Correlation light measuring device based on inclined plane mirror and control method

Abstract

The invention relates to a related light measuring device based on a bevel mirror and a control method, and solves the problem that the existing three-dimensional detection technology is insufficient in comprehensive adaptability and cannot meet the requirements of precision detection for stability, precision and data collaboration, and the related light measuring device comprises a laser emission head, a double-bevel mirror, a light path receiving module, a zoom camera and a circuit processing unit. The first bevel mirror is provided with a through hole for laser to directly irradiate a target; the light path receiving module receives and reflects the laser-to-electric signal; the second bevel mirror reflects the target and the light spot light to the camera to obtain visual information; and the circuit processing unit controls all the components, integrates laser and visual information, and realizes three-dimensional detection of the target. The method has the following effects: through the light path collaborative design of the double-bevel mirror, precise and synchronous acquisition of laser ranging signals and visual contour / light spot information is realized, so that high-precision three-dimensional detection of the distance, the flatness and the segment difference of a target object is efficiently completed.

Description

Technical Field

[0001] This invention relates to the technical field of precision optical engineering, and in particular to a correlation light measurement device and control method based on an inclined mirror. Background Technology

[0002] In the fields of industrial precision manufacturing and component inspection, the detection of three-dimensional parameters of target objects, such as distance, flatness, and step difference, is a core requirement for ensuring product accuracy. With the upgrading of manufacturing processes, the industry increasingly needs to simultaneously acquire multi-dimensional information such as laser geometric data, visual contours, and spot positions to achieve efficient and accurate three-dimensional analysis.

[0003] Current mainstream 3D inspection technologies can be divided into three categories: laser-only measurement technologies (such as triangulation and reflective ranging), which obtain depth parameters through laser signals; pure vision technologies, which rely on image acquisition and algorithms to extract the contours of objects; and laser-vision combined technologies, which are equipped with both laser modules and cameras to attempt to integrate the two types of information.

[0004] Existing technologies have certain limitations: laser measurement alone is easily affected by environmental interference, resulting in low efficiency for large-scale detection; pure vision technology has difficulty directly acquiring depth parameters, limiting accuracy; and the laser-vision combined solution suffers from insufficient optical path coordination, low matching degree between the two types of data, and poor integration effect, making it difficult to fully meet the actual needs of precision detection. Summary of the Invention

[0005] In order to achieve precise synchronous acquisition of laser ranging signals and visual contour / spot information through the optical path collaborative design of double inclined mirrors, thereby efficiently completing high-precision three-dimensional detection of target object distance, flatness, and step difference, this application provides a correlation light measurement device and control method based on inclined mirrors.

[0006] In a first aspect, this application provides a correlation light measurement device based on an inclined face mirror, which adopts the following technical solution:

[0007] A correlation light measurement device based on an inclined plane mirror, comprising:

[0008] A laser emitter is used to emit a laser beam that propagates in a preset direction and is directed toward a target object.

[0009] The first inclined mirror is set on the laser propagation path between the laser emitter and the target object. Its inclined surface is a mirror glass surface and has a through hole for the laser beam to pass through. The axis of the through hole is collinear with the light output direction of the laser emitter, so that the laser beam passes through the through hole and directly hits the target object.

[0010] The optical path receiving module is arranged adjacent to the laser transmitter. The two are located on the side of the first inclined mirror away from the target object. It is used to receive the laser beam that returns along the original path after being reflected by the target object and passes through the through hole, and convert the reflected laser beam into an electrical signal.

[0011] The second inclined mirror is set at a preset angle to the inclined surface of the first inclined mirror. Its mirror surface faces the side where the first inclined mirror is located, and the mirror surface is a mirror glass surface. It is configured to reflect the light from the target object and the light spot formed by the laser beam on the target object.

[0012] The zoom camera has its lens facing the surface of the second inclined mirror to receive the light reflected from the second inclined mirror and obtain the visual outline of the target object and the spatial position information of the light spot formed by the laser beam on the target object.

[0013] The circuit processing unit is electrically connected to the laser transmitter, the optical path receiving module, and the zoom camera, respectively. It is used to control the emission timing of the laser transmitter, receive the electrical signals output by the optical path receiving module and process them to calculate the distance, flatness, and step difference of the target object, control the zoom camera to work and process the visual information it acquires, and realize the three-dimensional detection of the target object by integrating the laser signal and the visual information.

[0014] By adopting the above technical solution, the device uses two inclined mirrors working together. The first inclined mirror provides direct laser light to the target, while the second inclined mirror reflects visual light. The laser and vision components are arranged on the same side, resulting in a compact structure that saves space. The laser ranging and visual contour information are fused by the circuit processing unit to accurately calculate the target distance, flatness, and step difference. This enables three-dimensional target detection, balancing space utilization and detection accuracy, and is suitable for measurement needs in various scenarios such as industrial applications and precision parts manufacturing.

[0015] Secondly, this application provides a control method for a correlation light measurement device based on an inclined plane mirror, employing the following technical solution:

[0016] A control method for a correlation light measurement device based on an inclined plane mirror includes:

[0017] The laser emitter is controlled to emit a laser beam according to a preset timing sequence, so that the laser beam is directly aimed at the target object along the through-hole axis of the first inclined mirror. At the same time, the zoom camera is controlled to acquire the target area image reflected by the second inclined mirror, and the initial pixel coordinates of the laser spot and the initial contour features of the target are extracted from the image to form initial visual data.

[0018] The laser signal reflected from the target and passing through the through hole is collected by the optical path receiving module, converted into an electrical signal, and then the laser ranging value is calculated. Combined with the initial pixel coordinates of the light spot in the initial visual data, a mapping relationship between the laser ranging value and the visual pixel coordinates is established, with the initial contour features as the spatial reference.

[0019] Based on the above mapping relationship, the laser signal collected in real time by the optical path receiving module is converted into an electrical signal and calculated into real-time target distance data. Simultaneously, the dynamic image reflected by the second inclined mirror is obtained through the zoom camera, and the real-time pixel coordinates of the light spot and the real-time contour features of the target are identified.

[0020] Call the preset data fusion rules, associate real-time distance data, real-time pixel coordinates of light spots and real-time contour features, and calculate the target flatness and step difference based on the initial contour features;

[0021] It integrates real-time distance data, corrected flatness and step difference information, and outputs detection results including the target object's three-dimensional coordinates, flatness and step difference parameters.

[0022] By adopting the above technical solution, this method first synchronously collects initial visual data and laser ranging values, quickly establishes the mapping relationship between the two, and lays the foundation for accurate association; then, it synchronously acquires distance data and visual features in real time to ensure the timeliness of detection; through data fusion, it accurately calculates flatness and step difference to improve parameter accuracy; finally, it integrates and outputs three-dimensional detection results. The process is coherent and efficient, and can accurately complete the three-dimensional detection of targets, adapting to scenarios such as precision parts measurement. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the overall use of a correlation light measurement device based on an inclined mirror according to an embodiment of this application.

[0024] Figure 2 This is a schematic flowchart of a control method for a correlation light measurement device based on an inclined mirror according to an embodiment of this application.

[0025] In the diagram, 1 is the laser transmitter; 2 is the first inclined mirror; 3 is the optical path receiving module; 4 is the second inclined mirror; 5 is the zoom camera; and 6 is the circuit processing unit. Detailed Implementation

[0026] The present application will be further described in detail below with reference to the accompanying drawings.

[0027] Reference Figure 1 This application discloses a correlation light measurement device based on an inclined mirror, which mainly consists of a laser emitter 1, a first inclined mirror 2, an optical path receiving module 3, a second inclined mirror 4, a zoom camera 5, and a circuit processing unit 6. The functions and cooperation relationships of each component are as follows:

[0028] The laser emitter 1 can emit a laser beam in a preset direction, and the propagation path of the laser beam is directed towards the target object. The first inclined mirror 2 is located on the laser propagation path between the laser emitter 1 and the target object. Its inclined surface is made of mirror glass, and a through hole is opened on the inclined surface for the laser beam to pass through. The inner wall of the through hole is provided with a black light-absorbing coating to reduce stray light interference. The diameter of the through hole is 0.5-1mm larger than the diameter of the laser beam spot, which ensures that the laser beam passes through smoothly and avoids excessive ambient light from entering. The axis of the through hole is collinear with the light output direction of the laser emitter 1, ensuring that the laser beam directly hits the target object.

[0029] The optical path receiving module 3 and the laser transmitter 1 are arranged adjacent to each other, and both are located on the side of the first inclined mirror 2 away from the target object. They are used to receive the laser beam that returns along the original path after being reflected by the target object and passes through the through hole, and convert the reflected laser beam into an electrical signal. The second inclined mirror 4 is arranged at a preset angle with the inclined surface of the first inclined mirror 2. The mirror surface faces the side where the first inclined mirror 2 is located and is made of mirror glass. It can reflect the light from the target object and the light from the laser beam forming a light spot on the surface of the target object.

[0030] The lens of the zoom camera 5 faces the mirror surface of the second inclined mirror 4, and can receive the reflected light to obtain the visual outline of the target object and the spatial position information of the light spot. The circuit processing unit 6 is electrically connected to the laser emitter 1, the optical path receiving module 3 and the zoom camera 5 respectively. It can control the laser emission timing and the working state of the zoom camera 5, process the electrical signal of the optical path receiving module 3 to calculate the target distance, flatness and step difference, and finally realize the three-dimensional detection of the target object by integrating the laser signal and visual information.

[0031] Reference Figure 2 A control method for a correlation light measurement device based on an inclined mirror, comprising:

[0032] Step S100: Control the laser emitter 1 to emit a laser beam according to a preset timing sequence, so that the laser beam is directly pierced by the through hole axis of the first inclined mirror 2 and directed at the target object. At the same time, control the zoom camera 5 to collect the target area image reflected by the second inclined mirror 4, extract the initial pixel coordinates of the laser spot and the initial contour features of the target in the image, and form the initial visual data.

[0033] The preset timing sequence refers to the time sequence in which the laser transmitter 1 emits the laser beam. It is usually controlled by the circuit processing unit 6 to ensure that the laser emission and subsequent signal acquisition operations are performed at appropriate times. For example, if the preset timing sequence is to emit a laser pulse every 10ms, the circuit processing unit 6 will send a trigger signal to the laser transmitter 1 according to this time interval.

[0034] Initial visual data: including the initial pixel coordinates of the laser spot and the initial contour features of the target. This data is acquired and processed by the zoom camera 5 and used for subsequent measurement and calculation.

[0035] The necessary process is described below:

[0036] 1. Warm-up and Calibration of Laser Emitter 1: Before starting the measurement, laser emitter 1 needs to be warmed up to ensure the stability of its emitted laser beam parameters. Simultaneously, calibration is performed using a standard reflective target at a known distance to adjust the emission parameters of laser emitter 1, enabling it to accurately emit the laser beam.

