CN117555376B - Residential building environment energy-saving adjusting method and system based on big data - Google Patents
Residential building environment energy-saving adjusting method and system based on big data Download PDFInfo
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Abstract
The invention discloses a residential building environment energy-saving regulation method and system based on big data, which belong to the field of electric digital data processing.
Description
Technical Field
The invention belongs to the field of electric digital data processing, and particularly relates to a residential building environment energy-saving adjusting method and system based on big data.
Background
In the prior art, when the temperature and humidity of the living environment are regulated, specific regulation values are usually required to be set for regulation, and for scenes with special requirements on the living environment in hospital wards or nursing homes and the like, proper temperature and humidity cannot be accurately calculated and exported through the physical characteristics of human bodies in the building, so that the temperature and humidity regulation cannot meet different patient demands, and the problems exist in the prior art;
for example, in chinese patent with application publication number CN115688388A, a residential building environment adjusting method based on big data is disclosed, which belongs to the technical field of environment adjustment, and includes: and obtaining environmental parameters of the interior and the exterior of the building, and constructing a model with the same specification according to the type and the structure of the building. Conditions and times required to adjust the environment within the building to preset values are simulated and determined in the model based on the environmental parameters. Determining the rule of personnel activities, and carrying out targeted adjustment on a specific area in the building by combining the information fed back in the model. The model constructed by the residential building environment adjusting method based on big data can truly simulate an actual building, an adjusting route with stronger referential can be planned in advance through simulation in the model, and the consumption of resources is greatly reduced and the energy saving level is improved through determining the activity rule of personnel.
The problems proposed in the background art exist in the above patents: the prior art generally needs to set specific adjustment values for adjustment, and for scenes with special requirements on living environments in hospital wards or nursing homes and the like, proper humiture cannot be accurately calculated and exported through human body physical characteristics in a building, so that the temperature and humidity adjustment cannot meet the requirements of patients.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a large-data-based residential building environment energy-saving regulation method and system.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the residential building environment energy-saving adjusting method based on big data comprises the following specific steps:
s1, monitoring human body constitution data information in a residential building through an intelligent constitution data monitoring module worn on a human body, collecting indoor real-time temperature and humidity data, and storing and transmitting the monitored human body constitution data information and the real-time temperature and humidity data;
s2, acquiring human body constitution data information, and leading the acquired human body constitution data information into a critical illness state value calculation strategy to calculate critical illness state values of personnel;
s3, acquiring historical human body constitution data information and proper temperature and humidity data of a historical human body, constructing and training a neural network model which is input into the human body constitution data information and output into the proper temperature and humidity data of the human body;
s4, acquiring corresponding human body constitution data information, importing the information into the constructed neural network model, and outputting proper temperature and humidity data of each person;
s5, acquiring a disease critical value of the corresponding person and the calculated proper temperature and humidity data of the corresponding person, and importing the disease critical value and the calculated proper temperature and humidity data into a temperature and humidity value calculation strategy to calculate the final temperature and humidity;
s6, the temperature and humidity controller in the building adjusts the residential building environment to the final temperature and humidity in real time.
