;

[0077] Exogenous variable vector as follows:

[0078] ;

[0079] in, This indicates that the coherent Doppler wind lidar is at time... Distance Gate The eastward wind speed and air volume obtained at the location; This indicates that the coherent Doppler wind lidar is at time... Distance Gate Northward wind speed and volume obtained at [location], exogenous variable vector The dimension is 2 The corresponding eastward and northward wind speed components for each distance gate;

[0080] The basic vector autoregressive exogenous variable prediction model expression is as follows:

[0081] ;

[0082] in, The lag order of the model is determined using the Akaike information criterion. for dimensional endogenous variable coefficient matrix, This characterizes the effect of lagged terms of endogenous variables on current terms; for The exogenous variable coefficient matrix represents the influence of exogenous variables and their lagged terms on endogenous variables. for A dimensional random error vector, satisfying , Here is the error covariance matrix;

[0083] Visibility and scattered echo signal exhibit a monotonically decreasing relationship. The mapping model between visibility and scattered echo signal is as follows:

[0084] ;

[0085] in, For at any time Distance Gate Visibility, The mapping function between visibility and the scattered echo signal is given. The calculation of the aerosol extinction coefficient from the predicted distance squared correction signal adopts the classic Cernard method. The visibility calculation adopts the conventional calculation method, which will not be elaborated here.

[0086] Predicting endogenous variables as follows:

[0087] ;

[0088] in, For micropulse lidar at time Distance Gate The predicted distance squared correction signal obtained at the location, To predict the step size;

[0089] The predicted visibility is as follows:

[0090] ;

[0091] in, For at any time Distance Gate Predicted visibility.

[0092] By analyzing the characteristics of lidar and atmospheric data, the model is improved by introducing a range gate spatial correlation term and adjusting the weights of dynamic exogenous variables.

[0093] Atmospheric aerosols and wind fields exhibit spatial continuity, and data from adjacent distance gates show strong correlations. Existing prediction modules do not consider this characteristic, leading to limited prediction accuracy. This is addressed by introducing a spatial correlation term to correct the endogenous variable vector. as follows:

[0094] ;

[0095] in, The spatial correlation coefficient, whose value was determined through cross-validation, ranges from 0.3 to 0.6, and is adjusted based on the atmospheric homogeneity of the observation area. For The dimensional weight matrix is ​​obtained using distance decay weights, and the expression for distance decay weights is as follows:

[0096] ;

[0097] in, For the first The distance between the gates is the first The spatial influence weight of each distance gate As a spatial correlation threshold, select 5-10 distance gate spacings. Represented as distance gate and distance door The distance between them.

[0098] The impact of wind fields on visibility varies depending on the distance from the gate and the time of day (e.g., near-surface wind fields have a greater impact on low-altitude visibility). Existing prediction modules using fixed coefficient matrices cannot adapt to this dynamic characteristic, so a dynamic weight matrix is ​​introduced. The exogenous variables at each time point are weighted and adjusted, and the adjusted exogenous variable vector is as follows:

[0099] ;

[0100] in, for 3D diagonal matrix, diagonal elements The expression is as follows:

[0101] ;

[0102] in, The first exogenous variable vector Each element corresponds to a wind component at distance from the door. The weights are obtained by normalizing the absolute values ​​of the covariances of endogenous and exogenous variables in real time, thereby automatically weighting and enhancing exogenous variables with significant influence. Let covariance function be used. For traversal The maximum absolute value of the covariance obtained from each exogenous variable;

[0103] The covariance of endogenous variables and each exogenous variable is normalized by real-time calculation, thereby achieving automatic weighting and enhancement of exogenous variables with significant influence.

[0104] The expression for the vector autoregressive exogenous variable prediction model, after introducing the distance gate spatial correlation term and dynamically adjusted exogenous variable weights, is as follows:

[0105] ;

[0106] in, For lag Time-dependent endogenous variables are adjusted. For lag Time-based adjustments to exogenous variables. Improved prediction accuracy is achieved by integrating the spatial correlation term of the distance gate with dynamic adjustments to the weights of exogenous variables.

