阅读说明：本技术 基于云计算的年龄均值估算方法 (Age mean estimation method based on cloud computing ) 是由 周群 于 2018-07-25 设计创作，主要内容包括：本发明涉及一种基于云计算的年龄均值估算方法,该方法包括提供一种基于云计算的年龄均值估算方法来进行估算,所述基于云计算的年龄均值估算平台包括：电压测量设备,与供电电源连接,用于对所述供电电源的电压进行稳定性测量,以获得所述供电电源的电压对应的电压稳定等级；U型滑板主体,包括U型滑轨、轨道列车、滑轨基座、供电电源和PLC逻辑器件；云计算服务器,用于接收已执行图像,获取所述已执行图像中的每一个像素点的红色通道值,将红色通道值落在人体红色通道上限值和人体红色通道下限值之间的像素点确定为人体区域,获取所述已执行图像中的多个人体区域,分别确定每一个人体区域对应的年龄。(The invention relates to an age mean estimation method based on cloud computing, which comprises the following steps of providing an age mean estimation method based on cloud computing for estimation, wherein the age mean estimation platform based on cloud computing comprises: the voltage measuring equipment is connected with the power supply and is used for measuring the stability of the voltage of the power supply so as to obtain the voltage stability grade corresponding to the voltage of the power supply; the U-shaped sliding plate main body comprises a U-shaped sliding rail, a rail train, a sliding rail base, a power supply and a PLC (programmable logic controller) logic device; the cloud computing server is used for receiving the executed image, obtaining a red channel value of each pixel point in the executed image, determining the pixel point of which the red channel value falls between an upper limit value of a human body red channel and a lower limit value of the human body red channel as a human body area, obtaining a plurality of human body areas in the executed image, and respectively determining the age corresponding to each human body area.)

1. A cloud computing-based age mean estimation method, which comprises providing a cloud computing-based age mean estimation method for estimation, wherein the cloud computing-based age mean estimation platform comprises:

the voltage measuring equipment is connected with the power supply and is used for measuring the stability of the voltage of the power supply so as to obtain the voltage stability grade corresponding to the voltage of the power supply;

the U-shaped sliding plate main body comprises a U-shaped sliding rail, a rail train, a sliding rail base, a power supply and a PLC (programmable logic controller) logic device, wherein the U-shaped sliding rail is arranged above the sliding rail base, the rail train is connected with the U-shaped sliding rail, the PLC logic device provides control logic for the sliding of the rail train on the U-shaped sliding rail, and the power supply is respectively connected with the rail train and the PLC logic device and is used for respectively providing power supply for the rail train and the PLC logic device;

the train camera device is arranged right above the rail train, is connected with the PLC logic device and is used for shooting the rail train when the rail train is in a static state acquired by the PLC logic device so as to acquire and output a corresponding rail train image;

the noise sequencing equipment is connected with the train camera device and used for receiving the rail train image, sequencing various noise types in the rail train image from large to small in maximum amplitude, and outputting the number of the noise types with the preset number in the front as the maximum noise number;

the quantity counting equipment is used for receiving the rail train image, acquiring the quantity of various noise types in the rail train image and outputting the quantity of various noise types in the rail train image as reference noise quantity;

the layer number acquisition equipment is connected with the noise sequencing equipment and used for receiving the maximum noise number and determining the layer number for signal segmentation based on the maximum noise number, wherein the more the maximum noise number is, the more the layer number for signal segmentation is, and the layer number acquisition equipment outputs the determined layer number for signal segmentation as a target layer number;

the de-noising reference device is connected with the quantity counting device and used for receiving the reference noise quantity and determining the percentage value for reducing the wavelet coefficient based on the reference noise quantity, wherein the more the reference noise quantity is, the smaller the determined percentage value for reducing the wavelet coefficient is, and the de-noising reference device outputs the percentage value for determining reducing the wavelet coefficient as a target percentage value;

the denoising execution device is respectively connected with the noise sequencing device, the denoising reference device and the denoising reference device, and is used for receiving the rail train image, the target layer number and the target percentage value, performing signal decomposition on the target layer number on the rail train image based on the target layer number by adopting a haar wavelet base to obtain each high-frequency coefficient from the first layer to the highest layer and each low-frequency coefficient of the highest layer, performing numerical shrinkage on each high-frequency coefficient from the first layer to the highest layer based on the target percentage value to obtain each shrunk high-frequency coefficient from the first layer to the highest layer, and reconstructing an executed image corresponding to the rail train image based on each shrunk high-frequency coefficient from the first layer to the highest layer and each low-frequency coefficient of the highest layer;

