CN118280606B - Intelligent accompanying equipment control method and system - Google Patents
Intelligent accompanying equipment control method and system Download PDFInfo
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Abstract
The invention discloses a control method and a system for intelligent accompanying equipment, and relates to the technical field of intelligent accompanying. The method comprises the following steps: the time sequence of the physiological state parameters is respectively subjected to data normalization and time sequence coding according to the dimension of the parameter sample to obtain a blood pressure time sequence associated hidden characteristic vector and a blood sugar time sequence associated hidden characteristic vector; and then, the blood pressure time sequence association hidden characteristic vector and the blood sugar time sequence association hidden characteristic vector are subjected to a characteristic fusion module guided based on Gaussian prior distribution to obtain the time sequence association characteristic vector under the blood sugar-blood pressure prior constraint so as to determine whether to generate a physiological state emergency early warning prompt signal to a community medical center. Therefore, the problems of missing report, false report and hysteresis caused by a threshold monitoring alarm mode in the traditional scheme can be avoided, and personalized physiological state monitoring service can be provided for different old people to better meet the health management requirements of users.
Description
Technical Field
The invention relates to the technical field of intelligent accompanying, in particular to an intelligent accompanying equipment control method and system.
Background
With the increasing degree of aging of the population, the health management and nursing demands of the aged population are increasingly prominent. The accompanying device is used as a monitoring and management device and can provide continuous monitoring, health management and emergency rescue services for the elderly, patients or people needing special nursing.
However, the conventional accompanying equipment can only realize simple user physiological data monitoring and threshold alarming functions, lacks efficient data processing and analyzing capabilities, cannot provide personalized health management and physiological state emergency early warning services according to individual differences of users, and often has the situations of false alarm and false alarm. In addition, the degree of intellectualization of conventional accompanying equipment is low, and the mode of threshold monitoring and alarming can lead to hysteresis of alarming. That is, the alarm is given when the physiological state of the old user is abnormal, and the emergency of the user cannot be responded in time, so that an effective rescue mechanism is lacked.
Accordingly, a smart companion device control scheme is desired.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
In a first aspect, the present invention provides a method for controlling an intelligent accompanying device, the method comprising:
Receiving a time sequence of physiological state parameters of a monitored elderly subject acquired by a smart wearable device, wherein the physiological state parameters include a blood pressure value and a blood glucose value;
The time sequence of the physiological state parameters is respectively subjected to data normalization according to the dimension of the parameter sample to obtain a time sequence of a blood pressure value and a time sequence of a blood sugar value;
Respectively carrying out time sequence coding on the time sequence of the blood pressure value and the time sequence of the blood glucose value to obtain a blood pressure time sequence associated hidden characteristic vector and a blood glucose time sequence associated hidden characteristic vector;
the blood pressure time sequence association hidden feature vector and the blood glucose time sequence association hidden feature vector are subjected to a feature fusion module guided based on Gaussian prior distribution to obtain a time sequence association feature vector under the constraint of blood glucose-blood pressure prior;
determining whether to generate a physiological state emergency early warning prompt signal to a community medical center based on the time sequence association feature vector under the blood sugar-blood pressure priori constraint;
the blood pressure time sequence association hidden feature vector and the blood sugar time sequence association hidden feature vector are subjected to a feature fusion module guided based on Gaussian prior distribution to obtain the time sequence association feature vector under the blood sugar-blood pressure prior constraint, which comprises the following steps:
calculating prior factors of the blood pressure time sequence association hidden feature vector and the blood sugar time sequence association hidden feature vector respectively to obtain a blood pressure time sequence association prior feature vector and a blood sugar time sequence association prior feature vector;
And calculating the position-wise summation between the blood pressure time sequence association prior feature vector and the blood glucose time sequence association prior feature vector to obtain the time sequence association feature vector under the blood glucose-blood pressure prior constraint.
Optionally, performing time sequence encoding on the time sequence of the blood pressure value and the time sequence of the blood glucose value to obtain a blood pressure time sequence associated implicit feature vector and a blood glucose time sequence associated implicit feature vector, including: the time sequence of the blood pressure value passes through a blood pressure sequence encoder based on an RNN model to obtain the blood pressure time sequence association implicit characteristic vector; and (3) passing the time sequence of blood glucose values through a blood glucose sequence encoder based on an RNN model to obtain the blood glucose time sequence association implicit characteristic vector.
Optionally, calculating the prior factors of the blood pressure time sequence association implicit feature vector and the blood glucose time sequence association implicit feature vector to obtain a blood pressure time sequence association prior feature vector and a blood glucose time sequence association prior feature vector, including: multiplying the blood pressure time sequence association hidden characteristic vector and the blood glucose time sequence association hidden characteristic vector by a preset weight super parameter according to positions to obtain a weight modulation blood pressure time sequence association hidden characteristic vector and a weight modulation blood glucose time sequence association hidden characteristic vector; taking each position characteristic value in the weight-modulation blood pressure time sequence related implicit characteristic vector and the weight-modulation blood glucose time sequence related implicit characteristic vector as an index of a natural constant to calculate an index function value based on the natural constant according to the position so as to obtain a weight-modulation blood pressure time sequence related support characteristic vector and a weight-modulation blood glucose time sequence related support characteristic vector; multiplying the weight-modulated blood pressure time sequence association class support feature vector by a first Gaussian distribution random number function value to obtain the blood pressure time sequence association prior feature vector; and multiplying the weight-modulated blood glucose time sequence association class support feature vector by a second Gaussian distribution random number function value to obtain the blood glucose time sequence association prior feature vector.
