A colorimetric sensor array color feature automatic acquisition method and system

Abstract

The present invention discloses a method and system for automatically acquiring color features of a colorimetric sensor array, and relates to the field of colorimetric sensor technology. The method for automatically acquiring color features of a colorimetric sensor array comprises the following steps: color acquisition data collection, colorimetric environment compliance judgment, colorimetric environment optimization judgment, and colorimetric texture optimization judgment. The present invention evaluates the degree of environmental compliance of automatic acquisition of color features to determine whether to perform color feature acquisition environment optimization, then evaluates the effect of color feature acquisition environment optimization to determine whether to perform color feature acquisition texture optimization, and finally obtains the colorimetric acquisition texture optimization index after executing color feature acquisition texture optimization and determines whether to perform color feature acquisition adaptive optimization, thereby achieving the effect of improving the accuracy of automatic acquisition of color features of a colorimetric sensor array in an industrial production environment, and solving the problem of inaccurate automatic acquisition of color features of a colorimetric sensor array in an industrial production environment in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of colorimetric sensing, and in particular to a method and system for automatically acquiring color characteristics of a colorimetric sensing array. Background Art

[0002] With the advancement of automation and intelligent manufacturing, the demand for accurate acquisition and analysis of color features is increasing. As an important physical property, color is often used to characterize the state of materials, products or processes. Therefore, colorimetric sensing technology has been widely used in many fields. Traditional color measurement requires manual intervention, while colorimetric sensor arrays can automatically acquire color features without manual intervention, improve production efficiency, reduce labor costs and reduce human errors, which is of great significance for improving product quality, optimizing production processes, reducing costs and promoting intelligent manufacturing.

[0003] Existing color feature automatic acquisition methods of colorimetric sensor arrays mainly rely on the combination of multiple advanced hardware and software technologies, usually including sensor arrays, data acquisition and processing algorithms, and automated control systems. Through efficient data analysis and processing methods, the collected color features are compared with preset standards or targets to automatically determine whether the color of the product meets the quality standards, thereby achieving efficient and accurate color detection, and providing strong support for modern industrial automation and intelligent manufacturing.

[0004] For example, the patent application with publication number: CN112945949A discloses a method for identifying organic matter based on a colorimetric sensor array that is sensitive to flavor substances in liquor, including: constructing a colorimetric sensor array of substances that chemically react or act on different flavor substances, the array has a significantly differentiated response to the most important flavor substances in liquor, and can further distinguish different types of flavor substances through PCA and HCA, and the array is further used for identifying and distinguishing liquor.

[0005] For example, the invention patent with announcement number: CN110987915B discloses a colorimetric sensor array and its preparation method and application, including: synthesizing a gold nanorod solution; using the gold nanorod solution as a sensor, constructing a colorimetric sensing detection array of the wine sample to be tested by adding a certain concentration of silver nitrate, the wine sample to be tested and a reducing agent; obtaining a colorimetric fingerprint spectrum of the colorimetric sensing detection array; and using a classical pattern recognition method to distinguish and identify the colorimetric fingerprint spectrum.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In industrial production environments, lighting conditions can vary greatly, especially on large-scale production lines. Differences in natural light, artificial lighting, and light sources in different factory environments can interfere with the readings of colorimetric sensors. In addition, environmental factors such as temperature, humidity, and vibration during industrial production processes can affect the colorimetric sensor array, reducing its stability and accuracy. For example, in environments with high temperature, high humidity, or corrosive gases, colorimetric sensors may easily fail or become damaged.

[0008] Another thing to consider is that in some production processes, especially for materials such as plastics, metals or glass, the surface gloss or texture changes greatly, which will interfere with the measurement results of the colorimetric sensor. For example, the metal surface may have reflections, affecting the accurate acquisition of color; and when the surface of cloth, leather and other materials has texture, it will also affect the accuracy of the colorimetric sensor, and there is a problem of inaccurate automatic acquisition of color characteristics of the colorimetric sensor array in industrial production environments. Summary of the invention

[0009] The embodiments of the present application solve the problem of inaccurate automatic acquisition of color features of colorimetric sensor arrays in industrial production environments in the prior art by providing a method and system for automatic acquisition of color features of colorimetric sensor arrays, thereby improving the accuracy of automatic acquisition of color features of colorimetric sensor arrays in industrial production environments.

[0010] The embodiment of the present application provides a method for automatically acquiring color features of a colorimetric sensor array, comprising the following steps: S1, collecting color acquisition related data in a preset industrial production environment through a colorimetric sensor array, and preprocessing the color acquisition related data to obtain color acquisition data; S2, evaluating the environmental compliance degree of automatic acquisition of color features according to the color acquisition data to obtain a colorimetric acquisition environmental compliance index, and judging whether to perform color feature acquisition environment optimization based on the colorimetric acquisition environment compliance index, wherein the colorimetric acquisition environment compliance index is used to quantitatively evaluate the environmental compliance degree of automatic acquisition of color features; S3, after performing color feature acquisition environment optimization, evaluating the effect of color feature acquisition environment optimization to obtain a colorimetric acquisition environment optimization index, judging whether to perform color feature acquisition texture optimization based on the colorimetric acquisition environment optimization index, wherein the colorimetric acquisition environment optimization index is used to quantitatively evaluate the effect of color feature acquisition environment optimization; S4, obtaining a colorimetric acquisition texture optimization index after performing color feature acquisition texture optimization, judging whether to perform color feature acquisition adaptive optimization based on the colorimetric acquisition environment compliance index, and the colorimetric acquisition texture optimization index, wherein the colorimetric acquisition texture optimization index is used to quantitatively evaluate the effect of color feature acquisition texture optimization.

[0011] Furthermore, the specific steps for obtaining the colorimetric acquisition environment compliance index are as follows: obtaining colorimetric environment assessment related data within a preset time interval, the colorimetric environment assessment related data including color acquisition RGB vector, color acquisition related maximum assessment value and color acquisition related standard deviation; the color acquisition related maximum assessment value includes color acquisition maximum hue value, color acquisition maximum saturation and color acquisition maximum brightness value; the color acquisition related standard deviation includes color acquisition hue standard deviation, color acquisition saturation standard deviation and color acquisition brightness standard deviation; combining the colorimetric environment assessment related data with colorimetric environment assessment reference data obtained from a preset database to obtain the colorimetric acquisition environment compliance index, the colorimetric environment assessment reference data including color acquisition reference RGB vector, color acquisition reference value, color acquisition standard deviation reference value and colorimetric environment compliance assessment weight; the colorimetric environment compliance assessment weight includes colorimetric environment influencing factor, colorimetric similarity assessment weight and colorimetric stability assessment weight.

[0012] Furthermore, the method for obtaining the colorimetric acquisition environment compliance index is as follows:

[0013]

[0014] In the formula, Indicates that the colorimetric acquisition environment complies with the index, represents the colorimetric environmental influencing factor, Indicates the number of the preset time in the preset time interval, , Indicates the total number of preset moments in a preset time interval. represents the colorimetric similarity evaluation weight, represents the colorimetric stability assessment weight, Indicates the color acquisition RGB vector at the nth preset time within the preset time interval. Indicates the color acquisition reference RGB vector at the nth preset time within the preset time interval, Indicates that the color within the preset time interval obtains the maximum hue value. Indicates that the color gets the reference hue value. Indicates that the color within the preset time interval obtains the maximum saturation. Indicates that the color gets the reference saturation. Indicates that the color within the preset time interval obtains the maximum brightness value. Indicates that the color gets the reference brightness value. Indicates the color hue standard deviation within the preset time interval. Indicates the standard deviation of the reference hue obtained by the color. Indicates the color saturation standard deviation within the preset time interval. Indicates the reference saturation standard deviation of the color. Indicates the color brightness standard deviation within the preset time interval. Indicates the color gets the reference brightness standard deviation, Represents a natural constant.

