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

Abstract

The invention 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, and the method comprises the steps: carrying out the probability analysis of a snowflake point set and a non-snowflake point set, and generating a snowflake point probability and a non-snowflake point probability; the precision is improved through variance analysis, a snowflake point variance analysis module and a non-snowflake point variance analysis module are introduced, intensity distribution characteristics of different reflection sources can be deeply understood, a reflection intensity threshold value is output based on the magnitude of variance, and the precision of laser ranging is optimized. Laser points reflected by snowflakes and laser points not reflected by snowflakes can be effectively identified, and accurate classification of laser data is realized; in consideration of possible jittering of laser equipment under the influence of wind power, jittering data of the equipment are acquired in real time through a jittering acquisition module and an analysis module, and correlation analysis is performed on the jittering data and a reflection intensity threshold value, so that the authenticity of the data is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of laser curtain walls, and in particular to a laser curtain wall image optimization system and method based on laser ranging analysis. Background Art

[0002] Laser curtain wall object monitoring technology is an application based on laser ranging and point cloud data processing. It is widely used in various fields such as security surveillance, environmental monitoring, smart buildings, and traffic management. Laser curtain walls transmit a laser beam 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, thereby calculating the object's distance and position. This principle is based on the constant propagation speed of light. Laser measurement is a non-contact method that can monitor objects without disturbing them, 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. On snowy days, the presence of snowflakes can cause the laser beam to reflect and absorb the laser signal, causing the intensity of the received laser point to vary. This can affect 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 above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] The object of the present invention is to provide a laser curtain wall image optimization system and method based on laser ranging analysis to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] Laser curtain wall image optimization system based on laser ranging analysis, including:

[0008] The laser point cloud acquisition module is used to set up the laser equipment. When it snows, the laser equipment emits laser and receives the laser reflected by the snow in the air and the laser reflected by the reflective equipment. All the received laser point cloud data are collected. The laser point cloud data includes the intensity of N laser points. The laser point cloud data is sorted from small to large according to the received laser intensity. The probability of the point cloud data with the corresponding intensity after sorting is q k ;q k The probability of occurrence of point cloud data of the kth laser receiving intensity, 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 receiving intensity in the snowflake point set A is less than the reflection intensity threshold k, and the laser receiving intensity in the non-snowflake point set B is greater than or equal to the reflection intensity threshold;

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

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

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

[0013] A 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 a snowflake intensity variance segmentation coefficient, and obtain a determined value of the reflection intensity threshold k based on the snowflake intensity variance segmentation coefficient;

[0014] Jitter acquisition module, used to collect jitter data of laser equipment;

[0015] Jitter analysis module, used to perform correlation analysis on jitter data and generate laser jitter value Y;

[0016] Snowflake collection module, used to set up laser test transmitting equipment and laser test receiving equipment in normal weather conditions, collect test light intensity and test distance, and collect snowflake parameters at the same time;

[0017] Attenuation analysis module, used to perform correlation analysis on the parameters collected by the snow collection module and generate the snow light intensity attenuation coefficient;

[0018] The output module is used to set the laser jitter threshold, perform correlation analysis on 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 measured distance based on the light intensity attenuation coefficient of snow.

[0019] Furthermore, the snowflake point set A and the non-snowflake point set B are subjected to correlation analysis to generate the snowflake point probability Q A And the non-snowflake probability Q B , based on the formula:

[0020]

[0021] Furthermore, the snowflake point probability and the snowflake point set A are correlated to generate the snowflake point mean J A and snowflake variance F A , based on the formula:

[0022]

[0023] Perform correlation analysis on the non-snowflake point probability and the non-snowflake point set B to generate the non-snowflake point mean J B and non-snowflake variance F B , based on the formula:

[0024]

[0025] Furthermore, for the snowflake probability Q A , non-snowflake probability Q B , snowflake variance F A , non-snowflake variance F B Correlation analysis is performed to generate the snowflake intensity variance partition coefficient ρ, based on the formula:

[0026] ρ=Q A *F A 2 +Q B *F B 2 .

[0027] Furthermore, the snowflake intensity variance partition coefficient ρ was fitted and analyzed by MATLAB software, and the value of the reflection intensity threshold k was output when the snowflake intensity variance partition coefficient ρ was set to the minimum.

[0028] Furthermore, the jitter data of the laser device is measured by an acceleration sensor. The 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. The formula is as follows:

[0029]

[0030] Among them, α is the linear velocity weight factor, and its value range is [0,1].

