An audio control panel installation device and a fixing performance detection method thereof

Abstract

The present invention relates to the field of circuit board production technology, and in particular to an audio control panel installation device and a fixing performance detection method thereof. In terms of the installation device, its structural design is reasonable, and the arrangement of various components such as the front panel, the false panel, the bottom shell, etc., helps to achieve a stable installation of the audio control panel. In terms of the fixing performance detection method, by collecting laser point cloud data at preset position nodes to build a database, the status information of the audio control panel after installation can be fully obtained. The data is classified and denoised to obtain a pure and accurate data set for each plane area, and then a characteristic three-dimensional model diagram is constructed to accurately present the actual structure after installation. Positioning analysis based on this model diagram is performed to determine whether the process performance requirements are met. It can not only effectively eliminate the influence of factors such as measurement errors and environmental interference, but also accurately evaluate the fixing performance of the installation device, thereby improving the quality and reliability of the audio control panel installation.

Description

Technical Field

[0001] The invention relates to the technical field of circuit board production, in particular to an audio control panel installation device and a fixing performance detection method thereof. Background Art

[0002] With the development of miniaturization, integration and intelligence of electronic products, audio control panels have been widely used in various electronic devices. Their installation accuracy and fixing performance are directly related to the reliability and service life of the equipment. Traditional audio control panel installation devices have problems such as low installation accuracy, difficult operation, insufficient fixing strength and backward detection methods, which can easily cause the panel to loosen, fall off or be damaged, affecting the normal operation and user experience of the equipment. In addition, most of the existing technologies lack refined positioning analysis and process performance evaluation of the panel after installation, making it difficult to accurately judge whether the panel meets the design requirements, resulting in difficulty in ensuring the installation quality. Therefore, there is an urgent need to develop a device and method that can effectively improve the installation accuracy and fixing performance of the audio control panel and realize automatic detection. Summary of the invention

[0003] The invention overcomes the shortcomings of the prior art and provides an audio control panel installation device and a fixing performance detection method thereof.

[0004] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is:

[0005] The present invention discloses a method for detecting the fixing performance of an audio control panel installation device, comprising the following steps:

[0006] After the audio control panel is installed and fixed by the installation device, laser point cloud data of the installed audio control panel is collected at a plurality of preset position nodes, a database is constructed, and the acquired laser point cloud data is stored in the database;

[0007] After the acquisition is completed, the laser point cloud data in the database are classified and denoised to obtain a laser point cloud data set of each plane area in the audio control panel after installation;

[0008] Constructing a characteristic three-dimensional model diagram of the installed audio control panel according to the laser point cloud data set of each plane area in the installed audio control panel;

[0009] The installed audio control panel is positioned and analyzed according to the characteristic three-dimensional model diagram to determine whether the installed audio control panel meets the process performance requirements.

[0010] Preferably, the laser point cloud data in the database are classified and denoised to obtain a laser point cloud data set of each plane area in the audio control panel after installation, specifically:

[0011] Creating a regression tree structure, initializing a number of root nodes in the regression tree structure, and mapping each laser point cloud data in the database to each root node separately;

[0012] The cosine similarity algorithm is introduced to calculate the cosine similarity between the laser point cloud data in each root node based on the cosine similarity algorithm; the two root nodes with the largest cosine similarity are merged to obtain several leaf nodes;

[0013] Perform feature extraction processing on the laser point cloud data in each leaf node to obtain the feature vector of the laser point cloud data in each leaf node; perform weighted average processing on the feature vector of the laser point cloud data in each leaf node to obtain the node center of each leaf node;

[0014] The Euclidean distances between all laser point cloud data in each leaf node and the corresponding node center are calculated respectively, and the Euclidean distances between all laser point cloud data in each leaf node and the corresponding node center are weighted averaged to obtain the saturation of each leaf node; and the saturation of each leaf node is compared with a preset threshold;

[0015] When the saturation of each leaf node is greater than the preset threshold, a stop merge operation instruction is generated; otherwise, each leaf node is regarded as a root node and the merge operation is continued until a stop merge operation instruction is obtained;

[0016] After obtaining the instruction to stop the merge operation, the last merge operation result is obtained, and a regression tree model is generated according to the last merge operation result.

[0017] Preferably, the laser point cloud data in the database are classified and denoised to obtain a laser point cloud data set of each plane area in the audio control panel after installation, further comprising the following steps:

[0018] Obtain each leaf node in the regression tree model, and calculate the Mahalanobis distance between the node centers of every two leaf nodes in the regression tree model;

[0019] Compare the Mahalanobis distance between the node centers of every two leaf nodes in the regression tree model with the preset distance value;

[0020] If the Mahalanobis distance between the node centers of two leaf nodes in the regression tree model is less than the preset distance value, the two leaf nodes are marked as a pair of leaf nodes with similar features; the two leaf nodes marked as a pair of leaf nodes with similar features are merged;

[0021] Update the state of each leaf node in the regression tree model, and calculate the local outlier factor value of the laser point cloud data in each leaf node in the regression tree model;

[0022] If the local outlier factor value of a laser point cloud data in a leaf node is greater than a preset value, the laser point cloud data is marked as noise data and completely deleted;

[0023] The status of each leaf node in the regression tree model is updated again to obtain the final regression tree model; the final regression tree model is subjected to leaf pruning to obtain the laser point cloud data set of each plane area in the audio control panel after installation.

