阅读说明：本技术 一种基于深度学习的室内环境舒适度调节方法及系统 (Indoor environment comfort level adjusting method and system based on deep learning ) 是由 林绍福 杨佳伟 陈华敏 于 2021-07-19 设计创作，主要内容包括：本发明公开了一种基于深度学习的基于深度学习的室内环境舒适度调节方法及系统,在数据采集模块利用传感器设备进行环境数据采集,并将环境状态数据发送至边缘预处理与控制模块；基于深度学习LSTM神经网络构建模型来预测未来一定时间段室内环境的变化；将当前时刻传感器上传的多源异构环境数据,作为室内环境预测模型的输入；室内环境预测模型根据上传来的室内多源异构环境数据,完成预测并将结果发送给下一步中热舒适度控制模型；构建热舒适度控制模型,设置初始环境参数以及环境参数调节范围,提出在不同场景模式约束下提高人体舒适度的室内环境调节方案。本发明通过动态预测为用户提供舒适的室内环境的同时,使能耗有效降低。(The invention discloses a deep learning-based indoor environment comfort level adjusting method and system, wherein a data acquisition module acquires environment data by using sensor equipment and sends the environment state data to an edge preprocessing and control module; predicting the change of an indoor environment in a certain time period in the future based on a deep learning LSTM neural network construction model; the multisource heterogeneous environment data uploaded by the sensor at the current moment are used as the input of an indoor environment prediction model; the indoor environment prediction model completes prediction according to the transmitted indoor multi-source heterogeneous environment data and sends the result to the next thermal comfort level control model; and constructing a thermal comfort level control model, setting initial environmental parameters and an environmental parameter adjusting range, and providing an indoor environment adjusting scheme for improving the comfort level of the human body under the constraint of different scene modes. The invention provides comfortable indoor environment for users through dynamic prediction and effectively reduces energy consumption.)

1. An indoor environment comfort level governing system based on degree of depth study, its characterized in that includes:

the indoor environment comfort level adjusting system is divided into 4 modules according to the functional attributes, and the modules are a data acquisition module, an edge preprocessing and control module, an indoor environment prediction module and a thermal comfort level adjusting module based on mode characteristic constraint from bottom to top respectively;

the data acquisition module is mainly responsible for acquiring environmental data and comprises a temperature and humidity sensor, a wind speed sensor, an equipment controller and the like;

the edge preprocessing and control module is mainly used for preprocessing data of multi-source heterogeneous environment data uploaded by the sensor, the data preprocessing module is used for improving the data availability by means of noise reduction, abnormal value removal and the like on the data, the accuracy of data input by the prediction model is ensured, the precision of the prediction model is improved, and the preprocessed data are uploaded to the edge preprocessing and control module;

the indoor environment prediction module is a core layer of the indoor environment comfort level regulation system and is responsible for completing prediction of environment changes in the stadium within a certain time period in the future based on the deep learning neural network;

the thermal comfort degree adjusting module based on mode feature constraint is responsible for interaction between an indoor environment control system and a user, and analyzes environment data through an algorithm to regulate and control indoor environment equipment, so that reasonable adjustment of the environment in a venue under different mode constraints is realized, and the purpose of improving the thermal comfort degree of the indoor environment is achieved.

2. An indoor environment comfort level adjusting method based on deep learning is characterized by comprising the following steps:

s1, acquiring environmental data by using sensor equipment in the data acquisition module, and sending the environmental state data to the edge preprocessing and control module;

s2, after the environment state data are collected by the edge preprocessing and control module, preprocessing the data through the miniature server, and then sending the data to the indoor environment prediction module through the gateway;

s3, storing the environmental state data in the indoor environment prediction module, inputting the real-time environmental state data into a deep learning LSTM model which is trained in advance, and obtaining the change of the indoor environment at the next moment;

s4, the indoor environment prediction module sends a signal of the indoor environment change at the next moment output by the prediction model to the thermal comfort regulation module, the regulation module sends the regulation and control information of the system to the edge preprocessing and control module through an algorithm, and the edge preprocessing and control module sends an instruction to the related environment control equipment;

s5, the data center is directly connected with the edge preprocessing and control module, if the regulation and control result of the thermal comfort degree regulation module does not meet the self requirement of the user enough, the instruction can be adjusted through the application program, the application program sends the system instruction to the edge preprocessing and control module, and then the edge preprocessing and control module sends the instruction to the related environment control equipment.

