A sandblasting precision detection method for the bottom case of a wearable watch

Abstract

The present invention discloses a method for detecting the sandblasting accuracy of a wearable watch bottom case, which relates to the field of detecting the sandblasting accuracy of a bottom case and includes the following steps: determining the window mutual information value, pixel point gradient vector, pixel protrusion state, pixel depression state, and sandblasting texture of a visual image to obtain the sandblasting visual evaluation value of the bottom case, and analyzing the sandblasting visual evaluation value of the bottom case to obtain the sandblasting visual evaluation set H of the bottom case; deeply analyzing the sandblasting of the bottom case based on the surface physical characteristics, particle distribution characteristics, mechanical state, material performance parameters, and surface optical performance of the sandblasting of the bottom case to obtain the deep secondary corrosion value of the sandblasting of the bottom case, and analyzing the deep secondary corrosion value of the sandblasting of the bottom case to obtain the depth evaluation set W of the sandblasting of the bottom case; performing a union operation on the sets H and W, and analyzing to obtain the warehousing operations and label displays corresponding to the comprehensive evaluation signals at each level, which can determine the sandblasting accuracy of the watch bottom case from multiple dimensions, can timely adjust the sandblasting process, and reduce the production cost of enterprises.

Description

Technical Field

[0001] The present invention relates to the field of sandblasting precision detection for the bottom case, and specifically to a method for detecting the sandblasting precision of the bottom case of a wearable watch. Background Art

[0002] With the continuous improvement of consumers' requirements for the appearance delicacy and quality of watches, the bottom case of a watch is an important part of the watch, so the surface treatment quality is becoming increasingly crucial. The sandblasting process can endow the watch bottom case with a high-quality appearance effect and a unique texture at the bottom of the watch. Therefore, the precision detection of sandblasting is of great importance. The traditional detection methods mainly rely on the visual judgment of personnel and conventional measuring tools. Therefore, a method for detecting the sandblasting precision of the bottom case of a wearable watch has emerged.

[0003] When the traditional method for detecting the sandblasting precision of the bottom case of a wearable watch is running, it is impossible to judge the sandblasting precision of the watch bottom case in multiple dimensions, classify and store the watch bottom cases with different sandblasting precision qualities, and remind the process parameters with unqualified sandblasting precision in time. As a result, the sandblasting quality of the watch bottom cases produced by the enterprise is uneven, affecting the reputation of the enterprise, and it is impossible to adjust the sandblasting process in time, increasing the production cost of the enterprise, and there are situations of a large number of reworks and scraps due to quality problems.

[0004] To solve the above defects, a technical solution is provided now. Summary of the Invention

[0005] To solve the technical problems raised in the above background art, the present invention is proposed. An embodiment of the present invention provides a method for detecting the sandblasting precision of the bottom case of a wearable watch.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A method for detecting the sandblasting precision of the bottom case of a wearable watch, including the following steps:

[0007] Step 1: Collect images of the sandblasted bottom case through a camera, and judge the window mutual information value, pixel point gradient vector, pixel convex state, pixel concave state, and sandblasting texture of the visual image to obtain the visual evaluation value of the sandblasted bottom case;

[0008] Step 2: Analyze and judge the visual evaluation value of the sandblasted bottom case and the reference interval to obtain the visual evaluation set H of the sandblasted bottom case;

[0009] Step 3: Deeply analyze the sandblasted bottom case based on the surface physical characteristics, particle distribution characteristics, mechanical state, material performance parameters, and surface optical performance of the sandblasted bottom case to obtain the deep secondary corrosion value of the sandblasted bottom case;

[0010] Step 4: Analyze and judge the deep secondary corrosion value of the sandblasted bottom case and the reference threshold to obtain the depth evaluation set W of the sandblasted bottom case;

[0011] Step Five: By performing a union operation on sets H and W and analyzing, obtain the warehousing operations and label displays corresponding to the comprehensive evaluation signals at each level.

[0012] Furthermore, the analysis steps for the sandblasting visual evaluation value of the bottom shell are as follows:

[0013] Step 106: Divide the obtained normalized image into several regions. Taking the pixel points in each region as the center, mark the gray value of the central pixel point as zh and the gray value of the neighborhood as L q , where q = 1, 2,..., 8, representing the serial number of the neighborhood pixels. According to the formula Obtain the relative shape value X of the gray value of each neighborhood pixel to the central pixel point q , where a1, a2, and a3 are natural constants respectively. According to the formula Obtain the shape value DZZ of the central pixel point, where e is a natural constant. Statistically analyze the shape values of all central pixel points in the image. According to the distribution of the shape values of the central pixel points in the image, divide them into G intervals and establish a histogram of the shape values of the central pixel points. According to the formula Obtain the symmetric distance DJ between intervals A and B, where g is the serial number of the interval, taking positive integers, and the maximum value is G. Statistically analyze the symmetric distances between every two regions in the image and sum them to obtain the texture consistency deviation value of the bottom shell sandblasting;

[0014] Step 107: Calculate the sandblasting visual evaluation value Spz of the bottom shell by calculating the sandblasting information consistency value, the sandblasting ladder impurity value, the sandblasting convexity value, the sandblasting concavity value, and the texture consistency deviation value of the bottom shell sandblasting.

