CN114678097A - Artificial intelligence and digital twinning system and method for intelligent clothes - Google Patents
Artificial intelligence and digital twinning system and method for intelligent clothes Download PDFInfo
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Abstract
The invention discloses an artificial intelligence and digital twinning system and method for intelligent clothes, wherein the system comprises an intelligent clothes module and a digital twinning module; the intelligent clothing module comprises clothing, a sensor module and a wireless communication module, and is used for acquiring user data and performing data interaction with the digital twin module; the digital twin module comprises a deep learning algorithm module, a digital twin body module and a cloud server module, the cloud server module judges and stores the current state of the user by using the deep learning algorithm, the user entity is mapped to the digital twin body by using a digital twin technology to be displayed and interacted in real time, and when the user is detected to be in danger, the cloud server module automatically warns. The invention innovatively combines an intelligent wearing technology, an artificial intelligence technology and a digital twin technology, obtains more comprehensive user data, accurately judges the user state, can record and analyze the current state of the user in real time and take corresponding measures when the user encounters danger, and effectively ensures the life safety of the user.
Description
Technical Field
The invention belongs to the field of intelligent clothes, and particularly relates to an artificial intelligence and digital twinning system and method for intelligent clothes.
Background
With the innovative development of the clothing industry, intelligent clothing has been widely applied to a plurality of fields such as life health, medical health, safety protection, military equipment and the like, and is developed towards the direction that materials are more intelligent, functions are more comprehensive and services are more diversified. The intelligent wearable equipment can improve the difference of uneven distribution of medical resources, so that the laggard areas can fairly enjoy medical services; meanwhile, aiming at the safety problem of children and the aging population health problem, the physiological state and the daily activity state of the children can be monitored in real time through the intelligent clothes, and sufficient protection is provided.
The existing intelligent clothes only have a simple detection function on human bodies, the acquired data volume is small, the user state judgment is not accurate enough, the display is not visual enough, the product function is not comprehensive enough, the practicability is poor, and the actual requirements of users cannot be met. For example, chinese patent publication No. CN 107432739 a discloses "an intelligent clothing system for health monitoring", according to the scheme, information such as heart rate, body temperature, and acceleration of a human body can be collected, and a remote intelligent terminal is provided. However, the scheme has less detection data, the current posture of the user is difficult to be effectively judged only by the three-axis acceleration sensor at the waist of the human body, the detection algorithm is simple and crude, user feedback is lacked, no user interaction exists, historical data cannot be checked, and the protection on the user is poor.
Disclosure of Invention
Aiming at the defects or the improvement requirements of the prior art, the invention provides an artificial intelligence and digital twin system and method for intelligent clothing, which aim to monitor, record and display the self state of a user in real time, remind the user when the posture of the user is identified to be incorrect, automatically send out a warning when the danger of the user is detected, inform relatives and guarantee the health of the user.
To achieve the above object, according to one aspect of the present invention, there is provided an artificial intelligence and digital twinning system for intelligent clothing, comprising an intelligent clothing module and a digital twinning module;
the intelligent clothing module comprises clothing, a sensor module and a wireless communication module, and is used for acquiring user data and performing data interaction with the digital twin module; the digital twin module comprises a deep learning algorithm module, a digital twin body module and a cloud server module, wherein the deep learning algorithm module is embedded in the cloud server module, the cloud server module judges and stores the current state of a user by using a deep learning algorithm in the deep learning algorithm module, and a digital twin technology in the digital twin body module is used for mapping a user entity to a digital twin body for real-time display and interaction;
the deep learning algorithm module comprises a data preprocessing module and a neural network module;
the data preprocessing module is used for preprocessing the sensor data to obtain input data of the neural network module;
the neural network module performs supervised training on the labeled data by using a neural network combined with attention so as to accurately judge the physiological state of a user, and comprises two control units which are connected at a time and used for controlling information flow in the network, an intermediate state unit, an attention unit and an output unit.
