CN113538114A - Mask recommendation platform and method based on small programs - Google Patents
Mask recommendation platform and method based on small programs Download PDFInfo
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- CN113538114A CN113538114A CN202111068440.7A CN202111068440A CN113538114A CN 113538114 A CN113538114 A CN 113538114A CN 202111068440 A CN202111068440 A CN 202111068440A CN 113538114 A CN113538114 A CN 113538114A
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Abstract
The application is applicable to the technical field of medical treatment, and provides a mask recommendation platform based on small programs, which comprises a front-end interaction system and a rear-end management system; the front-end interaction system comprises a mask recommending module; the mask recommending module is used for acquiring a face image of a user and displaying a recommending result corresponding to the face image; the back-end management system comprises an interactive interface module; the interactive interface module is used for packaging a mask recommendation algorithm, calling the mask recommendation algorithm to determine a recommendation result corresponding to the face image, sending the recommendation result to a front-end interactive system, obtaining a face image of a user and quickly constructing a three-dimensional head model, calling a deep neural network algorithm which completes face and mask tightness association training based on the three-dimensional head model and various types of mask models to perform accurate and quick fitting, recommending a mask type suitable for the user to wear, improving the tightness when the mask is worn, and reducing the infection risk.
Description
Technical Field
The application belongs to the technical field of medical treatment, and particularly relates to a mask recommendation platform and method based on a small program.
Background
The medical mask can effectively filter micro particles suspended in the air and block pollutants such as respiratory droplets, body fluid, secretion and the like. Utilize with the structure that user's face closely closes, the polluted environment of isolated gauze mask outside reduces respiratory infection and contact transmission risk. The medical protective mask is used as the last line of defense of personal hygiene protection, and it is very important to correctly wear the mask meeting the epidemic prevention standard.
The fit (close fit) of the mask to the face of the user is one of the key technical indicators for evaluating the protective ability of the mask. If the selected mask cannot be tightly fit with the face of a user, and harmful particles pass through other places, the protective mask cannot provide effective breathing protection.
Therefore, how to determine the mask suitable for the user is an urgent problem to be solved.
Disclosure of Invention
The embodiment of the application provides a mask recommending platform and method based on an applet, and a suitable mask can be accurately recommended to a user.
In a first aspect, an embodiment of the application provides a mask recommendation platform based on an applet, which includes a front-end interaction system and a back-end management system;
the front-end interaction system comprises a mask recommending module; the mask recommending module is used for acquiring a face image of a user and displaying a recommending result corresponding to the face image;
the back-end management system comprises an interactive interface module; the interactive interface module is used for packaging a mask recommendation algorithm, calling the mask recommendation algorithm to determine a recommendation result corresponding to the face image, and sending the recommendation result to the front-end interactive system.
In an implementation manner of the first aspect, the interactive interface module is further configured to encapsulate a three-dimensional face reconstruction algorithm, and call the three-dimensional face reconstruction algorithm to perform three-dimensional reconstruction on the face image, so as to obtain a three-dimensional head model.
In an implementation manner of the first aspect, the mask recommendation module of the front-end interaction system includes a photo obtaining unit and a result display unit:
the photo acquisition unit is used for acquiring a face image of a user;
the result display unit is used for displaying recommendation results, and the recommendation results comprise face key information, mask recommendation scores and three-dimensional display results.
In one implementation manner of the first aspect, the front-end interaction system further includes a mask little knowledge module and a personal center module:
the mask little knowledge module is used for displaying the relevant knowledge of various masks;
the personal center module is used for managing personal information of the user.
In one implementation manner of the first aspect, the mask little knowledge module includes a mask display unit, a mask technical parameter unit, and a mask usage guide unit:
the mask display unit is used for displaying mask information, and the mask information comprises pictures, model structures and performance parameters of various masks;
the mask technical parameter unit is used for displaying authentication or detection information of a common protective mask, technical parameters of a medical protective mask and technical parameters of other types of masks;
the mask use guide unit is used for displaying a use guide of the mask and a mask processing method.
In one implementation manner of the first aspect, the personal center module includes a registration unit, a personal information changing unit, and a recommendation history viewing unit:
the registration unit is used for acquiring and storing registration information filled by a new user;
the personal information changing unit is used for checking and changing the registered information after the user registers;
the recommendation history viewing unit is used for displaying the mask recommendation records of the user in a list text mode.