Optionally, the first gaussian distribution random number function value and the second gaussian distribution random number function value are both generated as gaussian distribution random number functions with a mean of 0 and a variance of 1.
Optionally, determining whether to generate the physiological status emergency early warning prompt signal to the community medical center based on the time sequence associated feature vector under the blood glucose-blood pressure prior constraint comprises: and the time sequence associated feature vector under the prior constraint of blood sugar and blood pressure is passed through a classifier-based controller to obtain a control instruction, wherein the control instruction is used for indicating whether a physiological state emergency early warning prompt signal is generated to a community medical center.
Optionally, the time sequence associated feature vector under the prior constraint of blood sugar and blood pressure is passed through a classifier-based controller to obtain a control instruction, wherein the control instruction is used for indicating whether to generate a physiological state emergency early warning prompt signal to a community medical center, and the method comprises the following steps of: performing full-connection coding on the time sequence associated feature vector under the prior constraint of blood sugar and blood pressure by using a plurality of full-connection layers of the classifier-based controller to obtain a coded classification feature vector; and the coding classification feature vector is passed through a Softmax classification function of the classifier-based controller to obtain the control instruction.
In a second aspect, the present invention provides an intelligent accompanying apparatus control system, the system comprising:
The physiological state parameter acquisition module is used for receiving a time sequence of physiological state parameters of the monitored elderly object acquired by the intelligent wearable equipment, wherein the physiological state parameters comprise a blood pressure value and a blood glucose value;
The data normalization module is used for performing data normalization on the time sequence of the physiological state parameters according to the dimension of the parameter sample to obtain a time sequence of a blood pressure value and a time sequence of a blood sugar value;
The time sequence coding module is used for respectively carrying out time sequence coding on the time sequence of the blood pressure value and the time sequence of the blood glucose value so as to obtain a blood pressure time sequence associated hidden characteristic vector and a blood glucose time sequence associated hidden characteristic vector;
the feature fusion module is used for enabling the blood pressure time sequence related implicit feature vector and the blood glucose time sequence related implicit feature vector to be guided through the feature fusion module based on Gaussian prior distribution so as to obtain the time sequence related feature vector under the constraint of blood glucose-blood pressure prior;
The emergency early warning prompt determining module is used for determining whether to generate a physiological state emergency early warning prompt signal to a community medical center based on the time sequence association feature vector under the blood sugar-blood pressure priori constraint;
wherein, the feature fusion module includes:
The prior factor calculating unit is used for calculating prior factors of the blood pressure time sequence association hidden characteristic vector and the blood sugar time sequence association hidden characteristic vector respectively to obtain a blood pressure time sequence association prior characteristic vector and a blood sugar time sequence association prior characteristic vector;
And the position-based addition calculation unit is used for calculating the position-based addition between the blood pressure time sequence association priori feature vector and the blood sugar time sequence association priori feature vector to obtain the time sequence association feature vector under the blood sugar-blood pressure priori constraint.
Optionally, the timing encoding module includes: the blood pressure sequence coding unit is used for enabling the time sequence of the blood pressure values to pass through a blood pressure sequence coder based on an RNN model to obtain the blood pressure time sequence association implicit characteristic vector; and the blood glucose sequence coding unit is used for enabling the time sequence of the blood glucose values to pass through a blood glucose sequence coder based on an RNN model to obtain the blood glucose time sequence association implicit characteristic vector.
By adopting the technical scheme, the blood pressure time sequence associated hidden feature vector and the blood sugar time sequence associated hidden feature vector are obtained by respectively carrying out data normalization and time sequence coding on the time sequence of the physiological state parameters according to the dimension of the parameter sample; and then, the blood pressure time sequence association hidden characteristic vector and the blood sugar time sequence association hidden characteristic vector are subjected to a characteristic fusion module guided based on Gaussian prior distribution to obtain the time sequence association characteristic vector under the blood sugar-blood pressure prior constraint so as to determine whether to generate a physiological state emergency early warning prompt signal to a community medical center. Therefore, the problems of missing report, false report and hysteresis caused by a threshold monitoring alarm mode in the traditional scheme can be avoided, and personalized physiological state monitoring service can be provided for different old people to better meet the health management requirements of users.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
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The above and other features, advantages and aspects of embodiments of the present invention will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale. In the drawings:
fig. 1 is a flowchart illustrating a method of controlling a smart accompanying device according to an example embodiment.
Fig. 2 is a block diagram illustrating a smart accompanying device control system, according to an example embodiment.
Fig. 3 is a block diagram of an electronic device, according to an example embodiment.