[0015] Furthermore, the analysis steps for the sandblasting convexity value and the sandblasting concavity value of the bottom shell are as follows:

[0016] Step 104: For the obtained normalized image, set a gray threshold one. When the pixel points in the image are greater than the gray threshold one, mark the pixel points as candidate points one for the convex region. Calculate the reflection light gray dispersion degree value of each pixel point. The reflection light gray dispersion degree value is the standard deviation of the reflection light gray in the region centered on the pixel points in the candidate points one for the convex region. When the reflection light gray dispersion degree value is greater than the set threshold PT1, mark the loudness point as candidate points two for the convex region. Merge candidate points one and two for the convex region to obtain the convex region. Obtain the difference between the highest gray value of the convex region minus the average gray value of the non - convex region, and perform a weighted calculation with the number of values in the convex region, multiplying by the corresponding weight factor coefficient to obtain the sandblasting convexity value of the bottom shell;

[0017] Step 105: For the obtained normalized image, set the gray threshold two. When the pixel points in the image are less than the gray threshold two, mark the pixel points as the candidate points one for the convex regions. Calculate the discrete degree value of the reflected light gray levels of each pixel point. When the discrete degree value of the reflected light gray levels is greater than the set threshold PT2, mark the loudness point as the candidate point two for the convex regions. Merge the candidate points one and two for the convex regions to obtain the convex regions. Obtain the difference between the average gray value of the non-convex regions minus the lowest gray value of the convex regions, and perform a weighted calculation with the number of values in the convex regions, multiply by the corresponding weight factor coefficient to obtain the sandblasting concave depth value of the bottom shell.

[0018] Furthermore, the analysis steps for the consistency value of the bottom shell sandblasting and the sandblasting gradient and impurity value are as follows:

[0019] Step 101: The light source irradiates the surface of the watch bottom shell. Use a camera to obtain image information, perform normalization processing on the image, perform gray processing. For the normalized image, start from a window of 1×1 pixel and gradually expand it to a window of n×n pixels in sequence, and calculate the gray values of each point in each window as the reflected light intensity of each point in the window;

[0020] Step 102: Divide the reflected light intensity ranges of two different window scales into m intervals. Calculate the probabilities of the reflected light intensities of each window scale in each interval respectively, marked as the marginal probabilities, and the probability that the intensities of the two window sizes fall into their respective specific intervals simultaneously, marked as the joint probability. For each pair of interval combinations that appear, there are m×m in total. Divide the joint probability by the product of the two marginal probabilities, marked as the division and multiplication value one. Multiply the logarithm of the division and multiplication value one by the joint probability to obtain the product value. Add up all the product values to obtain the mutual information value of two different window scales. Repeat the above steps to obtain the mutual information values of all two different window scales and add them up to obtain the consistency value of the bottom shell sandblasting information;

[0021] Step 103: For the obtained normalized image, use the image gradient calculation algorithm to obtain the gradient vectors of each pixel point in the image. Statistically sum the standard deviation of the gradient vector directions in the image and the variable extreme value of the gradient vector modulus in the image to obtain the sandblasting gradient and impurity value of the bottom shell.

[0022] Furthermore, the analysis steps for the warehousing operations and label displays corresponding to the comprehensive evaluation signals at all levels are as follows:

[0023] Step 501: Perform the union operation on the visual evaluation set H of the bottom shell sandblasting and the depth evaluation set W of the bottom shell sandblasting. When H∪W = {h1, w1}, a primary comprehensive evaluation signal for the bottom shell sandblasting accuracy determination is generated; when H∪W = {h1, w2} or {h1, w3} or {h2, w1} or {h2, w2} or {h3, w1}, a secondary comprehensive evaluation signal for the bottom shell sandblasting accuracy determination is generated; when H∪W = {h2, w3} or {h3, w2}, a tertiary comprehensive evaluation signal for the bottom shell sandblasting accuracy determination is generated; when H∪W = {h3, w3}, a quaternary comprehensive evaluation signal for the bottom shell sandblasting accuracy determination is generated.

[0024] Step 502: If a primary comprehensive evaluation signal for the bottom shell sandblasting accuracy determination is generated for the bottom shell sandblasting, then reclassify the bottom shell sandblasting into set Y1, store it in the warehouse, and display label one; if a secondary comprehensive evaluation signal for the bottom shell sandblasting accuracy determination is generated for the bottom shell sandblasting, then reclassify the bottom shell sandblasting into set Y2, store it in the warehouse, and display label one; if a tertiary comprehensive evaluation signal for the bottom shell sandblasting accuracy determination is generated for the bottom shell sandblasting, then reclassify the bottom shell sandblasting into set Y3, do not allow it to be stored in the warehouse, and display label one; if a quaternary comprehensive evaluation signal for the bottom shell sandblasting accuracy determination is generated for the bottom shell sandblasting, then reclassify the bottom shell sandblasting into set Y4, do not allow it to be stored in the warehouse, and display label one.

[0025] Further, the analysis steps for the depth evaluation set W of the bottom shell sandblasting are as follows:

[0026] Step 402: When the deep residual corrosion value of the bottom shell sandblasting is greater than or equal to the reference threshold TG1, a tertiary depth evaluation determination signal for the corresponding bottom shell sandblasting is sent; when the deep residual corrosion value of the bottom shell sandblasting is less than the reference threshold TG1 and greater than the reference threshold TG2, a secondary depth evaluation determination signal for the corresponding bottom shell sandblasting is sent; when the deep residual corrosion value of the bottom shell sandblasting is less than or equal to the reference threshold TG2, a primary depth evaluation determination signal for the corresponding bottom shell sandblasting is sent.

[0027] Step 403: Establish set W based on the depth evaluation determination signal of the bottom shell sandblasting, and determine the primary, secondary, and tertiary depth evaluation determination signals of the bottom shell sandblasting as elements w1, w2, and w3 respectively.

[0028] Further, the analysis steps for the depth evaluation set W of the bottom shell sandblasting are as follows:

[0029] Step 302: Normalize the surface feature value of the bottom shell sandblasting, the particle distribution value of the bottom shell sandblasting, the force deviation residual value of the bottom shell sandblasting, the material corrosion resistance value of the bottom shell sandblasting, and the surface light variation value of the bottom shell sandblasting. Use the force deviation residual value of the bottom shell sandblasting as the radius of the upper surface of the frustum, the material corrosion resistance value of the bottom shell sandblasting as the radius of the lower surface of the frustum, and the surface light variation value of the bottom shell sandblasting as the height of the frustum to construct a frustum. Take the center point of the frustum as the center of the sphere and construct a sphere inside the frustum. Use the sum of the surface feature value of the bottom shell sandblasting and the particle distribution value of the bottom shell sandblasting as the radius of the sphere, and identify the non-overlapping volume formed by the frustum and the sphere, which is marked as the deep secondary corrosion residue value of the bottom shell sandblasting.

