CN113705467B - Temperature adjusting method and device based on image recognition, electronic equipment and medium - Google Patents
Temperature adjusting method and device based on image recognition, electronic equipment and medium Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
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- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/10—Temperature
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2120/00—Control inputs relating to users or occupants
- F24F2120/10—Occupancy
- F24F2120/12—Position of occupants
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2120/00—Control inputs relating to users or occupants
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Abstract
The invention relates to the field of artificial intelligence, and discloses a temperature adjusting method based on image recognition, which comprises the following steps: acquiring face images and limb images of a plurality of users in a target area; carrying out expression analysis on the facial image by utilizing a pre-constructed face recognition model to obtain facial expression information; performing behavior analysis on the limb image by using a pre-constructed motion recognition model to obtain limb motion information; determining the coldness degree of a plurality of users and the heat degree of the plurality of users according to the facial expression information and the limb action information; acquiring the current temperature of the target area, and calculating a temperature change value of the current temperature according to the current temperature, the cold degree, the hot degree and a preset temperature regulation model; and controlling the current temperature of the target area to be adjusted to the target temperature according to the temperature change value. The invention also provides a temperature adjusting device based on image recognition, electronic equipment and a medium. The invention can solve the problem of inaccurate temperature regulation.
Description
Technical Field
The present invention relates to the field of artificial intelligence, and in particular, to a temperature adjustment method, apparatus, electronic device, and medium based on image recognition.
Background
Most of the current temperature regulating devices in public places are located in central positions, and most of the regulating modes of the temperature regulating devices are manual regulation or are driven by an app to regulate and control temperature. However, in a large space, there are often cases where some people are far away from the temperature adjusting device switch, when the temperature of the device is too high or too low, the people cannot adjust the temperature of the device, and the temperature variation ranges of different positions in the space are large, so that the accuracy of the temperature adjusting device in adjusting the temperature is low.
Disclosure of Invention
The invention provides a temperature adjusting method, a device, electronic equipment and a computer medium based on image recognition, and mainly aims to solve the problem of inaccurate temperature adjustment.
In order to achieve the above object, the present invention provides a temperature adjustment method based on image recognition, including:
acquiring face images and limb images of a plurality of users in a target area;
carrying out expression analysis on the facial image by utilizing a pre-constructed face recognition model to obtain facial expression information;
Performing behavior analysis on the limb image by using a pre-constructed motion recognition model to obtain limb motion information;
determining the coldness degree of a plurality of users and the warmth degree of a plurality of users according to the facial expression information and the limb action information;
Acquiring the current temperature of the target area, and calculating a temperature change value of the current temperature according to the current temperature, the cold degree, the hot degree and a preset temperature regulation model;
and controlling the current temperature of the target area to be adjusted to be the target temperature according to the temperature change value.
Optionally, the determining the coldness degree of a plurality of the users and the warmth degree of a plurality of the users according to the facial expression information and the limb motion information includes:
matching the facial expression information and the limb motion information with a preset cold face limb library and a preset hot face limb library respectively;
dividing the facial expression information into a cold facial expression and a hot facial expression according to a matching result, and dividing the limb actions into a cold limb action and a hot limb action according to the matching result;
Identifying the occurrence times of the cold facial expressions and the cold limb actions, determining cold feeling values of a plurality of users according to the occurrence times of the cold facial expressions and the cold limb actions, and determining the cold degrees of the users according to the cold feeling values;
and identifying the occurrence times of the thermal facial expressions and the thermal limb actions, determining thermal sensation values of a plurality of users according to the occurrence times of the thermal facial expressions and the thermal limb actions, and determining the thermal degrees of the users according to the thermal sensation values.
Optionally, the classifying the facial expression information into a cold facial expression and a hot facial expression according to the matching result includes:
If the facial expression information is matched with information in a preset cold facial limb library, determining that the facial expression information is a cold facial expression;
and if the facial expression information is matched with information in a preset hot facial limb library, determining that the facial expression information is a hot facial expression.
Optionally, the preset temperature regulation model includes a cold model and a hot model, and the calculating the temperature change value of the current temperature according to the current temperature, the cold degree, the hot degree and the preset temperature regulation model includes:
when the cooling degree is in a first preset range, calculating a cooling temperature change value of the current temperature by using the cooling model and the current temperature;
And when the heat degree is in a second preset range, calculating a heat temperature change value of the current temperature by using the thermal model and the current temperature.
Optionally, the calculating, by using a cold model in the temperature regulation model, a cold temperature variation value of the current temperature includes:
Calculating a cold temperature variation value of the current temperature by the following cold model:
Wherein u (T) represents a cold temperature variation value, K p represents a proportionality coefficient, e (T) represents a current temperature, dlt L a cold feeling value, T D a differential time constant and T a regulation period.
Optionally, the performing expression analysis on the face image by using a pre-constructed face recognition model to obtain facial expression information includes:
preprocessing the face image to obtain a standard face image;
extracting the standard face image by using a convolution pooling layer in a preset face recognition model to perform feature recognition to obtain a feature face image;
And outputting the characteristic face image by using an activation function in a full connection layer in the face recognition model to obtain face expression information.