[0024] Preferably, a characteristic three-dimensional model diagram of the installed audio control panel is constructed according to the laser point cloud data set of each plane area in the installed audio control panel, specifically:

[0025] Normalize the coordinates of the laser point cloud data of each plane area to determine a unified coordinate origin and coordinate axis direction, so that the data of different plane areas can be expressed in the same coordinate system;

[0026] Performing feature analysis on each plane region point cloud data to obtain feature conditions of each plane region point cloud data, and determining the geometric constraint relationship between each plane region according to the feature conditions of each plane region point cloud data; wherein the feature conditions include density change and spatial distribution;

[0027] The plane regions are spliced ​​according to the geometric constraint relationship between the plane regions to obtain a three-dimensional frame structure; the relative coordinate information between the laser point cloud data is obtained in the three-dimensional frame structure;

[0028] A coordinate data set is generated according to the relative coordinate information between each laser point cloud data, and a characteristic three-dimensional model diagram of the audio control panel after installation is constructed according to the coordinate data set.

[0029] Preferably, the audio control panel after installation is positioned and analyzed according to the characteristic three-dimensional model diagram to determine whether the audio control panel after installation meets the process performance requirements, specifically:

[0030] Obtaining standard installation process engineering drawing information of the audio control panel, and constructing a standard installation three-dimensional model diagram of the audio control panel according to the standard installation process engineering drawing information;

[0031] Retrieving the installation positioning surface in the standard installation three-dimensional model diagram, and retrieving the installation positioning surface in the characteristic three-dimensional model diagram;

[0032] Integrate the standard installation three-dimensional model diagram and the characteristic three-dimensional model diagram according to the installation positioning surface, and after the integration, analyze the overlap between the standard installation three-dimensional model diagram and the characteristic three-dimensional model diagram;

[0033] If the overlap between the standard installation three-dimensional model diagram and the characteristic three-dimensional model diagram is greater than the preset overlap, it means that the audio control panel after installation meets the process performance requirements;

[0034] If the degree of overlap between the standard installation three-dimensional model diagram and the characteristic three-dimensional model diagram is not greater than the preset degree of overlap, it means that the audio control panel after installation does not meet the process performance requirements.

[0035] Preferably, performing a positioning analysis on the installed audio control panel according to the characteristic three-dimensional model diagram to determine whether the installed audio control panel meets the process performance requirements further includes the following steps:

[0036] If the overlap between the standard installation three-dimensional model diagram and the characteristic three-dimensional model diagram is not greater than a preset overlap, dividing the standard installation three-dimensional model diagram and the characteristic three-dimensional model diagram into a plurality of sub-areas;

[0037] Perform overlap analysis on each sub-area, extract only the sub-area corresponding to the overlap not greater than the preset overlap, and mark it as the installation offset sub-area;

[0038] The installation position correction processing is performed on the installed audio control panel that does not meet the process performance requirements according to the installation offset sub-area.

[0039] The present invention also discloses an audio control panel installation device, which is applied to any one of the methods for detecting the fixing performance of an audio control panel installation device, wherein the audio control panel installation device comprises a knob cap, a first screw, a front panel, a dummy panel, a bottom shell and a second screw;

[0040] The top of the front panel is provided with a first milling groove and a second milling groove, and the bottom of the front panel is provided with a waist-shaped hole and a screw hole;

[0041] A rivet stud 1 is disposed on the top of the dummy panel, and a through hole, a rivet stud 2 and an encoder are disposed on the bottom of the dummy panel;

[0042] The top of the bottom shell is provided with a countersunk hole, and the bottom of the bottom shell is provided with a buckle.

[0043] The present invention solves the technical defects existing in the background technology, and the present invention has the following beneficial effects: in terms of the installation device, its structural design is reasonable, and the arrangement of various components such as the front panel, the false panel, the bottom shell, etc. helps to achieve the stable installation of the audio control panel. In terms of the fixing performance detection method, by collecting laser point cloud data at preset position nodes to build a database, the status information of the audio control panel after installation can be fully obtained. The data is classified and denoised to obtain a pure and accurate data set for each plane area, and then a characteristic three-dimensional model diagram is constructed to accurately present the actual structure after installation. Positioning analysis based on this model diagram is performed to determine whether the process performance requirements are met. It can not only effectively eliminate the influence of factors such as measurement errors and environmental interference, but also accurately evaluate the fixing performance of the installation device, thereby improving the quality and reliability of the audio control panel installation and ensuring the normal operation of related equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0045] Figure 1 The overall method flow chart of this detection method is shown in FIG.

[0046] Figure 2 This is a first exploded schematic diagram of the installation;

[0047] Figure 3 is a second exploded schematic diagram of the installation;

[0048] Figure 4 It is a first labeled schematic diagram of each component in the installation device;

[0049] Figure 5 is a second annotated schematic diagram of the parts in the installation device;

[0050] In the figure: 1. Knob cap; 2. Screw 1; 3. Milling groove 1; 4. Milling groove 2; 5. Rivet stud 1; 6. Countersunk hole; 7. Screw 2; 8. Buckle; 9. Through hole; 10. Rivet stud 2; 11. Encoder; 12. Waist-shaped hole; 13. Screw hole. DETAILED DESCRIPTION

[0051] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0052] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0053] like Figure 1 As shown, the present invention discloses a method for detecting the fixing performance of an audio control panel installation device, comprising the following steps:

[0054] S102: After the audio control panel is installed and fixed by the installation device, laser point cloud data of the installed audio control panel is collected at a plurality of preset position nodes, a database is constructed, and the acquired laser point cloud data is stored in the database;

[0055] S104: After the acquisition is completed, the laser point cloud data in the database is classified and denoised to obtain a laser point cloud data set of each plane area in the audio control panel after installation;

[0056] S106: constructing a characteristic three-dimensional model diagram of the installed audio control panel according to the laser point cloud data set of each plane area in the installed audio control panel;

[0057] S108: Performing positioning analysis on the installed audio control panel according to the characteristic three-dimensional model diagram to determine whether the installed audio control panel meets the process performance requirements.

