CN118285983A - Endocrine patient foot nursing device - Google Patents
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Abstract
An endocrine patient foot care device that obtains in-space temperature values at a plurality of predetermined time points over a predetermined period of time and rotational speed values of a fan at the plurality of predetermined time points; performing time sequence correlation analysis on the temperature values in the space of the preset time points and the rotating speed values of the fans of the preset time points to obtain temperature-rotating speed interaction feature vectors; and determining that the rotational speed value of the fan at the current time point should be increased or decreased based on the temperature-rotational speed interaction feature vector. In this way, an automatic and intelligent control of the fan speed can be achieved to optimize the use experience.
Description
Technical Field
The application relates to the technical field of intelligent nursing, and in particular relates to a foot nursing device for endocrine patients.
Background
Patients in endocrinology rooms often face the problem of foot wounds, which are slow to heal due to endocrine abnormalities in the patient, increasing the risk of infection. Once infection occurs, the condition may further worsen. At the same time, the wound is prone to inflammation, which can lead to local temperature increases.
In order to reduce the risk of infection for endocrine disruptors, some endocrine disruptors foot care devices are currently on the market. However, when the device is used in hot summer, the patient's wounds and inflammations can feel stuffy to the patient's feet, resulting in lower comfort to the patient's feet, as the spatial environment of the device is more closed. The comfort level of patient's foot is improved through setting up the fan in foot care device to current technical scheme, but current fan rotational speed is carried out manual control by the user, and this makes the user have to bend down to operate fan rotational speed operating button in the use, brings inconvenience for the user.
Accordingly, an optimized endocrine patient foot care device is desired.
Disclosure of Invention
The present application has been made to solve the above-mentioned technical problems. The embodiment of the application provides a foot nursing device for endocrine patients, which is used for acquiring temperature values in space at a plurality of preset time points in a preset time period and rotating speed values of fans at the preset time points; performing time sequence correlation analysis on the temperature values in the space of the preset time points and the rotating speed values of the fans of the preset time points to obtain temperature-rotating speed interaction feature vectors; and determining that the rotational speed value of the fan at the current time point should be increased or decreased based on the temperature-rotational speed interaction feature vector. In this way, an automatic and intelligent control of the fan speed can be achieved to optimize the use experience.
In a first aspect, there is provided an endocrine patient foot care device comprising:
the shell is provided with a containing cavity, wherein the top end and the side surface of the shell are provided with air holes;
An ultraviolet lamp tube and a leg support bracket which are arranged in the accommodating cavity;
the fan is arranged in the accommodating cavity and faces the leg support bracket; and
And a controller for controlling an operation mode of the fan.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a block diagram of an endocrine patient foot care device in accordance with an embodiment of the present application.
Fig. 2 is a block diagram of the controller in an endocrine patient foot care device in accordance with an embodiment of the present application.
Fig. 3 is a flow chart of a method of endocrine patient foot care in accordance with an embodiment of the present application.
Fig. 4 is a schematic diagram of an endocrine patient foot care method architecture, according to an embodiment of the present application.
Fig. 5 is an application scenario diagram of an endocrine patient foot care device, according to an embodiment of the present application.
Detailed Description
The following description of the technical solutions according to the embodiments of the present application will be given with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Unless defined otherwise, all technical and scientific terms used in the embodiments of the application have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the present application is for the purpose of describing particular embodiments only and is not intended to limit the scope of the present application.
In describing embodiments of the present application, unless otherwise indicated and limited thereto, the term "connected" should be construed broadly, for example, it may be an electrical connection, or may be a communication between two elements, or may be a direct connection, or may be an indirect connection via an intermediate medium, and it will be understood by those skilled in the art that the specific meaning of the term may be interpreted according to circumstances.
It should be noted that, the term "first\second\third" related to the embodiment of the present application is merely to distinguish similar objects, and does not represent a specific order for the objects, it is to be understood that "first\second\third" may interchange a specific order or sequence where allowed. It is to be understood that the "first\second\third" distinguishing objects may be interchanged where appropriate such that embodiments of the application described herein may be practiced in sequences other than those illustrated or described herein.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
The foot nursing device for endocrine patients is nursing equipment specially designed for endocrine patients, aims at providing comfortable and effective treatment and helps to relieve foot discomfort and symptoms caused by endocrine diseases.