3. The deep learning-based indoor environment comfort level adjustment method according to claim 2, wherein the step S1 includes:

the temperature and humidity and wind speed sensor is deployed to detect current indoor temperature, humidity, wind speed and other environmental conditions, and the temperature and humidity and wind speed sensor equipment is communicated with the edge preprocessing and control module through a Zigbee or echo Lite network protocol.

4. The step S2 of claim 2, comprising:

the edge preprocessing and control module is responsible for collecting data from the sensors and sending the data to the indoor environment prediction module through the MQTT protocol.

5. The deep learning-based indoor environment comfort level adjustment method according to claim 2, wherein the step S3 includes:

s31, in the data preprocessing stage of the model, when sequence data consisting of a plurality of variable sequences are used for predicting environment data, dimensions of different variables are different, and numerical value difference is large; considering the input and output range of the nonlinear activation function in the model, in order to avoid neuron saturation, normalization processing needs to be carried out on the variable time sequence;

s32, setting the input dimension of the LSTM and the time step of input data by using an LSTM network related module in a Keras framework based on the Keras deep learning framework through a prediction model;

s33, LSTM input data reading batch size and window length LSTM model optimizer and learning rate;

s34, counting the number of the cryptomelanic ganglion points; the number of model iterations; and continuously adjusting parameters, checking the convergence degree of the model according to the model loss, and preferentially selecting high-convergence parameters to form an indoor environment prediction model based on the LSTM.

6. The deep learning-based indoor environment comfort level adjusting method of claim 2, wherein the step S4 includes:

the thermal comfort adjustment adopts a fuzzy control algorithm, T_upIs the upper temperature limit, T, of the target temperature region_downIs the lower temperature limit; w_upIs the upper temperature limit of the target temperature region, W_downIs a lower limit of temperature T_upIs the upper temperature limit, T, of the target temperature region_downIs the lower temperature limit; firstly, acquiring an indoor temperature value T and a wind speed value W at the next moment from an indoor environment prediction model; and according to the difference of indoor thermal comfort degree corresponding to the PMV thermal sensing ruler, different adjusting amplitudes are adopted according to a fuzzy control algorithm to give a target temperature.

Technical Field

The invention relates to the field of indoor environment control, in particular to an indoor environment comfort level adjusting method and system based on deep learning.

Background

When most of the next families or office areas adopt a single constant-temperature control indoor temperature regulation mode, the comfort level of human bodies is improved only by means of regulating the indoor temperature, and other controllable environmental factors influencing the indoor comfort level are not considered; and the control of temperature is accomplished through setting for air conditioning system of people's subjective consciousness also, and temperature variation has the characteristics of nonlinearity and non-real-time nature in the indoor environment in addition, and this kind of control mode can not satisfy people to the real needs of indoor environment travelling comfort, and the operating process that relapses simultaneously can make people's indoor environment experience feel relatively poor. Therefore, the control strategy for establishing reasonable indoor environment thermal comfort has practical requirements and application value.

In order to avoid guidance and adjustment without practical significance caused by the defects of indexes in the comfort control of the indoor environment, various environmental factors influencing the comfort of the human body are comprehensively considered, an environmental comfort prediction model based on deep learning is established on the basis, and the environmental comfort control model is further established according to the predicted value of the model. The comfort level of the indoor environment is quantitatively evaluated by means of the American Society of heating, refrigeration and Air-Conditioning Engineers (ASHRAE) standard, and meanwhile, the comfort level of the indoor environment can be used as a control variable of an indoor comfort control system to achieve effective regulation of the comfort level of the indoor environment through indirect control of indexes. Meanwhile, considering the influence of indoor working scenes and operation modes on the indoor environment, the invention further provides an environment comfort level control model limited by the indoor operation mode, so that the environment comfort level control is more in line with the actual requirements.