[0058] It should be noted that after the audio control panel is installed and fixed by the installation device, its laser point cloud data is collected at several preset position nodes. The selection of these preset position nodes can be based on the consideration of the overall structure and key parts of the audio control panel to fully obtain the panel status information after installation. The collected data is constructed into a database and stored, which is the basic data source for all subsequent analysis and processing. After the data is collected, the laser point cloud data in the database is classified and denoised. The classification operation can distinguish the data from different plane areas, which helps to analyze the situation of each area more accurately. The denoising process removes abnormal data points that may be caused by measurement errors, environmental interference and other factors, making the data purer and more reliable, thereby obtaining the laser point cloud data set of each plane area in the audio control panel after installation. These processed data sets can more accurately reflect the actual status of each plane area. The characteristic three-dimensional model diagram of the installed audio control panel is constructed based on the laser point cloud data set of each plane area. This process integrates the data of the plane area into a three-dimensional model diagram that can fully reflect the overall structure and shape of the audio control panel after installation. When constructing the model diagram, operations such as coordinate conversion and splicing of the data of each plane area are involved to ensure that the model diagram can accurately present the actual installation status of the audio control panel. Finally, the installed audio control panel is positioned and analyzed based on the characteristic three-dimensional model diagram to determine whether it meets the process performance requirements. The positioning analysis includes the analysis of the position, angle, and relative relationship of the audio control panel in the equipment. By comparing the actual installed model with the process requirements, such as whether the flatness of the installation and the spacing with other components meet the standards, it is determined whether the installed audio control panel meets the process performance requirements.

[0059] Preferably, the laser point cloud data in the database are classified and denoised to obtain a laser point cloud data set of each plane area in the audio control panel after installation, specifically:

[0060] Creating a regression tree structure, initializing a number of root nodes in the regression tree structure, and mapping each laser point cloud data in the database to each root node separately;

[0061] The cosine similarity algorithm is introduced to calculate the cosine similarity between the laser point cloud data in each root node based on the cosine similarity algorithm; the two root nodes with the largest cosine similarity are merged to obtain several leaf nodes;

[0062] Perform feature extraction processing on the laser point cloud data in each leaf node to obtain the feature vector of the laser point cloud data in each leaf node; perform weighted average processing on the feature vector of the laser point cloud data in each leaf node to obtain the node center of each leaf node;

[0063] The Euclidean distances between all laser point cloud data in each leaf node and the corresponding node center are calculated respectively, and the Euclidean distances between all laser point cloud data in each leaf node and the corresponding node center are weighted averaged to obtain the saturation of each leaf node; and the saturation of each leaf node is compared with a preset threshold;

[0064] When the saturation of each leaf node is greater than the preset threshold, a stop merge operation instruction is generated; otherwise, each leaf node is regarded as a root node and the merge operation is continued until a stop merge operation instruction is obtained;

[0065] After obtaining the instruction to stop the merge operation, the last merge operation result is obtained, and a regression tree model is generated according to the last merge operation result.

[0066] It should be noted that the regression tree structure is first created and several root nodes are initialized, which is the framework basis for data processing. Then, each laser point cloud data in the database is mapped to each root node separately. This step is to prepare for the subsequent merging operation and to preliminarily classify and place the data. The cosine similarity algorithm is introduced to calculate the cosine similarity between the laser point cloud data in each root node. Cosine similarity can measure the similarity between two vectors. In this way, the two most similar (cosine similarity is the largest) root nodes are found and merged, and the data is gradually clustered to obtain several leaf nodes after merging. The laser point cloud data in each leaf node is subjected to feature extraction processing to obtain feature vectors. These feature vectors can represent the characteristics of the data in the leaf node. Then, the feature vectors are weighted averaged to obtain the node center of each leaf node, which can be regarded as a representative point of the leaf node data. The Euclidean distance between all laser point cloud data in each leaf node and the corresponding node center is calculated respectively. The Euclidean distance can measure the degree of discreteness between the data point and the center. Then, these distances are weighted averaged to obtain the saturation of each leaf node. The saturation is compared with the preset threshold, which reflects the degree of data aggregation in the leaf node. If the saturation of each leaf node is greater than the preset threshold, it means that the data in the leaf node is sufficiently aggregated, and a stop merging operation instruction is generated. Otherwise, each leaf node is regarded as a root node and the merging operation is continued. This process is an iterative process, which continuously optimizes the clustering of the data until the stop merging operation instruction is obtained. Finally, a regression tree model is generated based on the result of the last merging operation. This regression tree model can effectively classify the laser point cloud data.

[0067] Through the above series of data processing operations, the laser point cloud data of the installed audio control panel can be effectively classified and denoised. The classification operation makes the data reasonably grouped according to similarity, and the denoising effect is reflected in the reasonable exclusion of noise data through the judgment of indicators such as saturation, and finally the laser point cloud data sets of each plane area are obtained. These data sets are purer and more orderly, providing a high-quality data foundation for the subsequent construction of an accurate feature 3D model diagram of the installed audio control panel, so as to more accurately detect the fixing performance of the audio control panel installation device.

[0068] Preferably, the laser point cloud data in the database are classified and denoised to obtain a laser point cloud data set of each plane area in the audio control panel after installation, further comprising the following steps:

[0069] Obtain each leaf node in the regression tree model, and calculate the Mahalanobis distance between the node centers of every two leaf nodes in the regression tree model;

[0070] Compare the Mahalanobis distance between the node centers of every two leaf nodes in the regression tree model with the preset distance value;

[0071] If the Mahalanobis distance between the node centers of two leaf nodes in the regression tree model is less than the preset distance value, the two leaf nodes are marked as a pair of leaf nodes with similar features; the two leaf nodes marked as a pair of leaf nodes with similar features are merged;

[0072] Update the state of each leaf node in the regression tree model, and calculate the local outlier factor value of the laser point cloud data in each leaf node in the regression tree model;

[0073] If the local outlier factor value of a laser point cloud data in a leaf node is greater than a preset value, the laser point cloud data is marked as noise data and completely deleted;

[0074] The status of each leaf node in the regression tree model is updated again to obtain the final regression tree model; the final regression tree model is subjected to leaf pruning to obtain the laser point cloud data set of each plane area in the audio control panel after installation.

