A laser measuring device for aluminum alloy door and window dimensions

Abstract

The application relates to the technical field of laser measurement, and discloses an aluminum alloy door and window size laser measurement device which comprises a rack, a laser detector, a conveyor and a gain multiple regulation system. The gain multiple regulation system is integrated with an environmental context module, a target characteristic module, a core feedback module, a signal quality module and a dynamic stability module. According to multi-dimensional parameters such as target distance, environmental light intensity, target surface reflectivity, laser incidence angle and received signal strength, the gain multiple of a laser receiving module is output and dynamically adjusted through an environmental model, a geometric optical compensation model, a constraint automatic gain control model, a signal form diagnosis model and a smooth filtering and dynamic response model. The application solves the problems of signal saturation or measurement failure caused by the fact that the existing laser measurement device cannot adapt to complex working conditions due to a fixed gain strategy, and realizes high-precision and high-stability self-adaptive measurement of the size of aluminum alloy doors and windows under various environments and target characteristics.

Description

Technical Field

[0001] This invention belongs to the field of laser measurement technology, and in particular relates to a laser measurement device for the dimensions of aluminum alloy doors and windows. Background Technology

[0002] Dimensional measurement of aluminum alloy doors and windows is crucial throughout the entire process of product manufacturing, on-site installation, and post-installation maintenance, ensuring a precise match between the doors / windows and the building structure. To improve measurement efficiency and objectivity, non-contact measuring devices based on laser ranging principles are increasingly being applied in this field. These devices emit laser pulses and receive reflected signals from the target, calculating distance values ​​based on flight time, effectively avoiding subjective interference from manual operation.

[0003] However, in actual measurement scenarios for aluminum alloy doors and windows, the environment and target characteristics exhibit high dynamism and complexity. The surface materials of doors and windows vary widely; the reflectivity of a dark-colored anodized window frame may be less than one-third that of a light-colored coated surface, leading to significant differences in reflected signal intensity. Ambient lighting conditions fluctuate drastically, ranging from dark indoor environments to bright outdoor sunlight at midday, with intensity spanning several orders of magnitude. The measurement angle often deviates from the vertical direction due to the limitations of the door and window structure, and changes in the laser incident angle affect the reflection path. Furthermore, the measurement distance itself varies with different door and window specifications. These factors combined cause unpredictable fluctuations in the signal intensity captured by the laser receiving module: when the signal is too weak, the effective echo is overwhelmed by noise, and the device cannot trigger distance calculation; when the signal is too strong, the receiving circuit enters a saturation state, causing a sharp drop in time measurement accuracy or even complete failure.

[0004] Existing laser measurement equipment generally employs fixed gain settings or relies on user experience to manually adjust gain parameters. This static control mechanism lacks the ability to sense real-time measurement conditions and cannot dynamically optimize system sensitivity based on signal strength changes. When continuously measuring workpieces with different reflectivities on a door and window production line, or when encountering sudden strong light interference at the installation site, operators must frequently interrupt the process for manual intervention. This not only reduces measurement continuity but also leads to numerous invalid measurement attempts due to adjustment lag. Especially in the curved transition areas of door and window frames or at the joints between glass and metal, signal quality deteriorates sharply, and existing devices often repeatedly report errors or output abnormal values, forcing users to repeatedly remeasure. This lack of adaptability severely restricts the widespread application of laser measurement technology in the door and window industry. There is an urgent need for an intelligent measurement solution that can autonomously sense environmental and target characteristics and optimize gain parameters in real time to achieve stable and reliable measurement under all operating conditions.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] The purpose of this invention is to provide a laser measuring device for aluminum alloy door and window dimensions, in order to solve the above-mentioned problems.

[0007] This invention is implemented as follows: a laser measurement device for aluminum alloy door and window dimensions includes a frame and a laser detector mounted on top of the frame, with a conveyor positioned below the laser detector. It also includes: a gain factor control system connected to the laser receiving module of the laser detector; an environmental context module that outputs a basic gain coefficient based on the target distance and ambient light intensity using an environmental model; a target characteristic module that outputs a compensation coefficient based on the target surface reflectivity and laser incident angle using a geometric optics compensation model; a core feedback module that outputs a real-time gain coefficient based on the received signal strength and signal-to-noise ratio, as well as the basic gain coefficient and compensation coefficient, using a constrained automatic gain control model; a signal quality module that outputs a confidence coefficient based on the pulse width and rise time using a signal morphology diagnostic model; and a dynamic stabilization module that, based on the relative motion speed, real-time gain coefficient, and confidence coefficient, outputs a target gain factor using a smoothing filter and dynamic response model and adjusts the current gain factor to the target gain factor.

[0008] A further technical solution, the specific steps of outputting a target gain factor and adjusting the current gain factor to the target gain factor based on relative motion speed, real-time gain coefficient, and confidence coefficient through smoothing filtering and dynamic response model are as follows: The current relative motion speed is linearly mapped to a relative motion speed exponent using a linear scaling function, and this exponent increases linearly from zero to one as the motion speed increases; based on the relative motion speed exponent, real-time gain coefficient, and confidence coefficient, a first-order inertial filtering function is used to obtain the target gain coefficient; the target gain coefficient is positively correlated with the real-time gain coefficient and the confidence coefficient, the inertial time constant of the filtering function is positively correlated with the relative motion speed exponent, and the smoothness of the gain adjustment increases with the increase of motion speed.

[0009] A further technical solution involves the following steps for outputting a confidence coefficient based on pulse width and rise time using a signal morphology diagnostic model: The current pulse width is linearly mapped to a pulse width exponent based on the system's inherent minimum pulse width and the maximum allowable effective pulse width, which increases linearly from zero to one as the pulse width increases; the pulse rise time is linearly mapped to a rise time exponent based on the system's inherent minimum rise time and the maximum allowable effective rise time, which also increases linearly from zero to one as the rise time increases; the arithmetic mean of the pulse width exponent and the rise time exponent is calculated, and this mean is processed using a monotonically decreasing function to obtain the confidence coefficient, which is negatively correlated with both the pulse width exponent and the rise time exponent.

[0010] A further technical solution, the specific steps of outputting the real-time gain coefficient based on the received signal strength and signal-to-noise ratio, as well as the basic gain coefficient and the compensation coefficient, through a constrained automatic gain control model are as follows: The current received signal strength value is linearly mapped to a received signal strength index based on its minimum resolvable level and maximum unsaturated level. This index increases linearly from zero to one as the received signal strength increases. The current signal-to-noise ratio value is linearly scaled and mapped to a signal-to-noise ratio index based on the minimum operating signal-to-noise ratio required by the system and a preset ideal target signal-to-noise ratio. When the signal-to-noise ratio reaches or exceeds the ideal target value, this index is constrained to one, and it increases from zero to one as the signal-to-noise ratio increases. The signal strength index, signal-to-noise ratio index, basic gain coefficient, and compensation coefficient are fused to obtain the real-time gain coefficient. The real-time gain coefficient is negatively correlated with the signal strength index, negatively correlated with the signal-to-noise ratio index when it is below the preset target value, and positively correlated with both the basic gain coefficient and the compensation coefficient.