[0030] Further, the analysis steps for the material corrosion resistance value of the bottom shell sandblasting and the surface light variation value of the bottom shell sandblasting are as follows:

[0031] The material performance parameters of the bottom shell sandblasting refer to the weighted calculation of the sandblasting chemical activity, the galvanic corrosion sensitivity of the bottom shell, and the biocompatibility, and then multiply by the corresponding influence factor coefficient to obtain the material corrosion resistance value of the bottom shell sandblasting. The galvanic corrosion sensitivity of the bottom shell refers to measuring the polarization curve of the sandblasted bottom shell to obtain the value of the corrosion current density. The biocompatibility refers to the number of times of itching, erythema, rash, swelling, redness, and fever that occur during a certain wearing time of the wearable watch. The surface optical performance of the bottom shell sandblasting refers to the sum of the surface light scattering feature value, the surface color stability change value, and the optical reflectivity anisotropy value to obtain the surface light variation value of the bottom shell sandblasting. The surface light scattering feature value refers to obtaining the light scattering spectrum through a light scattering instrument, obtaining the range of the scattered light intensity and the scattering angle in the spectrum, and taking the sum value. The surface color stability change value refers to the chromaticity change value of the bottom shell sandblasting after a certain time in the salt spray test. The optical reflectivity anisotropy value refers to measuring the optical reflectivity of each angle of the bottom shell sandblasting through a multi-angle spectrophotometer to obtain the standard deviation of the optical reflectivity.

[0032] Further, the analysis steps for the surface physical characteristic value, particle distribution value, and force deviation residual value of the bottom case sandblasting are as follows: Step 301: The surface physical characteristics of the bottom case sandblasting refer to the surface friction distribution value, surface flatness, surface glossiness, and surface wetting value. Divide the sum of the surface flatness, surface glossiness, and surface wetting value by the surface friction distribution value to obtain the surface physical characteristic value of the bottom case sandblasting. The surface friction distribution value refers to the standard deviation of the friction force through different regions of the bottom case sandblasting surface, and the surface wetting value refers to the numerical value of the contact angle of the bottom case sandblasting; The particle distribution characteristics of the bottom case sandblasting refer to the roundness of the sandblasting particles, the electrostatic distribution state value of the sandblasting particles, and the crushing rate of the sandblasting particles. Divide the roundness of the sandblasting particles by the sum of the electrostatic distribution state value of the sandblasting particles and the crushing rate of the sandblasting particles to obtain the particle distribution value of the bottom case sandblasting. The electrostatic distribution state value of the sandblasting particles refers to the electrostatic quantity of the sandblasting particles; The mechanical state of the bottom case sandblasting refers to the particle adhesion deviation force and the residual stress being added together to obtain the force deviation residual value of the bottom case sandblasting. Among them, the particle adhesion deviation force refers to using a standard tape to stick on the sandblasted surface of the bottom case and tearing the tape from the bottom case surface at a certain speed, and the number of particles on the tape.

[0033] Further, the analysis steps for the visual evaluation set H of the bottom case sandblasting are as follows:

[0034] Step 201: When the visual evaluation value of the bottom case sandblasting is within the gradient reference interval NM1, the corresponding bottom case sandblasting emits a third-level visual evaluation determination signal for the bottom case sandblasting. When the visual evaluation value of the bottom case sandblasting is within the gradient reference interval NM2, the corresponding bottom case sandblasting emits a second-level visual evaluation determination signal for the bottom case sandblasting. When the visual evaluation value of the bottom case sandblasting is within the gradient reference interval NM3, the corresponding bottom case sandblasting emits a first-level visual evaluation determination signal for the bottom case sandblasting;

[0035] Step 202: Establish the set H based on the visual evaluation determination signal of the bottom case sandblasting. The first-level, second-level, and third-level visual evaluation determination signals of the bottom case sandblasting are respectively determined as elements h1, h2, and h3.

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] 1. The present invention determines the visual evaluation value of the bottom case sandblasting by judging the window mutual information value, pixel point gradient vector, pixel protrusion state, pixel depression state, and sandblasting texture of the visual image, analyzes and determines the visual evaluation value of the bottom case sandblasting with the reference interval to obtain the visual evaluation set H of the bottom case sandblasting, deeply analyzes the bottom case sandblasting based on the surface physical characteristics, particle distribution characteristics, mechanical state, material performance parameters, and surface optical performance of the bottom case sandblasting to obtain the deep corrosion residual value of the bottom case sandblasting, analyzes and determines the deep corrosion residual value of the bottom case sandblasting with the reference threshold to obtain the depth evaluation set W of the bottom case sandblasting, and can judge the sandblasting accuracy of the watch bottom case from multiple dimensions, increasing the accuracy of sandblasting accuracy identification.

[0038] 2. By performing the union operation on sets H and W and analyzing the warehousing operations and label displays corresponding to comprehensive evaluation signals at all levels, the present invention can classify and store watch bottom cases with different sandblasting precision qualities, timely remind process parameters with unqualified sandblasting precision, improve the reputation of the enterprise, timely adjust the sandblasting process, reduce the production cost of the enterprise, and reduce the situations of rework and scrapping due to quality problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. The following drawings are not deliberately drawn to scale in actual size, and the focus is on showing the gist of the present invention. Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings. Obviously, the described embodiments are only partial embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts also belong to the scope of protection of the present invention.