Optionally, the performing behavior analysis on the limb image by using the pre-constructed motion recognition model to obtain limb motion information includes:
acquiring limb joint position coordinates in the limb image by using a convolution pooling layer in a pre-constructed action recognition model;
Calculating a motion trail of a limb joint according to the change of the position coordinates of the limb joint, and generating a limb joint change matrix according to the motion trail of the limb joint;
And outputting the limb joint change matrix by using an activation function in a full-connection layer in the motion recognition model to obtain limb motion information.
In order to solve the above problems, the present invention also provides a temperature adjusting device based on image recognition, the device comprising:
the face and limb image acquisition module is used for acquiring face images and limb images of a plurality of users in the target area;
The facial and limb expression analysis module is used for carrying out expression analysis on the facial image by utilizing a pre-constructed face recognition model to obtain facial expression information; performing behavior analysis on the limb image by using a pre-constructed motion recognition model to obtain limb motion information;
The cold degree and heat degree acquisition module is used for determining the cold degrees of a plurality of users and the heat degrees of a plurality of users according to the facial expression information and the limb action information;
the temperature change value calculation module is used for obtaining the current temperature of the target area, and calculating the temperature change value of the current temperature according to the current temperature, the cold degree, the hot degree and a preset temperature regulation model.
And the temperature adjustment module is used for controlling the temperature of the target area to be adjusted from the current temperature to the target temperature according to the temperature change value.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
a memory storing at least one computer program; and
And a processor executing the computer program stored in the memory to realize the temperature adjustment method based on image recognition.
In order to solve the above-mentioned problems, the present invention also provides a computer medium having stored therein at least one computer program that is executed by a processor in an electronic device to implement the above-mentioned image recognition-based temperature adjustment method.
In the embodiment of the invention, facial expression information is obtained by acquiring face images and limb images of a plurality of users in a target area and performing expression analysis on the face images, and limb action information is obtained by performing behavior analysis on the limb images; and determining the cold degree and the hot degree of a plurality of users according to the facial expression information and the limb action information, and adjusting the temperature of the target area by determining the cold degree and the hot degree of the plurality of users and combining the cold degree and the hot degree of the plurality of users in the target area, thereby realizing accurate temperature adjustment. Therefore, the temperature adjustment method, the device, the electronic equipment and the medium based on image recognition provided by the embodiment of the invention can solve the problem of inaccurate temperature adjustment.
Drawings
Fig. 1 is a schematic flow chart of a temperature adjustment method based on image recognition according to an embodiment of the present invention;
Fig. 2 is a detailed flowchart illustrating one step of the image recognition-based temperature adjustment method provided in fig. 1 according to an embodiment of the present invention.
FIG. 3 is a schematic block diagram of a temperature adjusting device based on image recognition according to an embodiment of the present invention;
Fig. 4 is a schematic diagram of an internal structure of an electronic device implementing a temperature adjustment method based on image recognition according to an embodiment of the present invention;
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides a temperature adjusting method based on image recognition. The execution subject of the image recognition-based temperature adjustment method includes, but is not limited to, at least one of a server, a terminal, and the like, which can be configured to execute the method provided by the embodiment of the application. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms. In other words, the image recognition-based temperature adjustment method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Referring to fig. 1, which is a schematic flow chart of an image recognition-based temperature adjustment method according to an embodiment of the present invention, in an embodiment of the present invention, the image recognition-based temperature adjustment method includes:
s1, acquiring face images and limb images of a plurality of users in a target area.
In this embodiment, the target area is an area controlled by the temperature adjustment device. For example, the temperature regulation range covered by a central air conditioner in a conference room.
The plurality of users in the target area may be all users in the target area, or more than two-thirds of the users.
Or the plurality of users within the target area include: dividing the target area into a plurality of subareas, selecting at least two users from each subarea, and determining that the at least two users selected from each subarea form a plurality of users.
In the embodiment of the invention, the face images of a plurality of users can be acquired through the cameras.
In an embodiment of the present invention, the acquiring of the limb image includes: the video is acquired through the camera, the acquired video is subjected to framing processing, and limb information contained in each frame of video is extracted to obtain a limb image.
S2, carrying out expression analysis on the facial image by utilizing a pre-constructed face recognition model to obtain facial expression information.
In the embodiment of the invention, the pre-constructed face recognition model can perform feature recognition on the face image so as to obtain the face expression information, wherein the face recognition model comprises: convolution pooling layer and full connection layer.
In detail, the facial image using the pre-constructed face recognition model performs expression analysis, and facial expression information includes:
preprocessing the face image to obtain a standard face image;
extracting the standard face image by using a convolution pooling layer in a preset face recognition model to perform feature recognition to obtain a feature face image;
And outputting the characteristic face image by using an activation function in a full connection layer in the face recognition model to obtain face expression information.
In an embodiment of the present invention, the preprocessing operation performed on the face image may be light compensation, gray level transformation, histogram equalization, normalization, geometric correction, filtering, and sharpening operations performed on the face image.
