CN112001413B - Human body appearance data prediction system based on human body size database - Google Patents
Human body appearance data prediction system based on human body size database Download PDFInfo
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Abstract
The invention discloses a human body appearance data prediction system based on a human body size database, which comprises the steps of obtaining historical size data of a size database about a target individual, carrying out correlation analysis on the additional characteristic data, and dividing the additional characteristic data into a training set and a prediction set; inputting the additional feature data in the training set into a sample model for training; inputting the additional characteristic data in the prediction set into a trained sample model for prediction to obtain prediction size data; clustering the predicted size data, obtaining an optimal cluster group by DB-index analysis, using data aggregation, aggregating all kinds of data based on a cluster center to form characteristic curves of each cluster, using model combination to train K prediction models for each cluster curve, and outputting a size data prediction model. According to the invention, the required human body dimension project value of the tested person is predicted and obtained according to the similar sample and the obtained known dimension data, and the accuracy of the predicted value is high and the cost is low.
Description
Technical Field
The invention relates to the field of data prediction and information extraction, in particular to a human body appearance data prediction system based on a human body size database.
Background
The human body appearance data is basic data of products and environment designs used by owners of clothing, automobiles, airplanes, ships, buildings and the like, and has great practical value in the aspects of human engineering, national defense industry, light industry, safety engineering, mechanical design, sports science, health care and the like.
With the development of technology, personal customization is adopted by more and more enterprises. The personal customization needs to acquire personal detailed human body shape data, the human body data needed by different products are different, the automobile seat design needs to sit high, thigh length, hip width and other data, and the coat design needs to have chest circumference, arm length and other data. Therefore, the application field of human body appearance data is wider and wider, and the influence on the safety and the use experience of products is also larger and wider.
Human body shape data is generally obtained through human body measurement, and human body measurement is a professional work, and special measuring instruments and measuring technologies are needed, for example, main measuring tools for manual measurement are flexible rule, angle rule, altimeter, distance meter, sliding meter and the like, and non-contact measurement is performed by a three-dimensional human body scanner, a photographic method and the like. Anthropometric measurements require expertise and special environmental configurations. Moreover, when the scanning system such as laser and white light or the photographic method is adopted to carry out the human body measurement, the measured person is required to take off the clothes, only a small amount of measurement underwear and head covers are worn, the measured person is required to keep a constant posture within a few minutes, the measurement process is complex and time-consuming, and the interference of the measured person is easy to cause. Accordingly, there is a need for a human body profile data prediction system that can be based on a human body size database.
Disclosure of Invention
The invention aims to provide a human body appearance data prediction system based on a human body size database.
The aim of the invention is achieved by the following technical scheme:
the invention comprises the following steps:
a, acquiring historical size data of a size database about a target individual, wherein the historical size data comprises main size project data of height, weight, waistline, head circumference, hand length and foot length; extracting additional feature data from the historical size data, the additional feature data including gender, age, region and occupation,
b, carrying out correlation analysis on the additional characteristic data and dividing the additional characteristic data into a training set and a prediction set; inputting the additional feature data in the training set into a sample model for training; inputting the additional characteristic data in the prediction set into a trained sample model for prediction to obtain prediction size data;
c, clustering is carried out on the predicted size data, and DB-index analysis is used for obtaining an optimal cluster group;
d, aggregating all kinds of data by utilizing data aggregation and taking a cluster center as a basis to form characteristic curves of all clusters;
and E, respectively training K prediction models for each cluster curve by using model combination, judging whether the relative error of the predicted size data and the real size data is smaller than the comparison judgment of an error threshold value, judging whether the relative error of the predicted size data and the real size data is smaller than the error threshold value, and if so, taking the trained sample model as the size data prediction model.
Further, the size database includes a plurality of sample models, and the step of inputting the additional feature data in the training set into the sample models for training includes: respectively inputting the additional characteristic data in the training set into each sample model in the size database for training; respectively inputting the additional characteristic data in the prediction set into each sample model in the trained size database to predict so as to obtain a plurality of prediction data; dividing a plurality of data into a multi-dimensional training set and a multi-dimensional prediction set; and inputting a plurality of prediction data in the multidimensional training set into a sample model in the size database for training.
A method of predicting human body dimensions of different individuals, the method comprising: inputting a target individual to be predicted into the size data prediction model trained by the training method so as to acquire size data of the target individual to be predicted in a future preset range, wherein the size data comprises individual data; inputting the size data into a preset mapping function to obtain an age sample of an individual to be predicted; the mapping function includes a number of age samples, each age sample corresponding to a range of size data.
A model training system for size data prediction comprises a historical data acquisition module, an additional feature data extraction module, an additional feature data division module, a training module, a prediction module and an error judgment module; the history data acquisition module is used for acquiring history size data of a size database about a target individual, the history size data comprises history size individual data, the additional feature data extraction module is used for extracting additional feature data from the history size data, and the additional feature data comprises gender, age, region and occupation information.