Disclosure of Invention

The invention aims to provide an indoor environment comfort level adjusting method and system based on deep learning. From the two angles of considering the evaluation index (PMV) representing the human thermal response and saving energy consumption, a relatively comfortable indoor environment is provided for a user, excessive change and non-real-time adjustment of the indoor environment are avoided through adjustment of the indoor environment, the comfort of the user is improved, and meanwhile, the energy consumption of indoor environment adjusting equipment is saved.

The invention provides an indoor environment comfort level adjusting system based on deep learning. The system is divided into 4 modules according to functional attributes, and the modules are a data collection module, an edge preprocessing and control module, an indoor environment prediction module based on deep learning and a thermal comfort control module based on mode feature constraint from bottom to top.

The data collection module is mainly responsible for collecting multisource heterogeneous environment data under the current scene, including temperature, humidity and wind speed, and the multisource heterogeneous environment data are converged and fused to the edge preprocessing and control module through the data collection module. The edge preprocessing and control module applies noise reduction, abnormal value removal and other means to the data to improve the data availability, ensure the accuracy of the input data of the prediction model and improve the precision of the prediction model. The indoor environment prediction module is based on a deep learning neural network and is responsible for completing prediction of indoor environment changes in a certain time period in the future. The thermal comfort control module based on mode feature constraint realizes reasonable adjustment of indoor environment under different mode constraints, thereby achieving the purpose of improving the thermal comfort of the indoor environment.

The invention provides an indoor environment comfort level adjusting method based on deep learning, which comprises the following steps:

s1, the data collection module collects indoor real-time multi-source heterogeneous environment data and sends the environment data to the edge preprocessing and control module;

s2, preprocessing data of the multi-source heterogeneous environment data uploaded by the sensor in an edge preprocessing and control module;

s3, predicting the change of the indoor environment in a certain period of time in the future based on the deep learning LSTM neural network construction model;

s4, the multi-source heterogeneous environment data processed by the edge preprocessing and control module is used as the input of an indoor environment prediction model;

s5, the indoor environment prediction model completes prediction of indoor environment change according to the transmitted indoor multi-source heterogeneous environment data, and sends the result to the thermal comfort level control model based on different mode feature constraints in the next step;

s6, constructing a thermal comfort level control model based on different mode characteristic constraints, and providing an indoor environment adjusting scheme for improving the comfort level of a human body under different scene mode constraints;

s7, the data center is directly connected with the edge preprocessing and control module, if the regulation and control result can not meet the self requirement of the user, the instruction can be adjusted through the application program, the application program sends the system instruction to the edge preprocessing and control module, and then the edge preprocessing and control module sends the instruction to the related environment control equipment;

step S2 described as the present invention includes a preprocessing operation on the multi-source heterogeneous environment deployment, including:

s21, deleting abnormal values or setting missing bits aiming at multi-source heterogeneous environment data uploaded by sensors affected by environment fluctuation and faults in a venue, wherein interpolation or elimination is adopted for processing the missing values;

s22, processing multisource heterogeneous environment data based on a Kalman filtering algorithm, calculating Kalman gain, continuously correcting and updating to realize noise reduction of the environment data, and transmitting the preprocessed data to an indoor environment prediction model;

step S3 described in the present invention includes building and training a deep learning model, including:

s31, carrying out normalization and other processing on the preprocessed multi-source heterogeneous historical environment data set containing temperature, humidity and wind speed, and dividing the data set into training and verification data sets;

s32, setting the number of layers of a neural network, input dimension and time step of input data, LSTM input data reading batch size, window length LSTM model optimizer, learning rate and model iteration times;

s33, finally, continuously adjusting parameters, checking the convergence degree of the model according to the model loss, and preferentially selecting high-convergence-degree parameters to form an indoor environment prediction model based on the LSTM;

step S6, which is described as the present invention, includes the adjustment of the indoor environment, including:

s61, setting an environmental parameter adjusting range according to different scene modes;

s62, receiving the results of the indoor environment prediction model in the previous step, calculating comfort level and fuzzy algorithm by utilizing a PMV formula in combination with the indoor environment parameter variation range set under the constraint of scene mode to obtain target temperatures of the indoor environment for different modes, and generating an environment control signal according to the target temperatures

S63, finally, feeding back the adjustment result to the indoor environment prediction model for training and updating the model, and improving the system efficiency;

compared with the prior art, the invention has the beneficial effects that: according to the indoor environment comfort level adjusting method and system based on deep learning, environmental data uploaded by the sensor are analyzed through the prediction model, future changes of indoor environment are predicted and fed back to the indoor thermal comfort level control model, the control model provides a human body thermal comfort level optimization scheme based on current scene mode constraints, results are fed back to the prediction model, meanwhile, relevant equipment is controlled to adjust the indoor environment to a proper state, repeated adjustment of the environment is effectively avoided, energy consumption is reduced, and a reasonable scheme for indoor environment adjustment can be provided according to different scenes and operation modes.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.