[0011] A further technical solution involves the following steps for outputting the basic gain coefficient based on the target distance and ambient light intensity using an environmental model: The current target distance value is linearly mapped to a target distance index based on its preset minimum and maximum effective distances. This index increases linearly from zero to one as the distance increases. The current ambient light intensity value is first compressed using a logarithmic function to match human eye perception characteristics, and then mapped to an ambient light intensity index based on preset typical indoor illuminance and outdoor strong illuminance ranges. This index increases non-linearly from zero to one as the ambient light intensity increases. The ambient light intensity index and the target distance index are processed using a weighted average function and a limiting function to obtain the basic gain coefficient. The basic gain coefficient is positively correlated with the target distance index and negatively correlated with the ambient light intensity index.

[0012] A further technical solution, the specific steps of outputting compensation coefficients based on the target surface reflectivity and laser incident angle through a geometric optics compensation model are as follows: the current laser incident angle value is linearly mapped to a laser incident angle exponent based on its zero degree and a preset maximum effective incident angle. This exponent increases linearly from zero to one as the laser incident angle increases. The reflectivity and the laser incident angle exponent are processed by a weighted average function and a limiting function to obtain the compensation coefficient. The compensation coefficient is negatively correlated with the reflectivity and positively correlated with the laser incident angle exponent.

[0013] In a further technical solution, two guide shafts are horizontally fixed on the frame above the conveyor, and a translation plate is slidably connected to the two guide shafts. The laser detector is fixed on the translation plate, and a drive assembly for driving the translation plate to move horizontally is provided on the frame.

[0014] In a further technical solution, the drive assembly includes a lead screw rotatably connected to the frame and a motor fixed on the side wall of the frame. The rotating end of the motor is connected to the lead screw, the lead screw is threadedly connected to the translation plate, and the lead screw is arranged parallel to the guide shaft.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0016] 1. The system achieves adaptive measurement under all working conditions, significantly improving success rate and accuracy. Through multi-module collaboration, the system senses changes in the environment and target in real time and dynamically optimizes the gain, effectively overcoming the limitations of fixed gain strategies in dealing with changes in distance, light intensity, reflectivity, and incident angle. This fundamentally avoids measurement failures and errors caused by signals that are too weak or too strong.

[0017] 2. Enhanced stability and reliability of dynamic measurements. The system adaptively adjusts the smoothness of gain regulation based on relative motion speed, suppressing jitter at high speeds and responding quickly at low speeds, ensuring stable and reliable data during continuous scanning. Simultaneously, a signal quality assessment mechanism proactively identifies signal distortion risks, making gain control more precise.

[0018] 3. Improved automation and efficiency in measurement. The combination of intelligent gain control and a precise scanning mechanism enables the device to automatically complete the two-dimensional measurement of doors and windows without frequent manual intervention, significantly improving measurement efficiency and reducing labor intensity and subjective errors. Attached Figure Description

[0019] Figure 1 A flowchart of the gain multiplier control system provided by the present invention;

[0020] Figure 2 A schematic diagram of the structure of a laser measuring device for aluminum alloy door and window dimensions provided by the present invention;

[0021] Figure 3 Provided by the present invention Figure 2 A schematic diagram of the structure at the rear angle.

[0022] In the attached diagram: 1. Frame; 2. Laser detector; 3. Conveyor; 4. Translation plate; 5. Guide shaft; 6. Lead screw; 7. Motor. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0024] In the process of measuring the dimensions of aluminum alloy doors and windows using laser ranging technology, factors such as changes in ambient light intensity, differences in target distance, variations in target surface reflectivity, and changes in the laser incident angle cause unstable fluctuations in the received signal strength. When the signal strength is too weak, the measurement process cannot be completed; when the signal strength is too strong, the receiving circuit saturates, both introducing measurement errors or causing ranging failure. This instability in signal strength directly affects measurement accuracy and reliability, making the measurement data unable to accurately reflect the actual dimensions. This, in turn, affects key performance indicators in subsequent production, installation, and replacement processes, including measurement success rate, data accuracy, and system stability.

[0025] For example, during indoor measurements at aluminum alloy door and window installation sites, the ambient lighting conditions are often dim, and the target surface, being a dark-colored window frame, has low reflectivity. In this scenario, the laser signal intensity is significantly weakened after reflection from the target, and the signal received by the receiving module falls below the effective threshold, leading to measurement failure. Operators must repeatedly adjust the position or remeasure, prolonging the measurement process. Furthermore, when measuring a white wall in strong outdoor light, increased ambient light interference and an excessively strong received signal can cause circuit saturation, again preventing the acquisition of effective distance data. Specifically, increasing the target distance exacerbates signal attenuation, and increasing the laser incident angle, based on geometric optics principles, causes a non-linear decrease in reflection intensity. These two factors combined cause the signal intensity fluctuation range to exceed the effective operating range of the receiving module, further reducing the adaptability of the measuring device in practical applications.

[0026] If the issue of unstable received signal strength is not resolved, the measuring device will frequently experience measurement failures or data deviations under complex and variable actual operating conditions. Consequently, the mismatch between aluminum alloy door and window products and installation opening dimensions will significantly increase, requiring factory returns for modification or on-site adjustments. This not only extends the production cycle and increases economic costs but also reduces overall measurement efficiency and user satisfaction. In the long run, this problem limits the reliable application of laser ranging technology in the field of door and window measurement, hinders the improvement of industry automation levels, and renders existing devices unable to meet the actual needs of high-precision and high-reliability measurements.

[0027] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0028] like Figure 1 and Figure 2As shown, an embodiment of the present invention provides a laser measurement device for aluminum alloy door and window dimensions, including a frame 1 and a laser detector 2 disposed on the top of the frame 1. A conveyor 3 is disposed below the laser detector 2. The device also includes: a gain factor control system connected to the laser receiving module of the laser detector 2; an environmental context module that outputs a basic gain coefficient based on the target distance and ambient light intensity through an environmental model; a target characteristic module that outputs a compensation coefficient based on the target surface reflectivity and laser incident angle through a geometric optics compensation model; a core feedback module that outputs a real-time gain coefficient based on the received signal strength and signal-to-noise ratio, as well as the basic gain coefficient and the compensation coefficient, through a constrained automatic gain control model; a signal quality module that outputs a confidence coefficient based on the pulse width and rise time through a signal morphology diagnostic model; and a dynamic stabilization module that outputs a target gain factor and adjusts the current gain factor to the target gain factor based on the relative motion speed, real-time gain factor, and confidence factor through a smoothing filter and dynamic response model.