In an optional embodiment of the present invention, feature recognition of the processed face image may be to detect key points such as facial contours, eyebrows, nose, eyes, etc. of the user, and identify facial expressions such as shaking head, blowing mouth, emotion (such as dysphoria and cold trembling), eye sight, etc.
In an embodiment of the present invention, a feature facial image is input into a full connection layer, and an activation function (such as a Softmax function) in the full connection layer is used to output the feature facial image, so as to obtain facial expression information.
S3, performing behavior analysis on the limb image by using a pre-constructed motion recognition model to obtain limb motion information.
In the embodiment of the present invention, the pre-constructed motion recognition model may obtain the position coordinates of the limb joint in the limb image through a convolution pooling layer, calculate the motion track of the limb joint according to the change of the position coordinates of the limb joint, generate a limb joint change matrix, implement the behavior analysis of the limb image, and output the limb joint change matrix through a full connection layer to obtain the limb motion information, where the motion recognition model includes: convolution pooling layer and full connection layer.
In detail, the performing behavior analysis on the limb image by using the pre-constructed motion recognition model to obtain limb motion information includes:
acquiring limb joint position coordinates in the limb image by using a convolution pooling layer in a pre-constructed action recognition model;
Calculating a motion trail of a limb joint according to the change of the position coordinates of the limb joint, and generating a limb joint change matrix according to the motion trail of the limb joint;
And outputting the limb joint change matrix by using an activation function in a full-connection layer in the motion recognition model to obtain limb motion information.
In one implementation of the invention, the upper limb joint in the limb image can be set as a key point, the position coordinates of the key point are obtained, and the motion track of the limb joint is obtained according to the transformation of the position coordinates, so that a series of motion information of the upper limb is extracted, and the motion information is stored in a limb joint matrix to realize the behavior analysis of the limb image.
In an embodiment of the present invention, the limb behavior corresponding to the motion track of the limb joint may be a behavior such as arm rubbing, chest holding with both hands, fanning with both hands, and sleeve lifting.
In an embodiment of the present invention, the limb joint change matrix is output by using an activation function (e.g., sigmod functions) in the full-connection layer, so as to obtain facial expression information.
S4, determining the coldness degree of a plurality of users and the heat degree of a plurality of users according to the facial expression information and the limb action information.
In the embodiment of the invention, the facial expression information and the limb motion information can be respectively matched with a preset cold face limb library and a preset hot face limb library, and then the cold and hot degrees of the user are determined according to the matching result.
In the embodiment of the invention, the facial expression information and the limb motion information are respectively matched with a preset cold face limb library and a preset hot face limb library, by respectively matching the facial expression information and the limb motion information with the preset cold face limb library and respectively matching the facial expression information and the limb motion information with the preset hot face limb library.
In an embodiment of the present invention, the cold face limb library may include: facial tremor, mouth haunch, arm friction, chest holding with both hands, tremors, clothing, contracture, sneeze, nasal discharge, and other expression and limb feature vectors; the thermal facial limb library may comprise: expression and limb feature vectors such as frequent head shake, emotional agitation, forehead wiping, clothes removing, sleeves lifting and the like.
In detail, referring to fig. 2, fig. 2 is a detailed flowchart illustrating one step of the image recognition-based temperature adjustment method provided in fig. 1 according to an embodiment of the present invention.
The determining the coldness degree of a plurality of users and the warmth degree of a plurality of users according to the facial expression information and the limb motion information comprises the following steps:
S40, matching the facial expression information and the limb motion information with a preset cold face limb library and a preset hot face limb library respectively;
s41, dividing the facial expression information into a cold facial expression and a hot facial expression according to a matching result, and dividing the limb actions into a cold limb action and a hot limb action according to the matching result;
s42, recognizing the occurrence times of the cold facial expressions and the cold limb actions, determining cold feeling values of a plurality of users according to the occurrence times of the cold facial expressions and the cold limb actions, and determining the cold degrees of the users according to the cold feeling values;
s43, recognizing the occurrence times of the thermal facial expressions and the thermal limb actions, determining thermal sensation values of a plurality of users according to the occurrence times of the thermal facial expressions and the thermal limb actions, and determining the thermal degrees of the users according to the thermal sensation values.
Further, the facial expression information is divided into a cold facial expression and a hot facial expression according to a matching result, and the method comprises the following steps:
If the facial expression information is matched with information in a preset cold facial limb library, determining that the facial expression information is a cold facial expression;
and if the facial expression information is matched with information in a preset hot facial limb library, determining that the facial expression information is a hot facial expression.
In an optional embodiment of the present invention, the cold feeling value of the user may be determined by identifying the cold facial expression and the number of times of occurrence of the cold limb motion, and the cold degree of the user may be determined according to the magnitude of the cold feeling value.
For example, when a plurality of users have rubbed arms, held chest by both hands, tremors, sneezes and nasal discharge, and each occurrence of the two users has occurred, it is determined that the coldness value dlt L of the plurality of users is-5; when the cold feeling value dlt L is within the range of-3 to-5, it can be determined that the cold degree of a plurality of users is generally, and when the cold feeling value dlt L is within the range of-6 to-10, it can be determined that the cold degree of a plurality of users is very cold.
In yet another embodiment of the present invention, the process of determining the degree of heat is similar to the degree of cold, and will not be described again.
