Laser curtain wall image optimization system and method based on laser ranging analysis

Abstract

The application provides a laser curtain wall image optimization system and method based on laser ranging analysis, and relates to the technical field of laser curtain walls.The application generates snowflake point probability and non-snowflake point probability by performing probability analysis on a snowflake point set and a non-snowflake point set, improves precision through variance analysis, and introduces a variance analysis module for snowflake points and non-snowflake points, which helps to deeply understand the intensity distribution characteristics of different reflection sources, outputs a reflection intensity threshold based on the size of the variance, optimizes the precision of laser ranging, and effectively identifies laser points that have passed through snowflake reflection and laser points that have not passed through snowflake reflection through the setting of a laser point cloud collection module and a division module, thereby realizing accurate classification of laser data; considering the possible jitter of the laser equipment under the influence of wind, the jitter data of the equipment are acquired in real time through a jitter collection module and an analysis module, and correlation analysis is performed with the reflection intensity threshold, thereby ensuring the authenticity of the data.

Description

Technical Field

[0001] This invention relates to the field of laser curtain wall technology, specifically to a laser curtain wall image optimization system and method based on laser ranging analysis. Background Technology

[0002] Laser wall monitoring technology is an application based on laser ranging and point cloud data processing, widely used in various fields such as security monitoring, environmental monitoring, smart buildings, and traffic management. Laser walls emit laser beams and measure the time it takes for the laser signal to travel from the source to the object's surface and back to the receiver, thus calculating the object's distance and position. This principle is based on the constant speed of light. Laser measurement is a non-contact method that allows monitoring without disturbing the object, making it suitable for fragile objects or hazardous environments.

[0003] In laser curtain wall applications, changes in environmental factors can significantly impact the performance of laser equipment. In snowy weather, the presence of snowflakes causes reflection of the laser beam and absorption of the laser signal, leading to changes in the intensity of the received laser point. This affects the precision and accuracy of the laser equipment, potentially causing inaccurate laser ranging results and making it difficult to accurately analyze objects on the curtain wall.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a laser curtain wall image optimization system and method based on laser ranging analysis to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A laser curtain wall image optimization system based on laser ranging analysis includes:

[0008] A laser point cloud acquisition module is used to set up a laser device. In snowy weather, the laser device emits laser light and receives laser light reflected from snow in the air, as well as laser light reflected by a reflecting device. All received laser point cloud data is acquired, including the intensity of N laser points. The laser point cloud data is then sorted according to the received laser intensity from smallest to largest. After sorting, the probability of occurrence of point cloud data with the corresponding intensity is q. k ;q k The probability of point cloud data appearing for the k-th laser reception intensity is represented by k = 1, 2, ..., m; m represents the maximum laser intensity.

[0009] The partitioning module is used to set the reflection intensity threshold k, perform correlation analysis on the laser point cloud data, and generate a snowflake point set A and a non-snowflake point set B. The laser received intensity in the snowflake point set A is less than the reflection intensity threshold k, and the laser received intensity in the non-snowflake point set B is greater than or equal to the reflection intensity threshold k.

[0010] The probability analysis module is used to perform correlation analysis on the snowflake point set A and the non-snowflake point set B, and generate the snowflake point probability Q. A and the probability Q of non-snowflake points B ;

[0011] The snowflake point variance analysis module is used to perform correlation analysis on the snowflake point probability and the snowflake point set A, and generate the snowflake point mean J. A and the variance F of the snowflake point A ;

[0012] The non-snowflake point variance analysis module is used to perform correlation analysis on the probability of non-snowflake points and the set of non-snowflake points B, and generate the mean J of non-snowflake points. B The variance F of non-snowflake points B ;

[0013] The segmentation coefficient analysis module is used to perform correlation analysis on the snowflake point probability, non-snowflake point probability, snowflake point variance, and non-snowflake point variance, generate the snowflake intensity variance segmentation coefficient, and obtain the determined value of the reflection intensity threshold k based on the snowflake intensity variance segmentation coefficient.