Specifically, the step S1 includes the following specific steps:
s11, human body constitution data information in residential buildings is monitored through an intelligent constitution data monitoring module worn on a human body, wherein the human body constitution data information comprises collected human body temperature data, human body blood pressure data, human body shaking frequency data and human body heart rate data, and the obtained indoor human body constitution data is stored in a storage module in a first dimension vector form and transmitted;
s12, acquiring indoor real-time temperature and humidity data through an indoor environment temperature acquisition module, and storing the acquired indoor real-time temperature and humidity data in a storage module in a second dimension vector form and transmitting the second dimension vector;
it should be noted that the acquired human body constitution data information and indoor real-time temperature and humidity data are only used in the system calculation process, the leakage mode of no hacking invasion and the like can not be leaked to the outside, and the human body constitution data information and indoor real-time temperature and humidity data can not be acquired on the premise that external personnel do not perform network attack, so that the specific confidentiality problem is not considered;
specifically, the disease critical value calculation strategy in S2 includes the following specific contents:
s21, setting an acquisition period, and acquiring the average data of the body temperature, the average data of the blood pressure, the average data of the jitter frequency and the average data of the heart rate of the human body in the acquired acquisition period;
s22, importing the acquired human body temperature average data, human blood pressure average data, human jitter frequency average data, human heart rate average data, stored human body temperature safety range data, stored human blood pressure safety range data, stored human jitter frequency safety range data and stored human heart rate safety range data in an acquired acquisition period into a disease state risk degree calculation formula of a calculator, wherein the disease state risk degree calculation formula of the calculator is as follows:wherein n is the number of data items of human body temperature average data, human blood pressure average data, human jitter frequency average data and human heart rate average data, +.>The i-th item of human body temperature average data, human blood pressure average data, human jitter frequency average data and human heart rate average data is->Is the median value of the safety range corresponding to the ith item in the average human body temperature data, average human blood pressure data, average human jitter frequency data and average human heart rate data, < ->Is the maximum value of the safety range corresponding to the ith item in the average data of human body temperature, average data of human blood pressure, average data of human jitter frequency and average data of human heart rate, < + >>Is average data of human body temperature, average data of human blood pressure and human jitter frequencyThe ith item in the rate average data and the human heart rate average data corresponds to the minimum value of the safety range,/>Is the duty ratio coefficient of the ith item in the human body temperature average data, the human blood pressure average data, the human jitter frequency average data and the human heart rate average data,；
s23, setting a critical degree threshold value, comparing the calculated critical degree of the illness with the critical degree threshold value, setting corresponding personnel as environment-adjusting personnel if the critical degree of the illness is greater than or equal to the critical degree threshold value, and setting the corresponding personnel as the environment-adjusting personnel if the critical degree of the illness is less than the critical degree threshold value.
Specifically, the content of S3 includes the following specific steps:
s31, extracting historical human body temperature average data, human blood pressure average data, human jitter frequency average data, human heart rate average data and corresponding human body suitable temperature and humidity data, constructing a deep learning neural network model which is input into human body constitution data information and output into human body suitable temperature and humidity data;
s32, setting a parameter training set and a parameter testing set according to the ratio of 9:1 by extracting historical human body temperature average data, human blood pressure average data, human jitter frequency average data, human heart rate average data and corresponding human body proper temperature and humidity data; inputting 90% of parameter training sets into a deep learning neural network model for training to obtain an initial deep learning neural network model; testing the initial deep learning neural network model by using a 10% parameter test set, and outputting an optimal initial deep learning neural network model meeting the preset probability of the corresponding human body suitable temperature and humidity data as the deep learning neural network model, wherein an output strategy formula of a q+1 th-layer item neuron in the deep learning neural network model is as follows:wherein->For the output of the q+1 layer s term neuron,/->For the connection weight of the q-th layer p-th neuron and the q+1 layer s-th neuron,/for the connection weight of the q-th layer p-th neuron and the q+1 layer s-th neuron>Input representing the p-th neuron of the q-th layer,>bias representing the linear relationship of the q-th layer p-th neuron and the q+1-th layer s-th neuron,/and/or->Representing Sigmoid activation function, M is the number of neurons in the q-th layer deep learning neural network model.
Specifically, the specific content of S4 includes the following specific steps:
s41, acquiring human body temperature average data, human blood pressure average data, human jitter frequency average data and human heart rate average data of an environment regulator;
s42, importing the obtained average human body temperature data, average human blood pressure data, average human jitter frequency data and average human heart rate data of the environment regulating personnel into a deep learning neural network model to output the proper temperature and humidity data of each environment regulating personnel.
Specifically, the specific content of the temperature and humidity value calculation strategy of S5 includes the following specific steps:
s51, acquiring proper temperature and humidity data of environment regulating personnel in residential buildings and marking the data as respectivelyWherein->The temperature and humidity data suitable for the jth environmental regulator is provided, and v is the environmental regulator in the residential buildingSimultaneously extracting critical illness state values of environmental regulators;
s52, substituting the critical illness state value of the environmental regulator and the proper temperature and humidity data of the environmental regulator into a temperature and humidity value calculation formula to calculate the final temperature and humidity, wherein the temperature and humidity value calculation formula is as follows:wherein->The disease state criticality of the jth environmental regulator is achieved.