[0107] like Figure 2 As shown, the apparatus for performing the above-described omnidirectional slant-range visibility prediction method based on lidar includes:

[0108] Micropulse lidar is used to acquire scattered echo signal data and calculate slant path visibility data.

[0109] Coherent Doppler wind lidar is used to acquire wind data.

[0110] The data acquisition and processing module is used for the acquisition and processing of scattered echo signal data, slant path visibility data, and wind data.

[0111] The prediction module based on the vector autoregressive exogenous variable model is used to predict the slant visibility value at future times based on the scattered echo signal data, slant visibility data and wind data.

[0112] Both the micropulse lidar and the coherent Doppler wind lidar are connected to the data acquisition and processing module, which in turn is connected to the prediction module based on a vector autoregression exogenous variable model.

[0113] The model fully leverages the complementary advantages of multi-instrument collaborative observations, quantifies the coupling relationship between atmospheric aerosols and wind fields through mathematical modeling, and solves the problems of insufficient accuracy and poor robustness of traditional univariate or linear prediction models, providing an efficient mathematical tool and implementation path for atmospheric visibility prediction.

[0114] To verify the effectiveness of the technical solution in this embodiment, the following tests were conducted.

[0115] Visibility is measured using micropulse lidar, such as... Figures 3-4 As shown, the prediction data of the technical solution in this embodiment is as follows: Figures 5-6 As shown, Figure 7 As shown, a forward-scattering visibility meter is used to improve visibility prediction accuracy. Near-surface measurement data (at 150m) from the lidar is extracted from the visibility profile and compared with the forward-scattering visibility meter results. Starting from 15:53, the figure shows good consistency between the model's predicted future visibility and the forward-scattering visibility meter's measurement results. The technical solution adopted in this embodiment utilizes a coherent Doppler wind lidar to accurately acquire the atmospheric dynamics change trend (wind data) along the laser path, while simultaneously combining it with a micro-pulse lidar to provide the aerosol content change trend along the path. Through joint analysis of these two trends and model establishment, high-precision prediction of future visibility changes along the laser emission path is achieved.

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for predicting omnidirectional slant-path visibility based on lidar, characterized in that, The specific steps are as follows: Step S1: Determine the laser emission path based on the measurement area, and acquire the scattered echo signal data, slant path visibility data, and wind data at the same distance along the laser emission path using micropulse lidar and coherent Doppler wind lidar, respectively. Step S2: Store and process the data obtained in step S1; Step S3: Input the processed data into the vector autoregressive exogenous variable prediction model after introducing the distance gate spatial correlation term and dynamically adjusted exogenous variable weights to obtain the predicted slant visibility value for the future time. Endogenous variable vector modified by introducing spatial correlation terms as follows: ； in, For the vector of endogenous variables, The spatial correlation coefficient is determined through cross-validation. for The dimensional weight matrix is ​​obtained using distance decay weights, and the expression for distance decay weights is as follows: ； in, For the first The distance between the gates is the first The spatial influence weight of each distance gate For spatial correlation threshold, Represented as distance gate and distance door The distance between them; Introducing a dynamic weight matrix The exogenous variables at each time point are weighted and adjusted, and the adjusted exogenous variable vector is as follows: ； in, For exogenous variable vectors, for 3D diagonal matrix, diagonal elements The expression is as follows: ； in, The first exogenous variable vector Each element corresponds to a wind component at distance from the door. The weights are obtained by normalizing the absolute value of the covariance between endogenous and exogenous variables in real time, thus automatically weighting and enhancing exogenous variables with significant influence. Let covariance function be used. For traversal The maximum absolute value of the covariance obtained from each exogenous variable; The covariance of endogenous variables and exogenous variables is normalized by real-time calculation, thereby achieving automatic weighting and enhancement of exogenous variables; The expression for the vector autoregressive exogenous variable prediction model, after introducing the distance gate spatial correlation term and dynamically adjusted exogenous variable weights, is as follows: ； in, For lag Time-dependent endogenous variables are adjusted. For lag Time-based correction of exogenous variables, The lag order of the model is determined using the Akaike information criterion. This characterizes the effect of lagged terms of endogenous variables on current terms; for The exogenous variable coefficient matrix represents the influence of exogenous variables and their lagged terms on endogenous variables.