The device is generally composed of the following components: foot wraps, which are the core of the device, are used to wrap the patient's feet. Are often made of soft, breathable materials to ensure comfort and proper ventilation. The temperature regulating system is arranged in the device, and can provide proper temperature according to the needs of patients. This may be achieved by heating or cooling the air within the device to maintain a comfortable temperature of the foot. The intelligent control system is provided with the device, and the temperature and other parameters can be automatically adjusted according to the condition and the requirement of a patient. For example, based on temperature sensor feedback, the intelligent control system may automatically adjust the rotational speed of the fan to maintain a suitable temperature environment. With the interface, there may be a user-friendly interface on the device for monitoring and adjusting the settings of the device. The user can know the current temperature, set the target temperature and make necessary adjustments through the interface.
The endocrine patient foot care device can provide comfortable treatment environment and relieve foot discomfort and symptoms caused by endocrine diseases. The temperature and other parameters can be automatically adjusted according to the needs of the patient, providing personalized treatment. Moreover, the simple and easy-to-use interface enables the patient to conveniently monitor and adjust the settings of the device. Can be used in home, medical institution and other environments to provide long-term care support.
In one embodiment of the present application, FIG. 1 is a block diagram of an endocrine patient foot care device in accordance with an embodiment of the present application. As shown in fig. 1, an endocrine patient foot care device 100 according to an embodiment of the present application includes: a shell 1 with a containing cavity, wherein the top end and the side surface of the shell 1 are provided with air holes; an ultraviolet lamp tube 2 and a leg support bracket 3 which are arranged in the accommodating cavity; a fan 4 disposed in the housing chamber, the fan 4 being directed toward the leg support bracket 3; and a controller 5 for controlling an operation mode of the fan 4.
Wherein, endocrine patient's foot care device's effect is the treatment environment that provides comfortablely, promotes health and the comfortableness of foot. The device is provided with the fan and the controller, and the running mode of the fan can be adjusted according to the requirement so as to control the temperature of the feet, which is very important for endocrine patients, because endocrine dyscrasia can cause abnormal temperature of the feet, such as excessive sweating or cold sweat, and the like, and the device can help to maintain the proper temperature of the feet and provide a comfortable treatment environment by adjusting the running mode of the fan.
The ultraviolet lamp tube is arranged in the device and can provide ultraviolet radiation for feet. Ultraviolet radiation has bactericidal and disinfectant effects, can help prevent or mitigate the risk of foot infection, and for endocrine patients they may be more susceptible to infection due to immune system abnormalities, and ultraviolet radiation can provide additional protection to promote foot health.
Leg support brackets are arranged in the device, so that comfortable support and stability can be provided. For endocrine patients who may face problems with leg muscles and joints, such as leg pain or instability, the leg support brace may provide additional support, relieve leg stress, promote comfort and rehabilitation.
In other words, endocrine patient foot care devices can provide personalized care support, help patients maintain foot health and comfort, can be used in homes or medical institutions, and are equipped with intelligent control systems and user interfaces to meet the personalized needs of the patient.
Fig. 2 is a block diagram of the controller in an endocrine patient foot care device in accordance with an embodiment of the present application. As shown in fig. 2, the controller 5 includes: a data acquisition module 110, configured to acquire temperature values in space at a plurality of predetermined time points in a predetermined time period and rotational speed values of fans at the plurality of predetermined time points; the data analysis module 120 is configured to perform time-sequence correlation analysis on the temperature values in the space at the plurality of predetermined time points and the rotational speed values of the fans at the plurality of predetermined time points to obtain a temperature-rotational speed interaction feature vector; and a fan speed control module 130 configured to determine, based on the temperature-speed interaction feature vector, whether the speed value of the fan at the current time point should be increased or decreased.
The data acquisition module 110 is configured to acquire temperature values in a space and rotational speed values of the fan at a plurality of predetermined time points in a predetermined time period. When the module is used, the accuracy and the reliability of the temperature sensor and the fan sensor are ensured so as to acquire accurate data. At the same time, attention is paid to the frequency and timing of data acquisition to ensure that sufficient data is acquired for subsequent analysis and control.
The data analysis module 120 performs time-series correlation analysis on the temperature values in the space and the rotational speed values of the fan at a plurality of predetermined time points to obtain a temperature-rotational speed interaction feature vector. In performing the time series correlation analysis, the relationship between the temperature and the rotation speed is considered, and an appropriate analysis method, such as correlation analysis or regression analysis, is determined. Through analyzing the obtained characteristic vector, the interaction mode between the temperature and the rotating speed can be known, and a basis is provided for subsequent fan rotating speed control.
The fan speed control module 130 determines whether the speed value of the fan at the current point in time should be increased or decreased based on the temperature-speed interaction feature vector. When the fan rotating speed control is performed, the current temperature state and the rotating speed state are judged according to the information in the characteristic vector so as to determine whether the rotating speed of the fan needs to be adjusted or not. Through the intelligent control algorithm, the fan rotating speed can be adaptively adjusted according to the real-time temperature data, so that the optimal foot care effect is provided.