The residential building environment energy-saving regulation system based on big data is realized based on the above-mentioned residential building environment energy-saving regulation method based on big data, which comprises a data acquisition module, a critical value calculation module, a neural network model construction module, a proper temperature and humidity output module, a final temperature and humidity calculation module, a temperature and humidity regulation module and a control module, wherein the data acquisition module is used for monitoring human body constitution data information in a residential building through an intelligent constitution data monitoring module worn on a human body, simultaneously collecting indoor real-time temperature and humidity data, storing and transmitting the monitored human body constitution data information and the real-time temperature and humidity data, the critical value calculation module is used for acquiring human body constitution data information, importing the human body constitution data information into a disease critical value calculation strategy to calculate the illness critical value of personnel, and the neural network model construction module is used for acquiring historical human body constitution data information, and proper temperature and humidity data of a historical human body, constructing and training and inputting the information into the human body constitution data information, and outputting a neural network model which is proper temperature and humidity data of the human body.
Specifically, the suitable temperature and humidity output module is used for acquiring corresponding human body constitution data information, importing the information into the constructed neural network model, outputting suitable temperature and humidity data of each person, the final temperature and humidity calculation module is used for acquiring a disease state critical value of the corresponding person and the calculated suitable temperature and humidity data of the corresponding person, importing the data into a temperature and humidity value calculation strategy to calculate final temperature and humidity, and the temperature and humidity adjustment module is used for adjusting the residential building environment to the final temperature and humidity in real time through a temperature and humidity controller in a building.
Specifically, the controller is used for controlling the operation of the data acquisition module, the critical value calculation module, the neural network model construction module, the suitable temperature and humidity output module, the final temperature and humidity calculation module and the temperature and humidity adjustment module.
An electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor executes the residential building environment energy-saving adjusting method based on big data by calling the computer program stored in the memory.
A computer readable storage medium storing instructions that when executed on a computer cause the computer to perform a residential building environment energy conservation adjustment method based on big data as described above.
Compared with the prior art, the invention has the beneficial effects that:
according to the intelligent physical data monitoring system, physical data information of a living building is monitored through an intelligent physical data monitoring module worn on a human body, indoor real-time temperature and humidity data are collected, the monitored physical data information and the real-time temperature and humidity data are stored and transmitted, physical data information is acquired and is imported into a disease critical value calculation strategy to calculate a disease critical value of a person, historical physical data information and proper temperature and humidity data of the historical human body are acquired, the historical physical data information and proper temperature and humidity data of the historical human body are built and trained and input into the physical data information, a neural network model which is the proper temperature and humidity data of the human body is output, the corresponding physical data information is acquired and imported into the built neural network model, proper temperature and humidity data of each person is output, the disease critical value of the corresponding person and the proper temperature and humidity data of the corresponding person obtained through calculation are acquired and imported into a temperature and humidity value calculation strategy to calculate final temperature and humidity, a temperature and humidity controller in the building adjusts living building environment in real time to final temperature and humidity, physical characteristics of the person in the living building are analyzed, and a deep learning neural network model is built to accurately calculate proper temperature and humidity in the living environment, and proper temperature is exported, and is especially suitable for a hospital living environment, and a special scene.
Drawings
FIG. 1 is a schematic flow chart of a residential building environment energy-saving regulation method based on big data;
fig. 2 is a schematic diagram of an overall framework of the residential building environment energy-saving adjusting system based on big data.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
Example 1
Referring to fig. 1, an embodiment of the present invention is provided: the residential building environment energy-saving adjusting method based on big data comprises the following specific steps:
s1, monitoring human body constitution data information in a residential building through an intelligent constitution data monitoring module worn on a human body, collecting indoor real-time temperature and humidity data, and storing and transmitting the monitored human body constitution data information and the real-time temperature and humidity data;
it should be noted that, S1 includes the following specific steps:
s11, human body constitution data information in residential buildings is obtained through collection through an intelligent constitution data monitoring module worn on a human body, the human body constitution data information comprises human body temperature data, human body blood pressure data, human body shaking frequency data and human body heart rate data, and the obtained indoor human body constitution data is stored in a storage module in a first dimension vector form and transmitted;