[0037] 2. Alignment and Verification of the First Inclined Mirror 2: After the device is assembled, the alignment and verification of the through-hole axis of the first inclined mirror 2 needs to be performed. A laser collimator can be used to emit a laser beam, and the alignment can be judged by observing whether the laser can pass smoothly through the through-hole and directly hit the target object. If a deviation is found, the position and angle of the inclined mirror need to be finely adjusted until the laser can accurately pass through the through-hole.

[0038] 3. Parameter Settings and Image Acquisition of Zoom Camera 5: Adjust the focal length of zoom camera 5 according to the size and distance of the target object to ensure it can clearly capture images of the target area. When acquiring images, ensure that parameters such as exposure time and gain of the camera are set appropriately to obtain high-quality images. For example, in darker environments, increase the exposure time or increase the gain; in brighter environments, decrease the exposure time or decrease the gain.

[0039] 4. Image Processing and Initial Visual Data Extraction: Acquired images typically contain a large amount of noise and interference, requiring preprocessing using image processing algorithms. First, filtering algorithms (such as Gaussian filtering) can be used to remove noise from the image. Then, edge detection algorithms are used to extract the contour features of the target object. Thresholding algorithms or region growing algorithms are then used to locate the laser spot and calculate its initial pixel coordinates. For example, assuming the contour of the target object in the acquired image is relatively clear, the contour can be extracted using the Canny algorithm. Then, pixels with brightness higher than a certain threshold within the contour region are searched to determine the location of the laser spot.

[0040] Assuming the target object is a flat metal plate with high surface reflectivity, the laser spot diameter formed on the target object surface is approximately 2mm. In step S100, the laser emitter 1 is first preheated and calibrated to stably emit a laser beam with a wavelength of 650nm and a power of 5mW. Then, the axis of the through hole of the first inclined mirror 2 is aligned and verified using a laser collimator to ensure that the laser beam can accurately pass through the through hole and directly hit the target object. Next, based on the distance to the target object, the focal length of the zoom camera 5 is adjusted to 50mm, and the exposure time is set to 1 / 1000s with a gain of 1.5 to acquire a clear image of the target area. After acquiring the image, a Gaussian filtering algorithm is used to denoise the image, and then the contour features of the target object are extracted using the Canny edge detection algorithm. Within the contour region, a threshold segmentation algorithm (with a threshold set to 128) is used to locate the laser spot, calculating its initial pixel coordinates as (300, 200). Simultaneously, the initial contour features of the target object are extracted as a rectangular region with its upper left corner coordinates at (100, 100) and lower right corner coordinates at (500, 400). This initial visual data will serve as the basis for subsequent measurements and calculations.

[0041] In step S200, the laser signal reflected from the target and passing through the through hole is collected by the optical path receiving module 3, converted into an electrical signal, and the laser ranging value is calculated. Combined with the initial pixel coordinates of the light spot in the initial visual data, a mapping relationship between the laser ranging value and the visual pixel coordinates is established, with the initial contour features as the spatial reference.

[0042] The laser ranging value is calculated from the laser signal received by the optical path receiving module 3, determining the distance between the target object and the measuring device. It is typically based on Time-of-Flight (ToF) or phase difference principles. The mapping relationship refers to the correspondence between the laser ranging value and the visual pixel coordinates. Establishing this relationship links the distance data obtained from laser ranging with the pixel positions in the image, providing a foundation for subsequent 3D reconstruction and measurement. The initial contour features are the contour features of the target object extracted from the initial visual data, used as a spatial reference to determine the target object's position and shape in the image. These are typically obtained through image processing algorithms such as edge detection.

[0043] The necessary process is described below:

[0044] 1. Calibration and calibration of optical path receiving module 3: Before formal measurement, optical path receiving module 3 needs to be calibrated and calibrated to ensure that it can accurately receive and convert laser signals. Calibration can be performed using a standard reflective target at a known distance, adjusting the parameters of optical path receiving module 3 to enable it to accurately measure distance.

[0045] 2. Calculation of Laser Ranging Value: Based on the electrical signal acquired by the optical path receiving module 3, the laser ranging value is calculated using a specific algorithm. For example, when using the time-of-flight method, the distance is calculated by measuring the round-trip time of the laser. Assuming the electrical signal acquired by the optical path receiving module 3 represents the laser round-trip time as t, the distance d can be calculated using the following formula: Where c is the speed of light.

[0046] 3. Establishing the mapping relationship: A mapping relationship is established by associating the laser ranging value with the initial pixel coordinates of the light spot in the initial visual data. Specifically, the laser ranging value is matched with the pixel coordinates of the light spot in the image. For example, the laser ranging value corresponding to each pixel coordinate can be determined through a calibration process. Assume that during the calibration process, the initial pixel coordinates of the light spot in the image are known... The corresponding laser ranging value is Then a mapping relationship can be established: .

[0047] Initial contour feature matching: Initial contour features are used as a spatial reference to determine the position and shape of the target object in the image. This can be achieved by matching the contour features with pixel coordinates in the image. For example, assuming the initial contour feature is a rectangular region with its top-left corner coordinates (x1, y1) and bottom-right corner coordinates (x2, y2), the position of the target object in the image can be determined using these coordinates.

[0048] Step S300: Based on the above mapping relationship, the laser signal collected in real time by the optical path receiving module 3 is converted into an electrical signal and calculated into real-time target distance data. Simultaneously, the dynamic image reflected by the second inclined mirror 4 is obtained through the zoom camera 5, and the real-time pixel coordinates of the light spot and the real-time contour features of the target are identified.

[0049] Among them, dynamic images refer to the image sequence acquired in real time by the zoom camera 5, reflecting the real-time state of the target object during the measurement process. Real-time data synchronization refers to aligning the real-time distance data acquired by the optical path receiving module 3 with the dynamic image data acquired by the zoom camera 5 in time, ensuring that the two correspond at the same moment, and providing an accurate data foundation for subsequent 3D reconstruction.

[0050] The necessary process is described below:

[0051] 1. Real-time laser signal acquisition and conversion: The optical path receiving module 3 receives the laser signal and converts it into an electrical signal, which is then converted into a digital signal after amplification and filtering.

[0052] 2. Real-time distance data calculation: The circuit processing unit 6 calculates the real-time distance data based on the digital signal using the time-of-flight method. For example, if the laser round-trip time is 12ns, the calculated distance is 1.8m.

[0053] 3. Acquisition and processing of dynamic images: The zoom camera 5 acquires dynamic images and uses Gaussian filtering to remove noise, ensuring image clarity.

[0054] 4. Extraction of real-time pixel coordinates of the light spot: The real-time pixel coordinates of the light spot are extracted using a threshold segmentation algorithm. For example, if the threshold is set to 128, the extracted light spot coordinates are (320, 220).

[0055] 5. Extraction of real-time contour features of the target object: The Canny algorithm is used to extract the real-time contour features of the target object. For example, the extracted contour features are a rectangular region with the upper left corner coordinates (110, 110) and the lower right corner coordinates (510, 410).

[0056] 6. Data Association and Synchronization: Real-time distance data is associated with real-time pixel coordinates of light spots through mapping relationships to ensure data synchronization and provide accurate data for subsequent 3D detection.

[0057] Step S400: Invoke the preset data fusion rules, associate real-time distance data, real-time pixel coordinates of light spots and real-time contour features, and calculate the target flatness and step difference based on the initial contour features.

[0058] Data fusion rules refer to the rules and methods for integrating data from different sensors (such as the optical path receiving module 3 and the zoom camera 5) to improve the accuracy and reliability of measurements. Data fusion involves calling preset data fusion rules to integrate real-time distance data, real-time pixel coordinates of the light spot, and real-time contour features. For example, algorithms such as Kalman filters or particle filters are used for data fusion to improve the accuracy and reliability of measurements.

[0059] Flatness: refers to the smoothness of the surface of a target object, usually assessed by measuring the height differences between points on the surface. Flatness calculation: using initial contour features as a reference, calculate the height differences between points on the target object's surface. For example, use the least squares method to fit the plane equation of the target object's surface, then calculate the distance from each point to the fitted plane to evaluate the flatness.

[0060] Step difference: refers to the height difference between different areas of a target object's surface, used to evaluate the surface's smoothness and consistency. Step difference calculation: The step difference is calculated by comparing the height values ​​of different areas of the target object's surface. For example, a region segmentation algorithm can be used to divide the target object's surface into multiple regions, and then the height difference between each region can be calculated.

[0061] The necessary process is described below:

[0062] 1. Data Fusion: This function integrates real-time distance data, real-time pixel coordinates of light spots, and real-time contour features by invoking preset data fusion rules. For example, a Kalman filter can be used to fuse real-time data, improving the accuracy and reliability of measurements.

[0063] 2. Flatness Calculation: Based on the initial contour features, the least squares method is used to fit the plane equation of the target object's surface. The distance from each point to the fitted plane is calculated, and the flatness is evaluated. For example, assuming the fitted plane equation is z = ax + by + c, the distance from each point (x...) to the fitted plane is calculated. i ,y i ,z i Distance to the plane :

[0064] Flatness is assessed by statistically analyzing the distribution of these distances.

[0065] 3. Segment Difference Calculation: The target object surface is divided into multiple regions using a region segmentation algorithm. The average height of each region is calculated, and then the height difference between regions is calculated to evaluate the segment difference. For example, assuming the target object surface is divided into two regions, with region 1 having an average height of h1 and region 2 having an average height of h2, then the segment difference Δh is: .

[0066] Step S500: Integrate real-time distance data, corrected flatness and step difference information, and output detection results including the target object's three-dimensional coordinates, flatness and step difference parameters.

[0067] 3D coordinates: These refer to the position coordinates of each point on the surface of the target object in three-dimensional space, usually represented as (x, y, z). Detection results: These refer to the integrated measurement data, including the target object's 3D coordinates, flatness, and step parameters, used to describe the shape and surface characteristics of the target object.

[0068] Detection result output: The calculated 3D coordinates, flatness and step difference parameters are integrated into a data structure and output as the detection result.

[0069] A control method for a correlation light measurement device based on an inclined plane mirror also includes a mapping relationship update method, as follows:

[0070] Step S301: At each preset interval, extract the real-time contour features of the target and synchronously record the device status and environmental variables related to contour acquisition. The device status includes the real-time emission power of the laser emitter 1, the focus lock status of the zoom camera 5, and the signal gain value of the optical path receiving module 3. The environmental variables include the real-time illumination intensity of the target area.