[0014] The jitter acquisition module is used to acquire jitter data from the laser equipment.

[0015] The jitter analysis module is used to perform correlation analysis on jitter data and generate the laser jitter amount Y;

[0016] The snowflake acquisition module is used to set up the laser test transmitting and receiving equipment under normal weather conditions, acquire the test light intensity and test distance, and simultaneously acquire snowflake parameters.

[0017] The attenuation analysis module is used to perform correlation analysis on the parameters collected by the snowflake acquisition module and generate the light intensity attenuation coefficient in snowy weather.

[0018] The output module is used to set the laser jitter threshold, perform correlation analysis between the laser jitter amount Y and the reflection intensity threshold k, output whether the laser reflection point is a snowflake point, and output the actual measurement distance based on the light intensity attenuation coefficient in snowy weather.

[0019] Furthermore, a correlation analysis is performed on the snowflake point set A and the non-snowflake point set B to generate the snowflake point probability Q. A and the probability Q of non-snowflake points B The formula used is:

[0020]

[0021] Furthermore, a correlation analysis was performed on the snowflake point probability and the snowflake point set A to generate the snowflake point mean J. A and the variance F of the snowflake point A The formula used is:

[0022]

[0023] Correlation analysis was performed on the probability of non-snowflake points and the set of non-snowflake points B to generate the mean J of non-snowflake points. B The variance F of non-snowflake points B The formula used is:

[0024]

[0025] Furthermore, regarding the probability Q of the snowflake point... A The probability of non-snowflake points Q B Snowflake point variance F A Non-snowflake point variance F B Correlation analysis was performed to generate the snowflake intensity variance segmentation coefficient ρ, based on the following formula:

[0026] ρ=Q A *F A 2 +Q B *F B 2 .

[0027] Furthermore, the snowflake intensity variance segmentation coefficient ρ was fitted and analyzed using MATLAB software. When the value of the snowflake intensity variance segmentation coefficient ρ was minimized, the value of the reflection intensity threshold k was output.

[0028] Furthermore, the jitter data of the laser device is measured using an accelerometer. This jitter data includes acceleration a, velocity v, angular velocity w, laser reception time T, and test distance L. Correlation analysis is performed on the jitter data to generate the laser jitter amount Y, based on the following formula:

[0029]

[0030] Where α is the linear velocity weighting factor, and its value ranges from [0,1].

[0031] Furthermore, under normal weather conditions, laser testing emission and receiving equipment are set up to collect test light intensity and test distance. The test light intensity includes the intensity of the emitted laser and the intensity of the received laser, with the emitted laser intensity QF... LThe emitted laser intensity at the test distance L, and the received laser intensity QJ are measured. L The received laser intensity at the test distance L. The snowflake parameters include the snowfall intensity X and the snowflake diameter D. The diameter of a single snowflake in a unit space is measured by microscopic image analysis, and the average value is taken as the snowflake diameter D.

[0032] Furthermore, the test emitted laser intensity QF L and the test received laser intensity QJ L are subjected to correlation analysis with the test distance L to generate the air absorption coefficient β a , and the formula used is:

[0033]

[0034] The air absorption coefficient β is generated by fitting analysis of multiple groups of test data using Matlab software. a The value of

[0035] Correlation analysis is performed on the snowfall intensity X and the snowflake diameter D to generate the snowflake absorption coefficient β b , and the formula used is:

[0036]

[0037] where σ sc is the snowflake scattering cross-section, n is the number of particles per unit volume, Q sc is the scattering efficiency factor, and V p is the volume of a single snowflake.

