Indoor decoration measuring and paying-off device and method

Abstract

The application discloses an indoor decoration measurement and line laying device and method, and belongs to the technical field of measurement. The method comprises the following steps: obtaining a laser bending risk coefficient based on indoor temperature and indoor wind speed; obtaining a laser brightness loss coefficient based on indoor ambient light intensity and indoor dust concentration; obtaining a laser projection risk coefficient based on the laser bending risk coefficient and the laser brightness loss coefficient; obtaining a projection surface evaluation coefficient based on projection surface roughness, projection surface lightness and projection surface glossiness; constructing a distance-wavelength-divergence angle adaptation model to output a distance-wavelength-divergence angle adaptation degree based on the laser projection risk coefficient, a laser working distance under the projection surface evaluation coefficient, a laser wavelength and a beam divergence angle; and constructing a laser intensity optimization model to output a target laser intensity based on the distance-wavelength-divergence angle adaptation degree and a current laser intensity. The application can improve the measurement accuracy and energy efficiency in a complex environment.

Description

Technical Field

[0001] This invention belongs to the field of measurement technology, and in particular relates to an indoor decoration measurement and layout device and method. Background Technology

[0002] In interior decoration construction, laser measurement and layout technology is widely used due to its high efficiency and accuracy. Traditional laser layout devices typically operate based on fixed parameters, such as laser intensity and wavelength, which are preset at the factory and lack adaptability to dynamic environmental changes. However, complex and variable environmental interferences exist at interior decoration sites, leading to a decrease in measurement accuracy, mainly manifested in:

[0003] Environmental disturbances: Temperature gradients and airflow (such as air conditioning air) cause laser refraction bending (mirage effect), and light intensity and dust concentration can significantly reduce laser visibility;

[0004] Inadequate projection surface adaptation: Rough, dark, or highly reflective walls can cause the laser line to be blurred, broken, or overexposed.

[0005] Fixed parameters: The laser intensity, wavelength, divergence angle and working distance lack dynamic collaborative optimization, and manual adjustment is required in long-distance or high-dust scenarios, which is inefficient. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an indoor decoration measurement and layout device and method, which solves the aforementioned problems.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for measuring and setting out lines in interior decoration, comprising the following steps:

[0008] A laser bending risk model is constructed based on indoor temperature and indoor wind speed, and the laser bending risk coefficient is output.

[0009] A laser brightness loss model is constructed based on indoor ambient light intensity and indoor dust concentration, and the laser brightness loss coefficient is output.

[0010] A laser projection risk model is constructed based on the laser bending risk coefficient and the laser brightness loss coefficient, and the laser projection risk coefficient is output.

[0011] A projection surface evaluation model is constructed based on the projection surface roughness, projection surface brightness, and projection surface gloss, and the projection surface evaluation coefficient is output.

[0012] Based on the laser projection risk coefficient and projection surface evaluation coefficient, a distance-wavelength-divergence angle adaptation model is constructed to output the distance-wavelength-divergence angle adaptation degree for laser working distance, laser wavelength, and beam divergence angle.

[0013] A laser intensity optimization model is constructed based on the distance-wavelength-divergence angle fit and the current laser intensity to output the target laser intensity.

[0014] Based on the above technical solutions, the present invention also provides the following optional technical solutions:

[0015] A further technical solution: The laser intensity optimization model is expressed as follows:

[0016]

[0017] in, Indicates the target laser intensity. Indicates the current laser intensity. Indicates the minimum strength guarantee factor. Indicates the fitness sensitivity factor. This indicates the current distance-wavelength-divergence angle fit. This indicates the ideal distance-wavelength-divergence angle fit.

[0018] Further technical solution: Based on the laser projection risk coefficient and projection surface evaluation coefficient, the steps for constructing a distance-wavelength-divergence angle adaptation model and outputting the distance-wavelength-divergence angle adaptation degree are as follows:

[0019] The laser wavelength index is obtained by performing maximum-minimum normalization on the laser wavelength.

[0020] The distance index and divergence angle index are obtained by comparing the laser working distance and beam divergence angle with the corresponding maximum allowable values.

[0021] A distance-wavelength-divergence angle adaptation model is constructed based on the laser projection risk coefficient and the projection surface evaluation coefficient, along with the laser wavelength index, distance index, and divergence angle index. This model is expressed as follows:

[0022]

[0023] in, This indicates the distance-wavelength-divergence angle fit. Indicates the evaluation coefficient of the projection surface. Indicates the risk factor of laser projection. Indicates the laser wavelength index. Represents the distance index. Indicates the divergence index. Indicates the wavelength-divergence coupling index. Represents the distance-divergence angle coupling index, the Furthermore, the larger the value, the better the distance-wavelength-divergence angle adaptability;

[0024] Import the current projection surface evaluation coefficient, current laser projection risk coefficient, current laser wavelength index, current distance index, and current divergence angle index into the distance-wavelength-divergence angle adaptation model to output the current distance-wavelength-divergence angle adaptation degree.

[0025] Further technical solution: The steps for constructing a projection surface evaluation model and outputting projection surface evaluation coefficients based on projection surface roughness, projection surface brightness, and projection surface gloss are as follows:

[0026] The lightness and gloss of the projection surface are obtained by performing maximum-min normalization.