Further, through data acquisition and analysis, temperature and rotation speed information of each time point can be obtained, and personalized fan rotation speed control is carried out according to individual requirements, so that more comfortable and effective foot care is provided. Based on the temperature-rotating speed interaction feature vector, the rotating speed control module of the fan can adjust the rotating speed of the fan in real time so as to adapt to different temperature changes and provide a stable foot care environment. Through analysis and processing of the data, a correlation mode between the temperature and the rotating speed can be obtained, scientific basis is provided for decision of the rotating speed of the fan, and the effect and the efficiency of foot nursing are improved. Specifically, the data acquisition module 110 is configured to acquire temperature values in a space at a plurality of predetermined time points in a predetermined time period and rotational speed values of a fan at the plurality of predetermined time points. Aiming at the technical problems, the application has the technical conception that the temperature collector is arranged in the foot care device of the endocrine patient so as to collect the temperature value in the foot care device of the endocrine patient in real time through the temperature sensor, and then the rotating speed of the fan is adaptively adjusted based on the collected temperature data, so that the automatic and intelligent control of the rotating speed of the fan is realized to optimize the user experience.
Specifically, in the technical scheme of the application, firstly, the temperature values in the space at a plurality of preset time points in a preset time period and the rotating speed values of the fans at the preset time points are obtained. It should be appreciated that as the temperature in the space increases, the fan speed may increase to provide more air volume and lower temperature. Thus, the pattern and trend of such contact may be determined by statistical analysis and data mining of the data.
The rotation speed change condition of the fan under different temperature conditions can be used for optimizing the control strategy of the fan so as to provide a more comfortable treatment environment. For example, the fan speed may be adaptively adjusted according to a temperature value at a predetermined time point such that it is automatically increased when the temperature is increased and automatically decreased when the temperature is decreased to maintain a desired temperature range.
Acquiring temperature values in space at a plurality of predetermined time points over a predetermined period of time and fan speed values at the plurality of predetermined time points may provide valuable data for subsequent processing. These data may be used to analyze and optimize the performance of the foot care device to provide a better user experience and therapeutic effect.
Further, by analyzing the correlation of the temperature values in the space and the user feedback data, the most comfortable temperature felt by the user in different temperature ranges can be determined, and the adjustment strategy of the fan rotating speed can be optimized, so that the optimal treatment environment can be provided. By analyzing the relationship between the fan rotation speed value and the temperature value in the space, the temperature regulation effect under different fan rotation speeds can be determined, and the optimal fan control strategy can be determined, so that quick and accurate temperature regulation can be realized. By analyzing the relationship between the temperature value and the fan rotational speed value in the space and the treatment effect, the treatment effect under different temperature conditions can be evaluated, and the optimal temperature range and the fan control strategy can be determined to provide the optimal treatment effect. By analyzing the relationship between the fan rotation speed value and the energy consumption, the energy utilization efficiency under different fan control strategies can be evaluated, and the fan control strategy can be optimized, so that the energy saving and environmental protection aims can be realized.
That is, obtaining the in-space temperature values at a plurality of predetermined time points over a predetermined period of time and the fan speed values at the plurality of predetermined time points have an important impact on subsequent processing, may help optimize performance and therapeutic effectiveness of the foot care device. Specifically, the data analysis module 120 is configured to perform time-series correlation analysis on the temperature values in the space at the plurality of predetermined time points and the rotational speed values of the fan at the plurality of predetermined time points to obtain a temperature-rotational speed interaction feature vector. Comprising the following steps: a data time sequence distribution arrangement unit, configured to arrange the temperature values in the space at the plurality of predetermined time points and the rotational speed values of the fans at the plurality of predetermined time points into an indoor temperature time sequence input vector and a fan rotational speed time sequence input vector according to a time dimension, respectively; the data parameter time sequence associated coding unit is used for extracting time sequence associated characteristics of the indoor temperature time sequence input vector and the fan rotating speed time sequence input vector through a time sequence characteristic extractor based on a deep neural network model so as to obtain an indoor temperature time sequence characteristic vector and a fan rotating speed time sequence characteristic vector; and the data time sequence feature fusion unit is used for fusing the indoor temperature time sequence feature vector and the fan rotating speed time sequence feature vector by using a cascading function so as to obtain the temperature-rotating speed interaction feature vector.
Wherein the deep neural network model includes a first convolution layer and a second convolution layer.