[0071] Among them, the preset period refers to a specified time interval used to periodically perform certain operations, such as data acquisition or status checks. Equipment status refers to the current operating status of each component in the measuring device, including the real-time emission power of the laser emitter 1, the focus lock status of the zoom camera 5, and the signal gain value of the optical path receiving module 3. Environmental variables refer to external factors in the measurement environment that may affect the measurement results, such as the real-time illumination intensity of the target area.

[0072] The necessary process is described below:

[0073] 1. Real-time contour feature extraction of the target: At preset intervals (e.g., every 10 seconds), the zoom camera 5 acquires images of the target object. The Canny edge detection algorithm is used to extract the real-time contour features of the target object. For example, the extracted contour features are a rectangular region with the upper left corner coordinates (110, 110) and the lower right corner coordinates (510, 410).

[0074] 2. Recording of Equipment Status: Synchronously record the equipment status related to contour acquisition, including the real-time emission power of laser emitter 1, the focus lock status of zoom camera 5, and the signal gain value of optical path receiving module 3. For example, record that the real-time emission power of laser emitter 1 is 5mW, the focus lock status of zoom camera 5 is 50mm, and the signal gain value of optical path receiving module 3 is 1.5.

[0075] 3. Recording of environmental variables: Synchronously record the real-time light intensity of the target area. For example, record the light intensity of the target area as 500 lux.

[0076] Step S302: The iterative nearest point algorithm is used to match the real-time contour features with the initial contour features, and the root mean square of the coordinate offset of the feature points is calculated as the basic contour deviation.

[0077] Among them, the iterative nearest point algorithm is an algorithm used to match two sets of point clouds or contour features. It finds the nearest point pair iteratively and calculates the optimal transformation matrix to align the two sets of points. Basic contour deviation refers to the deviation between the real-time contour features and the initial contour features, which is usually evaluated by calculating the root mean square of the coordinate offset of the feature point pair.

[0078] The necessary process is described below:

[0079] 1. Matching Real-Time Contour Features with Initial Contour Features: The ICP algorithm is used to match real-time contour features with initial contour features. The ICP algorithm iteratively finds the nearest point pair and calculates the optimal transformation matrix to align the two sets of contour features. In short, the ICP algorithm continuously adjusts the position and orientation of the real-time contour features to make them as close as possible to the initial contour features.

[0080] 2. Calculate the basic contour deviation: Calculate the root mean square (RMS) of the coordinate offsets of feature points as the basic contour deviation. Specifically, calculate the coordinate offset of each feature point, and then calculate the root mean square value of these offsets. The smaller the root mean square value, the closer the real-time contour is to the initial contour.

[0081] Step S303: Based on the acquired equipment status, calculate the equipment status deviation using a preset equipment status deviation calculation method; based on the acquired environmental variables, calculate the environmental variable deviation using a preset environmental deviation calculation method.

[0082] Equipment state deviation: refers to the difference between the current operating state of each component in the measuring device and the standard or initial state, such as the power change of laser emitter 1, the focal length change of the camera, etc. Environmental variable deviation: refers to the difference between the external factors in the measurement environment that may affect the measurement results and the standard or initial state, such as changes in light intensity.

[0083] The equipment condition deviation is calculated as follows:

[0084] 1. Power deviation of laser emitter 1: The power of laser emitter 1 is monitored in real time by circuit processing unit 6, and the difference between it and the standard power is calculated.

[0085] 2. Zoom camera focal length deviation: Obtain the current focal length through the camera's control interface and calculate the difference between it and the standard focal length.

[0086] 3. Optical path receiving module 3 gain deviation: The current gain value is obtained through the system's preset signal processing unit, and the difference between it and the standard gain value is calculated. The signal processing unit is usually an independent module (or circuit, chip) at the system level, used to centrally process the electrical signals of multiple modules (including the optical path receiving module).

[0087] The environmental variable deviation is calculated as follows:

[0088] Light intensity deviation: The light intensity of the target area is measured in real time by an environmental sensor (such as a light sensor), and the difference between the light intensity and the standard light intensity is calculated.

[0089] Step S304: Based on the equipment state deviation and environmental variable deviation, assign correction coefficients to the basic profile deviation to obtain the corrected profile deviation.

[0090] The correction factor is a coefficient used to adjust the basic contour deviation based on equipment condition deviation and environmental variable deviation, reflecting the degree of influence of the deviation on the measurement results. The corrected contour deviation, adjusted by the correction factor, more accurately reflects the actual deviation of the target object's contour.

[0091] The necessary process is described below:

[0092] Equipment condition correction factor: Calculated based on equipment condition deviation. For example, assuming the equipment condition deviation is... Correction factor It can be represented as: ,in, It is a preset proportional coefficient.

[0093] Environmental variable correction factor: The correction factor is calculated based on the deviation of the environmental variables. For example, assuming the deviation of the environmental variables is... Correction factor It can be represented as: ,in, It is a preset proportional coefficient.

[0094] Calculate the corrected profile deviation: Adjust the basic profile deviation using equipment condition correction factors and environmental variable correction factors. Assume the basic profile deviation is... Corrected contour deviation It can be represented as: .

[0095] Step S305: If the corrected contour deviation exceeds the preset threshold, then based on the contour transformation matrix output by the iterative nearest point algorithm, combined with the correction coefficients of the device status and environmental variables, the mapping relationship transformation parameters between the laser ranging value and the visual pixel coordinates are adjusted by the least squares method.

[0096] The contour transformation matrix, output by the Iterative Closest Point (ICP) algorithm, describes the spatial transformation relationship between real-time contour features and initial contour features. Mapping transformation parameters are used to convert laser ranging values ​​into visual pixel coordinates; these parameters may need to be adjusted based on the contour transformation matrix and correction coefficients.

[0097] The necessary process is described below:

[0098] 1. Determine the corrected contour deviation: Check if the corrected contour deviation exceeds the preset threshold. If it does, proceed to the next adjustment; if it does not, keep the current mapping relationship transformation parameters unchanged.

[0099] 2. Calculate the mapping transformation parameters: Using the contour transformation matrix and correction coefficients, adjust the mapping transformation parameters using the least squares method. For example, if the contour transformation matrix is... The correction factor is and Then the new mapping transformation parameters It can be calculated in the following ways: Where d is the distance function and P is the transformation parameter of the original mapping relationship. These are visual pixel coordinates.

[0100] Step S306: If the corrected contour deviation is less than or equal to the preset threshold, then it remains unchanged.

[0101] Among them, the preset threshold is a pre-set value used to determine whether the mapping relationship transformation parameters need to be adjusted.

[0102] The necessary process is described below:

[0103] 1. Determine contour deviation: Compare the corrected contour deviation with the preset threshold to determine whether the mapping relationship conversion parameters need to be adjusted.

[0104] 2. Maintain current parameters: If the corrected contour deviation is less than or equal to the preset threshold, then keep the current mapping relationship conversion parameters unchanged.

[0105] Based on the equipment condition deviation and environmental variable deviation, correction coefficients are assigned to the basic profile deviation to obtain the corrected profile deviation, which includes:

[0106] Step S3041: Based on the preset angle parameters between the first inclined mirror 2 and the second inclined mirror 4, the real-time contour features, spot pixel coordinates and laser ranging data are geometrically corrected by a preset spatial coordinate transformation algorithm to eliminate the inherent spatial deviation caused by the double inclined mirror reflection and obtain the corrected real-time contour features.

[0107] Geometric correction refers to adjusting measurement data using algorithms to eliminate systematic errors caused by the geometric layout of the measuring device. Spatial coordinate transformation algorithm: An algorithm used to transform measurement data from one coordinate system to another to correct for the effects caused by the device's geometric layout.

[0108] The necessary process is described below:

[0109] 1. Determine geometric transformation parameters: Determine the geometric transformation parameters such as the angle, position, and direction of the inclined mirror through the system calibration process.

[0110] 2. Application of spatial coordinate transformation algorithm: Using these parameters, real-time contour features, spot pixel coordinates, and laser ranging data are corrected. The algorithm includes operations such as rotation, translation, and scaling to adapt to different geometric transformation requirements.

[0111] 3. Perform geometric transformations:

[0112] Rotation Correction: The contour feature is rotated and corrected based on the angle θ between the inclined mirrors. For example, if the contour feature coordinates are (x, y), the rotated coordinates (x′, y′) are calculated as follows:

[0113] ; .

[0114] Translation Correction: If the position of the inclined mirror causes image shift, translation correction is needed for the contour features. Translation Vector This can be obtained through measurement system calibration, and then applied as follows: ; , These are the two-dimensional coordinate values ​​after translation compensation.

[0115] Scaling Correction (if needed): If the reflection from the beveled mirror causes a change in image scale, scaling correction is required for the contour features. The scaling factor S can be obtained through measurement system calibration and then applied as follows: ; , This represents the final target coordinates after scaling transformation.

[0116] 4. Integrate and correct data: Integrate the corrected real-time contour features, spot pixel coordinates, and laser ranging data for use in subsequent steps.

[0117] Step S3042: Based on the corrected real-time contour features, and combined with the initial contour features, the corrected basic contour deviation is obtained through a preset feature matching algorithm.

[0118] Among them, the feature matching algorithm is an algorithm used to identify and match similar or identical feature points in two sets of feature data (such as contour features).

[0119] The necessary process is described below:

[0120] 1. Feature Extraction: Extract key feature points from the corrected real-time contour features and the initial contour features. These feature points can be inflection points, vertices, or other significant geometric features on the contour.

[0121] 2. Application of Feature Matching Algorithms: Using a preset feature matching algorithm, such as the Iterative Closest Point (ICP) algorithm, feature points in the real-time contour features are matched with feature points in the initial contour features. For example, if a point in the real-time contour features is located at (150, 250), the algorithm will find the point in the initial contour features that is closest to this point and match it.

[0122] 3. Deviation Calculation: For each pair of matched feature points and Calculate their differences in spatial coordinates. This can be done by calculating the Euclidean distance:

[0123] .

[0124] 4. Comprehensive Deviation Assessment: Calculate a comprehensive deviation value based on the deviations of all matched feature points, such as the average deviation. Where n is the total number of matching feature points. It is the deviation of the i-th pair of feature points.

[0125] Key feature points are extracted from the corrected real-time contour features and the initial contour features. These feature points can be inflection points, vertices, or other significant geometric features on the contour.

[0126] Step S3043: Based on the acquired equipment status deviation values, the deviation values ​​are divided into preset deviation levels using a preset interval mapping algorithm, and a preset equipment correction coefficient is assigned accordingly.

[0127] The interval mapping algorithm is an algorithm used to map actual measured deviation values ​​to preset deviation level intervals. Deviation level: Based on the magnitude of the deviation value, the deviation is divided into different levels to facilitate the application of different correction strategies. Correction coefficient: A coefficient corresponding to the deviation level, used to adjust the measurement results to compensate for the influence of the deviation.