[0027] Importing the projection surface roughness into the formula The roughness index of the projection surface is obtained from the data. Indicates the roughness of the projection surface. Indicates the maximum permissible surface roughness;

[0028] A projection surface evaluation model is constructed based on the projection surface brightness index, projection surface gloss index, and projection surface roughness index. This projection surface evaluation model (weighted harmonic average) is expressed as follows:

[0029]

[0030] in, Indicates the evaluation coefficient of the projection surface. Indicates the roughness index of the projection surface. Indicates the brightness index of the projection surface. Indicates the gloss index of the projection surface. Represents the weight coefficient and The Furthermore, the larger the value, the better the imaging effect of the projection surface;

[0031] Import the current projection surface roughness index, current projection surface brightness index, and current projection surface gloss index into the projection surface evaluation model to obtain the current projection surface evaluation coefficient.

[0032] A further technical solution: The laser projection risk model is expressed as follows:

[0033]

[0034] in, Indicates the risk factor of laser projection. Indicates the risk factor of laser bending. The laser brightness loss coefficient is represented by the following. Furthermore, the higher the value, the greater the risk to the quality of laser projection.

[0035] Further technical solution: The steps for constructing a laser bending risk model and outputting the laser bending risk coefficient based on indoor temperature and indoor wind speed are as follows:

[0036] The indoor temperature index is obtained by performing maximum-minimum normalization on the indoor temperature, and the indoor wind speed index is obtained by performing ratio processing of indoor wind speed and maximum permissible wind speed.

[0037] A laser bending risk model is constructed based on the indoor temperature index and the indoor wind speed index. The laser bending risk model is expressed as follows:

[0038]

[0039] in, Indicates the risk factor of laser bending. Indicates the indoor temperature index. Indicates the indoor wind speed index. Represents the weight coefficient and The Furthermore, the higher the value, the greater the risk of laser bending;

[0040] Import the current indoor temperature index and the current indoor wind speed index into the laser bending risk model to output the current laser bending risk coefficient.

[0041] Further technical solution: The steps for constructing a laser brightness loss model and outputting laser brightness loss coefficients based on indoor ambient light intensity and indoor dust concentration are as follows:

[0042] The indoor ambient light intensity and indoor dust concentration were obtained by performing maximum-minimum normalization.

[0043] A laser brightness loss model is constructed based on the indoor ambient light intensity index and the indoor dust concentration index. The laser brightness loss model is expressed as follows:

[0044]

[0045] in, Indicates the laser brightness loss coefficient. Indicates the indoor ambient light intensity index. Indicates the indoor dust concentration index. Represents the weight coefficient and The Furthermore, the larger the value, the greater the loss in laser brightness;

[0046] Import the current indoor ambient light intensity index and the current indoor dust concentration index into the laser brightness loss model to output the current laser brightness loss coefficient.

[0047] A further technical solution: compare the current distance-wavelength-divergence angle fit with the distance-wavelength-divergence angle fit threshold, and adjust the laser wavelength or beam divergence angle to make the distance-wavelength-divergence angle fit within the distance-wavelength-divergence angle fit threshold.

[0048] An indoor decoration measurement and layout device is provided, which uses the above-mentioned indoor decoration measurement and layout method for layout processing.

[0049] This invention provides an indoor decoration measurement and layout device and method, which has the following advantages compared with the prior art:

[0050] 1. This application can automatically optimize the combination of laser parameters based on real-time environmental data and projection surface status. In long-distance operation scenarios, it maintains the clarity of the laser line by dynamically adjusting the matching relationship between wavelength and divergence angle. In high dust environments, it reduces light scattering loss by optimizing the ratio of working distance to divergence angle. This solution solves the problem of frequent manual adjustment caused by fixed parameters in traditional equipment. It can still maintain the stability and measurement accuracy of laser projection when there are sudden changes in wall roughness or sudden airflow interference.

[0051] 2. This application solves the measurement error problem caused by laser intensity curing. It can automatically adjust the laser intensity in complex environments, such as reducing the intensity to reduce refraction interference when the laser bends due to high temperature airflow, or increasing the intensity to improve visibility when the projection surface is rough, thereby reducing the frequency of manual intervention and improving measurement stability. Attached Figure Description

[0052] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0053] 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.

[0054] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0055] Please see Figure 1 The present invention provides an embodiment of an indoor decoration measurement and layout method, comprising the following steps:

[0056] A laser bending risk model is constructed based on indoor temperature and indoor wind speed, and the laser bending risk coefficient is output.

[0057] A laser brightness loss model is constructed based on indoor ambient light intensity and indoor dust concentration, and the laser brightness loss coefficient is output.

[0058] A laser projection risk model is constructed based on the laser bending risk coefficient and the laser brightness loss coefficient, and the laser projection risk coefficient is output.

[0059] A projection surface evaluation model is constructed based on the projection surface roughness, projection surface brightness, and projection surface gloss, and the projection surface evaluation coefficient is output.

[0060] Based on the laser projection risk coefficient and projection surface evaluation coefficient, a distance-wavelength-divergence angle adaptation model is constructed to output the distance-wavelength-divergence angle adaptation degree for laser working distance, laser wavelength, and beam divergence angle.

[0061] A laser intensity optimization model is constructed based on the distance-wavelength-divergence angle fit and the current laser intensity to output the target laser intensity.