[0015] Furthermore, the specific steps of judging whether to execute color feature acquisition environment optimization based on the colorimetric acquisition environment compliance index are as follows: comparing the colorimetric acquisition environment compliance index with a preset colorimetric environment compliance threshold range obtained from a preset database; if the colorimetric acquisition environment compliance index is within the preset colorimetric environment compliance threshold range, color feature acquisition environment optimization is not executed, and the color acquisition data and the colorimetric acquisition environment compliance index are backed up in real time; if the colorimetric acquisition environment compliance index exceeds the preset colorimetric environment compliance threshold range, color feature acquisition environment optimization is executed, and the colorimetric acquisition environment optimization index is obtained.

[0016] Furthermore, the specific steps of the color feature acquisition environment optimization are as follows: S21, optimizing the automatic color feature acquisition process through ambient light compensation, when the colorimetric acquisition environment compliance index monitored after the ambient light compensation optimization is within the preset colorimetric environment compliance threshold range, the color feature acquisition environment optimization is stopped, otherwise S22 is executed; S22, optimizing the automatic color feature acquisition process through ambient temperature and humidity compensation, when the colorimetric acquisition environment compliance index monitored after the ambient temperature and humidity compensation optimization is within the preset colorimetric environment compliance threshold range, the color feature acquisition environment optimization is stopped, otherwise S23 is executed; S23, optimizing the automatic color feature acquisition process through ambient vibration monitoring, and at the same time evaluating the effect of the color feature acquisition environment optimization to obtain the colorimetric acquisition environment optimization index.

[0017] Furthermore, the specific steps of obtaining the colorimetric acquisition environment optimization index are as follows: obtaining initial feature acquisition data before executing color feature acquisition environment optimization, the initial feature acquisition data including colorimetric initial light intensity, colorimetric initial light source temperature and colorimetric initial signal-to-noise ratio; obtaining feature acquisition optimization data after executing color feature acquisition environment optimization, the feature acquisition optimization data including colorimetric optimized light intensity, colorimetric optimized light source temperature and colorimetric optimized signal-to-noise ratio; obtaining the colorimetric acquisition environment optimization index by combining the initial feature acquisition data and the feature acquisition optimization data with the feature acquisition reference data obtained from a preset database; the feature acquisition reference data including feature acquisition reference deviation and environment optimization influencing factors.

[0018] Furthermore, the specific steps of judging whether to execute color feature acquisition texture optimization based on the colorimetric environment optimization index are as follows: compare the colorimetric environment optimization index with a preset colorimetric environment optimization threshold range obtained from a preset database; if the colorimetric environment optimization index is within the preset colorimetric environment optimization threshold range, color feature acquisition texture optimization is not executed, and the colorimetric environment optimization index is visualized; if the colorimetric environment optimization index exceeds the preset colorimetric environment optimization threshold range, color feature acquisition texture optimization is executed, and the colorimetric texture optimization index after executing the color feature acquisition texture optimization is obtained; the color feature acquisition texture optimization includes color feature roughness optimization and color feature texture directionality optimization.

[0019] Furthermore, the specific steps of obtaining the colorimetric texture optimization index after executing color feature texture optimization are as follows: obtaining texture optimization evaluation data after executing color feature texture optimization, the texture optimization evaluation data including texture optimization colorimetric acquisition environment compliance index, colorimetric texture optimization roughness, colorimetric texture optimization light reflectivity and colorimetric optimization texture directional contrast; obtaining the colorimetric texture optimization index according to the texture optimization evaluation data, the optimized colorimetric acquisition environment compliance index and the colorimetric reference texture directional contrast, texture roughness influencing factor, texture directional influencing factor and texture light reflection influencing factor obtained from a preset database.

[0020] Furthermore, the specific steps of judging whether to execute color feature acquisition adaptive optimization according to the colorimetric acquisition environment compliance index, the colorimetric acquisition environment optimization index and the colorimetric acquisition texture optimization index are as follows: S41, judging whether the acquired colorimetric acquisition environment compliance index is within the preset colorimetric environment compliance threshold range, if the colorimetric acquisition environment compliance index exceeds the preset colorimetric environment compliance threshold range, executing S42, otherwise the color feature acquisition adaptive optimization is not executed; S42, judging whether the acquired colorimetric acquisition environment optimization index is within the preset colorimetric environment optimization threshold range, if the colorimetric acquisition environment optimization index exceeds the preset colorimetric environment optimization threshold range, executing S43, otherwise the color feature acquisition adaptive optimization is stopped; S43, judging whether the acquired colorimetric acquisition texture optimization index is within the preset colorimetric texture optimization threshold range, if the colorimetric acquisition texture optimization index is within the preset colorimetric texture optimization threshold range, the color feature acquisition adaptive optimization is not executed, otherwise the color feature acquisition adaptive optimization is executed; the color feature acquisition adaptive optimization includes color feature acquisition environment adaptive optimization and color feature acquisition texture adaptive optimization.

[0021] The embodiment of the present application provides a color feature automatic acquisition system of a colorimetric sensor array, comprising: a color acquisition data acquisition module, a colorimetric environment compliance judgment module, a colorimetric environment optimization judgment module, and a colorimetric texture optimization judgment module; wherein the color acquisition data acquisition module is used to acquire color acquisition related data in a preset industrial production environment through a colorimetric sensor array, and pre-process the color acquisition related data to obtain color acquisition data; the colorimetric environment compliance judgment module is used to evaluate the environmental compliance degree of automatic acquisition of color features according to the color acquisition data to obtain a colorimetric acquisition environment compliance index, and judge whether to execute color feature acquisition environment optimization based on the colorimetric acquisition environment compliance index, and the colorimetric acquisition environment compliance index is used to quantitatively evaluate the automatic acquisition of color features. the colorimetric environment optimization judgment module is used to evaluate the effect of color feature acquisition environment optimization after executing color feature acquisition environment optimization to obtain a colorimetric acquisition environment optimization index, and judge whether to execute color feature acquisition texture optimization based on the colorimetric acquisition environment optimization index, and the colorimetric acquisition environment optimization index is used to quantitatively evaluate the effect of color feature acquisition environment optimization; the colorimetric texture optimization judgment module is used to obtain the colorimetric acquisition texture optimization index after executing color feature acquisition texture optimization, and judge whether to execute color feature acquisition adaptive optimization according to the colorimetric acquisition environment compliance index, the colorimetric acquisition environment optimization index and the colorimetric acquisition texture optimization index, and the colorimetric acquisition texture optimization index is used to quantitatively evaluate the effect of color feature acquisition texture optimization.

[0022] One or more technical solutions provided in the embodiments of the present application have at least the following beneficial effects:

[0023] 1. By evaluating the environmental compliance degree of automatic acquisition of color features to determine whether to perform color feature acquisition environment optimization, then evaluating the effect of color feature acquisition environment optimization to determine whether to perform color feature acquisition texture optimization, finally obtaining the colorimetric acquisition texture optimization index after executing color feature acquisition texture optimization and determining whether to perform color feature acquisition adaptive optimization, thereby achieving quantitative evaluation and optimization of the environmental compliance degree in automatic acquisition of color features, and further achieving improved accuracy of automatic acquisition of color features of colorimetric sensor arrays in industrial production environments, effectively solving the problem of inaccurate automatic acquisition of color features of colorimetric sensor arrays in industrial production environments in the prior art.