Further, the specific processing procedure of the neural network module is as follows:
taking the data processed by the data preprocessing module as input;
using two control units for controlling in a networkInformation flow, respectively reset unitsRsAnd an update unitUpThe reset unit controls the number of the retained past states, the update unit controls how many copies of the old state are retained in the new state,
wherein the content of the first and second substances,
is a parameter to be optimized for the network;
is a bias parameter of the network;M t is a certain momenttA two-dimensional parameter matrix generated by a data preprocessing module;s t-1is the output immediately before the network;δ(X) Is a non-linear function of the signal,
a matrix of R x C is represented,
for each row in X
And each column
Is traversed, compressing the value between 0 and 1, wherein Z represents an integer set;
intermediate state obtained by intermediate state unit in networkHSIt is shown that,
wherein, in the step (A),
is a parameter to be optimized for the intermediate state;B hs is a bias parameter for the intermediate state; the symbol is a binary operation with the input of two matrices of the same shape and the output of a matrix of the same shape with elements of each position equal to the product of the elements of the same position of the two input matrices;
the attention that the attention unit in the network takes is formed by a weighted sum of intermediate states, in particular
，
WhereinW a A weight matrix representing the attention of the user,SW t refers to an intermediate state of attention that is,SA t the attention value obtained by final calculation is referred to;
output unit in networktThe output of the time iss t ，
In the formula (I), wherein,λfor a hyper-parameter, the last cell state is passedδNon-linear function to obtain user state output
。
Further, a sensor module in the intelligent clothing module comprises an attitude sensor, a heart rate sensor, an infrared temperature sensor and a GPS chip; the infrared temperature sensor captures the current temperature of the human body, and the heart rate sensor captures the current heart rate of the human body; the attitude sensor is used for acquiring Euler angles, accelerations and coordinates of the attitude sensors of all parts; the working cycles of different sensors are different, the posture sensor takes 0.2s as one cycle, the heart rate sensor takes 30s as one cycle, and the infrared temperature sensor takes 300s as one cycle; the 15 posture sensors are distributed on key nodes of all parts of a human body, specifically, 1 posture sensor is distributed on each of the cervical vertebra, the chest and the abdomen, and 1 posture sensor is distributed on each of the left and right sides of the shoulder, the elbow, the wrist, the hip, the knee and the ankle.
Furthermore, the wireless communication module in the intelligent clothing module further comprises a linear motor, the wireless communication module is connected with the sensor on the intelligent clothing through a Bluetooth technology, data transmission is carried out through the wireless communication technology and the server, real-time acquisition and data management of data of the sensor are achieved, and when a specific instruction of the server is received, the linear motor is controlled to vibrate to prompt a user.
Furthermore, the data preprocessing module combines the acquired sensor data into a one-dimensional vector
5 one-dimensional vectors acquired every 1s at inputvScreening maximum and minimum values to generate 2 one-dimensional vectorsv max Andv min the 2 one-dimensional vectors and the first 5 one-dimensional vectors are spliced to form a two-dimensional matrix
Performing an operation ofMAndLand sending the data to a neural network module for training.
Further, the digital twin module forms a five-tuple representation between the entity and the twin according to a mapping function, D = (RE, VT, AD, TD, MR), where RE in the formula represents a real entity, and refers to a part of the entity existing in reality, that is, a cloud server module; the VT represents a virtual twin, namely a digital twin module, the digital modeling technology is used for performing characteristic display on the entity in two physical and physiological dimensions, and the characteristic display is presented to clients such as Web, a mobile phone App and the like; AD represents activity data of an entity, in particular to data obtained by multi-source acquisition of the entity by a plurality of sensors; TD represents twin data, namely user state data obtained by a neural network module; MR represents the mapping relationship between the entity and the twin.
