Intelligent mask fitting process monitoring system and method based on data analysis

Abstract

The invention relates to the technical field of general control systems, in particular to an intelligent facial mask fitting process monitoring system and method based on data analysis. Facial mask fitting quality is analyzed based on a three-dimensional image of the face of a user and a three-dimensional image jointly formed by a facial mask after facial mask fitting and the face of the user; the method comprises the steps of analyzing the mask fitting stability based on circuit data on a mask, predicting the mask fitting effect based on the mask fitting stability and mask fitting quality, judging whether fitting adjustment is needed or not according to the obtained mask fitting effect, and comprehensively analyzing and evaluating the fitting quality and the circuit stability in the mask fitting process. Therefore, the fitting quality of the mask is accurately predicted and analyzed, and the fitting quality and the fitting efficiency of the mask fitting process are improved.

Description

Technical Field

[0001] This application relates to the technical field of general control systems, and particularly to an intelligent mask fitting process monitoring system and method based on data analysis. Background Art

[0002] Intelligent masks are an innovative product in the beauty and skincare industry in recent years. They integrate modern technology and traditional beauty techniques to provide users with a more personalized and efficient skincare experience. Intelligent masks usually integrate high-tech components such as sensors and microprocessors, and can provide customized care plans according to the user's skin condition. With the increasing demand for personalized skincare among consumers and the continuous progress of technology, with the development of big data and Internet of Things technologies, intelligent masks are expected to further achieve seamless connection with users' daily lives and provide more comprehensive and convenient beauty solutions.

[0003] In the process of intelligent mask fitting in the prior art, the prior art cannot comprehensively analyze the fitting effect based on the fitting situation of the mask and the matching situation of the human face, and cannot comprehensively estimate the circuit stability after the mask is fitted, resulting in an inability to accurately predict and analyze the mask fitting quality. In view of this, an intelligent mask fitting process monitoring system and method based on data analysis are proposed. Summary of the Invention

[0004] In order to overcome the defects and deficiencies existing in the prior art, this application provides an intelligent mask fitting process monitoring system and method based on data analysis. The mask fitting quality is analyzed based on the three-dimensional image of the user's face and the three-dimensional image jointly formed by the mask and the user's face after the mask is fitted. The mask fitting stability is analyzed based on the circuit data on the mask. The mask fitting effect is predicted based on the mask fitting stability and the mask fitting quality. It is judged whether fitting adjustment is needed according to the obtained mask fitting effect, and the fitting quality and circuit stability in the mask fitting process are comprehensively analyzed and evaluated, so as to accurately predict and analyze the mask fitting quality, and improve the fitting quality and fitting efficiency of the mask fitting process.

[0005] To achieve the above object, this application adopts the following technical solutions:

[0006] In the first aspect, this application provides an intelligent mask fitting process monitoring method based on data analysis, including the following steps:

[0007] S1: Obtain the three-dimensional image of the user's face, and at the same time obtain the three-dimensional image jointly formed by the mask and the user's face after the mask is fitted, and obtain the circuit data on the mask;

[0008] S2: Analyze the mask fitting quality based on the three-dimensional image of the user's face and the three-dimensional image jointly formed by the mask and the user's face after the mask is attached.

[0009] S3: Analyze the mask fitting stability based on the circuit data on the mask.

[0010] S4: Predict the mask fitting effect based on the mask fitting stability and the mask fitting quality.

[0011] S5: Determine whether fitting adjustment is needed according to the obtained mask fitting effect.

[0012] S6: After the fitting is completed, operate and use the mask.

[0013] Optionally, the steps of obtaining the three-dimensional image of the user's face, simultaneously obtaining the three-dimensional image jointly formed by the mask and the user's face after the mask is attached, and obtaining the circuit data on the mask include the following specific steps:

[0014] S11: Obtain the three-dimensional image of the user's face through a three-dimensional image acquisition component.

[0015] S12: After attaching the mask, obtain the three-dimensional image jointly formed by the mask and the user's face through a three-dimensional image acquisition component.

[0016] S13: Obtain the data on the distribution of silver paste on the circuit after the mask is attached, and store the obtained data in different corresponding storage components respectively. Here, the storage method is, for example, to set different types of storage components corresponding to different data types first.