[0075] It should be noted that the leaf nodes in the regression tree model are first obtained, and then the Mahalanobis distance between the node centers of each two leaf nodes is calculated. The Mahalanobis distance takes into account the covariance structure of the data and can more accurately measure the distance between the centers of two nodes in the multivariate space. This distance is compared with the preset distance value. If the Mahalanobis distance between the node centers of two leaf nodes is less than the preset distance value, the two leaf nodes are marked as feature-similar leaf node pairs and merged. This step is to further classify the data and merge the leaf nodes with similar features to make the data classification more reasonable. After updating the status of each leaf node in the regression tree model, the local outlier factor value of the laser point cloud data in each leaf node is calculated. The local outlier factor value can measure the degree of deviation of a data point relative to its local neighborhood. If the local outlier factor value of a laser point cloud data in a leaf node is greater than the preset value, it indicates that the data point is noise data and it is completely deleted. This step realizes the denoising function and removes those interfering data points that are greatly different from the surrounding data. After updating the status of each leaf node in the regression tree model again, the final regression tree model is obtained, and then the final regression tree model is subjected to leaf pruning. Leaf pruning is to remove some unnecessary branches or data structures, and finally obtain the laser point cloud dataset of each plane area in the installed audio control panel. This dataset has undergone multiple rounds of classification and denoising processing, and more accurately reflects the characteristics of each plane area of ​​the audio control panel.

[0076] The classification and denoising effects of laser point cloud data are further optimized by calculating the Mahalanobis distance of leaf node centers and merging them, calculating local outlier factor values ​​to remove noise data, and performing leaf pruning on the final regression tree model. Data with similar features can be more accurately classified together, while noise data can be effectively removed, making the final laser point cloud data set of each plane area in the installed audio control panel purer and more accurate, providing a high-quality data foundation for constructing the characteristic three-dimensional model diagram of the installed audio control panel, thereby facilitating more accurate detection of the fixing performance of the audio control panel installation device.

[0077] Preferably, a characteristic three-dimensional model diagram of the installed audio control panel is constructed according to the laser point cloud data set of each plane area in the installed audio control panel, specifically:

[0078] Normalize the coordinates of the laser point cloud data of each plane area to determine a unified coordinate origin and coordinate axis direction, so that the data of different plane areas can be expressed in the same coordinate system;

[0079] Performing feature analysis on each plane region point cloud data to obtain feature conditions of each plane region point cloud data, and determining the geometric constraint relationship between each plane region according to the feature conditions of each plane region point cloud data; wherein the feature conditions include density change and spatial distribution;

[0080] The plane regions are spliced ​​according to the geometric constraint relationship between the plane regions to obtain a three-dimensional frame structure; the relative coordinate information between the laser point cloud data is obtained in the three-dimensional frame structure;

[0081] A coordinate data set is generated according to the relative coordinate information between each laser point cloud data, and a characteristic three-dimensional model diagram of the audio control panel after installation is constructed according to the coordinate data set.

[0082] It should be noted that normalizing the coordinates of the laser point cloud data of each plane area is a basic step in constructing a feature 3D model diagram. By determining a unified coordinate origin and coordinate axis direction, data from different plane areas can be integrated into the same coordinate system. This helps to subsequently process data from different areas and avoid splicing and modeling errors caused by differences in coordinate systems. Feature analysis is performed on the point cloud data of each plane area, focusing on the characteristics of density change and spatial distribution. Density change can reflect the density of point cloud data in the plane area, which is related to the structural characteristics or installation conditions of the area; spatial distribution can reflect the arrangement of point cloud data in space. According to these characteristics, the geometric constraint relationship between each plane area is determined, such as the parallel and vertical relationship between planes or the connection method between adjacent planes. This geometric constraint relationship is the key basis for accurately splicing each plane area. The plane areas are spliced ​​according to the determined geometric constraint relationship to obtain a 3D frame structure. In this 3D frame structure, the relative coordinate information between each laser point cloud data can be obtained. The relative coordinate information describes the positional relationship of the point cloud data in the 3D frame and is important data for building an accurate 3D model. Finally, a coordinate data set is generated based on the relative coordinate information between the laser point cloud data. This coordinate data set contains the position information required to build a three-dimensional model. Based on this coordinate data set, a characteristic three-dimensional model diagram of the installed audio control panel is constructed. The model diagram can accurately reflect the three-dimensional shape, structure and relationship between the various parts of the installed audio control panel.

[0083] Through a series of operations such as coordinate normalization, feature analysis to determine geometric constraints, plane area splicing to obtain relative coordinate information, and model building based on coordinate data sets, a characteristic three-dimensional model diagram of the installed audio control panel can be constructed. This model diagram accurately reflects the three-dimensional structural characteristics of the audio control panel, and provides a reliable basis for subsequent positioning analysis of the installed audio control panel and judging whether it meets the process performance requirements based on the model diagram, which helps to accurately evaluate the fixing performance of the audio control panel installation device.