[0024] 2. By acquiring colorimetric environmental assessment related data within a preset time interval, and then combining the colorimetric environmental assessment related data with colorimetric environmental assessment reference data obtained from a preset database to obtain a colorimetric environmental compliance index, a numerical evaluation of the degree of compliance of the color feature automatic acquisition environment is achieved, thereby achieving a more accurate evaluation of the degree of compliance of the color feature automatic acquisition environment.

[0025] 3. The texture optimization evaluation data after the execution color feature texture optimization is obtained, and then the colorimetric texture optimization index is obtained based on the texture optimization evaluation data, the optimized colorimetric environment compliance index and the colorimetric reference texture directional contrast, texture roughness influence factor, texture directionality influence factor and texture light reflection influence factor obtained from the preset database. This realizes the numerical evaluation of the texture optimization effect of color feature acquisition, and then realizes a more accurate evaluation of the texture optimization effect of color feature acquisition. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A flow chart of a method for automatically acquiring color characteristics of a colorimetric sensor array provided in an embodiment of the present application;

[0027] Figure 2 A schematic diagram of the change of the colorimetric texture optimization index provided in the embodiment of the present application, wherein (a) is a schematic diagram of the change of the colorimetric texture optimization index with the texture optimization colorimetric acquisition environment compliance index, and (b) is a schematic diagram of the change of the colorimetric texture optimization index with the colorimetric texture optimization roughness;

[0028] Figure 3 A schematic diagram of the structure of a colorimetric sensor array color feature automatic acquisition system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] The embodiment of the present application solves the problem of inaccurate automatic acquisition of color features of colorimetric sensor arrays in industrial production environments in the prior art by providing a method and system for automatic acquisition of color features of a colorimetric sensor array. The method preprocesses color acquisition related data to obtain color acquisition data, then evaluates the degree of environmental compliance of automatic color feature acquisition based on the color acquisition data to obtain a colorimetric acquisition environmental compliance index and determines whether to perform color feature acquisition environmental optimization. Then, the effect of the color feature acquisition environmental optimization is evaluated to obtain a colorimetric acquisition environmental optimization index. Based on the colorimetric acquisition environmental optimization index, it is determined whether to perform color feature acquisition texture optimization. Finally, the colorimetric acquisition texture optimization index after the color feature acquisition texture optimization is obtained. Based on the colorimetric acquisition environmental compliance index, the colorimetric acquisition environmental optimization index and the colorimetric acquisition texture optimization index, it is determined whether to perform color feature acquisition adaptive optimization. This improves the accuracy of automatic acquisition of color features of colorimetric sensor arrays in industrial production environments.

[0030] The technical solution in the embodiment of the present application is to solve the problem of inaccurate automatic acquisition of color features of the colorimetric sensor array in the above industrial production environment. The overall idea is as follows:

[0031] The degree of environmental compliance of automatic acquisition of color features is evaluated to determine whether to execute color feature acquisition environment optimization, and then the effect of color feature acquisition environment optimization is evaluated to determine whether to execute color feature acquisition texture optimization. Finally, the colorimetric acquisition texture optimization index after executing color feature acquisition texture optimization is obtained and it is determined whether to execute color feature acquisition adaptive optimization, thereby achieving the effect of improving the accuracy of automatic acquisition of color features of colorimetric sensor arrays in industrial production environments.

[0032] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0033] like Figure 1 As shown, it is a flow chart of a color feature automatic acquisition method of a colorimetric sensor array provided in an embodiment of the present application, and the method includes the following steps: S1, collecting color acquisition related data in a preset industrial production environment through a colorimetric sensor array, and preprocessing the color acquisition related data to obtain color acquisition data; S2, evaluating the environmental compliance degree of automatic acquisition of color features according to the color acquisition data to obtain a colorimetric acquisition environmental compliance index, judging whether to perform color feature acquisition environment optimization based on the colorimetric acquisition environment compliance index, and the colorimetric acquisition environment compliance index is used to quantitatively evaluate the environmental compliance degree of automatic acquisition of color features; S3, after performing color feature acquisition environment optimization, evaluating the effect of color feature acquisition environment optimization to obtain a colorimetric acquisition environment optimization index, judging whether to perform color feature acquisition texture optimization based on the colorimetric acquisition environment optimization index, and the colorimetric acquisition environment optimization index is used to quantitatively evaluate the effect of color feature acquisition environment optimization; S4, obtaining a colorimetric acquisition texture optimization index after performing color feature acquisition texture optimization, judging whether to perform color feature acquisition adaptive optimization based on the colorimetric acquisition environment compliance index, the colorimetric acquisition environment optimization index and the colorimetric acquisition texture optimization index, and the colorimetric acquisition texture optimization index is used to quantitatively evaluate the effect of color feature acquisition texture optimization.

[0034] In this embodiment, specifically, color acquisition related data is acquired through a colorimetric sensor array; wherein the color acquisition related data includes color acquisition RGB value, color acquisition hue value, color acquisition saturation and color acquisition brightness value.

[0035] It should be understood that preprocessing includes data normalization and feature extraction; data normalization is performed by RGB color space normalization, and feature extraction is achieved by color feature extraction and spectral feature extraction, for example, the difference between color values ​​is calculated by Euclidean distance.

[0036] By acquiring color acquisition data to evaluate the degree of environmental compliance in automatic color feature acquisition, optimization of the automatic color feature acquisition under abnormal environmental compliance conditions is achieved, thereby improving the accuracy of automatic color feature acquisition.

[0037] Furthermore, the specific steps for obtaining the colorimetric acquisition environment compliance index are as follows: obtaining colorimetric environment assessment related data within a preset time interval, the colorimetric environment assessment related data including color acquisition RGB vector, color acquisition related maximum assessment value and color acquisition related standard deviation; color acquisition related maximum assessment value including color acquisition maximum hue value, color acquisition maximum saturation and color acquisition maximum brightness value; color acquisition related standard deviation including color acquisition hue standard deviation, color acquisition saturation standard deviation and color acquisition brightness standard deviation; combining the colorimetric environment assessment related data with the colorimetric environment assessment reference data obtained from a preset database to obtain the colorimetric acquisition environment compliance index, the colorimetric environment assessment reference data including color acquisition reference RGB vector, color acquisition reference value, color acquisition standard deviation reference value and colorimetric environment compliance assessment weight; colorimetric environment compliance assessment weight including colorimetric environment influencing factor, colorimetric similarity assessment weight and colorimetric stability assessment weight.

[0038] The method for obtaining the colorimetric acquisition environment compliance index is as follows:

[0039] ;

[0040] In the formula, Indicates that the colorimetric acquisition environment complies with the index, represents the colorimetric environmental influencing factor, Indicates the number of the preset time in the preset time interval, , Indicates the total number of preset moments in a preset time interval. represents the colorimetric similarity evaluation weight, represents the colorimetric stability assessment weight, Indicates the color acquisition RGB vector at the nth preset time within the preset time interval. Indicates the color acquisition reference RGB vector at the nth preset time within the preset time interval, Indicates that the color within the preset time interval obtains the maximum hue value. Indicates that the color gets the reference hue value. Indicates that the color within the preset time interval obtains the maximum saturation. Indicates that the color gets the reference saturation. Indicates that the color within the preset time interval obtains the maximum brightness value. Indicates that the color gets the reference brightness value. Indicates the color hue standard deviation within the preset time interval. Indicates the standard deviation of the reference hue obtained by the color. Indicates the color saturation standard deviation within the preset time interval. Indicates the reference saturation standard deviation of the color. Indicates the color brightness standard deviation within the preset time interval. Indicates the color gets the reference brightness standard deviation, Represents a natural constant.