[0017] Optionally, the analysis of the mask fitting quality includes the following specific steps:

[0018] S21: Obtain the three-dimensional image jointly formed by the mask and the user's face after the mask is attached and the three-dimensional image of the user's face, and obtain the mask thickness data of each point on the face after fitting based on the three-dimensional image jointly formed by the mask and the user's face and the three-dimensional image of the user's face.

[0019] S22: Analyze the mask fitting fluctuation coefficient based on the mask thickness data of each point on the face after fitting and the fluctuation of the mask thickness data of each point on the face after fitting. Among them, the mask fitting fluctuation coefficient can be calculated by formulas representing the fluctuation of the data set such as variance or standard deviation.

[0020] S23. Obtain the difference between the mask thickness data at each point on the face after fitting and the standard thickness. At the same time, set the points where the absolute value of the difference is greater than or equal to the set thickness difference threshold as abnormal points. Obtain the distances between pairs of abnormal points, and perform abnormal analysis of the mask fitting quality based on the distances between pairs of abnormal points and the difference between the mask thickness data at abnormal points and the standard thickness;

[0021] S24. Perform weighted summation on the obtained mask fitting fluctuation coefficient and the mask fitting quality abnormal value, and then take the reciprocal to obtain the mask fitting quality.

[0022] Optionally, the analysis of the mask fitting stability based on the circuit data on the mask includes the following specific steps:

[0023] Obtain the data of the silver paste distribution on the circuit after mask fitting, obtain the area positions where the silver paste distribution density is not within the safe range, and evaluate the mask running stability based on the data of the silver paste distribution on the circuit. Among them, the mask running stability calculation formula is: where Lm is the length of the position where the silver paste is located, Lx is the average distance of the area positions where the silver paste distribution density is not within the safe range, S is the length of the area positions where the silver paste distribution density is not within the safe range, dl is the length integral, ρl is the silver paste distribution density at the area position with length l, ρm is the minimum value of the safe range of the silver paste distribution density.

[0024] Optionally, the prediction of the mask fitting effect based on the mask fitting stability and the mask fitting quality includes:

[0025] Obtain the calculated mask running stability and the mask fitting quality, and import them into the mask fitting effect calculation formula to calculate the mask fitting effect. Among them, the mask fitting effect calculation formula is: Mz＝bTh+(1-b)Pc , where b is the mask fitting quality proportion coefficient, and Th is the mask fitting quality.

[0026] Optionally, the judgment of whether to perform fitting adjustment according to the obtained mask fitting effect includes:

[0027] Compare the obtained mask fitting effect with the set mask fitting effect threshold. If the mask fitting effect is greater than or equal to the set mask fitting effect threshold, it means that the fitting is complete and no fitting adjustment is required. If the mask fitting effect is less than the set mask fitting effect threshold, it means that the fitting is incomplete and fitting adjustment is required.

[0028] In a second aspect, the present application provides an intelligent mask fitting process monitoring system based on data analysis, including:

[0029] A data acquisition module, configured to acquire a three-dimensional image of the user's face, and at the same time acquire a three-dimensional image jointly formed by the mask and the user's face after the mask is attached, and acquire the circuit data on the mask;

[0030] A fitting quality analysis module, configured to analyze the mask fitting quality based on the three-dimensional image of the user's face and the three-dimensional image jointly formed by the mask and the user's face after the mask is attached;

[0031] A fitting stability analysis module, configured to analyze the mask fitting stability based on the circuit data on the mask;

[0032] A fitting effect prediction module, configured to predict the mask fitting effect based on the mask fitting stability and the mask fitting quality;

[0033] A fitting adjustment module, configured to determine whether fitting adjustment is required according to the obtained mask fitting effect;

[0034] An operation and use module, configured to operate and use the mask after the fitting is completed.

[0035] In a third aspect, the present application provides an electronic device, including: a processor and a memory, wherein, a computer program that can be called by the processor is stored in the memory, and the processor executes an intelligent mask fitting process monitoring method based on data analysis by calling the computer program stored in the memory.

[0036] In a fourth aspect, the present application provides a computer-readable storage medium, storing instructions, when the instructions are run on a computer, enabling the computer to execute an intelligent mask fitting process monitoring method based on data analysis.