s12, acquiring indoor real-time temperature and humidity data through an indoor environment temperature acquisition module, and storing the acquired indoor real-time temperature and humidity data in a storage module in a second dimension vector form and transmitting the second dimension vector;
it should be noted that the acquired human body constitution data information and indoor real-time temperature and humidity data are only used in the system calculation process, the leakage mode of no hacking invasion and the like can not be leaked to the outside, and the human body constitution data information and indoor real-time temperature and humidity data can not be acquired on the premise that external personnel do not perform network attack, so that the specific confidentiality problem is not considered;
s2, acquiring human body constitution data information, and leading the acquired human body constitution data information into a critical illness state value calculation strategy to calculate critical illness state values of personnel;
it should be noted that, the disease critical value calculation strategy in S2 includes the following specific contents:
s21, setting an acquisition period, and acquiring the average data of the body temperature, the average data of the blood pressure, the average data of the jitter frequency and the average data of the heart rate of the human body in the acquired acquisition period;
s22, importing the acquired human body temperature average data, human blood pressure average data, human jitter frequency average data, human heart rate average data, stored human body temperature safety range data, stored human blood pressure safety range data, stored human jitter frequency safety range data and stored human heart rate safety range data in an acquired acquisition period into a disease state risk degree calculation formula of a calculator, wherein the disease state risk degree calculation formula of the calculator is as follows:wherein n is the number of data items of human body temperature average data, human blood pressure average data, human jitter frequency average data and human heart rate average data, +.>The i-th item of human body temperature average data, human blood pressure average data, human jitter frequency average data and human heart rate average data is->Is the median value of the safety range corresponding to the ith item in the average human body temperature data, average human blood pressure data, average human jitter frequency data and average human heart rate data, < ->Is the maximum value of the safety range corresponding to the ith item in the average data of human body temperature, average data of human blood pressure, average data of human jitter frequency and average data of human heart rate, < + >>Is the minimum value of the safety range corresponding to the ith item in the average human body temperature data, the average human blood pressure data, the average human jitter frequency data and the average human heart rate data, < + >>Is the duty ratio coefficient of the ith item in the human body temperature average data, the human blood pressure average data, the human jitter frequency average data and the human heart rate average data,；
s23, setting a critical degree threshold value, comparing the calculated critical degree of the illness with the critical degree threshold value, setting corresponding personnel as environment-regulating personnel if the critical degree of the illness is greater than or equal to the critical degree threshold value, and setting the corresponding personnel as the environment-regulating personnel if the critical degree of the illness is less than the critical degree threshold value;
s3, acquiring historical human body constitution data information and proper temperature and humidity data of a historical human body, constructing and training a neural network model which is input into the human body constitution data information and output into the proper temperature and humidity data of the human body;
the content of S3 includes the following specific steps:
s31, extracting historical human body temperature average data, human blood pressure average data, human jitter frequency average data, human heart rate average data and corresponding human body suitable temperature and humidity data, constructing a deep learning neural network model which is input into human body constitution data information and output into human body suitable temperature and humidity data;
the following is a simple Python code example, a deep learning neural network model is constructed by using a TensorFlow library, the deep learning neural network model is input as human body constitution data information, and the deep learning neural network model is output as human body suitable temperature and humidity data, and note that the example is only used as a starting point, and in practical application, adjustment may be needed according to actual conditions and data sets.
First, a TensorFlow library is installed:
bash
pip install tensorflow
then, the neural network model code is written:
import tensorflow as tf
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
definition of# input and output layers
input_dyes= [ 'body temperature', 'blood pressure', 'jitter frequency', 'heart rate', ]
output_dyes= [ 'suitable temperature', 'suitable humidity' ]
# suppose that there is already a dataset containing physique data
# random data is used here as an example
data = {
'body temperature': 36.5, 37.2, 36.8, 37.1],
'blood pressure' [120, 140, 130, 135],
'dithering frequency' [40, 50, 45, 55],
'heart rate' [70, 80, 75, 85],
suitable temperatures are [25, 26, 25.5, 26.5],
'suitable humidity': 50, 60, 55, 65]
}
# creation training dataset
train_data = data.copy()
train_data = dict(zip(input_layers, train_data.values()))
# creation of test dataset
test_data = data.copy()
test_data = dict(zip(input_layers, test_data.values()))
# definition neural network model
model = Sequential([
Dense(64, activation='relu', input_shape=(len(input_layers),)),
Dense(64, activation='relu'),
Dense(2)
])
# compiling model
model.compile(optimizer='adam', loss='mse', metrics=['mae'])