The invention also discloses an artificial intelligence and digital twinning method for the intelligent garment, which comprises the following steps:
s1: acquiring the current temperature and heart rate of a human body and Euler angles, accelerations and coordinates of all parts of the human body through a sensor;
s2: the method comprises the steps that sensor data are obtained through a wireless communication module, and data interaction is conducted between the sensor data and a cloud server; wherein the wireless communication module is internally provided with a linear motor;
s3: the cloud server judges and stores the current state of the user by using a deep learning algorithm, wherein input data of the deep learning algorithm is obtained by preprocessing the sensor data in the step S1;
s4: mapping a user entity to a digital twin body by utilizing a digital twin technology according to the current state of a user for showing and interacting;
s5: when the server judges that the user is in a bad state for a long time, a specific instruction is sent, the linear motor in the wireless communication module vibrates to prompt the user, and when the server judges that the user is in a danger, the current state and the coordinates of the user can be automatically reported.
Further, the deep learning algorithm in step S3 includes the following steps:
s31: combining the sensor data acquired at the same time into a one-dimensional vector, screening the maximum value and the minimum value of a plurality of acquired one-dimensional vectors to generate two new most-valued vectors, generating a two-dimensional matrix by splicing all the vectors, classifying the two-dimensional matrix data by using an artificial labeling mode to generate a human activity labelL；
S32: judging the current state of the user by using a neural network combining attention, wherein the specific processing process is as follows;
the data after the processing of step S31 is taken as input;
controlling information flow in a network using two control units, respectively reset unitsRsAnd an update unitUpThe reset unit controls the number of the retained past states, the update unit controls how many copies of the old state are retained in the new state,
wherein the content of the first and second substances,
is a parameter to be optimized for the network;
is a bias parameter of the network;M t is a certain momenttGenerating a two-dimensional parameter matrix;s t-1is the output immediately before the network;δ(X) Is a non-linear function of the signal,
a matrix of R x C is represented,
for each row in X
And each column
Is traversed, compressing the value between 0 and 1, wherein Z represents an integer set;
intermediate states in a network are composed ofHSIt is shown that,
wherein, in the step (A),
is a parameter to be optimized for the intermediate state;B hs is the bias parameter for the intermediate state; the symbol is a binary operation with the input of two matrices of the same shape and the output of a matrix of the same shape with elements of each position equal to the product of the elements of the same position of the two input matrices;
attention in the network is formed by a weighted sum of intermediate states, in particular
，
WhereinW a A weight matrix representing the attention of the user,SW t refers to an intermediate state of attention that is,SA t the attention value obtained by final calculation is referred to;
network attThe output of the time iss t ，
In the formula (I), wherein,λfor a hyper-parameter, the last cell state is passedδNon-linear function to obtain user state output
。
Further, the digital twinning technique described in step S4 above forms a quintuple representation between the entity and the twin according to the mapping function; the digital twin body can show the current state of a user, and the user can send a specific instruction to the wireless communication module by operating the digital twin body, so that the wireless communication module reports the current sensor information or generates vibration.
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects:
(1) the artificial intelligence and digital twin system and method for intelligent clothing uses a neural network combining attention, and utilizes a nonlinear function compared with other neural networks
The calculation of the neural network is simplified, the utilization rate of the data is improved through attention, so that important data obtain larger weight, and the accuracy of the network is improved;
(2) according to the artificial intelligence and digital twin system and method for the intelligent garment, provided by the invention, the behavior category and the health state of a user can be accurately judged by identifying the physical condition of the user in real time in the system operation process; the digital twin body corresponding to the real entity is calculated and deduced, the state change of the user is reflected visually, and when the user operates the digital twin body to send a specific instruction or the system identifies an abnormal state, a warning can be sent out and corresponding measures can be taken, so that the life safety of the user is effectively guaranteed, and the risk of danger is reduced.
Drawings
Fig. 1 is a schematic flow chart of an artificial intelligence and digital twin system for intelligent clothing according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of an attitude sensor distribution provided by an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Fig. 1 is a schematic flow chart of an artificial intelligence and digital twin system for intelligent clothing according to an embodiment; the artificial intelligence and digital twin system for the intelligent garment comprises an intelligent garment module and a digital twin module.