[0038] Correlation analysis is performed on the snowflake absorption coefficient β b and the air absorption coefficient β a to generate the snow light intensity attenuation coefficient β c , and the formula used is:

[0039]

[0040] Furthermore, the laser jitter threshold is δ, and δ is set to 1.35. Correlation analysis is performed on the laser jitter amount Y and the reflection intensity threshold k. When Y < δ, the points where the received laser intensity is less than the reflection intensity threshold k are marked as snowflake points, and the points where the received laser intensity is greater than the reflection intensity threshold k are marked as non-snowflake points. At this time, substituting the snow light intensity attenuation coefficient into the laser attenuation formula can output the actual measured distance. When Y ≥ δ and Y ≥ R, it is impossible to determine whether it is a snowflake point, and no mark is output. When Y ≥ δ and Y < R, the points where the received laser intensity is less than the reflection intensity threshold k are marked as snowflake points, and the points where the received laser intensity is greater than the reflection intensity threshold k are marked as non-snowflake points. The confidence level is Where R is the maximum width of the snowflake, the actual measured distance can be output by substituting the snow light intensity attenuation coefficient into the laser attenuation formula.

[0041] The laser curtain wall image optimization method based on laser ranging analysis is characterized by the following steps:

[0042] Step 1: Sort the point cloud data of the laser received by the laser receiving device according to the intensity of the received laser from smallest to largest, and obtain the probability q of the occurrence of point cloud data for each intensity. i ;

[0043] Step 2: Set the reflection intensity threshold k, where k is a positive integer, k∈[1,m]. Based on k, divide the laser point cloud into snowflake points and non-snowflake points to obtain the snowflake point set A and the non-snowflake point set B.

[0044] Step 3: Based on the snowflake point set A and the non-snowflake point set B and the probability q k The probability of generating snowflake points Q A and the probability Q of non-snowflake points B ;

[0045] Step 4: Calculate the variance F of non-snowflake points B and the variance F of the snowflake point A ;

[0046] Step 5: Based on Q A Q B F B and F A Calculate the snowflake intensity variance segmentation coefficient ρ; based on the variance segmentation coefficient ρ, obtain the determined value of the reflection intensity threshold;

[0047] Step 6: Measure the jitter data of the laser equipment using an accelerometer to generate the laser jitter amount Y;

[0048] Step 7: Set up the laser test transmitting and receiving equipment under normal weather conditions, and collect test light intensity, test distance, and snowflake parameters;

[0049] Step 8: Perform correlation analysis on the parameters collected in Step 7 to generate the snowy day light intensity attenuation coefficient;

[0050] Step 9: Based on the laser jitter Y and k, determine whether the received laser is a snowflake, and calculate the actual distance according to the light intensity attenuation coefficient in snowy weather.

[0051] Compared with the prior art, the beneficial effects of the present invention are:

[0052] The laser curtain wall image optimization system and method based on laser ranging analysis provided by this invention generates snowflake point probabilities and non-snowflake point probabilities by performing probability analysis on snowflake point sets and non-snowflake point sets. Further analysis and optimization are performed based on the intensity probabilities of laser points. Variance analysis is used to improve accuracy, and a variance analysis module for snowflake and non-snowflake points is introduced to help understand the intensity distribution characteristics of different reflection sources. A reflection intensity threshold is output based on the variance, optimizing the accuracy of laser ranging. By setting up a laser point cloud acquisition module and a segmentation module, laser points reflected by snowflakes (snowflake points) and those not reflected by snowflakes (non-snowflake points) can be effectively identified, achieving accurate classification of laser data. Considering the potential vibration of laser equipment under wind influence, a vibration acquisition module and an analysis module are used to acquire equipment vibration data in real time and perform correlation analysis with the reflection intensity threshold. Snow-related parameters are also collected, and a snow-day light intensity attenuation coefficient is generated, providing more accurate calculation data for laser ranging. This effectively compensates for data deviations caused by equipment vibration and ensures data authenticity. Attached Figure Description

[0053] Figure 1 This is a schematic diagram of the overall system flow of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0055] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0056] Example:

[0057] Please see Figure 1 The present invention provides a technical solution:

[0058] A laser curtain wall image optimization system based on laser ranging analysis includes:

[0059] In snowy weather, snowflakes can affect the laser emitted by laser equipment and cause it to reflect back, leading to inaccurate installation of the laser equipment on the curtain wall. To analyze snowflake points and non-snowflake points, this invention includes the following modules:

[0060] The laser point cloud acquisition module is used to set up a laser device. In snowy weather, the laser device emits laser light and receives laser light reflected by snow in the air and laser light reflected by the reflecting device. It collects all the received laser point cloud data, which includes the intensity of N laser points.

[0061] The point cloud data of the laser received by the laser receiving device are sorted in ascending order of laser intensity, with N data points. The laser intensity of the received laser point cloud data is defined as 1, 2, 3, ..., k, ... m, and the probability of point cloud data with the corresponding intensity is q1, q2, q3, ..., qm. k , ..., q m Point cloud data represents the laser intensity of all received laser beams.

[0062] To analyze the intensity of all received lasers, they are divided into two parts: one part is the snowflake point reflection part, and the other part is the non-snowflake point reflection part.

[0063] The partitioning module is used to set the reflection intensity threshold k, perform correlation analysis on the laser point cloud data, and generate a snowflake point set A and a non-snowflake point set B. The laser received intensity in the snowflake point set A is less than the reflection intensity threshold k, and the laser received intensity in the non-snowflake point set B is not less than the reflection intensity threshold k. The snowflake point set A is used to reflect the laser point cloud data reflected by snowflakes, and the non-snowflake point set B is used to reflect the laser point cloud data that has not been reflected by snowflakes.

[0064] Correlation analysis is performed on the snowflake point set A and the non-snowflake point set B to generate the snowflake point probability Q. A and the probability Q of non-snowflake points B The formula used is:

[0065]

[0066] Snowflake probability Q A This is used to reflect the probability that any of the received laser points is a reflection from a snowflake point, and the probability of a non-snowflake point is Q. B It is used to reflect the probability that among all received laser points, there is no reflection after passing through the snowflake point, where i is used to index the intensity of the received reflected laser, and the value range is 1, 2, 3, ..., k, ... m.

[0067] The probability analysis module is used to perform correlation analysis on the snowflake point set A and the non-snowflake point set B, and generate the snowflake point probability and the non-snowflake point probability.

[0068] The snowflake point variance analysis module is used to perform correlation analysis on the snowflake point probability and the snowflake point set A, and generate the snowflake point mean and snowflake point variance.

[0069] The non-snowflake point variance analysis module is used to perform correlation analysis on the probability of non-snowflake points and the set of non-snowflake points B, and generate the mean and variance of non-snowflake points.

[0070] Correlation analysis is performed on the snowflake point probability and the snowflake point set A to generate the snowflake point mean J. A and the variance F of the snowflake point A The formula used is:

[0071]

[0072] Snowflake point mean J A The mean value of the laser reflection intensity of the snowflake points, and the variance F of the snowflake points. A The variance used to reflect the intensity of laser reflection at the snowflake point;

[0073] Correlation analysis was performed on the probability of non-snowflake points and the set of non-snowflake points B to generate the mean J of non-snowflake points. B The variance F of non-snowflake points B The formula used is:

[0074]

[0075] Non-snowflake point mean J B The mean value used to reflect the laser reflection intensity of non-snowflake points, and the variance F of non-snowflake points. B The variance used to reflect the intensity of laser reflection from non-snowflake points.

[0076] Obtain the variance F of the non-snowflake point B and the variance F of the snowflake point A Afterwards, since the laser reflection intensity values ​​of all snowflake points will be relatively close, the variance F of the snowflake points will be... A The numerical value will be relatively small. Similarly, since the laser reflection intensity values ​​of all non-snowflake points will be quite similar, the variance F of the non-snowflake points will be smaller. B The value will be relatively small.