[0041] In this embodiment, the color sensor module (such as TCS3200) in the colorimetric sensor outputs the color acquisition RGB value, and the color of each pixel is represented by the intensity values ​​of the red (R), green (G) and blue (B) channels to form a color acquisition RGB vector; RGB to HSV conversion is performed through OpenCV in Python to obtain the color acquisition hue value, color acquisition saturation and color acquisition brightness value; the color acquisition maximum hue value, color acquisition maximum saturation and color acquisition maximum brightness value are obtained through the statistical toolbox (such as max(data)) in MATLAB; the color acquisition hue standard deviation, color acquisition saturation standard deviation and color acquisition brightness standard deviation are obtained through the statistical toolbox (such as std(data)) in MATLAB.

[0042] The color acquisition reference value includes a color acquisition reference hue value, a color acquisition reference saturation, and a color acquisition reference brightness value; the color acquisition reference hue value is represented by summing and averaging the collected historical color acquisition hue values, the color acquisition reference saturation is represented by summing and averaging the collected historical color acquisition saturations, and the color acquisition reference brightness value is represented by summing and averaging the collected historical brightness values.

[0043] The color acquisition standard deviation reference value includes the color acquisition reference hue standard deviation, the color acquisition reference saturation standard deviation and the color acquisition reference brightness standard deviation; the color acquisition reference hue standard deviation is represented by summing and averaging the collected historical color acquisition hue standard deviations, the color acquisition reference saturation standard deviation is represented by summing and averaging the collected historical color acquisition saturation standard deviations, and the color acquisition brightness standard deviation is represented by summing and averaging the collected historical color acquisition brightness standard deviations.

[0044] The colorimetric similarity evaluation weight reflects the influence of color acquisition similarity on the color acquisition environment compliance index. A mapping set is formed by color acquisition similarity and the weights corresponding to the colorimetric similarity evaluation preset in the preset database. The real-time color acquisition similarity is input into the mapping set to obtain the corresponding weight. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0, 1].

[0045] In this example, the sum of the colorimetric similarity evaluation weight and the colorimetric stability evaluation weight is 1. The colorimetric stability evaluation weight is used to describe the influence of the color acquisition stability on the color acquisition environment compliance index.

[0046] The colorimetric acquisition environment compliance index is used to quantitatively evaluate the degree of environmental compliance of automatic acquisition of color features. Specifically, the colorimetric acquisition environment compliance index includes parameters from multiple aspects, and the various parameters are related to each other and do not exist independently. For example, in color space, brightness and saturation are usually inversely related. The higher the saturation, the lower the brightness may be. Similarly, if the saturation is high, the brightness may be low, that is, as the maximum saturation of color acquisition increases, the maximum brightness value of color acquisition decreases accordingly; in addition, the hue standard deviation is used to reflect the fluctuation of the hue value of color acquisition. As the hue standard deviation increases, it indicates that the hue change is unstable, that is, the colorimetric acquisition environment compliance index decreases accordingly; therefore, the correlation and mutual influence between various factors are taken into account, and the colorimetric acquisition environment compliance index is obtained through comprehensive analysis, which realizes the numerical evaluation of the degree of environmental compliance of automatic acquisition of color features. The degree of environmental compliance of automatic acquisition of color features is judged through numerical evaluation, thereby realizing a more accurate evaluation of the degree of environmental compliance of automatic acquisition of color features.

[0047] Furthermore, the specific steps for determining whether to execute color feature acquisition environment optimization based on the colorimetric acquisition environment compliance index are as follows: compare the colorimetric acquisition environment compliance index with a preset colorimetric environment compliance threshold range obtained from a preset database; if the colorimetric acquisition environment compliance index is within the preset colorimetric environment compliance threshold range, color feature acquisition environment optimization is not executed, and the color acquisition data and the colorimetric acquisition environment compliance index are backed up in real time; if the colorimetric acquisition environment compliance index exceeds the preset colorimetric environment compliance threshold range, color feature acquisition environment optimization is executed, and the colorimetric acquisition environment optimization index is obtained.

[0048] In this embodiment, specifically, the preset colorimetric environment meets the threshold range set by professionals according to standards in the field. For example, the preset colorimetric environment meets the threshold range is set to 0.8 to 1. By combining the preset colorimetric environment to meet the threshold range for judgment and optimizing the color feature acquisition environment, a more accurate judgment of the degree of compliance of the color feature automatic acquisition environment is achieved.

[0049] It should be added that the specific steps of color feature acquisition environment optimization are as follows: S21, optimize the ambient light compensation for the automatic color feature acquisition process. When the colorimetric acquisition environment compliance index monitored after the ambient light compensation optimization is within the preset colorimetric environment compliance threshold range, stop the color feature acquisition environment optimization, otherwise execute S22; S22, optimize the ambient temperature and humidity compensation for the automatic color feature acquisition process. When the colorimetric acquisition environment compliance index monitored after the ambient temperature and humidity compensation optimization is within the preset colorimetric environment compliance threshold range, stop the color feature acquisition environment optimization, otherwise execute S23; S23, optimize the ambient vibration monitoring for the automatic color feature acquisition process, and evaluate the effect of the color feature acquisition environment optimization to obtain the colorimetric acquisition environment optimization index.

[0050] Specifically, the optimization of ambient lighting compensation is achieved through color correction algorithms such as white balance algorithms and light source color compensation algorithms. For example, by obtaining the gray or white area in the color feature image to be acquired, the average values ​​of its R, G, and B channels are calculated, and the RGB values ​​in the color feature image to be acquired are proportionally adjusted to match the white point; the optimization of ambient temperature and humidity compensation is achieved through temperature compensation algorithms, humidity compensation algorithms, or Kalman filtering. For example, according to the temperature response characteristics of the sensor, a functional relationship between temperature and color measurement (such as linear regression) is established, a temperature correction factor is added to the measurement result, and the color value is adjusted through linear interpolation or a polynomial regression model; the optimization of environmental vibration monitoring is achieved through vibration compensation algorithms and image stabilization algorithms (such as optical flow methods). For example, motion estimation is performed on continuous images, and the displacement of feature points in the image is tracked using an optical flow method (such as the Lucas-Kanade method), and the collected image is corrected according to the displacement information to reduce blur and color measurement errors caused by vibration; the color feature acquisition environment is optimized, and the reliability of automatic color feature acquisition in abnormal environmental conditions is improved.

[0051] Furthermore, the specific steps for obtaining the colorimetric acquisition environment optimization index are as follows: obtaining initial feature acquisition data before executing color feature acquisition environment optimization, the initial feature acquisition data including colorimetric initial light intensity, colorimetric initial light source temperature and colorimetric initial signal-to-noise ratio; obtaining feature acquisition optimization data after executing color feature acquisition environment optimization, the feature acquisition optimization data including colorimetric optimized light intensity, colorimetric optimized light source temperature and colorimetric optimized signal-to-noise ratio; obtaining the colorimetric acquisition environment optimization index by combining the initial feature acquisition data and the feature acquisition optimization data with the feature acquisition reference data obtained from a preset database; the feature acquisition reference data including feature acquisition reference deviation and environmental optimization influencing factors.