Training model #
model.fit(train_data, test_data, epochs=100, batch_size=32, validation_split=0.2)
# predictive new data
New_data= { 'body temperature': [37], 'blood pressure': 130], 'jitter frequency': 50], 'heart rate': 80] }
predictions = model.predict(new_data)
print(predictions)
The code defines a simple neural network model, inputs the model into the body temperature, the blood pressure, the shaking frequency and the heart rate of the human body, outputs the model into proper temperature and humidity, and adjusts the model structure and parameters according to the actual data set and the requirements;
note that: in this example, assuming that there is already a data set containing physical data, in practical applications, it may be necessary to acquire and process the data from a practical data source (such as a database, a file, or an API), and in addition, to improve the accuracy of the model, it may be necessary to perform operations such as preprocessing, feature engineering, and dimension reduction on the data;
s32, setting a parameter training set and a parameter testing set according to the ratio of 9:1 by extracting historical human body temperature average data, human blood pressure average data, human jitter frequency average data, human heart rate average data and corresponding human body proper temperature and humidity data; inputting 90% of parameter training sets into a deep learning neural network model for training to obtain an initial deep learning neural network model; testing the initial deep learning neural network model by using 10% of parameter test set, and outputting the optimal initial deep learning meeting the preset probability of the corresponding human body suitable temperature and humidity dataThe neural network model is used as a deep learning neural network model, wherein the output strategy formula of the q+1th-item neuron in the deep learning neural network model is as follows:wherein->For the output of the q+1 layer s term neuron,/->For the connection weight of the q-th layer p-th neuron and the q+1 layer s-th neuron,/for the connection weight of the q-th layer p-th neuron and the q+1 layer s-th neuron>Input representing the p-th neuron of the q-th layer,>bias representing the linear relationship of the q-th layer p-th neuron and the q+1-th layer s-th neuron,/and/or->Representing a Sigmoid activation function, wherein M is the number of neurons in a q-th layer deep learning neural network model;
s4, acquiring corresponding human body constitution data information, importing the information into the constructed neural network model, and outputting proper temperature and humidity data of each person;
the specific content of S4 includes the following specific steps:
s41, acquiring human body temperature average data, human blood pressure average data, human jitter frequency average data and human heart rate average data of an environment regulator;
s42, importing the obtained average human body temperature data, average human blood pressure data, average human jitter frequency data and average human heart rate data of the environment regulator into a deep learning neural network model to output the proper temperature and humidity data of each environment regulator;
s5, acquiring a disease critical value of the corresponding person and the calculated proper temperature and humidity data of the corresponding person, and importing the disease critical value and the calculated proper temperature and humidity data into a temperature and humidity value calculation strategy to calculate the final temperature and humidity;
the specific content of the temperature and humidity value calculation strategy of S5 includes the following specific steps:
s51, acquiring proper temperature and humidity data of environment regulating personnel in residential buildings and marking the data as respectivelyWherein->The data of the temperature and humidity suitable for the jth environmental regulator is obtained, v is the number of the environmental regulators in the residential building, and meanwhile, the critical value of the illness state of the environmental regulators is extracted;
s52, substituting the critical illness state value of the environmental regulator and the proper temperature and humidity data of the environmental regulator into a temperature and humidity value calculation formula to calculate the final temperature and humidity, wherein the temperature and humidity value calculation formula is as follows:wherein->The disease state criticality of the jth environmental regulator;
s6, a temperature and humidity controller in the building regulates the environment of the residential building to the final temperature and humidity in real time;
the following is a simple Python code example, which uses PID control algorithm to adjust the temperature and humidity in the building in real time, note that this example is only used as a starting point, and may need to be adjusted according to the actual situation and the equipment interface in practical application;
first, a PID library is installed:
bash
pip install python-pid
then, a temperature and humidity controller code is written:
import time
import threading
from python_pid import PID
# definition device interface (here, only by way of example, the actual application needs to be modified according to the actual situation)
def set_temperature(temperature):
print (f 'set temperature { temperature } degrees Celsius')
def set_humidity(humidity):
print (f 'set humidity to { humidity }%')
# creation PID controller
pid_temp = PID(Kp=1.0, Ki=0.5, Kd=0.1, output_limits=(0, 100))
pid_hum = PID(Kp=1.0, Ki=0.5, Kd=0.1, output_limits=(0, 100))
# set target temperature and humidity
target_temperature = 25
target_humidity = 50
# create two threads for real-time temperature and humidity adjustment, respectively
def temperature_controller():
while True:
Reading the current temperature #
current_temperature = get_temperature()
Calculation error #
error = target_temperature - current_temperature
# calculation output
output = pid_temp.update(error)
Setting temperature #
set_temperature(current_temperature + output)
# wait for a period of time
time.sleep(1)
def humidity_controller():
while True:
Reading the current humidity #
current_humidity = get_humidity()
Calculation error #