[0077] Define the probability Q of the snowflake point using a function. A The probability of non-snowflake points Q B Snowflake point variance F A Non-snowflake point variance F B Correlation analysis was performed to calculate the minimum interference, and the following module was proposed:

[0078] The segmentation coefficient analysis module is used to perform correlation analysis on the snowflake point probability, non-snowflake point probability, snowflake point variance, and non-snowflake point variance to generate the snowflake intensity variance segmentation coefficient. The snowflake intensity variance segmentation coefficient is used to reflect the degree of variance difference in the received laser intensity.

[0079] The probability Q of the snowflake point A The probability of non-snowflake points Q B Snowflake point variance F A Non-snowflake point variance F B Correlation analysis was performed to generate the snowflake intensity variance segmentation coefficient ρ, based on the following formula:

[0080] ρ=Q A *F A 2 +Q B *F B 2

[0081] The snowflake intensity variance segmentation coefficient ρ is used to reflect the magnitude of the variance after all laser points are divided into snowflake points and non-snowflake points reflecting laser light.

[0082] The snowflake intensity variance segmentation coefficient ρ was fitted and analyzed using MATLAB software. When the value of the snowflake intensity variance segmentation coefficient ρ was minimized, the value of the reflection intensity threshold k was output. Points where the laser intensity received by the laser was greater than the reflection intensity threshold k were marked as non-snowflake points, and points where the laser intensity received by the laser was less than the reflection intensity threshold k were marked as snowflake points.

[0083] In snowy weather, the wind causes the laser equipment to vibrate, resulting in displacement of the laser receiving point and thus inaccurate data. Therefore, this invention proposes the following module for analysis:

[0084] The jitter acquisition module is used to acquire jitter data from the laser equipment.

[0085] The jitter analysis module is used to perform correlation analysis on jitter data and generate the laser jitter amount Y;

[0086] The jitter data of the laser device is measured using an accelerometer. This jitter data includes acceleration *a*, velocity *v*, angular velocity *w*, laser reception time *T*, and test distance *L*. Correlation analysis is performed on the jitter data to generate the laser jitter amount *Y*, based on the following formula:

[0087]

[0088] Where α is the linear velocity weighting factor, with a value range of [0,1], and the laser jitter Y is used to reflect the degree of laser jitter caused by wind in snowy weather. The laser receiving time is the time from when the laser device emits the laser to when it is reflected back by the receiving and reflecting device, and the test distance is the distance between the laser device and the reflecting device.

[0089] The snowflake acquisition module is used to set up the laser test transmitting and receiving equipment under normal weather conditions, acquire the test light intensity and test distance, and simultaneously acquire snowflake parameters, including snowfall intensity X and snowflake diameter D.

[0090] Under normal weather conditions (no rain, snow, wind, or sandstorms), both the laser transmitting and receiving equipment are set up. During laser ranging, only the attenuation of the laser light in the air needs to be considered. The test light intensity and test distance are collected. The test light intensity includes the intensity of the transmitted laser and the intensity of the received laser. The intensity of the transmitted laser is QF. L Let QJ be the emitted laser intensity at a test distance of L, and QJ be the received laser intensity. L To measure the received laser intensity at a test distance of L, the diameter of a single snowflake within a unit space is obtained by microscopic image analysis, and the average value is taken as the snowflake diameter D. A microscopic photograph of the snowflake is taken, and the geometric parameters of the snowflake within the range are extracted and averaged using image processing software. Similarly, the number of snowflakes per unit volume can be measured using this method.

[0091] The attenuation analysis module is used to perform correlation analysis on the parameters collected by the snowflake acquisition module and generate a snowy day light intensity attenuation coefficient, which reflects the degree of attenuation of laser intensity in snowy weather.