[0084] Preferably, the audio control panel after installation is positioned and analyzed according to the characteristic three-dimensional model diagram to determine whether the audio control panel after installation meets the process performance requirements, specifically:

[0085] Obtaining standard installation process engineering drawing information of the audio control panel, and constructing a standard installation three-dimensional model diagram of the audio control panel according to the standard installation process engineering drawing information;

[0086] Retrieving the installation positioning surface in the standard installation three-dimensional model diagram, and retrieving the installation positioning surface in the characteristic three-dimensional model diagram;

[0087] Integrate the standard installation three-dimensional model diagram and the characteristic three-dimensional model diagram according to the installation positioning surface, and after the integration, analyze the overlap between the standard installation three-dimensional model diagram and the characteristic three-dimensional model diagram;

[0088] If the overlap between the standard installation three-dimensional model diagram and the characteristic three-dimensional model diagram is greater than the preset overlap, it means that the audio control panel after installation meets the process performance requirements;

[0089] If the degree of overlap between the standard installation three-dimensional model diagram and the characteristic three-dimensional model diagram is not greater than the preset degree of overlap, it means that the audio control panel after installation does not meet the process performance requirements.

[0090] Preferably, performing a positioning analysis on the installed audio control panel according to the characteristic three-dimensional model diagram to determine whether the installed audio control panel meets the process performance requirements further includes the following steps:

[0091] If the overlap between the standard installation three-dimensional model diagram and the characteristic three-dimensional model diagram is not greater than a preset overlap, dividing the standard installation three-dimensional model diagram and the characteristic three-dimensional model diagram into a plurality of sub-areas;

[0092] Perform overlap analysis on each sub-area, extract only the sub-area corresponding to the overlap not greater than the preset overlap, and mark it as the installation offset sub-area;

[0093] The installation position correction processing is performed on the installed audio control panel that does not meet the process performance requirements according to the installation offset sub-area.

[0094] It should be noted that, first, the standard installation process engineering drawing information of the audio control panel is obtained to construct a standard installation three-dimensional model diagram. This standard model diagram represents the shape of the audio control panel after installation according to the process requirements under ideal conditions. Then, the installation positioning surface is retrieved from the standard installation three-dimensional model diagram and the characteristic three-dimensional model diagram (the model diagram after actual installation obtained by the previous steps). The installation positioning surface is a key plane used to determine the position and posture of the panel. By finding this surface, a benchmark can be provided for subsequent model integration and comparison. The standard installation three-dimensional model diagram and the characteristic three-dimensional model diagram are integrated according to the installation positioning surface, so that the two models can be compared under the same reference system. Then, their overlap is analyzed. The overlap is an indicator to measure the degree of compliance between the actual installation situation and the standard process requirements. If the overlap is greater than the preset overlap, it indicates that the actual installation situation is close to the standard process requirements, and the audio control panel meets the process performance requirements after installation; otherwise, it does not meet the requirements. When the overlap between the standard installation three-dimensional model diagram and the characteristic three-dimensional model diagram is not greater than the preset overlap, the two models are further divided into several sub-areas. The overlap analysis is performed on each sub-area, and only the sub-areas corresponding to the overlap not greater than the preset overlap are extracted and marked as installation offset sub-areas. These installation offset sub-areas accurately point out the specific locations where the actual installation does not conform to the standard process. Based on these installation offset sub-areas, the installation position of the installed audio control panel that does not meet the process performance requirements can be corrected, thereby improving the accuracy of the installation and the degree of compliance with the process requirements.

[0095] By constructing a standard installation 3D model diagram, retrieving the installation positioning surface, and performing model integration and coincidence analysis, it is possible to determine whether the audio control panel meets the process performance requirements after installation. And if it does not meet the requirements, the sub-area analysis is used to find the installation offset sub-area, and then the installation position is corrected. This series of operations realizes the accurate evaluation and effective correction of the installation of the audio control panel, which helps to ensure the installation quality of the audio control panel, improve the fixing performance of the installation device, and ensure that the product meets the process requirements and operates normally.

[0096] In practical application, the detection method may further include the following steps:

[0097] Applying various preset pressures to the installed audio control panel at several time points, and obtaining displacement amplitude values ​​of the installed audio control panel after applying various preset pressures to the installed audio control panel; so as to perform a point pressure test on the installed audio control panel;

[0098] The Hermite curve algorithm is introduced, and the Hermite basis function is preset. Based on the Hermite basis function, the displacement amplitude value of the installed audio control panel after various preset pressures are applied to the installed audio control panel is fitted and calculated to obtain the Hermite curve equation;

[0099] Draw a Hermite curve after performing a point pressure test on the installed audio control panel according to the Hermite curve equation;

[0100] Obtaining process performance requirements of the audio control panel, determining a pressure displacement amplitude range for the audio control panel according to the process performance requirements, and determining a definition domain range of the Hermite point pressure test curve according to the pressure displacement amplitude range;

[0101] Calculating the curve length of the Hermite point pressure test curve within the definition domain, and calculating the curve length of the Hermite point pressure test curve outside the definition domain;

[0102] The curve length of the Hermitian point pressure test curve outside the definition domain is processed by ratio with the curve length of the Hermitian point pressure test curve within the definition domain, so as to obtain the point pressure stability deviation coefficient of the audio control panel after installation;

[0103] Comparing the point pressure stability deviation coefficient of the installed audio control panel with a preset coefficient threshold; if the point pressure stability deviation coefficient of the installed audio control panel is greater than the preset coefficient threshold, it means that the fixing performance of the installed audio control panel does not meet the process requirements;

[0104] If the point pressure stability deviation coefficient of the audio control panel after installation is not greater than the preset coefficient threshold, it means that the fixing performance of the audio control panel after installation meets the process requirements.