For example, if a plurality of users have a hand fan, a forehead wiping, a clothes removing, and a sleeve lifting, the heat sensation value dlt R of the plurality of users can be determined to be 4; when the thermal sensation value dlt R is in the range of 3 to 5, it can be determined that the degree of heat of a plurality of users is generally in the range of 6 to 10, and when the thermal sensation value dlt R is in the range of 6 to 10, it can be determined that the degree of heat of a plurality of users is very hot.
In the embodiment of the invention, the cold feeling value and the hot feeling value are determined according to the occurrence times of the facial expressions and the limb actions, so that if a certain user carelessly happens the expression actions of the cold face limb library or the hot face limb library, the expression actions are not used as the cold feeling value or the hot feeling value, and the accuracy of the subsequent calculation of the temperature change value can be improved.
S5, acquiring the current temperature of the target area, and calculating a temperature change value of the current temperature according to the current temperature, the cold degree, the hot degree and a preset temperature regulation model.
In the embodiment of the present invention, the current temperature of the target area is a temperature determined based on the outdoor environment temperature and the indoor environment temperature and suitable for most users.
In the embodiment of the present invention, the temperature change value is a change value calculated according to the cooling degree, the current temperature, and the preset temperature regulation model, or a change value calculated according to the heating degree, the current temperature, and the preset temperature regulation model, where the temperature change value includes: a cold temperature change value or a hot temperature change value; the preset temperature regulation model comprises the following components: cold and hot models.
In detail, the calculating the temperature change value of the current temperature according to the current temperature, the cold degree, the hot degree and a preset temperature regulation model includes:
when the cooling degree is in a first preset range, calculating a cooling temperature change value of the current temperature by using the cooling model and the current temperature;
And when the heat degree is in a second preset range, calculating a heat temperature change value of the current temperature by using the thermal model and the current temperature.
In an embodiment of the present invention, the first preset range may be-3 to-10, and the second preset range may be 3 to 10, and in addition, when the cold degree and the hot degree occur simultaneously in the present invention, a temperature change value with a stronger degree may be calculated, if the degree is stronger, the temperature change value of the cold degree is calculated, and if the degree is stronger, the temperature change value of the hot degree is calculated.
In an embodiment of the present invention, the cold temperature change value of the current temperature may be calculated by a cold model of the following temperature regulation models:
Wherein u (T) represents a cold temperature variation value, K p represents a proportionality coefficient, e (T) represents a current temperature, dlt L a cold feeling value, T D a differential time constant and T a regulation period.
In yet another embodiment of the present invention, the thermal temperature change value of the current temperature may be calculated by a thermal model of the temperature regulation model:
Wherein u (T) represents a thermal temperature variation value, K p represents a proportionality coefficient, e (T) represents a current temperature, dlt R a thermal sensation value, T D a differential time constant, and T a regulation period.
And S6, controlling the current temperature of the target area to be adjusted to be the target temperature according to the temperature change value.
In the embodiment of the present invention, the controlling the current temperature of the target area according to the temperature variation value to adjust to the target temperature includes: and adjusting the temperature of the target area to be the target temperature by combining the temperature change value on the basis of the current temperature.
For example, the temperature change value is a cold temperature change value, specifically, the cold temperature change value is 3 ℃, and if the current temperature is 27 ℃, the target temperature of the target area is controlled to be reduced by 3 ℃ from 27 ℃, that is, the target temperature is 24 ℃.
In the embodiment of the invention, when the temperature of the target area is controlled to be adjusted from the current temperature to the target temperature, the temperature adjusting device can rotate the adjusting knob according to the temperature change value, so that the rotating rod rotates, the gear is driven to rotate through the rotation of the rotating rod, the adjustment of the height of the terminal is realized, and the terminal can improve the blowing effect through the adjustment of the height.
In the embodiment of the invention, facial expression information is obtained by acquiring face images and limb images of a plurality of users in a target area and performing expression analysis on the face images, and limb action information is obtained by performing behavior analysis on the limb images; and determining the cold degree and the hot degree of a plurality of users according to the facial expression information and the limb action information, and adjusting the temperature of the target area by determining the cold degree and the hot degree of the plurality of users and combining the cold degree and the hot degree of the plurality of users in the target area, thereby realizing accurate temperature adjustment. Therefore, the temperature adjustment method based on image recognition provided by the embodiment of the invention can solve the problem of inaccurate temperature adjustment.
As shown in fig. 3, a functional block diagram of the temperature adjusting device based on image recognition according to the present invention is shown.
The image recognition-based temperature adjustment device 100 of the present invention may be installed in an electronic apparatus. Depending on the functions implemented, the image recognition-based temperature adjustment device may include a face and limb image acquisition module 101, a face and limb expression analysis module 102, a cold and hot degree acquisition module 103, a temperature change value calculation module 104, a temperature adjustment module 105, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and are stored in a memory of the electronic device.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the face and limb image acquisition module 101 is configured to acquire face images and limb images of a plurality of users in a target area.
In this embodiment, the target area is an area controlled by the temperature adjustment device. For example, the temperature regulation range covered by a central air conditioner in a conference room.