In one implementation manner of the first aspect, the backend management system further includes: a result management module;
the result management module is used for creating a list for the user test record, completing the design of user storage logic and the realization of a storage process, and performing associated storage on the original picture, the three-dimensional head model file, the face feature classification and the mask recommendation score in the user test record.
In a second aspect, an embodiment of the present application provides a mask recommendation method, including:
acquiring a face image;
constructing a three-dimensional head model according to the face image;
determining a recommendation for the facial image match based on the three-dimensional head model;
and displaying the recommendation result.
In an implementation manner of the second aspect, the constructing a three-dimensional head model according to the face image includes:
carrying out image preprocessing on the face image to obtain face point cloud information;
and generating the three-dimensional head model according to the human face point cloud information.
In one implementation manner of the second aspect, determining a recommendation result for the face image matching according to the three-dimensional head model includes:
calling a mask recommendation algorithm to process the depth information of the three-dimensional head model to obtain recommendation scores of various masks; the mask recommendation algorithm is a deep neural network algorithm which completes the training of the correlation between the face and the mask tightness.
In a third aspect, an embodiment of the present application provides a terminal device, where the terminal device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, and the processor, when executing the computer program, implements the method according to the second aspect or any optional manner of the second aspect.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium storing a computer program which, when executed by a processor, implements a method according to the second aspect or any alternative of the second aspect.
In a fifth aspect, embodiments of the present application provide a computer program product, which, when run on a terminal device, causes the terminal device to execute the method of the second aspect or any alternative form of the second aspect.
Compared with the prior art, the embodiment of the application has the advantages that:
the mask recommending platform and method based on the small programs, the terminal device and the computer program product have the following beneficial effects that: the three-dimensional head model is quickly constructed by acquiring the face picture of the user, the deep neural network algorithm which is used for completing face and mask tightness association training is called to accurately and quickly fit based on the three-dimensional head model and various mask models, the mask type suitable for being worn is recommended for the user, the tightness when the mask is worn can be improved, the infection risk is reduced, and meanwhile mask protection knowledge and the correct using mode of the mask can be popularized.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic structural diagram of a facial mask recommendation platform based on an applet provided in the present application;
FIG. 2 is a flowchart of an exemplary implementation of a method for recommending a mask according to an embodiment of the present application;
fig. 3 is a schematic diagram illustrating an effect of a mask recommendation method according to an embodiment of the present application;
fig. 4 is a flowchart of an exemplary implementation of S12 in the mask recommendation method provided in an embodiment of the present application;
fig. 5 is a schematic structural diagram of a terminal device according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a computer-readable storage medium according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It should be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items. Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
It should also be appreciated that reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
An applet is an application that can be used without installation, and is widely used due to its installation-free and easy-to-use characteristics. The mask recommendation platform provided by the embodiment of the application is developed and realized based on an applet. The following will describe in detail a facial mask recommendation platform based on small programs provided in the embodiments of the present application:
referring to fig. 1, fig. 1 is a schematic diagram illustrating a framework of a facial mask recommendation platform based on an applet according to an embodiment of the present application. As shown in fig. 1, the applet-based mask recommendation platform provided in the embodiment of the present application includes a front-end interaction system 100 and a back-end management system 200. In the embodiment of the present application, the front-end interactive system 100 is displayed through an applet.
The front-end interaction system 100 includes a mask little knowledge module 110, a mask recommendation module 120, and a personal center module 130.
The backend management system 200 includes a results management module 210 and an interaction interface module 220.
The mask little knowledge module 110 is used for displaying the relevant knowledge of various masks. Each of the above types of masks includes, but is not limited to: general protective masks, medical protective masks, and other types of masks.
The mask little knowledge module 110 may include a mask display unit, a mask technical parameter unit, and a mask use guide unit.
The mask display unit can be used for displaying mask information, and the mask information comprises pictures, model structures and performance parameters of various masks.
The mask technical unit is used for displaying authentication or detection information of a common protective mask, technical parameters of a medical protective mask and technical parameters of other types of masks.
The mask use guide unit is used for displaying a use guide of the mask and a mask processing method. It should be noted that the above-mentioned guide unit for mask use can also show the related notice of the mask.
The mask recommendation module 120 is configured to obtain a face image of a user and display a recommendation result corresponding to the face image.