The intelligent clothing module comprises clothing, a sensor module and a wireless communication module, and is used for acquiring user data and performing data interaction with the digital twin module;
the digital twin module comprises a deep learning algorithm module, a digital twin body module and a cloud server module, wherein the deep learning algorithm module is embedded in the cloud server module, the cloud server module judges and stores the current state of a user by using a deep learning algorithm in the deep learning algorithm module, a digital twin technology in the digital twin body module is used for mapping a user entity to the digital twin body for real-time display and interaction, and automatic warning is given when the user is detected to run into danger.
The sensor module comprises an attitude sensor, a heart rate sensor, an infrared temperature sensor and a GPS chip;
the infrared temperature sensor captures the current temperature of a human body, and the heart rate sensor captures the current heart rate of the human body; the attitude sensor is used for acquiring Euler angles, accelerations and coordinates of the attitude sensors of all parts; the working cycles of different sensors are different, the posture sensor takes 0.2s as one cycle, the heart rate sensor takes 30s as one cycle, and the infrared temperature sensor takes 300s as one cycle; the GPS chip is used for positioning;
wherein, 15 attitude sensors distribute on human every key node, specifically, 1 each of cervical vertebra, chest, belly, shoulder, elbow, wrist, buttock, knee, ankle respectively 1 about, as shown in fig. 2.
The wireless communication module is used for data interaction and further comprises a linear motor, the wireless communication module is connected with a sensor on the intelligent garment through a Bluetooth technology, data transmission is carried out through the wireless communication technology and the server, real-time acquisition and data management of sensor data are achieved, and when a specific instruction of the server is received, the linear motor can be controlled to vibrate to prompt a user.
The digital twin module comprises a deep learning algorithm module, a digital twin body module and a cloud server;
the deep learning algorithm module comprises a data preprocessing module and a neural network module;
the data preprocessing module combines the acquired sensor data into a one-dimensional vector
Wherein, in the step (A),
each representing the data of 15 sensors,hrrepresenting the data of the heartbeat of the human body,btrepresenting the human body temperature data and generating the human body activity label by using an artificial labeling modeLIncluding a plurality of human body states: walking, running, jumping, sitting still, lying down, lying on one side, sleeping, climbing the legs, bending over, falling down, and abnormal heart rate, inputting 5 one-dimensional vectors obtained every 1svScreening maximum and minimum values to generate 2 one-dimensional vectorsv max Andv min the 2 one-dimensional vectors and the first 5 one-dimensional vectors are spliced to form a two-dimensional matrix
Performing an operation ofMAndLand sending the data to a neural network module for training.
The neural network module performs supervised training on the labeled data by using a neural network combined with attention so as to accurately judge the physiological state of a user, and comprises two control units which are connected at a time and used for controlling information flow in the network, an intermediate state unit, an attention unit and an output unit. The specific processing process of the neural network module is as follows:
controlling information flow in a network using two control units, respectively reset unitsRsAnd an update unitUpThe reset unit controls the number of the retained past states, and the update unit controls the new statesHow many copies of the old state are retained in the state,
wherein, the first and the second end of the pipe are connected with each other,
is a parameter to be optimized for the network;
is a bias parameter of the network;M t is a certain momenttA two-dimensional parameter matrix generated by a data preprocessing module;s t-1is the output immediately before the network;δ(X) Is a non-linear function of the signal,
a matrix of R x C is represented,
for each row in X
And each column
Is traversed, compressing the value between 0 and 1, wherein Z represents an integer set;
intermediate state obtained by intermediate state unit in networkHSIt is shown that,
wherein, in the step (A),
is in an intermediate stateChanging parameters;B hs is a bias parameter for the intermediate state; the symbol is a binary operation with the input of two matrices of the same shape and the output of a matrix of the same shape with elements of each position equal to the product of the elements of the same position of the two input matrices;
the attention acquired by the attention unit in the network is formed by a weighted sum of intermediate states, in particular
，
In whichW a A weight matrix representing the attention of the user,SW t refers to an intermediate state of attention that is,SA t the attention value obtained by final calculation is referred to;
output unit in networktThe output of the time iss t ，
In the formula (I), the reaction is carried out,λfor a hyperparameter (default 0.3), the last cell state is passedδNonlinear function to obtain user state output
。
Data obtained by sensorsvJudging the current state of the user and giving a human activity labelLAnd taking a plurality of data as a training set to train the neural network for a plurality of times, and storing the network weight with the best performance for prediction. And during prediction, loading the trained network weight, acquiring the current sensor data of the user, sending the data into a network, and outputting the current state of the user after network operation.