[0062] The laser bending risk model is a mathematical model that assesses the probability of laser path deviation through the coupling effect of temperature and wind speed. It can be implemented using linear weighting and cross-term combinations to quantify the impact of airflow disturbance on measurement accuracy. The laser brightness loss model is a calculation model based on light intensity and dust concentration to calculate the degree of laser visibility attenuation. It can be implemented through normalization and nonlinear superposition to assess the weakening effect of environmental factors on laser line sharpness. The laser projection risk model is a composite risk index that integrates bending risk and brightness loss. It can be implemented using the geometric mean method to reflect the comprehensive threat of environmental interference to measurement quality. The projection surface evaluation model is an index system that evaluates the imaging quality of the projection surface through the fusion of multiple parameters such as roughness, brightness, and gloss. It can be implemented using a weighted harmonic average method to quantify the adaptability of different surface materials to laser imaging. The distance-wavelength-divergence angle adaptation model is an optimization model that dynamically matches laser parameters with environmental risks and projection surface characteristics. It can be implemented through a combination of exponential and ratio operations to determine the optimal combination of laser parameters. The laser intensity optimization model is a nonlinear function that adjusts the laser output power according to the parameter fit. Specifically, it can be implemented by combining exponential decay and a minimum protection factor to maintain measurement accuracy while avoiding energy waste.

[0063] Specifically, environmental data is first collected using temperature sensors and anemometers. After normalization, this data is input into the laser bending risk model, outputting a risk coefficient reflecting the stability of the laser path. Simultaneously, environmental data is acquired using light sensors and dust detectors. After standardization, this data is input into the laser brightness loss model, outputting a loss coefficient characterizing the degree of laser visibility attenuation. These two coefficients are then input into the laser projection risk model to generate a comprehensive environmental risk index. Projection surface characteristics are measured using a surface roughness meter, colorimeter, and gloss meter. After exponential conversion, this data is input into the projection surface evaluation model, outputting an evaluation coefficient reflecting the surface imaging quality. The environmental risk coefficient and projection surface evaluation coefficient are combined with the current laser working distance, wavelength, and divergence angle parameters, input into a distance-wavelength-divergence angle adaptation model to calculate the parameter fit. Finally, based on the fit and the current laser intensity, the output power is dynamically adjusted using a nonlinear function to form a closed-loop control.

[0064] Compared to existing technologies, current laser projection devices only use fixed wavelength and divergence angle parameters, failing to automatically adjust according to environmental changes. This method achieves dynamic matching between laser parameters and environmental conditions by establishing an environmental risk quantification model and a projection surface evaluation system. For example, when a high-temperature, high-wind environment is detected, the laser intensity is automatically increased to compensate for path deviation risk; when the projection surface is a dark, rough wall, the divergence angle and wavelength combination is optimized to enhance imaging contrast. This multi-parameter collaborative optimization mechanism overcomes the limitations of traditional single-parameter adjustment.

[0065] Through the above technical solution, this application can sense the intensity of environmental interference in real time and quantify the imaging characteristics of the projection surface. It automatically matches the optimal wavelength and divergence angle combination based on the laser working distance and dynamically adjusts the laser output intensity. For example, under strong air conditioning conditions, the laser intensity is enhanced to counteract the effects of airflow disturbance; when dust concentration suddenly increases, wavelength parameters are optimized to reduce scattering loss; and when facing highly reflective walls, the divergence angle is adjusted to avoid overexposure of the light spot. This closed-loop control mechanism effectively solves the measurement errors caused by dynamic environmental interference, improves the imaging clarity under different projection surface conditions, and reduces the frequency of manual parameter adjustments.

[0066] Preferably, the steps for constructing a laser bending risk model based on indoor temperature and indoor wind speed and outputting the laser bending risk coefficient are as follows:

[0067] The indoor temperature index is obtained by performing maximum-minimum normalization on the indoor temperature, and the indoor wind speed index is obtained by performing ratio processing of indoor wind speed and maximum permissible wind speed.

[0068] A laser bending risk model is constructed based on the indoor temperature index and the indoor wind speed index. The laser bending risk model is expressed as follows:

[0069]

[0070] in, Indicates the risk factor of laser bending. Indicates the indoor temperature index. Indicates the indoor wind speed index. Represents the weight coefficient and The Furthermore, the higher the value, the greater the risk of laser bending;

[0071] Import the current indoor temperature index and the current indoor wind speed index into the laser bending risk model to output the current laser bending risk coefficient.

[0072] The indoor temperature index refers to the conversion of the actual temperature into a dimensionless standardized value through maximum-minimum normalization. Specifically, it can be achieved by collecting real-time data from a temperature sensor and then performing a linear transformation based on a preset temperature range. This is used to eliminate differences in temperature dimensions and quantify the degree of influence of temperature on laser refraction.

[0073] The indoor wind speed index is the ratio of the actual wind speed to the preset maximum allowable wind speed. Specifically, it can be achieved by dividing the real-time wind speed measured by an anemometer by the equipment's safety threshold. It is used to characterize whether the airflow disturbance exceeds the critical conditions for stable laser propagation.

[0074] The laser bending risk model is a weighted calculation formula that includes linear terms of temperature index, linear terms of wind speed index, and the intersection of the two. Specifically, the weighting coefficients can be determined by linear regression or empirical formulas to capture the superimposed effect of the combined effect of temperature and wind speed on laser path deviation.

[0075] Specifically, environmental data is collected using temperature sensors and anemometers. Temperature data is normalized to an index within the 0-1 range to eliminate interference from different units of measurement in the model calculation. The measured wind speed is compared to the maximum permissible wind speed of the equipment; when the wind speed exceeds a threshold, the index exceeds 1, directly triggering a risk warning. In model construction, the linear term of the temperature index reflects the impact of a single temperature change on refraction, the linear term of the wind speed index characterizes the intensity of airflow disturbance, and the cross term quantifies the coupling effect of wind speed amplifying refraction under high-temperature conditions. The weighting coefficients can be adjusted based on experimental data; for example, setting the temperature weight to 0.4, the wind speed weight to 0.3, and the cross term weight to 0.3 makes the model more closely reflect actual working conditions. The final output risk coefficient reflects the degree of environmental disturbance in real time, providing dynamic input for subsequent laser intensity optimization.