error = target_humidity - current_humidity
# calculation output
output = pid_hum.update(error)
# set humidity
set_humidity(current_humidity + output)
# wait for a period of time
time.sleep(1)
# create thread and start
threading.Thread(target=temperature_controller).start()
threading.Thread(target=humidity_controller).start()
# keep main program running
while True:
time.sleep(10)
The code defines a simple temperature and humidity controller, and the temperature and humidity in the building are regulated in real time by using a PID algorithm, and the code is regulated according to the actual equipment interface and the requirements;
according to the intelligent physical data monitoring system, physical data information of a living building is monitored through an intelligent physical data monitoring module worn on a human body, indoor real-time temperature and humidity data are collected, the monitored physical data information and the real-time temperature and humidity data are stored and transmitted, physical data information is acquired and is imported into a disease critical value calculation strategy to calculate a disease critical value of a person, historical physical data information and proper temperature and humidity data of the historical human body are acquired, the historical physical data information and proper temperature and humidity data of the historical human body are built and trained and input into the physical data information, a neural network model which is the proper temperature and humidity data of the human body is output, the corresponding physical data information is acquired and imported into the built neural network model, proper temperature and humidity data of each person is output, the disease critical value of the corresponding person and the proper temperature and humidity data of the corresponding person obtained through calculation are acquired and imported into a temperature and humidity value calculation strategy to calculate final temperature and humidity, a temperature and humidity controller in the building adjusts living building environment in real time to final temperature and humidity, physical characteristics of the person in the living building are analyzed, and a deep learning neural network model is built to accurately calculate proper temperature and humidity in the living environment, and proper temperature is exported, and is especially suitable for a hospital living environment, and a special scene.
Example 2
As shown in fig. 2, the big data-based residential building environment energy-saving regulation system is realized based on the big data-based residential building environment energy-saving regulation method, and the big data-based residential building environment energy-saving regulation system comprises a data acquisition module, a critical value calculation module, a neural network model construction module, a proper temperature and humidity output module, a final temperature and humidity calculation module, a temperature and humidity regulation module and a control module, wherein the data acquisition module is used for monitoring human body constitution data information in a residential building through an intelligent constitution data monitoring module worn on a human body, collecting indoor real-time temperature and humidity data, storing and transmitting the monitored human body constitution data information and the real-time temperature and humidity data, the critical value calculation module is used for acquiring human body constitution data information, importing the human body constitution data information into a disease critical value calculation strategy to calculate a disease critical value of a person, and the neural network model construction module is used for acquiring historical human body constitution data information, and proper temperature and humidity data of a historical human body, constructing and training and inputting the historical human body constitution data information into the neural network model of proper temperature and humidity data of the human body;
in this embodiment, the suitable temperature and humidity output module is used for acquiring corresponding human body constitution data information, importing the information into the constructed neural network model, outputting suitable temperature and humidity data of each person, the final temperature and humidity calculation module is used for acquiring a disease critical value of the corresponding person and the calculated suitable temperature and humidity data of the corresponding person, importing the disease critical value and the calculated suitable temperature and humidity data into the temperature and humidity value calculation strategy to calculate final temperature and humidity, and the temperature and humidity adjustment module is used for adjusting the residential building environment to the final temperature and humidity in real time through a temperature and humidity controller in the building;
in this embodiment, the controller is configured to control operations of the data acquisition module, the critical value calculation module, the neural network model building module, the suitable temperature and humidity output module, the final temperature and humidity calculation module, and the temperature and humidity adjustment module.
Example 3
The present embodiment provides an electronic device including: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor executes the above-described big data-based residential building environment energy-saving adjustment method by calling a computer program stored in the memory.
The electronic device may vary greatly in configuration or performance, and can include one or more processors (Central Processing Units, CPU) and one or more memories, where the memories store at least one computer program that is loaded and executed by the processors to implement the big data based energy saving regulation method for residential building environments provided by the above method embodiments. The electronic device can also include other components for implementing the functions of the device, for example, the electronic device can also have wired or wireless network interfaces, input-output interfaces, and the like, for inputting and outputting data. The present embodiment is not described herein.