[0041] As Figure 1 shown, a method for detecting the sandblasting precision of a wearable watch bottom case is as follows:

[0042] Step 1: Collect the image of the bottom case sandblasting through a camera, and determine the window mutual information value, pixel point gradient vector, pixel convex state, pixel concave state, and sandblasting texture of the visual image to obtain the sandblasting visual evaluation value of the bottom case.

[0043] Step 2: Analyze and determine the sandblasting visual evaluation value of the bottom case and the reference interval to obtain the sandblasting visual evaluation set H of the bottom case.

[0044] Step 3: Deeply analyze the bottom case sandblasting based on the surface physical characteristics, particle distribution characteristics, mechanical state, material performance parameters, and surface optical performance of the bottom case sandblasting to obtain the deep secondary corrosion value of the bottom case sandblasting.

[0045] Step 4: Analyze and determine the deep secondary corrosion value of the bottom case sandblasting and the reference threshold to obtain the sandblasting depth evaluation set W of the bottom case.

[0046] Step 5: Perform the union operation on sets H and W, and analyze the warehousing operations and label displays corresponding to comprehensive evaluation signals at all levels.

[0047] Among them, the specific steps for solving the sandblasting visual evaluation value of the bottom case are as follows:

[0048] Step 101: The light source irradiates the surface of the watch bottom case, and a camera is used to obtain image information. The image is normalized, subjected to grayscale processing, and the normalized image is gradually expanded from a 1×1 pixel window to an n×n pixel window in sequence. The grayscale values of each point in each window are calculated as the reflected light intensity of each point in the window.

[0049] Step 102: The reflected light intensity ranges of two different window scales are divided into m intervals. The probabilities of the reflected light intensity of each window scale in each interval are calculated respectively and marked as marginal probabilities, and the probability that the intensities of the two window sizes fall within their respective specific intervals simultaneously is marked as the joint probability. For each pair of interval combinations that appear, there are m×m in total. The joint probability is divided by the product of the two marginal probabilities and marked as the division-multiplication value one. The logarithm of the division-multiplication value one is multiplied by the joint probability to obtain the product value. All the product values are added up to obtain the mutual information value of two different window scales. Repeat the above steps to obtain the mutual information values of all two different window scales and add them up to obtain the consistency value of the bottom case sandblasting information.

[0050] Step 103: For the obtained normalized image, an image gradient calculation algorithm is used to obtain the gradient vectors of each pixel point in the image. The standard deviation of the gradient vector directions in the image and the extreme value of the change in the modulus of the gradient vectors in the image are statistically summed to obtain the gradient noise value of the bottom case sandblasting, where the extreme value of the change in the modulus of the gradient vectors in the image refers to the maximum value of the modulus of the gradient vectors in the image minus the minimum value.

[0051] Step 104: For the obtained normalized image, a grayscale threshold one is set. When the pixel point in the image is greater than the grayscale threshold one, the pixel point is marked as a candidate point one for the raised area. The reflected light grayscale dispersion value of each pixel point is calculated, where the reflected light grayscale dispersion value is the standard deviation of the reflected light grayscale in the area centered on the pixel points in the candidate points one for the raised area. When the reflected light grayscale dispersion value is greater than the set threshold PT1, the pixel point is marked as a candidate point two for the raised area. The candidate points one and two for the raised area are merged to obtain the raised area. The difference between the highest grayscale value of the raised area minus the average grayscale value of the non-raised area is weighted and calculated with the number value of the raised area, and multiplied by the corresponding weight factor coefficient to obtain the convexity value of the bottom case sandblasting.

[0052] Step 105: For the normalized image obtained, set the gray threshold two. When the pixel points in the image are less than the gray threshold two, mark the pixel points as candidate points one for the concave area. Calculate the discrete degree value of the reflected light gray of each pixel point. When the discrete degree value of the reflected light gray is greater than the set threshold PT2, mark the loudness point as candidate point two for the concave area. Combine candidate points one and two for the concave area to obtain the concave area. Obtain the difference between the average gray value of the non-concave area minus the lowest gray value of the concave area, and perform a weighted calculation with the number value of the concave area, multiply by the corresponding weight factor coefficient to obtain the sandblasting concave process value of the bottom shell;

[0053] Step 106: Divide the normalized image obtained into several regions. Taking the pixel points in each region as the center, mark the gray value of the center pixel point as zh, and the neighborhood gray value as L q , where q = 1, 2,..., 8, representing the serial numbers of the neighborhood pixels. According to the formula obtain the relative shape value X of the gray value of each neighborhood pixel to the center pixel point q , where a1, a2, and a3 are natural constants respectively, and a2 > a1 > a3. The specific values of a1, a2, and a3 correspond to 1.25, 1.35, and 0. According to the formula obtain the shape value DZZ of the center pixel point, where e is a natural constant, and the specific value is 2.718. Statistically analyze the shape values of all center pixel points in the image. According to the distribution of the shape values of the center pixel points in the image, divide them into G intervals, and establish a histogram of the shape values of the center pixel points. According to the formula obtain the symmetric distance DJ between intervals A and B, where g is the serial number of the interval, taking positive integers, and the maximum value is G. Statistically analyze the symmetric distances between each two regions in the image and sum them to obtain the sandblasting texture consistency deviation value of the bottom shell. It should be noted that the smaller the symmetric distance between each region, the better the texture direction consistency and coherence between the two regions;

[0054] Step 107: Mark the sandblasting information consistency value, sandblasting ladder impurity value, sandblasting convex process value, sandblasting concave process value, and sandblasting texture consistency deviation value of the bottom shell as yzz, pzz, ptz, atz, and wgp respectively, and perform normalization processing. Substitute them into the set formula Spz = ta1 × yzz / (ta2 × pzz + ta3 × ptz + ta4 × atz + ta5 × wgp) for calculation to obtain the sandblasting visual evaluation value Spz of the bottom shell, where ta1, ta2, ta3, ta4, and ta5 are the set influence factor coefficients of the sandblasting information consistency value, sandblasting ladder impurity value, sandblasting convex process value, sandblasting concave process value, and sandblasting texture consistency deviation value of the bottom shell respectively, and the specific values are determined by professionals in this field;