The digital twin body module forms a representation of five-tuple between an entity and a twin body according to a mapping function, D = (RE, VT, AD, TD, MR), wherein RE in the formula represents a real entity, and refers to an entity part existing in reality, namely a cloud server module; the VT represents a virtual twin, namely a digital twin module, the digital modeling technology is used for performing characteristic display on the entity in two physical and physiological dimensions, and the characteristic display is presented to clients such as Web, a mobile phone App and the like; AD represents activity data of an entity, in particular to data obtained by multi-source acquisition of the entity by a plurality of sensors; TD represents twin data, namely user state data obtained by a neural network module; MR represents the mapping relationship between the entity and the twin.
The cloud server module is used as a computing center of the whole system and is responsible for specific display of the virtual twin body, and the user data is stored in a data warehouse mode by adopting a distributed computing method. Specifically, modeling is carried out through Unity and other software, a digital twin portrait of a user is constructed, sensor data uploaded by an intelligent clothing module is calculated through a deep learning algorithm, accurate judgment is given, the current state of the user is reflected in a three-dimensional mode, and corresponding operation is adopted when the situation of the user is identified; the stored data is stored in a data warehouse, and a report can be generated and exported by taking the user information as a theme, and is sent to a doctor for analysis;
the user condition can be browsed by the user, and also can be given to a guardian, a child or a doctor of the user to check after authorization, so that the system not only can better look after the child or the old to judge the current activity condition of the child or the old, but also can be given to medical care personnel to analyze the physical condition; based on the digital twin module, the patient can provide long-term activity data when carrying out online consultation with experts in various places, and the patient can help doctors to judge physical conditions and adjust daily life habits.
After authorization, a guardian or a child of the user can operate a digital twin body or a cloud server to send a corresponding instruction to a wireless communication module after judging that the user is in an adverse state for a long time (30 seconds for lifting the legs, 3 minutes for bending over and humping back and 45 minutes for sitting), and the clothes are vibrated by a linear motor to prompt the user; when the user is judged to be in danger (fall, abnormal heart rate and the like), the relative is automatically contacted according to the reserved emergency contact way, the current state and the coordinates of the user are reported, and the life safety of the user is protected.
The embodiment of the invention provides an artificial intelligence and digital twinning method for intelligent clothing, which comprises the following steps:
s1: acquiring the current temperature and heart rate of a human body and Euler angles, accelerations and coordinates of all parts of the human body through a sensor;
s2: the method comprises the steps that sensor data are obtained through a wireless communication module, and data interaction is conducted between the sensor data and a cloud server; wherein the wireless communication module is internally provided with a linear motor;
s3: the cloud server judges and stores the current state of the user by using a deep learning algorithm, wherein input data of the deep learning algorithm is obtained by preprocessing the sensor data in the step S1;
s4: mapping a user entity to a digital twin body by utilizing a digital twin technology according to the current state of a user for showing and interacting;
s5: when the server judges that the user is in a bad state for a long time, a specific instruction is sent, the linear motor in the wireless communication module vibrates to prompt the user, and when the server judges that the user is in a danger, the current state and the coordinates of the user can be automatically reported.