[0076] Compared to existing technologies, traditional methods only determine whether temperature or wind speed exceeds limits using fixed thresholds, without considering the dynamic coupling relationship between the two. This solution, however, achieves multi-parameter collaborative analysis through normalization, accurately capturing the abrupt refraction changes caused by the superposition of high temperature and high wind speed using cross-terms, thus solving the problem of inaccurate assessment by a single parameter.

[0077] Through the above technical solution, this application can dynamically quantify the combined influence of temperature gradient and airflow disturbance on the laser propagation path, and generate a bending risk coefficient in real time, providing a precise basis for laser parameter adjustment. In scenarios where air conditioning supply causes local temperature differences, it can effectively identify the refraction offset risk caused by the combined effect of temperature and wind speed, avoiding the accumulation of measurement errors caused by sudden environmental changes.

[0078] Preferably, the step of constructing a laser brightness loss model and outputting laser brightness loss coefficients based on indoor ambient light intensity and indoor dust concentration is as follows:

[0079] The indoor ambient light intensity and indoor dust concentration were obtained by performing maximum-minimum normalization.

[0080] A laser brightness loss model is constructed based on the indoor ambient light intensity index and the indoor dust concentration index. The laser brightness loss model is expressed as follows:

[0081]

[0082] in, Indicates the laser brightness loss coefficient. Indicates the indoor ambient light intensity index. Indicates the indoor dust concentration index. Represents the weight coefficient and The Furthermore, the larger the value, the greater the loss in laser brightness;

[0083] Import the current indoor ambient light intensity index and the current indoor dust concentration index into the laser brightness loss model to output the current laser brightness loss coefficient.

[0084] The maximum-minimum normalization process refers to a standardization method that maps the original environmental parameters to the 0-1 range. Specifically, it can be achieved by proportionally calculating real-time data collected by sensors and preset thresholds, thus eliminating the dimensional differences between light intensity and dust concentration. The indoor ambient light intensity index is a quantitative indicator reflecting the degree of interference of ambient light on laser visibility. Specifically, it can be achieved by normalizing the measured values ​​from a visible spectrum light intensity sensor, thus characterizing the masking effect of light pollution on laser brightness. The indoor dust concentration index is a quantitative indicator reflecting the scattering effect of suspended particulate matter in the air on laser light. Specifically, it can be achieved by acquiring data from a laser scattering particulate matter detector and performing normalization, thus assessing the laser energy attenuation caused by dust. The interaction term in the laser brightness loss model refers to the product of light intensity and dust concentration. Specifically, it can be achieved by constructing a nonlinear relationship expression using a multinomial regression algorithm, thus capturing the combined attenuation effect under the combined influence of the two environmental factors.

[0085] Specifically, environmental data is collected in real time using a light intensity sensor and a dust detector. The original light intensity value is divided by the preset maximum allowable light intensity value to obtain the light intensity index. For example, when the maximum allowable light intensity is set to 1000 lux, a detected value of 500 lux corresponds to an index of 0.5. The dust concentration index is calculated by dividing the measured concentration value by the upper limit of the device's range. For example, when the detector's range is 0-500 μg / m³, a measured concentration of 200 μg / m³ corresponds to an index of 0.4. These two indices are substituted into a brightness loss model containing linear and interaction terms. The linear terms represent the independent effects of a single environmental factor on brightness loss, while the interaction term reflects the additional attenuation caused by the combined effect of high dust levels and strong light. The weighting coefficients are determined through fitting experimental data. For example, when dust concentration has a greater impact on brightness loss, a weighting coefficient of 0.5 can be used. A weight value of 0.5 is assigned. The resulting brightness loss coefficient is then fed back to the laser intensity optimization module in real time, providing a quantitative basis for dynamically adjusting the laser power.

[0086] Compared to existing technologies, traditional laser measurement equipment judges environmental interference based solely on fixed thresholds, such as triggering fixed intensity compensation when dust concentration exceeds a set value. This solution, however, establishes a mathematical model incorporating interactions, quantifying not only the impact of individual environmental factors but also capturing the synergistic effect of light and dust. For example, when the dust concentration index is 0.6 and the light intensity index is 0.8, the interaction term generates an additional attenuation of 0.48, a nonlinear characteristic that traditional linear superposition models cannot reflect. Furthermore, normalization processing makes environmental parameters of different dimensions comparable; for instance, 800 lux of light and 300 μg / m³ dust are uniformly converted into index values ​​in the 0-1 range, facilitating cross-dimensional calculations by the model.

[0087] Through the above technical solution, this application can accurately quantify the combined impact of dynamically changing environmental factors on laser brightness. For example, in construction sites where dust concentration suddenly increases and light intensity fluctuates, the model adjusts the laser intensity in a timely manner by calculating the interaction term values ​​in real time, avoiding the situation where combined interference is ignored because a single environmental parameter does not exceed the standard. This solution effectively solves the problem of brightness compensation lag caused by isolated judgment of environmental parameters in traditional methods. For example, in scenarios with moderate dust concentration but high light intensity, the interaction term can provide early warning of brightness loss risk, triggering the intensity compensation mechanism earlier than existing technologies, thereby maintaining the visibility of the laser projection line.

