High-precision multi-angle workpiece production detection system based on simulation vision

Abstract

The invention discloses a high-precision multi-angle workpiece production detection system based on simulation vision, relates to the field of simulation, and is used for solving the problem that when a workpiece is disassembled or assembled, deviation exists between multi-angle image data fusion and illumination characteristics in a real scene, so that the detection accuracy is reduced. Illumination characteristic information and material characteristic information in a simulation environment and a real scene are collected, a data analysis model is established, deviation evaluation coefficients are obtained and compared with a deviation threshold value, a comparison result is obtained, the deviation evaluation coefficients smaller than the deviation threshold value are defined as fine deviations, and the fine deviations are calculated. Deviation evaluation coefficients larger than or equal to the deviation threshold value are defined as deviation angles, a set of fuzzy rules are formulated according to the alarm threshold value trigger frequency for fuzzy reasoning, a deviation adjustment scheme is determined, the detection frequency is set according to production operation of workpieces, deviation of all the angles can be effectively adjusted, and the detection accuracy is improved. And the detection decision flexibility and efficiency are higher.

Description

Technical Field

[0001] The present invention relates to the field of simulation technology, and more specifically, to a high-precision multi-angle workpiece production detection system based on simulation vision. Background Art

[0002] In modern manufacturing, workpiece production inspection is a key link to ensure product quality and production efficiency. With the development of automation and intelligent technology, the use of simulated vision can effectively reduce the contact with workpieces during workpiece production inspection operations. At the same time, multi-angle inspection can collect workpiece data from different perspectives, thereby improving inspection efficiency and accuracy.

[0003] The prior art has the following deficiencies:

[0004] At present, based on simulated vision, simulated scenes are generated according to real scene data to perform multi-angle generation and detection of workpieces. However, in the modern workpiece production process, workpieces often need to be disassembled or assembled, and simulated vision technology is always built based on the light source and material data information of the real scene, which leads to deviations from the lighting characteristics in the real scene when multi-angle image data is fused, thereby reducing the accuracy of detection. Therefore, a high-precision multi-angle workpiece production detection system based on simulated vision is proposed.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a high-precision multi-angle workpiece production inspection system based on simulated vision, which solves the problems raised in the above-mentioned background technology by using different product inspection methods.

[0007] To achieve the above object, the present invention provides the following technical solution: a high-precision multi-angle workpiece production inspection system based on simulated vision, comprising a data acquisition module, a data processing module, a deviation analysis module and a deviation adjustment module; signal connections between the modules;

[0008] The data acquisition module is used to collect the illumination characteristic information and material characteristic information in the simulation environment and the real scene, obtain the illumination reflectivity difference, the bidirectional reflection distribution difference, the background light intensity difference and the illumination uniform distribution difference through data processing, and send the illumination characteristic information and material characteristic information in the simulation environment and the real scene to the data processing module;

[0009] The data processing module is used to obtain the illumination characteristic information and material characteristic information in the simulation environment and the real scene, establish a data analysis model, obtain the deviation evaluation coefficient and send it to the deviation analysis module;

[0010] The deviation analysis module is used to obtain the deviation evaluation coefficients of multiple angles, and compare them with the deviation threshold to obtain the comparison results. The deviation evaluation coefficients less than the deviation threshold are defined as slight deviations, and the deviation evaluation coefficients greater than or equal to the deviation threshold are defined as individual deviation angles and sent to the deviation adjustment module. The maximum deviation evaluation coefficient and the surrounding deviation evaluation coefficients greater than or equal to the deviation threshold are statistically calculated and compared with the alarm threshold to evaluate the maximum deviation angle.

[0011] The deviation adjustment module is used to obtain each deviation angle, obtain each deviation value and collect the alarm threshold trigger frequency, and use fuzzy logic to determine the deviation adjustment plan for each deviation value and alarm threshold trigger frequency.