Further, the training module comprises a training unit, a prediction unit, a dividing unit and a training unit; the training unit is used for inputting the additional characteristic data in the training set into the size database for training; the prediction unit is used for inputting the additional characteristic data in the prediction set into the trained size database for prediction to obtain prediction data; the dividing unit is used for dividing the prediction data into a training set and a prediction set; the training unit is used for inputting the predicted data in the training set into the size database for training; the prediction module is used for inputting the prediction data in the prediction set into the trained size database to conduct prediction so as to obtain the prediction size data.
Further, the size database comprises a plurality of sample models, and the training unit is used for respectively inputting additional characteristic data in a training set into each sample model in the size database for training; the prediction unit is used for respectively inputting the additional characteristic data in the prediction set into each sample model in the trained size database to predict so as to obtain a plurality of prediction data; the dividing unit is used for dividing a plurality of data into a multi-dimensional training set and a multi-dimensional prediction set; the training unit is used for inputting a plurality of prediction data in the multidimensional training set into a sample model in the size database for training; the prediction module is used for inputting a plurality of prediction data in the multidimensional prediction set into the trained sample model in the size database to conduct prediction so as to obtain prediction size data.
An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the model training method for size data prediction described above when executing the computer program; or, the processor executes the computer program to implement the above-mentioned prediction method for different individual human body sizes.
A computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the model training method for dimensional data prediction described above; or, the computer program when executed by the processor implements the steps of the above-described method for predicting the size of the human body of the different individuals.
One or embodiments of the present invention may have the following advantages over the prior art:
(1) The invention has the advantages that the required input data is easy to obtain, only the main size items which are easy to measure, such as gender, age, region, occupation and the like of the tested person, such as height, weight, waistline, head circumference, hand length, foot length and the like, are required, the sample which is similar to the main size item value is matched through the latest Chinese human body size database, the required human body size item value of the tested person is predicted according to the similar sample and the related model of each size data, and the accuracy of the predicted value is high and the cost is low.
(2) The algorithm of the invention is based on the human body data with large sample size in the latest Chinese human body size database, has lower requirement on the number of input data items and quality of the tested person, and can calculate accurate human body outline size data of the tested person through a correlation model among measurement items with large sample size even if all values of basic information items and main size items can not be provided.
(3) The method for predicting the human body appearance data is realized by a representative human body size database with large sample size and only needs a tested person to provide basic information and a small amount of main size project values, can be used in the fields of network fitting, network customization and the like, can greatly shorten the measurement time, improves the human body appearance prediction precision and efficiency, and has the advantages of faster measurement speed, more accurate human body appearance data and lower measurement cost compared with manual measurement and three-dimensional non-contact measurement.
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FIG. 1 is a flow chart of a human body profile data prediction system based on a human body size database;
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the following examples and the accompanying drawings.
As shown in fig. 1, a, obtaining historical size data of a size database about a target individual, wherein the historical size data comprises main size item data of height, weight, waistline, head circumference, hand length and foot length; extracting additional feature data from the historical size data, the additional feature data including gender, age, region and occupation,
b, dividing the additional feature data into a training set and a prediction set; inputting the additional feature data in the training set into a sample model for training; inputting the additional characteristic data in the prediction set into a trained sample model for prediction to obtain prediction size data;
and carrying out correlation analysis among human body dimension items according to the Chinese human body dimension database. The correlation between the main body dimensions can be found by the following law:
the correlation between the height and the height measurement item is stronger, and the correlation coefficient between the height measurement item and the standing posture height item is larger than the correlation coefficient between the height measurement item and the sitting posture height item; the height and trunk length items also have more obvious linear correlation; the weight and the circumference class, the width class and the thickness class have obvious linear correlation; chest circumference, waistline, hip circumference have a strong correlation with weight, circumference class, thickness class and width class measurement items. In the hand and foot measurement project, the hand length and foot length are obviously related to the height, and the hand width and foot width are obviously related to the weight.
According to the correlation analysis result of human body dimension items, main dimension items (such as height, weight, waistline, head circumference, arm length, leg length, hand length, foot length and the like) which are easy to measure and have large correlation with other dimension items are found out, the dimensions of sex, age, region, occupation and the like are divided, and a correlation model between each human body dimension item and the main dimension item is respectively established.
Searching a Chinese human body size database according to the age, region, occupation, main size item and other data of the tested person, matching samples similar to the main size item value, and predicting to obtain the required human body size item value of the tested person according to the similar samples and the related model of each size data.
According to the latest collected national human body size data, the Chinese human body size database is updated regularly, and the human body size project correlation model is corrected, so that the human body size data is predicted more accurately.
