CN114510595A - Intelligent data analysis system based on big data - Google Patents
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Abstract
The invention discloses an intelligent data analysis system based on big data, which comprises a big data updating library, a clothing classification module and a shoe outlet control module, and is characterized in that: the big data updating library is used for big data learning and updating clothes type characteristics, the clothes classification module is used for analyzing the type of the person wearing the shoe on the same day and judging the current trip travel type of the person, the shoe-out control module is used for controlling the shoe cabinet where the corresponding type of shoe is located to be pushed out according to the trip travel of the person, so that the person can directly and randomly select the shoes according with the travel type, the big data updating library is electrically connected with the clothes classification module, the clothes classification module is electrically connected with the shoe-out control module, the big data updating library comprises a data acquisition module and a characteristic learning module, and the data acquisition module is used for acquiring main profile characteristics of different types of clothes through a network.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to an intelligent data analysis system based on big data.
Background
In daily life, the basic requirement of the people is the indispensable life requirement, but along with the improvement of living conditions, the requirement of people on daily wearing is higher and higher, and different dressing effects are needed on different occasions. After people change appropriate clothes in the clothes and hat room, people also need to go to the shoe cabinet to select corresponding shoes so as to be in line with the whole wearing and taking trip.
Along with the continuous progress of society and the continuous improvement of people's standard of living, people are more and more high to the pursuit of beauty, the shoes number volume becomes more and more in the shoe cabinet, but no matter be the clothing or shoes all wholly divide into three big types of commercial type, leisure class and sports type, wherein commercial type clothes are mostly western-style clothes, the version is level and smooth, the dress profile is straighter, the whole version style of leisure type is various, the dress profile has the ripple and comparatively intensive, and sports type dress is comparatively loose, the dress profile is big wave form law change in the department of buckling.
However, the pace of urban life is continuously accelerated, and many times, when people fit clothes in a clothes and hat room to go out, the shoes matched with the clothes are difficult to distinguish and find out at a glance because of a large number of shoes in the shoe cabinet, a large amount of time needs to be spent in finding out the shoes in the shoe cabinet, the time for going out is seriously wasted, and even the journey is delayed. Therefore, there is a need to design intelligent data analysis systems based on big data that are robust and can be used to derive footwear based on clothing intelligence.
Disclosure of Invention
The invention aims to provide an intelligent data analysis system based on big data to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: big data-based intelligent data analysis system, including big data update storehouse, clothing classification module and play shoes control module, big data update storehouse is used for big data study to update clothes type characteristic, clothing classification module is used for analyzing individual belonged type of wearing on the same day and judges individual current trip stroke type, it is used for corresponding type shoes place shoe cabinet department to push out according to individual trip stroke control and stretches out to go out, is convenient for individual direct arbitrary the selecting in the shoes that accord with the stroke type, big data update storehouse is connected with clothing classification module electricity, clothing classification module is connected with a shoes control module electricity.
According to the technical scheme, the big data updating library comprises a data acquisition module and a feature learning module, the data acquisition module is used for acquiring main contour features of different types of clothes through a network, the feature learning module is electrically connected with the data acquisition module, and the feature learning module is used for learning and sorting comprehensive feature contour curve data of different types of clothes.
According to the technical scheme, the clothing classification module comprises a photosensitive shooting unit, an imaging data analysis module and a logic judgment module, the photosensitive shooting unit is used for triggering shooting of images according to photosensitive conversion signals, the imaging data analysis module is electrically connected with the photosensitive shooting unit, the imaging data analysis module is used for analyzing and calculating the type of clothing worn by a current person according to imaging information, the logic judgment module is electrically connected with the imaging data analysis module and a big data updating library, and the logic judgment module is used for analyzing and judging the type of the current trip travel of the person according to the type of the clothing.
According to the technical scheme, the shoe outlet control module comprises an induction tag unit and an execution unit, the induction tag unit is used for recording shoe data and is attached to the shoe cabinet for induction transmission, and the execution unit is used for controlling the shoe cabinet to move at the bottom of the corresponding shoe position so that the corresponding shoe can be moved out of the shoe cabinet.
According to the technical scheme, the imaging data analysis module comprises an identification matting submodule, a contour fitting submodule and a data calculation submodule, wherein the identification matting submodule is used for carrying out clothing contour buckling and cutting on an imaging picture, the contour fitting submodule is used for buckling and cutting a contour line of the clothing picture, and the data calculation submodule is used for calculating a fluctuation numerical value of the contour line and analyzing and judging the clothing type.