The plurality of users in the target area may be all users in the target area, or more than two-thirds of the users.
Or the plurality of users within the target area include: dividing the target area into a plurality of subareas, selecting at least two users from each subarea, and determining that the at least two users selected from each subarea form a plurality of users.
In the embodiment of the invention, the face images of a plurality of users can be acquired through the cameras.
In an embodiment of the present invention, the acquiring of the limb image includes: the video is acquired through the camera, the acquired video is subjected to framing processing, and limb information contained in each frame of video is extracted to obtain a limb image.
The facial and limb expression analysis module 102 is configured to perform expression analysis on the facial image by using a pre-constructed face recognition model to obtain facial expression information; and performing behavior analysis on the limb image by using a pre-constructed motion recognition model to obtain limb motion information.
In the embodiment of the invention, the pre-constructed face recognition model can perform feature recognition on the face image so as to obtain the face expression information, wherein the face recognition model comprises: convolution pooling layer and full connection layer.
In detail, the facial and limb expression analysis module 102 performs expression analysis on the facial image using a pre-constructed face recognition model by performing operations including:
preprocessing the face image to obtain a standard face image;
extracting the standard face image by using a convolution pooling layer in a preset face recognition model to perform feature recognition to obtain a feature face image;
And outputting the characteristic face image by using an activation function in a full connection layer in the face recognition model to obtain face expression information.
In an embodiment of the present invention, the preprocessing operation performed on the face image may be light compensation, gray level transformation, histogram equalization, normalization, geometric correction, filtering, and sharpening operations performed on the face image.
In an optional embodiment of the present invention, feature recognition of the processed face image may be to detect key points such as facial contours, eyebrows, nose, eyes, etc. of the user, and identify facial expressions such as shaking head, blowing mouth, emotion (such as dysphoria and cold trembling), eye sight, etc.
In an embodiment of the present invention, a feature facial image is input into a full connection layer, and an activation function (such as a Softmax function) in the full connection layer is used to output the feature facial image, so as to obtain facial expression information.
In the embodiment of the present invention, the pre-constructed motion recognition model may obtain the position coordinates of the limb joint in the limb image through a convolution pooling layer, calculate the motion track of the limb joint according to the change of the position coordinates of the limb joint, generate a limb joint change matrix, implement the behavior analysis of the limb image, and output the limb joint change matrix through a full connection layer to obtain the limb motion information, where the motion recognition model includes: convolution pooling layer and full connection layer.
In detail, the facial and limb expression analysis module 102 performs behavior analysis on the limb image by using a pre-constructed motion recognition model by performing the following operations, to obtain limb motion information, including:
acquiring limb joint position coordinates in the limb image by using a convolution pooling layer in a pre-constructed action recognition model;
Calculating a motion trail of a limb joint according to the change of the position coordinates of the limb joint, and generating a limb joint change matrix according to the motion trail of the limb joint;
And outputting the limb joint change matrix by using an activation function in a full-connection layer in the motion recognition model to obtain limb motion information.
In one implementation of the invention, the upper limb joint in the limb image can be set as a key point, the position coordinates of the key point are obtained, and the motion track of the limb joint is obtained according to the transformation of the position coordinates, so that a series of motion information of the upper limb is extracted, and the motion information is stored in a limb joint matrix to realize the behavior analysis of the limb image.
In an embodiment of the present invention, the limb behavior corresponding to the motion track of the limb joint may be a behavior such as arm rubbing, chest holding with both hands, fanning with both hands, and sleeve lifting.
In an embodiment of the present invention, the limb joint change matrix is output by using an activation function (e.g., sigmod functions) in the full-connection layer, so as to obtain facial expression information.
The cold and hot degree obtaining module 103 is configured to determine cold degrees of a plurality of users and hot degrees of a plurality of users according to the facial expression information and the limb motion information.
In the embodiment of the invention, the facial expression information and the limb motion information can be respectively matched with a preset cold face limb library and a preset hot face limb library, and then the cold and hot degrees of the user are determined according to the matching result.
In the embodiment of the invention, the facial expression information and the limb motion information are respectively matched with a preset cold face limb library and a preset hot face limb library, by respectively matching the facial expression information and the limb motion information with the preset cold face limb library and respectively matching the facial expression information and the limb motion information with the preset hot face limb library.
In an embodiment of the present invention, the cold face limb library may include: facial tremor, mouth haunch, arm friction, chest holding with both hands, tremors, clothing, contracture, sneeze, nasal discharge, and other expression and limb feature vectors; the thermal facial limb library may comprise: expression and limb feature vectors such as frequent head shake, emotional agitation, forehead wiping, clothes removing, sleeves lifting and the like.