[0128] The necessary process is described below:

[0129] 1. Determine the deviation level range: Preset different deviation level ranges, such as: [0,0.01], (0.1,0.2], (0.2,0.3], etc., each range corresponds to a different deviation level.

[0130] 2. Applying an interval mapping algorithm: The actual equipment status deviation value is mapped to a preset deviation level interval using an interval mapping algorithm. For example, if the equipment status deviation value is 0.15, it is mapped to the interval (0.1, 0.2).

[0131] 3. Assign a correction factor: Preset a correction factor for each deviation level. For example: For the range [0, 0.01], the correction factor might be 1.00 (no deviation or very small deviation, no correction needed). For the range (0.1, 0.2], the correction factor might be 1.01 (slight deviation, slight correction needed). For the range (0.2, 0.3], the correction factor might be 1.02 (moderate deviation, moderate correction needed).

[0132] 4. Calculate the correction factor: Based on the deviation level obtained from the mapping, select the corresponding correction factor from the preset correction factor table. For example, if the deviation value of 0.15 is mapped to the interval (0.1, 0.2], then select the correction factor 1.01.

[0133] Step S3044: Using a preset coefficient fusion algorithm, the preset device correction coefficient and the preset environment correction coefficient are multiplied to obtain the comprehensive correction coefficient.

[0134] The system comprises the following components: **Preset Equipment Correction Coefficient:** This coefficient corresponds to the deviation level determined by an interval mapping algorithm based on the equipment state deviation value. It is used to compensate for the impact of changes in equipment state on the measurement results. **Preset Environmental Correction Coefficient:** This coefficient is obtained by calculating the environmental variable deviation value using a corresponding method. It is used to compensate for the impact of changes in environmental factors on the measurement results. **Comprehensive Correction Coefficient:** This coefficient is obtained by multiplying the preset equipment correction coefficient and the preset environmental correction coefficient. It is used to comprehensively consider the impact of both equipment state and environmental factors on the measurement results, providing a more comprehensive correction to the basic profile deviation. **Coefficient Fusion Algorithm:** This algorithm is used to fuse the preset equipment correction coefficient and the preset environmental correction coefficient. In this step, a product operation is employed.

[0135] The necessary process is described below:

[0136] 1. Determine the preset equipment correction coefficient: Based on the equipment status deviation value, use a preset interval mapping algorithm to divide it into the corresponding deviation level interval, and then select the corresponding equipment correction coefficient from the preset correction coefficient table. For example, if the power deviation value of laser emitter 1 is 0.15, it is mapped to the interval (0.1, 0.2] through the interval mapping algorithm, and the corresponding equipment correction coefficient is 1.01.

[0137] 2. Determine the preset environmental correction coefficient: Based on the deviation value of the environmental variable, the environmental correction coefficient is obtained through a preset calculation method. Assuming the environmental variable is light intensity with a deviation value of 50 lux, the environmental correction coefficient is calculated to be 1.005 using a preset relationship model between light intensity deviation and correction coefficient.

[0138] 3. Apply the coefficient fusion algorithm: Multiply the preset equipment correction coefficient 1.01 obtained above with the preset environment correction coefficient 1.005, i.e., 1.01×1.005=1.01505, to obtain the comprehensive correction coefficient 1.01505.

[0139] Step S3045: Multiply the corrected basic profile deviation by the comprehensive correction coefficient to obtain the initial corrected profile deviation.

[0140] Step S3046: Apply a preset upper limit constraint to the comprehensive correction coefficient: If the comprehensive correction coefficient exceeds the preset threshold, then replace the comprehensive correction coefficient with the threshold and recalculate the corrected profile deviation.

[0141] The preset upper limit threshold is a pre-defined maximum value used to limit the maximum possible value of the comprehensive correction coefficient. When the comprehensive correction coefficient exceeds this threshold, it will be replaced by this threshold to prevent inaccurate or unstable measurement results caused by an excessively large correction coefficient. The corrected profile deviation is the profile deviation value recalculated after applying the upper limit constraint. This value is used for subsequent measurements and analysis to ensure the accuracy and reliability of the measurement results.

[0142] The necessary process is described below:

[0143] 1. Set a preset upper limit threshold: Based on experiments and experience, the preset upper limit threshold for the comprehensive correction coefficient is set to 1.1. This value was obtained through multiple experiments and data analysis to ensure the stability and accuracy of measurement results under various conditions.

[0144] 2. Check if the overall correction coefficient exceeds the threshold: Compare the overall correction coefficient with the preset upper limit threshold. If the overall correction coefficient exceeds 1.1, then replace the overall correction coefficient with 1.1. For example, assume that the overall correction coefficient calculated in step S3044 is 1.12. Because 1.12 is greater than 1.1, the overall correction coefficient is replaced with 1.1.

[0145] 3. Recalculate the corrected profile deviation: Recalculate the corrected profile deviation using the new comprehensive correction coefficient (i.e., the preset upper limit threshold of 1.1). Assuming the corrected basic profile deviation is 0.5 mm, the recalculated corrected profile deviation is: 0.5 × 1.1 = 0.55 mm.

[0146] Step S3047: Output the corrected profile deviation after adjustment by the upper limit constraint.

[0147] Corrected profile deviation after upper limit constraint adjustment: The profile deviation value is recalculated after applying an upper limit constraint to the comprehensive correction coefficient in step S3046. If the comprehensive correction coefficient exceeds a preset upper limit threshold, the threshold is used to replace the comprehensive correction coefficient, and the corrected profile deviation is recalculated.

[0148] A control method based on a correlation light measurement device using an inclined mirror further includes a step following the simultaneous acquisition of a dynamic image reflected by a second inclined mirror 4 via a zoom camera 5, and the identification of the real-time pixel coordinates of the light spot and the real-time contour features of the target, as follows:

[0149] In step S3A0, the circuit processing unit 6 controls the spectral acquisition submodule, which is electrically connected to the zoom camera 5 and the optical path receiving module 3, to acquire the spectral distribution of the target area image and the laser signal spectrum in real time.

[0150] The spectral acquisition submodule, electrically connected to the zoom camera 5 and the optical path receiving module 3, is used to acquire the image spectral distribution and laser signal spectrum of the target area in real time. It provides spectral information about the target area, aiding in the identification and processing of stray light. Image spectral distribution: Spectral information at different locations in the target area image, reflecting the spectral characteristics of that area. Laser signal spectrum: The spectral characteristics of the laser signal emitted by the laser transmitter 1, used for comparison and analysis with the image spectral distribution of the target area.

[0151] The necessary process is described below:

[0152] 1. Initialize the Spectrum Acquisition Submodule: Circuit processing unit 6 sends an initialization signal to start the spectrum acquisition submodule. Ensure that the spectrum acquisition submodule is properly connected to the zoom camera 5 and the optical path receiving module 3, and prepare to start acquisition.

[0153] 2. Real-time acquisition of image spectral distribution: The spectral acquisition submodule acquires images of the target area through the zoom camera 5, and simultaneously acquires the spectral distribution of each pixel in the image. This spectral data reflects the spectral characteristics of the target area, including the light intensity of different wavelengths.

[0154] 3. Real-time acquisition of laser signal spectrum: The spectrum acquisition submodule simultaneously acquires the spectrum of the laser signal emitted by laser transmitter 1. The laser signal spectrum is known, but the acquired data can be used to compare with the image spectral distribution to identify stray light.

[0155] 4. Data Transmission and Preliminary Processing: The acquired image spectral distribution and laser signal spectrum data are transmitted to the processing module via the circuit processing unit 6. The circuit processing unit 6 performs preliminary processing on these data, such as filtering and normalization, for subsequent analysis.

[0156] Step S3B0: Analyze the spectral distribution of the acquired image and the spectrum of the laser signal using a preset feature matching algorithm to identify stray light types, including outdoor direct sunlight and indoor high-frequency flicker light.

[0157] The feature matching algorithm is used to compare and match the spectral distribution of acquired images with the spectrum of laser signals to identify stray light types. This algorithm is based on a pre-set feature library and determines the type of stray light by comparing spectral features. Stray light types refer to non-target light sources that interfere with the measurement, such as direct sunlight outdoors and high-frequency flicker light indoors. These stray lights can affect the accuracy and reliability of the measurement. Direct sunlight outdoors refers to light rays directly from the sun, which have a broad spectral distribution and high intensity, and may strongly interfere with the measurement. High-frequency flicker light indoors refers to high-frequency flickering light generated by indoor lighting equipment (such as fluorescent lamps), whose spectral distribution and frequency characteristics differ from outdoor sunlight.

[0158] The necessary process is described below:

[0159] 1. Acquire data: Obtain the image spectral distribution and laser signal spectrum of the target area from the spectral acquisition submodule.

[0160] 2. Preprocessing the acquired data: The acquired spectral data is preprocessed, such as by filtering and normalization, to eliminate noise and improve data quality.

[0161] 3. Feature extraction: Extract key features, such as spectral peaks and spectral width, from the spectral distribution of the preprocessed image.

[0162] 4. Feature matching: Using a preset feature matching algorithm, the extracted features are compared with a preset stray light feature library to identify the type of stray light.

[0163] 5. Identify stray light type: Determine the stray light type based on the matching results. If the characteristics of direct outdoor sunlight are matched, it is identified as direct outdoor sunlight; if the characteristics of indoor high-frequency flickering light are matched, it is identified as indoor high-frequency flickering light.

[0164] In step S3C0, for direct sunlight outdoors, the control circuit processing unit 6 switches the emission band of the laser emitter 1 so that the overlap between the switched emission band and the sunlight interference band is less than or equal to a preset ratio, and matches the peak reflection band of the narrow-band reflective film coated on the surface of the second inclined mirror 4. Simultaneously, the white balance parameters of the zoom camera 5 are adjusted to increase the laser wavelength channel signal gain by a preset ratio.

[0165] Among them, the sunlight interference band refers to the spectral distribution range of outdoor sunlight, which is typically wide and high in intensity. The peak reflection band of the narrow-band reflective film on the second inclined mirror 4 refers to the center wavelength range of the narrow-band reflective film coated on the second inclined mirror 4, used for efficient reflection of laser light in specific wavelengths. The white balance parameter of the zoom camera 5 is a parameter used to adjust the camera's color balance to ensure accurate color reproduction under different lighting conditions. The laser wavelength channel signal gain is a gain parameter used to enhance the signal of a specific laser wavelength, improving signal strength and clarity.

[0166] The necessary process is described below:

[0167] 1. Identify direct sunlight outdoors: The feature matching algorithm in step S3B0 has identified the interference of direct sunlight in the current environment.