Further, the deep learning algorithm in step S3 includes the following steps:
s31: combining the sensor data acquired at the same time into a one-dimensional vector, screening the maximum value and the minimum value of a plurality of acquired one-dimensional vectors to generate two new most-valued vectors, generating a two-dimensional matrix by splicing all the vectors, classifying the two-dimensional matrix data by using an artificial labeling mode to generate a human activity labelL；
S32: judging the current state of the user by using a neural network combined with attention, wherein the specific processing process is as follows;
the data after the processing of step S31 is taken as input;
controlling information flow in a network using two control units, respectively reset unitsRsAnd an update unitUpThe reset unit controls the number of the retained past states, the update unit controls how many copies of the old state are retained in the new state,
wherein, the first and the second end of the pipe are connected with each other,
is a parameter to be optimized for the network;
is a bias parameter of the network;M t is a certain momenttGenerating a two-dimensional parameter matrix;s t-1is the output immediately before the network;δ(X) Is a non-linear function of the signal,
a matrix of R x C is represented,
for each row in X
And each column
Is traversed, compressing the value between 0 and 1, wherein Z represents an integer set;
intermediate states in a network are composed ofHSIt is shown that,
wherein, in the step (A),
is a parameter to be optimized for the intermediate state;B hs is a bias parameter for the intermediate state; the symbol is an Aldamard product, a binary operation with two inputsThe output is a matrix which has the same shape and the element of each position is equal to the product of the elements of the same position of the two input matrixes;
attention in the network is formed by a weighted sum of intermediate states, in particular
，
WhereinW a A weight matrix representing the attention of the user,SW t refers to an intermediate state of attention that is,SA t the attention value obtained by final calculation is referred to;
network attThe output of the time iss t ，
In the formula (I), wherein,λfor a hyper-parameter, the last cell state is passedδNon-linear function to obtain user state output
。
Further, the digital twinning technique described in step S4 forms a quintuple representation between the entity and the twin according to the mapping function; the digital twin body can show the current state of a user, and the user can send a specific instruction to the wireless communication module by operating the digital twin body, so that the wireless communication module reports the current sensor information or generates vibration.
The artificial intelligence and digital twin system and method for the intelligent garment, provided by the invention, can accurately acquire physiological data of a user, simplifies the complexity of the network by using a neural network combined with attention, accurately judges the current state of the user in real time, performs display and interaction by using a digital twin body, and gives a warning in time and takes corresponding measures when the user sends a specific instruction or the system identifies an abnormal state, so that the life safety of the user is effectively guaranteed, the risk of danger is reduced, and the system and method have certain application values.
It will be understood by those skilled in the art that the foregoing is only an exemplary embodiment of the present invention, and is not intended to limit the invention to the particular forms disclosed, since various modifications, substitutions and improvements within the spirit and scope of the invention are possible and within the scope of the appended claims.
Claims (9)
1. The utility model provides an artificial intelligence and digital twinning system towards wisdom clothing which characterized in that: the intelligent garment comprises an intelligent garment module and a digital twinning module, wherein the intelligent garment module comprises a garment, a sensor module and a wireless communication module and is used for acquiring user data and performing data interaction with the digital twinning module; the digital twin module comprises a deep learning algorithm module, a digital twin body module and a cloud server module, wherein the deep learning algorithm module is embedded in the cloud server module, the cloud server module judges and stores the current state of a user by using a deep learning algorithm in the deep learning algorithm module, and a digital twin technology in the digital twin body module is used for mapping a user entity to a digital twin body for real-time display and interaction;
the deep learning algorithm module comprises a data preprocessing module and a neural network module;
the data preprocessing module is used for preprocessing the sensor data to obtain input data of the neural network module;
the neural network module performs supervised training on the labeled data by using a neural network combined with attention so as to accurately judge the physiological state of a user, and comprises two control units which are connected at a time and used for controlling information flow in the network, an intermediate state unit, an attention unit and an output unit.