Fig. 4 is an application scenario diagram illustrating a method of controlling a smart accompanying device according to an example embodiment.
Detailed Description
Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While the invention is susceptible of embodiment in the drawings, it is to be understood that the invention may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided to provide a more thorough and complete understanding of the invention. It should be understood that the drawings and embodiments of the invention are for illustration purposes only and are not intended to limit the scope of the present invention.
It should be understood that the various steps recited in the method embodiments of the present invention may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the invention is not limited in this respect.
The term "including" and variations thereof as used herein are intended to be open-ended, i.e., including, but not limited to. The term "based on" is based at least in part on. The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments. Related definitions of other terms will be given in the description below.
It should be noted that the terms "first," "second," and the like herein are merely used for distinguishing between different devices, modules, or units and not for limiting the order or interdependence of the functions performed by such devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those skilled in the art will appreciate that "one or more" is intended to be construed as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of such messages or information.
Conventional companion devices have limitations in terms of user physiological monitoring and alerting, mainly providing basic physiological data tracking and simple threshold crossing alerting, but these functions often do not have deep data analysis capabilities. Therefore, it is difficult for these devices to provide customized health management schemes according to the personal health condition of the user, and also difficult to implement early warning of specific health risks of the user, which may cause false alarm or missing report problems, affecting the use experience and health safety of the user.
Furthermore, conventional accompanying devices have a relatively low level of intelligibility, and they typically only alarm when the user's physiological index exceeds a preset threshold, which may cause delays in the alarm. That is, the device will trigger an alarm mechanism only when a significant abnormality has occurred in the physiological state of the elderly user, which is detrimental to a quick response and timely rescue to an emergency health condition.
In order to promote the utility and accuracy of the accompanying device, the following improvement directions can be considered:
1. By applying advanced data analysis techniques, such as machine learning and data mining, the ability to interpret physiological data of a user is improved, thereby providing a more accurate health assessment.
2. According to individual differences of ages, health conditions, living habits and the like of users, personalized monitoring schemes and health management plans are designed.
3. A system capable of monitoring the physiological state of a user in real time and giving early warning in time is developed so that actions can be taken at the early stage of health crisis occurrence.
4. The alarm mechanism is improved, so that the alarm mechanism is not only judged based on a threshold value, but also combined with historical data of a user and pattern recognition, and false alarm and missing report are reduced.
5. The companion device is combined with an emergency response service that can immediately contact the medical service provider or family member upon detection of an anomaly.
6. The user interface is designed to be easy to understand and operate, ensuring that the elderly user can use the device conveniently.
Through these improvements, the accompanying device can be more intelligent and personalized, and more accurate and timely health management service is provided for users.
In order to solve the problems, the invention provides an intelligent accompanying equipment control method and a system thereof, wherein the time sequence of physiological state parameters is respectively subjected to data normalization and time sequence coding according to parameter sample dimensions to obtain a blood pressure time sequence associated hidden characteristic vector and a blood sugar time sequence associated hidden characteristic vector; and then, the blood pressure time sequence association hidden characteristic vector and the blood sugar time sequence association hidden characteristic vector are subjected to a characteristic fusion module guided based on Gaussian prior distribution to obtain the time sequence association characteristic vector under the blood sugar-blood pressure prior constraint so as to determine whether to generate a physiological state emergency early warning prompt signal to a community medical center. Therefore, the problems of missing report, false report and hysteresis caused by a threshold monitoring alarm mode in the traditional scheme can be avoided, and personalized physiological state monitoring service can be provided for different old people to better meet the health management requirements of users.
The following describes specific embodiments of the present invention in detail with reference to the drawings.
Fig. 1 is a flowchart illustrating a method of controlling a smart accompanying device, according to an exemplary embodiment, as shown in fig. 1, the method including:
step 101, receiving a time sequence of physiological state parameters of a monitored elderly person object acquired by intelligent wearable equipment, wherein the physiological state parameters comprise a blood pressure value and a blood glucose value;
102, respectively carrying out data normalization on the time sequence of the physiological state parameters according to the dimension of the parameter sample to obtain a time sequence of a blood pressure value and a time sequence of a blood sugar value;
Step 103, respectively performing time sequence coding on the time sequence of the blood pressure value and the time sequence of the blood glucose value to obtain a blood pressure time sequence associated hidden feature vector and a blood glucose time sequence associated hidden feature vector;
104, the blood pressure time sequence association hidden feature vector and the blood glucose time sequence association hidden feature vector are subjected to a feature fusion module guided based on Gaussian prior distribution to obtain a time sequence association feature vector under the constraint of blood glucose-blood pressure prior;
And step 105, determining whether to generate a physiological state emergency early warning prompt signal to a community medical center based on the time sequence association feature vector under the blood sugar-blood pressure priori constraint.