[0055] Among them, the specific solution steps for the visual evaluation set H of the bottom shell sandblasting are as follows:

[0056] Step 201: Set the reference intervals NM1, NM2, and NM3 for the visual evaluation values of the bottom shell sandblasting, and substitute the visual evaluation values of the bottom shell sandblasting into the preset gradient reference intervals NM1, NM2, and NM3 for comparative analysis. Among them, the interval values of NM1, NM2, and NM3 increase in a gradient;

[0057] When the visual evaluation value of the bottom shell sandblasting is within the gradient reference interval NM1, a bottom shell sandblasting third-level visual evaluation determination signal is sent for the corresponding bottom shell sandblasting. When the visual evaluation value of the bottom shell sandblasting is within the gradient reference interval NM2, a bottom shell sandblasting second-level visual evaluation determination signal is sent for the corresponding bottom shell sandblasting. When the visual evaluation value of the bottom shell sandblasting is within the gradient reference interval NM3, a bottom shell sandblasting first-level visual evaluation determination signal is sent for the corresponding bottom shell sandblasting;

[0058] Step 202: Establish the set H based on the visual evaluation determination signal of the bottom shell sandblasting. The bottom shell sandblasting first-level visual evaluation determination signal is determined as the element h1, the bottom shell sandblasting second-level visual evaluation determination signal is determined as the element h2, and the bottom shell sandblasting third-level visual evaluation determination signal is determined as the element h3. And the element h1 ∈ set H, the element h2 ∈ set H, and the element h3 ∈ set H;

[0059] Among them, the specific solution steps for the deep secondary corrosion value of the bottom shell sandblasting are as follows:

[0060] Step 301: Conduct in-depth analysis of the sandblasted bottom shell based on its surface physical characteristics, particle distribution characteristics, mechanical state, material property parameters, and surface optical properties. The surface physical characteristics of the sandblasted bottom shell refer to the surface friction distribution value, surface flatness, surface glossiness, and surface wetting value. Divide the sum of the surface flatness, surface glossiness, and surface wetting value by the surface friction distribution value to obtain the surface physical characteristic value of the sandblasted bottom shell. The surface friction distribution value refers to the standard deviation of the friction force through different regions of the sandblasted bottom shell surface, and the surface wetting value refers to the numerical value of the contact angle of the sandblasted bottom shell. The particle distribution characteristics of the sandblasted bottom shell refer to the roundness of the sandblasting particles, the electrostatic distribution value of the sandblasting particles, and the particle breakage rate. Divide the roundness of the sandblasting particles by the sum of the electrostatic distribution value of the sandblasting particles and the particle breakage rate to obtain the particle distribution value of the sandblasted bottom shell. The electrostatic distribution value of the sandblasting particles refers to the electrostatic quantity of the sandblasting particles. The mechanical state of the sandblasted bottom shell refers to the addition of the particle attachment deviation force and the residual stress to obtain the force deviation residual value of the sandblasted bottom shell. The particle attachment deviation force refers to the number of particles on the tape when a standard tape is pasted on the sandblasted surface of the bottom shell and the tape is torn off from the bottom shell surface at a certain speed. The material property parameters of the sandblasted bottom shell refer to the weighted calculation of the sandblasting chemical activity, the galvanic corrosion sensitivity of the bottom shell, and the biocompatibility, and multiply by the corresponding influence factor coefficient to obtain the material property corrosion compatibility value of the sandblasted bottom shell. The galvanic corrosion sensitivity of the bottom shell refers to the measurement of the polarization curve of the sandblasted bottom shell to obtain the numerical value of the corrosion current density. The biocompatibility refers to the number of times of itching, erythema, rash, swelling, redness, and fever that occur when wearing the watch for a certain period of time. When the sandblasting accuracy is higher, the physical contact between the skin and the watch bottom plate is more gentle, and the number of adverse reaction times is less. The surface optical properties of the sandblasted bottom shell refer to the summation of the surface light scattering characteristic value, the surface color stability change value, and the optical reflectivity anisotropy value to obtain the surface light variation value of the sandblasted bottom shell. The surface light scattering characteristic value refers to obtaining the light scattering spectrum through a light scattering instrument, obtaining the range of the scattered light intensity and the scattering angle in the spectrum, and taking the sum value. The surface color stability change value refers to the chromaticity change value of the sandblasted bottom shell after a certain time in the salt spray test. The optical reflectivity anisotropy value refers to measuring the optical reflectivity of each angle of the sandblasted bottom shell through a multi-angle spectrophotometer to obtain the standard deviation of the optical reflectivity. Step 302: Normalize the surface physical characteristic value, particle distribution value, force deviation residual value, material property corrosion compatibility value, and surface light variation value of the sandblasted bottom shell. Take the force deviation residual value of the sandblasted bottom shell as the radius of the upper surface of the frustum, take the material property corrosion compatibility value of the sandblasted bottom shell as the radius of the lower surface of the frustum, take the surface light variation value of the sandblasted bottom shell as the height of the frustum, construct a frustum, take the center point of the frustum as the center of the sphere, construct a sphere inside the frustum, take the sum of the surface physical characteristic value and the particle distribution value of the sandblasted bottom shell as the radius of the sphere, identify the non-overlapping volume formed by the frustum and the sphere, and mark it as the deep secondary corrosion residual value of the sandblasted bottom shell;

[0061] Among them, the specific solution steps for the bottom shell sandblasting depth evaluation set W are as follows:

[0062] Step 401: Set the reference thresholds TG1 and TG2 for the deep secondary corrosion value of the bottom shell sandblasting, where the reference threshold TG1 > TG2, and compare and analyze the deep secondary corrosion value of the bottom shell sandblasting with the reference thresholds TG1 and TG2;

[0063] Step 402: When the deep secondary corrosion value of the bottom shell sandblasting is greater than or equal to the reference threshold TG1, a bottom shell sandblasting level-three depth evaluation determination signal is sent for the corresponding bottom shell sandblasting; when the deep secondary corrosion value of the bottom shell sandblasting is less than the reference threshold TG1 and greater than the reference threshold TG2, a bottom shell sandblasting level-two depth evaluation determination signal is sent for the corresponding bottom shell sandblasting; when the deep secondary corrosion value of the bottom shell sandblasting is less than or equal to the reference threshold TG2, a bottom shell sandblasting level-one depth evaluation determination signal is sent for the corresponding bottom shell sandblasting;

[0064] Step 403: Establish the set W based on the bottom shell sandblasting depth evaluation determination signal. The bottom shell sandblasting level-one depth evaluation determination signal is determined as the element w1, the bottom shell sandblasting level-two depth evaluation determination signal is determined as the element w2, and the bottom shell sandblasting level-three depth evaluation determination signal is determined as the element w3, and the element w1 ∈ set W, the element w2 ∈ set W, the element w3 ∈ set W;

[0065] Among them, the specific solution steps for the warehousing operation and label display corresponding to the comprehensive evaluation signals at each level are as follows:

[0066] Step 501: Perform the union operation on the sets H and W. When H ∪ W = {h1, w1}, a bottom shell sandblasting accuracy determination level-one comprehensive evaluation signal is generated;

[0067] When H ∪ W = {h1, w2} or {h1, w3} or {h2, w1} or {h2, w2} or {h3, w1}, a bottom shell sandblasting accuracy determination level-two comprehensive evaluation signal is generated;

[0068] When H ∪ W = {h2, w3} or {h3, w2}, a bottom shell sandblasting accuracy determination level-three comprehensive evaluation signal is generated;

[0069] When H ∪ W = {h3, w3}, a bottom shell sandblasting accuracy determination level-four comprehensive evaluation signal is generated;

[0070] Step 502: If the sandblasting of the bottom shell generates a first-level comprehensive evaluation signal for the determination of the sandblasting precision of the bottom shell, then reclassify the sandblasted bottom shell into set Y1, store it in the warehouse, and label it as "The sandblasting precision of this bottom shell is good"; if the sandblasting of the bottom shell generates a second-level comprehensive evaluation signal for the determination of the sandblasting precision of the bottom shell, then reclassify the sandblasted bottom shell into set Y2, store it in the warehouse, and label it as "The sandblasting precision of this bottom shell passes"; if the sandblasting of the bottom shell generates a third-level comprehensive evaluation signal for the determination of the sandblasting precision of the bottom shell, then reclassify the sandblasted bottom shell into set Y3, and do not allow it to be stored in the warehouse, and label it as "The sandblasting precision of this bottom shell is unqualified, rework is required, and the sandblasting process parameters and procedures at this time need to be checked and adjusted"; if the sandblasting of the bottom shell generates a fourth-level comprehensive evaluation signal for the determination of the sandblasting precision of the bottom shell, then reclassify the sandblasted bottom shell into set Y4, and do not allow it to be stored in the warehouse, and label it as "The sandblasting precision of this bottom shell is seriously unqualified, scrapped, and the sandblasting process parameters and procedures at this time need to be checked and adjusted".

[0071] The foregoing is a description of the present invention and should not be construed as a limitation thereof. Although several exemplary embodiments of the present invention have been described, those skilled in the art will readily understand that many modifications can be made to the exemplary embodiments without departing from the novel teachings and advantages of the present invention. Accordingly, all such modifications are intended to be included within the scope of the present invention as defined by the claims. It should be understood that the foregoing is a description of the present invention and should not be considered limited to the specific embodiments disclosed, and modifications to the disclosed embodiments as well as other embodiments are intended to be included within the scope of the appended claims. The present invention is defined by the claims and their equivalents.

Claims

1. A sandblasting accuracy detection method for a wearable watch bottom shell, characterized in that: The following steps are involved: Step 1: Use a camera to collect an image of the sandblasted bottom shell, and determine the window mutual information value, pixel gradient vector, pixel convex state, pixel concave state, and sandblasted texture of the visual image to obtain the visual evaluation value of the sandblasted bottom shell; The bottom shell sandblasting visual evaluation analysis steps are as follows: Step 106: The normalized image is divided into several regions, with the pixel in each region as the center. The gray value of the center pixel is marked as zh, and the gray value of the area is marked as L. q , where q = 1, 2, ..., 8, represents the sequence number of the field pixel, according to the formula Get the grayscale value of each neighborhood pixel relative to the central pixel X q , where a1, a2 and a3 are natural constants, according to the formula The pair value DZZ of the central pixel is obtained, where e is a natural constant. The pair values ​​of all central pixels in the image are counted, and divided into G intervals according to the pair value distribution of the central pixel of the image. The pair value histogram of the central pixel is established according to the formula The symmetric distance DJ between the A and B intervals is obtained, where g is the interval number, which is a positive integer and has a maximum value of G. The symmetric distances between each two regions in the image are counted and summed to obtain the uniform deviation of the sandblasting texture of the bottom shell; Step 107: Calculate the bottom shell sandblasting information consistency value, the bottom shell sandblasting gradient value, the bottom shell sandblasting convex distance value, the bottom shell sandblasting concave distance value and the bottom shell sandblasting texture deviation value to obtain the bottom shell sandblasting visual evaluation value Spz; Step 2: Analyze and determine the bottom shell sandblasting visual evaluation value and the reference interval to obtain the bottom shell sandblasting visual evaluation set H; Step 3: Conduct in-depth analysis of the bottom shell sandblasting based on the surface physical characteristics, particle distribution characteristics, mechanical state, material performance parameters and surface optical properties of the bottom shell sandblasting to obtain the deep residual corrosion value of the bottom shell sandblasting; The steps for analyzing the deep residual corrosion value of the bottom shell sandblasting are as follows: Step 302: normalize the surface material characteristic value of sandblasting of the bottom shell, the particle distribution value of sandblasting of the bottom shell, the force bias residual value of sandblasting of the bottom shell, the material corrosion capacity value of sandblasting of the bottom shell, and the surface light variation value of sandblasting of the bottom shell, and construct a frustum with the force bias residual value of sandblasting of the bottom shell as the radius of the circle on the upper surface of the frustum, the material corrosion capacity value of sandblasting of the bottom shell as the radius of the circle on the lower surface of the frustum, and the surface light variation value of sandblasting of the bottom shell as the height of the frustum. The center point of the frustum is taken as the center of the sphere, and a sphere is constructed inside the frustum. The sum of the surface material characteristic value of sandblasting of the bottom shell and the particle distribution value of sandblasting of the bottom shell is taken as the radius of the sphere, and the non-overlapping volume formed by the frustum and the sphere is identified, and marked as the deep residual corrosion value of sandblasting of the bottom shell; Step 4: Analyze and determine the bottom shell sandblasting depth residual corrosion value and the reference threshold value to obtain the bottom shell sandblasting depth evaluation set W; Step 5: By performing a union process on the sets H and W, and analyzing them, the storage operations and label displays corresponding to the comprehensive evaluation signals at each level are obtained.