[0105] It should be noted that at several time points, different preset pressures are applied to the installed audio control panel, and the corresponding displacement amplitude values ​​are obtained. This step simulates the pressure conditions that may be encountered in actual use. By measuring the displacement amplitude value, the response of the audio control panel under pressure is quantified to provide raw data for subsequent analysis. The Hermite curve algorithm is introduced and the Hermite basis function is preset. The collected displacement amplitude values ​​are fitted and calculated to obtain the Hermite curve equation. The Hermite curve algorithm can construct a smooth curve based on the given data points. In this scenario, it can well describe the relationship between pressure and displacement amplitude, making the data presentation more intuitive and continuous. The pressure displacement amplitude range is determined according to the process performance requirements of the audio control panel, and then the definition domain range of the Hermite point pressure test curve is determined. The process performance requirements specify the reasonable range of pressure and displacement of the audio control panel under normal working conditions. This range is reflected in the curve as the definition domain range, which is an important basis for judging whether the audio control panel meets the process requirements. The curve lengths of the Hermite point pressure test curves within and outside the definition domain are calculated respectively, and then the curve length outside the range is ratioed with the curve length within the range to obtain the point pressure stability deviation coefficient. The calculation of the curve length can quantify the scale of the curve in different definition domains, while the deviation coefficient comprehensively reflects the proportional relationship between the portion of the curve that exceeds the normal process range and the portion of the curve that is within the normal range. The point pressure stability deviation coefficient is compared with the preset coefficient threshold to determine whether the fixing performance of the audio control panel after installation meets the process requirements. If the deviation coefficient is greater than the threshold, it means that the situation beyond the normal process range is more serious and the fixing performance does not meet the requirements; otherwise, it meets the requirements. Specifically, in the point pressure test, the definition domain range of the Hermite point pressure test curve is defined according to the pressure displacement amplitude range determined according to the process performance requirements. This range is the normal working range for the audio control panel to meet the process performance requirements. The curve length of the Hermite point pressure test curve within the definition domain represents the changes within the normal pressure displacement range, while the curve length of the Hermite point pressure test curve outside the definition domain represents the changes beyond the normal process requirements. The larger the ratio between the two, the more significant the change beyond the normal range is relative to the change in the normal range, which means that the audio control panel deviates more from the stable state (the state that meets the process performance requirements) under the point pressure test; conversely, the smaller the ratio, the closer the audio control panel is to the stable state that meets the process performance requirements under the point pressure test.

[0106] By performing a point pressure test on the installed audio control panel, collecting pressure-displacement data and fitting it using the Hermite curve algorithm, determining the curve definition domain range, calculating the point pressure stability deviation coefficient, and then comparing it with the preset threshold, it is possible to accurately determine whether the fixing performance of the audio control panel after installation meets the process requirements. This method comprehensively considers the displacement response under actual pressure, process requirements, and the quantitative characteristics of the curve, providing a comprehensive, scientific, and quantitative evaluation method for the fixing performance detection of audio control panel installation devices.

[0107] In practical application, the detection method may further include the following steps:

[0108] Before installing the audio control panel using the audio control panel installation device, scanning and detecting the fixed point area in the audio control panel installation device using an ultrasonic detector to obtain a regional three-dimensional model diagram of the fixed point area;

[0109] Performing feature extraction processing on the three-dimensional model image of the region to obtain crack feature information existing in the fixed point region; calculating crack concentration values ​​of cracks existing in the fixed point region according to the crack feature information;

[0110] If the crack concentration value is greater than a preset threshold, a finite element analysis method is introduced to obtain the tip stress of each crack in the fixed point area;

[0111] Constructing a sorting table, importing the tip stress of each crack into the sorting table for sorting, and extracting the minimum tip stress;

[0112] Obtaining a preset press-fitting process of the audio control panel, and obtaining a preset load force of the audio control panel on the fixed point area after installation according to the preset press-fitting process;

[0113] It is determined whether the preset load force is greater than the minimum tip stress. If so, the audio control panel installation device is considered as a device with unqualified fixing performance and is not used to install and fix the audio control panel.

[0114] It should be noted that before installing the audio control panel, an ultrasonic detector is used to scan and detect the fixed point area of ​​the installation device to obtain a three-dimensional model map of the area. Then, the model map is feature extracted to obtain crack feature information, and then the crack concentration value is calculated. The crack concentration value is a quantitative representation of the crack condition in the fixed point area, which reflects the overall severity of the cracks in the area. When the crack concentration value is greater than the preset threshold, it means that the crack condition in the fixed point area is relatively serious and requires further analysis. The finite element analysis method is introduced to obtain the tip stress of each crack. The finite element analysis method is a powerful numerical analysis method that can simulate complex physical phenomena. It is used here to accurately calculate the stress condition at the crack tip. A sorting table is constructed to sort the tip stress of each crack by size, so as to obtain the minimum tip stress. This minimum tip stress is the stress value borne by the crack that is least likely to expand due to stress in the fixed point area, and it plays a key role in the subsequent judgment of the fixing performance of the installation device. According to the preset press-fitting process of the audio control panel, the preset load force of the audio control panel on the fixed point area after installation is obtained. This preset load force is the force applied by the audio control panel to the fixed point area under normal installation conditions. Then determine whether the preset load force is greater than the minimum tip stress. If it is greater, it means that during the normal installation process, even the cracks that are least likely to expand may expand because they cannot withstand the load force, thereby affecting the fixing performance of the installation device. Therefore, the installation device is judged as a device with unqualified fixing performance and is not used for the installation and fixation of the audio control panel. By performing a series of operations such as ultrasonic detection, crack analysis, finite element stress calculation, stress sorting, and combined with press-fitting process judgment on the fixing point area of ​​the audio control panel installation device before installation, the fixing performance of the installation device can be evaluated in advance. It can effectively screen out installation devices with serious crack problems that may not be able to withstand the load after installation, avoid using unqualified installation devices to install the audio control panel, thereby improving the reliability and stability of the audio control panel installation, and reducing problems such as loose fixation of the audio control panel due to defects in the installation device itself.

