CN212155235U

CN212155235U - Fan based on AI visual convolution neural network technique

Info

Publication number: CN212155235U
Application number: CN202020858207.3U
Authority: CN
Inventors: 张业超; 刘峰; 王振鹏
Original assignee: Shangshi Hongtu Shenzhen Technology Co ltd
Current assignee: Shangshi Hongtu Shenzhen Technology Co ltd
Priority date: 2020-05-20
Filing date: 2020-05-20
Publication date: 2020-12-15
Anticipated expiration: 2030-05-20

Abstract

The utility model provides a fan based on AI vision convolution neural network technique, include: a fan body for generating an air flow; the camera is arranged on the fan body, the direction of the camera is basically the same as the blowing direction of the fan body, and the camera is used for acquiring environmental information; the base bears the fan body and is rotationally connected with the fan body; the driver is used for driving the fan body to rotate relative to the base; and the AI chip module is provided with a visual convolution neural network model and is used for controlling the driver to rotate so as to enable the fan body to blow along with the human body structure when the environmental information acquired by the camera comprises the human body structure information through calculation and judgment of the convolution neural network model. The utility model discloses on using the fan with current AI technique, can follow the user and blow in order to guarantee to last the user of removing, improve the intelligence and the practicality of fan.

Description

Fan based on AI visual convolution neural network technique

Technical Field

The utility model relates to a domestic fan especially relates to a fan based on AI vision convolution neural network technique.

Background

When summer comes, the fan becomes a sharp instrument for relieving summer heat and reducing fever. The fan generates air flow through the rotation of the fan blades, the temperature of a human body can be reduced in the air flowing process, and compared with air-conditioning cooling, the fan is more natural in cooling and more beneficial to body health, so that the fan is very popular. The existing fan is used for cooling a human body by repeatedly and continuously rotating, or is fixed to blow towards one direction to cool the human body, so that the existing fan is not convenient enough, and especially when people blow air while moving to do things, the fan cannot continuously blow towards the human body.

SUMMERY OF THE UTILITY MODEL

In view of this, in order to solve one of the technical problems in the related art to a certain extent, it is necessary to provide a fan based on an AI visual convolutional neural network technology, which applies the existing AI technology to the fan, can follow the user to ensure that the moving user is continuously blown, and improve the intelligence and the practicability of the fan.

The utility model provides a fan based on AI vision convolution neural network technique, include:

a fan body for generating an air flow;

the camera is arranged on the fan body, the direction of the camera is basically the same as the blowing direction of the fan body, and the camera is used for acquiring environmental information;

the base bears the fan body and is rotationally connected with the fan body;

the driver is used for driving the fan body to rotate relative to the base;

and the AI chip module is provided with a visual convolution neural network model and is used for controlling the driver to rotate so as to enable the fan body to blow along with the human body structure when the environmental information acquired by the camera comprises the human body structure information through calculation and judgment of the convolution neural network model.

Further, the AI chip module comprises a CMOS image sensor and a GPU for operating a convolutional neural network model, and the CMOS image sensor and the GPU are combined together in an SOC mode.

Further, the human body structure comprises a human face and/or a human figure.

Further, when the environment information acquired by the camera does not include the human body structure information through the calculation of the convolutional neural network model and is judged, the driver is controlled to continuously rotate so as to detect the human body structure.

Further, the fan still includes the adapter, the adapter is used for gathering environmental sound information, the AI chip module still has AI voice model for according to the environmental sound information control that the adapter gathered the driver execution with the instruction that the pronunciation correspond.

According to the above technical scheme, the utility model discloses a camera acquires environmental information, calculates and judges whether including human structure information in the environmental information that the camera acquireed through AI chip module, when calculating and judging through convolution neural network model that the camera acquireed includes human structure information in, then through control the driver rotates so that the fan body is followed human structure bloies for the direction of blowing of the fan body is facing to the human body all the time, and the effect of blowing is better.

Drawings

Fig. 1 is a schematic structural diagram of the present invention.

Fig. 2 is a schematic circuit diagram of the present invention.

Fig. 3 is a specific circuit diagram of the present invention.

The following detailed description will further illustrate the invention in conjunction with the above-described figures.

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 of the present invention without any inventive step, are within the scope of the present invention. It is to be understood that the drawings are provided solely for the purposes of reference and illustration and are not intended as a definition of the limits of the invention. The connection relationships shown in the drawings are for clarity of description only and do not limit the manner of connection.

It will be understood that when an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It should also be noted that, unless expressly stated or limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly and can include, for example, fixed connections, removable connections, or integral connections; either mechanically or electrically, and may be internal to both elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

It should be noted that in the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, which are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be configured in a specific orientation, and operate, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.

As shown in fig. 1 and 2, the present invention provides a fan based on AI visual convolutional neural network technology, which applies the existing AI visual convolutional neural network technology to the fan.

The fan includes: a fan body 10 for generating an air flow; the camera 20 is arranged on the fan body 10 and faces to the direction which is basically the same as the blowing direction of the fan body 10, and the camera 20 is used for acquiring environmental information; a base 30 for supporting the fan body 10 and rotatably connected to the fan body 10; a driver 40 for driving the fan body 10 to rotate relative to the base 30; the AI chip module 50 has a visual convolutional neural network model, and is configured to control the driver 40 to rotate so that the fan body 10 blows along with the human body structure when it is calculated and determined that the environmental information acquired by the camera 20 includes the human body structure information through the convolutional neural network model.