[0012] In a preferred embodiment, the material characteristic information includes the difference in illumination reflectance and the difference in bidirectional reflection distribution; the illumination characteristic information includes the difference in background light intensity and the difference in illumination uniform distribution;

[0013] The light reflectance difference Rd is obtained by measuring the reflected luminous flux and the luminous flux incident on the workpiece surface under the material properties and light source conditions set in the real scene and the simulation environment, and calculating the ratio and difference respectively. i ; Where i is the i-th angle;

[0014] The bidirectional reflection distribution value in the real scene and the bidirectional reflection distribution value in the simulation environment are brought into the root mean square error calculation to obtain the bidirectional reflection distribution difference Bd i ;

[0015] Use the light sensor and set the virtual light source and scene parameters to measure the overall ambient light intensity of the simulated environment and the real scene, and calculate the difference between them to obtain the background light intensity difference Bl i ;

[0016] The illumination uniform distribution difference Ud is obtained by the absolute difference between the difference between the maximum and minimum illumination intensities in the real scene and the simulation environment and the ratio of the corresponding average illumination intensities. i .

[0017] In a preferred embodiment, the illumination characteristic information and material characteristic information in the simulation environment and the real scene are obtained, including the illumination reflectivity difference Rd i , Bidirectional reflection distribution difference Bd i 、Background light intensity difference Bl i And the uniform distribution difference of illumination Udi , establish a data analysis model and generate the deviation evaluation coefficient De i , the specific formula is:

[0018]

[0019] In the formula, De i is the deviation assessment coefficient, as well as is the preset proportionality factor for the difference in illumination reflectance, the difference in bidirectional reflectance distribution, the difference in background light intensity, and the difference in illumination uniformity distribution, and as well as Both are greater than 0.

[0020] In a preferred embodiment, after obtaining the deviation evaluation coefficients from multiple angles, the deviation evaluation coefficients are compared and analyzed with the continuously iterated deviation thresholds;

[0021] If the deviation evaluation coefficient is greater than or equal to the deviation threshold, the illumination characteristic information obtained at the angle corresponding to the deviation evaluation coefficient is marked as an obvious deviation, and an obvious signal is generated;

[0022] If the deviation evaluation coefficient is less than the deviation threshold, the illumination characteristic information acquired at the angle corresponding to the deviation evaluation coefficient is marked as a slight deviation, and an end signal is generated.

[0023] In a preferred embodiment, the deviation evaluation coefficients of the multi-angle illumination characteristics that are greater than or equal to the deviation threshold are counted, and the deviation evaluation coefficients are sorted to obtain each deviation angle and send it to the deviation adjustment module to identify the maximum deviation evaluation coefficient De max , perform statistical calculations on the values ​​of the deviation assessment coefficients around it that are greater than or equal to the deviation threshold to obtain the maximum possible deviation;

[0024] The maximum possible deviation is collected and compared with the alarm threshold. If it is greater than the alarm threshold, an alarm is issued and the angle of the maximum possible deviation is marked as the maximum deviation angle.

[0025] In a preferred embodiment, each deviation angle is obtained, each deviation value is obtained, and the alarm threshold trigger frequency is collected;

[0026] Each deviation value is obtained by calculating the light reflectivity, bidirectional reflection distribution, background light intensity, and uniform light distribution at each deviation angle and the parameter difference between them and the corresponding real scene angle.

[0027] In a preferred embodiment, the alarm threshold triggering frequency is obtained by counting the number of times the illumination characteristic deviation between the simulation environment and the real scene is detected and exceeds the alarm threshold at different detection angles and performing ratio calculation.

[0028] In a preferred embodiment, each deviation value and alarm threshold trigger frequency are defined as input variables and divided into different fuzzy sets respectively;

[0029] The deviation adjustment scheme is defined as the output variable and divided into fuzzy sets;

[0030] Formulate fuzzy rules to describe the impact of each deviation value and alarm threshold triggering frequency on the deviation adjustment plan;

[0031] Perform fuzzy reasoning based on fuzzy rules and determine the deviation adjustment plan.

