CN113317941A

CN113317941A - Intelligent health management auxiliary nursing system and method

Info

Publication number: CN113317941A
Application number: CN202110228829.7A
Authority: CN
Inventors: 邢燕; 刘倩云; 徐铭聪; 杨舒尧; 邢利; 徐宏涛
Original assignee: Zaozhuang Mental Health Center Zaozhuang Municipal Second Hospital
Current assignee: Zaozhuang Mental Health Center Zaozhuang Municipal Second Hospital
Priority date: 2021-03-02
Filing date: 2021-03-02
Publication date: 2021-08-31

Abstract

The utility model provides an intelligent health management auxiliary nursing system, which comprises a processor, an acquisition module and a storage alarm unit, wherein the acquisition module is used for acquiring the real-time data of humidity, pressure and temperature of a nursing pad and transmitting the acquired real-time data to the processor; the processor processes the real-time data to generate an instruction signal and transmits the instruction signal to the storage alarm unit; the storage alarm unit receives an instruction signal of the processor to perform storage and/or alarm actions; convenient to use, flexibility, application range is wide, can integrate into the system and use, for the recovered mode of medical treatment provides high-end, systematic, the nursing appurtenance of scale, can divide the unit to use by machine again, be used for a single unit old man or patient, also can use in patient's at home care, very big reduction disability old man and patient's misery, rely on the supplementary nursing machinery in the ward, reduced heavy nursing labour force cooperation work load to and nurse, the heavy physical labor amount of nurse.

Description

Intelligent health management auxiliary nursing system and method

Technical Field

The disclosure relates to the technical field related to nursing of disabled old people and patients, in particular to an intelligent health management and auxiliary nursing device system and method for disabled old people and patients.

Background

The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.

Along with the improvement of living standard of people, the aged population rapidly increases, the body functions of the old people gradually decline and are ill, the burden of family care is increased, the disabled old people and patients are more and more, most of the old people, especially the disabled old people, need to live into a nursing home or a medical care rehabilitation hospital for a security late year, the nursing labor force is obviously insufficient, the disabled old people and the patients are more and more difficult to nurse, and the nursing quality cannot be guaranteed. How to solve the health problems of the elderly, the disabled or the patients, improve the life quality of the elderly, maintain and promote physical and mental health of the disabled or the patients, and realize the strategic target of healthy aging is undoubtedly a major concern in the current society and the state. According to the requirements of the old department of medical care and rehabilitation and the shortage of nursing staff, the intelligent health management auxiliary nursing device, the intelligent health management auxiliary nursing system and the intelligent health management auxiliary nursing method for the disabled old and the patients are invented, practice proves that the effect is good, the comfort level of the disabled old and the patients can be effectively improved, manpower is saved, and the nursing service quality is improved.

The hospital and each area have four hospital areas, the total number of the hospital and each area is 2000 or more, the receiving rate is more than 96%, the number of disabled old people and living patients can not be managed by themselves, and 800 or more old people are provided with 308 nursing staff and 121 nursing workers, according to the comprehensive bed-nursing ratio of 1: 0.35 calculates far away not enough, because the disability old man and can't take care of oneself patient are more, if the nursing can't keep up, cause the lung infection easily, calculus, bedsore, complication such as stomach and intestine dysfunction, so the nurse will work ceaselessly, and nursing items do one by one, and nursing staff overload work, about 80 patients of a department usually, it needs very long time to patrol the nursing patient at every turn, for example: the turning-over of each patient is carried out for 3 minutes averagely, the body width and the weight of some old people generally need to be carried out by two nursing personnel in a cooperative way, the turning-over of the old people and the patients is conventionally divided into 2 groups, 2 nursing personnel in each group need to take 2 hours after turning over for one time, the next nursing is carried out after one-time patrol nursing is finished, and the nursing personnel can have a rest for a moment between two times of patrol nursing, and the skin of the patient is damaged due to the frequent occurrence of operations such as rough turning-over and the like; the old or the patient frequently gets out of the body to get in bed and get cold at night; in addition, heavy nursing work is carried out, the nursing labor force deficiency is obvious day by day, in order to reduce heavy work of nursing staff, reasonably look at each disabled old man and each disabled patient, know the health index of the disabled old man and each disabled patient, reduce or reduce pain of the disabled old man and each disabled patient, complications caused by long-term lying in bed and the like, and improve the comfort level of the old man.