[0031] Furthermore, in normal weather, a laser test transmitting device and a laser test receiving device are set up to collect the test light intensity and test distance. The test light intensity includes the test transmitting laser intensity and the test receiving laser intensity. The test transmitting 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 through Matlab software. a The value of β is obtained.

[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 laser intensity of the received laser is less than the reflection intensity threshold k are marked as snowflake points, and the points where the laser intensity of the received laser 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 "unmarked" is output. When Y ≥ δ and Y < R, the points where the laser intensity of the received laser is less than the reflection intensity threshold k are marked as snowflake points, and the points where the laser intensity of the received laser 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 a snowflake. Substituting the light intensity attenuation coefficient in snowy weather into the laser attenuation formula can output the actual measured distance.

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

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

[0043] Step 2: Set the reflection intensity threshold k, where k is a positive integer, k∈[1,m]. Based on k, the laser point cloud is divided 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 , generate snowflake point probability Q A And the non-snowflake probability Q B ;

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

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

[0047] Step 6: Measure the jitter data of the laser device using an acceleration sensor to generate the laser jitter value Y;

[0048] Step 7: Set up the laser test transmitter and receiver in 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 snow light intensity attenuation coefficient;

[0050] Step 9: Based on the laser jitter Y and k, determine whether the received laser is a snow point, and calculate the actual distance based on the light intensity attenuation coefficient of snow.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] The laser curtain wall image optimization system and method based on laser ranging analysis provided by the present invention generates snowflake point probability and non-snowflake point probability by performing probability analysis on a snowflake point set and a non-snowflake point set, performs further analysis and optimization based on the intensity probability of the laser point, improves the accuracy 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 accuracy of laser ranging, and effectively identifies laser points reflected by snowflakes (snowflake points) and laser points not reflected by snowflakes (non-snowflake points) by setting a laser point cloud acquisition module and a division module, thereby achieving accurate classification of laser data; considering the possible jitter of the laser equipment under the influence of wind, the jitter data of the equipment is obtained in real time through the jitter acquisition module and the analysis module, and correlation analysis is performed with the reflection intensity threshold, and snow-related parameters are collected to analyze and generate the snow-based light intensity attenuation coefficient, providing more accurate calculation data for laser ranging, thereby effectively compensating for the data deviation caused by the equipment jitter and ensuring the authenticity of the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic diagram of the overall system flow of the present invention. DETAILED DESCRIPTION

[0054] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is 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 the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0056] Example:

[0057] See also Figure 1 , the present invention provides a technical solution:

[0058] Laser curtain wall image optimization system based on laser ranging analysis, including:

[0059] On snowy days, snowflakes can affect the laser light emitted by the laser device and cause it to reflect back, resulting in inaccurate settings of the laser device curtain wall. In order to analyze snowflake points and non-snowflake points, the present invention sets the following modules:

[0060] The laser point cloud acquisition module is used to set up the laser equipment. When it snows, the laser equipment emits laser light and receives laser light reflected by snow in the air and laser light reflected by the reflective device. The module then collects all received laser point cloud data, which includes the intensity of N laser points.

[0061] The point cloud data of the laser receiving device is sorted from small to large according to the intensity of the received laser. The number of data points is N, where the laser intensity of the received laser point cloud data is defined from small to large as 1, 2, 3, ..., k, ... m, and the probability of occurrence of the point cloud data of the corresponding intensity is q1, q2, q3, ..., q k ,…,q m The point cloud data is the laser intensity of all received lasers.

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

[0063] The division 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 receiving intensity in the snowflake point set A is less than the reflection intensity threshold k, and the laser receiving 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 not reflected by snowflakes.

[0064] Perform correlation analysis on the snowflake point set A and the non-snowflake point set B to generate the snowflake point probability Q A And the non-snowflake probability Q B , based on the formula:

[0065]

[0066] Snowflake probability Q A It is used to reflect the probability that all received laser points are reflected by snowflake points, and the probability of non-snowflake points Q B It is used to reflect the probability that all received laser points have not been reflected by snowflake points. i is used to index the intensity of the received reflected laser, and its 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 non-snowflake point probability and the non-snowflake point set B to generate the non-snowflake point mean and non-snowflake point variance;

[0070] Perform correlation analysis on the snowflake point probability and the snowflake point set A to generate the snowflake point mean J A and snowflake variance F A , based on the formula:

[0071]

[0072] Snowflake mean J A Used to reflect the mean value of the laser reflection intensity of the snowflake point, the snowflake point variance F A Used to reflect the variance of the laser reflection intensity of the snowflake point;

[0073] Perform correlation analysis on the non-snowflake point probability and the non-snowflake point set B to generate the non-snowflake point mean J B and non-snowflake variance F B , based on the formula:

[0074]

[0075] Non-snowflake point mean J B Used to reflect the mean of the laser reflection intensity of non-snowflake points, the variance F of non-snowflake points B Used to reflect the variance of laser reflection intensity at non-snowflake points.