C, clustering is carried out on the predicted size data, and DB-index analysis is used for obtaining an optimal cluster group;
d, aggregating all kinds of data by utilizing data aggregation and taking a cluster center as a basis to form characteristic curves of all clusters;
and E, respectively training K prediction models for each cluster curve by using model combination, judging whether the relative error of the predicted size data and the real size data is smaller than the comparison judgment of an error threshold value, judging whether the relative error of the predicted size data and the real size data is smaller than the error threshold value, and if so, taking the trained sample model as the size data prediction model.
In this embodiment 1, 2-3 measurement points are extracted from each region according to population distribution, and the anthropometric work is performed according to standardized measurement methods such as national standard GB/T5703, anthropometric basic project for technical design, etc., to collect basic information (sex, age, native place, residence, occupation, etc.) and anthropometric data (mainly including 100 items of anthropomorphic parameter data (such as body weight, height, eye height, shoulder height, elbow height, sitting height, shoulder-elbow distance, elbow-wrist distance, hand length, palm length, foot length, head circumference, etc.) of each sample to be measured, such as standing position, head-face portion in sitting position, body trunk, limb position, foot, etc.).
In this embodiment 2, the size database includes a plurality of sample models, and the step of inputting the additional feature data in the training set into the sample models for training includes: respectively inputting the additional characteristic data in the training set into each sample model in the size database for training; respectively inputting the additional characteristic data in the prediction set into each sample model in the trained size database to predict so as to obtain a plurality of prediction data; dividing a plurality of data into a multi-dimensional training set and a multi-dimensional prediction set; and inputting a plurality of prediction data in the multidimensional training set into a sample model in the size database for training.
A method of predicting human body dimensions of different individuals, the method comprising: inputting a target individual to be predicted into the size data prediction model trained by the training method to obtain size data of the target individual to be predicted in a future preset range, wherein the size data comprises individual data; inputting the size data into a preset mapping function to obtain an age sample of an individual to be predicted; the mapping function includes a number of age samples, each age sample corresponding to a range of size data.
A model training system for size data prediction comprises a historical data acquisition module, an additional feature data extraction module, an additional feature data division module, a training module, a prediction module and an error judgment module; the history data acquisition module is used for acquiring history size data of a size database about a target individual, the history size data comprises history size individual data, the additional feature data extraction module is used for extracting additional feature data from the history size data, and the additional feature data comprises gender, age, region and occupation information.
In this embodiment 3, the training module includes a training unit, a prediction unit, a dividing unit, and a training unit; the training unit is used for inputting the additional characteristic data in the training set into the size database for training; the prediction unit is used for inputting the additional characteristic data in the prediction set into the trained size database for prediction to obtain prediction data; the dividing unit is used for dividing the prediction data into a training set and a prediction set; the training unit is used for inputting the predicted data in the training set into the size database for training; the prediction module is used for inputting the prediction data in the prediction set into the trained size database to conduct prediction so as to obtain the prediction size data.
In this embodiment 4, the size database includes a plurality of sample models, and the training unit is configured to input additional feature data in a training set to each sample model in the size database for training; the prediction unit is used for respectively inputting the additional characteristic data in the prediction set into each sample model in the trained size database to predict so as to obtain a plurality of prediction data; the dividing unit is used for dividing a plurality of data into a multi-dimensional training set and a multi-dimensional prediction set; the training unit is used for inputting a plurality of prediction data in the multidimensional training set into a sample model in the size database for training; the prediction module is used for inputting a plurality of prediction data in the multidimensional prediction set into the trained sample model in the size database to conduct prediction so as to obtain prediction size data.
In this embodiment 5, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the model training method for size data prediction described above when executing the computer program; or, the processor executes the computer program to implement the above-mentioned prediction method for different individual human body sizes.
In this embodiment 6, a computer-readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the model training method for size data prediction described above; or, the computer program when executed by the processor implements the steps of the above-described method for predicting the size of the human body of the different individuals.
The electronic device may further communicate with one or more external devices (e.g., keyboard, pointing device, etc.). Such communication may be through an input/output (I/O) interface. And, the electronic device may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through a network adapter. The network adapter communicates with other modules of the electronic device via a bus. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with an electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, data backup storage systems, and the like.
It should be noted that although several units/modules or sub-units/modules of an electronic device are mentioned in the above detailed description, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more units/modules described above may be embodied in one unit/module according to embodiments of the present application. Conversely, the features and functions of one unit/module described above may be further divided into ones that are embodied by a plurality of units/modules.
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the prediction method of embodiment 1.
More specifically, among others, readable storage media may be employed including, but not limited to: portable disk, hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible embodiment, the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps of implementing the prediction method in embodiment 1 or embodiment 2, when said program product is run on the terminal device.