Through the deep neural network model in the data parameter time sequence correlation coding unit, time sequence correlation characteristic extraction can be carried out on the indoor temperature time sequence input vector and the fan rotating speed time sequence input vector, so that time sequence relation and interaction between the indoor temperature time sequence input vector and the fan rotating speed time sequence input vector can be captured, and the influence of temperature and the fan rotating speed on each other can be better understood. Through the cascade function in the data time sequence feature fusion unit, the indoor temperature time sequence feature vector and the fan rotating speed time sequence feature vector can be fused to obtain a temperature-rotating speed interaction feature vector, the influence of temperature and the fan rotating speed is comprehensively considered, and more comprehensive feature representation is provided to support subsequent processing tasks. By using the method of time sequence feature extraction and fusion, richer and accurate feature representation can be provided, and the model performance of subsequent processing tasks can be improved. For example, in temperature and fan speed control, the adjustment strategy for fan speed may be optimized based on the temperature-speed interaction feature vector, thereby providing better therapeutic effect and user experience.
The data time sequence distribution arrangement unit, the data parameter time sequence associated coding unit and the data time sequence feature fusion unit can be used for effectively processing and extracting the features of indoor temperature and fan rotating speed data, so that beneficial effects are provided.
Then, considering that the temperature value in the space and the rotating speed value of the fan have respective dynamic change rules in the time dimension, in order to effectively establish the association relationship between the temperature value in the space and the rotating speed value of the fan, the rotating speed of the fan is accurately controlled in real time based on the temperature value in the space, and the time sequence collaborative association interaction characteristics of the temperature value in the space and the rotating speed value of the fan are required to be extracted. Specifically, first, the spatial temperature values at the predetermined time points and the rotational speed values of the fans at the predetermined time points are arranged into an indoor temperature time sequence input vector and a fan rotational speed time sequence input vector according to a time dimension, so that the spatial temperature values and the time sequence distribution information of the rotational speed values of the fans are integrated.
The arrangement in the time dimension means that the temperature values and the fan rotational speed values in the space at a plurality of predetermined time points are arranged in the time sequence in which they occur, so that the change process of the indoor temperature and the fan rotational speed can be expressed as a time sequence.
Specifically, for the indoor temperature values, they may be arranged in time order as one indoor temperature timing input vector. For example, if the indoor temperature values at 5 predetermined time points are [23 ℃,24 ℃,25 ℃,26 ℃,27 ℃ respectively ], the indoor temperature timing input vector may be represented as [23,24,25,26,27].
Similarly, the fan speed values may be arranged in time series as a fan speed time series input vector. Assuming that the fan speed values for the corresponding 5 predetermined time points are [1000RPM,1100RPM,1200RPM, 1400RPM ], respectively, the fan speed timing input vector may be represented as [1000,1100,1200,1300,1400].
By arranging according to the time dimension, the change process of the indoor temperature and the fan rotating speed can be converted into an input vector with time sequence, and a basis is provided for subsequent time sequence feature extraction and analysis.
Further, since the temperature value in the space and the rotational speed value of the fan have volatility and uncertainty in the time dimension, and time sequence change information of the temperature value in the space and the rotational speed value of the fan is weak, the capability of the traditional feature extraction scheme for extracting time sequence change features of the temperature value in the space and the rotational speed value of the fan is weak. Therefore, in order to effectively capture time sequence variation characteristic information of the temperature value in the space and the rotating speed value of the fan in the time dimension, in the technical scheme of the application, the indoor temperature time sequence input vector and the fan rotating speed time sequence input vector are further respectively passed through a time sequence characteristic extractor comprising a first convolution layer and a second convolution layer to obtain an indoor temperature time sequence characteristic vector and a fan rotating speed time sequence characteristic vector. In particular, the first convolution layer and the second convolution layer use one-dimensional convolution kernels with different scales to perform feature mining on the indoor temperature time sequence input vector and the fan rotating speed time sequence input vector respectively, so as to extract multi-scale time sequence associated feature distribution information of the temperature value in the space and the rotating speed value of the fan under different time spans respectively.
By using a timing feature extractor comprising a first convolution layer and a second convolution layer, useful timing features can be extracted from the indoor temperature timing input vector and the fan speed timing input vector, which can help capture timing patterns and related information in the input vector.
The first convolution layer may perform a convolution operation on the input vector by sliding a window to extract local timing features to help the model capture short term variations and trends in indoor temperature and fan speed. The second convolution layer may further extract global timing characteristics by performing a convolution operation on the output of the first convolution layer to help the model capture long-term variations and trends in indoor temperature and fan speed.
By extracting the indoor temperature timing feature vector and the fan speed timing feature vector, these features can be used for subsequent data processing and analysis. These features may be used, for example, to predict future indoor temperatures and fan speeds, or to optimize control algorithms for foot care devices.