the cloud computing server is connected with the denoising execution device and used for receiving the executed image, acquiring a red channel value of each pixel point in the executed image, determining pixel points of which the red channel values fall between an upper limit value of a human body red channel and a lower limit value of the human body red channel as human body regions, acquiring a plurality of human body regions in the executed image, respectively determining the age corresponding to each human body region, and performing mean value calculation on a plurality of ages respectively corresponding to the plurality of human body regions in the executed image to obtain a corresponding mean value and output the mean value as a human body age mean value;

the style switching equipment is connected with the cloud computing server and used for receiving the human age mean value, determining a corresponding music style based on the human age mean value and outputting the corresponding music style;

and the music playing equipment is connected with the style switching equipment and is used for receiving the corresponding music style and playing the music content corresponding to the corresponding music style.

2. The method of claim 1, wherein the platform further comprises:

and the music storage equipment is connected with the music playing equipment and is used for storing various types of music content with music styles in advance.

3. The method of claim 2, wherein:

the denoising execution device comprises a signal receiving unit, a signal shrinking unit and a signal output unit.

4. The method of claim 3, wherein:

the signal receiving unit is used for receiving the rail train image, the target layer number and the target percentage value.

5. The method of claim 4, wherein:

and in the denoising execution device, performing data retention processing on each low-frequency coefficient of the highest layer.

6. The method of claim 5, wherein:

the signal contraction unit is respectively connected with the signal receiving unit and the signal output unit.

7. The method of claim 6, wherein:

in the noise ranking device, outputting the number of the preset number of noise types with the sequence number preceding as the maximum noise number includes: the preset number and the resolution of the rail train image form a positive correlation relationship.

8. The method of claim 7, wherein:

the cloud computing server, the style switching device, the music playing device and the music storage device are all arranged right above the rail train.

9. The method of claim 8, wherein:

the cloud computing server, the style switching device and the music storage device are placed in a box of an instrument box.

10. The method of any of claims 1-9, wherein:

the music playing device is embedded in the shell of the instrument box and plays music content towards the rail train.

Technical Field

The invention relates to the field of cloud computing, in particular to an age mean estimation method based on cloud computing.

Background

Cloud computing was yet another huge change following the large transition of mainframe computers to client-servers in the 1980 s.

Cloud computing is a product of development and fusion of traditional computer and network technologies, such as distributed computing, parallel computing, utility computing, network storage, virtualization, load balancing, hot backup redundancy and the like.

Disclosure of Invention

In order to solve the technical problems of complexity in field calculation of image data and lack of customization, the invention provides an age mean value estimation method based on cloud calculation, wherein a cloud calculation server is introduced to carry out age detection and subsequent mean value processing on each human body in U-shaped skateboard equipment so as to obtain a human body age mean value, and a corresponding music style is determined based on the human body age mean value so as to play corresponding music content, so that the intelligent degree of the U-shaped skateboard equipment is improved; in a specific image filtering mechanism, the number of noise types with the preset number in front is used as the maximum noise number, the number of layers for signal segmentation is determined based on the maximum noise number, the number of various noise types in the high-definition image is used as the reference noise number, and the percentage value for reducing the wavelet coefficient is determined based on the reference noise number, so that the targeted image denoising based on the image content is realized.

According to an aspect of the present invention, there is provided a cloud-computing-based age mean estimation method, the method including providing a cloud-computing-based age mean estimation method for estimation, the cloud-computing-based age mean estimation platform including:

the voltage measuring equipment is connected with the power supply and is used for measuring the stability of the voltage of the power supply so as to obtain the voltage stability grade corresponding to the voltage of the power supply; the U-shaped sliding plate main body comprises a U-shaped sliding rail, a rail train, a sliding rail base, a power supply and a PLC (programmable logic controller) logic device, wherein the U-shaped sliding rail is arranged above the sliding rail base, the rail train is connected with the U-shaped sliding rail, the PLC logic device provides control logic for the sliding of the rail train on the U-shaped sliding rail, and the power supply is respectively connected with the rail train and the PLC logic device and is used for respectively providing power supply for the rail train and the PLC logic device; the train camera device is arranged right above the rail train, is connected with the PLC logic device and is used for shooting the rail train when the rail train is in a static state acquired by the PLC logic device so as to acquire and output a corresponding rail train image; the noise sequencing equipment is connected with the train camera device and used for receiving the rail train image, sequencing various noise types in the rail train image from large to small in maximum amplitude, and outputting the number of the noise types with the preset number in the front as the maximum noise number; the quantity counting equipment is used for receiving the rail train image, acquiring the quantity of various noise types in the rail train image and outputting the quantity of various noise types in the rail train image as reference noise quantity; the layer number acquisition equipment is connected with the noise sequencing equipment and used for receiving the maximum noise number and determining the layer number for signal segmentation based on the maximum noise number, wherein the more the maximum noise number is, the more the layer number for signal segmentation is, and the layer number acquisition equipment outputs the determined layer number for signal segmentation as a target layer number; the de-noising reference device is connected with the quantity counting device and used for receiving the reference noise quantity and determining the percentage value for reducing the wavelet coefficient based on the reference noise quantity, wherein the more the reference noise quantity is, the smaller the determined percentage value for reducing the wavelet coefficient is, and the de-noising reference device outputs the percentage value for determining reducing the wavelet coefficient as a target percentage value; the denoising execution device is respectively connected with the noise sequencing device, the denoising reference device and the denoising reference device, and is used for receiving the rail train image, the target layer number and the target percentage value, performing signal decomposition on the target layer number on the rail train image based on the target layer number by adopting a haar wavelet base to obtain each high-frequency coefficient from the first layer to the highest layer and each low-frequency coefficient of the highest layer, performing numerical shrinkage on each high-frequency coefficient from the first layer to the highest layer based on the target percentage value to obtain each shrunk high-frequency coefficient from the first layer to the highest layer, and reconstructing an executed image corresponding to the rail train image based on each shrunk high-frequency coefficient from the first layer to the highest layer and each low-frequency coefficient of the highest layer; the cloud computing server is connected with the denoising execution device and used for receiving the executed image, acquiring a red channel value of each pixel point in the executed image, determining pixel points of which the red channel values fall between an upper limit value of a human body red channel and a lower limit value of the human body red channel as human body regions, acquiring a plurality of human body regions in the executed image, respectively determining the age corresponding to each human body region, and performing mean value calculation on a plurality of ages respectively corresponding to the plurality of human body regions in the executed image to obtain a corresponding mean value and output the mean value as a human body age mean value; the style switching equipment is connected with the cloud computing server and used for receiving the human age mean value, determining a corresponding music style based on the human age mean value and outputting the corresponding music style; and the music playing equipment is connected with the style switching equipment and is used for receiving the corresponding music style and playing the music content corresponding to the corresponding music style.

More specifically, in the cloud computing-based age mean estimation platform, the method further includes:

and the music storage equipment is connected with the music playing equipment and is used for storing various types of music content with music styles in advance.

More specifically, in the cloud computing-based age mean estimation platform: the denoising execution device comprises a signal receiving unit, a signal shrinking unit and a signal output unit.

More specifically, in the cloud computing-based age mean estimation platform: the signal receiving unit is used for receiving the rail train image, the target layer number and the target percentage value.

More specifically, in the cloud computing-based age mean estimation platform: and in the denoising execution device, performing data retention processing on each low-frequency coefficient of the highest layer.

More specifically, in the cloud computing-based age mean estimation platform: the signal contraction unit is respectively connected with the signal receiving unit and the signal output unit.

More specifically, in the cloud computing-based age mean estimation platform: in the noise ranking device, outputting the number of the preset number of noise types with the sequence number preceding as the maximum noise number includes: the preset number and the resolution of the rail train image form a positive correlation relationship.

More specifically, in the cloud computing-based age mean estimation platform: the cloud computing server, the style switching device, the music playing device and the music storage device are all arranged right above the rail train.

More specifically, in the cloud computing-based age mean estimation platform: the cloud computing server, the style switching device and the music storage device are placed in a box of an instrument box.

More specifically, in the cloud computing-based age mean estimation platform: the music playing device is embedded in the shell of the instrument box and plays music content towards the rail train.

Drawings

Embodiments of the invention will now be described with reference to the accompanying drawings, in which:

fig. 1 is a schematic structural diagram illustrating a fixed rod body of a U-shaped skateboard body of a cloud-computing-based age mean estimation platform according to an embodiment of the present invention.