According to the technical scheme, the operation method of the intelligent data analysis system based on big data comprises the following steps:
step S1: the big data updating library continuously updates the main style data of different types of clothes at present, and sorts the main style characteristic data of the study to obtain main characteristic databases of different types of clothes;
step S2: after the person gets up, the person enters a clothes and hat room according to the current travel type to be changed into a clothes type matched with the travel, and the clothes are arranged in front of the mirror;
step S3: when the person is in the mirror, the clothing classification module shoots the current personal image, analyzes and judges the clothing type, judges the current travel type of the person, and transmits the judgment result to the shoe outlet control module in the form of an electric signal;
step S4: the shoe outlet control module is matched with the same type of induction tag units according to the current travel type, and executes movement on the shoe cabinet where the same induction tag units are located, so as to control the corresponding type of shoes to be moved out of the shoe cabinet.
According to the above technical solution, the step S3 further includes the following steps:
step S31: the photosensitive shooting unit senses ambient light in real time and converts a sensed photosensitive signal into an electric signal;
step S32: when a person is in the mirror, the body shields the light source in the cloakroom, the photosensitive shooting unit senses that the light is weakened to trigger an electric signal for shooting, and the electric signal of the shot image is transmitted to the imaging data analysis module;
step S33: the imaging data analysis module acquires personal dress image data in front of the mirror surface, performs data analysis on the image data, and transmits a data analysis result electric signal to the logic judgment module;
step S34: the logic judgment module acquires the main characteristic data of different types of clothes in the big data update library, judges the data analysis result of the clothes worn by the current individual and obtains the current trip type of the individual after judging the type of the current individual clothes.
According to the above technical solution, the step S33 further includes the following steps:
step S331: after image data shot by a photosensitive shooting unit is obtained, an identification matting submodule carries out intelligent identification on an image;
step S332: matting the personal picture in the image according to the difference between the personal depth and the background depth in the image;
step S333: the image after the matting is a personal body image, the contour fitting submodule calibrates the contour of the personal body image according to a fixed spacing distance, and a calibrated point connecting line forms a personal body contour fitting line;
step S334: the data calculation module intercepts a segment of personal body fitting line, connects two end points of the segment to obtain a standard line, and sequentially measures the distance from all the calibration point values in the segment of personal body fitting line to the standard line;
step S335: and taking one side of the standard line as positive and the other side as negative to obtain a distance sequence { l) from all the calibration points to the standard line1、l2、l3...ln}；
Step S336: randomly acquiring any numerical value in the numerical sequence, marking the current numerical value when the numerical value is positive or negative with the numerical values on the two sides of the numerical sequence, finally acquiring the number m of the marked numerical values in the current numerical sequence, and acquiring the fluctuation frequency H of the current contour according to the ratio of the marked numerical value m to all the calibration points n;
step S337: calculating the fluctuation degree S of all the calibration points in the personal body fitting line relative to the standard line through a variance formula2。
According to the above technical solution, the calculation formula of the fluctuation frequency H of the current contour in step S336 is as follows:
in the formula, when the number m of the marked numerical values is larger, more adjacent numerical values in the numerical sequence are positive and negative, and the fluctuation frequency is higher relative to the standard line.
The fluctuation degree S of all the calibration points in the personal body fitting line relative to the standard line in the step S3372The calculation formula of (2) is as follows:
in the formula, the fluctuation degree of the array relative to the standard line is calculated through a variance formula, and when the dispersion degree of the whole calibration point and the standard line is large, the fluctuation degree is larger.
According to the above technical solution, the step S34 further includes:
step S341: the logic judgment module respectively obtains the fluctuation frequency and fluctuation degree standards of the main body styles of the clothes corresponding to the business type clothes, the leisure type clothes and the sports type clothes by acquiring the big data updating library;
step S342: acquiring data analyzed and calculated by a current data calculation submodule;
step S343: when the fluctuation frequency H is large, the logic judgment module judges that the clothes are leisure clothes and outputs the type of the free trip;
step S344: when the fluctuation frequency H is small and the fluctuation degree S2When the size is larger, the logic judgment module judges the clothes are sports, and outputs the travel type of the sports trip;
step S345: when the fluctuation frequency H is small and the fluctuation degree S2And when the number is smaller, the logic judgment module judges that the clothing is a business type clothing and outputs the travel type of business trip.