[0168] 2. Switching the laser emission band: The circuit processing unit 6 controls the laser emitter 1 to switch to a new emission band, ensuring that the overlap between the switched emission band and the sunlight interference band does not exceed a preset ratio. For example, if the preset ratio is 10%, the overlap between the switched emission band and the sunlight interference band must not exceed 10%. Simultaneously, the switched emission band should match the peak reflection band of the narrowband reflective film of the second inclined mirror 4 to ensure efficient reflection and reception of the laser signal.

[0169] 3. Adjust the white balance parameters of the zoom camera 5: The circuit processing unit 6 synchronously adjusts the white balance parameters of the zoom camera 5 to adapt to the new lighting conditions and ensure the color accuracy of the image.

[0170] 4. Increase the signal gain of the laser wavelength channel: The circuit processing unit 6 increases the signal gain of the laser wavelength channel by a preset ratio to enhance the intensity and clarity of the laser signal. For example, if the preset ratio is 20%, the signal gain will be increased by 20%.

[0171] In step S3D0, for indoor high-frequency flickering light, the control circuit processing unit 6 synchronizes the laser emission timing with the flickering light period in reverse to ensure that the peak moment is avoided and the timing deviation is ≤ preset duration. At the same time, the zoom camera 5 is controlled to enable anti-flicker mode and the frame rate is set to a preset multiple of the flickering light period.

[0172] Among them, the laser emission timing sequence is the time sequence of laser emission from the laser emitter 1, which can be adjusted by the circuit processing unit 6. The flicker period is the period of the high-frequency flicker light indoors, typically generated by lighting equipment (such as fluorescent lamps). The timing deviation is the deviation between the laser emission timing sequence and the flicker period, which needs to be controlled within a preset range. The anti-flicker mode is a working mode of the zoom camera 5 used to reduce the impact of high-frequency flicker light on image acquisition. The frame rate is the number of image frames acquired per second by the zoom camera 5, which can be adjusted by the circuit processing unit 6.

[0173] The necessary process is described below:

[0174] 1. Identify indoor high-frequency flickering light: Through the feature matching algorithm in step S3B0, the interference of indoor high-frequency flickering light in the current environment has been identified.

[0175] 2. Adjusting the laser emission timing: The circuit processing unit 6 controls the emission timing of the laser emitting head 1 to synchronize it in the opposite direction to the scintillation period. Specifically, the laser emission timing should avoid the peak moment of the scintillation light to ensure that the timing deviation does not exceed the preset duration.

[0176] 3. Enable the anti-flicker mode of zoom camera 5: The circuit processing unit 6 controls the zoom camera 5 to enable the anti-flicker mode in order to reduce the impact of high-frequency flicker light on image acquisition.

[0177] 4. Set the frame rate of zoom camera 5: The circuit processing unit 6 sets the frame rate of zoom camera 5 to a preset multiple of the flicker light period to ensure stable operation in anti-flicker mode.

[0178] In step S3E0, the real-time contour features and real-time pixel coordinates of the light spot acquired after adjustment are marked with confidence level. Outdoor areas under direct sunlight are marked as low confidence level, while indoor areas without high-frequency flicker and other areas without significant stray light interference are marked as high confidence level.

[0179] Among them, real-time contour features: real-time contour information of the target object acquired and processed by the zoom camera 5. Real-time pixel coordinates of the laser spot: real-time pixel position of the laser spot in the image acquired and processed by the zoom camera 5. Confidence labeling: the quality of the acquired data is evaluated and labeled as high or low confidence to distinguish the reliability of the data in subsequent processing.

[0180] The necessary process is described below:

[0181] 1. Obtain the adjusted data: Obtain the adjusted real-time contour features and real-time pixel coordinates of the light spot from the previous steps.

[0182] 2. Assess environmental conditions: Assess the reliability of the data based on current environmental conditions (such as whether there is direct sunlight outdoors or high-frequency flickering light indoors).

[0183] 3. Confidence Level Labeling: Outdoor areas under direct sunlight are labeled as low confidence, as direct sunlight may lead to larger data errors. Indoor areas without high-frequency flicker and other areas without significant stray light interference are labeled as high confidence, as the data from these areas are relatively reliable.

[0184] The system invokes preset data fusion rules, associates real-time distance data, real-time pixel coordinates of the light spot, and real-time contour features, and calculates the target flatness and step difference based on the initial contour features, including:

[0185] Step S410: Based on the labeled high-confidence data and low-confidence data, assign a preset high weight to the high-confidence data and a preset low weight to the low-confidence data. Then, use a weighted fusion algorithm to associate and fuse real-time distance data, real-time pixel coordinates of light spots, and real-time contour features to generate a weighted 3D point cloud dataset with confidence weights.

[0186] The dataset includes: Preset high weights: Pre-defined weight values ​​used for high-confidence data to enhance its influence during the fusion process. Preset low weights: Pre-defined weight values ​​used for low-confidence data to reduce its influence during the fusion process. Weighted 3D point cloud dataset: A 3D point cloud dataset generated using a weighted fusion algorithm, where each point contains location information and a corresponding confidence weight.

[0187] The necessary process is described below:

[0188] 1. Data Acquisition: Obtain real-time distance data, real-time pixel coordinates of the light spot, real-time contour features, and corresponding confidence labels from the previous steps. This data will serve as input to the weighted fusion algorithm. For example, suppose we have the following data: 1. Real-time distance data: d1, d2, d3; 2. Real-time pixel coordinates of the light spot: (x1, y1), (x2, y2), (x3, y3); 3. Real-time contour features: (c1, c2, c3); 4. Confidence labels: high confidence, low confidence, high confidence.

[0189] 2. Weight Allocation: Based on the confidence level, assign a pre-defined high weight to high-confidence data and a pre-defined low weight to low-confidence data. For example, assume the pre-defined high weight is 0.9 and the pre-defined low weight is 0.1.

[0190] 3. A weighted fusion algorithm is used to correlate and fuse real-time distance data, real-time pixel coordinates of light spots, and real-time contour features. Specifically, for each data point, a weighted 3D point cloud data point is generated by combining its position information, distance information, and weight.

[0191] 4. Integrate all generated weighted 3D point cloud data points into a single dataset. The final weighted 3D point cloud dataset contains the location information, distance information, and corresponding weight for each point.

[0192] Step S420: Using the initial contour features as a spatial reference, extract point cloud data of high-confidence regions from the weighted 3D point cloud dataset, and obtain a reference plane model by fitting it with a preset plane fitting algorithm.

[0193] The preset plane fitting algorithm is a pre-defined algorithm used to fit a plane model from the point cloud data. Commonly used algorithms include the least squares method. The reference plane model is a plane model obtained by fitting point cloud data of a high-confidence region, which is used as a reference benchmark for subsequent measurements and calculations.

[0194] Extracting point cloud data from high-confidence regions: Extracting data points with weights higher than a preset threshold from a weighted 3D point cloud dataset. For example, assuming the preset threshold is 0.8, extracting point cloud data with weights greater than or equal to 0.8 from the dataset.

[0195] Applying a pre-defined plane fitting algorithm: A pre-defined plane fitting algorithm (such as least squares) is used to fit the extracted high-confidence point cloud data to obtain a baseline plane model. Least squares fits the plane by minimizing the sum of squared perpendicular distances from points to the plane. Assume the extracted high-confidence point cloud data is as follows: ; Therefore, the reference plane model obtained by fitting using the least squares method can be expressed as: , where a, b, c, d are the fitted plane parameters.

[0196] Step S430: Using the reference plane model as a reference, calculate the vertical deviation between the point cloud data of the low confidence area and the reference plane model using a preset spatial distance algorithm, and calculate the average deviation value; if the average deviation value exceeds the preset deviation threshold, call the historical data of multiple frames in the area for cross-validation, and take the deviation value after verification as the effective deviation.

[0197] The system includes: **Preset Spatial Distance Algorithm:** A pre-defined algorithm used to calculate the vertical distance between point cloud data and a reference plane model. Commonly used algorithms include the point-to-plane distance formula. **Vertical Deviation Value:** The vertical distance between point cloud data and the reference plane model, reflecting the deviation of data points relative to the reference plane. **Preset Deviation Threshold:** A pre-set upper limit for the deviation value, used to determine if the deviation is within an acceptable range. **Multi-Frame Historical Data:** Data collected at multiple time points, used to cross-validate the accuracy of the current data. **Cross-Validation:** Verifying the reliability and accuracy of the current data by comparing data from multiple time points. **Valid Deviation:** The deviation value confirmed after cross-validation, used for subsequent calculations and analysis.

[0198] The necessary process is described below:

[0199] 1. Calculate the vertical deviation value: Use a preset spatial distance algorithm to calculate the vertical deviation value between the point cloud data of the low confidence area and the reference plane model. Assume the reference plane model is... For each low-confidence data point Its vertical deviation value It can be calculated using the following formula:

[0200] ;

[0201] For example, assuming the baseline plane model is 2x + 3y + 4z + 5 = 0, and the point cloud data for the low-confidence region is (1, 2, 3), then its vertical deviation value is:

[0202] ;

[0203] 2. Calculate the average deviation value: Calculate the average deviation value by statistically analyzing the vertical deviation values ​​obtained from the point cloud data of all low-confidence regions. Assuming there are three points in the low-confidence region with vertical deviation values ​​D1=4.64, D2=3.21, and D3=2.78, the average deviation value is:

[0204] Average deviation value = .

[0205] For details on determining whether the preset deviation threshold is exceeded, calling up multiple frames of historical data for cross-validation, and determining the effective deviation, please refer to steps S431 to S435. These will not be elaborated here.

[0206] Step S440: Integrate the reference plane model and the effective deviation, and calculate the overall flatness of the target using a preset flatness algorithm.

[0207] Among them, the preset flatness algorithm is a pre-defined algorithm used to calculate the overall flatness of the target object; commonly used algorithms include the least squares method. Overall target flatness is a parameter reflecting the smoothness of the target object's surface, evaluated by calculating the deviation of each point on the surface relative to a reference plane.

[0208] The necessary process is described below:

[0209] 1. Integrating the datum plane model and effective deviation: The datum plane model and effective deviation are combined to form a complete dataset for subsequent flatness calculations. For example, assuming the datum plane model is 2x+3y+4z+5=0 and the effective deviation is 3.48, the integrated dataset includes the datum plane model and the effective deviation values ​​for all points.

[0210] 2. Apply a preset flatness algorithm: Calculate the overall flatness of the target object using a preset flatness algorithm. A commonly used algorithm is the least squares method, which fits the plane by minimizing the sum of the squares of the perpendicular distances from each point to the plane, and evaluates the flatness by calculating the distance from each point to the fitted plane. For example, assuming the integrated dataset includes a reference plane model and the effective deviation values ​​of all points, the plane model obtained by fitting using the least squares method can be expressed as: ax + by + cz + d = 0. Here, a, b, c, and d are the fitted plane parameters.