Aiming at the technical problems, in the technical scheme of the invention, an intelligent accompanying equipment control method is provided, which can monitor and collect physiological state parameters of an old person object in real time through an intelligent wearing equipment, wherein the physiological state parameters comprise blood pressure values and blood sugar values, and time sequence collaborative correlation analysis of time sequence data of the physiological state parameters is carried out at the rear end by utilizing a data processing and analyzing algorithm based on artificial intelligence and deep learning, so that time sequence change modes and characteristics of blood pressure and blood sugar of the old person object are learned and captured, and the time sequence characteristics of the blood pressure and the time sequence characteristics of the blood sugar are used as priori information to monitor and emergency early warn the physiological state of the old person object. The method can not only avoid the problems of missing report, false report and hysteresis caused by the threshold monitoring alarm mode of the traditional scheme, but also provide personalized physiological state monitoring service for different elderly people to better meet the health management requirements of users.
Specifically, in the technical scheme of the invention, firstly, a time sequence of physiological state parameters of a monitored elderly person object acquired by intelligent wearable equipment is received, wherein the physiological state parameters comprise a blood pressure value and a blood sugar value. Next, it is considered that since the physiological state parameter of the monitored elderly subject includes a blood pressure value and a blood glucose value, the blood pressure value and the blood glucose value are continuously changed with the lapse of time in a time dimension. Therefore, in order to analyze and capture the time sequence change pattern and trend of the blood pressure value and the blood glucose value of the elderly person in the time dimension, respectively, so as to more accurately monitor the personalized physiological state and pre-warn the physiological state of the elderly person, in the technical scheme of the invention, the time sequence of the physiological state parameters needs to be data-normalized according to the parameter sample dimension to obtain the time sequence of the blood pressure value and the time sequence of the blood glucose value.
It should be appreciated that there may be individual differences in the pattern and characteristics of time-series variations in physiological parameters of different individuals in the time dimension, and thus personalized health management becomes particularly important. In order to perform personalized physiological state monitoring and early warning for the monitored elderly person by performing personalized feature analysis and capturing on the blood pressure time sequence pattern and the blood glucose time sequence pattern of the monitored elderly person, the blood pressure time sequence feature and the blood glucose time sequence feature information existing in the time sequence of the blood pressure value and the time sequence of the blood glucose value are required to be extracted. Specifically, in the technical scheme of the invention, the time sequence of the blood pressure value is encoded in a blood pressure sequence encoder based on an RNN model so as to extract the time sequence dynamic implicit correlation characteristic of the blood pressure value in the time dimension, reflect the time sequence mode and the change trend of the blood pressure and obtain the blood pressure time sequence correlation implicit characteristic vector. And the time sequence of the blood glucose value is encoded in a blood glucose sequence encoder based on an RNN model so as to extract time sequence dynamic implicit correlation characteristics of the blood glucose value in a time dimension, reflect a time sequence mode and a change trend of the blood glucose and obtain a blood glucose time sequence correlation implicit characteristic vector.
In one embodiment of the present invention, time-series encoding is performed on the time series of blood pressure values and the time series of blood glucose values to obtain a blood pressure time-series associated implicit feature vector and a blood glucose time-series associated implicit feature vector, respectively, including: the time sequence of the blood pressure value passes through a blood pressure sequence encoder based on an RNN model to obtain the blood pressure time sequence association implicit characteristic vector; and (3) passing the time sequence of blood glucose values through a blood glucose sequence encoder based on an RNN model to obtain the blood glucose time sequence association implicit characteristic vector.
Further, since blood pressure and blood glucose level are important indicators of human health, there are certain correlation and mutual influence between them, and there are differences between different time-series variation patterns and characteristics of physiological parameters of elderly individuals in the time dimension. Therefore, in order to monitor and emergency early-warning the physiological state of the monitored elderly object more accurately, in the technical scheme of the invention, the blood pressure time sequence association hidden feature vector and the blood sugar time sequence association hidden feature vector are further subjected to a feature fusion module guided based on Gaussian prior distribution so as to obtain the time sequence association feature vector under the prior constraint of blood sugar and blood pressure. Through the processing of the feature fusion module based on Gaussian prior distribution guidance, the time sequence dynamic implicit association feature of the blood pressure value and the time sequence dynamic implicit association feature of the blood sugar value can be subjected to interaction and association analysis so as to learn and mine time sequence mode features and correlation relations between the blood pressure and the blood sugar of the monitored elderly object, and therefore the learned and captured feature Gaussian prior distribution is used as prior knowledge to guide the blood pressure-blood sugar time sequence association mode and constraint expression of the features of the elderly object. In this way, certain constraint and smoothness can be maintained in the process of carrying out association and fusion of the blood pressure time sequence characteristics and the blood sugar time sequence characteristics of the monitored elderly subjects, the physiological state time sequence modes and the change trends of the elderly subjects are better represented by utilizing the known blood pressure time sequence modes and blood sugar time sequence mode information, so that the comprehensiveness and comprehensiveness of the physiological state time sequence characteristics are improved, the understanding and deducing capability of the model on the health condition of the subjects are improved, the early discovery of abnormal change modes or trends is facilitated, the possible health risks are early warned, and corresponding intervention measures are adopted.