In detail, the cool and hot degree acquiring module 103 determines cool degrees of a plurality of the users and hot degrees of the plurality of the users according to the facial expression information and the limb motion information by performing operations including:
matching the facial expression information and the limb motion information with a preset cold face limb library and a preset hot face limb library respectively;
dividing the facial expression information into a cold facial expression and a hot facial expression according to a matching result, and dividing the limb actions into a cold limb action and a hot limb action according to the matching result;
Identifying the occurrence times of the cold facial expressions and the cold limb actions, determining cold feeling values of a plurality of users according to the occurrence times of the cold facial expressions and the cold limb actions, and determining the cold degrees of the users according to the cold feeling values;
and identifying the occurrence times of the thermal facial expressions and the thermal limb actions, determining thermal sensation values of a plurality of users according to the occurrence times of the thermal facial expressions and the thermal limb actions, and determining the thermal degrees of the users according to the thermal sensation values.
Further, the facial expression information is divided into a cold facial expression and a hot facial expression according to a matching result, and the method comprises the following steps:
If the facial expression information is matched with information in a preset cold facial limb library, determining that the facial expression information is a cold facial expression;
and if the facial expression information is matched with information in a preset hot facial limb library, determining that the facial expression information is a hot facial expression.
In an optional embodiment of the present invention, the cold feeling value of the user may be determined by identifying the cold facial expression and the number of times of occurrence of the cold limb motion, and the cold degree of the user may be determined according to the magnitude of the cold feeling value.
For example, when a plurality of users have rubbed arms, held chest by both hands, tremors, sneezes and nasal discharge, and each occurrence of the two users has occurred, it is determined that the coldness value dlt L of the plurality of users is-5; when the cold feeling value dlt L is within the range of-3 to-5, it can be determined that the cold degree of a plurality of users is generally, and when the cold feeling value dlt L is within the range of-6 to-10, it can be determined that the cold degree of a plurality of users is very cold.
In yet another embodiment of the present invention, the process of determining the degree of heat is similar to the degree of cold, and will not be described again.
For example, if a plurality of users have a hand fan, a forehead wiping, a clothes removing, and a sleeve lifting, the heat sensation value dlt R of the plurality of users can be determined to be 4; when the thermal sensation value dlt R is in the range of 3 to 5, it can be determined that the degree of heat of a plurality of users is generally in the range of 6 to 10, and when the thermal sensation value dlt R is in the range of 6 to 10, it can be determined that the degree of heat of a plurality of users is very hot.
In the embodiment of the invention, the cold feeling value and the hot feeling value are determined according to the occurrence times of the facial expressions and the limb actions, so that if a certain user carelessly happens the expression actions of the cold face limb library or the hot face limb library, the expression actions are not used as the cold feeling value or the hot feeling value, and the accuracy of the subsequent calculation of the temperature change value can be improved.
The temperature change value calculation module 104 is configured to obtain a current temperature of the target area, and calculate a temperature change value of the current temperature according to the current temperature, the cold degree, the hot degree, and a preset temperature regulation model.
In the embodiment of the present invention, the current temperature of the target area is a temperature determined based on the outdoor environment temperature and the indoor environment temperature and suitable for most users.
In the embodiment of the present invention, the temperature change value is a change value calculated according to the cooling degree, the current temperature, and the preset temperature regulation model, or a change value calculated according to the heating degree, the current temperature, and the preset temperature regulation model, where the temperature change value includes: a cold temperature change value or a hot temperature change value; the preset temperature regulation model comprises the following components: cold and hot models.
In detail, the temperature change value calculation module 104 calculates a temperature change value of the current temperature according to the current temperature, the cold degree, the hot degree, and a preset temperature regulation model by performing operations including:
when the cooling degree is in a first preset range, calculating a cooling temperature change value of the current temperature by using the cooling model and the current temperature;
And when the heat degree is in a second preset range, calculating a heat temperature change value of the current temperature by using the thermal model and the current temperature.
In an embodiment of the present invention, the first preset range may be-3 to-10, and the second preset range may be 3 to 10, and in addition, when the cold degree and the hot degree occur simultaneously in the present invention, a temperature change value with a stronger degree may be calculated, if the degree is stronger, the temperature change value of the cold degree is calculated, and if the degree is stronger, the temperature change value of the hot degree is calculated.
In an embodiment of the present invention, the cold temperature change value of the current temperature may be calculated by a cold model of the following temperature regulation models:
Wherein u (T) represents a cold temperature variation value, K p represents a proportionality coefficient, e (T) represents a current temperature, dlt L a cold feeling value, T D a differential time constant and T a regulation period.
In yet another embodiment of the present invention, the thermal temperature change value of the current temperature may be calculated by a thermal model of the temperature regulation model:
/>
Wherein u (T) represents a thermal temperature variation value, K p represents a proportionality coefficient, e (T) represents a current temperature, dlt R a thermal sensation value, T D a differential time constant, and T a regulation period.
The temperature adjustment module 105 is configured to control the temperature of the target area to be adjusted from the current temperature to a target temperature according to the temperature change value.
In an embodiment of the present invention, the controlling the temperature of the target area according to the temperature variation value to adjust from the current temperature to the target temperature includes: and adjusting the temperature of the target area to be the target temperature by combining the temperature change value on the basis of the current temperature.
For example, the temperature change value is a cold temperature change value, specifically, the cold temperature change value is 3 ℃, and if the current temperature is 27 ℃, the target temperature of the target area is controlled to be reduced by 3 ℃ from 27 ℃, that is, the target temperature is 24 ℃.