Wherein the program code for carrying out the invention may be written in any combination of one or more programming languages, which program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on the remote device or entirely on the remote device.
Although the embodiments of the present invention are described above, the embodiments are only used for facilitating understanding of the present invention, and are not intended to limit the present invention. Any person skilled in the art can make any modification and variation in form and detail without departing from the spirit and scope of the present disclosure, but the scope of the present disclosure is still subject to the scope of the appended claims.
Claims (7)
1. Human body appearance data prediction system based on human body size database, characterized by:
the method comprises the following steps:
a, acquiring historical size data of a Chinese human body size database about a target individual, wherein the historical size data comprises main size project data of height, weight, waistline, head circumference, hand length and foot length; additional feature data is extracted from the historical size data,
the Chinese human body size database comprises a plurality of sample models;
b, carrying out correlation analysis on the additional characteristic data and dividing the additional characteristic data into a training set and a prediction set; inputting the additional feature data in the training set into a sample model for training; inputting the additional characteristic data in the prediction set into a trained sample model for prediction to obtain prediction size data;
according to the Chinese human body size database, carrying out correlation analysis among human body size items, finding out main size items which are easy to measure and have large correlation with other sizes according to the human body size item correlation analysis result, dividing gender, age, region and occupation dimension, and respectively establishing correlation models among all human body size items and the main size items;
searching a Chinese human body size database according to age, region, occupation and main size item data of the tested person, matching samples similar to the main size item values, and predicting to obtain the required human body size item values of the tested person according to the similar samples and related models of the size data;
c, clustering the predicted size data, and obtaining an optimal cluster group by DB-index analysis;
d, aggregating all kinds of data by utilizing data aggregation and taking a cluster center as a basis to form characteristic curves of all clusters; and E, respectively training K prediction models for each cluster curve by using model combination, judging whether the relative error of the predicted size data and the real size data is smaller than an error threshold value, and if so, taking the trained sample model as a size data prediction model.
2. The human body shape data prediction system based on a human body size database of claim 1, wherein the step of inputting additional feature data in the training set into the sample model for training comprises: respectively inputting the additional characteristic data in the training set into each sample model in the Chinese human body size database for training;
respectively inputting the additional characteristic data in the prediction set into each sample model in the trained Chinese human body size database to predict so as to obtain a plurality of prediction data; dividing a plurality of data into a multi-dimensional training set and a multi-dimensional prediction set; and inputting a plurality of prediction data in the multidimensional training set into a sample model in the Chinese human body size database for training.
3. A method for predicting human body dimensions of different individuals, the method comprising: inputting a human body size of a target individual to be predicted to a human body shape data prediction system based on a human body size database according to any one of claims 1-2 to obtain size data of the target individual to be predicted within a preset range in the future, the size data including individual data; inputting the size data into a preset mapping function to obtain an age sample of an individual to be predicted; the mapping function includes a number of age samples, each age sample corresponding to a range of size data.
4. A model training system for size data prediction, characterized by:
the human body appearance data prediction system based on the human body size database according to any one of claims 1-2, further comprising a historical data acquisition module, an additional feature data extraction module, an additional feature data division module, a training module, a prediction module and an error judgment module; the history data acquisition module is used for acquiring history size data of the Chinese human body size database about a target individual, the history size data comprise history size individual data, the additional feature data extraction module is used for extracting additional feature data from the history size data, and the additional feature data comprise gender, age, region and occupation information.
5. The model training system for size data prediction as claimed in claim 4, wherein said training module comprises a training unit, a prediction unit, a partitioning unit; the training unit is used for inputting the additional characteristic data in the training set into the size database for training; the prediction unit is used for inputting the additional characteristic data in the prediction set into the trained size database for prediction to obtain prediction data; the dividing unit is used for dividing the prediction data into a training set and a prediction set; the training unit is used for inputting the predicted data in the training set into the size database for training; the prediction module is used for inputting the prediction data in the prediction set into the trained size database for prediction to obtain the prediction size data.
6. A model training system for size data prediction as claimed in claim 5, wherein said training unit is adapted to input additional feature data in a training set into each sample model in said size database for training, respectively; the prediction unit is used for respectively inputting the additional characteristic data in the prediction set into each sample model in the trained size database to predict so as to obtain a plurality of prediction data; the dividing unit is used for dividing a plurality of data into a multi-dimensional training set and a multi-dimensional prediction set; the training unit is used for inputting a plurality of prediction data in the multidimensional training set into a sample model in the size database for training; the prediction module is used for inputting a plurality of prediction data in the multidimensional prediction set into the trained sample model in the size database to conduct prediction so as to obtain prediction size data.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the model training system for size data prediction of any of claims 4 to 6 when the computer program is executed by the processor; or, the processor, when executing the computer program, implements the method for predicting different individual human body dimensions according to claim 3.
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