In a specific application, the mask recommending module 120 may include a photo obtaining unit and a result displaying unit.
The photo acquisition unit is used for acquiring a face image of a user. In the embodiment of the application, face images of a user at multiple angles, such as a left face image, a front face image and a right face image, can be acquired. The photo obtaining unit may send the face image of the user to the interactive interface module 220 after obtaining the face image, the interactive interface module 220 determines a recommendation result corresponding to the face image based on a packaged mask recommendation algorithm, and then sends the recommendation result to the front-end interactive system 100, and the result display unit displays the recommendation result, where the recommendation result may include face key information, a mask recommendation score, and a three-dimensional display result (a display result of a wearing effect).
In a specific application, the personal center module 130 includes a registration unit, a personal information changing unit and a recommendation history viewing unit.
The registration unit is used for acquiring and storing registration information filled by a new user.
The personal information changing unit is used for checking and changing the registered information after the user registers;
the recommendation history viewing unit is used for displaying the mask recommendation records of the user in a list character mode.
In a specific application, the result management module 210 is configured to create a list for a user test record, complete the design of a user storage logic and the implementation of a storage process, and perform associated storage on an original picture, a three-dimensional head model file, a face feature classification and a mask recommendation score in the user test record.
In a specific application, the interactive interface module 220 is configured to encapsulate a mask recommendation algorithm, call the mask recommendation algorithm to determine a recommendation result corresponding to the face image, and send the recommendation result to the front-end interactive system 100.
The interactive interface module 220 is further configured to encapsulate a three-dimensional face reconstruction algorithm, and call the three-dimensional face reconstruction algorithm to perform three-dimensional reconstruction on the face image, so as to obtain a three-dimensional head model.
The mask recommendation algorithm can determine a recommendation result matched with the three-dimensional head model based on the reconstructed three-dimensional head model, specifically, the mask recommendation algorithm can be called to process the depth information of the three-dimensional head model to obtain recommendation scores of various types of masks, and the mask recommendation algorithm is a deep neural network algorithm which is trained by correlation of the face and the mask.
It should be noted that, the interactive interface module 220 may further package a face classification algorithm to classify the face.
Therefore, the mask recommendation platform based on the small program provided by the embodiment of the application can be used for calling the deep neural network algorithm which completes the correlation training of the face and the mask tightness to perform accurate and quick fitting based on the three-dimensional head model and various types of mask models by acquiring the face picture of a user and quickly constructing the three-dimensional head model, recommending the mask type suitable for the user to wear, improving the tightness when the mask is worn, reducing the infection risk, and simultaneously promoting the mask protection knowledge and the correct use mode of the mask.
Based on the mask recommendation platform shown in fig. 1, the embodiment of the application further provides a mask recommendation method. Referring to fig. 2, fig. 2 is a schematic flow chart of a mask recommendation method according to an embodiment of the present application. It should be noted that an execution main body of the mask recommendation method provided in the embodiment of the application is a terminal device including an applet, and the terminal device may be a mobile terminal such as a smart phone, a tablet computer, or a wearable device, or may be a device such as a computer in various application scenarios.
As shown in fig. 2, the method for recommending a mask provided in the embodiment of the present application may include steps S11 to S14, which are detailed as follows:
s11: and acquiring a human face image.
In the embodiment of the application, the terminal device can call the face image in the album or shoot the face image through the camera.
Specifically, the terminal device displays a photo obtaining unit in the applet, the photo obtaining unit comprises a photo obtaining control, the terminal device can display images in the album for a user to select after detecting that the user clicks or triggers the photo obtaining control, and the user clicks a face image to be selected to obtain the face image.
Specifically, after detecting that the user clicks or triggers the photo acquisition control, the terminal device may call a camera to shoot the face image.
For example, as shown in fig. 3 (a), when a camera is called to perform face shooting, a user shooting guidance process may be entered, where the guidance process includes obtaining a front face image of a face by a front-face reference schematic under a shooting auxiliary frame, obtaining a left-side image of the face by a left-side reference schematic, and finally obtaining a right-side image of the face by a right-side reference schematic. The number of face images acquired at a time may be 1 to 3. The pictures are shot and selected and then browsed and displayed, and the selected pictures can be deleted and added.
S12: and constructing a three-dimensional head model according to the face image.
In the embodiment of the application, a human face three-dimensional reconstruction algorithm can be called to construct a three-dimensional head model.