In the embodiment of the invention, when the temperature of the target area is controlled to be adjusted from the current temperature to the target temperature, the temperature adjusting device can rotate the adjusting knob according to the temperature change value, so that the rotating rod rotates, the gear is driven to rotate through the rotation of the rotating rod, the adjustment of the height of the terminal is realized, and the terminal can improve the blowing effect through the adjustment of the height.
In the embodiment of the invention, facial expression information is obtained by acquiring face images and limb images of a plurality of users in a target area and performing expression analysis on the face images, and limb action information is obtained by performing behavior analysis on the limb images; and determining the cold degree and the hot degree of a plurality of users according to the facial expression information and the limb action information, and adjusting the temperature of the target area by determining the cold degree and the hot degree of the plurality of users and combining the cold degree and the hot degree of the plurality of users in the target area, thereby realizing accurate temperature adjustment. Therefore, the temperature adjusting device based on image recognition provided by the embodiment of the invention can solve the problem of inaccurate temperature adjustment.
As shown in fig. 4, a schematic structural diagram of an electronic device implementing a temperature adjustment method based on image recognition according to the present invention is shown.
The electronic device may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program stored in the memory 11 and executable on the processor 10, such as a temperature regulation program based on image recognition.
The memory 11 includes at least one type of medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD) or the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only for storing application software installed in an electronic device and various types of data, such as codes of a temperature adjustment program based on image recognition, but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various parts of the entire electronic device using various interfaces and lines, executes or executes programs or modules (e.g., a temperature adjustment program based on image recognition, etc.) stored in the memory 11, and invokes data stored in the memory 11 to perform various functions of the electronic device and process data.
The communication bus 12 may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The communication bus 12 is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.
Fig. 4 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 4 is not limiting of the electronic device and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power source (such as a battery) for supplying power to the respective components, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
Optionally, the communication interface 13 may comprise a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices.
Optionally, the communication interface 13 may further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The image recognition based temperature regulation program stored in the memory 11 in the electronic device is a combination of a plurality of computer programs, which when run in the processor 10, can realize:
acquiring face images and limb images of a plurality of users in a target area;
carrying out expression analysis on the facial image by utilizing a pre-constructed face recognition model to obtain facial expression information;
Performing behavior analysis on the limb image by using a pre-constructed motion recognition model to obtain limb motion information;
determining the coldness degree of a plurality of users and the warmth degree of a plurality of users according to the facial expression information and the limb action information;
Acquiring the current temperature of the target area, and calculating a temperature change value of the current temperature according to the current temperature, the cold degree, the hot degree and a preset temperature regulation model;
and controlling the current temperature of the target area to be adjusted to be the target temperature according to the temperature change value.
In particular, the specific implementation method of the processor 10 on the computer program may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.
Further, the electronic device integrated modules/units may be stored in a computer readable medium if implemented in the form of software functional units and sold or used as stand alone products. The computer readable medium may be non-volatile or volatile. The computer readable medium may include: any entity or device capable of carrying the computer program code to be described, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
Embodiments of the present invention may also provide a computer medium storing a computer program which, when executed by a processor of an electronic device, may implement:
acquiring face images and limb images of a plurality of users in a target area;
carrying out expression analysis on the facial image by utilizing a pre-constructed face recognition model to obtain facial expression information;
Performing behavior analysis on the limb image by using a pre-constructed motion recognition model to obtain limb motion information;
determining the coldness degree of a plurality of users and the warmth degree of a plurality of users according to the facial expression information and the limb action information;
Acquiring the current temperature of the target area, and calculating a temperature change value of the current temperature according to the current temperature, the cold degree, the hot degree and a preset temperature regulation model;
and controlling the current temperature of the target area to be adjusted to be the target temperature according to the temperature change value.
Further, the computer usable medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The blockchain (Blockchain), essentially a de-centralized database, is a string of data blocks that are generated in association using cryptographic methods, each of which contains information from a batch of network transactions for verifying the validity (anti-counterfeit) of its information and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A temperature adjustment method based on image recognition, the method comprising:
acquiring face images and limb images of a plurality of users in a target area;
carrying out expression analysis on the facial image by utilizing a pre-constructed face recognition model to obtain facial expression information;
Performing behavior analysis on the limb image by using a pre-constructed motion recognition model to obtain limb motion information;
Determining the coldness degree of a plurality of users and the warmth degree of a plurality of users according to the facial expression information and the limb action information, wherein the method comprises the following steps: matching the facial expression information and the limb motion information with a preset cold face limb library and a preset hot face limb library respectively, and determining a plurality of cold degrees of users and a plurality of hot degrees of the users according to matching results, wherein the cold face limb library comprises: the facial expression feature vector of the mouth bar haar and the limb feature vector of the wearing clothes, and the hot face limb library comprises: expression feature vectors of frequent head shaking and limb feature vectors of desquamation;
Acquiring the current temperature of the target area, and calculating a temperature change value of the current temperature according to the current temperature, the cold degree, the hot degree and a preset temperature regulation model;
and controlling the current temperature of the target area to be adjusted to be the target temperature according to the temperature change value.