[0211] 3. Calculate the overall flatness of the target: For each data point Calculate its perpendicular distance to the fitting plane. : .

[0212] The vertical distances of all data points are statistically analyzed, and the mean, standard deviation, and other statistical measures are calculated to assess the overall flatness of the target object. For example, assuming the vertical distances of all data points are D1=3.48, D2=3.50, and D3=3.45, the average flatness is:

[0213] Average flatness = The standard deviation can further assess the uniformity of flatness; the smaller the standard deviation, the more uniform the flatness.

[0214] Step S450: Extract adjacent boundary points of real-time contour features from the weighted 3D point cloud dataset, associate them with the corresponding real-time distance data, and calculate the vertical distance difference between adjacent boundary points in the normal direction based on the normal direction of the reference plane model to obtain the initial segment difference value.

[0215] Among them, the adjacent boundary points of the real-time contour feature are the boundary points adjacent to the real-time contour feature in the weighted 3D point cloud dataset, used to calculate the step difference. The normal direction of the reference plane model is the direction of the normal vector of the reference plane model, used to determine the direction for calculating the vertical distance. The initial step difference value is the difference in vertical distance between adjacent boundary points in the normal direction of the reference plane model, reflecting the step difference of the target object's surface.

[0216] The necessary process is described below:

[0217] 1. Extracting adjacent boundary points of real-time contour features: Extracting boundary points adjacent to the real-time contour features from the weighted 3D point cloud dataset. These boundary points are used to calculate the step difference. For example, assuming the real-time contour feature is a rectangular region, extract the four boundary points of that rectangular region.

[0218] 2. Associate with corresponding real-time distance data: Associate the extracted adjacent boundary points with the corresponding real-time distance data.

[0219] 3. Calculate the normal direction of the reference plane model: Obtain the normal direction of the reference plane model from step S420. Assume the reference plane model is ax + by + cz + d = 0, and its normal vector is (a, b, c).

[0220] 4. Calculate the vertical distance difference between adjacent boundary points: For each pair of adjacent boundary points, calculate the vertical distance difference between them in the normal direction of the reference plane model. For example, for boundary points... and Calculate the difference in their vertical distances. :

[0221] .

[0222] Repeat the above process to calculate the vertical distance difference between all adjacent boundary points.

[0223] 5. Obtain the initial segment difference value: Integrate all the calculated vertical distance differences to obtain the initial segment difference value.

[0224] Step S460: For the initial segment difference value of the low confidence region, a preset compensation coefficient is added according to a preset low weight ratio to obtain the corrected segment difference value.

[0225] The initial segment difference value in the low confidence region is calculated in step S450 and is located in the low confidence region. These values ​​may be affected by measurement errors or environmental interference. The preset low weight ratio is a pre-set weight ratio used to adjust the initial segment difference value in the low confidence region to reduce its impact on the final result. The preset compensation coefficient is a pre-set compensation value used to correct the initial segment difference value in the low confidence region to improve the accuracy of the measurement results. The corrected segment difference value is the segment difference value after weight adjustment and compensation, which is closer to the true value and is used for subsequent analysis and application.

[0226] The necessary process is described below:

[0227] 1. Obtain the initial segment difference value of the low confidence region: Obtain the initial segment difference value of the low confidence region from step S450. For example, assume the initial segment difference value is [0.5, 0.6, 0.4].

[0228] 2. Apply preset low weight ratio: Adjust the initial segment difference value of the low confidence region according to the preset low weight ratio. Assuming the preset low weight ratio is 0.3, the adjusted segment difference value is: [0.5×0.3,0.6×0.3,0.4×0.3]=[0.15,0.18,0.12].

[0229] 3. Add a preset compensation coefficient: Add a preset compensation coefficient to the adjusted segment difference value. Assuming the preset compensation coefficient is 0.2, the corrected segment difference value is as follows:

[0230] [0.15+0.2,0.18+0.2,0.12+0.2]=[0.35,0.38,0.32].

[0231] 4. Obtain the corrected segment difference value: The corrected segment difference value [0.35, 0.38, 0.32] is closer to the true value and can be used for subsequent analysis and application.

[0232] If the average deviation value exceeds the preset deviation threshold, multi-frame historical data for that region is retrieved for cross-validation, and the deviation value after verification is taken as the valid deviation, including:

[0233] Step S431: When the average deviation value of the low confidence area exceeds the preset threshold, extract the preset number of historical data of the area from the historical data record, compare the environmental features of the historical data with the current data through the preset time series similarity algorithm, and filter the valid historical data with the matching degree reaching the preset standard. The environmental features include light and temperature.

[0234] The data includes: Historical Data Records: Multiple frames of historical data stored for cross-validation of the accuracy of the current data. Preset Frame Count: The pre-defined number of data frames to be extracted from the historical data records. Preset Time Series Similarity Algorithm: A pre-defined algorithm used to compare the environmental characteristics of historical data and current data to evaluate their similarity. Environmental Characteristics: External conditions affecting measurement results, such as lighting and temperature. Valid Historical Data: Historical data selected through similarity comparison that meets the pre-defined standard for matching the environmental characteristics of the current data.

[0235] The necessary process is described below:

[0236] 1. Determine if the average deviation value exceeds the preset threshold: Compare the average deviation value of the low confidence area with the preset threshold. If the average deviation value exceeds the preset threshold, it indicates that the current data may have a large error or interference, and further verification is required.

[0237] 2. Extract historical data for a preset frame count: Extract historical data for the preset frame count for this region from the historical data records. For example, assuming the preset frame count is 5, extract the data for the most recent 5 frames from the historical data records.

[0238] 3. Compare environmental features between historical and current data: Use a preset time series similarity algorithm to compare environmental features, including lighting and temperature, between historical and current data. The similarity algorithm can be based on methods such as Euclidean distance and cosine similarity to evaluate the similarity between historical and current data.

[0239] 4. Filter valid historical data: Based on the comparison results, filter out historical data that matches the current data environment features to a preset standard. For example, assuming the preset matching standard is 0.8, only retain historical data with a similarity greater than or equal to 0.8.

[0240] Step S432: Perform spatial coordinate correction on the valid historical data to ensure consistency with the current coordinate system, and then perform time-series filtering to obtain a stable historical deviation dataset.

[0241] The process includes: Spatial coordinate correction: Transforming historical data to align with the current coordinate system to ensure data comparability. Temporal filtering: Filtering the corrected historical data to reduce noise and fluctuations, resulting in a stable historical deviation dataset. Historical deviation dataset: The corrected and filtered historical dataset used for subsequent analysis, containing the deviation value at each time point.

[0242] The necessary process is described below:

[0243] 1. Obtain valid historical data: Obtain the filtered valid historical data from step S431. This data has been compared with the current data to ensure that it has similar environmental conditions.

[0244] 2. Perform spatial coordinate correction: Perform spatial coordinate correction on the valid historical data to ensure it is consistent with the current coordinate system. This step typically involves coordinate transformation algorithms, such as translation, rotation, and scaling. For example, assume the current coordinate system is... The coordinate system for historical data is The following steps are required for correction:

[0245] Translation correction: The origin of the historical data coordinate system is translated to the origin of the current coordinate system.

[0246] ;

[0247] in, .

[0248] Rotation correction: Adjust the coordinate axis orientation of historical data as needed to make it consistent with the current coordinate system.

[0249] ; where R is the rotation matrix.

[0250] Scaling correction: If necessary, scale the historical data to match the scale of the current data.

[0251] .

[0252] in, It is the scaling factor.

[0253] 3. Perform time-series filtering: Apply time-series filtering to the corrected historical data to reduce noise and fluctuations. Commonly used filtering algorithms include moving average filtering and Kalman filtering. For example, using moving average filtering to process historical deviation data:

[0254] Where D(t) is the deviation at time point t, and N is the window size of the moving average.

[0255] 4. Obtain a stable historical deviation dataset: After correction and filtering, a stable historical deviation dataset is obtained. This data will be used for subsequent analysis and validation.

[0256] Step S433: Dynamically allocate the fusion weight of the current deviation value and the historical deviation dataset based on the external interference weight. The external interference weight is calculated by a preset hierarchical analysis algorithm: the environmental interference factors and equipment status deviation factors are classified according to preset influence factors. The deviation values ​​of each factor are calculated with the corresponding preset weight coefficients by a preset weighted summation formula and then accumulated to obtain the weight value that quantifies the comprehensive interference degree of external factors.

[0257] The algorithm comprises the following components: External Interference Weight: A weight value that quantifies the influence of external factors (such as environmental interference and equipment condition deviation) on the measurement results, used to dynamically adjust the fusion weight of the current deviation value and the historical deviation dataset. Analytic Hierarchy Process (AHP): A multi-criteria decision analysis method used to determine the relative importance of each factor and calculate the comprehensive weight. Environmental Interference Factors: External environmental factors affecting the measurement results, such as light intensity and temperature. Equipment Condition Deviation Factors: Equipment condition factors affecting the measurement results, such as laser emitter power deviation and camera focal length deviation. Preset Influence Factors: Pre-defined relative importance levels of each factor. Preset Weight Coefficients: Pre-defined weight coefficients for each factor, used to calculate the comprehensive weight. Fusion Weight: Used to dynamically adjust the weight of the current deviation value and the historical deviation dataset to improve the accuracy of the fusion results.

[0258] The necessary process is described below:

[0259] 1. Obtain environmental interference factors and equipment status deviation factors: Obtain current environmental interference factors (such as light intensity and temperature) and equipment status deviation factors (such as changes in the power of laser emitter 1 and changes in camera focal length) from the measurement environment and equipment status monitoring module.

[0260] 2. Construct a hierarchical structure model: Use the hierarchical analysis algorithm to construct a hierarchical structure model, classifying environmental interference factors and equipment state deviation factors into preset influencing factors. For example, light intensity and temperature are considered as primary factors, while changes in the power of laser emitter 1 and the focal length of the camera are considered as secondary factors.

[0261] 3. Calculate the relative importance of each factor: Calculate the relative importance of each factor using the analytic hierarchy process (AHP). For example, assume the relative importance of light intensity is 0.4, temperature is 0.3, the change in power of laser emitter 1 is 0.2, and the change in camera focal length is 0.1.

[0262] 4. Calculate the deviation values ​​of each factor: Obtain the actual deviation values ​​of each factor. For example, assume that the current light intensity deviation is 0.1, the temperature deviation is 0.2, the power change of laser emitter 1 is 0.15, and the focal length change of the camera is 0.05.

[0263] 5. Apply the preset weighted summation formula: After calculating the deviation value of each factor with the corresponding preset weight coefficient using the preset weighted summation formula, the weight value is accumulated to obtain the weight value that quantifies the comprehensive interference degree of external factors.