[0037] Compared with the prior art, the present application has the following advantages and beneficial effects:

[0038] The present application analyzes the mask fitting quality based on the three-dimensional image of the user's face and the three-dimensional image jointly formed by the mask and the user's face after the mask is attached, analyzes the mask fitting stability based on the circuit data on the mask, predicts the mask fitting effect based on the mask fitting stability and the mask fitting quality, determines whether fitting adjustment is required according to the obtained mask fitting effect, comprehensively analyzes and evaluates the fitting quality and circuit stability in the mask fitting process, and then accurately predicts and analyzes the mask fitting quality, improving the fitting quality and fitting efficiency of the mask fitting process. Description of the Drawings

[0039] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, objects, and advantages of the present application will become more obvious:

[0040] Figure 1It is a schematic diagram of the overall process of the intelligent mask fitting process monitoring method based on data analysis provided by an embodiment of the present application;

[0041] Figure 2 It is a schematic diagram of the process of step S2 of the intelligent mask fitting process monitoring method based on data analysis provided by an embodiment of the present application;

[0042] Figure 3 It is a schematic diagram of the process of step S5 of the intelligent mask fitting process monitoring method based on data analysis provided by an embodiment of the present application;

[0043] Figure 4 It is a schematic diagram of the structure of the intelligent mask fitting process monitoring system based on data analysis provided by an embodiment of the present application. Detailed implementation manners

[0044] The technical solutions of the present application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific features in the embodiments of the present application are detailed descriptions of the technical solutions of the present application, rather than limitations on the technical solutions of the present application. Without conflict, the technical features in the embodiments of the present application and the embodiments can be combined with each other.

[0045] Please refer to Figure 1 , Figure 1 It is a schematic diagram of the overall process of the intelligent mask fitting process monitoring method based on data analysis provided by an embodiment of the present application, which specifically includes the following steps:

[0046] S1: Obtain the three-dimensional image of the user's face, and at the same time obtain the three-dimensional image jointly composed of the mask and the user's face after the mask is attached, and obtain the circuit data on the mask;

[0047] In this embodiment, obtaining the three-dimensional image of the user's face, and at the same time obtaining the three-dimensional image jointly composed of the mask and the user's face after the mask is attached, and obtaining the circuit data on the mask includes the following specific steps:

[0048] S11. Obtain the three-dimensional image of the user's face through a three-dimensional image acquisition component, and the following technologies and steps can be adopted: Three-dimensional scanning technology: Use structured light scanning, laser scanning or depth camera and other technologies to capture the three-dimensional data of the user's face; Structured light scanning: Project a series of known pattern lights onto the face and capture the deformation of these lights after being reflected by the face to reconstruct the three-dimensional shape of the face; Laser scanning: Scan the face with a laser beam and determine the three-dimensional coordinates of the face by measuring the time or angle at which the laser beam is reflected back; Depth camera: Depth cameras such as Kinect or Intel RealSense can directly capture the three-dimensional data of the face;

[0049] S12. After applying the facial mask, use a three-dimensional image acquisition component to acquire a three-dimensional image jointly formed by the facial mask and the user's face after the facial mask is applied;

[0050] S13. Obtain data on the distribution of silver paste on the circuit after the facial mask is applied, and store the obtained data in different corresponding storage components respectively. Here, the storage method is, for example, to set different types of storage components corresponding to different data types first;

[0051] S2: Analyze the facial mask fitting quality based on the three-dimensional image of the user's face and the three-dimensional image jointly formed by the facial mask and the user's face after the facial mask is applied;

[0052] In this embodiment, as Figure 2 shown, analyzing the facial mask fitting quality includes the following specific steps:

[0053] S21. Obtain the three-dimensional image jointly formed by the facial mask and the user's face after the facial mask is applied, and the three-dimensional image of the user's face. Based on the three-dimensional image jointly formed by the facial mask and the user's face after the facial mask is applied and the three-dimensional image of the user's face, obtain the facial mask thickness data of each point on the face after fitting;

[0054] S22. Based on the facial mask thickness data of each point on the face after fitting, analyze the facial mask fitting fluctuation coefficient based on the fluctuation of the facial mask thickness data of each point on the face after fitting. Among them, the facial mask fitting fluctuation coefficient can be calculated by formulas representing the fluctuation of the data set such as variance or standard deviation. For example, the facial mask fitting fluctuation coefficient calculation formula is: where n is the number of facial acquisition points, xi is the facial mask thickness data of the i-th point, and xm is the average facial mask thickness of each point on the face after fitting;