Compared with the prior art, the invention has the following beneficial effects: according to the invention, by arranging the big data updating library, the clothing classification module and the shoe outlet control module, daily wearing clothing can be monitored, the wearing clothing type is judged, the shoe cabinet is automatically controlled to move out shoes of the same type for individuals to directly pick up and match the wearing clothing, the time delay caused by finding the matched shoes in the trip is greatly reduced, and the trip efficiency is improved.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram of the system module composition of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention provides the following technical solutions: the intelligent data analysis system based on the big data comprises a big data updating library, a clothing classification module and a shoe outlet control module, wherein the big data updating library is used for big data learning and updating clothes type characteristics, the clothing classification module is used for analyzing the type of personal wear on the day and judging the type of personal current trip travel, the shoe outlet control module is used for controlling the shoe cabinet where the corresponding type of shoes are located to push and extend according to the personal trip travel, so that the individual can directly and freely select the shoes according with the travel types, the big data updating library is electrically connected with the clothing classification module, and the clothing classification module is electrically connected with the shoe outlet control module; by arranging the big data updating library, the clothing classification module and the shoe outlet control module, daily clothing can be monitored, the clothing type is judged, the shoe cabinet is automatically controlled to move out of shoes of the same type for individuals to directly pick up and match the shoes, the time delay caused by finding the matched shoes during trip is greatly reduced, and the trip efficiency is improved.
The big data updating library comprises a data acquisition module and a feature learning module, the data acquisition module is used for acquiring main profile features of different types of clothes through a network, the feature learning module is electrically connected with the data acquisition module, and the feature learning module is used for learning and sorting comprehensive feature profile curve data of the different types of clothes.
The clothing classification module comprises a photosensitive shooting unit, an imaging data analysis module and a logic judgment module, the photosensitive shooting unit is used for triggering shooting of images according to photosensitive conversion signals, the imaging data analysis module is electrically connected with the photosensitive shooting unit, the imaging data analysis module is used for analyzing and calculating the type of clothing worn by a person according to imaging information, the logic judgment module is electrically connected with the imaging data analysis module and a big data updating library, and the logic judgment module is used for analyzing and judging the type of a current trip travel of the person according to the type of clothing.
The shoe outlet control module comprises an induction label unit and an execution unit, the induction label unit is used for recording shoe data and is attached to the shoe cabinet for induction transmission, and the execution unit is used for controlling the shoe cabinet to move at the bottom of the corresponding shoe position so that the corresponding shoe can be moved out of the shoe cabinet.
The imaging data analysis module comprises an identification matting submodule, a contour fitting submodule and a data calculation submodule, wherein the identification matting submodule is used for carrying out clothing contour buckling and cutting on an imaging picture, the contour fitting submodule is used for fitting contour lines of the clothing picture after buckling and cutting, and the data calculation submodule is used for calculating fluctuation numerical values of the contour lines and analyzing and judging clothing types.
The operation method of the intelligent data analysis system based on big data comprises the following steps:
step S1: the big data updating library continuously updates the main style data of different types of clothes at present, and sorts the main style characteristic data of the study to obtain main characteristic databases of different types of clothes;
step S2: after the person gets up, the person enters a clothes and hat room according to the current travel type to be changed into a clothes type matched with the travel, and the clothes are arranged in front of the mirror;
step S3: when the person is in the mirror, the clothing classification module shoots the current personal image, analyzes and judges the clothing type, judges the current travel type of the person, and transmits the judgment result to the shoe outlet control module in the form of an electric signal;
step S4: the shoe outlet control module is matched with the same type of induction tag units according to the current travel type, and executes movement on the shoe cabinet where the same induction tag units are located, so as to control the corresponding type of shoes to be moved out of the shoe cabinet; and further, the time for selecting and searching the corresponding type of shoes in the shoe cabinet during traveling is greatly reduced, and the delay of the traveling process is effectively avoided.