Detailed Description

Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

Cloud computing is an increasing, usage and delivery model for internet-based related services, typically involving the provision of dynamically scalable and often virtualized resources over the internet. Cloud is a metaphor of network and internet. In the past, telecommunications networks were often represented by clouds and later also by the abstraction of the internet and the underlying infrastructure.

Therefore, cloud computing can enable you to experience even 10 trillion times per second computing power, and the powerful computing power can simulate nuclear explosion, forecast climate change and market development trend. A user accesses the data center through a computer, a notebook, a mobile phone and the like and operates according to the own requirements.

There are various references to the definition of cloud computing. At least 100 interpretations can be found for what is exactly cloud computing. It is now widely accepted that the National Institute of Standards and Technology (NIST) defines: cloud computing is a pay-per-use model that provides available, convenient, on-demand network access into a configurable shared pool of computing resources (resources including networks, servers, storage, applications, services) that can be provisioned quickly, with little administrative effort, or interaction with service providers.

In order to overcome the defects, the invention provides the age mean estimation method based on the cloud computing, the method comprises the step of providing the age mean estimation method based on the cloud computing for estimation, and the age mean estimation platform based on the cloud computing can effectively solve the corresponding technical problems.

Fig. 1 is a schematic structural diagram illustrating a fixed rod body of a U-shaped skateboard body of a cloud-computing-based age mean estimation platform according to an embodiment of the present invention. The fixed rod body comprises montant 2 and base 3, be provided with train camera device 1 on the fixed rod body.

The cloud computing-based age mean estimation platform shown according to the embodiment of the invention comprises:

the voltage measuring equipment is connected with the power supply and is used for measuring the stability of the voltage of the power supply so as to obtain the voltage stability grade corresponding to the voltage of the power supply;

the U-shaped sliding plate main body comprises a U-shaped sliding rail, a rail train, a sliding rail base, a power supply and a PLC (programmable logic controller) logic device, wherein the U-shaped sliding rail is arranged above the sliding rail base, the rail train is connected with the U-shaped sliding rail, the PLC logic device provides control logic for the sliding of the rail train on the U-shaped sliding rail, and the power supply is respectively connected with the rail train and the PLC logic device and is used for respectively providing power supply for the rail train and the PLC logic device;

the train camera device is arranged right above the rail train, is connected with the PLC logic device and is used for shooting the rail train when the rail train is in a static state acquired by the PLC logic device so as to acquire and output a corresponding rail train image;

the noise sequencing equipment is connected with the train camera device and used for receiving the rail train image, sequencing various noise types in the rail train image from large to small in maximum amplitude, and outputting the number of the noise types with the preset number in the front as the maximum noise number;

the quantity counting equipment is used for receiving the rail train image, acquiring the quantity of various noise types in the rail train image and outputting the quantity of various noise types in the rail train image as reference noise quantity;

the layer number acquisition equipment is connected with the noise sequencing equipment and used for receiving the maximum noise number and determining the layer number for signal segmentation based on the maximum noise number, wherein the more the maximum noise number is, the more the layer number for signal segmentation is, and the layer number acquisition equipment outputs the determined layer number for signal segmentation as a target layer number;

the de-noising reference device is connected with the quantity counting device and used for receiving the reference noise quantity and determining the percentage value for reducing the wavelet coefficient based on the reference noise quantity, wherein the more the reference noise quantity is, the smaller the determined percentage value for reducing the wavelet coefficient is, and the de-noising reference device outputs the percentage value for determining reducing the wavelet coefficient as a target percentage value;

the denoising execution device is respectively connected with the noise sequencing device, the denoising reference device and the denoising reference device, and is used for receiving the rail train image, the target layer number and the target percentage value, performing signal decomposition on the target layer number on the rail train image based on the target layer number by adopting a haar wavelet base to obtain each high-frequency coefficient from the first layer to the highest layer and each low-frequency coefficient of the highest layer, performing numerical shrinkage on each high-frequency coefficient from the first layer to the highest layer based on the target percentage value to obtain each shrunk high-frequency coefficient from the first layer to the highest layer, and reconstructing an executed image corresponding to the rail train image based on each shrunk high-frequency coefficient from the first layer to the highest layer and each low-frequency coefficient of the highest layer;

the cloud computing server is connected with the denoising execution device and used for receiving the executed image, acquiring a red channel value of each pixel point in the executed image, determining pixel points of which the red channel values fall between an upper limit value of a human body red channel and a lower limit value of the human body red channel as human body regions, acquiring a plurality of human body regions in the executed image, respectively determining the age corresponding to each human body region, and performing mean value calculation on a plurality of ages respectively corresponding to the plurality of human body regions in the executed image to obtain a corresponding mean value and output the mean value as a human body age mean value;

the style switching equipment is connected with the cloud computing server and used for receiving the human age mean value, determining a corresponding music style based on the human age mean value and outputting the corresponding music style;

and the music playing equipment is connected with the style switching equipment and is used for receiving the corresponding music style and playing the music content corresponding to the corresponding music style.