The traditional nursing method has the following defects: 1. the disabled old man and the patient with low comfort level have the advantages that the one-level patient can patrol for one time in 1 hour and turn over for 1 time in 2 hours, the bed of the patient is dry when the patient turns over sometimes, but the patient can possibly urinate and pull again after moving after turning over, the nursing staff cannot find the patient in time, the patient can only be replaced by a urine pad when being soaked in urine wet for next patrol, the comfort level of the old man is reduced, the risk of pressure sore of the patient is increased due to the fact that the urine pad is soaked for a long time, and the living quality of the patient is reduced.

2. When a patient is easy to have complications during patrol or turn over, whether the patient urinates or defecates or not is observed, the old or the patient is easy to catch a cold when the old or the patient is checked, and the disabled old and the patient are often poor in physique or have a plurality of diseases. Exacerbating the induction of primary or other diseases. Such as cold diarrhea, asthma, bronchitis, etc.

3. Nurse intensity of labour is in order to promote old man physical and mental health, hospital's administrative or technical offices regulation pushes away the patient outdoor activity or shines the sun more than 1 hour in the afternoon every day, and the patient gets up the bed, gets up the wheelchair, needs three nurse cooperation just can accomplish one and get up the bed: one nurse encircles the back waist of the patient, and 2 nurses encircle two legs and the hip respectively, and the operation can be completed in a concerted manner. The patient is taken, lifted up and laid down by the wheelchair for two times, heavy physical strength is required to be paid, the nursing staff can feel tired waist and back ache after getting down, and the waist is strained by the nursing staff sometimes.

4. The doctor in the department of hospital carefully cares the personnel, and can not expose the patient by lifting the quilt, stretch to the butt of the patient and touch whether to wet the urine, touch the urine and possibly touch the stool on the hand, thereby polluting the hands of the nursing personnel and increasing the risk of cross infection.

Disclosure of Invention

In order to solve the problems, the intelligent health management assistant nursing system and method are provided and applied in the disclosure, and the assistant nursing device is mainly applied to disabled old people, disabled people and patients who need care in daily life, is suitable for families, hospitals, nursing homes and medical rehabilitation hospitals, and has satisfactory effect through clinical practice.

In a first aspect, the present disclosure provides an intelligent health management assisted nursing system, comprising a processor, a collection module and a storage alarm unit, wherein the collection module is used for collecting real-time data of humidity, pressure and temperature of a nursing pad and transmitting the collected real-time data to the processor; the processor processes the real-time data to generate an instruction signal and transmits the instruction signal to the storage alarm unit; and the storage alarm unit receives the instruction signal of the processor to carry out storage and/or alarm action.

In a second aspect, the present disclosure provides a method of using the intelligent health management assistance nursing system according to the first aspect, including:

collecting pressure, temperature and humidity data of the nursing pad, storing the data to form historical data, and acquiring real-time pressure, temperature and humidity data of the nursing pad;

analyzing the historical data to obtain a human body physiological curve, and marking a plurality of measuring point data of the human body physiological curve as preset values;

and comparing the acquired real-time nursing pad pressure, temperature and humidity data with preset values and human body physiological curves, and alarming to remind nursing staff to nurse and store in time when the real-time nursing pad pressure, temperature and humidity data deviate from the preset values and/or the human body physiological curves.

Compared with the prior art, this disclosure possesses following beneficial effect:

1. the intelligent health management auxiliary nursing device, the intelligent health management auxiliary nursing system and the intelligent health management auxiliary nursing method are convenient and flexible to use, wide in application range, capable of being integrated into a system to be used in a medical rehabilitation area rehabilitation mode, capable of intelligently analyzing health trends and indexes of patients, capable of providing high-end, systematic and large-scale auxiliary nursing tools and methods for the medical rehabilitation mode, capable of being used in units and units in a machine mode, used for old people or patients in a bed, and capable of being used for home care of the patients.