[0032] Technical effects and advantages of the present invention:

[0033] 1. The present invention collects illumination characteristic information and material feature information in a simulation environment and a real scene, establishes a data analysis model, obtains a deviation assessment coefficient and compares it with a deviation threshold, obtains a comparison result, defines a deviation assessment coefficient less than the deviation threshold as a slight deviation, defines a deviation assessment coefficient greater than or equal to the deviation threshold as each deviation angle, collects the maximum deviation assessment coefficient and its surrounding deviation assessment coefficients greater than or equal to the deviation threshold for statistical calculation, and compares it with the alarm threshold to evaluate the maximum deviation angle, thereby simplifying the traditional simulation environment that changes in real time according to the real scene, setting the detection frequency according to the production operation of the workpiece, giving early warning and accurately discovering the maximum deviation angle, and saving detection time.

[0034] 2. The present invention obtains each deviation value through each deviation angle, and formulates a set of fuzzy rules for fuzzy reasoning with the alarm threshold trigger frequency to determine the deviation adjustment plan, improve the automation of the system, reduce human intervention, so that the deviation of each angle can be effectively adjusted, further optimize the existing model, and make the detection decision more flexible and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a module schematic diagram of a high-precision multi-angle workpiece production inspection system based on simulated vision in the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] The present invention performs multi-angle inspection on workpiece production through a simulation scene generated based on simulated vision, extracts the three-dimensional model of the workpiece, including the geometric shape and surface material information of the workpiece, through three-dimensional scanning technology, and then sets a multi-angle virtual light source consistent with the direction, color, and intensity of the light source in the real scene to simulate the real lighting conditions of the workpiece production environment. After rendering with the GGX model, multi-angle images of the workpiece are collected, the lighting characteristics of the workpiece are obtained based on the images, and an inspection report is generated to ensure the quality of the workpiece.

[0038] Among them, the surface illumination characteristics extracted from the fused image are compared with the surface illumination characteristics obtained in the real scene, and the deviation of the illumination characteristics is analyzed to verify the accuracy of the test report;

[0039] Example 1

[0040] The present invention discloses a high-precision multi-angle workpiece production detection system based on simulated vision, such as Figure 1 As shown, it includes a data acquisition module, a data processing module, a deviation analysis module and a deviation adjustment module; and the modules are connected by signals.

[0041] The data acquisition module is used to collect the lighting characteristic information and material characteristic information in the simulation environment and the real scene, and obtain the lighting reflectance difference, bidirectional reflection distribution difference, background light intensity difference and lighting uniform distribution difference through data processing, and send the lighting characteristic information and material characteristic information in the simulation environment and the real scene to the data processing module.

[0042] Among them, the material feature information includes the difference in light reflectance and the difference in bidirectional reflection distribution; the light characteristic information includes the difference in background light intensity and the difference in light uniformity distribution.

[0043] It should be noted that the light reflectance difference can reflect the reflectivity of the workpiece surface to light, including specular reflection and diffuse reflection. The workpiece production and processing process involves grinding, assembly, and disassembly. Therefore, the acquisition module collects data based on the number of operations during the workpiece production and processing process to evaluate the deviation between the light characteristics in the simulation environment and the real scene in real time.

[0044] The difference in light reflectivity refers to the difference in the reflectivity of the workpiece surface to the incident light in the simulation environment and the real scene. Specifically, the reflectivity indicates the proportion of the incident light reflected by the surface material;

[0045] The acquisition logic is to use a photometer or illuminometer to measure the luminous flux reflected from the surface of the workpiece in the real scene and the luminous flux incident on the surface of the workpiece and calculate the ratio to obtain the reflectivity Rr in the real scene. iThen, according to the material properties and light source conditions set in the simulation environment, the luminous flux reflected by the workpiece surface is combined with the luminous flux on the workpiece surface to obtain the reflectivity Rs in the simulation environment. i , according to the formula: Rd i =|Rs i -Rr i |Get the light reflectance difference Rd i ; Where i is the i-th angle;