By using a timing feature extractor comprising a first convolution layer and a second convolution layer, beneficial timing features can be extracted from the indoor temperature timing input vector and the fan speed timing input vector, thereby improving the performance and effectiveness of the model.
And then, after the multi-scale time sequence change characteristics of the temperature value in the space and the rotating speed value of the fan in the time dimension are respectively obtained, further using a cascading function to fuse the indoor temperature time sequence characteristic vector and the rotating speed time sequence characteristic vector of the fan so as to obtain time sequence cooperative interaction association characteristic information of the temperature value in the space and the rotating speed value of the fan, thereby obtaining a temperature-rotating speed interaction characteristic vector. In particular, as the cascade function can construct the association between two features, in particular, the cascade function can enable a network to have a certain logic reasoning capability and mine the association feature information among objects. Therefore, in the technical scheme of the application, the cascade function is used for fusing the time sequence multi-scale change characteristic of the indoor temperature and the time sequence multi-scale change characteristic of the fan rotating speed, so that a network can be promoted to better sense the time sequence interaction cooperative correlation characteristic information of the indoor temperature value and the fan rotating speed value in the time dimension, thereby being beneficial to adaptively adjusting the fan rotating speed based on the temperature change condition in the actual device space, and further being better suitable for the needs of patients.
By using a cascading function to fuse the indoor temperature time sequence feature vector and the fan rotating speed time sequence feature vector, time sequence cooperative interaction association feature information between the indoor temperature time sequence feature vector and the fan rotating speed time sequence feature vector can be obtained, and therefore the temperature-rotating speed interaction feature vector is obtained. Such fusion may further enhance the modeling ability of the model to the relationship between temperature and rotational speed, thereby providing more accurate and efficient predictions and controls.
The cascade function can connect the indoor temperature time sequence feature vector and the fan rotating speed time sequence feature vector in time sequence to form a longer feature vector. In this way, the model can take into account both temperature and rotational speed timing information and capture the cross-correlation between them.
By acquiring the temperature-rotation speed interaction feature vector, the relationship between the indoor temperature and the rotation speed of the fan can be better understood. This is very beneficial for optimizing the control algorithm of the foot care device and for providing a better treatment environment. For example, the fan speed may be adjusted based on the temperature-speed interaction feature vector to achieve more accurate temperature regulation and to improve user comfort.
The cascade function is used for fusing the indoor temperature time sequence feature vector and the fan rotating speed time sequence feature vector to obtain the temperature-rotating speed interaction feature vector, so that the performance and the effect of the model are further improved, and the control and treatment effects of the foot care device are beneficial.
Specifically, the fan speed control module 130 is configured to determine, based on the temperature-speed interaction feature vector, whether the speed value of the fan at the current time point should be increased or decreased. Further used for: and the temperature-rotating speed interaction characteristic vector is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating that the rotating speed value of the fan at the current time point is increased or decreased.
And then, the temperature-rotating speed interaction characteristic vector is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating that the rotating speed value of the fan at the current time point is increased or decreased. That is, in the technical solution of the present application, the label of the classifier includes that the rotational speed value of the fan at the current time point should be increased (first label) and that the rotational speed value of the fan at the current time point should be decreased (second label), wherein the classifier determines to which classification label the temperature-rotational speed interaction feature vector belongs through a soft maximum function. That is, the time sequence association relation between the temperature value in the space and the fan rotating speed value is established by performing classification processing according to the time sequence change characteristic of the temperature in the space and the time sequence change characteristic of the rotating speed of the fan, so that the self-adaptive control of the rotating speed of the fan is performed in real time based on the actual temperature change condition in the space, and the comfort is improved and the energy consumption is reduced.
By using a classifier to classify the temperature-rotational speed interaction feature vector, a classification result may be obtained that may represent a suggestion that the current point in time fan rotational speed value should be increased or decreased, which may help optimize the control strategy of the foot care device to provide a more comfortable and efficient treatment experience.
The classifier may learn a relationship between the temperature-rotation speed interaction feature vector and the fan rotation speed value based on the training data and classify the new temperature-rotation speed interaction feature vector according to the relationship. For example, a supervised learning algorithm (e.g., support vector machine, random forest, or neural network) may be used to train the classifier and classify the new feature vectors according to the trained model.
By obtaining the classification result, the rotation speed value of the fan at the current time point can be adjusted according to the suggestion. If the classification result indicates that the rotational speed should be increased, the rotational speed of the fan may be increased accordingly; if the classification result indicates that the rotational speed should be reduced, the rotational speed of the fan may be reduced accordingly. Thus, the rotating speed of the fan can be adjusted in real time according to the current temperature-rotating speed interaction characteristics so as to provide more proper treatment environment and user experience.