1. The main machine and the extension machines can find abnormal conditions in time through a sensor technology, a health trend analysis method and an AI intelligent algorithm, analyze and find the problems of bed wetting, catching a cold, missing turning over and the like in real time, and greatly relieve the pain of disabled old people and patients. The intensive nursing labor force cooperation workload and the heavy physical labor amount of nurses and nursing staff are reduced by depending on auxiliary nursing machinery in the sickroom.

2. The intelligent health management auxiliary nursing device, system and method for disabled old people and patients mainly comprise two subsystems, wherein the first subsystem is a nursing monitoring and analyzing health trend and index and alarm system, and the system can timely alarm the urine wet mattress, the body local pressure time is too long, the body temperature is abnormal and the like of the disabled old people or patients, respond to nursing staff or care personnel, perform targeted processing, give out the health trend and index through data analysis, and improve the service satisfaction degree. The second subsystem is the auxiliary nursing device, and this subsystem truns into simple light operation to heavy nursing, saves the nursing labour, and the nurse is liberated in by heavy physical effort, makes the nursing operation humanized more, scientific.

3. The comfort level of the old is increased, and the intelligent health management auxiliary nursing device, system and method for disabled old and patients can find that a certain disabled old and patient can urinate and wet a mattress, defecate and sweat in time; kicking off the quilt body with too low temperature or fever; under the conditions of long-time pressing of local body and the like, the temperature, pressure and humidity sensors in the bed nursing pad are transmitted to the extension set or the host respectively configured in each sickbed or each room or each ward, the alarm on the extension set or the host of each sickbed or each room or the whole ward is alarmed, the alarm ring is heard, the nursing staff can quickly arrive at the patient, the treatment is carried out, the comfort level of the old people is increased, and the health trend and index are given out through data analysis.

4. The intelligent health management auxiliary nursing device, system and method for disabled old people and patients effectively prevent complications, and information data is used for reading humidity, temperature and pressure conditions of the comforts of the patients, so that the risk that the patients catch cold unnecessarily and aggravate to induce original or other diseases caused by the fact that the patients open the quilt to see whether to wet or not is avoided, and pressure sores caused by long-time partial pressure or wet friction stimulation are prevented.

5. The nursing labour is saved "can not reach the old man and patient intelligent health management assists nursing device system and method", about 1/3 uses manpower sparingly, and current situation manpower configuration is obviously not enough, and the patient gets up to get up to go up the wheelchair, needs three nurse cooperation just can accomplish, and this assists nursing device makes originally by three nurse cooperation could accomplish the operation each time, and the current situation manpower configuration not enough condition is alleviated to light completion by a nurse now.

6. Simple prescription is convenient and easy to operate "the supplementary nursing device of disability old man and patient intelligent health management, system and method" single light operation, receive alarm signal and in time arrive patient's side, with the help of the supplementary nursing device of installation in the ward, press the patient sling in the button slip nursing mechanical system with the hand gently, the patient is wrapped gently, quick clean smooth change urine pad, wash the perineum, operation such as stand up, easily accomplish alone, heavy nurse, the heavy physical strength amount of labour of nurse worker has been reduced.

7. 'disabled old people and patients intelligent health management auxiliary nursing device, system and method' for improving cleanness of ward, in a nursing pad of a sickbed, a probe point which is not easy to break is implanted to serve as a humidity measuring point, and whether the old people urinate wet the mattress or not and whether stool and sweat wet exceed threshold values or not is collected in time. And the auxiliary nursing devices are all arranged on the wall, do not occupy the ground space, are indoor and orderly, and simultaneously reduce the risk of cross infection among patients.

Advantages of additional aspects of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate exemplary embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.