Processing the blood pressure time sequence association hidden characteristic vector and the blood glucose time sequence association hidden characteristic vector through the characteristic fusion module guided by Gaussian prior distribution according to the following fusion formula to obtain the time sequence association characteristic vector under the blood glucose-blood pressure prior constraint;
Wherein, the fusion formula is:
；
wherein, Is the blood pressure time sequence associated implicit characteristic vector,Is the blood glucose time sequence association implicit characteristic vector,AndIn order for the weight to exceed the parameters,AndThe method uses a Gaussian distribution random number function with a mean value of 0 and a variance of 1 as a hyper-parameter of a Gaussian distribution function coefficient,In the case of a vector addition,And (5) time sequence associated feature vectors under the prior constraint of blood sugar and blood pressure.
The first Gaussian distribution random number function value and the second Gaussian distribution random number function value are generated by Gaussian distribution random number functions with the mean value of 0 and the variance of 1.
In one embodiment of the present invention, the blood pressure time sequence association implicit feature vector and the blood glucose time sequence association implicit feature vector are obtained by a feature fusion module guided based on gaussian prior distribution, and the method includes: calculating prior factors of the blood pressure time sequence association hidden feature vector and the blood sugar time sequence association hidden feature vector respectively to obtain a blood pressure time sequence association prior feature vector and a blood sugar time sequence association prior feature vector, wherein the prior factors refer to parameters or variables determined based on prior knowledge when probability inference is carried out; and calculating the position-wise summation between the blood pressure time sequence association prior feature vector and the blood glucose time sequence association prior feature vector to obtain the time sequence association feature vector under the blood glucose-blood pressure prior constraint.
Further, calculating prior factors of the blood pressure time sequence association hidden feature vector and the blood glucose time sequence association hidden feature vector respectively to obtain a blood pressure time sequence association prior feature vector and a blood glucose time sequence association prior feature vector, including: multiplying the blood pressure time sequence association hidden characteristic vector and the blood glucose time sequence association hidden characteristic vector by a preset weight super parameter according to positions to obtain a weight modulation blood pressure time sequence association hidden characteristic vector and a weight modulation blood glucose time sequence association hidden characteristic vector; taking each position characteristic value in the weight-modulation blood pressure time sequence related implicit characteristic vector and the weight-modulation blood glucose time sequence related implicit characteristic vector as an index of a natural constant to calculate an index function value based on the natural constant according to the position so as to obtain a weight-modulation blood pressure time sequence related support characteristic vector and a weight-modulation blood glucose time sequence related support characteristic vector; multiplying the weight-modulated blood pressure time sequence association class support feature vector by a first Gaussian distribution random number function value to obtain the blood pressure time sequence association prior feature vector; and multiplying the weight-modulated blood glucose time sequence association class support feature vector by a second Gaussian distribution random number function value to obtain the blood glucose time sequence association prior feature vector.
And then, the time sequence associated feature vector under the prior constraint of the blood sugar and the blood pressure is passed through a controller based on a classifier to obtain a control instruction, wherein the control instruction is used for indicating whether a physiological state emergency early warning prompt signal is generated to a community medical center. That is, the physiological state of the aged person is monitored and the emergency early warning is performed by performing classification processing by using time sequence associated characteristic information between the blood pressure time sequence characteristic and the blood sugar time sequence characteristic of the monitored aged person under the constraint of mode prior information. Therefore, the intelligent degree of the accompanying equipment can be improved, the problems of missing report, false report and hysteresis caused by a threshold monitoring alarm mode in the traditional scheme can be avoided, and personalized physiological state monitoring services can be provided for different old people to better meet the health management requirements of users.
In one embodiment of the present invention, determining whether to generate a physiological status emergency early warning prompt signal to a community medical center based on the time sequence associated feature vector under the blood glucose-blood pressure prior constraint includes: and the time sequence associated feature vector under the prior constraint of blood sugar and blood pressure is passed through a classifier-based controller to obtain a control instruction, wherein the control instruction is used for indicating whether a physiological state emergency early warning prompt signal is generated to a community medical center.
The time sequence associated feature vector under the prior constraint of blood sugar and blood pressure is passed through a classifier-based controller to obtain a control instruction, wherein the control instruction is used for indicating whether a physiological state emergency early warning prompt signal is generated to a community medical center or not, and the method comprises the following steps: performing full-connection coding on the time sequence associated feature vector under the prior constraint of blood sugar and blood pressure by using a plurality of full-connection layers of the classifier-based controller to obtain a coded classification feature vector; and the coding classification feature vector is passed through a Softmax classification function of the classifier-based controller to obtain the control instruction.