By using a classifier to classify the temperature-rotational speed interaction feature vector, a classification result may be obtained that may indicate a suggestion that the current point in time fan rotational speed value should be increased or decreased. This approach may help optimize the control strategy of the foot care device, providing better therapeutic results and user experience.
Further, in the present application, the endocrine patient foot care device further comprises a training module for training the timing feature extractor comprising the first convolution layer and the second convolution layer and the classifier. Wherein, training module includes: the training data acquisition unit is used for acquiring temperature values in training spaces of a plurality of preset time points in a preset time period and rotating speed values of training fans of the preset time points, and the rotating speed value of the fan at the current time point is increased or reduced; the training data parameter time sequence arrangement unit is used for arranging the temperature values in the training space at a plurality of preset time points and the rotating speed values of the training fans at a plurality of preset time points into training indoor temperature time sequence input vectors and training fan rotating speed time sequence input vectors according to time dimensions respectively; the training data time sequence change feature extraction unit is used for enabling the training indoor temperature time sequence input vector and the training fan rotating speed time sequence input vector to respectively pass through the time sequence feature extractor comprising the first convolution layer and the second convolution layer so as to obtain a training indoor temperature time sequence feature vector and a training fan rotating speed time sequence feature vector; the training feature fusion unit is used for fusing the training indoor temperature time sequence feature vector and the training fan rotating speed time sequence feature vector by using a cascading function to obtain a training temperature-rotating speed interaction feature vector; the training optimization unit is used for carrying out convergence equalization on the training temperature-rotating speed interaction feature vector so as to obtain an optimized training temperature-rotating speed interaction feature vector; the classification loss unit is used for enabling the optimized training temperature-rotating speed interaction feature vector to pass through the classifier to obtain a classification loss function value; and a model training unit for training the time sequence feature extractor including the first convolution layer and the second convolution layer and the classifier by taking a weighted sum of the classification loss function value and the common manifold implicit similarity loss function value as a loss function value and propagating in a gradient descent direction.
In particular, in the technical solution of the present application, the training indoor temperature time sequence feature vector and the training fan rotational speed time sequence feature vector express local time sequence correlation features of a training indoor temperature value and a training fan rotational speed value respectively, and when a cascade function is used to fuse the training indoor temperature time sequence feature vector and the training fan rotational speed time sequence feature vector, in consideration of source data differences and distribution differences in a time sequence direction caused by the source data differences, and noise distribution differences caused by the source data differences, although a certain correlation of the training indoor temperature time sequence feature vector and the training fan rotational speed time sequence feature vector can be constructed, as a cascade expression, the training temperature-rotational speed interaction feature vector can cause the training temperature-rotational speed interaction feature vector to have a local distribution non-uniform granularity based on feature values due to a feature value distribution alignment difference corresponding to the training indoor temperature time sequence feature vector and the training fan rotational speed time sequence feature vector, and the training fan rotational speed interaction feature vector has a classification effect when the local temperature-rotational speed interaction feature vector is fused in a high-dimensional feature space, thereby having a classification non-uniform interaction probability.
Therefore, the training temperature-rotating speed interaction feature vector is optimized each time the training temperature-rotating speed interaction feature vector is classified and iterated by the classifier, specifically expressed as the training optimizing unit, which is used for: optimizing the training temperature-rotating speed interaction feature vector by using the following optimization formula to obtain the optimized training temperature-rotating speed interaction feature vector;
Wherein, the optimization formula is:
Wherein V represents the training temperature-rotation speed interaction feature vector, V i and V j are the feature values of the ith and jth positions of the training temperature-rotation speed interaction feature vector V, respectively, M μ represents a first intermediate matrix, M σ represents a second intermediate matrix, M μ (i, j) represents the feature value of the (i, j) position of the first intermediate matrix, M σ (i, j) represents the feature value of the (i, j) position of the second intermediate matrix, Indicating that the addition is performed by the position point,Representing matrix multiplication, and V' represents the optimal training temperature-rotating speed interaction characteristic vector.
The training temperature-rotating speed interaction feature vector V is obtained by introducing local statistical information distribution of the training temperature-rotating speed interaction feature vector V as an external information source to perform feature vector retrieval enhancement so as to avoid non-uniform feature distribution class mapping illusion of the training temperature-rotating speed interaction feature vector V caused by local overflow information distribution based on local statistical dense information structuring, and accordingly information trusted response reasoning is obtained on the basis of local distribution group dimension retention corresponding to the training indoor temperature time sequence feature vector and the training fan rotating speed time sequence feature vector, namely trusted distribution response of the training temperature-rotating speed interaction feature vector V in a probability density space based on non-uniform local feature distribution is obtained, so that class probability density space convergence effect is improved, and training speed and training result accuracy are improved. Thus, the self-adaptive control of the fan rotating speed can be performed in real time based on the temperature change condition in the actual space, so that the comfort level of the foot of an endocrine patient is improved, and the energy consumption is reduced.