2. The intelligent garment-oriented artificial intelligence and digital twin system of claim 1, wherein: the specific processing process of the neural network module is as follows:
taking the data processed by the data preprocessing module as input;
controlling information flow in a network using two control units, respectively reset unitsRsAnd an update unitUpThe reset unit controls the number of the retained past states, the update unit controls how many copies of the old state are retained in the new state,
wherein the content of the first and second substances,
is a parameter to be optimized for the network;
is a bias parameter of the network;M t is a certain momenttA two-dimensional parameter matrix generated by a data preprocessing module;s t-1is the output immediately before the network;δ(X) Is a non-linear function of the signal,
a matrix of R x C is represented,
for each row in X
And each column
Is traversed, compressing the value between 0 and 1, wherein Z represents an integer set;
in-network intermediate state unit acquisitionIs in an intermediate state ofHSIt is shown that,
wherein, in the step (A),
is a parameter to be optimized for the intermediate state;B hs is a bias parameter for the intermediate state; the symbol is a binary operation with the input of two matrices of the same shape and the output of a matrix of the same shape with elements of each position equal to the product of the elements of the same position of the two input matrices;
the attention acquired by the attention unit in the network is formed by a weighted sum of intermediate states, in particular
，
WhereinW a A weight matrix representing the attention of the user,SW t refers to an intermediate state of attention that is,SA t the attention value obtained by final calculation is referred to;
output unit in networktThe output of the time iss t ，
In the formula (I), wherein,λfor a hyper-parameter, the last cell state is passedδNon-linear function to obtain user state output
。
3. The intelligent garment-oriented artificial intelligence and digital twin system of claim 1, wherein: the sensor module in the intelligent clothing module comprises an attitude sensor, a heart rate sensor, an infrared temperature sensor and a GPS chip; the infrared temperature sensor captures the current temperature of the human body, and the heart rate sensor captures the current heart rate of the human body; the attitude sensor is used for acquiring Euler angles, accelerations and coordinates of the attitude sensors of all parts; the working cycles of different sensors are different, the posture sensor takes 0.2s as one cycle, the heart rate sensor takes 30s as one cycle, and the infrared temperature sensor takes 300s as one cycle; the 15 posture sensors are distributed on key nodes of all parts of a human body, specifically, 1 posture sensor is distributed on each of the cervical vertebra, the chest and the abdomen, and 1 posture sensor is distributed on each of the left and right sides of the shoulder, the elbow, the wrist, the hip, the knee and the ankle.
4. The intelligent garment-oriented artificial intelligence and digital twin system of claim 1, wherein: the wireless communication module in the intelligent clothing module further comprises a linear motor, the wireless communication module is connected with a sensor on the intelligent clothing through a Bluetooth technology, data transmission is carried out through the wireless communication technology and the server, real-time acquisition and data management of data of the sensor are achieved, and when a specific instruction of the server is received, the linear motor is controlled to vibrate to prompt a user.
5. The intelligent garment-oriented artificial intelligence and digital twin system of claim 3, wherein:
the data preprocessing module combines the acquired sensor data into a one-dimensional vector
Wherein, in the step (A),
each representing the data of 15 sensors,hrrepresenting the data of the heartbeat of the human body,btrepresenting human body temperature data and generating human body activity labels in a mode of artificial markingLIncluding a plurality of human body states: walking, running, jumping, sitting still, lying down, lying on side, sleeping, lifting legs, bending back, falling down, and having different heart ratesFrequently;
5 one-dimensional vectors acquired every 1s at inputvScreening maximum and minimum values to generate 2 one-dimensional vectorsv max Andv min the 2 one-dimensional vectors and the first 5 one-dimensional vectors are spliced to form a two-dimensional matrix
Performing an operation ofMAndLand sending the data to a neural network module for training.
6. The intelligent garment-oriented artificial intelligence and digital twin system of claim 1, wherein: the digital twin module forms a quintuple representation between an entity and the twin according to a mapping function, D = (RE, VT, AD, TD, MR), wherein RE in the formula represents a real entity, and refers to an entity part existing in reality, namely a cloud server module; the VT represents a virtual twin, namely a digital twin module, the digital modeling technology is used for performing characteristic display on the entity in two physical and physiological dimensions, and the characteristic display is presented to clients such as Web, a mobile phone App and the like; AD represents activity data of an entity, in particular to data obtained by multi-source acquisition of the entity by a plurality of sensors; TD represents twin data, namely user state data obtained by a neural network module; MR represents the mapping relationship between the entity and the twin.