[0029] The environmental context module quantifies distance attenuation and light interference based on target distance and ambient light intensity, providing initial environmental compensation for target gain adjustment. The target characteristics module integrates target surface reflectivity and laser incident angle parameters to generate compensation coefficients to correct signal loss caused by target physical characteristics. The core feedback module dynamically balances signal amplification and saturation risk by receiving signal strength and signal-to-noise ratio, ensuring the signal remains within its effective operating range. The signal quality module quantifies signal integrity using pulse width and rise time, providing reliability weights for gain decisions. The dynamic stabilization module combines relative motion velocity to perform weighted smoothing of real-time gain, achieving dynamic matching between gain adjustment and motion state. Furthermore, the gain multiplier control system, as the central hub, integrates the outputs of each module, avoiding the insufficient adaptability of traditional fixed gain strategies through closed-loop adaptive adjustment, thereby ensuring improved measurement accuracy and reliability under various real-world conditions.

[0030] In this embodiment of the invention, the conveyor 3 is used for loading and unloading aluminum alloy doors and windows. The gain multiplier control system is connected to the laser receiving module of the laser detector 2 to achieve automatic adjustment of the gain parameter. The laser detector 2 can be specifically implemented as a commercial laser rangefinder sensor. Its laser receiving module adopts a 905 nm wavelength laser source and a high-sensitivity photodiode array, which has nanosecond-level time resolution to accurately capture the temporal characteristics of the reflected signal. The environmental context module is a functional unit that outputs a basic gain coefficient based on the target distance and ambient light intensity. It can be implemented by using a lookup table or a polynomial fitting function. For example, it can be queried through a pre-stored distance-light intensity-coefficient mapping table, or experimental data can be fitted using a quadratic polynomial. Its main purpose is to provide an initial compensation basis for the influence of environmental factors on laser signal attenuation and background noise. The target characteristic module is a functional unit that outputs a compensation coefficient based on the target surface reflectivity and laser incident angle. It can be implemented by simplifying calculations using a physical model or by calibration curve interpolation. For example, it can estimate the reflection intensity change based on the principle of geometric optics, or obtain the compensation value through interpolation of experimental calibration data. Its main purpose is to correct the signal loss caused by differences in target surface characteristics. The core feedback module is a functional unit that outputs a real-time gain coefficient based on the received signal strength and signal-to-noise ratio, as well as the basic gain coefficient and compensation coefficient. It can be implemented using a weighted average algorithm or a proportional-integral (PI) controller. For example, it can weight and fuse the signal strength index and the signal-to-noise ratio index, or use PI control to dynamically adjust the gain. Its main purpose is to ensure that the received signal is within the effective operating range. The signal quality module is a functional unit that outputs a confidence coefficient based on the pulse width and rise time. It can be implemented using threshold judgment methods or statistical analysis methods. For example, it can set a pulse width threshold to determine the degree of signal distortion, or calculate the standard deviation of the rise time to assess signal stability. Its main purpose is to quantify the reliability weight of signal integrity. The dynamic stabilization module is a functional unit that outputs a target gain multiple based on relative motion speed, real-time gain coefficient, and confidence coefficient. It can be implemented using exponential smoothing filtering or Kalman filtering algorithms. For example, it can adjust the filter coefficient according to the speed, or use state estimation to optimize the gain output. Its main purpose is to match the dynamic characteristics of the gain adjustment process with the motion state. This system integrates environmental perception, target characteristic analysis, real-time signal feedback, and motion stability control to form a closed-loop adjustment mechanism. This avoids the insufficient adaptability of traditional fixed-gain strategies under complex working conditions, thereby maintaining the effectiveness of the signal and the accuracy of the measurement results during the measurement of aluminum alloy door and window dimensions.

[0031] During the measurement of aluminum alloy door and window dimensions, a laser detector 2 is mounted on top of the frame 1 of the device, and a conveyor 3 is configured below it for automatic loading and unloading of doors and windows. The gain multiplier control system is connected to the laser receiving module of the laser detector 2. The environmental context module calculates and outputs a basic gain coefficient based on the target distance and ambient light intensity using an environmental model. This coefficient increases with increasing target distance to compensate for signal attenuation and decreases with increasing ambient light intensity to suppress background noise interference. The target characteristic module generates a compensation coefficient using a geometric optics compensation model based on the target surface reflectivity and laser incident angle. This coefficient increases with decreasing reflectivity to compensate for weak signals and increases with increasing laser incident angle to correct for reflection intensity loss caused by non-perpendicular incidence. The core feedback module integrates the received signal strength, signal-to-noise ratio, basic gain coefficient, and compensation coefficient, and determines the real-time gain coefficient through a constrained automatic gain control model. This coefficient is negatively correlated with signal strength to avoid saturation, negatively correlated with the signal-to-noise ratio when it is below the target value to optimize signal quality, and positively correlated with the basic gain coefficient and compensation coefficient to integrate environmental and target characteristic compensation. The signal quality module evaluates and outputs a confidence coefficient based on pulse width and rise time using a signal morphology diagnostic model. This coefficient decreases as the pulse width widens or the rise time lengthens, reflecting the decrease in signal integrity caused by diffuse reflection, multiple reflections, or scattering. The dynamic stabilization module ultimately combines relative motion speed, real-time gain coefficient, and confidence coefficient, employing a smoothing filter and dynamic response model to calculate the target gain factor. It then adjusts the current gain factor to the target value in real time. The filtering time constant increases with relative motion speed to enhance gain stability under high-speed motion and reduce adjustment fluctuations.

[0032] like Figure 1 As shown, in a preferred embodiment of the present invention, the specific steps of outputting a target gain multiple and adjusting the current gain multiple to the target gain multiple based on the relative motion speed, real-time gain coefficient, and confidence coefficient through smoothing filtering and dynamic response model are as follows:

[0033] The relative motion speed between the device and the target is obtained through the encoder signal of the drive motor of conveyor 3;

[0034] The current relative velocity is linearly mapped to a relative velocity exponent by a linear scaling function, which increases linearly from zero to one as the velocity increases.

[0035] The linear scaling function specifically involves taking the ratio of the current relative motion speed to the maximum relative motion speed that the system design can handle, and obtaining the relative motion speed index.

[0036] The relative motion speed index is a dimensionless quantization parameter that normalizes the relative motion speed to the 0-1 range. It can be implemented by performing ratio calculations using software algorithms. The purpose is to eliminate the dimensional influence of the absolute speed value, so that the system can be universally adapted to different speed conditions.