In a preferred embodiment of the foregoing aspect, passing the time-series-associated feature vector under the blood glucose-blood pressure a priori constraint through a classifier-based controller to obtain the control instructions includes:
calculating the position-by-position average value of the blood pressure time sequence association hidden characteristic vector and the blood sugar time sequence association hidden characteristic vector to obtain a blood pressure-blood sugar time sequence association hidden characteristic average value vector;
Calculating an exponential function based on a natural constant according to the position of each characteristic value of the blood pressure-blood glucose time sequence associated hidden characteristic mean value vector to obtain a blood pressure-blood glucose time sequence associated hidden characteristic regression vector;
Determining a blood pressure time sequence association characteristic matrix and a blood sugar time sequence association characteristic matrix of the classifier aiming at the blood pressure time sequence association implicit characteristic vector and the blood sugar time sequence association implicit characteristic vector;
The blood pressure-blood sugar time sequence correlation hidden characteristic regression vector is multiplied by the blood pressure time sequence correlation characteristic matrix in a matrix mode, then the blood pressure-blood sugar time sequence correlation characteristic matrix is multiplied by the blood pressure time sequence correlation characteristic matrix in a matrix mode, and points are carried out on the characteristic vector obtained after multiplication and the blood pressure-blood sugar time sequence correlation hidden characteristic regression vector to obtain a blood pressure-blood sugar time sequence correlation correction vector;
Performing point multiplication fusion on the blood pressure-blood glucose time sequence correlation correction vector and the time sequence correlation feature vector under the blood glucose-blood pressure prior constraint to obtain an optimized time sequence correlation feature vector under the blood glucose-blood pressure prior constraint;
And the time sequence associated characteristic vector under the optimized blood sugar-blood pressure priori constraint is passed through a classifier-based controller to obtain a control instruction.
Here, the blood pressure time series-associated implicit feature vector and the blood glucose time series-associated implicit feature vector have different time series dimension feature representations of the heterologous data on the model parallel branch lines. Therefore, in order to solve the problem that regression specific features on the parallel branch lines of the model of the blood pressure time sequence association implicit feature vector and the blood sugar time sequence association implicit feature vector learn the high-order fusion representation under the condition of keeping the distinguishing property of the heterogeneous data time sequence association features, the step takes the mean value of the blood pressure time sequence association implicit feature vector and the blood sugar time sequence association implicit feature vector as an intermediate feature, and calculates an exponential function based on a natural constant to obtain a class regression feature, so as to perform query-support type matching on the blood pressure time sequence association feature matrix and the blood sugar time sequence association feature matrix of the classifier, and strengthen the distinguishing degree learning of the feature corresponding relation enrichment type under the parallel branch structure of the model through feature class regression association under different representation dimensions. And then correcting the time sequence correlation characteristic vector under the blood glucose-blood pressure prior constraint by using the blood pressure-blood glucose time sequence correlation correction vector, so that generalization of the time sequence correlation characteristic vector under the blood glucose-blood pressure prior constraint based on classification regression of regression specific characteristic distinguishing can be improved, and accuracy of classification results in model inference can be improved. Therefore, the physiological state of the old people can be monitored more accurately by using the intelligent accompanying equipment, so that abnormal conditions can be found in time and emergency early warning can be carried out, corresponding measures can be taken in time, and the life quality and the health level of the old people can be improved.
In summary, by adopting the above scheme, the physiological state parameters of the elderly are monitored and collected in real time through the intelligent wearable device, including the blood pressure value and the blood glucose value, and the time sequence collaborative correlation analysis of the time sequence data of the physiological state parameters is performed at the rear end by utilizing the data processing and analysis algorithm based on artificial intelligence and deep learning, so that the time sequence change modes and characteristics of the blood pressure and the blood glucose of the elderly are learned and captured, and the time sequence characteristics and the blood glucose time sequence characteristics are used as priori information to monitor the physiological state and perform emergency early warning of the elderly. The method can not only avoid the problems of missing report, false report and hysteresis caused by the threshold monitoring alarm mode of the traditional scheme, but also provide personalized physiological state monitoring service for different elderly people to better meet the health management requirements of users.
Fig. 2 is a block diagram illustrating a smart accompanying device control system, according to an example embodiment. As shown in fig. 2, the system 200 includes:
a physiological state parameter acquisition module 201, configured to receive a time sequence of physiological state parameters of a monitored elderly person subject acquired by a smart wearable device, where the physiological state parameters include a blood pressure value and a blood glucose value;
A data normalization module 202, configured to normalize the time sequence of the physiological state parameter according to the dimension of the parameter sample to obtain a time sequence of a blood pressure value and a time sequence of a blood glucose value;
the time sequence encoding module 203 is configured to perform time sequence encoding on the time sequence of the blood pressure value and the time sequence of the blood glucose value respectively to obtain a blood pressure time sequence associated implicit feature vector and a blood glucose time sequence associated implicit feature vector;
the feature fusion module 204 is configured to obtain a time sequence associated feature vector under a blood glucose-blood pressure prior constraint by using the blood pressure time sequence associated implicit feature vector and the blood glucose time sequence associated implicit feature vector through a feature fusion module guided based on gaussian prior distribution;
The emergency early warning prompt determining module 205 is configured to determine whether to generate a physiological status emergency early warning prompt signal to a community medical center based on the time sequence association feature vector under the blood glucose-blood pressure prior constraint.
In one embodiment of the present invention, the timing encoding module 203 includes: the blood pressure sequence coding unit is used for enabling the time sequence of the blood pressure values to pass through a blood pressure sequence coder based on an RNN model to obtain the blood pressure time sequence association implicit characteristic vector; and the blood glucose sequence coding unit is used for enabling the time sequence of the blood glucose values to pass through a blood glucose sequence coder based on an RNN model to obtain the blood glucose time sequence association implicit characteristic vector.