The base 30 may serve as a support part for supporting the fan body 10 to facilitate the placement of the fan. The fan may be a desk fan or a floor fan, but is not limited thereto.

In the present invention, the AI chip module 50 may adopt an NB1001 chip, and a specific circuit structure is shown in fig. 3. The AI chip module 50 may include a CMOS image sensor and a GPU running a convolutional neural network model, and the CMOS image sensor and the GPU are joined together in a SOC manner, so that a space required for system construction is simplified, lower power consumption can be provided, a size required for motherboard design can be minimized, an area required for PCB fabrication is reduced, and the AI chip module is more environment-friendly.

The visual convolutional neural network model of the AI chip module 50 may be a visual convolutional neural network model that is already available in the prior art. The visual convolution neural network model belongs to a mature algorithm model, and can detect interesting features including human body structures, such as human faces and/or human shapes, based on a deep neural network algorithm. The AI chip module 50 may select an existing single-algorithm or dual-algorithm visual convolutional neural network model.

Based on a visual convolutional neural network model of a single algorithm, a feature of interest, such as a human face or a human figure, can be selected, feature coordinates are labeled in an image, and the movement of the feature is calculated. The visual convolution neural network model based on the dual algorithm can simultaneously calculate the face feature and the human shape feature in the environment information image acquired by the camera 20, and specify the coordinates of the face and the human shape in the image to calculate the movement of the face and the human shape. The GPIO interface through AI chip module 50 outputs the instruction of the high-low level of control to control driver 40 rotates, makes fan body 10 for base 30 rotates, and then realizes that camera 20 follows people's face or humanoid, fan body 10 can keep aiming at the user automatically and blow, and is convenient more intelligent, improves the practicality and the intellectuality of fan greatly.

Based on the existing visual convolution neural network model, gestures can be taken as features to perform gesture recognition so as to change the gear of the fan.

The fan has a self-checking mode, which can be switched on and off by a power-on button 60 arranged on the base 30, and is switched on when the fan is powered on or fails to follow. The method specifically comprises the following steps: when the environmental information acquired by the camera 20 does not include the human structure information through the calculation of the convolutional neural network model, the driver 40 is controlled to continuously rotate to detect the human structure until the environmental information acquired by the camera 20 includes the human structure information through the calculation of the convolutional neural network model, the self-checking mode is stopped when the calibration is successful, and at this time, the indicator lamp 70 can be normally turned on to indicate that the calibration is successful.

During the self-test mode, the indicator light 70 may flash, and if the self-test is not successful after a predetermined time period, the low power consumption standby mode may be entered, or the power-off may be performed. Under the low-power consumption mode, be favorable to reducing the battery energy consumption, improve duration, improve the life of battery, reduce the discarded pollution of battery.

The fan may further include a sound pickup 80, the sound pickup 80 is configured to collect environmental sound information, and the AI chip module 50 further has an AI voice model configured to control the driver 40 to execute an instruction corresponding to the voice according to the environmental sound information collected by the sound pickup 80. The bottom layer is added with trained voice tags such as 'head shaking', 'shutdown' and 'follow stop', when a user sends voice commands such as 'head shaking', 'shutdown' and 'follow stop', after calculation and recognition are successful, an AI chip module 50 gives a command for controlling high and low levels through a GPIO port, the rotating speed of the driver 40 is controlled, and head shaking/shutdown/angle fixing is realized through controlling the driver 40.

Of course, the utility model discloses can also increase bluetooth function for connect the bluetooth remote controller in order to realize the remote control of fan, for example on & off and/or rotational speed and/or gear and/or shake the head or not.

Throughout the description and claims of this application, the words "comprise/comprises" and the words "have/includes" and variations of these are used to specify the presence of stated features, values, steps or components but do not preclude the presence or addition of one or more other features, values, steps, components or groups thereof.

Some features of the invention, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, certain features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable combination in different embodiments.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims

1. A fan based on AI visual convolutional neural network technology, comprising:

a fan body for generating an air flow;

the base bears the fan body and is rotationally connected with the fan body;

the driver is used for driving the fan body to rotate relative to the base;

2. The AI visual convolutional neural network technology-based fan of claim 1, wherein the AI chip module comprises a CMOS image sensor and a GPU running a convolutional neural network model, the CMOS image sensor and the GPU being bonded together by means of a SOC.

3. The AI visual convolutional neural network technology-based fan of claim 1, wherein the human structure comprises a human face and/or a human figure.

4. The AI visual convolutional neural network technology-based fan of claim 1, wherein when it is calculated and determined by a convolutional neural network model that the environmental information acquired by the camera does not include human structure information, the driver is controlled to continuously rotate to detect human structures.

5. The AI visual convolutional neural network technology-based fan of claim 1, further comprising a microphone for collecting environmental sound information, wherein the AI chip module further has an AI voice model for controlling the driver to execute instructions corresponding to the voice according to the environmental sound information collected by the microphone.