[0076] In obtaining the non-snowflake point variance F B and snowflake variance F A After that, since the laser reflection intensity values of all snowflake points are close, the snowflake point variance F A The value will be smaller. Similarly, since the laser reflection intensity values of all non-snowflake points are close, the variance F of non-snowflake points is B The value will be smaller.

[0077] Set the function to the probability Q of snowflake points A , non-snowflake probability Q B , snowflake variance F A , non-snowflake variance F B Perform correlation analysis, calculate the minimum value of interference, and propose the following modules:

[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 of the received laser intensity.

[0079] The probability of snowflake point Q A , non-snowflake probability Q B , snowflake variance F A , non-snowflake variance F B Correlation analysis is performed to generate the snowflake intensity variance partition coefficient ρ, based on the formula:

[0080] ρ=Q A *F A 2 +Q B *F B 2

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

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

[0083] On snowy days, the laser device may vibrate due to wind, which in turn may cause the laser receiving point to shift, thus causing the collected data to be untrue. Therefore, the present invention proposes the following module to analyze it:

[0084] Jitter acquisition module, used to collect jitter data of laser equipment;

[0085] Jitter analysis module, used to perform correlation analysis on jitter data and generate laser jitter value Y;

[0086] The jitter data of the laser device is measured by an acceleration sensor. The jitter data includes acceleration a, velocity v, angular velocity w, laser reception time T, and test distance L. The jitter data is correlated and analyzed to generate the laser jitter value Y. The formula is as follows:

[0087]

[0088] Where α is the linear velocity weighting factor, ranging from 0 to 1. The laser jitter Y reflects the degree of laser jitter caused by wind in snowy conditions. The laser reception time is the time from the laser device emitting the laser to the laser receiving device reflecting the laser. The test distance is the distance between the laser device and the reflector.

[0089] Snowflake collection module, used to set up laser test transmitting equipment and laser test receiving equipment in normal weather, collect test light intensity and test distance, and simultaneously collect snowflake parameters, including snowfall intensity X and snowflake diameter D;

[0090] The laser test transmitting equipment and the laser test receiving equipment are set up in normal weather. The normal weather is the weather without rain, snow, wind and sand. When measuring laser distance, only the attenuation of the laser in the air needs to be considered. The test light intensity and test distance are collected. The test light intensity includes the test emission laser intensity and the test reception laser intensity. The test emission laser intensity QF L is the emission laser intensity at the test distance L, and the test receiving laser intensity QJ L To measure the received laser intensity at a test distance of L, the diameter of a single snowflake per unit space is measured using microscopic image analysis, and the average value is taken as the snowflake diameter D. A microscopic photograph of the snowflakes is taken, and the geometric parameters of the snowflakes 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 snow collection module and generate the snow light intensity attenuation coefficient. The snow light intensity attenuation coefficient is used to reflect the attenuation degree of laser intensity in snowy weather.

[0092] The test laser intensity QF L , test the received laser intensity QJ L , perform correlation analysis on the test distance L and generate the air absorption coefficient β a , based on the formula:

[0093]

[0094] This formula is obtained by transforming the light absorption law formula. The MATLAB software is used to test the laser intensity QF of multiple groups. L , test the received laser intensity QJ L , perform fitting analysis at the test distance L, generate and output the fitted air absorption coefficient β a The value of the air absorption coefficient β a Used to reflect the attenuation absorption coefficient of laser intensity in 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 used is:

[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 used is:

[0099]

[0100] where the snow sky light intensity attenuation coefficient β c is the attenuation degree of the laser intensity in snowy weather. 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 is 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 a snowflake. Substituting the light intensity attenuation coefficient in snowy weather into the laser attenuation formula can output the actual measured distance.