[0046] Bidirectional reflection distribution difference refers to the difference between the distribution characteristics of the workpiece surface for incident and reflected light in the simulation environment and the real scene. Specifically, the bidirectional reflection distribution (BREF) describes the intensity distribution of light reflected at different angles after entering the surface from an incident direction. The corresponding bidirectional reflection distribution is calculated by the function formula for the real scene and the simulation environment through optical measurement sensors and GGX model rendering. The specific formula is as follows:

[0047]

[0048] In the formula, ω i is the incident direction of the light, ω o is the reflection direction, dL o (ω o ) is in the direction ω o Radiance reflected from above, dE i (ω i ) is from the direction ω i The incident radiant illumination;

[0049] The BRDF values ​​in the real scene and the BRDF values ​​in the simulation environment are brought into the root mean square error calculation, and the formula is expressed as:

[0050]

[0051] Where Bd i is the bidirectional reflection distribution difference, y i is the BRDF value in the i-th real scene, is the BRDF value in the i-th simulation environment;

[0052] The background light intensity difference refers to the diffuse light from all directions in the simulation environment and the real scene, which does not rely on direct illumination from a specific light source (such as ambient light, reflected light, etc.); its acquisition logic is to use the light sensor and set the virtual light source and scene parameters to measure the overall ambient light intensity of the simulation environment and the real scene, and calculate the difference between them to obtain the background light intensity difference Bl i ;

[0053] Among them, if the difference in background light intensity is greater, the deviation of the corresponding illumination characteristics will be greater, resulting in the inconsistency between the performance of the workpiece in the simulation environment and the real scene, resulting in reduced accuracy of the test report;

[0054] The difference in illumination uniformity refers to the difference between the illumination uniformly distributed on the entire workpiece surface or the area with uneven brightness in the simulation environment and the real scene. It is understandable that in the real scene, due to factors such as the position and number of light sources and the geometric characteristics of the scene, the illumination is not necessarily uniformly distributed, that is, the distribution is random. In the simulation environment, the illumination uniformity is determined by the configuration of the virtual light source, the illumination model and the rendering technology, that is, the distribution is controllable. Although the distribution is controlled by adjusting the light source parameters, there is still a slight deviation from the illumination distribution in the real scene in the complex process of workpiece production.

[0055] The acquisition logic is to obtain the uniform illumination distribution difference Ud by the absolute difference between the difference between the maximum and minimum illumination intensities in the real scene and the simulation environment and the ratio of the corresponding average illumination intensities. i ;

[0056] It should be noted that the illumination characteristic information and material feature information in the above-mentioned simulation environment and real scene are obtained by multi-angle images taken by a multi-angle camera set in the real scene and a multi-angle virtual camera set in the virtual environment corresponding to the real scene position; among them, the specific image detection method (such as edge detection, texture analysis, drawing color and brightness histogram, etc.) is implemented by the experimenter according to the specific actual situation and is not limited here;

[0057] The data processing module is used to obtain the lighting characteristic information and material feature information in the simulation environment and the real scene, establish a data analysis model, obtain the deviation evaluation coefficient and send it to the deviation analysis module.

[0058] Obtain lighting characteristic information and material feature information in the simulation environment and real scene, including the lighting reflectivity difference Rd i , Bidirectional reflection distribution difference Bd i 、Background light intensity difference Bl i And the uniform distribution difference of illumination Ud i , establish a data analysis model and generate the deviation evaluation coefficient De i , the specific formula is:

[0059]

[0060] In the formula, De i is the deviation assessment coefficient, as well as is the preset proportionality factor for the difference in illumination reflectance, the difference in bidirectional reflectance distribution, the difference in background light intensity, and the difference in illumination uniformity distribution, and as well as Both are greater than 0.

[0061] Among them, the difference in illumination reflectivity, the difference in bidirectional reflection distribution, the difference in background light intensity, and the difference in illumination uniformity distribution all directly express the differences in illumination characteristics between the current angle simulation environment and the real scene.