FIG. 1 is a block diagram of a schematic structure of an intelligent health management assisted care system according to an embodiment 1 of the present disclosure;

FIG. 2 is a schematic view of a machine for assisted care according to embodiment 1 of the present disclosure;

FIG. 3 is a flow chart of a method of use of embodiment 2 of the present disclosure;

FIG. 4 is a health trend analysis chart of example 2 of the present disclosure;

wherein: 1. the device comprises a humidity measuring point module, a pressure measuring point module, a temperature measuring point module, a collecting and sorting module, a processor, a display module, a sub-transmission module, a display and storage alarm unit, a central processing unit, a first transmission module, a second transmission module, a third transmission module, a fourth transmission module, a fifth transmission module, a longitudinal moving track, a fifth transmission module, a sixth transmission module, a transverse moving track, a fifth transmission module, a sixth transmission module, a fifth transmission module, a fourth transmission module, a fifth transmission module, a fourth transmission.

The specific implementation mode is as follows:

the present disclosure is further described with reference to the following drawings and examples.

It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular is intended to include the plural unless the context clearly dictates otherwise, and it should be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of features, steps, operations, elements, components, and/or combinations thereof.

Belongs to the explanation:

ridge regression (ridge regression), a biased estimation regression method dedicated to linear data analysis, is an improved least squares estimation method, with some data being more likely than least squares. The obtained regression coefficient is more practical, and the fitting to the pathological data is stronger than that of a least square method. Ridge regression analysis is used to solve the problem of multiple collinearity. In medical research and practical application, an algorithm is not required to be created and is used.

Example 1

As shown in figure 1 of the drawings, in which,

the utility model provides an intelligent health management auxiliary nursing system, which comprises a processor, an acquisition module and a storage alarm unit, wherein the acquisition module is used for acquiring the real-time data of humidity, pressure and temperature of a nursing pad and transmitting the acquired real-time data to the processor; the processor processes the real-time data to generate an instruction signal and transmits the instruction signal to the storage alarm unit; and the storage alarm unit receives the instruction signal of the processor to carry out storage and/or alarm action.

Furthermore, the humidity measuring point module, the pressure measuring point module and the temperature measuring point module of the sickbed are all arranged in the nursing pad; the humidity measuring point module comprises a probe point which is not easy to break off, and the probe is implanted into the nursing pad; the pressure measuring point module and the temperature measuring point module are arranged at corresponding positions in the nursing pad according to the body pressure part of the prone position of the human body.

Furthermore, a quick connection plug is also arranged, and the quick connection plug connects the acquisition module in the nursing pad to a processor configured in each bed or room;

the system further comprises a central processing unit, wherein the central processing unit is connected with a processor configured in each bed or room, receives data transmitted by the processor and transmits the data to a storage alarm unit; the storage alarm unit is used for providing storage data and an alarm function; the central processing unit and the processor can both send out command signals and transmit the command signals to the storage alarm unit, and the storage alarm unit carries out corresponding actions according to the command signals. The central processing unit is a host, and the processor is an extension.

Furthermore, the humidity measuring point module adopts a humidity sensor to detect humidity, the temperature and humidity sensor uses a temperature and humidity integrated probe as a temperature measuring element, temperature and humidity signals are collected and converted into current signals or voltage signals which are in linear relation with the temperature and the humidity after being processed by circuits such as voltage stabilizing filtering, operational amplification, nonlinear correction, V/I conversion, constant current and reverse protection and the like, and the current signals or the voltage signals are output, and the interface output of RS485 or RS232 and the like can also be directly carried out through a main control chip.

Further, the pressure measuring point module adopts a pressure sensor for pressure detection, and the pressure sensor can adopt a resistance strain gauge pressure sensor, a semiconductor strain gauge pressure sensor, a piezoresistive pressure sensor, an inductive pressure sensor, a capacitive pressure sensor, a resonant pressure sensor or a capacitive acceleration sensor.

Further, the temperature measuring point module adopts a temperature sensor for temperature detection, and the temperature sensor can adopt a low-temperature gas thermometer, a paramagnetic salt thermometer, a quantum thermometer, a low-temperature thermal resistance thermometer or a thermocouple temperature sensor.

The system further comprises a display module connected with the processor, wherein the display module can adopt a liquid crystal display screen to display the data of the processor or the working state of each module in real time.