[0088] Preferably, the laser projection risk model is expressed as:

[0089]

[0090] in, Indicates the risk factor of laser projection. Indicates the risk factor of laser bending. The laser brightness loss coefficient is represented by the following. Furthermore, the higher the value, the greater the risk to the quality of laser projection.

[0091] The laser bending risk coefficient is a quantitative indicator of the degree of laser refraction bending caused by temperature and airflow disturbance. Specifically, it can be calculated by a linear weighted sum and cross-term combination of indoor temperature index and wind speed index, which is used to reflect the influence of temperature and wind speed on the stability of the laser path.

[0092] The laser brightness loss coefficient is a quantitative indicator of the degree of laser visibility attenuation caused by ambient light intensity and dust concentration. Specifically, it can be calculated by a linear weighted sum and cross-term combination of light intensity index and dust concentration index, which reflects the weakening effect of light and dust on laser brightness.

[0093] Among them, the laser projection risk coefficient refers to the composite risk value that comprehensively characterizes the impact of environmental disturbances on the quality of laser projection. Specifically, it can be calculated by taking the square root of the product of the laser bending risk coefficient and the laser brightness loss coefficient, which is used to balance the nonlinear superposition effect of the two factors on the risk.

[0094] Specifically, the laser bending risk coefficient and the laser brightness loss coefficient are obtained through normalization and weighted calculation of environmental parameters such as temperature, wind speed, illumination, and dust. The laser projection risk model uses a square root function to mathematically process the product of the two coefficients. When either coefficient approaches zero, the risk coefficient decreases significantly, thus avoiding the over-amplification of a single environmental factor. When both coefficients increase simultaneously, the risk coefficient exhibits a non-linear increasing trend, which better reflects the physical law that multiple factors combined lead to a surge in risk in real-world scenarios. By constraining the risk coefficient within a standardized range, a quantifiable basis for dynamic adjustment of subsequent laser parameters can be provided.

[0095] Compared to existing technologies, traditional methods typically assess only a single environmental parameter independently, such as considering only the impact of temperature or dust on lasers, leading to a one-sided risk assessment. This approach constructs a composite risk model by coupling intermediate variables derived from environmental parameters such as temperature, wind speed, illumination, and dust, achieving a comprehensive characterization of complex environmental disturbances. Compared to existing techniques using linear superposition or simple weighted averaging, the nonlinear processing of the square root function better reflects the actual physical laws governing multi-factor coupling, effectively avoiding the underestimation or overestimation of a single factor.

[0096] Through the above technical solution, this application can accurately quantify the comprehensive impact of environmental disturbances on laser projection quality, solving the problem of decreased measurement accuracy caused by single-factor evaluation in traditional methods. By dynamically generating standardized risk coefficients, a reliable basis is provided for the coordinated optimization of parameters such as laser intensity and wavelength, thereby reducing laser line blurring, breakage, or overexposure caused by environmental interference and improving measurement stability in complex scenarios.

[0097] Preferably, the steps for constructing a projection surface evaluation model based on projection surface roughness, projection surface brightness, and projection surface gloss, and then outputting projection surface evaluation coefficients, are as follows:

[0098] The lightness and gloss of the projection surface are obtained by performing maximum-min normalization.

[0099] Importing the projection surface roughness into the formula The roughness index of the projection surface is obtained from the data. Indicates the roughness of the projection surface. Indicates the maximum permissible surface roughness;

[0100] A projection surface evaluation model is constructed based on the projection surface brightness index, projection surface gloss index, and projection surface roughness index. This projection surface evaluation model (weighted harmonic average) is expressed as follows:

[0101]

[0102] in, Indicates the evaluation coefficient of the projection surface. Indicates the roughness index of the projection surface. Indicates the brightness index of the projection surface. Indicates the gloss index of the projection surface. Represents the weight coefficient and The Furthermore, the larger the value, the better the imaging effect of the projection surface;

[0103] Import the current projection surface roughness index, current projection surface brightness index, and current projection surface gloss index into the projection surface evaluation model to obtain the current projection surface evaluation coefficient.

[0104] The projection surface roughness index is a standardized parameter calculated by subtracting the actual roughness from the maximum permissible roughness. It eliminates differences in roughness dimensions and transforms the data into a quantifiable and comparable index. The projection surface brightness index is a parameter that maps brightness values ​​to the 0-1 range through maximum-minimum normalization. It characterizes the influence of the projection surface's color depth on laser reflection intensity. The projection surface gloss index is a parameter that converts gloss values ​​to the 0-1 range through maximum-minimum normalization. It quantifies the impact of the projection surface's specular reflection characteristics on laser imaging sharpness. The weighted harmonic mean model is a calculation method that combines the three indices through harmonic averaging and introduces weighting coefficients. It is used to balance the contribution of each parameter to the imaging effect and suppress extreme value interference.

[0105] Specifically, the generation process of the projection surface evaluation coefficient first transforms brightness and gloss into dimensionless indices through normalization, and simultaneously converts roughness into a standardized index through difference calculation, making the parameters of different projection surfaces comparable. Then, the three indices are input into a weighted harmonic mean model. Harmonic averaging suppresses the impact of abnormal fluctuations in a single parameter on the evaluation results. For example, when there are localized high-reflectivity areas on the wall, harmonic averaging can reduce evaluation distortion caused by excessively high gloss indices. The weighting coefficients can be dynamically adjusted according to the actual scene. For example, in a dark wall scene, the weight of the brightness index can be increased to enhance the compensation effect of color depth on laser visibility. The final output evaluation coefficient directly reflects the comprehensive imaging capability of the projection surface, providing a quantitative basis for subsequent laser parameter optimization.