2. A sandblasting accuracy detection method for a bottom shell of a wearable watch according to claim 1, characterized in that: The analysis steps of the bottom shell sandblasting convex stroke value and the bottom shell sandblasting concave stroke value are as follows: Step 104: for the normalized image obtained, a grayscale threshold of 1 is set. When a pixel in the image is greater than the grayscale threshold of 1, the pixel is marked as a convex region candidate point 1. The reflected light grayscale dispersion value of each pixel is calculated. The reflected light grayscale dispersion value is the standard deviation of the reflected light grayscale in the area centered on the pixel in the convex region candidate point 1. When the reflected light grayscale dispersion value is greater than the set threshold PT1, the pixel is marked as a convex region candidate point 2. The convex region candidate points 1 and 2 are merged to obtain a convex region. The difference between the highest grayscale value of the convex region and the average grayscale value of the non-convex region is obtained, and a weighted calculation is performed with the number of convex regions, and the result is multiplied by the corresponding weight factor coefficient to obtain the bottom shell sandblasting convexity value. Step 105: For the normalized image obtained, set grayscale threshold 2. When a pixel in the image is less than grayscale threshold 2, mark the pixel as candidate point 1 for the concave area. Calculate the grayscale discreteness value of the reflected light of each pixel. When the grayscale discreteness value of the reflected light is greater than the set threshold PT2, mark the pixel as candidate point 2 for the concave area. Merge candidate points 1 and 2 for the concave area to obtain the concave area. Obtain the difference between the average grayscale value of the non-concave area and the minimum grayscale value of the concave area, and perform weighted calculation on the number of concave areas. Multiply by the corresponding weight factor coefficient to obtain the concave distance value of the bottom shell sandblasting.

3. The sandblasting accuracy detection method for the bottom shell of a wearable watch according to claim 1, characterized in that: The steps for analyzing the consistent value of the bottom shell sandblasting information and the bottom shell sandblasting gradient noise value are as follows: Step 101: The light source is irradiated onto the surface of the bottom case of the watch, and image information is obtained by using a camera. The image is normalized and gray-scaled. The normalized image is gradually expanded from a 1×1 pixel window to an n×n pixel window in sequence, and the gray value of each point in each window is calculated as the reflected light intensity of each point in the window; Step 102: Divide the reflected light intensity range of two different window scales into m intervals, calculate the probability of the emitted light intensity of each window scale in each interval, marked as edge probability, and the probability that the intensity of the two window sizes falls into their respective specific intervals at the same time, marked as joint probability, for each pair of interval combinations that appear, there are m×m, divide the joint probability by the product of the two edge probabilities, marked as division value one, take the logarithm of the division value one and multiply it by the joint probability to obtain the product value, add all the product values ​​to obtain the mutual information value of the two different window scales, repeat the above steps to obtain all the mutual information values ​​of the two different window scales, and add them to obtain the consistent value of the bottom shell sandblasting information; Step 103: For the normalized image obtained, an image gradient calculation algorithm is used to obtain the gradient vector of each pixel in the image, and the standard deviation of the gradient vector direction in the image and the extreme value of the gradient vector modulus in the image are summed to obtain the bottom shell sandblasting gradient noise value.

4. The sandblasting accuracy detection method for the bottom shell of a wearable watch according to claim 1, characterized in that: The steps of storage operation and label display analysis corresponding to the comprehensive evaluation signals of each level are as follows: Step 501: Perform a union process on the bottom shell sandblasting visual evaluation set H and the bottom shell sandblasting depth evaluation set W. When H∪W={h1, w1}, a first-level comprehensive evaluation signal for bottom shell sandblasting accuracy determination is generated; when H∪W={h1, w2} or {h1, w3} or {h2, w1} or {h2, w2} or {h3, w1}, a second-level comprehensive evaluation signal for bottom shell sandblasting accuracy determination is generated; when H∪W={h2, w3} or {h3, w2}, a third-level comprehensive evaluation signal for bottom shell sandblasting accuracy determination is generated; when H∪W={h3, w3}, a fourth-level comprehensive evaluation signal for bottom shell sandblasting accuracy determination is generated; Step 502: If the sandblasting of the bottom shell generates a first-level comprehensive evaluation signal for determining the accuracy of the bottom shell sandblasting, the sandblasting of the bottom shell will be reclassified into set Y1, put into storage, and the label will display label one; if the sandblasting of the bottom shell generates a second-level comprehensive evaluation signal for determining the accuracy of the bottom shell sandblasting, the sandblasting of the bottom shell will be reclassified into set Y2, put into storage, and the label will display label one; if the sandblasting of the bottom shell generates a third-level comprehensive evaluation signal for determining the accuracy of the bottom shell sandblasting, the sandblasting of the bottom shell will be reclassified into set Y3, and storage into storage is not allowed, and the label will display label one; if the sandblasting of the bottom shell generates a fourth-level comprehensive evaluation signal for determining the accuracy of the bottom shell sandblasting, the sandblasting of the bottom shell will be reclassified into set Y4, and storage into storage is not allowed, and the label will display label one.