[0062] Specifically, from the above formula, it can be seen that the greater the difference in illumination reflectance, bidirectional reflection distribution, background light intensity and illumination uniform distribution, the greater the deviation evaluation coefficient, and the greater the difference between the illumination characteristics in the image captured at a single angle and the material characteristics it reflects, the lower the degree of consistency between the built-in data of the simulation environment as the real scene changes. Conversely, the smaller the deviation evaluation coefficient.

[0063] The deviation analysis module is used to obtain the deviation assessment coefficients of multiple angles and compare them with the deviation threshold to obtain the comparison results. The deviation assessment coefficients less than the deviation threshold are defined as slight deviations, and the deviation assessment coefficients greater than or equal to the deviation threshold are defined as individual deviation angles and sent to the deviation adjustment module. The maximum deviation assessment coefficient and the deviation assessment coefficients around it that are greater than or equal to the deviation threshold are statistically calculated and compared with the alarm threshold to evaluate the maximum deviation angle.

[0064] The logic for obtaining the deviation threshold is to collect a set of distributed samples from the historical deviation evaluation information library, divide the data set into a training set and a test set, set the evaluation indicators and clustering algorithm, and in each round of cross-validation, train the model on the training set and evaluate the model performance on the test set. Then, adjust the deviation threshold based on the performance of the validation set. Therefore, the deviation threshold is constantly iteratively updated.

[0065] In the present invention, clustering algorithm is a type of unsupervised learning algorithm, which is used to divide the data points in the data set into groups or clusters with similarities; a common one is K-means clustering, which divides the data points in the data set into K clusters, so that the distance between the data point of each curve and the center point (centroid) of the cluster to which it belongs is minimized, and finally the effect of the adjusted deviation threshold is measured by the Euclidean distance, so as to set the deviation threshold.

[0066] After obtaining the deviation assessment coefficients from multiple angles, the deviation assessment coefficients are compared and analyzed with the continuously iterated deviation thresholds.

[0067] If the deviation evaluation coefficient is greater than or equal to the deviation threshold, the illumination characteristic information acquired at the angle corresponding to the deviation evaluation coefficient is marked as an obvious deviation, and an obvious signal is generated.

[0068] If the deviation evaluation coefficient is less than the deviation threshold, the illumination characteristic information acquired at the angle corresponding to the deviation evaluation coefficient is marked as a slight deviation, and an end signal is generated.

[0069] Count the deviation evaluation coefficients of multi-angle illumination characteristics that are greater than or equal to the deviation threshold, sort the deviation evaluation coefficients, get each deviation angle and send it to the deviation adjustment module to identify the maximum deviation evaluation coefficient De max , perform statistical calculations on the values ​​of the deviation assessment coefficients around it that are greater than or equal to the deviation threshold to obtain the maximum possible deviation;

[0070] Among them, the maximum deviation may refer to that when the deviation evaluation coefficient of the illumination characteristics of a certain angle is the maximum deviation value of this detection, the values ​​of the deviation evaluation coefficients greater than or equal to the deviation threshold value around it are statistically calculated; for example, the simulation environment sets up nine virtual cameras corresponding to the real scene, and the detection finds that the deviation evaluation coefficient of the image data obtained by the virtual camera at a position of 30 degrees to the left of the workpiece and the image data obtained by the camera corresponding to the real scene is the largest, then the deviation evaluation coefficients greater than or equal to the deviation threshold value at multiple angles such as 60 degrees to the left and 90 degrees to the left around it are brought into the statistical calculation to evaluate the maximum deviation of the deviation; among them, the experimenter can set different ranges according to the actual situation to fine-tune the possible values ​​of the maximum deviation, so as to facilitate better precise adjustment according to different information.

[0071] The maximum possible deviation is collected and compared with the alarm threshold. If it is greater than the alarm threshold, an alarm is issued and the angle of the maximum possible deviation is marked as the maximum deviation angle.

[0072] The maximum deviation angle refers to the angle of the illumination characteristics with the maximum deviation between the simulation environment and the real scene, and the alarm threshold is obtained through the current deviation evaluation coefficient and the historical deviation evaluation information library.