[0052] The method for obtaining the colorimetric environmental optimization index is as follows:

[0053] ;

[0054] In the formula, Indicates the colorimetric acquisition environment optimization index, Indicates that the optimized colorimetric acquisition environment meets the index, Indicates the colorimetric acquisition environment compliance index to be optimized at the preset time interval, Indicates the colorimetric ambient light intensity influence factor, Indicates the colorimetric ambient light temperature influence factor, represents the colorimetric environmental noise impact factor, Indicates colorimetric optimized light intensity, represents the colorimetric initial light intensity, Indicates the maximum deviation of the colorimetric reference ambient light intensity. Indicates the colorimetric optimized light source temperature, represents the initial light source temperature of the colorimetry, Indicates the maximum deviation of the colorimetric reference ambient light temperature. represents the colorimetric optimized signal-to-noise ratio, represents the colorimetric initial signal-to-noise ratio, represents the maximum deviation of the colorimetric reference signal-to-noise ratio, Represents a natural constant.

[0055] In this embodiment, the colorimetric initial light intensity refers to the colorimetric light intensity before the color feature acquisition environment optimization is performed, and the colorimetric light intensity is obtained by a light meter; the colorimetric initial light source temperature refers to the colorimetric light source temperature before the color feature acquisition environment optimization is performed, and the colorimetric light source temperature is obtained by a color temperature meter; the colorimetric initial signal-to-noise ratio refers to the colorimetric signal-to-noise ratio before the color feature acquisition environment optimization is performed, and the colorimetric signal-to-noise ratio is obtained by using a spectrometer or an imaging device.

[0056] The colorimetric optimized light intensity refers to the colorimetric light intensity after the color feature acquisition environment is optimized. The colorimetric optimized light source temperature refers to the colorimetric light source temperature after the color feature acquisition environment is optimized. The colorimetric optimized signal-to-noise ratio refers to the colorimetric signal-to-noise ratio after the color feature acquisition environment is optimized.

[0057] The feature acquisition reference deviation includes the maximum deviation of the colorimetric reference ambient light intensity, the maximum deviation of the colorimetric reference ambient light temperature and the maximum deviation of the colorimetric reference signal-to-noise ratio; the maximum deviation of the colorimetric reference ambient light is represented by the maximum value of the difference between the collected historical ambient light intensity and the standard light intensity, the maximum deviation of the colorimetric reference ambient light temperature is represented by the maximum value of the difference between the collected historical ambient light source temperature and the standard light source temperature, and the maximum deviation of the colorimetric reference signal-to-noise ratio is represented by the maximum value of the difference between the collected historical ambient signal-to-noise ratio and the standard signal-to-noise ratio.

[0058] Environmental optimization influencing factors include colorimetric ambient light intensity influencing factors, colorimetric ambient light temperature influencing factors and colorimetric ambient noise influencing factors; the colorimetric ambient light intensity influencing factors reflect the degree of influence of ambient light intensity on the colorimetric acquisition of the environmental optimization index, and can be directly obtained from the preset database when used. For example, a mapping set is formed by forming a mapping set between ambient light intensity and the influencing factors corresponding to the colorimetric ambient light intensity preset in the preset database, and the real-time ambient light intensity is input into the mapping set to obtain the corresponding weight. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0, 1].

[0059] The colorimetric ambient light temperature influence factor reflects the influence of the ambient light source temperature on the colorimetric acquisition environment optimization index. It can be directly obtained from the preset database when used. For example, the ambient light source temperature and the influence factor corresponding to the colorimetric ambient light temperature preset in the preset database form a mapping set, and the real-time ambient light source temperature is input into the mapping set to obtain the corresponding weight. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0, 1].

[0060] In this example, the sum of the colorimetric environment light intensity influence factor, the colorimetric environment light temperature influence factor, and the colorimetric environment noise influence factor is 1, which is the colorimetric environment noise influence factor.

[0061] Specific assumptions: the colorimetric acquisition environment compliance index to be optimized is 0.3, the colorimetric environment light intensity influence factor is 0.4, the colorimetric environment light temperature influence factor is 0.4, the colorimetric environment noise influence factor is 0.2, the colorimetric initial light intensity is 400lux, the colorimetric reference environment light intensity maximum deviation is 50lux, the colorimetric initial light source temperature is 6000K, the colorimetric reference environment light temperature maximum deviation is 500K, the colorimetric initial signal-to-noise ratio is 35, and the colorimetric reference signal-to-noise ratio maximum deviation is 5; the above method can be used to calculate the colorimetric acquisition environment optimization index, and the statistical table of the colorimetric acquisition environment optimization index is shown in Table 1:

[0062] Table 1 Statistics of changes in the colorimetric acquisition environment optimization index

[0063]

[0064] It can be seen from the data in Table 1 that with the increase of the colorimetric optimization illumination intensity, the colorimetric optimization light source temperature and the colorimetric optimization signal-to-noise ratio, the optimized colorimetric acquisition environment compliance index increases, which in turn leads to an increase in the colorimetric acquisition environment optimization index.

[0065] The colorimetric acquisition environment optimization index is used to quantitatively evaluate the effect of color feature acquisition environment optimization, wherein changes in the colorimetric optimization illumination intensity, the colorimetric optimization light source temperature, and the colorimetric optimization signal-to-noise ratio all lead to changes in the optimized colorimetric acquisition environment compliance index, which in turn affects the colorimetric acquisition environment optimization index. Specifically, for example, as the colorimetric optimization signal-to-noise ratio increases, the optimized colorimetric acquisition environment compliance index increases, which in turn leads to an increase in the colorimetric acquisition environment optimization index. In addition, the colorimetric acquisition environment optimization index includes multiple parameters, and each parameter is related to each other and does not exist independently. For example, As the colorimetric optimization illumination intensity increases, the signal intensity can usually be improved, thereby improving the signal-to-noise ratio, that is, the colorimetric optimization signal-to-noise ratio increases accordingly. As the colorimetric optimization signal-to-noise ratio increases, the optimized colorimetric acquisition environment compliance index increases accordingly, and the colorimetric acquisition environment optimization index also increases accordingly. Therefore, considering the correlation and mutual influence among various factors, the colorimetric acquisition environment optimization index is obtained through comprehensive analysis, and the numerical evaluation of the color feature acquisition environment optimization effect is realized. The color feature acquisition environment optimization effect is judged through numerical evaluation, thereby achieving a more accurate evaluation of the color feature acquisition environment optimization effect.

[0066] Furthermore, the specific steps of judging whether to execute color feature acquisition texture optimization based on the colorimetric acquisition environment optimization index are as follows: compare the colorimetric acquisition environment optimization index with a preset colorimetric environment optimization threshold range obtained from a preset database; if the colorimetric acquisition environment optimization index is within the preset colorimetric environment optimization threshold range, color feature acquisition texture optimization is not executed, and the colorimetric acquisition environment optimization index is visualized; if the colorimetric acquisition environment optimization index exceeds the preset colorimetric environment optimization threshold range, color feature acquisition texture optimization is executed, and the colorimetric acquisition texture optimization index after executing the color feature acquisition texture optimization is obtained; color feature acquisition texture optimization includes color feature roughness optimization and color feature texture directionality optimization.

[0067] In this embodiment, the environment optimization index is visualized by contrasting colors through visualization tools (such as Excel / Google Sheets); image noise is removed by using methods such as Gaussian filtering and mean filtering to optimize the accuracy of roughness calculation, thereby achieving color feature roughness optimization; color feature texture directionality optimization is achieved through a texture directionality optimization algorithm (for example, by applying Gabor filters at different directions, scales, and frequencies to optimize the directionality of colors and textures in an image).