[0092] Test the intensity of emitted laser QF L Test the intensity of the received laser QJ L Correlation analysis was performed on the test distance L to generate the air absorption coefficient β. a The formula used is:

[0093]

[0094] This formula is derived from a transformation of the law of light absorption. Multiple sets of tested emitted laser intensities QF were analyzed using MATLAB software. L Test the intensity of the received laser QJ L The test distance L is used for fitting analysis to generate and output the fitted air absorption coefficient β. a The value of the air absorption coefficient β a Used to reflect the attenuation and absorption coefficient of laser intensity under normal weather conditions within the test area;

[0095] Perform a correlation analysis on the snowfall intensity X and the snowflake diameter D to generate the snowflake absorption coefficient β b , and the formula is as follows:

[0096]

[0097] where, σ sc is the snowflake scattering cross-section, which is used to reflect the light scattering ability of each snowflake particle, n is the number of particles per unit volume, Q sc is the scattering efficiency factor, V p is the volume of a single snowflake, and the snowflake absorption coefficient β b is used to reflect the attenuation absorption coefficient of the snowflake to the laser intensity. Snowflakes usually have symmetry, and the snowflake volume is approximately calculated by . The scattering efficiency factor Q sc is an important parameter describing the scattering ability of particles to incident light. In the literature of meteorology and atmospheric science, the values of the scattering efficiency factors of common particles (such as water droplets, snowflakes, aerosols, etc.) are listed;

[0098] Perform a correlation analysis on the snowflake absorption coefficient β b and the air absorption coefficient β a to generate the snow sky light intensity attenuation coefficient β c , and the formula is as follows:

[0099]

[0100] where, the snow sky light intensity attenuation coefficient β c is the attenuation degree of the laser intensity in snow sky. At this time, substituting the snow sky light intensity attenuation coefficient into the laser attenuation formula can output the actual measured distance.

[0101] An output module, used to set the laser jitter threshold, perform a correlation analysis on the laser jitter amount Y and the reflection intensity threshold k, and output whether the laser reflection point is a snowflake point.

[0102] Furthermore, the laser jitter threshold is δ, and the value of δ is 1.35. Perform a correlation analysis on the laser jitter amount Y and the reflection intensity threshold k. When Y < δ, the points where the laser intensity received is less than the reflection intensity threshold k are marked as snowflake points, and the points where the laser intensity received is greater than the reflection intensity threshold k are marked as non-snowflake points. At this time, substituting the snow sky light intensity attenuation coefficient into the laser attenuation formula can output the actual measured distance; when Y ≥ δ and Y ≥ R, it is impossible to determine whether it is a snowflake point, and no mark is output; when Y ≥ δ and Y < R, the points where the laser intensity received is less than the reflection intensity threshold k are marked as snowflake points, and the points where the laser intensity received is greater than the reflection intensity threshold k are marked as non-snowflake points, and the confidence level is Where R is the maximum width of the snowflake, the actual measured distance can be output by substituting the snow light intensity attenuation coefficient into the laser attenuation formula.

[0103] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0104] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. 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 by 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.

[0105] 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; 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, depending on actual needs.

[0106] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes 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.