[0073] The present invention collects illumination characteristic information and material feature information in a simulation environment and a real scene, establishes a data analysis model, obtains a deviation assessment coefficient and compares it with a deviation threshold, obtains a comparison result, defines a deviation assessment coefficient less than the deviation threshold as a slight deviation, defines a deviation assessment coefficient greater than or equal to the deviation threshold as each deviation angle, collects the maximum deviation assessment coefficient and its surrounding deviation assessment coefficients greater than or equal to the deviation threshold for statistical calculation, compares them with the alarm threshold, and evaluates the maximum deviation angle, thereby simplifying the traditional simulation environment that changes in real time according to the real scene, setting the detection frequency according to the production operation of the workpiece, giving early warning and accurately discovering the maximum deviation angle, and saving detection time.

[0074] Example 2

[0075] In Example 1 of the present invention, examples are given to illustrate that a data analysis model is established through illumination characteristic information and material feature information in a simulation environment and a real scene, a deviation assessment coefficient is obtained and compared with a deviation threshold, a comparison result is obtained, a deviation assessment coefficient less than the deviation threshold is defined as a slight deviation, a maximum deviation assessment coefficient and its surrounding deviation assessment coefficients greater than or equal to the deviation threshold are collected for statistical calculation, and compared with the alarm threshold to evaluate the operation strategy of the maximum deviation angle; however, in Example 1, only the maximum deviation angle is found, and how to adjust the deviation is not considered. Obviously, although manual control can be performed after an alarm is issued to reduce the deviation, there is a possibility of multiple workpieces being polished, assembled or disassembled at the same time in the workpiece production environment, which will lead to insufficient system automation and excessive manpower consumption; in view of the above problems, Example 2 of the present invention is further refined;

[0076] The deviation adjustment module is used to obtain each deviation angle, obtain each deviation value and collect the alarm threshold trigger frequency, and use fuzzy logic to determine the deviation adjustment plan for each deviation value and alarm threshold trigger frequency;

[0077] Among them, each deviation value refers to the specific deviation value of each parameter in the illumination characteristics of the image data obtained at each deviation angle and each parameter in the illumination characteristics of the image data obtained from the corresponding real scene. Specifically, each parameter has been exemplified in Example 1 and will not be repeated here.

[0078] The acquisition logic is to calculate the parameter difference between the illumination reflectance, bidirectional reflection distribution, background light intensity and illumination uniform distribution at each deviation angle and the corresponding real scene angle. Specifically, if the difference is positive, it means that the parameter is set too low in the simulation environment. Conversely, if the difference is negative, it means that the parameter is set too high in the simulation environment.

[0079] The alarm threshold trigger frequency refers to the frequency of triggering the alarm when the system detects that the difference in lighting characteristics between the simulation environment and the real scene reaches or exceeds the preset alarm threshold during the workpiece detection process;

[0080] The acquisition logic is to obtain the alarm threshold trigger frequency by counting the number of times the illumination characteristics deviation between the simulation environment and the real scene is detected and exceeds the alarm threshold at different detection angles and performing ratio calculation;

[0081] Among them, the value of the alarm threshold trigger frequency is further limited. It is understandable that if the alarm threshold trigger frequency is continuously counted, there will be data redundancy. Therefore, the experimenter can limit the collection time of the alarm threshold trigger frequency based on the adjustment frequency of the angle and other information to reduce the problem of excessive data or too high recent trigger frequency, resulting in too high detection times and failure to timely discover the recent abnormality of the angle;

[0082] Determine the deviation adjustment plan using fuzzy logic based on each deviation value and the alarm threshold triggering frequency;

[0083] For example, "High", "Low", "Medium" for each deviation value, "Many", "Few", "Moderate" for the alarm threshold trigger frequency;

[0084] Formulate a set of fuzzy rules to describe the impact of different input variables on output variables. The definition of rules can be based on professional knowledge or obtained through data analysis and experiments. For example:

[0085] Mark each deviation value as X, the alarm threshold trigger frequency as U, and the deviation adjustment plan as C_Public;

[0086] Then we can define:

[0087] Rule 1:IF(X is High)AND(U is Many)THEN(C_Public is High)

[0088] Rule 2:IF(U is Low)AND(U is Few)THEN(C_Public is Low) ...