Example 4
The present embodiment proposes a computer-readable storage medium having stored thereon an erasable computer program;
the computer program, when run on the computer device, causes the computer device to perform the above-described big data based residential building environment energy saving adjustment method.
For example, the computer readable storage medium can be Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), compact disk Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM), magnetic tape, floppy disk, optical data storage device, etc.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (9)
1. The residential building environment energy-saving adjusting method based on big data is characterized by comprising the following specific steps of:
s1, monitoring human body constitution data information in a residential building through an intelligent constitution data monitoring module worn on a human body, collecting indoor real-time temperature and humidity data, and storing and transmitting the monitored human body constitution data information and the real-time temperature and humidity data;
s2, acquiring human body constitution data information, and leading the acquired human body constitution data information into a critical illness state value calculation strategy to calculate critical illness state values of personnel;
s3, acquiring historical human body constitution data information and proper temperature and humidity data of a historical human body, constructing and training a neural network model which is input into the human body constitution data information and output into the proper temperature and humidity data of the human body;
s4, acquiring corresponding human body constitution data information, importing the information into the constructed neural network model, and outputting proper temperature and humidity data of each person;
s5, acquiring a disease critical value of the corresponding person and the calculated proper temperature and humidity data of the corresponding person, and importing the disease critical value and the calculated proper temperature and humidity data into a temperature and humidity value calculation strategy to calculate the final temperature and humidity;
s6, the temperature and humidity controller in the building adjusts the residential building environment to the final temperature and humidity in real time.
2. The method for adjusting the energy conservation of the residential building environment based on big data according to claim 1, wherein the step S1 comprises the following specific steps:
s11, human body constitution data information in residential buildings is monitored through an intelligent constitution data monitoring module worn on a human body, wherein the human body constitution data information comprises collected human body temperature data, human body blood pressure data, human body shaking frequency data and human body heart rate data, and the obtained indoor human body constitution data is stored in a storage module in a first dimension vector form and transmitted;
s12, acquiring indoor real-time temperature and humidity data through an indoor environment temperature acquisition module, and storing the acquired indoor real-time temperature and humidity data in a storage module in a second dimension vector mode and transmitting the second dimension vector.
3. The big data based residential building environment energy saving adjustment method as set forth in claim 2, wherein the critical illness state value calculation strategy in S2 includes the following specific contents:
s21, setting an acquisition period, and acquiring the average data of the body temperature, the average data of the blood pressure, the average data of the jitter frequency and the average data of the heart rate of the human body in the acquired acquisition period;
s22, importing the acquired human body temperature average data, human blood pressure average data, human jitter frequency average data, human heart rate average data, stored human body temperature safety range data, stored human blood pressure safety range data, stored human jitter frequency safety range data and stored human heart rate safety range data in an acquired acquisition period into a disease state risk degree calculation formula of a calculator, wherein the disease state risk degree calculation formula of the calculator is as follows:wherein n is the number of data items of human body temperature average data, human blood pressure average data, human jitter frequency average data and human heart rate average data, +.>The i-th item of human body temperature average data, human blood pressure average data, human jitter frequency average data and human heart rate average data is->Is average data of human body temperature, average data of human blood pressure and human trembleMedian value of safety range corresponding to the ith item in the moving frequency average data and the human heart rate average data,/>Is the maximum value of the safety range corresponding to the ith item in the average data of human body temperature, average data of human blood pressure, average data of human jitter frequency and average data of human heart rate, < + >>Is the minimum value of the safety range corresponding to the ith item in the average human body temperature data, the average human blood pressure data, the average human jitter frequency data and the average human heart rate data, < + >>Is the duty ratio coefficient of the ith item in the human body temperature average data, the human blood pressure average data, the human jitter frequency average data and the human heart rate average data, +.>；
S23, setting a critical degree threshold value, comparing the calculated critical degree of the illness with the critical degree threshold value, setting corresponding personnel as environment-adjusting personnel if the critical degree of the illness is greater than or equal to the critical degree threshold value, and setting the corresponding personnel as the environment-adjusting personnel if the critical degree of the illness is less than the critical degree threshold value.