[0115] In practical application, the detection method may further include the following steps:

[0116] Obtain standard noise change data of the audio control panel at various vibration frequencies after installation through a big data network, introduce the graph network, and import the standard noise change data of the audio control panel at various vibration frequencies after installation into the graph network;

[0117] Using various vibration frequencies as first network nodes of the graph network, using corresponding standard noise change data as second network nodes, and constructing a network topology graph based on the first network nodes and the second network nodes;

[0118] Acquire an association matrix according to the network topology diagram, construct a vibration performance evaluation model based on a deep learning network, import the association matrix into the vibration performance evaluation model for training and learning, until the learning parameters meet the preset requirements, and output the trained vibration performance evaluation model;

[0119] Performing vibration tests on the installed audio control panel at several time points to obtain an actual noise data set emitted by the installed audio control panel, importing the actual noise data set into the vibration performance evaluation model for evaluation, and obtaining an evaluation result;

[0120] Based on the evaluation results, it is determined whether the vibration stability of the audio control panel after installation meets the process requirements.

[0121] It should be noted that in the detection of audio control panels, the standard noise change data at various vibration frequencies after installation is very critical information. These data reflect the relationship between different vibration frequencies and the noise changes generated by the audio control panel under ideal conditions. By importing these data into the graph network, it is a way of data structuring. The graph network can present complex data relationships in the form of a network, providing a basis for subsequent analysis. The network topology diagram is constructed by taking various vibration frequencies as the first network nodes and the corresponding standard noise change data as the second network nodes. This construction method intuitively links the vibration frequency and noise change data. In the diagram, the connection and layout between the nodes can reflect the intrinsic relationship between them. For example, some vibration frequencies are more closely related to specific noise change amplitudes, and this relationship pattern can be preliminarily observed through the topology diagram. According to the network topology diagram, the association matrix is ​​obtained, which quantifies the relationship between the vibration frequency node and the standard noise change data node. Then, a vibration performance evaluation model is constructed based on the deep learning network, and the association matrix is ​​imported into the model for training and learning. The deep learning network has a strong learning ability and can mine deep patterns and laws from the association matrix. During the training process, the parameters of the model are continuously adjusted until the preset requirements are met. At this time, the model can accurately process the input data. The installed audio control panel is subjected to vibration tests at several time points to obtain an actual noise data set. This actual noise data set is imported into the trained vibration performance evaluation model for evaluation. The significance of this step is to compare the actual situation with the model previously built and trained based on standard data. The model judges whether the actual noise data meets expectations based on the previously learned standard mode, thereby obtaining the evaluation results. Finally, based on the evaluation results, it is judged whether the vibration stability of the installed audio control panel meets the process requirements. This is the ultimate goal of the entire process. Through the previous series of operations based on data and models, the vibration stability of the audio control panel is quantitatively evaluated to determine whether it meets the requirements of the production process. Through the above series of steps, the standard noise data in big data can be used to construct a graph network, an association matrix, and a deep learning evaluation model, and then the noise data in the actual vibration test is compared and evaluated with the model built based on standard data, so as to accurately judge whether the vibration stability of the audio control panel after installation meets the process requirements, realizing the complete process from data acquisition, model construction to actual evaluation, and improving the accuracy and scientificity of the vibration stability detection of the audio control panel.

[0122] like Figures 2 to 5As shown, the present invention also provides an audio control panel installation device, including a knob cap 1, a screw 2, a front panel milling groove 1 3, a front panel milling groove 2 4, a false panel rivet column 1 5, a bottom shell countersunk hole 6, a screw 2 7, a bottom shell buckle 8, a false panel through hole 9, a false panel rivet column 2 10, an encoder 11, a front panel waist hole 12, and a front panel M3 screw hole 13.

[0123] The bottom shell buckle 8 is aligned with the fake panel through hole 9 and inserted. Then the bottom shell buckle 8 will pass through the fake panel through hole 9 and slide to align the bottom shell countersunk hole 6 with the fake panel rivet column 5. At the same time, the bottom shell buckle 8 will be firmly stuck on the fake panel through hole 9. Use screw 2 7 to pass through the bottom shell countersunk hole 6 and fix it with the fake panel rivet column 5. Then align the fake panel rivet column 2 10 with the front panel milling groove 2 4 and insert it. At the same time, the encoder 11 will pass through the front panel waist hole 12, and the bottom shell buckle 8 will also be in the front panel milling groove 3. Then screw 1 2 is passed through the front panel M3 screw hole 13 to fix it with the fake panel rivet column 2 10. Finally, press the knob cap 1 into the encoder 11 to fix it.

[0124] The entire control panel is fixed by screw 1 2, screw 2 7, and bottom shell buckle 8, with a total of 5 fixing points, which can greatly improve the firmness of the control panel. The knob cap 1 covers screw 1 2, so that no screw can be seen from the front, which improves the appearance of the front panel of the audio control panel. The bottom shell also changes from the original four screws to one screw, which improves the process and reduces three screws.

[0125] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. 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 can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0126] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; 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 present embodiment.

[0127] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0128] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0129] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected 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, server, or network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0130] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for detecting the fixing performance of an audio control panel installation device, characterized in that: The following steps are involved: After the audio control panel is installed and fixed by the installation device, laser point cloud data of the installed audio control panel is collected at a plurality of preset position nodes, a database is constructed, and the acquired laser point cloud data is stored in the database; After the acquisition is completed, the laser point cloud data in the database are classified and denoised to obtain a laser point cloud data set of each plane area in the audio control panel after installation; Constructing a characteristic three-dimensional model diagram of the installed audio control panel according to the laser point cloud data set of each plane area in the installed audio control panel; Performing positioning analysis on the installed audio control panel according to the characteristic three-dimensional model diagram to determine whether the installed audio control panel meets the process performance requirements; The laser point cloud data in the database are classified and denoised to obtain the laser point cloud data set of each plane area in the audio control panel after installation, specifically: Creating a regression tree structure, initializing a number of root nodes in the regression tree structure, and mapping each laser point cloud data in the database to each root node separately; The cosine similarity algorithm is introduced to calculate the cosine similarity between the laser point cloud data in each root node based on the cosine similarity algorithm; the two root nodes with the largest cosine similarity are merged to obtain several leaf nodes; Perform feature extraction processing on the laser point cloud data in each leaf node to obtain the feature vector of the laser point cloud data in each leaf node; perform weighted average processing on the feature vector of the laser point cloud data in each leaf node to obtain the node center of each leaf node; The Euclidean distances between all laser point cloud data in each leaf node and the corresponding node center are calculated respectively, and the Euclidean distances between all laser point cloud data in each leaf node and the corresponding node center are weighted averaged to obtain the saturation of each leaf node; and the saturation of each leaf node is compared with a preset threshold; When the saturation of each leaf node is greater than the preset threshold, a stop merge operation instruction is generated; otherwise, each leaf node is regarded as a root node and the merge operation is continued until a stop merge operation instruction is obtained; After obtaining the instruction to stop the merge operation, the last merge operation result is obtained, and a regression tree model is generated according to the last merge operation result.