[0055] S23. Obtain the difference between the facial mask thickness data of each point on the face after fitting and the standard thickness, and at the same time set the points where the absolute value of the difference is greater than or equal to the set thickness difference threshold as abnormal points. Obtain the distance between pairwise abnormal points, and perform abnormal analysis of the facial mask fitting quality based on the distance between pairwise abnormal points and the difference between the facial mask thickness data of the abnormal points and the standard thickness. Among them, the facial mask fitting quality abnormal value calculation formula is: where R is the radius of the facial mask, h is the average distance between pairwise abnormal points, M is the number of abnormal points, cj is the difference of the j-th abnormal point, and cm is the set thickness difference threshold;

[0056] S24. Perform weighted summation on the obtained facial mask fitting fluctuation coefficient and the facial mask fitting quality abnormal value, and then take the reciprocal to obtain the facial mask fitting quality;

[0057] S3: Analyze the facial mask fitting stability based on the circuit data on the facial mask;

[0058] In this embodiment, the analysis of the mask fitting stability is based on the circuit data on the mask, including the following specific steps:

[0059] Obtain the data of the silver paste distribution on the circuit after the mask is attached, obtain the area positions where the silver paste distribution density is not within the safe range, and evaluate the mask operation stability based on the data of the silver paste distribution on the circuit. Among them, the calculation formula for the mask operation stability is: where Lm is the length of the position where the silver paste is located, Lx is the average distance of the area positions where the silver paste distribution density is not within the safe range, S is the length of the area positions where the silver paste distribution density is not within the safe range, dl is the length integral, ρl is the silver paste distribution density of the area position with length l, ρm is the minimum value of the safe range of the silver paste distribution density;

[0060] S4: Predict the mask fitting effect based on the mask fitting stability and the mask fitting quality;

[0061] In this embodiment, predicting the mask fitting effect based on the mask fitting stability and the mask fitting quality includes:

[0062] Obtain the calculated mask operation stability and the mask fitting quality, and import them into the mask fitting effect calculation formula to calculate the mask fitting effect. Among them, the mask fitting effect calculation formula is: Mz＝bTh+(1-b)Pc , where b is the proportion coefficient of the mask fitting quality, and Th is the mask fitting quality;

[0063] S5: Determine whether fitting adjustment is required according to the obtained mask fitting effect;

[0064] In this embodiment, as Figure 3 shown, determining whether fitting adjustment is required according to the obtained mask fitting effect includes:

[0065] Compare the obtained mask fitting effect with the set mask fitting effect threshold. If the mask fitting effect is greater than or equal to the set mask fitting effect threshold, it means that the fitting is complete and no fitting adjustment is required. If the mask fitting effect is less than the set mask fitting effect threshold, it means that the fitting is incomplete and fitting adjustment is required;

[0066] S6: After the fitting adjustment meets the requirements, the mask is operated and used;

[0067] It should be noted here that the method for obtaining the values of the set parameters (such as the thickness difference threshold, the weighted value of the mask fitting fluctuation coefficient, the weighted value of the mask fitting quality outlier, etc.) in this application is as follows: Obtain the three-dimensional facial image of historical users, and at the same time obtain the three-dimensional image jointly composed of the mask and the user's face after the mask is attached. Obtain the circuit data on the mask, substitute it into the mask fitting effect calculation formula to calculate the mask fitting effect, and analyze whether the usage effect after the mask is used meets the requirements. Import the calculated mask fitting effect and the judgment result of whether the usage effect meets the requirements into fitting software such as Matlab to output the values of the set parameters (such as the thickness difference threshold, the weighted value of the mask fitting fluctuation coefficient, the weighted value of the mask fitting quality outlier, etc.) that meet the maximum judgment accuracy rate;

[0068] Through the above methods, the following can be achieved: Analyze the mask fitting quality based on the three-dimensional facial image of the user and the three-dimensional image jointly composed of the mask and the user's face after the mask is attached, analyze the mask fitting stability based on the circuit data on the mask, predict the mask fitting effect based on the mask fitting stability and the mask fitting quality, determine whether fitting adjustment is required according to the obtained mask fitting effect, comprehensively analyze and evaluate the fitting quality and circuit stability during the mask fitting process, and then accurately predict and analyze the mask fitting quality, improving the fitting quality and fitting efficiency of the mask fitting process.