[0068] Specifically, the preset colorimetric environment optimization threshold range is set by professionals according to standards in the field. For example, the optimized colorimetric acquisition environment conformity index range (generally 0.2 to 0.6), the colorimetric acquisition environment conformity index range to be optimized (generally 0 to 0.3), the colorimetric optimization light intensity range (generally 10lux to 800lux), the colorimetric initial light intensity range (generally 10lux to 350lux), the colorimetric optimization light source temperature range (generally 2000K to 10000K), the colorimetric initial light source temperature range (generally 1500K to 5000K), the colorimetric optimization signal-to-noise ratio range (generally 5 to 50) and the colorimetric initial signal-to-noise ratio range (generally 40 to 100) are set to determine the preset colorimetric environment optimization threshold range; by combining the preset colorimetric environment optimization threshold range, a more accurate judgment of the color feature acquisition environment optimization effect is achieved.

[0069] Furthermore, the specific steps of obtaining a colorimetric texture optimization index after executing color feature texture optimization are as follows: obtaining texture optimization evaluation data after executing color feature texture optimization, the texture optimization evaluation data including a texture optimization colorimetric environment compliance index, a colorimetric texture optimization roughness, a colorimetric texture optimization light reflectivity, and a colorimetric texture optimization directional contrast; obtaining a colorimetric texture optimization index based on the texture optimization evaluation data, the optimized colorimetric environment compliance index, and the colorimetric reference texture directional contrast, texture roughness influencing factor, texture directional influencing factor, and texture light reflection influencing factor obtained from a preset database.

[0070] The method of obtaining the colorimetric texture optimization index is as follows:

[0071] ;

[0072] In the formula, Indicates the colorimetric texture optimization index. represents the texture roughness influence factor, represents the texture directionality influence factor, Represents the texture light reflection influence factor, Indicates that the texture optimization colorimetric acquisition environment meets the index, Indicates that the optimized colorimetric acquisition environment meets the index, represents the colorimetric texture optimized roughness, Indicates that the colorimetric texture optimizes light reflectance, represents colorimetrically optimized texture directional contrast, Represents the colorimetric reference texture directional contrast.

[0073] In this embodiment, the texture optimization colorimetric acquisition environment compliance index refers to the colorimetric acquisition environment compliance index after executing color feature acquisition texture optimization, and the colorimetric texture optimization roughness refers to the texture roughness after executing color feature acquisition texture optimization, and the texture roughness is obtained by a surface roughness meter (such as a profilometer, a laser scanner).

[0074] Colorimetric texture optimized light reflectance refers to the texture light reflectance after texture optimization is obtained by executing color features, and the texture light reflectance is obtained by a spectral reflectance measuring instrument (such as a spectrophotometer, a spectrophotometer); colorimetric optimized texture directional contrast refers to the texture directional contrast after texture optimization is obtained by executing color features, and the texture directional contrast is obtained by a texture analysis algorithm based on image processing (such as a gray level co-occurrence matrix, a local binary pattern); the colorimetric reference texture directional contrast is represented by summing and averaging the collected historical colorimetric texture directional contrasts.

[0075] The texture roughness influencing factor reflects the influence of texture roughness on contrast color in obtaining texture optimization index. Specifically, it can be directly obtained from a preset database when used. For example, a mapping set is formed between texture roughness and the influencing factor corresponding to the preset texture roughness in the preset database, and the real-time texture roughness is input into the mapping set to obtain the corresponding weight. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0, 1].

[0076] The texture directionality influence factor reflects the influence of texture directionality contrast on the texture optimization index. It can be directly obtained from the preset database when used. For example, the texture directionality (texture direction contrast) and the influence factor corresponding to the preset texture directionality in the preset database form a mapping set, and the real-time texture directionality (texture direction contrast) is input into the mapping set to obtain the corresponding weight. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0, 1].

[0077] In this example, the sum of the texture roughness influence factor, the texture directionality influence factor, and the texture light reflection influence factor is 1. The texture light reflection influence factor reflects the influence of the texture light reflectivity on the contrast color to obtain the texture optimization index.

[0078] like Figure 2As shown, it is a schematic diagram of the change of the colorimetric texture optimization index provided in the embodiment of the present application, wherein: the texture roughness influence factor is 0.3, the texture directionality influence factor is 0.5, the texture light reflection influence factor is 0.2, the optimized colorimetric acquisition environment compliance index is 0.3, the colorimetric texture optimized light reflectivity is 0.7, the colorimetric optimized texture directional contrast is 0.6, and the colorimetric reference texture directional contrast is 0.5; wherein (a) is a schematic diagram of the change of the colorimetric acquisition texture optimization index with the texture optimized colorimetric acquisition environment compliance index, specifically, the colorimetric texture optimized roughness is set to 0.5 microns, and as the optimized colorimetric acquisition environment compliance index increases, the colorimetric acquisition texture optimization index increases accordingly; (b) is a schematic diagram of the change of the colorimetric acquisition texture optimization index with the colorimetric texture optimized roughness, specifically, the texture optimized colorimetric acquisition environment compliance index is set to 0.8, and as the colorimetric texture optimized roughness increases, the colorimetric acquisition texture optimization index decreases accordingly.

[0079] The colorimetric texture optimization index is used to quantitatively evaluate the effect of color feature texture optimization. Specifically, the colorimetric texture optimization index includes parameters in multiple aspects, and each parameter is related to each other and does not exist independently. For example, with the increase of colorimetric texture optimization roughness, the texture optimization colorimetric acquisition environment compliance index decreases, which in turn leads to a decrease in the colorimetric texture optimization index. In addition, with the increase of colorimetric texture optimization roughness, it shows that the higher the texture roughness, the more obvious the directional contrast, that is, the directional contrast of the colorimetric optimized texture increases. At the same time, increasing the light reflectance also affects the roughness. For example, with the increase of colorimetric texture optimization light reflectance, the colorimetric texture optimization roughness decreases. Texture optimization The colorimetric acquisition environment compliance index, colorimetric texture optimization roughness, colorimetric texture optimization light reflectance and colorimetric optimization texture directional contrast all have an impact on the colorimetric acquisition texture optimization index. Specifically, with the increase of the texture optimization colorimetric acquisition environment compliance index, the colorimetric acquisition texture optimization index increases accordingly, and with the decrease of the colorimetric texture optimization roughness, the colorimetric acquisition texture optimization index also increases accordingly. Therefore, the correlation and mutual influence between various factors are considered, and the colorimetric acquisition texture optimization index is obtained through comprehensive analysis, which realizes the numerical evaluation of the color feature acquisition texture optimization effect. The color feature acquisition texture optimization effect is judged through numerical evaluation, thereby achieving the improvement of the accuracy of the evaluation of the color feature acquisition texture optimization effect.

[0080] Furthermore, the specific steps of judging whether to execute color feature acquisition adaptive optimization according to the colorimetric acquisition environment compliance index, the colorimetric acquisition environment optimization index and the colorimetric acquisition texture optimization index are as follows: S41, judging whether the acquired colorimetric acquisition environment compliance index is within the preset colorimetric environment compliance threshold range, if the colorimetric acquisition environment compliance index exceeds the preset colorimetric environment compliance threshold range, executing S42, otherwise the color feature acquisition adaptive optimization is not executed; S42, judging whether the acquired colorimetric acquisition environment optimization index is within the preset colorimetric environment optimization threshold range, if the colorimetric acquisition environment optimization index exceeds the preset colorimetric environment optimization threshold range, executing S43, otherwise the color feature acquisition adaptive optimization is stopped; S43, judging whether the acquired colorimetric acquisition texture optimization index is within the preset colorimetric texture optimization threshold range, if the colorimetric acquisition texture optimization index is within the preset colorimetric texture optimization threshold range, the color feature acquisition adaptive optimization is not executed, otherwise the color feature acquisition adaptive optimization is executed; the color feature acquisition adaptive optimization includes color feature acquisition environment adaptive optimization and color feature acquisition texture adaptive optimization.