Step S3 further includes the steps of:
step S31: the photosensitive shooting unit senses ambient light in real time and converts a sensed photosensitive signal into an electric signal;
step S32: when a person is in the mirror, the body shields the light source in the cloakroom, the photosensitive shooting unit senses that the light is weakened to trigger an electric signal for shooting, and the electric signal of the shot image is transmitted to the imaging data analysis module;
step S33: the imaging data analysis module acquires personal dress image data in front of the mirror surface, performs data analysis on the image data, and transmits a data analysis result electric signal to the logic judgment module;
step S34: the logic judgment module acquires the main characteristic data of different types of clothes in the big data update library, judges the data analysis result of the clothes worn by the current individual and obtains the current trip type of the individual after judging the type of the current individual clothes.
Step S33 further includes the steps of:
step S331: after image data shot by a photosensitive shooting unit is obtained, an identification matting submodule carries out intelligent identification on an image;
step S332: matting the personal picture in the image according to the difference between the personal depth and the background depth in the image;
step S333: the image after the matting is a personal body image, the contour fitting submodule calibrates the contour of the personal body image according to a fixed spacing distance, and a calibrated point connecting line forms a personal body contour fitting line;
step S334: the data calculation module intercepts a segment of personal body fitting line, connects two end points of the segment to obtain a standard line, and sequentially measures the distance from all the calibration point values in the segment of personal body fitting line to the standard line;
step S335: and taking one side of the standard line as positive and the other side as negative to obtain a distance sequence { l) from all the calibration points to the standard line1、l2、l3...ln}；
Step S336: randomly acquiring any numerical value in the numerical sequence, marking the current numerical value when the numerical value is positive or negative with the numerical values on the two sides of the numerical sequence, finally acquiring the number m of the marked numerical values in the current numerical sequence, and acquiring the fluctuation frequency H of the current contour according to the ratio of the marked numerical value m to all the calibration points n;
step S337: calculating the fluctuation degree S of all the calibration points in the personal body fitting line relative to the standard line through a variance formula2。
The calculation formula of the fluctuation frequency H of the current contour in step S336 is:
in the formula, when the number m of the marked numerical values is larger, more adjacent numerical values in the numerical sequence are positive and negative, and the fluctuation frequency is higher relative to the standard line.
The fluctuation degree S of all the calibration points in the personal body fitting line relative to the standard line in step S3372The calculation formula of (2) is as follows:
in the formula, the fluctuation degree of the array relative to the standard line is calculated through a variance formula, and when the dispersion degree of the whole calibration point and the standard line is large, the fluctuation degree is larger.
Step S34 further includes:
step S341: the logic judgment module respectively obtains the fluctuation frequency and the fluctuation degree standard of the main body patterns of the clothes corresponding to the business clothes, the leisure clothes and the sports clothes by acquiring a big data update library;
step S342: acquiring data analyzed and calculated by a current data calculation submodule;
step S343: when the fluctuation frequency H is large, the logic judgment module judges that the clothes are leisure clothes and outputs the type of the free trip;
step S344: when the fluctuation frequency H is small and the fluctuation degree S2When the size is larger, the logic judgment module judges the clothes are sports, and outputs the travel type of the sports trip;
step S345: when the fluctuation frequency H is small and the fluctuation degree S2When the size is smaller, the logic judgment module judges the clothes are business clothes and outputs the travel type of business trip.
Example (b): after the person gets up, the current journey is trip business negotiation, the person enters a clothes and hat room to change the formal dress, the person arranges the clothes in front of the mirror,the clothing classification module shoots a current personal image, and carries out cutout calculation on image data to obtain the fluctuation frequency H of the body contour of the clothing under the normal clothing, which is 4%, and the fluctuation degree S2The shoe cabinet is 3cm, the fluctuation frequency obtained through the large database is larger than 30%, the fluctuation degree is larger than 10cm, the data is larger than 10cm, the logic judgment module judges that the shoe cabinet is a business type garment because 4% is smaller than 30% and 3cm is smaller than 10cm, the travel type of business trip is output, the shoe cabinet is matched with the position of the induction tag of the business type shoe, and finally the shoe cabinet moves at the bottom of the business type shoe, so that the business type shoe is moved out of the shoe cabinet.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. Intelligent data analysis system based on big data, including big data update storehouse, clothing classification module and play shoes control module, its characterized in that: the big data updating library is used for big data learning and updating clothes type characteristics, the clothing classification module is used for analyzing the types of personal wearing on the same day and judging the current travel type of the individual, the shoe outlet control module is used for controlling shoe cabinet positions where the corresponding types of shoes are located to be pushed out and extended according to the travel of the individual, the individual can be conveniently and directly selected at will in the shoes according with the travel types, the big data updating library is electrically connected with the clothing classification module, and the clothing classification module is electrically connected with the shoe outlet control module.