[0106] Compared to existing technologies, traditional methods typically consider only a single surface characteristic or employ simple weighted averages, making it difficult to effectively coordinate the coupled effects of roughness, brightness, and gloss. This solution constructs a weighted harmonic average model, which, while preserving the independent contributions of each parameter, utilizes the mathematical properties of harmonic averaging to suppress the excessive influence of extreme parameter values ​​on the evaluation results. Compared to conventional arithmetic averages, this model, in scenarios where rough and highly reflective walls coexist, avoids the problem of laser scattering caused by excessive roughness due to high gloss, thereby improving the reliability of the evaluation results.

[0107] Through the above technical solutions, this application can dynamically evaluate the comprehensive impact of different wall materials on laser imaging, effectively solving the problems of laser scattering caused by rough surfaces, laser attenuation caused by dark surfaces, and overexposure caused by highly reflective surfaces. For example, in the scenario of sprayed concrete walls, by accurately quantifying surface roughness and low brightness characteristics, the system can be guided to automatically increase laser intensity and adjust the divergence angle, thereby eliminating laser line breakage and blurring. In the scenario of polished marble walls, by accurately evaluating high gloss characteristics, the laser intensity can be automatically reduced to avoid overexposure while maintaining the clarity of the measurement lines.

[0108] Preferably, the steps for constructing a distance-wavelength-divergence angle adaptation model based on the laser projection risk coefficient and projection surface evaluation coefficient, including the laser working distance, laser wavelength, and beam divergence angle, and outputting the distance-wavelength-divergence angle adaptation degree, are as follows:

[0109] The laser wavelength index is obtained by performing maximum-minimum normalization on the laser wavelength.

[0110] The distance index and divergence angle index are obtained by comparing the laser working distance and beam divergence angle with the corresponding maximum allowable values.

[0111] A distance-wavelength-divergence angle adaptation model is constructed based on the laser projection risk coefficient and the projection surface evaluation coefficient, along with the laser wavelength index, distance index, and divergence angle index. This model is expressed as follows:

[0112]

[0113] in, This indicates the distance-wavelength-divergence angle fit. Indicates the evaluation coefficient of the projection surface. Indicates the risk factor of laser projection. Indicates the laser wavelength index. Represents the distance index. Indicates the divergence index. Indicates the wavelength-divergence coupling index. Represents the distance-divergence angle coupling index, the Furthermore, the larger the value, the better the distance-wavelength-divergence angle adaptability;

[0114] Import the current projection surface evaluation coefficient, current laser projection risk coefficient, current laser wavelength index, current distance index, and current divergence angle index into the distance-wavelength-divergence angle adaptation model to output the current distance-wavelength-divergence angle adaptation degree;

[0115] The current distance-wavelength-divergence angle fit is compared with the distance-wavelength-divergence angle fit threshold, and the laser wavelength or beam divergence angle is adjusted to make the distance-wavelength-divergence angle fit within the distance-wavelength-divergence angle fit threshold.

[0116] The laser wavelength index refers to the standardized index obtained by converting wavelength parameters of different dimensions into a normalized index through maximum-minimum normalization. Specifically, this can be achieved by linearly mapping the current wavelength value to the wavelength range allowed by the equipment, eliminating the impact of differences in physical dimensions on parameter synergy. The distance index is a relative value obtained by comparing the actual working distance with the maximum allowed working distance of the equipment. Specifically, this can be achieved by dividing the real-time measured distance value by a preset maximum distance threshold, used to quantify the degree of constraint of distance on laser projection. The divergence angle index is a relative value obtained by comparing the actual divergence angle with the maximum allowed divergence angle of the equipment, used to characterize the impact of beam diffusion on image quality. The wavelength-divergence angle coupling index is an adjustment parameter reflecting the nonlinear relationship between wavelength and divergence angle. Specifically, the index value can be determined through expert experience calibration or by fitting experimental data, used to balance the impact of divergence angle variations at different wavelengths on fit. The distance-divergence angle coupling index is an adjustment parameter reflecting the interaction between working distance and divergence angle. Specifically, it can be determined through expert experience calibration or by on-site testing of the impact of divergence angle on projection effect at different distances, used to dynamically adjust the matching relationship between distance and divergence angle.

[0117] Specifically, in the dynamic optimization of laser parameters, the laser wavelength is first normalized to eliminate the influence of different wavelength orders on the calculation. For example, when the device supports a wavelength range of 450-650nm, the exponent corresponding to a wavelength of 550nm is 0.5. Then, the ratio of the real-time measured working distance to the preset maximum allowable distance is calculated. When the measured distance is 10 meters and the maximum allowable distance is 20 meters, the distance exponent is 0.5. Simultaneously, the divergence angle measurement is compared to the maximum allowable divergence angle of the device. If the current divergence angle is 2 mrad and the maximum allowable value is 5 mrad, then the divergence angle exponent is 0.4. Next, the projection surface evaluation coefficient and the laser projection risk coefficient are used as adjustment factors for the numerator, and the denominator uses a combination of the wavelength exponent and the divergence angle exponent, as well as the distance exponent and the divergence angle exponent. For example, when the projection surface evaluation coefficient is 0.8 and the risk coefficient is 0.2, the numerator is 2 × 0.8 × (1 - 0.2) = 1.28. The denominator term is calculated based on the preset coupling indices k=1 and m=1, resulting in (0.5×0.4) - ¹)¹ + (0.5×0.4)¹ = (1.25) + (0.2) = 1.45, and the final fit M = 1.28 / 1.45 ≈ 0.88. This value is in the range of 0 to 2, which intuitively reflects the fit of the current parameter combination and provides a quantitative basis for subsequent laser intensity optimization.