[0264] 6. Dynamically allocate fusion weights: Based on the calculated external interference weights, dynamically allocate fusion weights between the current deviation value and the historical deviation dataset. For example, assuming the external interference weight is 0.135, the preset fusion weight adjustment formula is:

[0265] Fusion weights 当前 =1−External disturbance weight=1−0.135=0.865;

[0266] Fusion weights 历史 =External disturbance weight=0.135.

[0267] Step S434: Calculate the preliminary fusion deviation value using a preset weighted average algorithm.

[0268] Among them, the preset weighted average algorithm is an algorithm that uses given weights to perform a weighted average on the data in order to calculate a more accurate deviation value.

[0269] The necessary process is described below:

[0270] 1. Obtain the current deviation value and historical deviation dataset: Obtain the current deviation value from step S430 or S433, and obtain the historical deviation dataset from step S432. For example, assume the current deviation value is D. current =0.5, and the historical bias dataset is [0.45, 0.48, 0.52].

[0271] 2. Obtain the fusion weights: Obtain the fusion weights from step S433. Assume the weight of the current deviation value is W. current =0.865, the weight of the historical bias dataset is W history =0.135.

[0272] 3. Calculate the weighted average: Calculate the preliminary fusion deviation value using a preset weighted average algorithm. The weighted average formula is:

[0273] Where N is the number of data points in the historical deviation dataset;

[0274] 4. Obtain the preliminary fusion deviation value: Use the calculation result as the preliminary fusion deviation value for subsequent verification and analysis.

[0275] Step S435: Verify the initial fusion deviation value by a preset number of iterations. If the fluctuation range is ≤ a preset stable threshold, it is determined to be a valid deviation after verification; otherwise, repeat the screening to fusion step until the standard is met and the cause of the fluctuation is marked.

[0276] Among them, the preset iteration count is the maximum number of iterations in the verification process, used to control the termination condition of the verification process. The fluctuation amplitude is the degree of change in the initial fusion deviation value over multiple iterations, used to assess the stability of the deviation value. The preset stability threshold is the upper limit of the preset fluctuation amplitude, used to determine whether the deviation value is stable. The valid deviation after verification is the deviation value whose fluctuation amplitude is within the preset stability threshold after multiple iterations; this can be considered a reliable measurement result. The cause of fluctuation is the reason for the fluctuation in the deviation value, such as environmental interference, changes in equipment status, etc., used for subsequent analysis and improvement.

[0277] The necessary process is described below:

[0278] 1. Initialize verification parameters: Set the current iteration number to 0 and record the initial fusion deviation value as the initial verification value. For example, assume the initial fusion deviation value is 0.4977.

[0279] 2. Enter the iterative verification loop: Compare the current iteration count with the preset iteration count. If the current iteration count is less than the preset iteration count, continue the verification process; otherwise, terminate the verification and mark it as unsatisfactory.

[0280] 3. Calculate the fluctuation amplitude: In each iteration, recalculate the initial fusion deviation value and compare it with the previous deviation value to calculate the fluctuation amplitude. For example, assuming the preset stability threshold is 0.01, the deviation value after the first iteration is 0.4980, and the fluctuation amplitude is:

[0281] Fluctuation amplitude = |0.4980−0.4977| = 0.0003.

[0282] 4. Determine if the fluctuation amplitude is within the preset stability threshold: If the fluctuation amplitude is less than or equal to the preset stability threshold, determine the current deviation value as the valid deviation after verification, and terminate the verification process. Otherwise, increase the number of iterations and re-execute the screening to fusion step.

[0283] 5. Record the reasons for fluctuations: If the deviation value does not reach the stable threshold, record the possible reasons for the fluctuations, such as environmental interference, changes in equipment status, etc., for subsequent analysis and improvement.

[0284] Before cross-validating multiple frames of historical data from this region, a re-examination process is performed first, which includes:

[0285] Step S43A: When the average deviation value of the low confidence area exceeds the preset threshold, control the laser emitter 1 to emit laser beams a preset number of times, and simultaneously acquire multiple frames of target images of the area through the zoom camera 5.

[0286] The average deviation value of the low confidence region is calculated in step S430 and is used to evaluate the measurement error of the region.

[0287] Preset threshold: A pre-set upper limit for the deviation value, used to determine whether the deviation is within an acceptable range. Preset number of times: A pre-set number of laser emission times, used to control the amount of data collected during the re-inspection process. Multi-frame target images: Multiple frames of target area images acquired by the zoom camera 5 during the re-inspection process.

[0288] The necessary process is described below:

[0289] 1. Determine if the average deviation value exceeds the preset threshold: Compare the average deviation value of the low confidence area with the preset threshold. If the average deviation value exceeds the preset threshold, it indicates that the current data may have a large error or interference, and a re-examination is required.

[0290] 2. Control laser emitter 1 to emit laser beams: When the average deviation value exceeds a preset threshold, control laser emitter 1 to emit laser beams a preset number of times. For example, assuming the preset number of times is 5, laser emitter 1 will emit laser beams 5 times.

[0291] 3. Simultaneous Acquisition of Multiple Target Images: While the laser emitter 1 emits a laser beam, the zoom camera 5 simultaneously acquires multiple target images of the area. These images will be used for subsequent fusion and noise reduction processing. For example, assuming the zoom camera 5 acquires 5 frames as follows... .

[0292] Step S43B involves fusing and denoising the real-time contour features and spot pixel coordinates of multiple frames of images, assigning weights according to the clarity of each frame, and processing them using a preset weighted average algorithm.

[0293] Among them, the laser spot pixel coordinates are the pixel coordinates extracted from multiple frames of images. Fusion and noise reduction involves fusing data from multiple frames using an algorithm to reduce noise and errors, thereby improving data quality. Sharpness is an image quality metric used to evaluate the sharpness of an image. A preset weighted average algorithm is an algorithm that uses given weights to calculate a more accurate deviation value.

[0294] The necessary process is described below:

[0295] 1. Extract real-time contour features and spot pixel coordinates: Extract real-time contour features and spot pixel coordinates from multiple frames of images. For example, suppose the data extracted from 5 frames of images is as follows:

[0296] Frame 1: Spot pixel coordinates (100, 200); Frame 2: Spot pixel coordinates (102, 202); Frame 3: Spot pixel coordinates (101, 201); Frame 4: Spot pixel coordinates (103, 203); Frame 5: Spot pixel coordinates (102, 202).

[0297] 2. Calculate the sharpness of each frame: Use image processing algorithms to calculate the sharpness of each frame. Assume the sharpness is as follows:

[0298] Frame 1: 0.9; Frame 2: 0.85; Frame 3: 0.88; Frame 4: 0.92; Frame 5: 0.87.

[0299] 3. Assign weights: Assign weights based on the sharpness of each frame. Assume the weights are as follows: Frame 1: 0.20; Frame 2: 0.19; Frame 3: 0.20; Frame 4: 0.21; Frame 5: 0.20.

[0300] 4. Apply a preset weighted average algorithm: Use a weighted average algorithm to fuse and reduce noise in the pixel coordinates of the light spot. The calculation result is:

[0301] . .

[0302] 5. Obtain the fused data: The pixel coordinates of the fused spot are (101.6, 201.6).

[0303] Step S43C: Associate the fused data with real-time distance data, recalculate the deviation value between the region and the reference plane model, and use it as the re-detection deviation value.

[0304] The reference plane model is the plane model fitted in step S420, used for subsequent deviation calculation. The deviation value is the vertical distance between the fused data and the reference plane model, used to evaluate the deviation of the measurement area. The re-detection deviation value is the deviation value obtained after recalculation, used for subsequent verification and analysis.

[0305] A control method for a correlation light measurement device based on an inclined plane mirror further includes a step following the recalculation of the deviation value between the region and the reference plane model as the re-detection deviation value, as follows:

[0306] Step S43D: Extract multiple frames of historical data for the region and use a preset matching degree calculation algorithm to calculate the matching degree between the re-detection deviation value and the historical data.

[0307] The consistency calculation algorithm is an algorithm used to evaluate the similarity or consistency between the re-detection deviation value and historical data. Consistency is a quantitative indicator that measures the similarity between the re-detection deviation value and historical data; it is typically a value between 0 and 1, with values ​​closer to 1 indicating a higher consistency.

[0308] The necessary process is described below:

[0309] 1. Extract multiple frames of historical data: Extract multiple frames of historical data for this area from the data storage module. This historical data includes deviation values ​​and other relevant information recorded during previous measurements.

[0310] 2. Calculate the similarity score: Using a preset similarity score calculation algorithm, compare the re-detection deviation value with the deviation value in the historical data of each frame. The algorithm may be based on the difference between deviation values, statistical correlation, or other similarity measures. For example, suppose the re-detection deviation value is... =15.24, with historical deviations of [15.0, 15.1, 15.3, 15.2, 15.4]. The degree of agreement can be calculated using a simple measure of difference:

[0311] Fit = Where N is the number of frames in the historical data. It is the difference between the maximum and minimum deviation values ​​in historical data.

[0312] 3. Determine the consistency result: The calculated consistency value will be used in subsequent steps to determine whether the re-inspection deviation value can be used as a valid deviation.

[0313] In step S43E, if the degree of agreement is greater than or equal to the preset threshold, the re-inspection deviation is taken as the effective deviation.

[0314] Step S43F: Otherwise, the effective deviation is determined by adding the statistical correction amount of historical data to the re-examination deviation value as the benchmark.

[0315] Among them, the re-detection deviation value is the deviation value recalculated in step S43C, which is used for subsequent verification and analysis. The statistical correction amount for historical data is a correction amount calculated based on historical data, used to adjust the re-detection deviation value and improve its accuracy.

[0316] The embodiments described in this specific implementation are preferred embodiments of this application and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A correlation light measurement device based on an inclined plane mirror, characterized in that, include: A laser emitter (1) is used to emit a laser beam that propagates along a preset direction and is directed toward the target object; The first inclined mirror (2) is set on the laser propagation path between the laser emitting head (1) and the target object. Its inclined surface is a mirror glass surface and has a through hole for the laser beam to pass through. The axis of the through hole is collinear with the light output direction of the laser emitting head (1), so that the laser beam passes through the through hole and directly hits the target object. The optical path receiving module (3) is arranged adjacent to the laser emitting head (1). The two are located on the side of the first inclined mirror (2) away from the target object. It is used to receive the laser beam that returns along the original path after being reflected by the target object and passes through the through hole, and convert the reflected laser beam into an electrical signal. The second inclined mirror (4) is set at a preset angle with the inclined surface of the first inclined mirror (2), and its mirror surface faces the side where the first inclined mirror (2) is located. The mirror surface is a mirror glass surface and is configured to reflect the light from the target object and the light spot formed by the laser beam on the target object. The zoom camera (5) has its lens facing the mirror surface of the second inclined mirror (4) to receive the light reflected by the second inclined mirror (4) and obtain the visual outline of the target object and the spatial position information of the light spot formed by the laser beam on the target object. The circuit processing unit (6) is electrically connected to the laser transmitter (1), the optical path receiving module (3) and the zoom camera (5) respectively. It is used to control the emission timing of the laser transmitter (1), receive the electrical signal output by the optical path receiving module (3) and process it to calculate the distance, flatness and step difference of the target object, control the zoom camera (5) to work and process the visual information it acquires, and realize the three-dimensional detection of the target object by integrating the laser signal and the visual information.