Wherein the classification loss unit includes: the training classification subunit is configured to process the optimized training temperature-rotation speed interaction feature vector by using the classifier according to a training classification formula to generate a training classification result, where the training classification formula is: softmax { (W n,Bn):…:(W1,B1) —x }, where X represents the optimal training temperature-rotation speed interaction feature vector, W 1 to W n are weight matrices, and B 1 to B n represent bias matrices; and a loss function value calculation subunit for calculating a cross entropy value between the training classification result and a true value as the classification loss function value.
In summary, the endocrine patient foot care device 100 according to the embodiment of the present application is illustrated, in which a temperature collector is provided in the endocrine patient foot care device to collect temperature values in the endocrine patient foot care device in real time through a temperature sensor, and then the fan rotation speed is adaptively adjusted based on the collected temperature data, so as to realize automatic and intelligent control of the fan rotation speed, so as to optimize the user experience.
In one embodiment of the present application, FIG. 3 is a flow chart of a method of endocrine patient foot care in accordance with an embodiment of the present application. Fig. 4 is a schematic diagram of an endocrine patient foot care method architecture, according to an embodiment of the present application. As shown in fig. 3 and 4, the endocrine patient foot care method includes: 210, acquiring temperature values in space at a plurality of preset time points in a preset time period and rotating speed values of fans at the preset time points; 220, performing time sequence correlation analysis on the temperature values in the space of the plurality of preset time points and the rotating speed values of the fans of the plurality of preset time points to obtain temperature-rotating speed interaction feature vectors; and, 230, determining that the rotational speed value of the fan at the current time point should be increased or decreased based on the temperature-rotational speed interaction feature vector.
It will be appreciated by those skilled in the art that the specific operation of the various steps in the above-described endocrine patient foot care method has been described in detail in the above description of the endocrine patient foot care device with reference to fig. 1 to 2, and thus, repetitive descriptions thereof will be omitted.
Fig. 5 is an application scenario diagram of an endocrine patient foot care device, according to an embodiment of the present application. As shown in fig. 5, in the application scenario, first, taking in-space temperature values (e.g., C1 as illustrated in fig. 5) at a plurality of predetermined time points within a predetermined period of time and rotational speed values (e.g., C2 as illustrated in fig. 5) of the fans at the plurality of predetermined time points; the acquired in-space temperature value and rotational speed value of the fan are then input into a server (e.g., S as illustrated in fig. 5) deployed with an endocrine patient foot care algorithm, wherein the server is capable of processing the in-space temperature value and rotational speed value of the fan based on the endocrine patient foot care algorithm to determine whether the rotational speed value of the fan at the current point in time should be increased or decreased.
The basic principles of the present application have been described above in connection with specific embodiments, but it should be noted that the advantages, benefits, effects, etc. mentioned in the present application are merely examples and not intended to be limiting, and these advantages, benefits, effects, etc. are not to be construed as necessarily possessed by the various embodiments of the application. Furthermore, the specific details disclosed herein are for purposes of illustration and understanding only, and are not intended to be limiting, as the application is not necessarily limited to practice with the above described specific details.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or terminal device that comprises the element.
The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the application to the form disclosed herein. Although a number of example aspects and embodiments have been discussed above, a person of ordinary skill in the art will recognize certain variations, modifications, alterations, additions, and subcombinations thereof.
Claims (9)
1. An endocrine patient foot care device, comprising:
the shell is provided with a containing cavity, wherein the top end and the side surface of the shell are provided with air holes;
An ultraviolet lamp tube and a leg support bracket which are arranged in the accommodating cavity;
the fan is arranged in the accommodating cavity and faces the leg support bracket; and
And a controller for controlling an operation mode of the fan.
2. The endocrine patient foot care device of claim 1, wherein the controller comprises:
the data acquisition module is used for acquiring the temperature values in the space at a plurality of preset time points in a preset time period and the rotating speed values of the fans at the preset time points;
the data analysis module is used for carrying out time sequence correlation analysis on the temperature values in the space of the plurality of preset time points and the rotating speed values of the fans of the plurality of preset time points to obtain temperature-rotating speed interaction feature vectors; and
And the fan rotating speed control module is used for determining whether the rotating speed value of the fan at the current time point is increased or decreased based on the temperature-rotating speed interaction characteristic vector.