The system further comprises a sub-transmission module connected with the processor, wherein the sub-transmission module is used for transmitting the processor signal to the central processing unit; the sub-transmission module can adopt an RS-485 signal module or a module consisting of serial port chips MAX 485.

Furthermore, the display alarm unit is connected with an alarm device, the alarm is related to the linkage of the high and low level change of the quick I/O port of the microcontroller, and the alarm device comprises but is not limited to a mechanical alarm, an electronic alarm or an alarm lamp.

Furthermore, the system also comprises a timer connected with the processor, wherein the timer is used for acquiring real-time and nursing interval time, and can adopt an electromagnetic dotting timer, an electric spark timer, a persistence timer, an amplification timer or a windows timer.

Further, the processor is used for pre-judging and storing the turning-over time intervals of the voltage change signals after the resistivity conversion of the pressure measuring point modules by matching with a timer, sending the turning-over time intervals and the humidity measuring point module signals to the processor and the central processing unit, and displaying, alarming and storing when the conductivity signals are larger than the alarm value.

Furthermore, the auxiliary nursing machine is connected with the processor and comprises a longitudinal moving rail, a transverse moving rail and an electric hoist, wherein the transverse moving rail is hung on the longitudinal moving rail through a mute hanging wheel, and the electric hoist is installed on the transverse moving rail through a hanging wheel. Specifically, the auxiliary nursing machine is arranged at a proper height and position on the wall surfaces at two sides in a ward room, a side hanging piece is adopted to fix a longitudinal moving track at multiple points, the longitudinal moving track can be a bearing industrial hanging rail, and a transverse moving track is hung on the longitudinal moving track through a mute hanging wheel; the miniature silent electric hoist 14 or the winch is arranged on the transverse moving track 13 through two groups of silent hanging wheels.

Specifically, in a nursing pad of a sickbed, probe points which are not easy to break are implanted to serve as a humidity measuring point module 1; according to the characteristics of the body pressure part of the prone position of the human body, a plurality of pressure measuring point modules 2 and temperature measuring point modules 3 are added at corresponding positions, quick connecting plugs are arranged at proper positions of the nursing pad and connected to the extension set configured in each bed or room through the quick plugs, the extension set processor 5 displays the data of the collecting and arranging module 4 on the local extension display module 6, synchronously uploads the data to the first transmission module 10 at the host side (the other n extensions correspond to the nth transmission module 11) through the branch transmission module 7 and transmits the data to the central processing unit 9, and the relevant data are transmitted to the display, storage and alarm unit 8.

Example 2

A method of using an intelligent health management assisted care system, comprising:

Further, voltage change semaphore and pressure data are obtained through a pressure measuring point module, and when the voltage change semaphore exceeds a set voltage threshold, timing is started in cooperation with a timer, and nursing turnover time intervals are obtained and stored;

further, humidity data are obtained through a humidity measuring point module, temperature data are obtained through a temperature measuring point module, and the humidity, temperature and pressure data are transmitted to a processor;

furthermore, the human body physiological curve is analyzed through comparison and analysis of a plurality of measuring point data of the human body prone position curve set points and comparison of historical data, and when the human body physiological curve deviates from a preset value and the human body physiological curve, an alarm is given to remind a nursing staff to nurse and store in time.

Further, health assessment and health trend analysis are performed by adopting an AI intelligent algorithm according to historical data, and light and heavy and urgent classified nursing and point-to-point treatment can be performed according to the health trend, if a bed wetting rule is found, a part is pressed for a long time, pain of the part can be timely nursed, the health index is improved, and the user does not need to wait for checking rooms on time to find and then attend to the nursing. The AI intelligent algorithm includes, but is not limited to, least squares, ridge regression, or PAC algorithm, and can be selected by a software system according to data characteristics such as autocorrelation, multiple collinearity, etc. of the relevant data, and the algorithm is implemented according to data types. As an implementation manner of the embodiment, the least square method is adopted to process the historical data to obtain the human physiological curve and the preset value.