[0037] The target gain coefficient is obtained through a first-order inertial filter function based on the relative motion velocity index, real-time gain coefficient, and confidence coefficient.

[0038] The target gain coefficient is positively correlated with the real-time gain coefficient and the confidence coefficient, the inertial time constant of the filter function is positively correlated with the relative motion velocity index, and the smoothness of the gain adjustment increases with the increase of motion velocity.

[0039] The specific steps are as follows:

[0040] ,in, The filtering time constant is related to speed. It is the minimum time constant under static conditions. This represents the maximum time constant under high-speed motion. The relative velocity index;

[0041] ,in, This is the target gain coefficient for this sampling. The target gain coefficient from the previous sample. The filtering time constant is related to speed. This is the real-time gain coefficient. Confidence coefficient;

[0042] ,in, The target gain factor, This is the maximum gain allowed by the hardware. This is the minimum gain factor allowed by the hardware.

[0043] A first-order inertial filter function can be understood as a recursive low-pass filter, which can be implemented using a digital signal processing unit. Its purpose is to fuse historical gain information with current signal quality data to dynamically generate a smooth target gain coefficient; the velocity-dependent filter time constant... Specifically, the filter parameters are linearly adjusted based on the relative motion speed index, for example, by looking up a table or by real-time calculation. The purpose is to make the smoothness of the gain adjustment adapt to the motion speed and avoid the adjustment mismatch problem caused by fixed parameters.

[0044] The filtering time constant increases with the speed exponential, ensuring smoother gain adjustment during high-speed motion to suppress signal jitter and more sensitive adjustment during low-speed motion to respond quickly to signal changes. Finally, the target gain coefficient is linearly mapped to the hardware-allowed gain multiple range, the target gain multiple is output, and the current gain multiple of laser detector 2 is adjusted.

[0045] In one specific implementation, the dynamic stabilization module of this application is implemented by a microcontroller, wherein the relative motion speed is obtained through the encoder signal of the conveyor drive motor, the calculation of the first-order inertial filter function is performed by the digital signal processing unit of the microcontroller, and the adjustment of the target gain multiple is completed through the gain control interface of the laser detector 2.

[0046] Through the above technical solution, this application can dynamically adjust the smoothness of gain adjustment according to the moving speed of doors and windows. When moving at high speed, the smoothness is increased to avoid signal fluctuations, and when moving at low speed, the smoothness is reduced to improve the response speed, thereby effectively improving the stability and accuracy of the measurement process.

[0047] like Figure 1 As shown, in a preferred embodiment of the present invention, the specific steps for outputting the confidence coefficient based on the pulse width and rise time using a signal morphology diagnostic model are as follows:

[0048] Obtain the pulse width and rise time;

[0049] The current pulse width is linearly mapped to a pulse width exponent based on the system's inherent minimum pulse width and the maximum allowable effective pulse width. This exponent increases linearly from zero to one as the pulse width increases.

[0050] The pulse width index is obtained by taking the difference between the current pulse width and the system's inherent minimum pulse width and the difference between the system's maximum allowable effective pulse width and the system's inherent minimum pulse width, and then performing a ratio operation to obtain the pulse width index. Pulse broadening usually means diffuse reflection or multiple reflections, which degrades signal quality.

[0051] The pulse width index is a quantization parameter that normalizes the actual pulse width to the 0-1 range. It can be achieved by difference ratio processing. Specifically, the pulse width sampling and normalization calculation can be completed by a high-speed timing circuit in conjunction with a digital signal processor. Its purpose is to eliminate the evaluation bias caused by hardware differences, so that the index only reflects the relative width, thereby accurately associating diffuse reflection or multiple reflection phenomena with the physical nature of signal quality degradation.

[0052] The pulse rise time is linearly mapped to a rise time exponent based on the system's inherent minimum rise time and the maximum allowable effective rise time. This exponent increases linearly from zero to one as the rise time increases.

[0053] The rise time index is obtained by taking the difference between the current rise time and the system's inherent minimum rise time and the difference between the system's maximum allowable effective rise time and the system's inherent minimum rise time, and then performing a ratio operation to obtain the rise time index. A slower rise time indicates that the signal is weak or has been scattered, which will reduce the accuracy of time measurement.

[0054] The rise time exponent can be understood as a normalized index that characterizes the steepness of the rise time of a signal. It can be achieved by using a comparator circuit in conjunction with a time-to-digital converter to accurately capture and linearly map the rise time. Its purpose is to convert the phenomenon of a slow rise time into a quantifiable exponential value, avoid the misleading nature of absolute time values, and ensure that the time measurement accuracy is accurately reflected by scattering or weak signals.

[0055] The confidence coefficient is obtained by taking the arithmetic mean of the pulse width index and the rise time index, and then processing the average value through a monotonically decreasing function. The confidence coefficient is negatively correlated with both the pulse width index and the rise time index.

[0056] The monotonically decreasing function is: ,in, The confidence coefficient is... The pulse width index. The rising edge time index, The attenuation coefficient controls the rate at which the confidence level decreases as the signal quality deteriorates.

[0057] The confidence coefficient is a reliability assessment value generated by integrating the signal morphological characteristics through a monotonically decreasing function. It can be implemented using an exponential decay function or a piecewise linear decay function. Its purpose is to dynamically adjust the confidence based on the degree of signal quality degradation, so as to maintain stability when there are slight fluctuations and respond quickly when there is severe distortion, thus providing an accurate reliability basis for gain adjustment.

[0058] Specifically, the scheme in this application uses the original pulse width and rise time of the laser echo as basic input parameters. First, based on the boundary conditions of the system's inherent minimum and maximum effective values, the pulse width is linearly mapped to a normalized pulse width exponent. This mapping process eliminates the influence of absolute dimensions through difference ratio processing, making the exponent depend only on the relative broadening. Simultaneously, the rise time is mapped to a rise time exponent in the same way, ensuring that the exponent monotonically increases as the rise time slows down. Then, the two exponents are arithmetically averaged to comprehensively reflect the overall degradation of the signal morphology, and the average value is converted into a confidence coefficient using a monotonically decreasing function, where the attenuation coefficient... The introduction of this feature allows for an adjustable confidence rate decrease, enabling stability during slight signal quality degradation and rapid attenuation during severe degradation. The attenuation coefficient can be assigned a specific value through a preset strategy or dynamic algorithm. This process establishes a quantitative correlation between pulse characteristics and signal reliability through refined diagnosis of signal morphology, enabling the confidence coefficient to accurately reflect signal quality changes caused by diffuse reflection, multiple reflections, or scattering, thereby providing a reliable adjustment basis for the dynamic stabilization module.