2. The method for detecting the fixing performance of an audio control panel installation device according to claim 1, characterized in that: The laser point cloud data in the database are classified and denoised to obtain a laser point cloud data set of each plane area in the audio control panel after installation, further comprising the following steps: Obtain each leaf node in the regression tree model, and calculate the Mahalanobis distance between the node centers of every two leaf nodes in the regression tree model; Compare the Mahalanobis distance between the node centers of every two leaf nodes in the regression tree model with the preset distance value; If the Mahalanobis distance between the node centers of two leaf nodes in the regression tree model is less than the preset distance value, the two leaf nodes are marked as a pair of leaf nodes with similar features; the two leaf nodes marked as a pair of leaf nodes with similar features are merged; Update the state of each leaf node in the regression tree model, and calculate the local outlier factor value of the laser point cloud data in each leaf node in the regression tree model; If the local outlier factor value of a laser point cloud data in a leaf node is greater than a preset value, the laser point cloud data is marked as noise data and completely deleted; The status of each leaf node in the regression tree model is updated again to obtain the final regression tree model; the final regression tree model is subjected to leaf pruning to obtain the laser point cloud data set of each plane area in the audio control panel after installation.

3. The fixing performance detection method of the audio control panel installation device according to claim 1, characterized in that: According to the laser point cloud data set of each plane area in the installed audio control panel, a characteristic three-dimensional model diagram of the installed audio control panel is constructed, specifically: Normalize the coordinates of the laser point cloud data of each plane area to determine a unified coordinate origin and coordinate axis direction, so that the data of different plane areas can be expressed in the same coordinate system; Performing feature analysis on each plane region point cloud data to obtain feature conditions of each plane region point cloud data, and determining the geometric constraint relationship between each plane region according to the feature conditions of each plane region point cloud data; wherein the feature conditions include density change and spatial distribution; The plane regions are spliced ​​according to the geometric constraint relationship between the plane regions to obtain a three-dimensional frame structure; the relative coordinate information between the laser point cloud data is obtained in the three-dimensional frame structure; A coordinate data set is generated according to the relative coordinate information between each laser point cloud data, and a characteristic three-dimensional model diagram of the audio control panel after installation is constructed according to the coordinate data set.

4. The method for detecting the fixing performance of an audio control panel installation device according to claim 1, characterized in that: According to the characteristic three-dimensional model diagram, the audio control panel after installation is positioned and analyzed to determine whether the audio control panel after installation meets the process performance requirements, specifically: Obtaining standard installation process engineering drawing information of the audio control panel, and constructing a standard installation three-dimensional model diagram of the audio control panel according to the standard installation process engineering drawing information; Retrieving the installation positioning surface in the standard installation three-dimensional model diagram, and retrieving the installation positioning surface in the characteristic three-dimensional model diagram; Integrate the standard installation three-dimensional model diagram and the characteristic three-dimensional model diagram according to the installation positioning surface, and after the integration, analyze the overlap between the standard installation three-dimensional model diagram and the characteristic three-dimensional model diagram; If the overlap between the standard installation three-dimensional model diagram and the characteristic three-dimensional model diagram is greater than the preset overlap, it means that the audio control panel after installation meets the process performance requirements; If the degree of overlap between the standard installation three-dimensional model diagram and the characteristic three-dimensional model diagram is not greater than the preset degree of overlap, it means that the audio control panel after installation does not meet the process performance requirements.

5. A method for detecting the fixing performance of an audio control panel installation device according to claim 4, characterized in that: Performing a positioning analysis on the installed audio control panel according to the characteristic three-dimensional model diagram to determine whether the installed audio control panel meets the process performance requirements, further comprising the following steps: If the overlap between the standard installation three-dimensional model diagram and the characteristic three-dimensional model diagram is not greater than a preset overlap, dividing the standard installation three-dimensional model diagram and the characteristic three-dimensional model diagram into a plurality of sub-areas; Perform overlap analysis on each sub-area, extract only the sub-area corresponding to the overlap not greater than the preset overlap, and mark it as the installation offset sub-area; The installation position correction processing is performed on the installed audio control panel that does not meet the process performance requirements according to the installation offset sub-area.

6. An audio control panel installation device, applied to the fixing performance detection method of an audio control panel installation device according to any one of claims 1 to 5, characterized in that: The audio control panel installation device comprises a knob cap, a first screw, a front panel, a dummy panel, a bottom shell and a second screw; The top of the front panel is provided with a first milling groove and a second milling groove, and the bottom of the front panel is provided with a waist-shaped hole and a screw hole; A rivet stud 1 is disposed on the top of the dummy panel, and a through hole, a rivet stud 2 and an encoder are disposed on the bottom of the dummy panel; The top of the bottom shell is provided with a countersunk hole, and the bottom of the bottom shell is provided with a buckle.

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