2. The big-data based intelligent data analysis system of claim 1, wherein: the big data updating library comprises a data acquisition module and a feature learning module, wherein the data acquisition module is used for acquiring main profile features of different types of clothes through a network, the feature learning module is electrically connected with the data acquisition module, and the feature learning module is used for learning and sorting comprehensive feature profile curve data of different types of clothes.
3. The big-data based intelligent data analysis system of claim 2, wherein: the clothing classification module comprises a photosensitive shooting unit, an imaging data analysis module and a logic judgment module, the photosensitive shooting unit is used for triggering shooting of images according to photosensitive conversion signals, the imaging data analysis module is electrically connected with the photosensitive shooting unit, the imaging data analysis module is used for analyzing and calculating clothing types of current personal wear according to imaging information, the logic judgment module is electrically connected with the imaging data analysis module and a big data updating library, and the logic judgment module is used for analyzing and judging current travel types of individuals according to the clothing types.
4. The big-data based intelligent data analysis system of claim 3, wherein: the shoe outlet control module comprises an induction tag unit and an execution unit, the induction tag unit is used for recording shoe data and is attached to the shoe cabinet for induction transmission, and the execution unit is used for controlling the shoe cabinet to move at the bottom of the corresponding shoe, so that the corresponding shoe is moved out of the shoe cabinet.
5. The big-data based intelligent data analysis system of claim 4, wherein: the imaging data analysis module comprises an identification matting submodule, a contour fitting submodule and a data calculation submodule, wherein the identification matting submodule is used for carrying out clothing contour buckling and cutting on an imaging picture, the contour fitting submodule is used for buckling and cutting a clothing contour fitting contour line, and the data calculation submodule is used for calculating a fluctuation numerical value of the contour line and analyzing and judging the clothing type.
6. The big-data based intelligent data analysis system of claim 5, wherein: the operation method of the intelligent data analysis system based on big data comprises the following steps:
step S1: the big data updating library continuously updates the main style data of different types of clothes at present, and sorts the main style characteristic data of the study to obtain main characteristic databases of different types of clothes;
step S2: after the person gets up, the person enters a clothes and hat room according to the current travel type to be changed into a clothes type matched with the travel, and the clothes are arranged in front of the mirror;
step S3: when the person is in the mirror, the clothing classification module shoots the current personal image, analyzes and judges the clothing type, judges the current travel type of the person, and transmits the judgment result to the shoe outlet control module in the form of an electric signal;
step S4: the shoe outlet control module is matched with the same type of induction tag units according to the current travel type, and executes movement on the shoe cabinet where the same induction tag units are located, so as to control the corresponding type of shoes to be moved out of the shoe cabinet.
7. The big-data based intelligent data analysis system of claim 6, wherein: the step S3 further includes the steps of:
step S31: the photosensitive shooting unit senses ambient light in real time and converts a sensed photosensitive signal into an electric signal;
step S32: when a person is in the mirror, the body shields the light source in the cloakroom, the photosensitive shooting unit senses that the light is weakened to trigger an electric signal for shooting, and the electric signal of the shot image is transmitted to the imaging data analysis module;
step S33: the imaging data analysis module acquires personal dress image data in front of the mirror surface, performs data analysis on the image data, and transmits a data analysis result electric signal to the logic judgment module;
step S34: the logic judgment module acquires the main characteristic data of different types of clothes in the big data update library, judges the data analysis result of the clothes worn by the current individual and obtains the current trip type of the individual after judging the type of the current individual clothes.