[0059] As a specific implementation, the signal morphology diagnostic model of this application can be implemented by a field-programmable gate array (FPGA) in conjunction with a dedicated signal processing chip. The pulse width detection unit uses a high-speed comparator circuit to capture the start and end times of the laser echo pulse, and the rise time detection unit uses a time-to-digital converter to accurately measure the transition time of the signal from 10% amplitude to 90% amplitude. The normalization process is executed in the digital signal processor, which calculates the ratio of the original measured value to the system's preset minimum pulse width, maximum effective pulse width, minimum rise time, and maximum effective rise time. The confidence coefficient calculation module uses an exponential function processor with a configurable attenuation coefficient to output the confidence coefficient in real time based on the arithmetic mean. This coefficient is directly transmitted to the dynamic stabilization module for gain adjustment decision-making.

[0060] Through the above technical solution, this application can accurately diagnose signal reliability based on the pulse morphology characteristics of laser echo. When diffuse reflection, multiple reflections or scattering cause pulse width broadening or rise time extension, it can accurately quantify the degree of signal quality degradation, so that the confidence coefficient is highly matched with the actual signal conditions, thereby ensuring that the gain adjustment always meets the measurement requirements and effectively avoiding the problem of reduced measurement stability or loss of accuracy caused by inaccurate confidence assessment.

[0061] like Figure 1 As shown, in a preferred embodiment of the present invention, the specific steps for outputting real-time gain coefficients based on the received signal strength and signal-to-noise ratio, as well as the basic gain coefficient and compensation coefficient, through a constrained automatic gain control model are as follows:

[0062] Obtain the received signal strength and signal-to-noise ratio;

[0063] The current received signal strength value is linearly mapped to a received signal strength index based on its minimum resolvable level and maximum unsaturated level. This index increases linearly from zero to one as the received signal strength increases.

[0064] The specific method for obtaining the received signal strength index is to compare the difference between the current received signal strength and the minimum signal strength that the receiver can distinguish with the difference between the maximum signal strength of the receiver and the minimum signal strength that the receiver can distinguish under unsaturated conditions, and then obtain the received signal strength index.

[0065] The Received Signal Strength Index (RSSLI) is a standardized index that normalizes the absolute signal strength to the 0-1 range. It can be achieved by using software algorithms to linearly calculate the output value of the analog-to-digital converter. The purpose is to eliminate the influence of hardware differences on signal evaluation and ensure that the index can objectively reflect the degree to which the signal is close to saturation or too weak.

[0066] The current signal-to-noise ratio (SNR) value is linearly scaled and mapped to an SNR exponent based on the minimum operating SNR required by the system and the preset ideal target SNR. When the SNR reaches or exceeds the ideal target value, the exponent is constrained to one. The exponent increases from zero to one as the SNR increases.

[0067] The signal-to-noise ratio index is obtained by taking the difference between the current signal-to-noise ratio and the lowest signal-to-noise ratio that the system can effectively measure, and then taking the difference between the optimal signal-to-noise ratio of the system design and the lowest signal-to-noise ratio that the system can effectively measure.

[0068] The signal-to-noise ratio (SNR) index can be understood as a quantitative indicator of the relative position of the SNR with respect to the system's operating threshold. It can be calculated by performing spectral analysis on the original signal using a digital signal processing unit. Its purpose is to sensitively capture subtle changes in the low SNR region and provide a dynamic basis for gain adjustment.

[0069] The signal strength index, signal-to-noise ratio index, basic gain coefficient, and compensation coefficient are fused to obtain the real-time gain coefficient. The real-time gain coefficient is negatively correlated with the signal strength index, negatively correlated with the signal-to-noise ratio index when it is below a preset target value, and positively correlated with both the basic gain coefficient and the compensation coefficient.

[0070] The fusion process specifically involves: ,in, This is the real-time gain coefficient. Signal strength index The signal-to-noise ratio index. Basic gain coefficient, For compensation coefficient, For signal strength weighting coefficients, The value range is 0-1.

[0071] Fusion processing specifically refers to the computational process of generating real-time gain coefficients by integrating the quality parameters of multiple source signals. This can be implemented using a combination algorithm of weighted averaging and amplitude limiting functions executed by a microcontroller. The aim is to balance the contribution weights of signal strength and signal-to-noise ratio (SNR) to avoid adjustment deviations caused by a single parameter dominating the process. The signal strength weight coefficient is used in the fusion processing of the core feedback module to weigh the relative weights of the received signal strength and SNR on the real-time gain coefficient. Specifically, it can be assigned values ​​through preset strategies or dynamic algorithms. In practical applications, it can be fine-tuned according to the main challenges of the system: if signal saturation is the main risk in the measurement scenario (e.g., targets are mostly high-reflectivity surfaces), then the weight coefficient should be increased. A value (e.g., 0.6~0.8) allows the system to more actively suppress gain based on strong signals, preventing saturation. If the signal is weak and noise interference is the main problem in the measurement scenario (e.g., the target is mostly dark and the ambient light is complex), then the value should be reduced. Values ​​(such as 0.2~0.4) make the system more reliant on signal-to-noise ratio information, allowing it to extract effective signals from noise.

[0072] Specifically, the solution in this application achieves dynamic gain optimization by sequentially executing a logical process of signal acquisition, exponential mapping, and fusion calculation: First, the raw data of the received signal strength and signal-to-noise ratio output by the laser detector 2 are acquired as direct inputs for signal quality; then, the received signal strength is linearly normalized based on the minimum resolvable level and the maximum unsaturated level to generate a received signal strength exponent that increases with signal enhancement. This exponential linear mapping mechanism ensures that a continuously adjustable adjustment basis can still be provided when the reflectivity of the aluminum alloy door and window surface changes abruptly; simultaneously, the signal-to-noise ratio is constrained and scaled based on the minimum operating signal-to-noise ratio and the ideal target signal-to-noise ratio to generate a gain that increases with signal-to-noise ratio. The signal-to-noise ratio (SNR) index is increased, but with an upper limit of one. This constraint mechanism prevents excessive amplification of noise under high SNR conditions. Finally, the signal strength index, SNR index, base gain coefficient output by the environmental model, and compensation coefficient output by the geometric optics compensation model are weighted and integrated through fusion processing. The negative correlation between the signal strength index and the real-time gain coefficient can suppress circuit saturation caused by strong signals, the negative correlation of the SNR index in the low-value region can enhance weak signals to improve measurement accuracy, and the positive correlation between the base gain coefficient and the compensation coefficient inherits the pre-adjustment information of environmental and target characteristics, thus forming a comprehensive gain control mechanism that takes into account both real-time signal quality and prior knowledge.