[0118] Compared to existing technologies, traditional methods use a fixed combination of laser wavelength, divergence angle, and working distance. For example, in long-distance scenarios, simply increasing laser intensity is not considered, without considering the coordinated adjustment of divergence angle and wavelength. This proposed solution, however, establishes an adaptation model with multi-dimensional parameters. The numerator incorporates dual adjustments based on projection surface characteristics and environmental risk, while the denominator establishes nonlinear coupling relationships between wavelength and divergence angle, and distance and divergence angle, achieving dynamic balance among parameters. For instance, when an increase in divergence angle is detected, the model automatically adjusts the weighting of the wavelength and distance exponents, avoiding overcompensation caused by adjusting a single parameter. This improves the synergy of parameter optimization compared to traditional methods.

[0119] Through the above technical solution, this application can automatically optimize the laser parameter combination based on real-time environmental data and the projection surface status. In long-distance operation scenarios, it maintains laser line clarity by dynamically adjusting the matching relationship between wavelength and divergence angle; in high-dust environments, it reduces light scattering loss by optimizing the ratio between working distance and divergence angle. This solution solves the problem of frequent manual adjustments caused by fixed parameters in traditional equipment, and can maintain the stability and measurement accuracy of laser projection even when there are sudden changes in wall roughness or sudden airflow interference.

[0120] Preferably, the laser intensity optimization model is expressed as:

[0121]

[0122] in, Indicates the target laser intensity. Indicates the current laser intensity. Indicates the minimum strength guarantee factor. Indicates the fitness sensitivity factor. This indicates the current distance-wavelength-divergence angle fit. This indicates the ideal distance-wavelength-divergence angle fit.

[0123] The target laser intensity refers to the optimized laser output intensity obtained through model calculation. Specifically, it can be achieved by using a real-time feedback control system combined with a laser drive module to dynamically adjust the laser power to adapt to environmental changes.

[0124] The current laser intensity refers to the actual output intensity of the laser at present, which can be collected in real time by a laser power sensor (thermopile power sensor) to provide a reference parameter for the optimization process.

[0125] The intensity guarantee factor refers to the minimum proportional parameter that ensures the laser intensity is not lower than the preset safety threshold. It can be set through experimental calibration or empirical values ​​to avoid failure due to excessively low laser intensity caused by sudden changes in adaptability.

[0126] The fit sensitivity factor refers to the parameter that controls the sensitivity of the laser intensity response to the degree of fit deviation. Specifically, it can be achieved through dynamic adjustment algorithms or preset coefficients, and is used to amplify the impact of fit differences on intensity adjustment.

[0127] The current distance-wavelength-divergence angle fit refers to the degree of matching between the laser parameters calculated in real time and the environment and projection surface. Specifically, it can be reflected by normalization processing and weighted model output to reflect the laser projection quality in the current scene.

[0128] Ideal distance-wavelength-divergence angle fit refers to the preset optimal laser parameter matching benchmark value, which can be determined through laboratory calibration or theoretical calculation, and serves as a target reference for intensity optimization.

[0129] Specifically, the model uses the current laser intensity as a base and quantifies the fit difference as an intensity adjustment magnitude through an exponential function. When the distance-wavelength-divergence angle fit deviates from the ideal value, the exponential term decays with the square of the deviation, leading to a decrease in target intensity. When the distance-wavelength-divergence angle fit approaches the ideal value, the exponential term approaches 1, and the target intensity approaches the current intensity. An intensity floor coefficient ensures that the target intensity is never lower than a preset lower limit, preventing complete laser failure due to sudden environmental changes. The fit sensitivity factor controls the intensity response speed to changes in fit by adjusting the exponential decay rate. For example, in a high-dust environment, the fit sensitivity factor can be dynamically increased, allowing the laser intensity to rapidly increase to penetrate dust interference.

[0130] Compared to existing technologies, traditional laser beam-laying devices rely on fixed intensity parameters and cannot dynamically adjust according to environmental disturbances or projection surface characteristics, leading to decreased measurement accuracy requiring manual intervention. This solution, however, uses mathematical modeling to dynamically correlate environmental parameters, projection surface characteristics, and laser intensity, utilizing an exponential decay mechanism to achieve adaptive intensity optimization, maintaining measurement accuracy without manual adjustment.

[0131] Through the above technical solution, this application solves the measurement error problem caused by laser intensity curing. It can automatically adjust the laser intensity in complex environments, such as reducing the intensity to reduce refraction interference when the laser bends due to high temperature airflow, or increasing the intensity to improve visibility when the projection surface is rough, thereby reducing the frequency of manual intervention and improving measurement stability.

[0132] An indoor decoration measurement and layout device is provided, which uses the above-mentioned indoor decoration measurement and layout method for layout processing.