In one embodiment of the present invention, the feature fusion module 204 includes: the prior factor calculating unit is used for calculating prior factors of the blood pressure time sequence association hidden characteristic vector and the blood sugar time sequence association hidden characteristic vector respectively to obtain a blood pressure time sequence association prior characteristic vector and a blood sugar time sequence association prior characteristic vector; and the position-based addition calculation unit is used for calculating the position-based addition between the blood pressure time sequence association priori feature vector and the blood sugar time sequence association priori feature vector to obtain the time sequence association feature vector under the blood sugar-blood pressure priori constraint.
Referring now to fig. 3, a schematic diagram of an electronic device 600 suitable for use in implementing embodiments of the present invention is shown. The terminal device in the embodiment of the present invention may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), car terminals (e.g., car navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 3 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the invention.
As shown in fig. 3, the electronic device 600 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 601, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data required for the operation of the electronic apparatus 600 are also stored. The processing device 601, the ROM602, and the RAM603 are connected to each other through a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
In general, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, and the like; an output device 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, magnetic tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While fig. 3 shows an electronic device 600 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.
In particular, according to embodiments of the present invention, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program embodied on a non-transitory computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via communication means 609, or from storage means 608, or from ROM 602. The above-described functions defined in the method of the embodiment of the present invention are performed when the computer program is executed by the processing means 601.
The computer readable medium of the present invention may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText TransferProtocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.
Computer program code for carrying out operations of the present invention may be written in one or more programming languages, including, but not limited to, an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules involved in the embodiments of the present invention may be implemented in software or in hardware. The name of the module is not limited to the module itself in some cases, and for example, the test parameter obtaining module may also be described as "a module for obtaining the device test parameter corresponding to the target device".
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
In the context of the present invention, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Fig. 4 is an application scenario diagram illustrating a method of controlling a smart accompanying device according to an example embodiment. As shown in fig. 4, in this application scenario, first, a time series of physiological state parameters of a monitored elderly subject acquired by a smart wearable device is received, wherein the physiological state parameters include a blood pressure value (e.g., C1 illustrated in fig. 4) and a blood glucose value (e.g., C2 illustrated in fig. 4); the obtained blood pressure value and blood glucose value are then input to a server (e.g., S illustrated in fig. 4) deployed with a smart career device control algorithm, wherein the server is capable of processing the blood pressure value and the blood glucose value based on the smart career device control algorithm to determine whether to generate a physiological status emergency alert prompt signal to a community medical center.
The above description is only illustrative of the preferred embodiments of the present invention and of the principles of the technology employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in the present invention is not limited to the specific combinations of technical features described above, but also covers other technical features formed by any combination of the technical features described above or their equivalents without departing from the spirit of the disclosure. Such as the above-mentioned features and the technical features disclosed in the present invention (but not limited to) having similar functions are replaced with each other.
Moreover, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the invention. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims. The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Claims (8)
1. The intelligent accompanying equipment control method is characterized by comprising the following steps of:
Receiving a time sequence of physiological state parameters of a monitored elderly subject acquired by a smart wearable device, wherein the physiological state parameters include a blood pressure value and a blood glucose value;
The time sequence of the physiological state parameters is respectively subjected to data normalization according to the dimension of the parameter sample to obtain a time sequence of a blood pressure value and a time sequence of a blood sugar value;
Respectively carrying out time sequence coding on the time sequence of the blood pressure value and the time sequence of the blood glucose value to obtain a blood pressure time sequence associated hidden characteristic vector and a blood glucose time sequence associated hidden characteristic vector;
the blood pressure time sequence association hidden feature vector and the blood glucose time sequence association hidden feature vector are subjected to a feature fusion module guided based on Gaussian prior distribution to obtain a time sequence association feature vector under the constraint of blood glucose-blood pressure prior;
determining whether to generate a physiological state emergency early warning prompt signal to a community medical center based on the time sequence association feature vector under the blood sugar-blood pressure priori constraint;
the blood pressure time sequence association hidden feature vector and the blood sugar time sequence association hidden feature vector are subjected to a feature fusion module guided based on Gaussian prior distribution to obtain the time sequence association feature vector under the blood sugar-blood pressure prior constraint, which comprises the following steps:
calculating prior factors of the blood pressure time sequence association hidden feature vector and the blood sugar time sequence association hidden feature vector respectively to obtain a blood pressure time sequence association prior feature vector and a blood sugar time sequence association prior feature vector;
And calculating the position-wise summation between the blood pressure time sequence association prior feature vector and the blood glucose time sequence association prior feature vector to obtain the time sequence association feature vector under the blood glucose-blood pressure prior constraint.
2. The intelligent accompanying apparatus control method according to claim 1, wherein time-series encoding the time-series of blood pressure values and the time-series of blood glucose values to obtain a blood pressure time-series-associated implicit feature vector and a blood glucose time-series-associated implicit feature vector, respectively, comprises:
The time sequence of the blood pressure value passes through a blood pressure sequence encoder based on an RNN model to obtain the blood pressure time sequence association implicit characteristic vector;
And (3) passing the time sequence of blood glucose values through a blood glucose sequence encoder based on an RNN model to obtain the blood glucose time sequence association implicit characteristic vector.