[0073] In a preferred embodiment, the microcontroller acquires the output signal of the laser detector 2 in real time via a high-speed analog-to-digital converter and calculates the received signal strength and signal-to-noise ratio (SNR). Then, it executes a software algorithm to divide the difference between the received signal strength and the minimum resolvable signal strength of the receiver by a dynamic range threshold to obtain the received signal strength index. Simultaneously, it divides the difference between the SNR and the system's lowest operating SNR by a target SNR range to obtain the SNR index. Finally, it calculates the SNR index based on a preset signal strength weighting coefficient. The real-time gain coefficient is calculated using a formula, and the result is transmitted to the gain adjustment circuit of laser detector 2 to achieve dynamic adjustment of the gain factor.

[0074] Through the above scheme, this application can adaptively generate gain coefficients based on the real-time fluctuations of received signal strength and signal-to-noise ratio, effectively suppressing measurement failures caused by weak signals or circuit saturation caused by strong signals during the measurement of aluminum alloy doors and windows, and significantly improving the stability and data reliability of dimensional measurements under complex working conditions.

[0075] like Figure 1 As shown, in a preferred embodiment of the present invention, the specific steps for outputting the basic gain coefficient based on the target distance and ambient light intensity through the environment model are as follows:

[0076] Obtain target distance and ambient light intensity;

[0077] The current target distance value is linearly mapped to a target distance index based on its preset minimum effective distance and maximum effective distance. This index increases linearly from zero to one as the distance increases.

[0078] The target distance index is obtained by taking the ratio of the difference between the current target distance and the minimum effective range of the device and the difference between the maximum effective range of the device and the minimum effective range of the device.

[0079] The target distance index is a quantitative indicator that normalizes the actual ranging range to a standardized interval. It can be implemented using linear interpolation algorithms or lookup table methods. The purpose is to convert the physical distance into a dimensionless index, which facilitates subsequent mathematical processing and accurately reflects the nonlinear law of signal attenuation.

[0080] The current ambient light intensity value is first compressed using a logarithmic function to match the characteristics of human eye perception. Then, based on the preset range of typical indoor illuminance and outdoor strong illuminance, it is mapped to an ambient light intensity index, which increases non-linearly from zero to one as the ambient light intensity increases.

[0081] The formula for calculating the ambient light intensity index is: ,in, Ambient light intensity index, Given the current ambient light intensity, Typical indoor illuminance, To simulate clear outdoor light intensity, logarithmic compression is used to match the human eye's perception of light intensity. The stronger the ambient light, the more accurate the calculation. The closer the value is to 1, the lower the base gain should be to suppress noise.

[0082] The ambient light intensity index is a nonlinear index that characterizes the influence of ambient light intensity after logarithmic compression. It can be implemented using a photodiode in conjunction with a logarithmic amplifier circuit or a logarithmic operation module in a digital signal processor. The purpose is to match the nonlinear perception characteristics of the human eye to light intensity, so that small changes in low-light areas are sensitively captured while drastic changes in high-light areas are smoothed out. Logarithmic function compression refers to the preprocessing operation of applying a natural logarithmic transformation to the original light intensity value. It can be understood as being implemented through an analog logarithmic conversion circuit or a logarithmic function library in software. The purpose is to compress the dynamic range of ambient light intensity, avoid exponential saturation under strong light, and improve the resolution of weak light signals.

[0083] The ambient light intensity index and the target distance index are processed by a weighted average function and a limiting function to obtain a basic gain coefficient. The basic gain coefficient is positively correlated with the target distance index and negatively correlated with the ambient light intensity index.

[0084] The weighted average function and the amplitude limiting function are as follows: ,in, Basic gain coefficient, The target distance index. Ambient light intensity index, The distance factor weighting coefficient. The value range is 0-1. This means clamping the result to the 0-1 range.

[0085] The weighted average function and limiting function refer to mathematical processing mechanisms that fuse multi-source environmental parameters and constrain the output range. These can be implemented using a floating-point unit in a microcontroller or a dedicated gain control chip. Their purpose is to dynamically balance the relative influence of target distance and ambient light intensity, ensuring the base gain coefficient is output within a hardware safety range. The distance factor weighting coefficient is used in the environmental context module's fusion processing to weigh the relative weights of target distance and ambient light intensity on the base gain coefficient. Specifically, it can be assigned values ​​through preset strategies or dynamic algorithms. In practical applications, fine-tuning is possible: if the measurement distance in the application scenario varies greatly and is the dominant factor affecting signal strength (such as measuring extremely long doors and windows), the weighting coefficient should be increased. Values ​​(e.g., 0.6~0.8) are used to enhance compensation for distance attenuation. If the ambient lighting conditions in the application scenario are complex and fluctuate drastically (e.g., at outdoor installation sites), the value should be reduced. Values ​​(such as 0.2~0.4) enhance the suppression of ambient light noise.

[0086] Specifically, the solution in this application achieves dynamic generation of the basic gain coefficient through step-by-step collaborative processing of the environmental model: First, the target distance and ambient light intensity are acquired as raw input parameters, and changes in the measurement scene are sensed in real time; then, the target distance value is linearly mapped based on the minimum and maximum effective distance, and a target distance index is generated through difference ratio processing. This process strictly limits the index to linear change within the 0-1 range, thereby quantifying the degree of signal attenuation caused by increased distance; simultaneously, the ambient light intensity value is compressed using a logarithmic function and mapped to an ambient light intensity index. This compression process causes the index to increase non-linearly within the typical indoor to outdoor strong light range, accurately reflecting the physical law of increased noise interference when ambient light intensifies; finally, the ambient light intensity index and the target distance index are fused through a weighted average function, in which a weighting coefficient is introduced. Dynamically adjust the contribution ratio of the two and utilize By achieving a negative correlation conversion of the light intensity index and then constraining the output range through a limiting function, the basic gain coefficient can both increase with increasing distance to compensate for signal attenuation and decrease with increasing ambient light to suppress noise interference. This progressive processing mechanism of environmental awareness, nonlinear compression, and parameter fusion ensures that the basic gain coefficient can accurately adapt to actual measurement needs under various complex operating conditions.

[0087] As a specific implementation method, the solution of this application is implemented as follows: the ambient light intensity is acquired using a silicon photodiode sensor, and the output signal of the sensor is dynamically compressed by a logarithmic amplifier circuit; the target distance is acquired through the ranging module of the laser detector 2; the processing unit is specifically a microcontroller, which executes an environmental model algorithm, including the calculation of the target distance index and the ambient light intensity index, as well as weighted averaging and amplitude limiting processing; the output of the basic gain coefficient is used to adjust the receiving gain of the laser detector 2 in real time to ensure that the signal is always within the optimal working range during the measurement of aluminum alloy doors and windows.

[0088] Through the above scheme, this application can dynamically adapt to changes in target distance and fluctuations in ambient light intensity, effectively avoiding measurement failures caused by weak signals during long-distance measurements and accuracy degradation caused by receiver circuit saturation in strong light environments. Thus, it significantly improves the stability and reliability of dimensional measurements under complex working conditions such as dim indoor lighting, strong outdoor light, and changes in door and window positions.