Specifically, the health trend analysis method is characterized in that voltage change signal quantities obtained after resistivity conversion of a plurality of pressure measuring point modules 2 are matched with a timer to pre-judge and store the turn-over time intervals, and signals of the humidity measuring point modules 1 are sent to a processor 5 and a central processing unit 9 to be displayed, alarmed and stored when the conductivity signals are larger than alarm values;

the data of the temperature measuring point modules 3 are transmitted to the processor 5 and the central processing unit 9 after being processed, the physiological curve of the human body is compared with the historical data through comparison of a plurality of measuring point data of the human body prone position curve setting points, and when the human body prone position curve setting points deviate from the preset value and the physiological curve, an alarm is given to remind nursing staff to nurse and store the human body timely. A health trend analysis method adopts an AI intelligent algorithm to make health assessment and health trend analysis according to historical data, and can adopt mild and severe urgent classified nursing and point-to-point treatment according to the health trend, if a bedwetting rule is found, a part is pressed for a long time, so that pain of the part can be timely nursed, the health index can be improved, and the patient does not need to wait for the regular ward-round finding and then go to nursing.

The health trend analysis method flow is shown in fig. 3, the health trend analysis chart is shown in fig. 4, according to the pressure measuring point module 2, the resistance signal of the sensor is converted into a voltage signal or a high-low level signal, when the Xm omega-cm 2 signal value is greater than a set value and meets the pressure requirement, a timer is started to time, the turnover time is less than the set time, the timer is cleared and enters the pressure measuring point flow from the beginning, and if the timer exceeds the set time value, the next detection program is sent; and d1/d2 x% humidity (RH (%)), wherein the humidity is normal program flow when the humidity is less than the set value, and when the value d1 is greater than the set value, the humidity is sent to the next detection program, wherein d1 is a real-time measurement value of the density of the actually contained water vapor, and d2 is a preset water vapor density value. The temperature measuring method comprises the following steps that (1) n temperature (DEG C) sensors are matched with a ROM, after temperature conversion, the byte number of a plurality of temperature measuring points is read, when n measuring points are set to be smaller than a set value, the plurality of measuring point data entering a human body prone position curve set point are compared to judge temperature data, the temperature data are normally switched to a temperature measuring point process, and otherwise, the temperature data are sent to a next detection program; different algorithms are designed according to the data after logical judgment of each measuring point and historical data of a memory, an AI technology is used for selecting the algorithm, optimal selection can be tried in various algorithms such as a least square method, ridge regression, PAC and the like according to the data characteristics such as autocorrelation, multiple collinearity and the like of relevant data, and a software system implements algorithm selection according to the data types. As an implementation manner of this embodiment, the least square method is adopted to process the historical data to obtain a physiological curve and a preset value of the human body, and the data such as the temperature loss and the coolness, the frequency of urination, the excretion time law, the turnover time and the like are analyzed and judged to evaluate the health trend and index of the human body.

The health assessment and health trend analysis method comprises the following steps:

optimization objective of ridge regression: argmin | | Xw-y | | non-woven hair²+a||w||²

Matrix solving: w ═ X^TX+aI)^-1X^Ty, # I is the identity matrix

Ridge regression in sklern;

calling mode:

sklearn.linear_model.Ridgesklearn.linear_model.Ridge(alpha＝1.0,copy_ X＝True,fit_intercept＝True,max_iter＝None,normalize＝False,random_state ＝None,solver＝‘auto’,tol＝0.001)

alpha, regularization factor, corresponding to a in the loss function

fit _ interrupt indicating whether to calculate intercept

solver, method for setting calculation parameters, optional parameters 'auto', 'svd', 'sag', etc

Trend analysis example:

data: analyzing the health trend of the stored monitoring data;

the purpose is as follows: creating polynomial characteristics according to the existing data, and performing polynomial regression on the nursing information by using a ridge regression model to replace a general linear model;

the technical route is as follows:

sklearn.linear_model.Ridgefromsklearn.preprocessing.PolynomialFe

atures；

creating a polynomial feature:

PolynomialFeatures(degree＝2,

include_bias＝True,interaction_only＝False)

depth: the highest order curtain function;

an example of the procedure:

import numpy as np

import pandas as pd

from sky Ridge, linear _ model import Ridge # load Ridge regression method by sky Ridge, linear model;

from sklearn import model_selection

pyplot as plt # loads the cross validation module;

preprocessing import Polynomial Features # is used to create polynomials;

read _ csv (' data. csv ') # loads data using numpy's method;

data＝np.array(a)

plt. plot (data [: 5]) # uses plt to show development trend information;

plt.show()

x ═ data [: 1:4] # X is used to hold 0-4 dimensional data, i.e., attributes;

y ═ data [: 5] # y is used for saving the 5 th dimension data, i.e. the trend;

poly ═ polymomial features (5) # used to create polynomial features up to the power of 5;

fit _ transform (X) # X is a polynomial feature created;

train _ X, test _ X, train _ y, test _ y, model _ selection. train _ test _ split (X, y, test _ size 0.3, random _ state 0) # divides all data into a training set and a test set, test _ size indicates the proportion of the test set, and # random _ state is a random number;

clf Ridge (alpha 1.0, fit _ Intercept True) # creates a regressor and trains;

fit (train _ X, train _ y) # calls the fit function to train the regressor using the training set;

score (test _ X, test _ y) # calculates the goodness of the curve;

print ('curve similarity score:', score) # curve similarity score 0.737;

drawing a fitted curve with the start of 50# in the range of 50 to 200;

end＝200

predict (X) # calls the fitted value of the predict function;

time＝np.arange(start,end)

plt.plot(time,y[start:end],'b',label＝"real")

plt (time, y _ pre [ start: end ], ' r ', label ═ predict ') # shows real data, and synthetic data;

legend position of lot ═ upper left') #;

plt.show()。

the above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.

Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.

Claims

1. An intelligent health management auxiliary nursing system is characterized by comprising a processor, an acquisition module and a storage alarm unit, wherein the acquisition module is used for acquiring real-time data of humidity, pressure and temperature of a nursing pad and transmitting the acquired real-time data to the processor; the processor processes the real-time data to generate an instruction signal and transmits the instruction signal to the storage alarm unit; and the storage alarm unit receives the instruction signal of the processor to carry out storage and/or alarm action.

2. The intelligent health management assistant nursing system of claim 1, wherein the humidity measuring point module, the pressure measuring point module and the temperature measuring point module of the hospital bed are all arranged in a nursing pad; the humidity measuring point module comprises a probe point which is not easy to break off, and the probe is implanted into the nursing pad; the pressure measuring point module and the temperature measuring point module are arranged at corresponding positions in the nursing pad according to the body pressure part of the prone position of the human body.

3. The intelligent health management assisted-care system of claim 1, further provided with a quick-connect plug that connects the acquisition module in the care pad to the processor configured for each bed or room.

4. The intelligent health management assisted-care system of claim 1, further comprising a central processing unit connected to the processor configured for each bed or room, receiving data transmitted by the processor, and transmitting the data to the storage alarm unit.

5. The intelligent health management assisted care system of claim 1, wherein the humidity measurement point module comprises a humidity sensor, and the humidity sensor is used for humidity detection.

6. The intelligent health management assistive care system of claim 1, further comprising an assistive care machine connected to the processor, the assistive care machine comprising a longitudinal movement track, a transverse movement track, and an electric hoist, the transverse movement track suspended on the longitudinal movement track by a hanging wheel, the electric hoist mounted on the transverse movement track by the hanging wheel.

7. The intelligent health management assisted-care system of claim 1, further comprising a timer coupled to the processor.

8. A method of use employing the intelligent health management assisted care system of any of claims 1-7, comprising:

9. The use method of claim 8, wherein the pressure measuring point module is used for acquiring the voltage change semaphore and pressure data, and when the voltage change semaphore exceeds a set voltage threshold, the timer is used for starting timing, and the nursing turning-over time interval is acquired and stored.

10. The method of use of claim 8, wherein comparing the collected real-time pad pressure, temperature and humidity data to predetermined values and to the human body physiological curve comprises using an AI intelligence algorithm including, but not limited to, least squares, ridge regression or PAC algorithms to make health assessments and health trend analysis based on historical data.