2. The correlation light measurement device based on an inclined mirror according to claim 1, characterized in that, The inner wall of the through hole of the first inclined mirror (2) is provided with a black light-absorbing coating, and the diameter of the through hole is 0.5-1mm larger than the diameter of the laser beam spot.

3. A control method for a correlation light measurement device based on an inclined plane mirror, characterized in that, An applicable device for correlation light measurement based on a slanted mirror as described in any one of claims 1 to 2, comprising: The laser emitter (1) is controlled to emit a laser beam in a preset sequence, so that the laser beam is directly pierced along the through hole axis of the first inclined mirror (2) and the zoom camera (5) is controlled to collect the target area image reflected by the second inclined mirror (4), and the initial pixel coordinates of the laser spot and the initial contour features of the target are extracted in the image to form initial visual data. The laser signal reflected by the target and passing through the through hole is collected by the optical path receiving module (3), converted into an electrical signal and then the laser ranging value is calculated. Combined with the initial pixel coordinates of the light spot in the initial visual data, the mapping relationship between the laser ranging value and the visual pixel coordinates is established, with the initial contour features as the spatial reference. Based on the above mapping relationship, the laser signal collected in real time by the optical path receiving module (3) is converted into an electrical signal and calculated into the real-time distance data of the target. Simultaneously, the dynamic image reflected by the second inclined mirror (4) is obtained through the zoom camera (5), and the real-time pixel coordinates of the light spot and the real-time contour features of the target are identified. Call the preset data fusion rules, associate real-time distance data, real-time pixel coordinates of light spots and real-time contour features, and calculate the target flatness and step difference based on the initial contour features; It integrates real-time distance data, corrected flatness and step difference information, and outputs detection results including the target object's three-dimensional coordinates, flatness and step difference parameters.

4. The method according to claim 3, characterized in that, It also includes methods for updating mapping relationships, as detailed below: At each preset interval, the real-time contour features of the target are extracted, and the device status and environmental variables related to the contour acquisition are recorded synchronously. The device status includes the real-time emission power of the laser emitter (1), the focal length lock status of the zoom camera (5), and the signal gain value of the optical path receiving module (3). The environmental variables include the real-time illumination intensity of the target area. The iterative nearest point algorithm is used to match the real-time contour features with the initial contour features, and the root mean square of the coordinate offset of the feature points is calculated as the basic contour deviation. Based on the acquired equipment status, the equipment status deviation is calculated using a preset equipment status deviation calculation method. Based on the acquired environmental variables, the environmental variable deviation is calculated using a preset environmental deviation calculation method. Based on the equipment condition deviation and environmental variable deviation, correction coefficients are assigned to the basic profile deviation to obtain the corrected profile deviation. If the corrected contour deviation exceeds the preset threshold, the mapping relationship between the laser ranging value and the visual pixel coordinates is adjusted by least squares based on the contour transformation matrix output by the iterative nearest point algorithm, combined with the correction coefficients of the device status and environmental variables. If the corrected contour deviation is less than or equal to the preset threshold, it remains unchanged.

5. The method according to claim 4, characterized in that, Based on the equipment condition deviation and environmental variable deviation, correction coefficients are assigned to the basic profile deviation to obtain the corrected profile deviation, which includes: Based on the preset angle parameters between the first inclined mirror (2) and the second inclined mirror (4), the real-time contour features, spot pixel coordinates and laser ranging data are geometrically corrected by the preset spatial coordinate transformation algorithm to eliminate the inherent spatial deviation caused by the double inclined mirror reflection and obtain the corrected real-time contour features. Based on the corrected real-time contour features, and combined with the initial contour features, the corrected basic contour deviation is obtained through a preset feature matching algorithm. Based on the acquired equipment status deviation values, the deviations are divided into preset deviation levels using a preset interval mapping algorithm, and a preset equipment correction coefficient is assigned accordingly. The preset equipment correction coefficient and the preset environment correction coefficient are multiplied by a preset coefficient fusion algorithm to obtain a comprehensive correction coefficient. Multiply the corrected basic profile deviation by the comprehensive correction factor to obtain the initial corrected profile deviation; A preset upper limit constraint is applied to the comprehensive correction coefficient: if the comprehensive correction coefficient exceeds the preset threshold, the threshold is used to replace the comprehensive correction coefficient, and the corrected profile deviation is recalculated. Output the corrected profile deviation after adjustment by upper limit constraint.

6. The method according to claim 5, characterized in that... It also includes the following steps after the dynamic image reflected by the second inclined mirror (4) is acquired simultaneously through the zoom camera (5), and the real-time pixel coordinates of the light spot and the real-time contour features of the target are identified: The circuit processing unit (6) controls the spectral acquisition submodule, which is electrically connected to the zoom camera (5) and the optical path receiving module (3), to collect the spectral distribution of the target area image and the laser signal spectrum in real time. The spectral distribution of the acquired images and the spectrum of the laser signal are analyzed by a preset feature matching algorithm to identify stray light types, including direct sunlight outdoors and high-frequency flickering light indoors. In response to direct sunlight outdoors, the control circuit processing unit (6) switches the emission band of the laser emitter (1) so that the overlap between the switched emission band and the sunlight interference band is less than or equal to a preset ratio, and matches the peak reflection band of the narrow band reflective film coated on the surface of the second inclined mirror (4), and simultaneously adjusts the white balance parameters of the zoom camera (5) to increase the laser wavelength channel signal gain by a preset ratio. In response to indoor high-frequency flickering light, the control circuit processing unit (6) synchronizes the laser emission timing with the flickering light period in reverse to ensure that the peak moment is avoided and the timing deviation is less than or equal to the preset duration. At the same time, it controls the zoom camera (5) to enable the anti-flicker mode and sets the frame rate to a preset multiple of the flickering light period. The real-time contour features and real-time pixel coordinates of the light spots acquired after adjustment were marked with confidence level. Outdoor areas under direct sunlight were marked as low confidence level, while indoor areas without high-frequency flicker and other areas without significant stray light interference were marked as high confidence level.

7. The method according to claim 6, characterized in that, The system invokes preset data fusion rules, associates real-time distance data, real-time pixel coordinates of the light spot, and real-time contour features, and calculates the target flatness and step difference based on the initial contour features, including: Based on labeled high-confidence and low-confidence data, high-confidence data is assigned a preset high weight, and low-confidence data is assigned a preset low weight. The real-time distance data, real-time pixel coordinates of light spots, and real-time contour features are associated and fused through a weighted fusion algorithm to generate a weighted 3D point cloud dataset with confidence weights. Using the initial contour features as a spatial reference, point cloud data of high-confidence regions are extracted from the weighted 3D point cloud dataset, and a reference plane model is obtained by fitting the data using a preset plane fitting algorithm. Using a reference plane model, the vertical deviation between the point cloud data of the low confidence area and the reference plane model is calculated using a preset spatial distance algorithm, and the average deviation value is calculated. If the average deviation value exceeds the preset deviation threshold, multiple frames of historical data for that area are called for cross-validation, and the deviation value after verification is taken as the valid deviation. The overall flatness of the target is calculated by integrating the baseline plane model and the effective deviation using a preset flatness algorithm. The adjacent boundary points of the real-time contour features are extracted from the weighted 3D point cloud dataset, associated with the corresponding real-time distance data, and the vertical distance difference between adjacent boundary points in the normal direction based on the reference plane model is calculated to obtain the initial segment difference value. For the initial segment difference value in the low confidence region, a preset compensation coefficient is added according to a preset low weight ratio to obtain the corrected segment difference value.

8. The method according to claim 7, characterized in that, If the average deviation value exceeds the preset deviation threshold, multi-frame historical data for that region is retrieved for cross-validation, and the deviation value after verification is taken as the valid deviation, including: When the average deviation value of a low-confidence area exceeds a preset threshold, a preset number of historical data for that area is extracted from historical data records. The environmental features of the historical and current data are compared using a preset time series similarity algorithm, and valid historical data with a matching degree that meets the preset standard are selected. Environmental features include light and temperature. Spatial coordinate correction is performed on valid historical data to ensure consistency with the current coordinate system, and then time-series filtering is applied to obtain a stable historical deviation dataset. The current deviation value and the historical deviation dataset are dynamically allocated based on the external interference weight. The external interference weight is calculated by a preset hierarchical analysis algorithm: environmental interference factors and equipment status deviation factors are classified according to preset influence factors. The deviation values ​​of each factor are calculated with the corresponding preset weight coefficients by a preset weighted summation formula and then accumulated to obtain the weight value that quantifies the comprehensive interference degree of external factors. The initial fusion deviation value is calculated using a preset weighted average algorithm; The initial fusion deviation value is verified by a preset number of iterations. If the fluctuation range is less than or equal to the preset stability threshold, it is determined to be a valid deviation after verification; otherwise, the screening to fusion step is repeated until the standard is met and the cause of the fluctuation is marked.

9. The method according to claim 7, characterized in that, Before cross-validating multiple frames of historical data from this region, a re-examination process is performed first, which includes: When the average deviation value of the low confidence area exceeds the preset threshold, the laser emitter (1) is controlled to emit laser beams a preset number of times, and the zoom camera (5) simultaneously acquires multiple frames of target images of the area; The real-time contour features and spot pixel coordinates of multiple frames of images are fused and denoised, and weights are assigned according to the sharpness of each frame and processed by a preset weighted average algorithm. The fused data is correlated with real-time distance data, and the deviation between the region and the reference plane model is recalculated as the re-detection deviation value.

10. The method according to claim 9, characterized in that, It also includes a step following the recalculation of the deviation value between the region and the reference plane model, as the re-examination deviation value, as follows: Extract multiple frames of historical data for this region, and use a preset matching degree calculation algorithm to calculate the matching degree between the re-detection deviation value and the historical data; If the degree of agreement is greater than or equal to the preset threshold, the re-detection deviation is taken as the valid deviation. Otherwise, the effective deviation is determined by adding the statistical correction amount of historical data to the re-examination deviation value as the benchmark.

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