[0069] Please refer to Figure 4 , Figure 4 which is the structural schematic diagram of the intelligent mask fitting process monitoring system based on data analysis provided by the embodiment of this application. This embodiment provides an intelligent mask fitting process monitoring system based on data analysis, including:

[0070] A data acquisition module, which is used to obtain the three-dimensional facial image of the user, and at the same time obtain the three-dimensional image jointly composed of the mask and the user's face after the mask is attached, and obtain the circuit data on the mask;

[0071] A fitting quality analysis module, which analyzes the mask fitting quality based on the three-dimensional facial image of the user and the three-dimensional image jointly composed of the mask and the user's face after the mask is attached;

[0072] A fitting stability analysis module, which analyzes the mask fitting stability based on the circuit data on the mask;

[0073] A fitting effect prediction module, which predicts the mask fitting effect based on the mask fitting stability and the mask fitting quality;

[0074] A fitting adjustment module, which is used to determine whether fitting adjustment is required according to the obtained mask fitting effect;

[0075] An operation and usage module, which is used to perform the operation and usage of the mask after the fitting is completed.

[0076] For the steps of the parameters and each unit module in the intelligent mask fitting process monitoring system based on data analysis of the present application to implement corresponding functions, reference can be made to the parameters and steps in the embodiments of the intelligent mask fitting process monitoring method based on data analysis in the foregoing text, which will not be elaborated herein.

[0077] An embodiment of the present invention further provides an electronic device, including a memory, a processor, and a communication bus; the memory and the processor are connected through the communication bus. The memory stores instructions that can be loaded and executed by the processor and implement the intelligent mask fitting process monitoring method based on data analysis provided in the foregoing embodiment.

[0078] The memory can be used to store instructions, programs, codes, code sets, or instruction sets. The memory may include a program storage area and a data storage area. Among them, the program storage area can store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the intelligent mask fitting process monitoring method provided in the foregoing embodiment, etc.; the data storage area can store data involved in the intelligent mask fitting process monitoring method provided in the foregoing embodiment.

[0079] The processor may include one or more processing cores. The processor runs or executes instructions, programs, code sets, or instruction sets stored in the memory, calls data stored in the memory, and executes various functions of the present application and processes data. The processor can be at least one of an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a central processing unit (CPU), a controller, a microcontroller, and a microprocessor. It can be understood that for different devices, the electronic devices for implementing the above processor functions may be others, and the embodiments of the present application do not make specific limitations.

[0080] The communication bus may include a path for transmitting information between the above components. The communication bus can be a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The communication bus can be divided into an address bus, a data bus, a control bus, and the like.

[0081] An embodiment of the present application provides a computer-readable storage medium storing a computer program that can be loaded and executed by a processor to perform the intelligent mask fitting process monitoring method based on data analysis provided in the above embodiment.

[0082] In an embodiment of the present application, the computer-readable storage medium may be a tangible device that holds and stores instructions used by an instruction execution device. The computer-readable storage medium may be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination of the above. Specifically, the computer-readable storage medium may be a portable computer disk, a hard disk, a USB flash drive, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a podium random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, an optical disk, a magnetic disk, a mechanical encoding device, and any combination of the above.

[0083] The term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device.

[0084] The above description is only a preferred embodiment of the present application and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the application involved in the present application is not limited to the technical solution formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the foregoing application concept. For example, a technical solution formed by mutually replacing the above features with (but not limited to) technical features having similar functions applied in the present application.

Claims

1. An intelligent mask fitting process monitoring method based on data analysis, characterized in that, It includes the following steps: S1: Obtain the three-dimensional image of the user's face, and at the same time obtain the three-dimensional image jointly formed by the mask and the user's face after the mask is attached, and obtain the circuit data on the mask; S2: Analyze the mask attachment quality based on the three-dimensional image of the user's face and the three-dimensional image jointly formed by the mask and the user's face after the mask is attached; S3: Analyze the mask attachment stability based on the circuit data on the mask; S4: Predict the mask attachment effect based on the mask attachment stability and the mask attachment quality; S5: Determine whether attachment adjustment is needed according to the obtained mask attachment effect; S6: After the attachment is completed, the mask is operated and used.