[0081] In this embodiment, adaptive optimization of the color feature acquisition environment is achieved through an adaptive filtering algorithm (such as Kalman filtering, which is used to dynamically adjust color perception parameters to adapt to changing lighting conditions); adaptive optimization of the color feature acquisition texture is achieved through a local adaptive threshold algorithm (for example, by adaptively adjusting the local contrast of the image to optimize the extraction of color features and avoid excessive influence of noise from the texture).

[0082] Among them, the preset colorimetric texture optimization threshold range is obtained from a preset database. Specifically, the preset colorimetric texture optimization threshold range is set by professionals according to standards in the field. For example, the texture optimization colorimetric acquisition environment compliance index range (generally 0.77 to 1), the optimized colorimetric acquisition environment compliance index range (generally 0.2 to 0.6), the colorimetric texture optimization roughness range (generally 0.1 microns to 100 microns), the colorimetric texture optimization light reflectivity range (generally 0 (%) to 1 (%)) and the colorimetric optimization texture directional contrast range (generally 0 to 1) are set to determine the preset colorimetric texture optimization threshold range; by combining the preset colorimetric texture optimization threshold range for judgment, a more accurate judgment of the texture optimization effect of color feature acquisition is achieved.

[0083] like Figure 3As shown, it is a structural schematic diagram of a colorimetric sensor array color feature automatic acquisition system provided in an embodiment of the present application. The colorimetric sensor array color feature automatic acquisition system provided in an embodiment of the present application includes: a color acquisition data acquisition module, a colorimetric environment compliance judgment module, a colorimetric environment optimization judgment module, and a colorimetric texture optimization judgment module; wherein the color acquisition data acquisition module is used to collect color acquisition related data in a preset industrial production environment through the colorimetric sensor array, and pre-process the color acquisition related data to obtain color acquisition data; the colorimetric environment compliance judgment module is used to evaluate the environmental compliance degree of automatic color feature acquisition according to the color acquisition data to obtain a colorimetric acquisition environment compliance index, and judge whether to execute color feature acquisition environment optimization based on the colorimetric acquisition environment compliance index, and the colorimetric acquisition environment optimization is determined. The environmental compliance index is used to quantitatively evaluate the degree of environmental compliance of automatic acquisition of color features; the colorimetric environmental optimization judgment module is used to evaluate the effect of color feature acquisition environmental optimization after executing color feature acquisition environmental optimization to obtain the colorimetric acquisition environmental optimization index, and judge whether to execute color feature acquisition texture optimization based on the colorimetric acquisition environmental optimization index. The colorimetric acquisition environmental optimization index is used to quantitatively evaluate the effect of color feature acquisition environmental optimization; the colorimetric texture optimization judgment module is used to obtain the colorimetric acquisition texture optimization index after executing color feature acquisition texture optimization, and judge whether to execute color feature acquisition adaptive optimization based on the colorimetric acquisition environmental compliance index, the colorimetric acquisition environmental optimization index and the colorimetric acquisition texture optimization index. The colorimetric acquisition texture optimization index is used to quantitatively evaluate the effect of color feature acquisition texture optimization.

[0084] In this embodiment, color acquisition related data in a preset industrial production environment is collected by a colorimetric sensor array, thereby realizing the evaluation and optimization of the degree of compliance with the color feature automatic acquisition environment. By judging whether to perform color feature environment optimization based on the degree of compliance with the color feature automatic acquisition environment and judging whether to perform color feature acquisition texture optimization based on the color feature environment optimization effect, the quality of automatic color feature acquisition in the industrial production environment is improved.

[0085] To summarize, the embodiment of the present application evaluates the degree of environmental compliance of automatic acquisition of color features to determine whether to perform color feature acquisition environment optimization, then evaluates the effect of color feature acquisition environment optimization to determine whether to perform color feature acquisition texture optimization, and finally obtains the colorimetric acquisition texture optimization index after performing color feature acquisition texture optimization and determines whether to perform color feature acquisition adaptive optimization, thereby achieving quantitative evaluation and optimization of the degree of environmental compliance in automatic acquisition of color features, and further achieving improved accuracy of automatic acquisition of color features of colorimetric sensor arrays in industrial production environments, effectively solving the problem of inaccurate automatic acquisition of color features of colorimetric sensor arrays in industrial production environments in the prior art.

[0086] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0087] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0088] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0089] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0090] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0091] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for automatically acquiring color features of a colorimetric sensor array, characterized in that: The following steps are involved: S1, collecting color acquisition related data in a preset industrial production environment through a colorimetric sensor array, and preprocessing the color acquisition related data to obtain color acquisition data; S2, evaluating the environmental compliance degree of automatic acquisition of color features according to the color acquisition data to obtain a colorimetric acquisition environmental compliance index, and judging whether to perform color feature acquisition environment optimization based on the colorimetric acquisition environmental compliance index, wherein the colorimetric acquisition environmental compliance index is used to quantitatively evaluate the environmental compliance degree of automatic acquisition of color features; S3, after executing the color feature acquisition environment optimization, evaluating the effect of the color feature acquisition environment optimization to obtain a colorimetric acquisition environment optimization index, and judging whether to execute the color feature acquisition texture optimization based on the colorimetric acquisition environment optimization index, wherein the colorimetric acquisition environment optimization index is used to quantitatively evaluate the effect of the color feature acquisition environment optimization; S4, obtaining a colorimetric acquisition texture optimization index after executing the color feature acquisition texture optimization, and judging whether to execute the color feature acquisition adaptive optimization according to the colorimetric acquisition environment compliance index, the colorimetric acquisition environment optimization index and the colorimetric acquisition texture optimization index, wherein the colorimetric acquisition texture optimization index is used to quantitatively evaluate the effect of the color feature acquisition texture optimization; The specific steps of obtaining the colorimetric environmental compliance index are as follows: Acquire colorimetric environment assessment related data within a preset time interval, wherein the colorimetric environment assessment related data includes a color acquisition RGB vector, a color acquisition related maximum assessment value, and a color acquisition related standard deviation; The color acquisition related maximum evaluation value includes the color acquisition maximum hue value, the color acquisition maximum saturation value and the color acquisition maximum brightness value; The color acquisition related standard deviation includes the color acquisition hue standard deviation, the color acquisition saturation standard deviation and the color acquisition brightness standard deviation; Combining the colorimetric environment assessment related data with the colorimetric environment assessment reference data obtained from a preset database to obtain a colorimetric acquisition environment compliance index, wherein the colorimetric environment assessment reference data includes a color acquisition reference RGB vector, a color acquisition reference value, a color acquisition standard deviation reference value, and a colorimetric environment compliance assessment weight; The colorimetric environment compliance assessment weight includes a colorimetric environment influencing factor, a colorimetric similarity assessment weight, and a colorimetric stability assessment weight.

2. A method for automatically acquiring color characteristics of a colorimetric sensor array as claimed in claim 1, characterized in that: The method for obtaining the colorimetric environmental compliance index is as follows: In the formula, Indicates that the colorimetric acquisition environment complies with the index, represents the colorimetric environmental influencing factor, Indicates the number of the preset time in the preset time interval, , Indicates the total number of preset moments in a preset time interval. represents the colorimetric similarity evaluation weight, represents the colorimetric stability assessment weight, Indicates the color acquisition RGB vector at the nth preset time within the preset time interval. Indicates the color acquisition reference RGB vector at the nth preset time within the preset time interval, Indicates that the color within the preset time interval obtains the maximum hue value. Indicates that the color gets the reference hue value. Indicates that the color within the preset time interval obtains the maximum saturation. Indicates that the color gets the reference saturation. Indicates that the color within the preset time interval obtains the maximum brightness value. Indicates that the color gets the reference brightness value. Indicates the color hue standard deviation within the preset time interval. Indicates the standard deviation of the reference hue obtained by the color. Indicates the color saturation standard deviation within the preset time interval. Indicates the reference saturation standard deviation of the color. Indicates the color brightness standard deviation within the preset time interval. Indicates the color gets the reference brightness standard deviation, Represents a natural constant.