[0089] like Figure 1 As shown, in a preferred embodiment of the present invention, the specific steps for outputting compensation coefficients based on the target surface reflectivity and laser incident angle using a geometric optics compensation model are as follows:

[0090] The reflectivity of the target surface to the laser wavelength and the angle between the laser beam and the normal of the target surface are obtained. The angle between the laser beam and the normal of the target surface is the laser incident angle.

[0091] Target surface reflectivity refers to the quantified value of the ability of a target surface to reflect a specific laser wavelength. It can be obtained by querying a preset material reflectivity database or by real-time measurement based on a spectrometer. Its purpose is to provide basic parameters of surface optical properties and avoid signal weakness caused by low reflectivity materials such as dark aluminum alloy window frames. Laser incident angle refers to the angle between the laser beam and the normal of the target surface. It can be understood as being obtained through a miniature tilt sensor or geometric calculation based on binocular vision. Its purpose is to accurately capture the influence of the measurement posture on the reflected signal, which is especially suitable for curved or tilted measurement scenarios of doors and windows.

[0092] The current laser incident angle value is linearly mapped to the laser incident angle exponent based on its zero degree and the preset maximum effective incident angle. This exponent increases linearly from zero to one as the laser incident angle increases.

[0093] The laser incident angle index is obtained by comparing the current laser incident angle with the maximum effective incident angle allowed by the device.

[0094] The reflectivity and the laser incident angle exponent are processed by a weighted average function and a limiting function to obtain a compensation coefficient. The compensation coefficient is negatively correlated with the reflectivity and positively correlated with the laser incident angle exponent.

[0095] The laser incident angle index is a continuous quantity that normalizes the current laser incident angle to the range of 0-1. It can be implemented using a linear scaling function. Its purpose is to convert the angle change into a quantifiable index, which facilitates subsequent mathematical processing and eliminates the influence of dimensions. Ratio processing refers to dividing the current laser incident angle by the maximum effective incident angle. It can be understood as a standardization method. Its purpose is to ensure that the index changes linearly within the effective measurement range and avoids nonlinear errors introduced by absolute angle values.

[0096] The formula for the weighted average function limiting function is as follows: ,in, For compensation coefficient, For reflectivity, The laser incident angle index, The reflectivity factor weighting coefficient. The value range is 0-1. This means clamping the result to the 0-1 range.

[0097] The weighted average function is a mathematical operation that fuses reflectivity and incident angle exponents. It can be implemented using a linear combination method. Its purpose is to balance the contribution of different factors to the compensation coefficient, enabling targeted compensation for low-reflectivity surfaces and large incident angle scenes. The limiting function constrains the calculation result to the 0-1 range. It can be understood as a software clamping algorithm or a hardware limiting circuit. Its purpose is to prevent the compensation coefficient from exceeding a reasonable range, which could lead to signal saturation or distortion. The reflectivity factor weighting coefficient is used in the fusion processing of the target characteristic module to weigh the relative weights of the target surface reflectivity and the laser incident angle on the compensation coefficient. Specifically, it can be assigned values ​​through preset strategies or dynamic algorithms. In practical applications, it can be fine-tuned: if the surface material and color of the door or window to be measured have significant differences (large reflectivity variations), but the measurement angle is relatively standard, then the weighting coefficient should be increased. A value (e.g., 0.6~0.8) makes the system more sensitive to changes in reflectivity. If measurements are frequently performed under non-perpendicular incidence conditions (e.g., measuring curved surfaces or tilted doors and windows), and the surface material is relatively uniform, the value should be reduced. Values ​​(such as 0.2~0.4) enhance compensation for signal attenuation caused by the angle of incidence.

[0098] Specifically, the solution in this application uses the target surface reflectivity and laser incident angle as basic input parameters. First, it linearly maps the laser incident angle to a normalized laser incident angle exponent, which increases with the increase of the incident angle, accurately reflecting the physical law that the reflection signal weakens as the incident angle increases. Then, it converts the reflectivity into... The compensation coefficient is designed to highlight the need for low-reflectivity surfaces and is weighted and fused with the laser incident angle exponent. Finally, a limiting function ensures that the compensation coefficient is within the 0-1 range. This approach allows the compensation coefficient to dynamically adapt to changes in surface material and measurement orientation. A negative correlation with reflectivity ensures that low-reflectivity surfaces (such as dark window frames) automatically trigger higher compensation values ​​to enhance weak signals, while a positive correlation with the laser incident angle exponent provides additional gain to address signal attenuation caused by large incident angles, thus effectively suppressing signal fluctuations in gain control.

[0099] In one specific implementation, the reflectivity of the target surface is obtained by querying a pre-stored database of aluminum alloy door and window material reflectivity, for example, matching the corresponding reflectivity value based on the window frame color information; the laser incident angle is measured in real time by a miniature tilt sensor integrated in the laser detector 2; the laser incident angle index is obtained by calculating the ratio of the measured angle to the system's preset maximum effective incident angle; in the compensation coefficient calculation, the reflectivity factor weighting coefficient... The value is set between 0 and 1 according to actual needs to dynamically adjust the contribution ratio of reflectivity and incident angle; the limiting function is implemented by the microcontroller's built-in software algorithm to constrain the calculation results to the range of 0 to 1.

[0100] Through the above scheme, this application can dynamically quantify the influence of target surface reflectivity and laser incident angle on the reflected signal, generate adaptive compensation coefficients, effectively suppress signal intensity fluctuations caused by differences in surface material and changes in measurement angle, avoid signals that are too weak to be detected or too strong to cause circuit saturation, thereby improving the stability and accuracy of aluminum alloy door and window size measurement, especially suitable for curved or tilted measurement scenarios.

[0101] like Figure 2 and Figure 3 As shown, in a preferred embodiment of the present invention, two guide shafts 5 are horizontally fixed on the frame 1 above the conveyor 3, and a translation plate 4 is slidably connected to the two guide shafts 5. The laser detector 2 is fixed on the translation plate 4. A drive assembly for driving the translation plate 4 to move horizontally is provided on the frame 1. The direction of movement of the translation plate 4 is perpendicular to the conveying direction of the conveyor 3. The drive assembly includes a lead screw 6 rotatably connected to the frame 1 and a motor 7 fixed on the side wall of the frame 1. The rotating end of the motor 7 is connected to the lead screw 6. The lead screw 6 is threadedly connected to the translation plate 4, and the lead screw 6 is arranged parallel to the guide shafts 5.