[0103] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0104] 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 in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of each example 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 performed by 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, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0106] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. Laser curtain wall image optimization system based on laser ranging analysis, characterized by: include: The laser point cloud acquisition module is used to set up the laser equipment. When it snows, the laser equipment emits laser and receives the laser reflected by the snow in the air and the laser reflected by the reflective equipment. All the received laser point cloud data are collected. The laser point cloud data includes the intensity of N laser points. The laser point cloud data is sorted from small to large according to the received laser intensity. The probability of the point cloud data with the corresponding intensity after sorting is q k ;q k The probability of occurrence of point cloud data of the kth laser receiving intensity, 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 receiving intensity in the snowflake point set A is less than the reflection intensity threshold k, and the laser receiving intensity in the non-snowflake point set B is greater than or equal to the reflection intensity threshold; The probability analysis module is used to perform correlation analysis on the snowflake point set A and the non-snowflake point set B to generate the snowflake point probability Q A And the non-snowflake probability Q B ; Snowflake point variance analysis module is used to perform correlation analysis on snowflake point probability and snowflake point set A, and generate snowflake point mean J A and snowflake variance F A ; The non-snowflake point variance analysis module is used to perform correlation analysis on the non-snowflake point probability and the non-snowflake point set B, and generate the non-snowflake point mean J B and non-snowflake variance F B ; A 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 a snowflake intensity variance segmentation coefficient, and obtain a determined value of the reflection intensity threshold k based on the snowflake intensity variance segmentation coefficient; Jitter acquisition module, used to collect jitter data of laser equipment; Jitter analysis module, used to perform correlation analysis on jitter data and generate laser jitter value Y; Snowflake collection module, used to set up laser test transmitting equipment and laser test receiving equipment in normal weather conditions, collect test light intensity and test distance, and collect snowflake parameters at the same time; Attenuation analysis module, used to perform correlation analysis on the parameters collected by the snow collection module and generate the snow light intensity attenuation coefficient; The output module is used to set the laser jitter threshold, perform correlation analysis on 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 measured distance based on the light intensity attenuation coefficient of snow.

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

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

4. The laser curtain wall image optimization system based on laser ranging analysis according to claim 1, characterized in that: The probability of snowflake point Q A , non-snowflake probability Q B , snowflake variance F A , non-snowflake variance F B Correlation analysis is performed to generate the snowflake intensity variance partition coefficient ρ, based on the 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 is characterized in that: The snowflake intensity variance partition coefficient ρ is fitted and analyzed by MATLAB software, and the value of the reflection intensity threshold k is output when the snowflake intensity variance partition coefficient ρ is set to the minimum.

6. The laser curtain wall image optimization system based on laser ranging analysis according to claim 1, characterized in that: The laser device's jitter data is measured using an acceleration sensor. The 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 value Y. The formula used is: Among them, α is the linear velocity weight factor, and its value range is [0,1].

7. The laser curtain wall image optimization system based on laser ranging analysis according to claim 1, characterized in that: In normal weather, set up laser test transmitting equipment and laser test receiving equipment to collect test light intensity and test distance. The test light intensity includes the test transmitting laser intensity and the test receiving laser intensity. The test transmitting laser intensity QF L is the emission laser intensity at the test distance L, and the test receiving laser intensity QJ L is the received laser intensity at a test distance of L, and the snowflake parameters include snowfall intensity X and 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.

8. The laser curtain wall image optimization system based on laser ranging analysis according to claim 7, characterized in that: The test laser intensity QF L , test the received laser intensity QJ L , perform correlation analysis on the test distance L and generate the air absorption coefficient β a , based on the formula: Fitting and analyzing multiple sets of test data using Matlab software to generate the air absorption coefficient β a The value of Perform correlation analysis on snowfall intensity X and snowflake diameter D to generate snowflake absorption coefficient β b , based on the formula: Among them, σ sc is the snowflake scattering cross section, 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; Snowflake absorption coefficient β b and the air absorption coefficient β a Perform correlation analysis to generate the snow light intensity attenuation coefficient β c , based on the formula:

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 "unmarked" 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 using the laser curtain wall image optimization system based on laser ranging analysis according to claim 1, characterized in that: The specific steps include: Step 1: Sort the point cloud data of the laser received by the laser receiving device from small to large according to the intensity of the received laser, and obtain the probability q of the point cloud data corresponding to each intensity. i ; Step 2: Set the reflection intensity threshold k, where k is a positive integer, k∈[1,m]. Based on k, the laser point cloud is divided 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 , generate snowflake point probability Q A And the non-snowflake probability Q B ; Step 4: Calculate the variance F of non-snowflake points B and snowflake variance F A ; Step 5: Based on Q A , Q B 、F B and F A , calculate the snowflake intensity variance division coefficient ρ; obtain the determined value of the reflection intensity threshold based on the variance division coefficient ρ; Step 6: Measure the jitter data of the laser device using an acceleration sensor to generate the laser jitter value Y; Step 7: Set up the laser test transmitter and receiver in 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 snow light intensity attenuation coefficient; Step 9: Based on the laser jitter Y and k, determine whether the received laser is a snow point, and calculate the actual distance based on the light intensity attenuation coefficient of snow.

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