5. A sandblasting accuracy detection method for a bottom shell of a wearable watch according to claim 4, characterized in that: The bottom shell sandblasting depth assessment set W analysis steps are as follows: Step 402: when the deep residual corrosion value of the bottom shell sandblasting is greater than or equal to the reference threshold value TG1, the corresponding bottom shell sandblasting sends a bottom shell sandblasting level 3 depth assessment judgment signal; when the deep residual corrosion value of the bottom shell sandblasting is less than the reference threshold value TG1 and greater than the reference threshold value TG2, the corresponding bottom shell sandblasting sends a bottom shell sandblasting level 2 depth assessment judgment signal; when the deep residual corrosion value of the bottom shell sandblasting is less than or equal to the reference threshold value TG2, the corresponding bottom shell sandblasting sends a bottom shell sandblasting level 1 depth assessment judgment signal; Step 403: A set W is established according to the bottom shell sandblasting depth assessment and determination signal, and the bottom shell sandblasting first, second and third level depth assessment and determination signals are respectively determined as elements w1, w2 and w3.

6. The sandblasting accuracy detection method for the bottom shell of a wearable watch according to claim 1, characterized in that: The steps for analyzing the material corrosion capacity value and the surface light variation value of the bottom shell sandblasting are as follows: The material performance parameters of the bottom shell sandblasting refer to the weighted calculation of the sandblasting chemical activity, the galvanic corrosion sensitivity of the bottom shell, and the biocompatibility, and multiply them by the corresponding influencing factor coefficients to obtain the material corrosion capacity value of the bottom shell sandblasting. The galvanic corrosion sensitivity of the bottom shell refers to the polarization curve measurement of the bottom shell after sandblasting to obtain the value of the corrosion current density. Biocompatibility refers to the number of times itching, erythema, rash, swelling, redness and fever occur after wearing the watch for a certain period of time. The surface optical performance of the bottom shell sandblasting refers to the sum of the surface light scattering characteristic value, the surface color stability value, and the optical reflectivity anisotropy value to obtain the surface light variation value of the bottom shell sandblasting. The surface light scattering characteristic value refers to the light scattering spectrum obtained by the light scattering instrument, the range of scattered light intensity and scattering angle in the spectrum, and the sum value. The surface color stability value refers to the chromaticity change value of the bottom shell sandblasting after a certain period of time in the salt spray test. The optical reflectivity anisotropy value refers to the optical reflectivity of each angle of the bottom shell sandblasting measured by a multi-angle spectrophotometer to obtain the standard deviation of the optical reflectivity.

7. The sandblasting accuracy detection method for the bottom shell of a wearable watch according to claim 1, characterized in that: The analysis steps of the surface material characteristic value, particle distribution value and force residual value of the bottom shell sandblasting are as follows: Step 301: The surface physical characteristics of the bottom shell sandblasting refer to the surface friction distribution value, surface flatness, surface glossiness, and surface wettability. The sum of the surface flatness, surface glossiness, and surface wettability values ​​is divided by the surface friction distribution value to obtain the surface physical characteristics of the bottom shell sandblasting. The surface friction distribution value refers to the standard deviation of the friction force in different areas of the bottom shell sandblasting surface. The surface wettability value refers to the value of the contact angle of the bottom shell sandblasting. The particle distribution characteristics of the bottom shell sandblasting refer to the roundness of the sandblasting particles, the static value of the sandblasting particles, and the particle size distribution of the sandblasting particles. Electrostatic distribution value and sandblasting particle breakage rate. The roundness of sandblasting particles is divided by the sum of the electrostatic distribution value of sandblasting particles and the breakage rate of sandblasting particles to obtain the particle distribution value of bottom shell sandblasting. The electrostatic distribution value of sandblasting particles refers to the static electricity of sandblasting particles. The mechanical state of bottom shell sandblasting refers to the addition of particle attachment force and residual stress to obtain the force residual value of bottom shell sandblasting. Among them, the particle attachment force refers to the value of particles on the tape when a standard tape is pasted on the sandblasting surface of the bottom shell and the tape is torn off from the bottom shell surface at a certain speed.

8. The sandblasting accuracy detection method for the bottom shell of a wearable watch according to claim 4, characterized in that: The bottom shell sandblasting visual assessment set H analysis steps are as follows: Step 201: when the visual evaluation value of the bottom shell sandblasting is in the gradient reference interval NM1, the corresponding bottom shell sandblasting sends a bottom shell sandblasting level 3 visual evaluation judgment signal; when the visual evaluation value of the bottom shell sandblasting is in the gradient reference interval NM2, the corresponding bottom shell sandblasting sends a bottom shell sandblasting level 2 visual evaluation judgment signal; when the visual evaluation value of the bottom shell sandblasting is in the gradient reference interval NM3, the corresponding bottom shell sandblasting sends a bottom shell sandblasting level 1 visual evaluation judgment signal; Step 202: A set H is established based on the bottom shell sandblasting visual evaluation judgment signal, and the first, second and third level visual evaluation judgment signals of the bottom shell sandblasting are respectively determined as elements h1, h2 and h3.

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