4. The method for adjusting the energy conservation of the residential building environment based on big data according to claim 3, wherein the content of S3 comprises the following specific steps:
s31, extracting historical human body temperature average data, human blood pressure average data, human jitter frequency average data, human heart rate average data and corresponding human body suitable temperature and humidity data, constructing a deep learning neural network model which is input into human body constitution data information and output into human body suitable temperature and humidity data;
s32, extracting the average number of the historical human body temperaturesSetting a parameter training set and a parameter testing set according to the average data of the human blood pressure, the average data of the human shaking frequency, the average data of the human heart rate and the corresponding suitable temperature and humidity data of the human according to the proportion of 9:1; inputting 90% of parameter training sets into a deep learning neural network model for training to obtain an initial deep learning neural network model; testing the initial deep learning neural network model by using a 10% parameter test set, and outputting an optimal initial deep learning neural network model meeting the preset probability of the corresponding human body suitable temperature and humidity data as the deep learning neural network model, wherein an output strategy formula of a q+1 th-layer item neuron in the deep learning neural network model is as follows:wherein->For the output of the q+1 layer s term neuron,/->For the connection weight of the q-th layer p-th neuron and the q+1 layer s-th neuron,/for the connection weight of the q-th layer p-th neuron and the q+1 layer s-th neuron>Input representing the p-th neuron of the q-th layer,>bias representing the linear relationship of the q-th layer p-th neuron and the q+1-th layer s-th neuron,/and/or->Representing Sigmoid activation function, M is the number of neurons in the q-th layer deep learning neural network model.
5. The method for adjusting the energy conservation of the residential building environment based on big data according to claim 4, wherein the specific content of S4 comprises the following specific steps:
s41, acquiring human body temperature average data, human blood pressure average data, human jitter frequency average data and human heart rate average data of an environment regulator;
s42, importing the obtained average human body temperature data, average human blood pressure data, average human jitter frequency data and average human heart rate data of the environment regulating personnel into a deep learning neural network model to output the proper temperature and humidity data of each environment regulating personnel.
6. The method for adjusting the energy conservation of the residential building environment based on big data according to claim 5, wherein the specific content of the temperature and humidity value calculation strategy of S5 comprises the following specific steps:
s51, acquiring proper temperature and humidity data of environment regulating personnel in residential buildings and marking the data as respectivelyWhereinThe data of the temperature and humidity suitable for the jth environmental regulator is obtained, v is the number of the environmental regulators in the residential building, and meanwhile, the critical value of the illness state of the environmental regulators is extracted;
s52, substituting the critical illness state value of the environmental regulator and the proper temperature and humidity data of the environmental regulator into a temperature and humidity value calculation formula to calculate the final temperature and humidity, wherein the temperature and humidity value calculation formula is as follows:wherein->The disease state criticality of the jth environmental regulator is achieved.
7. The residential building environment energy-saving regulation system based on big data is realized based on the residential building environment energy-saving regulation method based on big data according to any one of claims 1-6, and is characterized by comprising a data acquisition module, a critical value calculation module, a neural network model construction module, a proper temperature and humidity output module, a final temperature and humidity calculation module, a temperature and humidity regulation module and a control module, wherein the data acquisition module is used for monitoring human body constitution data information in a residential building through an intelligent constitution data monitoring module worn on a human body, simultaneously collecting indoor real-time temperature and humidity data, storing and transmitting the monitored human body constitution data information and the real-time temperature and humidity data, the critical value calculation module is used for acquiring the human body constitution data information, importing the human body constitution critical value information into a disease critical value calculation strategy to calculate the illness critical value of a person, and the neural network model construction module is used for acquiring the historical human body constitution data information, the proper temperature and humidity data of a historical human body, constructing and training the neural network model which is input into the human body constitution data information and outputting the proper temperature and humidity data of the human body.
8. The energy-saving regulation system of residential building environment based on big data according to claim 7, wherein the suitable temperature and humidity output module is used for acquiring corresponding human body constitution data information, importing the information into a constructed neural network model, outputting suitable temperature and humidity data of each person, the final temperature and humidity calculation module is used for acquiring a illness state critical value of the corresponding person and the calculated suitable temperature and humidity data of the corresponding person, importing the information into a temperature and humidity value calculation strategy for calculating final temperature and humidity, and the temperature and humidity regulation module is used for regulating the residential building environment to the final temperature and humidity in real time through a temperature and humidity controller in a building.
9. The big data based residential building environment energy saving regulation system of claim 8, wherein the controller is configured to control operation of the data acquisition module, the critical value calculation module, the neural network model construction module, the suitable temperature and humidity output module, the final temperature and humidity calculation module and the temperature and humidity regulation module.
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