2. The omnidirectional slant path visibility prediction method based on lidar according to claim 1, characterized in that: In step S1, the micropulse lidar and the coherent Doppler wind lidar are powered on and start working simultaneously, and the spatiotemporal resolution is set uniformly. Micropulse lidar measures the backscattered echo signal at each distance gate along the laser emission path and calculates the slant path visibility in real time based on the backscattered echo signal; The wind data acquired by the coherent Doppler wind lidar includes wind speed and wind direction.

3. The omnidirectional slant path visibility prediction method based on lidar according to claim 1, characterized in that: In step S2, the data processing of the scattered echo signal includes photodetector nonlinearity correction, geometric factor correction, distance squared correction, and normalization processing; The processed scattered echo signal and wind data were subjected to spatiotemporal resolution unification.

4. The omnidirectional slant path visibility prediction method based on lidar according to claim 1, characterized in that: In step S3, the scattered echo signal with unified spatiotemporal resolution and wind data are input into the vector autoregressive exogenous variable prediction model; The endogenous variable is the scattered echo signal at each range gate of the micropulse lidar, and the exogenous variable is the wind data at each range gate of the coherent Doppler wind lidar; the stationarity of the input time series is tested using the sample autocorrelation function test.

5. The omnidirectional slant path visibility prediction method based on lidar according to claim 4, characterized in that: Vector of endogenous variables in a vector autoregressive model of exogenous variables as follows: ； in, For micropulse lidar at time Distance Gate The distance squared correction signal obtained at the location, ; The dimension of the endogenous variable vector represents the distance squared correction signal corresponding to each distance gate; Exogenous variable vector as follows: ； in, This indicates that the coherent Doppler wind lidar is at time... Distance Gate The eastward wind speed and air volume obtained at the location; This indicates that the coherent Doppler wind lidar is at time... Distance Gate Northward wind speed and volume obtained at [location], exogenous variable vector The dimension is 2 The corresponding eastward and northward wind speed components for each distance gate; The basic vector autoregressive exogenous variable prediction model expression is as follows: ； in, for A dimensional random error vector, satisfying , Here is the error covariance matrix; Visibility and scattered echo signal exhibit a monotonically decreasing relationship. The mapping model between visibility and scattered echo signal is as follows: ； in, For at any time Distance Gate Visibility, This is the mapping function between visibility and the scattered echo signal; Predicting endogenous variables as follows: ； in, For micropulse lidar at time Distance Gate The predicted distance squared correction signal obtained at the location, To predict the step size; The predicted visibility is as follows: ； in, For at any time Distance Gate Predicted visibility.

6. An apparatus for performing the omnidirectional slant-path visibility prediction method based on lidar as described in claim 5, characterized in that: include: Micropulse lidar is used to acquire scattered echo signal data and calculate slant range visibility data; Coherent Doppler wind lidar is used to acquire wind data; The data acquisition and processing module is used for the acquisition and processing of scattered echo signal data, slant path visibility data, and wind data. The prediction module based on the vector autoregressive exogenous variable model is used to predict the slant visibility value at future times based on the scattered echo signal data, slant visibility data and wind data. Both the micropulse lidar and the coherent Doppler wind lidar are connected to the data acquisition and processing module, which in turn is connected to the prediction module based on a vector autoregression exogenous variable model.

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