3. The intelligent accompanying device control method of claim 2, wherein calculating a priori factors of the blood pressure time sequence associated implicit feature vector and the blood glucose time sequence associated implicit feature vector to obtain a blood pressure time sequence associated priori feature vector and a blood glucose time sequence associated priori feature vector, respectively, comprises:
multiplying the blood pressure time sequence association hidden characteristic vector and the blood glucose time sequence association hidden characteristic vector by a preset weight super parameter according to positions to obtain a weight modulation blood pressure time sequence association hidden characteristic vector and a weight modulation blood glucose time sequence association hidden characteristic vector;
Taking each position characteristic value in the weight-modulation blood pressure time sequence related implicit characteristic vector and the weight-modulation blood glucose time sequence related implicit characteristic vector as an index of a natural constant to calculate an index function value based on the natural constant according to the position so as to obtain a weight-modulation blood pressure time sequence related support characteristic vector and a weight-modulation blood glucose time sequence related support characteristic vector;
Multiplying the weight-modulated blood pressure time sequence association class support feature vector by a first Gaussian distribution random number function value to obtain the blood pressure time sequence association prior feature vector;
and multiplying the weight-modulated blood glucose time sequence association class support feature vector by a second Gaussian distribution random number function value to obtain the blood glucose time sequence association prior feature vector.
4. The intelligent career device control method according to claim 3, wherein the first gaussian distribution random number function value and the second gaussian distribution random number function value are generated as gaussian distribution random number functions with a mean value of 0 and a variance of 1.
5. The intelligent career device control method of claim 4, wherein determining whether to generate a physiological status emergency pre-warning prompt signal to a community medical center based on the time-sequence-associated feature vector under the blood glucose-blood pressure prior constraint comprises: and the time sequence associated feature vector under the prior constraint of blood sugar and blood pressure is passed through a classifier-based controller to obtain a control instruction, wherein the control instruction is used for indicating whether a physiological state emergency early warning prompt signal is generated to a community medical center.
6. The intelligent career device control method according to claim 5, wherein the time sequence association feature vector under the prior constraint of blood sugar and blood pressure is passed through a classifier-based controller to obtain a control instruction, the control instruction is used for indicating whether to generate a physiological state emergency early warning prompt signal to a community medical center, and the method comprises the following steps:
performing full-connection coding on the time sequence associated feature vector under the prior constraint of blood sugar and blood pressure by using a plurality of full-connection layers of the classifier-based controller to obtain a coded classification feature vector;
and the coding classification feature vector is passed through a Softmax classification function of the classifier-based controller to obtain the control instruction.
7. An intelligent accompanying device control system, comprising:
The physiological state parameter acquisition module is used for receiving a time sequence of physiological state parameters of the monitored elderly object acquired by the intelligent wearable equipment, wherein the physiological state parameters comprise a blood pressure value and a blood glucose value;
The data normalization module is used for performing data normalization on the time sequence of the physiological state parameters according to the dimension of the parameter sample to obtain a time sequence of a blood pressure value and a time sequence of a blood sugar value;
The time sequence coding module is used for respectively carrying out time sequence coding on the time sequence of the blood pressure value and the time sequence of the blood glucose value so as to obtain a blood pressure time sequence associated hidden characteristic vector and a blood glucose time sequence associated hidden characteristic vector;
the feature fusion module is used for enabling the blood pressure time sequence related implicit feature vector and the blood glucose time sequence related implicit feature vector to be guided through the feature fusion module based on Gaussian prior distribution so as to obtain the time sequence related feature vector under the constraint of blood glucose-blood pressure prior;
The emergency early warning prompt determining module is used for determining whether to generate a physiological state emergency early warning prompt signal to a community medical center based on the time sequence association feature vector under the blood sugar-blood pressure priori constraint;
wherein, the feature fusion module includes:
The prior factor calculating unit is used for calculating prior factors of the blood pressure time sequence association hidden characteristic vector and the blood sugar time sequence association hidden characteristic vector respectively to obtain a blood pressure time sequence association prior characteristic vector and a blood sugar time sequence association prior characteristic vector;
And the position-based addition calculation unit is used for calculating the position-based addition between the blood pressure time sequence association priori feature vector and the blood sugar time sequence association priori feature vector to obtain the time sequence association feature vector under the blood sugar-blood pressure priori constraint.
8. The intelligent companion device control system of claim 7, wherein the timing encoding module comprises:
The blood pressure sequence coding unit is used for enabling the time sequence of the blood pressure values to pass through a blood pressure sequence coder based on an RNN model to obtain the blood pressure time sequence association implicit characteristic vector;
And the blood glucose sequence coding unit is used for enabling the time sequence of the blood glucose values to pass through a blood glucose sequence coder based on an RNN model to obtain the blood glucose time sequence association implicit characteristic vector.
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