2. The image recognition-based temperature adjustment method according to claim 1, wherein the matching the facial expression information and the limb motion information with a preset cold face limb library and a preset hot face limb library, respectively, and determining the coldness of a plurality of users and the warmness of a plurality of users according to the matching results comprises:
matching the facial expression information and the limb motion information with a preset cold face limb library and a preset hot face limb library respectively;
dividing the facial expression information into a cold facial expression and a hot facial expression according to a matching result, and dividing the limb actions into a cold limb action and a hot limb action according to the matching result;
Identifying the occurrence times of the cold facial expressions and the cold limb actions, determining cold feeling values of a plurality of users according to the occurrence times of the cold facial expressions and the cold limb actions, and determining the cold degrees of the users according to the cold feeling values;
and identifying the occurrence times of the thermal facial expressions and the thermal limb actions, determining thermal sensation values of a plurality of users according to the occurrence times of the thermal facial expressions and the thermal limb actions, and determining the thermal degrees of the users according to the thermal sensation values.
3. The image recognition-based temperature adjustment method according to claim 2, wherein the classifying the facial expression information into a cold facial expression and a hot facial expression according to a matching result includes:
If the facial expression information is matched with information in a preset cold facial limb library, determining that the facial expression information is a cold facial expression;
and if the facial expression information is matched with information in a preset hot facial limb library, determining that the facial expression information is a hot facial expression.
4. The image recognition-based temperature adjustment method according to claim 1, wherein the preset temperature regulation model includes a cold model and a hot model, and the calculating the temperature variation value of the current temperature according to the current temperature, the cold degree, the hot degree, and the preset temperature regulation model includes:
when the cooling degree is in a first preset range, calculating a cooling temperature change value of the current temperature by using the cooling model and the current temperature;
and when the heat degree is in a second preset range, calculating a heat temperature change value of the current temperature by using the heat model and the current temperature.
5. The image recognition-based temperature adjustment method according to claim 4, wherein the calculating a cold temperature variation value of the current temperature using a cold model of the temperature regulation models includes:
Calculating a cold temperature variation value of the current temperature by the following cold model:
Wherein u (T) represents a cold temperature variation value, K p represents a proportionality coefficient, e (T) represents a current temperature, dlt L a cold feeling value, T D a differential time constant and T a regulation period.
6. The image recognition-based temperature adjustment method according to any one of claims 1 to 5, wherein performing expression analysis on the face image using a pre-constructed face recognition model to obtain facial expression information includes:
preprocessing the face image to obtain a standard face image;
extracting the standard face image by using a convolution pooling layer in a preset face recognition model to perform feature recognition to obtain a feature face image;
And outputting the characteristic face image by using an activation function in a full connection layer in the face recognition model to obtain face expression information.
7. The image recognition-based temperature adjustment method according to any one of claims 1 to 5, wherein the performing behavior analysis on the limb image using a pre-constructed motion recognition model to obtain limb motion information includes:
acquiring limb joint position coordinates in the limb image by using a convolution pooling layer in a pre-constructed action recognition model;
Calculating a motion trail of a limb joint according to the change of the position coordinates of the limb joint, and generating a limb joint change matrix according to the motion trail of the limb joint;
And outputting the limb joint change matrix by using an activation function in a full-connection layer in the motion recognition model to obtain limb motion information.
8. A temperature adjustment device based on image recognition, characterized by comprising:
the face and limb image acquisition module is used for acquiring face images and limb images of a plurality of users in the target area;
The facial and limb expression analysis module is used for carrying out expression analysis on the facial image by utilizing a pre-constructed face recognition model to obtain facial expression information; performing behavior analysis on the limb image by using a pre-constructed motion recognition model to obtain limb motion information;
The cold degree and heat degree obtaining module is used for determining the cold degree of a plurality of users and the heat degree of a plurality of users according to the facial expression information and the limb action information, and comprises the following steps: matching the facial expression information and the limb motion information with a preset cold face limb library and a preset hot face limb library respectively, and determining a plurality of cold degrees of users and a plurality of hot degrees of the users according to matching results, wherein the cold face limb library comprises: the facial expression feature vector of the mouth bar haar and the limb feature vector of the wearing clothes, and the hot face limb library comprises: expression feature vectors of frequent head shaking and limb feature vectors of desquamation;
the temperature change value calculation module is used for obtaining the current temperature of the target area and calculating the temperature change value of the current temperature according to the current temperature, the cold degree, the hot degree and a preset temperature regulation model;
And the temperature adjustment module is used for controlling the temperature of the target area to be adjusted from the current temperature to the target temperature according to the temperature change value.
9. An electronic device, the electronic device comprising:
at least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores computer program instructions executable by the at least one processor to enable the at least one processor to perform the image recognition-based temperature regulation method of any one of claims 1 to 7.
10. A computer medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the image recognition-based temperature adjustment method according to any one of claims 1 to 7.
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| CN109766759A (en) * | 2018-12-12 | 2019-05-17 | 成都云天励飞技术有限公司 | Emotion identification method and Related product |
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| CN109766759A (en) * | 2018-12-12 | 2019-05-17 | 成都云天励飞技术有限公司 | Emotion identification method and Related product |
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