7. An artificial intelligence and digital twinning method for intelligent clothes is characterized by comprising the following steps:
s1: acquiring the current temperature and heart rate of a human body and Euler angles, accelerations and coordinates of all parts of the human body through a sensor;
s2: the method comprises the steps that sensor data are obtained through a wireless communication module, and data interaction is conducted between the sensor data and a cloud server; wherein the wireless communication module is internally provided with a linear motor;
s3: the cloud server judges and stores the current state of the user by using a deep learning algorithm, wherein input data of the deep learning algorithm is obtained by preprocessing the sensor data in the step S1;
s4: mapping a user entity to a digital twin body by utilizing a digital twin technology according to the current state of a user for showing and interacting;
s5: when the server judges that the user is in a bad state for a long time, a specific instruction is sent, the linear motor in the wireless communication module vibrates to prompt the user, and when the server judges that the user is in a danger, the current state and the coordinates of the user can be automatically reported.
8. The intelligent garment-oriented artificial intelligence and digital twinning method of claim 7, wherein: the deep learning algorithm described in step S3 includes the following steps:
s31: combining the sensor data acquired at the same time into a one-dimensional vector, screening the maximum value and the minimum value of a plurality of acquired one-dimensional vectors to generate two new most-valued vectors, generating a two-dimensional matrix by splicing all the vectors, classifying the two-dimensional matrix data by using an artificial labeling mode to generate a human activity labelL；
S32: judging the current state of the user by using a neural network combining attention, wherein the specific processing process is as follows;
the data after the processing of step S31 is taken as input;
controlling information flow in a network using two control units, respectively reset unitsRsAnd an update unitUpThe reset unit controls the number of the retained past states, the update unit controls how many copies of the old state are retained in the new state,
wherein the content of the first and second substances,
is a parameter to be optimized for the network;
is a bias parameter of the network;M t is a certain momenttGenerating a two-dimensional parameter matrix;s t-1is the output immediately before the network;δ(X) Is a non-linear function of the signal,
a matrix of R x C is represented,
for each row in X
And each column
Is traversed, compressing the value between 0 and 1, wherein Z represents an integer set;
intermediate states in a network are composed ofHSIt is shown that,
wherein, in the step (A),
is a parameter to be optimized for the intermediate state;B hs is a bias parameter for the intermediate state; the symbol is a binary operation with the input of two matrices of the same shape and the output of a matrix of the same shape with elements of each position equal to the product of the elements of the same position of the two input matrices;
attention in the network is formed by a weighted sum of intermediate states, in particular
，
In whichW a A weight matrix representing the attention of the user,SW t refers to an intermediate state of attention that is,SA t the attention value obtained by final calculation is referred to;
network attThe output of the time iss t ，
In the formula (I), wherein,λfor a hyper-parameter, the last cell state is passedδNon-linear function to obtain user state output
。
9. The intelligent garment-oriented artificial intelligence and digital twinning method of claim 7, wherein: the digital twinning technique described in step S4 forms a quintuple representation between the entity and the twin according to the mapping function; the digital twin body can show the current state of a user, and the user can send a specific instruction to the wireless communication module by operating the digital twin body, so that the wireless communication module reports the current sensor information or generates vibration.
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Effective date of registration: 20240320 Address after: No. 180 Qianyang West Road, Zhouji Office, Qianjiang City, Hubei Province, 433100 Patentee after: Hubei Bifan Garment Co.,Ltd. Country or region after: Zhong Guo Address before: 430073 No. 1 Textile Road, Hongshan District, Hubei, Wuhan Patentee before: Wuhan Textile University Country or region before: Zhong Guo |