Claims

1. A laser curtain wall image optimization system based on laser ranging analysis, characterized in that, include: A laser point cloud acquisition module is used to set up a laser device. In snowy weather, the laser device emits laser light and receives laser light reflected from snow in the air, as well as laser light reflected by a reflecting device. All received laser point cloud data is acquired, including the intensity of N laser points. The laser point cloud data is then sorted according to the received laser intensity from smallest to largest. After sorting, the probability of occurrence of point cloud data with the corresponding intensity is q. k ;q k The probability of point cloud data appearing for the k-th laser reception intensity is represented by k = 1, 2, ..., m; m represents the maximum laser intensity. The partitioning module is used to set the reflection intensity threshold k, perform correlation analysis on the laser point cloud data, and generate a snowflake point set A and a non-snowflake point set B. The laser received intensity in the snowflake point set A is less than the reflection intensity threshold k, and the laser received intensity in the non-snowflake point set B is greater than or equal to the reflection intensity threshold k. The probability analysis module is used to perform correlation analysis on the snowflake point set A and the non-snowflake point set B, and generate the snowflake point probability Q. A and the probability Q of non-snowflake points B ; The snowflake point variance analysis module is used to perform correlation analysis on the snowflake point probability and the snowflake point set A, and generate the snowflake point mean J. A and the variance F of the snowflake point A ; The non-snowflake point variance analysis module is used to perform correlation analysis on the probability of non-snowflake points and the set of non-snowflake points B, and generate the mean J of non-snowflake points. B The variance F of non-snowflake points B ; The segmentation coefficient analysis module is used to perform correlation analysis on the snowflake point probability, non-snowflake point probability, snowflake point variance, and non-snowflake point variance, generate the snowflake intensity variance segmentation coefficient, and obtain the determined value of the reflection intensity threshold k based on the snowflake intensity variance segmentation coefficient. The jitter acquisition module is used to acquire jitter data from the laser equipment. The jitter analysis module is used to perform correlation analysis on jitter data and generate the laser jitter amount Y; The snowflake acquisition module is used to set up the laser test transmitting and receiving equipment under normal weather conditions, acquire the test light intensity and test distance, and simultaneously acquire snowflake parameters. The attenuation analysis module is used to perform correlation analysis on the parameters collected by the snowflake acquisition module and generate the light intensity attenuation coefficient in snowy weather. The output module is used to set the laser jitter threshold, perform correlation analysis between the laser jitter amount Y and the reflection intensity threshold k, output whether the laser reflection point is a snowflake point, and output the actual measurement distance based on the light intensity attenuation coefficient in snowy weather.

2. The laser curtain wall image optimization system based on laser ranging analysis according to claim 1, characterized in that: Correlation analysis is performed on the snowflake point set A and the non-snowflake point set B to generate the snowflake point probability Q. A and the probability Q of non-snowflake points B The formula used is:

3. The laser curtain wall image optimization system based on laser ranging analysis according to claim 1, characterized in that: Correlation analysis is performed on the snowflake point probability and the snowflake point set A to generate the snowflake point mean J. A and the variance F of the snowflake point A The formula used is: Correlation analysis was performed on the probability of non-snowflake points and the set of non-snowflake points B to generate the mean J of non-snowflake points. B The variance F of non-snowflake points B The formula used is:

4. The laser curtain wall image optimization system based on laser ranging analysis according to claim 1, characterized in that: The probability Q of the snowflake point A The probability of non-snowflake points Q B Snowflake point variance F A Non-snowflake point variance F B Correlation analysis was performed to generate the snowflake intensity variance segmentation coefficient ρ, based on the following formula: ρ=Q A *F A 2 +Q B *F B 2 。 5. The laser curtain wall image optimization system based on laser ranging analysis according to claim 1, characterized in that: The snowflake intensity variance segmentation coefficient ρ was fitted and analyzed using MATLAB software. When the value of the snowflake intensity variance segmentation coefficient ρ was minimized, the value of the reflection intensity threshold k was output.

6. The laser curtain wall image optimization system based on laser ranging analysis according to claim 1, characterized in that: The jitter data of the laser device is measured using an accelerometer. This jitter data includes acceleration *a*, velocity *v*, angular velocity *w*, laser reception time *T*, and test distance *L*. Correlation analysis is performed on the jitter data to generate the laser jitter amount *Y*, based on the following formula: Where α is the linear velocity weighting factor, and its value ranges from [0,1].