Referring to fig. 4, in an embodiment of the present application, the S12 may include S21 to S22, which are detailed as follows:
s21: and carrying out image preprocessing on the face image to obtain face point cloud information.
S22: and generating the three-dimensional head model according to the human face point cloud information.
In the embodiment of the application, a human face three-dimensional reconstruction algorithm interface packaged in a background management system can be called, image preprocessing is performed on an obtained human face image to obtain human face point cloud information, and a three-dimensional head model is generated by performing pose estimation, point cloud fusion, texture mapping and other operations based on the human face point cloud information.
For example, the three-dimensional head model reconstructed based on the face image in fig. 3 (a) may be as shown in fig. 3 (b).
S13: a recommendation for face image matching is determined based on the three-dimensional head model.
In the specific application, a face classification algorithm and a mask recommendation algorithm interface packaged in a background management system are called, and face image preprocessing, face image detection, feature matching, feature extraction and other operations are sequentially completed to generate face feature information and face classification information. And the depth information of the three-dimensional head model is used as input to call a depth neural network algorithm which finishes the correlation training of the face and mask tightness in a background management system to carry out rapid fitting to generate the recommendation scores of various masks.
In an embodiment of the application, the mask recommendation method may further include training a deep neural network algorithm for incomplete face-mask tightness association training based on a large amount of historical data, so as to obtain the deep neural network algorithm for completed face-mask tightness association training.
S14: and displaying the recommendation result.
In this embodiment of the application, the front-end interactive system of the terminal device may obtain the three-dimensional model, the obj file, the material, the mtl file, and the texture, the png file generated in step S12 in the https protocol. And acquiring data such as generated face feature information, face classification information and recommendation scores of various types of masks in an object key value pair mode.
For example, as shown in fig. 3 (c), the acquired face feature information, face classification information, recommendation scores of various types of masks, and other data are used as input, mask recommendation scores and face information of masks are generated in a small program table, and a mask try-on effect is displayed.
In the embodiment of the application, threejs can be sequentially used to establish a scene in an applet canvas; loading a human face three-dimensional model and a mask three-dimensional model to a scene; adding lamplight; loading the material and texture of the three-dimensional model; controlling the rotation of the model; rendering, animation and other operations generate a mask try-on effect (three-dimensional display effect).
In an embodiment of the present application, the user may also select whether to save the corresponding recommendation result. And if the user selects not to store the recommendation result, the face image can be obtained again for analysis again.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
It can be seen from the above that, the mask recommendation algorithm provided by the embodiment of the application also carries out accurate and fast fitting by acquiring the face picture of the user and quickly constructing the three-dimensional head model, and calling the deep neural network algorithm which has completed the correlation training of the face and the mask tightness based on the three-dimensional head model and various types of mask models, so as to recommend the type of the mask suitable for the user to wear, improve the tightness when the mask is worn, and reduce the infection risk.
Fig. 5 is a schematic structural diagram of a terminal device according to another embodiment of the present application. As shown in fig. 5, the terminal device 5 provided in this embodiment includes: a processor 50, a memory 51 and a computer program 52, such as an image segmentation program, stored in said memory 51 and executable on said processor 50. The processor 50 implements the steps of the above-described mask recommendation method embodiments when executing the computer program 52. Alternatively, the processor 50 implements the functions of the modules/units in the above-mentioned mask recommendation platform based on applets when executing the computer program 52.
Illustratively, the computer program 52 may be partitioned into one or more modules/units, which are stored in the memory 51 and executed by the processor 50 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 52 in the terminal device 5.
The terminal device may include, but is not limited to, a processor 50, a memory 51. Those skilled in the art will appreciate that fig. 5 is merely an example of a terminal device 5 and does not constitute a limitation of terminal device 5 and may include more or fewer components than shown, or some components may be combined, or different components, e.g., the terminal device may also include input-output devices, network access devices, buses, etc.
The Processor 50 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 51 may be an internal storage unit of the terminal device 5, such as a hard disk or a memory of the terminal device 5. The memory 51 may also be an external storage device of the terminal device 5, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 5. Further, the memory 51 may also include both an internal storage unit and an external storage device of the terminal device 5. The memory 51 is used for storing the computer program and other programs and data required by the terminal device. The memory 51 may also be used to temporarily store data that has been output or is to be output.