[0133] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0134] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for measuring and setting out lines in interior decoration, characterized in that, Includes the following steps: A laser bending risk model is constructed based on indoor temperature and indoor wind speed, and the laser bending risk coefficient is output. A laser brightness loss model is constructed based on indoor ambient light intensity and indoor dust concentration, and the laser brightness loss coefficient is output. A laser projection risk model is constructed based on the laser bending risk coefficient and the laser brightness loss coefficient, and the laser projection risk coefficient is output. A projection surface evaluation model is constructed based on the projection surface roughness, projection surface brightness, and projection surface gloss, and the projection surface evaluation coefficient is output. Based on the laser projection risk coefficient and projection surface evaluation coefficient, a distance-wavelength-divergence angle adaptation model is constructed to output the distance-wavelength-divergence angle adaptation degree for laser working distance, laser wavelength, and beam divergence angle. A laser intensity optimization model is constructed based on the distance-wavelength-divergence angle fit and the current laser intensity, and the target laser intensity is output. The steps for constructing a distance-wavelength-divergence angle adaptation model based on the laser projection risk coefficient and projection surface evaluation coefficient, and outputting the distance-wavelength-divergence angle adaptation degree, are as follows: The laser wavelength index is obtained by performing maximum-minimum normalization on the laser wavelength. The distance index and divergence angle index are obtained by comparing the laser working distance and beam divergence angle with the corresponding maximum allowable values. A distance-wavelength-divergence angle adaptation model is constructed based on the laser projection risk coefficient and the projection surface evaluation coefficient, along with the laser wavelength index, distance index, and divergence angle index. This model is expressed as follows: in, This indicates the distance-wavelength-divergence angle fit. Indicates the evaluation coefficient of the projection surface. Indicates the risk factor of laser projection. Indicates the laser wavelength index. Represents the distance index. Indicates the divergence index. Indicates the wavelength-divergence coupling index. Represents the distance-divergence angle coupling index, the Furthermore, the larger the value, the better the distance-wavelength-divergence angle adaptability; Import the current projection surface evaluation coefficient, current laser projection risk coefficient, current laser wavelength index, current distance index, and current divergence angle index into the distance-wavelength-divergence angle adaptation model to output the current distance-wavelength-divergence angle adaptation degree.

2. The method for measuring and setting out interior decoration according to claim 1, characterized in that, The laser intensity optimization model is expressed as follows: in, Indicates the target laser intensity. Indicates the current laser intensity. Indicates the minimum strength guarantee factor. Indicates the fitness sensitivity factor. This indicates the current distance-wavelength-divergence angle fit. This indicates the ideal distance-wavelength-divergence angle fit.

3. The method for measuring and setting out interior decoration according to claim 2, characterized in that, The steps for constructing a projection surface evaluation model based on projection surface roughness, projection surface brightness, and projection surface gloss, and then outputting the projection surface evaluation coefficients, are as follows: The lightness and gloss of the projection surface are obtained by performing maximum-min normalization. Importing the projection surface roughness into the formula The roughness index of the projection surface is obtained from the data. Indicates the roughness of the projection surface. Indicates the maximum permissible surface roughness; A projection surface evaluation model is constructed based on the projection surface brightness index, projection surface gloss index, and projection surface roughness index. The projection surface evaluation model is expressed as follows: in, Indicates the evaluation coefficient of the projection surface. Indicates the roughness index of the projection surface. Indicates the brightness index of the projection surface. Indicates the gloss index of the projection surface. Represents the weight coefficient and The Furthermore, the larger the value, the better the imaging effect of the projection surface; Import the current projection surface roughness index, current projection surface brightness index, and current projection surface gloss index into the projection surface evaluation model to obtain the current projection surface evaluation coefficient.

4. The method for measuring and setting out interior decoration according to claim 2, characterized in that, The laser projection risk model is expressed as follows: in, Indicates the risk factor of laser projection. Indicates the risk factor of laser bending. The laser brightness loss coefficient is represented by the following. Furthermore, the higher the value, the greater the risk to the quality of laser projection.

5. The method for measuring and setting out interior decoration according to claim 4, characterized in that, The steps for constructing a laser bending risk model and outputting the laser bending risk coefficient based on indoor temperature and indoor wind speed are as follows: The indoor temperature index is obtained by performing maximum-minimum normalization on the indoor temperature, and the indoor wind speed index is obtained by performing ratio processing of indoor wind speed and maximum permissible wind speed. A laser bending risk model is constructed based on the indoor temperature index and the indoor wind speed index. The laser bending risk model is expressed as follows: in, Indicates the risk factor of laser bending. Indicates the indoor temperature index. Indicates the indoor wind speed index. Represents the weight coefficient and The Furthermore, the higher the value, the greater the risk of laser bending; Import the current indoor temperature index and the current indoor wind speed index into the laser bending risk model to output the current laser bending risk coefficient.

6. The method for measuring and setting out interior decoration according to claim 4, characterized in that, The steps for constructing a laser brightness loss model and outputting the laser brightness loss coefficient based on indoor ambient light intensity and indoor dust concentration are as follows: The indoor ambient light intensity and indoor dust concentration were obtained by performing maximum-minimum normalization. A laser brightness loss model is constructed based on the indoor ambient light intensity index and the indoor dust concentration index. The laser brightness loss model is expressed as follows: in, Indicates the laser brightness loss coefficient. Indicates the indoor ambient light intensity index. Indicates the indoor dust concentration index. Represents the weight coefficient and The Furthermore, the larger the value, the greater the loss in laser brightness; Import the current indoor ambient light intensity index and the current indoor dust concentration index into the laser brightness loss model to output the current laser brightness loss coefficient.

7. The method for measuring and setting out interior decoration according to claim 1, characterized in that, The current distance-wavelength-divergence angle fit is compared with the distance-wavelength-divergence angle fit threshold, and the laser wavelength or beam divergence angle is adjusted to make the distance-wavelength-divergence angle fit within the distance-wavelength-divergence angle fit threshold.

8. An indoor decoration measurement and layout device, characterized in that, The layout process is carried out using the indoor decoration measurement and layout method described in any one of claims 1-7.

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