Next, a further description of a specific structure of the cloud computing-based age mean estimation platform of the present invention is continued.

In the cloud computing-based age mean estimation platform, the method further comprises:

and the music storage equipment is connected with the music playing equipment and is used for storing various types of music content with music styles in advance.

In the cloud computing-based age mean estimation platform: the denoising execution device comprises a signal receiving unit, a signal shrinking unit and a signal output unit.

In the cloud computing-based age mean estimation platform: the signal receiving unit is used for receiving the rail train image, the target layer number and the target percentage value.

In the cloud computing-based age mean estimation platform: and in the denoising execution device, performing data retention processing on each low-frequency coefficient of the highest layer.

In the cloud computing-based age mean estimation platform: the signal contraction unit is respectively connected with the signal receiving unit and the signal output unit.

In the cloud computing-based age mean estimation platform: in the noise ranking device, outputting the number of the preset number of noise types with the sequence number preceding as the maximum noise number includes: the preset number and the resolution of the rail train image form a positive correlation relationship.

In the cloud computing-based age mean estimation platform: the cloud computing server, the style switching device, the music playing device and the music storage device are all arranged right above the rail train.

In the cloud computing-based age mean estimation platform: the cloud computing server, the style switching device and the music storage device are placed in a box of an instrument box.

In the cloud computing-based age mean estimation platform: the music playing device is embedded in the shell of the instrument box and plays music content towards the rail train.

In addition, the train camera device comprises a CMOS device and is used for shooting the rail train. Among them, CMOS (Complementary Metal-Oxide-Semiconductor), the chinese scientific name is Complementary Metal Oxide Semiconductor, which is an important chip in computer systems and stores the most basic data for system booting. The CMOS manufacturing technology is not different from that of a common computer chip, and mainly utilizes a semiconductor made of two elements, namely silicon and germanium, so that N (band-electric) and P (band + electric) level semiconductors coexist on the CMOS, and the current generated by the two complementary effects can be recorded and interpreted into an image by a processing chip. CMOS has later been processed to also serve as an image sensor in digital photography. For portable applications independent of the power grid, CMOS technology, which is known for its low power consumption characteristics, has a clear advantage: CMOS image sensors are designed for 5V and 3.3V supply voltages. The CCD chip requires a power supply voltage of about 12V, and therefore a voltage converter has to be employed, resulting in an increase in power consumption. Integrating control and system functions into a CMOS sensor would provide another benefit in terms of overall power consumption: he removes all external connection lines to other semiconductor elements. Drivers with their high power consumption have been abandoned today because the energy consumed to communicate inside the chip is much lower than with external implementations through a PCB or substrate.

By adopting the cloud computing-based age mean value estimation platform, aiming at the technical problems of complicated field computing of image data and lack of customization in the prior art, the cloud computing server is introduced to carry out age detection and subsequent mean value processing on each human body in the U-shaped skateboard device so as to obtain a human body age mean value, and the corresponding music style is determined based on the human body age mean value so as to play corresponding music content, so that the intelligent degree of the U-shaped skateboard device is improved; in a specific image filtering mechanism, the number of noise types with the preset number in the front is used as the maximum noise number, the number of layers for signal segmentation is determined based on the maximum noise number, the number of various noise types in the high-definition image is used as the reference noise number, and the percentage value for reducing the wavelet coefficient is determined based on the reference noise number, so that the targeted image denoising based on the image content is realized, and the technical problem is solved.

It is to be understood that while the present invention has been described in conjunction with the preferred embodiments thereof, it is not intended to limit the invention to those embodiments. It will be apparent to those skilled in the art from this disclosure that many changes and modifications can be made, or equivalents modified, in the embodiments of the invention without departing from the scope of the invention. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention are still within the scope of the protection of the technical solution of the present invention, unless the contents of the technical solution of the present invention are departed.