3. The endocrine patient foot care device of claim 2, wherein the data analysis module comprises:
a data time sequence distribution arrangement unit, configured to arrange the temperature values in the space at the plurality of predetermined time points and the rotational speed values of the fans at the plurality of predetermined time points into an indoor temperature time sequence input vector and a fan rotational speed time sequence input vector according to a time dimension, respectively;
The data parameter time sequence associated coding unit is used for extracting time sequence associated characteristics of the indoor temperature time sequence input vector and the fan rotating speed time sequence input vector through a time sequence characteristic extractor based on a deep neural network model so as to obtain an indoor temperature time sequence characteristic vector and a fan rotating speed time sequence characteristic vector;
And the data time sequence feature fusion unit is used for fusing the indoor temperature time sequence feature vector and the fan rotating speed time sequence feature vector by using a cascading function so as to obtain the temperature-rotating speed interaction feature vector.
4. The endocrine patient foot care device according to claim 3, wherein the deep neural network model comprises a first convolution layer and a second convolution layer.
5. The endocrine patient foot care device of claim 4, wherein the fan speed control module is configured to: and the temperature-rotating speed interaction characteristic vector is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating that the rotating speed value of the fan at the current time point is increased or decreased.
6. The endocrine patient foot care device according to claim 5, further comprising a training module for training the timing feature extractor comprising the first and second convolution layers and the classifier.
7. The endocrine patient foot care device of claim 6, wherein the training module comprises:
the training data acquisition unit is used for acquiring temperature values in training spaces of a plurality of preset time points in a preset time period and rotating speed values of training fans of the preset time points, and the rotating speed value of the fan at the current time point is increased or reduced;
The training data parameter time sequence arrangement unit is used for arranging the temperature values in the training space at a plurality of preset time points and the rotating speed values of the training fans at a plurality of preset time points into training indoor temperature time sequence input vectors and training fan rotating speed time sequence input vectors according to time dimensions respectively;
The training data time sequence change feature extraction unit is used for enabling the training indoor temperature time sequence input vector and the training fan rotating speed time sequence input vector to respectively pass through the time sequence feature extractor comprising the first convolution layer and the second convolution layer so as to obtain a training indoor temperature time sequence feature vector and a training fan rotating speed time sequence feature vector;
the training feature fusion unit is used for fusing the training indoor temperature time sequence feature vector and the training fan rotating speed time sequence feature vector by using a cascading function to obtain a training temperature-rotating speed interaction feature vector;
The training optimization unit is used for optimizing the training temperature-rotating speed interaction feature vector to obtain an optimized training temperature-rotating speed interaction feature vector;
The classification loss unit is used for enabling the optimized training temperature-rotating speed interaction feature vector to pass through the classifier to obtain a classification loss function value; and
And the model training unit is used for training the time sequence feature extractor comprising the first convolution layer and the second convolution layer and the classifier by taking the weighted sum of the classification loss function value and the common manifold implicit similarity loss function value as the loss function value and transmitting the weighted sum in the gradient descending direction.
8. The endocrine patient foot care device according to claim 7, wherein the training optimization unit is configured to: optimizing the training temperature-rotating speed interaction feature vector by using the following optimization formula to obtain the optimized training temperature-rotating speed interaction feature vector;
Wherein, the optimization formula is:
Wherein V represents the training temperature-rotation speed interaction feature vector, V i and V j are the feature values of the ith and jth positions of the training temperature-rotation speed interaction feature vector V, respectively, M μ represents a first intermediate matrix, M μ represents a second intermediate matrix, M μ (i, j) represents the feature value of the (i, j) position of the first intermediate matrix, M σ (i, j) represents the feature value of the (i, j) position of the second intermediate matrix, Indicating that the addition is performed by the position point,Representing matrix multiplication, and V' represents the optimal training temperature-rotating speed interaction characteristic vector.
9. The endocrine patient foot care device according to claim 8, wherein the categorical loss unit comprises:
The training classification subunit is configured to process the optimized training temperature-rotation speed interaction feature vector by using the classifier according to a training classification formula to generate a training classification result, where the training classification formula is: softmax { (W n,Bn):…:(W1,B1) —x }, where X represents the optimal training temperature-rotation speed interaction feature vector, W 1 to W n are weight matrices, and B 1 to B n represent bias matrices; and
And the loss function value calculating subunit is used for calculating a cross entropy value between the training classification result and a true value as the classification loss function value.
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| Publication Number | Publication Date |
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| CN118285983A true CN118285983A (en) | 2024-07-05 |
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