[0090] Perform fuzzy reasoning based on fuzzy rules to determine the deviation adjustment plan;

[0091] It should be noted that the division of fuzzy sets can be adjusted according to actual conditions. For example, although this embodiment takes three fuzzy sets as an example, in fact, the various deviation values ​​and alarm threshold trigger frequencies can be divided into more than three sets to facilitate better precise adjustment based on different public information.

[0092] Furthermore, for the judgment of each deviation value and the high, medium and low alarm threshold trigger frequency, the threshold can be set according to the actual situation. For example, when each deviation value exceeds 68% of the actual scene value, it is marked as "High", and when the alarm threshold trigger frequency is higher than 75%, it is marked as "Many", and so on. I will not go into details here.

[0093] Among them, the specific steps of GGX model rendering are to calculate the micro-normal distribution of the surface through the GGX microsurface distribution function (NDF) to describe how light is scattered on a rough or shiny surface. The GGX model allows light to be reflected in different directions by defining a distribution of micro-normals. Combined with the Fresnel reflection model, the reflection intensity of the incident light is calculated based on the refractive index and incident angle of the material. This accurately simulates light. Finally, the Smith G geometric shading-shadow model is used to determine the micro-normal distribution, calculate the Fresnel reflection effect, and combine it with geometric shading to ultimately generate a visual effect that matches the actual surface lighting conditions of the workpiece.

[0094] The present invention obtains each deviation value through each deviation angle, and formulates a set of fuzzy rules for fuzzy reasoning with the alarm threshold trigger frequency to determine the deviation adjustment plan, improve the automation of the system, reduce human intervention, so that the deviation of each angle can be effectively adjusted, further optimize the existing model, and make the detection decision more flexible and efficient.

[0095] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0096] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0097] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0098] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0099] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0100] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0101] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0102] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0103] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0104] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A high-precision multi-angle workpiece production inspection system based on simulated vision, characterized by: It includes data acquisition module, data processing module, deviation analysis module and deviation adjustment module; signal connection between each module; The data acquisition module is used to collect the illumination characteristic information and material characteristic information in the simulation environment and the real scene, obtain the illumination reflectivity difference, the bidirectional reflection distribution difference, the background light intensity difference and the illumination uniform distribution difference through data processing, and send the illumination characteristic information and material characteristic information in the simulation environment and the real scene to the data processing module; The data processing module is used to obtain the illumination characteristic information and material characteristic information in the simulation environment and the real scene, establish a data analysis model, obtain the deviation evaluation coefficient and send it to the deviation analysis module; The deviation analysis module is used to obtain the deviation evaluation coefficients of multiple angles, and compare them with the deviation threshold to obtain the comparison results. The deviation evaluation coefficients less than the deviation threshold are defined as slight deviations, and the deviation evaluation coefficients greater than or equal to the deviation threshold are defined as individual deviation angles and sent to the deviation adjustment module. The maximum deviation evaluation coefficient and the surrounding deviation evaluation coefficients greater than or equal to the deviation threshold are statistically calculated and compared with the alarm threshold to evaluate the maximum deviation angle. The deviation adjustment module is used to obtain each deviation angle, obtain each deviation value and collect the alarm threshold trigger frequency, and use fuzzy logic to determine the deviation adjustment plan for each deviation value and alarm threshold trigger frequency.