The embodiment of the application also provides a computer readable storage medium. Referring to fig. 6, fig. 6 is a schematic structural diagram of a computer-readable storage medium according to an embodiment of the present disclosure, as shown in fig. 6, a computer program 61 is stored in the computer-readable storage medium 60, and when the computer program 61 is executed by a processor, the mask recommendation method can be implemented.
The embodiment of the application provides a computer program product, and when the computer program product runs on a terminal device, the mask recommendation method can be realized when the terminal device executes the computer program product.
It is obvious to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional units and modules is merely used as an example, and in practical applications, the foregoing function distribution may be performed by different functional units and modules as needed, that is, the internal structure of the terminal device is divided into different functional units or modules to perform all or part of the above-described functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the description of each embodiment has its own emphasis, and parts that are not described or illustrated in a certain embodiment may refer to the description of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. A mask recommendation platform based on small programs is characterized by comprising a front-end interaction system and a rear-end management system;
the front-end interaction system comprises a mask recommending module; the mask recommending module is used for acquiring a face image of a user and displaying a recommending result corresponding to the face image;
the back-end management system comprises an interactive interface module; the interactive interface module is used for packaging a mask recommendation algorithm, calling the mask recommendation algorithm to determine a recommendation result corresponding to the face image, and sending the recommendation result to the front-end interactive system.
2. The applet-based mask recommendation platform according to claim 1, wherein the interactive interface module is further configured to encapsulate a three-dimensional face reconstruction algorithm, and call the three-dimensional face reconstruction algorithm to perform three-dimensional reconstruction on the face image to obtain a three-dimensional head model.
3. The applet-based mask recommendation platform according to claim 1, wherein the mask recommendation module of the front-end interactive system comprises a photo acquisition unit and a result display unit:
the photo acquisition unit is used for acquiring a face image of a user;
the result display unit is used for displaying recommendation results, and the recommendation results comprise face key information, mask recommendation scores and three-dimensional display results.
4. The applet-based mask recommendation platform according to claim 1, wherein the front-end interactive system further comprises a mask little knowledge module and a personal center module:
the mask little knowledge module is used for displaying the relevant knowledge of various masks;
the personal center module is used for managing personal information of the user.
5. The applet-based mask recommendation platform according to claim 4, wherein the mask little knowledge module comprises a mask display unit, a mask technical parameter unit, a mask use guide unit:
the mask display unit is used for displaying mask information, and the mask information comprises pictures, model structures and performance parameters of various masks;
the mask technical parameter unit is used for displaying authentication or detection information of a common protective mask and technical parameters of a medical protective mask;
the mask use guide unit is used for displaying a use guide of the mask and a mask processing method.
6. The applet-based mask recommendation platform according to claim 4, wherein the personal center module comprises a registration unit, a personal information change unit and a recommendation history viewing unit:
the registration unit is used for acquiring and storing registration information filled by a new user;
the personal information changing unit is used for checking and changing the registered information after the user registers;
the recommendation history viewing unit is used for displaying the mask recommendation records of the user in a list text mode.
7. The applet-based mask recommendation platform according to claim 1, wherein the back end management system further comprises: a result management module;
the result management module is used for creating a list for the user test record, completing the design of user storage logic and the realization of a storage process, and performing associated storage on the original picture, the three-dimensional head model file, the face feature classification and the mask recommendation score in the user test record.
8. A method of mask recommendation, comprising:
acquiring a face image;
constructing a three-dimensional head model according to the face image;
determining a recommendation for the facial image match based on the three-dimensional head model;
and displaying the recommendation result.
9. The mask recommendation method according to claim 8, wherein the constructing a three-dimensional head model from the face image comprises:
carrying out image preprocessing on the face image to obtain face point cloud information;
and generating the three-dimensional head model according to the human face point cloud information.
10. The mask recommendation method according to claim 8, wherein the determining of the recommendation result from the face image matching based on the three-dimensional head model includes:
calling a mask recommendation algorithm to process the depth information of the three-dimensional head model to obtain recommendation scores of various masks; the mask recommendation algorithm is a deep neural network algorithm which completes the training of the correlation between the face and the mask tightness.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114091131A (en) * | 2021-12-08 | 2022-02-25 | 中国矿业大学 | Optimal scheme decision system for cloth mask support design based on cloud platform |
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