[0102] In this embodiment of the invention, a rigid support path is established for the translation plate 4 through the guide shaft 5, enabling the translation plate 4 to resist external disturbances during the operation of the conveyor 3 and avoid laser path deviation caused by shaking due to the conveying of doors and windows. The sliding connection between the translation plate 4 and the guide shaft 5 maintains low friction characteristics during the movement process, reducing the impact of mechanical wear on long-term measurement accuracy. The laser detector 2 moves synchronously with the translation plate 4, dynamically adjusting the detection point according to the conveying position of the doors and windows, and can obtain continuous dimensional data along the width direction without manual intervention. The drive component drives the lead screw 6 to rotate through the motor 7, converting the rotational motion into linear displacement of the translation plate 4 through the threaded connection, achieving micron-level positioning accuracy. The setting that the moving direction of the translation plate 4 is perpendicular to the conveying direction of the conveyor 3 allows the vertical scanning to systematically capture width dimension data when the doors and windows move along the length direction, forming a complete two-dimensional dimensional measurement model. The parallel layout of the lead screw 6 and the guide shaft 5 eliminates the lateral force caused by angular deviation, ensuring that the translation plate 4 strictly follows the straight path, ensuring the perpendicularity of the laser ranging optical path, and reducing measurement deviations introduced by changes in the incident angle.

[0103] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An aluminum alloy door and window size laser measuring device, comprising a rack, and a laser detector arranged on the top of the rack, and a conveyor arranged below the laser detector, characterized in that, Also comprising: A gain multiple regulation system connected with the laser receiving module of the laser detector; An environmental context module outputting a base gain coefficient through an environmental model based on target distance and ambient light intensity; A target characteristic module outputting a compensation coefficient through a geometric optics compensation model based on target surface reflectivity and laser incidence angle; A core feedback module outputting a real-time gain coefficient through a constrained automatic gain control model based on received signal strength and signal-to-noise ratio, and the base gain coefficient and the compensation coefficient; A signal quality module outputting a confidence coefficient through a signal morphology diagnosis model based on pulse width and rising edge time; A dynamic stabilization module outputting a target gain multiple through a smoothing filter and dynamic response model based on relative motion speed, real-time gain coefficient and confidence coefficient, and adjusting the current gain multiple to the target gain multiple.

2. The aluminum alloy door and window size laser measuring device according to claim 1, characterized in that, The specific steps of outputting the target gain multiple through the smoothing filter and dynamic response model based on the relative motion speed, real-time gain coefficient and confidence coefficient, and adjusting the current gain multiple to the target gain multiple are as follows: Linearly mapping the current relative motion speed to a relative motion speed index through a linear proportional scaling function, which linearly increases from zero to one as the motion speed increases; Obtaining the target gain coefficient through a first-order inertial filter function based on the relative motion speed index, real-time gain coefficient and confidence coefficient; The target gain coefficient is positively correlated with the real-time gain coefficient and the confidence coefficient, and the inertial time constant of the filter function is positively correlated with the relative motion speed index, and the smoothing degree of gain adjustment increases as the motion speed increases.

3. The aluminum alloy door and window size laser measuring device according to claim 2, characterized in that, The specific steps of outputting the confidence coefficient through the signal morphology diagnosis model based on the pulse width and rising edge time are as follows: Linearly mapping the current pulse width to a pulse width index based on the system's inherent minimum pulse width and the maximum effective pulse width allowed for processing, which linearly increases from zero to one as the pulse width increases; Linearly mapping the pulse rising edge time to a rising edge time index based on the system's inherent minimum rising time and the maximum effective rising time allowed for processing, which linearly increases from zero to one as the rising edge time increases; Obtaining the confidence coefficient by taking the arithmetic mean of the pulse width index and the rising edge time index and processing the mean value through a monotonically decreasing function, which is negatively correlated with the pulse width index and the rising edge time index.

4. The aluminum alloy door and window size laser measuring device according to claim 2, characterized in that, The specific steps of outputting the real-time gain coefficient through the constrained automatic gain control model based on the received signal strength and signal-to-noise ratio, and the base gain coefficient and the compensation coefficient are as follows: Linearly mapping the current received signal strength value to a received signal strength index based on its minimum resolvable level and maximum unsaturated level, which linearly increases from zero to one as the received signal strength increases; Linearly scaling and mapping the current signal-to-noise ratio value to a signal-to-noise ratio index based on the system's required minimum working signal-to-noise ratio and the preset ideal target signal-to-noise ratio, which is constrained to one when the signal-to-noise ratio reaches or exceeds the ideal target value, and which increases from zero to one as the signal-to-noise ratio improves; The signal strength index, the signal-to-noise ratio index, the basic gain coefficient and the compensation coefficient are fused to obtain a real-time gain coefficient; the real-time gain coefficient is negatively correlated with the signal strength index, negatively correlated with the signal-to-noise ratio index when the signal-to-noise ratio index is lower than a preset target value, and positively correlated with the basic gain coefficient and the compensation coefficient.

5. The aluminum alloy door and window size laser measuring device according to claim 4, characterized in that, The specific steps of outputting the basic gain coefficient based on the target distance and the ambient light intensity through the ambient model are as follows: The current target distance value is linearly mapped to a target distance index based on the preset minimum effective ranging and maximum effective ranging, and the index linearly increases from zero to one as the distance increases; The current ambient light intensity value is first compressed by a logarithmic function to match the human eye perception characteristics, and then mapped to an ambient light intensity index based on the preset typical indoor illuminance and outdoor strong light illuminance range, and the index non-linearly increases from zero to one as the ambient light intensity increases; The ambient light intensity index and the target distance index are processed by a weighted average function and a limiting function to obtain a basic gain coefficient, and the basic gain coefficient is positively correlated with the target distance index and negatively correlated with the ambient light intensity index.

6. The aluminum alloy door and window size laser measuring device according to claim 4, characterized in that, The specific steps of outputting the compensation coefficient based on the target surface reflectivity and the laser incidence angle through the geometric optical compensation model are as follows: The current laser incidence angle value is linearly mapped to a laser incidence angle index based on zero and a preset maximum effective incidence angle, and the index linearly increases from zero to one as the laser incidence angle increases; The reflectivity and the laser incidence angle index are processed by a weighted average function and a limiting function to obtain a compensation coefficient, and the compensation coefficient is negatively correlated with the reflectivity and positively correlated with the laser incidence angle index.

7. The aluminum alloy door and window size laser measuring device according to claim 1, characterized in that, Two guide shafts are horizontally fixed above the conveyor on the rack, and a translation plate is slidably connected to the two guide shafts, and the laser detector is fixed on the translation plate.

8. The aluminum alloy door and window size laser measuring device according to claim 7, characterized in that, The driving assembly includes a lead screw rotatably connected to the rack, and a motor fixed on the side wall of the rack, the rotating end of the motor is connected with the lead screw, the lead screw is threadedly connected with the translation plate, and the lead screw is parallelly arranged with the guide shaft.

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