3. A method for automatically acquiring color characteristics of a colorimetric sensor array as claimed in claim 1, characterized in that: The specific steps of judging whether to perform color feature acquisition environment optimization based on the colorimetric acquisition environment compliance index are as follows: comparing the colorimetric acquisition environment compliance index with a preset colorimetric environment compliance threshold range obtained from a preset database; If the colorimetric acquisition environment compliance index is within the preset colorimetric environment compliance threshold range, the color feature acquisition environment optimization is not performed, and the color acquisition data and the colorimetric acquisition environment compliance index are backed up in real time; If the colorimetric acquisition environment compliance index exceeds a preset colorimetric environment compliance threshold range, the color feature acquisition environment optimization is performed, and the colorimetric acquisition environment optimization index is obtained.

4. A method for automatically acquiring color characteristics of a colorimetric sensor array as claimed in claim 3, characterized in that: The specific steps of color feature acquisition environment optimization are as follows: S21, performing ambient light compensation optimization on the color feature automatic acquisition process, when the colorimetric acquisition environment compliance index monitored after the ambient light compensation optimization is within the preset colorimetric environment compliance threshold range, then stopping the color feature acquisition environment optimization, otherwise executing S22; S22, performing environmental temperature and humidity compensation optimization on the color feature automatic acquisition process, when the colorimetric acquisition environment compliance index monitored after the environmental temperature and humidity compensation optimization is within the preset colorimetric environment compliance threshold range, then stopping the color feature acquisition environment optimization, otherwise executing S23; S23, performing environmental vibration monitoring optimization on the color feature automatic acquisition process, and evaluating the effect of the color feature acquisition environment optimization to obtain a colorimetric acquisition environment optimization index.

5. A method for automatically acquiring color characteristics of a colorimetric sensor array as claimed in claim 3, characterized in that: The specific steps of obtaining the colorimetric environmental optimization index are as follows: Acquire initial feature acquisition data before executing color feature acquisition environment optimization, wherein the initial feature acquisition data includes colorimetric initial illumination intensity, colorimetric initial light source temperature, and colorimetric initial signal-to-noise ratio; Acquire feature acquisition optimization data after executing color feature acquisition environment optimization, wherein the feature acquisition optimization data includes colorimetric optimization light intensity, colorimetric optimization light source temperature, and colorimetric optimization signal-to-noise ratio; The colorimetric acquisition environment optimization index is obtained by combining the feature acquisition initial data and the feature acquisition optimization data with the feature acquisition reference data obtained from a preset database; The feature acquisition reference data includes a feature acquisition reference deviation and an environmental optimization influencing factor.

6. A method for automatically acquiring color characteristics of a colorimetric sensor array as claimed in claim 1, characterized in that: The specific steps of determining whether to perform color feature acquisition texture optimization based on colorimetric acquisition environment optimization index are as follows: comparing the colorimetric environment optimization index with a preset colorimetric environment optimization threshold range obtained from a preset database; If the colorimetric acquisition environment optimization index is within the preset colorimetric environment optimization threshold range, the color feature acquisition texture optimization is not performed, and the colorimetric acquisition environment optimization index is visualized; If the colorimetric acquisition environment optimization index exceeds the preset colorimetric environment optimization threshold range, color feature acquisition texture optimization is performed, and the colorimetric acquisition texture optimization index after the color feature acquisition texture optimization is performed is obtained; The color feature acquisition texture optimization includes color feature roughness optimization and color feature texture directionality optimization.

7. A method for automatically acquiring color characteristics of a colorimetric sensor array as claimed in claim 6, characterized in that: The specific steps of obtaining the colorimetric texture optimization index after executing the color feature to obtain the texture optimization are as follows: Acquire texture optimization evaluation data after executing color feature acquisition texture optimization, wherein the texture optimization evaluation data includes texture optimization colorimetric acquisition environment compliance index, colorimetric texture optimization roughness, colorimetric texture optimization light reflectance, and colorimetric texture optimization directional contrast; The colorimetric texture optimization index is obtained by combining the texture optimization evaluation data, the optimized colorimetric environment compliance index, and the colorimetric reference texture directional contrast, texture roughness influence factor, texture directionality influence factor, and texture light reflection influence factor obtained from a preset database.

8. A method for automatically acquiring color characteristics of a colorimetric sensor array as claimed in claim 1, characterized in that: The specific steps of judging whether to perform color feature acquisition adaptive optimization according to the colorimetric acquisition environment compliance index, the colorimetric acquisition environment optimization index and the colorimetric acquisition texture optimization index are as follows: S41, judging whether the obtained colorimetric acquisition environment compliance index is within the preset colorimetric environment compliance threshold range, if the colorimetric acquisition environment compliance index exceeds the preset colorimetric environment compliance threshold range, executing S42, otherwise the color feature acquisition adaptive optimization is not executed; S42, determining whether the obtained colorimetric acquisition environment optimization index is within a preset colorimetric environment optimization threshold range, if the colorimetric acquisition environment optimization index exceeds the preset colorimetric environment optimization threshold range, executing S43, otherwise stopping the color feature acquisition adaptive optimization; S43, determining whether the obtained colorimetric texture optimization index is within a preset colorimetric texture optimization threshold range, if the colorimetric texture optimization index is within the preset colorimetric texture optimization threshold range, then not performing color feature acquisition adaptive optimization, otherwise performing color feature acquisition adaptive optimization; The color feature acquisition adaptive optimization includes color feature acquisition environment adaptive optimization and color feature acquisition texture adaptive optimization.

9. A system using the method for automatically acquiring color characteristics of a colorimetric sensor array as claimed in any one of claims 1 to 8, characterized in that: It includes: color acquisition data collection module, colorimetric environment compliance judgment module, colorimetric environment optimization judgment module, and colorimetric texture optimization judgment module; Wherein, the color acquisition data acquisition module is used to collect color acquisition related data in a preset industrial production environment through a colorimetric sensor array, and preprocess the color acquisition related data to obtain color acquisition data; The colorimetric environment compliance judgment module is used to evaluate the environmental compliance degree of automatic acquisition of color features according to the color acquisition data to obtain a colorimetric acquisition environment compliance index, and judge whether to perform color feature acquisition environment optimization based on the colorimetric acquisition environment compliance index, and the colorimetric acquisition environment compliance index is used to quantitatively evaluate the environmental compliance degree of automatic acquisition of color features; The colorimetric environment optimization judgment module is used to evaluate the effect of the color feature acquisition environment optimization after executing the color feature acquisition environment optimization to obtain a colorimetric acquisition environment optimization index, and judge whether to execute the color feature acquisition texture optimization based on the colorimetric acquisition environment optimization index, and the colorimetric acquisition environment optimization index is used to quantitatively evaluate the effect of the color feature acquisition environment optimization; The colorimetric texture optimization judgment module is used to obtain the colorimetric texture optimization index after executing the color feature acquisition texture optimization, and judge whether to execute the color feature acquisition adaptive optimization according to the colorimetric acquisition environment compliance index, the colorimetric acquisition environment optimization index and the colorimetric acquisition texture optimization index. The colorimetric texture optimization index is used to quantitatively evaluate the effect of the color feature acquisition texture optimization.

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