8. The big-data based intelligent data analysis system of claim 7, wherein: the step S33 further includes the steps of:
step S331: after image data shot by a photosensitive shooting unit is obtained, an identification matting submodule carries out intelligent identification on an image;
step S332: matting the personal picture in the image according to the difference between the personal depth and the background depth in the image;
step S333: the image after the matting is a personal body image, the contour fitting submodule calibrates the contour of the personal body image according to a fixed spacing distance, and a calibrated point connecting line forms a personal body contour fitting line;
step S334: the data calculation module intercepts a segment of personal body fitting line, connects two end points of the segment to obtain a standard line, and sequentially measures the distance from all the calibration point values in the segment of personal body fitting line to the standard line;
step S335: and taking one side of the standard line as positive and the other side as negative to obtain a distance sequence { l) from all the calibration points to the standard line1、l2、l3…ln}；
Step S336: randomly acquiring any numerical value in the numerical sequence, marking the current numerical value when the numerical value is positive or negative with the numerical values on the two sides of the numerical sequence, finally acquiring the number m of the marked numerical values in the current numerical sequence, and acquiring the fluctuation frequency H of the current contour according to the ratio of the marked numerical value m to all the calibration points n;
step S337: calculating the fluctuation degree S of all the calibration points in the personal body fitting line relative to the standard line through a variance formula2。
9. The big-data based intelligent data analysis system of claim 8, wherein: the calculation formula of the fluctuation frequency H of the current contour in step S336 is:
in the formula, when the number m of the marked numerical values is larger, more adjacent numerical values in the numerical sequence are positive and negative, and the fluctuation frequency is higher relative to the standard line.
The fluctuation degree S of all the calibration points in the personal body fitting line relative to the standard line in the step S3372The calculation formula of (2) is as follows:
in the formula, the fluctuation degree of the array relative to the standard line is calculated through a variance formula, and when the dispersion degree of the whole calibration point and the standard line is large, the fluctuation degree is larger.
10. The big-data based intelligent data analysis system of claim 9, wherein: the step S34 further includes:
step S341: the logic judgment module respectively obtains the fluctuation frequency and fluctuation degree standards of the main body styles of the clothes corresponding to the business type clothes, the leisure type clothes and the sports type clothes by acquiring the big data updating library;
step S342: acquiring data analyzed and calculated by a current data calculation submodule;
step S343: when the fluctuation frequency H is large, the logic judgment module judges that the clothes are leisure clothes and outputs the type of the free trip;
step S344: when the fluctuation frequency H is small and the fluctuation degree S2When the size is larger, the logic judgment module judges the clothes are sports, and outputs the travel type of the sports trip;
step S345: when the fluctuation frequency H is small and the fluctuation degree S2When the size is smaller, the logic judgment module judges the clothes are business clothes and outputs the travel type of business trip.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150062301A1 (en) * | 2013-08-30 | 2015-03-05 | National Tsing Hua University | Non-contact 3d human feature data acquisition system and method |
| CN107248099A (en) * | 2017-06-01 | 2017-10-13 | 深圳天珑无线科技有限公司 | Clothing matching recommends method, system and storage medium |
| CN107909445A (en) * | 2017-11-29 | 2018-04-13 | 努比亚技术有限公司 | A kind of information recommendation method, device and computer-readable recording medium |
| KR102185638B1 (en) * | 2019-10-31 | 2020-12-02 | 김다솜 | System of total coordination proposal with most famous full fashion style by using AI and operating method thereof |
| CN112115345A (en) * | 2019-06-21 | 2020-12-22 | 青岛海尔洗衣机有限公司 | Travel prompting method and intelligent travel equipment |
| CN113569643A (en) * | 2021-06-28 | 2021-10-29 | 上海电机学院 | Shoe cabinet with intelligent recommendation function |
-
2022
- 2022-02-21 CN CN202210156469.9A patent/CN114510595A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150062301A1 (en) * | 2013-08-30 | 2015-03-05 | National Tsing Hua University | Non-contact 3d human feature data acquisition system and method |
| CN107248099A (en) * | 2017-06-01 | 2017-10-13 | 深圳天珑无线科技有限公司 | Clothing matching recommends method, system and storage medium |
| CN107909445A (en) * | 2017-11-29 | 2018-04-13 | 努比亚技术有限公司 | A kind of information recommendation method, device and computer-readable recording medium |
| CN112115345A (en) * | 2019-06-21 | 2020-12-22 | 青岛海尔洗衣机有限公司 | Travel prompting method and intelligent travel equipment |
| KR102185638B1 (en) * | 2019-10-31 | 2020-12-02 | 김다솜 | System of total coordination proposal with most famous full fashion style by using AI and operating method thereof |
| CN113569643A (en) * | 2021-06-28 | 2021-10-29 | 上海电机学院 | Shoe cabinet with intelligent recommendation function |
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