7. The laser curtain wall image optimization system based on laser ranging analysis according to claim 1, characterized in that: Under normal weather conditions, laser test emission and laser test receiving equipment are set up to collect test light intensity and test distance. The test light intensity includes the intensity of the emitted laser and the intensity of the received laser, with the emitted laser intensity QF... L Let QJ be the emitted laser intensity at a test distance of L, and QJ be the received laser intensity. L To determine the received laser intensity at a test distance of L, the snowflake parameters include snowfall intensity X and snowflake diameter D; the diameter of a single snowflake within a unit space is measured by microscopic image analysis, and the average value is taken as the snowflake diameter D.

8. The laser curtain wall image optimization system based on laser ranging analysis according to claim 7, characterized in that: Test the intensity of emitted laser QF L Test the intensity of the received laser QJ L Correlation analysis was performed on the test distance L to generate the air absorption coefficient β. a The formula used is: The air absorption coefficient β was generated by fitting and analyzing multiple sets of test data using MATLAB software. a The value; Correlation analysis was performed on snowfall intensity X and snowflake diameter D to generate the snowflake absorption coefficient β. b The formula used is: Where, σ sc Let Q be the scattering cross section of the snowflake, n be the number of particles per unit volume, and Q be the scattering cross section of the snowflake. sc V is the scattering efficiency factor. p The volume of a single snowflake; Snowflake absorption coefficient β b and air absorption coefficient β a Correlation analysis was performed to generate the light intensity attenuation coefficient β during snowy weather. c The formula used is:

9. The laser curtain wall image optimization system based on laser ranging analysis according to claim 8, characterized in that: The laser jitter threshold is δ, and δ is set to 1.

35. A correlation analysis is performed on the laser jitter amount Y and the reflection intensity threshold k. When Y < δ, the points where the laser intensity received is less than the reflection intensity threshold k are marked as snowflake points, and the points where the laser intensity received is greater than or equal to the reflection intensity threshold k are marked as non-snowflake points. At this time, substituting the snow light intensity attenuation coefficient into the laser attenuation formula can output the actual measured distance; when Y ≥ δ and Y ≥ R, it is impossible to determine whether it is a snowflake point, and no mark is output; when Y ≥ δ and Y < R, the points where the laser intensity received is less than the reflection intensity threshold k are marked as snowflake points, and the points where the laser intensity received is greater than or equal to the reflection intensity threshold k are marked as non-snowflake points, and the confidence level is where R is the maximum width of the snowflake. At this time, substituting the snow light intensity attenuation coefficient into the laser attenuation formula can output the actual measured distance.

10. The method of the laser curtain wall image optimization system based on laser ranging analysis as described in claim 1, characterized in that: Specifically, the steps include the following: Step 1: Sort the point cloud data of the laser received by the laser receiving device according to the intensity of the received laser from smallest to largest, and obtain the probability q of the occurrence of point cloud data for each intensity. i ; Step 2: Set the reflection intensity threshold k, where k is a positive integer, k∈[1,m]. Based on k, divide the laser point cloud into snowflake points and non-snowflake points to obtain the snowflake point set A and the non-snowflake point set B. Step 3: Based on the snowflake point set A and the non-snowflake point set B and the probability q k The probability of generating snowflake points Q A and the probability Q of non-snowflake points B ; Step 4: Calculate the variance F of non-snowflake points B and the variance F of the snowflake point A ; Step 5: Based on Q A Q B F B and F A Calculate the snowflake intensity variance segmentation coefficient ρ; based on the variance segmentation coefficient ρ, obtain the determined value of the reflection intensity threshold; Step 6: Measure the jitter data of the laser equipment using an accelerometer to generate the laser jitter amount Y; Step 7: Set up the laser test transmitting and receiving equipment under normal weather conditions, and collect test light intensity, test distance, and snowflake parameters; Step 8: Perform correlation analysis on the parameters collected in Step 7 to generate the snowy day light intensity attenuation coefficient; Step 9: Based on the laser jitter Y and k, determine whether the received laser is a snowflake, and calculate the actual distance according to the light intensity attenuation coefficient in snowy weather.

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