2. The high-precision multi-angle workpiece production inspection system based on simulated vision according to claim 1, characterized in that: Material feature information includes the difference in illumination reflectance and the difference in bidirectional reflection distribution; illumination feature information includes the difference in background light intensity and the difference in illumination uniformity distribution; The light reflectance difference Rd is obtained by measuring the reflected luminous flux of the workpiece surface in the real scene and the material properties and light source conditions set in the simulation environment, as well as the luminous flux incident on the workpiece surface, and calculating the ratio and difference respectively. i ; Where i is the i-th angle; The bidirectional reflection distribution value in the real scene and the bidirectional reflection distribution value in the simulation environment are brought into the root mean square error calculation to obtain the bidirectional reflection distribution difference Bd i ; Use the light sensor and set the virtual light source and scene parameters to measure the overall ambient light intensity of the simulated environment and the real scene, and calculate the difference between them to obtain the background light intensity difference Bl i ; The illumination uniform distribution difference Ud is obtained by the absolute difference between the difference between the maximum and minimum illumination intensities in the real scene and the simulation environment and the ratio of the corresponding average illumination intensity. i .

3. The high-precision multi-angle workpiece production inspection system based on simulated vision according to claim 2, characterized in that: Obtain lighting characteristic information and material feature information in the simulation environment and real scene, including the lighting reflectivity difference Rd i , Bidirectional reflection distribution difference Bd i 、Background light intensity difference Bl i And the uniform distribution difference of illumination Ud i , establish a data analysis model and generate the deviation evaluation coefficient De i , the specific formula is: In the formula, De i is the deviation assessment coefficient, as well as is the preset proportional coefficient of the difference in illumination reflectance, the difference in bidirectional reflectance distribution, the difference in background light intensity, and the difference in illumination uniformity distribution, and as well as Both are greater than 0.

4. The high-precision multi-angle workpiece production inspection system based on simulated vision according to claim 1, characterized in that: After obtaining the deviation assessment coefficients from multiple angles, the deviation assessment coefficients are compared and analyzed with the continuously iterated deviation thresholds; If the deviation evaluation coefficient is greater than or equal to the deviation threshold, the illumination characteristic information obtained at the angle corresponding to the deviation evaluation coefficient is marked as an obvious deviation, and an obvious signal is generated; If the deviation evaluation coefficient is less than the deviation threshold, the illumination characteristic information acquired at the angle corresponding to the deviation evaluation coefficient is marked as a slight deviation, and an end signal is generated.

5. The high-precision multi-angle workpiece production inspection system based on simulated vision according to claim 4, characterized in that: Count the deviation evaluation coefficients of multi-angle illumination characteristics that are greater than or equal to the deviation threshold, sort the deviation evaluation coefficients, obtain each deviation angle and send it to the deviation adjustment module to identify the maximum deviation evaluation coefficient Dd max , perform statistical calculations on the values ​​of the deviation assessment coefficients around it that are greater than or equal to the deviation threshold to obtain the maximum possible deviation; The maximum possible deviation is collected and compared with the alarm threshold. If it is greater than the alarm threshold, an alarm is issued and the angle of the maximum possible deviation is marked as the maximum deviation angle.

6. The high-precision multi-angle workpiece production inspection system based on simulated vision according to claim 1, characterized in that: Obtain each deviation angle, obtain each deviation value and collect the alarm threshold trigger frequency; Each deviation value is obtained by calculating the light reflectivity, bidirectional reflection distribution, background light intensity, and uniform light distribution at each deviation angle and the parameter difference between them and the corresponding real scene angle.

7. The high-precision multi-angle workpiece production inspection system based on simulated vision according to claim 6, characterized in that: By counting the number of times the illumination characteristics of the simulation environment and the real scene deviate from each other and exceed the alarm threshold at different detection angles and performing ratio calculation, the alarm threshold triggering frequency is obtained.

8. The high-precision multi-angle workpiece production inspection system based on simulated vision according to claim 6, characterized in that: Each deviation value and alarm threshold trigger frequency are defined as input variables and divided into different fuzzy sets respectively; The deviation adjustment scheme is defined as the output variable and divided into fuzzy sets; Formulate fuzzy rules to describe the impact of each deviation value and alarm threshold triggering frequency on the deviation adjustment plan; Perform fuzzy reasoning based on fuzzy rules and determine the deviation adjustment plan.

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