2. The intelligent mask fitting process monitoring method based on data analysis according to claim 1, wherein The analysis of the mask attachment quality includes the following specific steps: Obtain the three-dimensional image jointly formed by the mask and the user's face after the mask is attached and the three-dimensional image of the user's face. Based on the three-dimensional image jointly formed by the mask and the user's face after the mask is attached and the three-dimensional image of the user's face, obtain the mask thickness data of each point on the face after attachment; Based on the mask thickness data of each point on the face after attachment, analyze the mask attachment fluctuation coefficient based on the fluctuation of the mask thickness data of each point on the face after attachment; Obtain the difference between the mask thickness data of each point on the face after attachment and the standard thickness. At the same time, set the points where the absolute value of the difference is greater than or equal to the set thickness difference threshold as abnormal points, obtain the distance between two abnormal points, and perform mask attachment quality abnormality analysis based on the distance between two abnormal points and the difference between the mask thickness data of the abnormal points and the standard thickness; Perform weighted summation on the obtained mask attachment fluctuation coefficient and the mask attachment quality abnormality value, and then take the reciprocal to obtain the mask attachment quality.

3. The intelligent mask fitting process monitoring method based on data analysis according to claim 2, wherein, The analysis of the mask attachment stability based on the circuit data on the mask includes the following specific steps: Obtain the data of the silver paste distribution on the circuit after the mask is attached, obtain the position of the area where the silver paste distribution density is not within the safe range, and evaluate the running stability of the mask based on the data of the silver paste distribution on the circuit. Among them, the calculation formula for the running stability of the mask is: Among them, Lm is the length of the position where the silver paste is located, Lx is the average distance of the position of the area where the silver paste distribution density is not within the safe range, S is the length of the position of the area where the silver paste distribution density is not within the safe range, dl is the length integral, ρl is the silver paste distribution density at the position of the area with length l, ρm is the minimum value of the safe range of the silver paste distribution density.

4. The intelligent mask fitting process monitoring method based on data analysis according to claim 1, characterized in that, The prediction of the mask attachment effect based on the mask attachment stability and the mask attachment quality includes: Obtain the calculated running stability and mask fitting quality of the mask, and import them into the mask fitting effect calculation formula to calculate the mask fitting effect. The mask fitting effect calculation formula is as follows: Mz＝bTh+(1-b)Pc , where b is the mask fitting quality proportion coefficient and Th is the mask fitting quality.

5. The intelligent mask fitting process monitoring method based on data analysis according to claim 1, characterized in that The determination of whether attachment adjustment is needed according to the obtained mask attachment effect includes: Compare the obtained mask attachment effect with the set mask attachment effect threshold. If the mask attachment effect is greater than or equal to the set mask attachment effect threshold, it means that the attachment is complete and no attachment adjustment is needed. If the mask attachment effect is less than the set mask attachment effect threshold, it means that the attachment is incomplete and attachment adjustment is needed.

6. The intelligent mask fitting process monitoring method based on data analysis according to claim 1, wherein, The calculation formula for the mask fitting fluctuation coefficient is as follows: Where n is the number of facial acquisition points, xi is the mask thickness data of the i-th point, and xm is the average mask thickness of each point on the face after fitting.

7. The intelligent facial mask fitting process monitoring method based on data analysis according to claim 1, characterized in that The calculation formula for the abnormal value of the mask fitting quality is as follows: where R is the radius of the mask, h is the average distance between pairs of abnormal points, M is the number of abnormal points, cj is the difference value of the j-th abnormal point, and cm is the set thickness difference threshold.

8. An intelligent facial mask fitting process monitoring system based on data analysis, which is implemented based on the intelligent facial mask fitting process monitoring method based on data analysis according to any one of claims 1-7, characterized in that, The system includes: A data acquisition module for obtaining the three-dimensional image of the user's face, and at the same time obtaining the three-dimensional image jointly formed by the mask and the user's face after the mask is attached, and obtaining the circuit data on the mask; An attachment quality analysis module for analyzing the mask attachment quality based on the three-dimensional image of the user's face and the three-dimensional image jointly formed by the mask and the user's face after the mask is attached; An attachment stability analysis module for analyzing the mask attachment stability based on the circuit data on the mask; An attachment effect prediction module for predicting the mask attachment effect based on the mask attachment stability and the mask attachment quality; An attachment adjustment module for determining whether attachment adjustment is needed according to the obtained mask attachment effect; An operation and use module for operating and using the mask after the attachment is completed.

9. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the intelligent mask fitting process monitoring method based on data analysis according to any one of claims 1-7 by calling the computer program stored in the memory.

10. A computer-readable storage medium, characterized in that, Stores instructions that, when run on a computer, cause the computer to execute the intelligent mask fitting process monitoring method based on data analysis according to any one of claims 1-7.

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