Fig. 1 is a flowchart of the indoor environment comfort level adjusting method and system based on deep learning according to the present invention.

Fig. 2 is a schematic structural diagram of an indoor environment comfort level adjustment system according to embodiment 1 of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are a part of the embodiments of the present invention, but not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

The invention is described in further detail below with reference to the attached drawing figures:

example 1

As shown in fig. 1, the present invention provides a method and a system for adjusting indoor environment comfort based on deep learning. Fig. 2 is a schematic structural diagram of an indoor environment comfort level adjustment system provided in embodiment 1 of the present invention, which takes an indoor environment as an example, and specifically includes the following steps:

s1, acquiring environmental data by using sensor equipment in the data acquisition module, and sending the environmental state data to the edge preprocessing and control module;

the method comprises the steps that sensors such as temperature, humidity and wind speed are deployed to detect current indoor temperature, humidity and wind speed and other environmental conditions, and the devices such as the temperature, humidity and wind speed sensors are communicated with an edge preprocessing and control module through network protocols such as Zigbee or echo Lite.

S2, after the indoor environment data are collected by the edge preprocessing and control module, the data are cached and preprocessed through the micro server, and then the data are sent to the data center through the gateway;

the edge preprocessing and control module is responsible for collecting data from sensors of temperature, humidity, wind speed and the like, and sending the data to an indoor environment prediction model and a data center after data cleaning and noise reduction processing.

Wherein, the data cleaning uses Statistical Product and Service Solutions software (SPSS) to delete abnormal values or set missing bits, and the missing values are processed by interpolation or elimination;

denoising the environmental data based on the principle of a Kalman filter; the state variable y at the next moment is predicted based on the optimal estimation x at a certain past moment, the state is observed to obtain an observed variable z, and then analysis is performed between prediction and observation, or the predicted quantity is corrected by the observed quantity, so that the optimal estimation at the next moment is obtained.

S3, storing the environmental state data in the edge preprocessing and control module, inputting the real-time environmental data into a deep learning LSTM prediction model which is trained in advance, and obtaining the change condition of the environment within a period of time in the future;

the core of the LSTM is the control unit state c, the control includes a forgetting gate f_tAnd input gate i_tAnd an output gate o_t. At the current time t, forget the door f_tResponsible for controlling the last moment c_t-1How much to save to the current moment c_t(ii) a Input door i_tIs responsible for controlling how much the instant state at the current moment is input into the current unit state c_t(ii) a Output gate o_tIs responsible for controlling the current cell state c_tHow many hidden layer outputs h as the current time_t. The calculation formulas are respectively as follows:

(1)f_t＝σ(w_f·[x_t,h_t-1]+b_f)

(2)i_t＝σ(w_i·[x_t,h_t-1]+b_i)

(3)o_t＝σ(w_o·[x_t,h_t-1]+b_o)

wherein, w_f、w_i、w_oWeight matrices for forgetting gate, input gate and output gate, respectively, b_f、b_i、b_oAre respectively offset terms of a forgetting gate, an input gate and an output gate, and sigma is a sigmoid function.

The inputs to the LSTM include: cell state at last moment c_t-1Last time LSTM hidden layer output value h_t-1Input value x of network at current time t_t(ii) a The output of the LSTM includes: cell state c at the present time_tHidden layer output value h of LSTM at current time_t。

Wherein the current input unit state is input by the network x at the current time t_tLast time LSTM hidden layer output value h_t-1Jointly determining, the calculation formula is as follows:

wherein, w_cIs a weight matrix of the states of the input cells, b_cIs a bias term of the state of the input cell, tanh is a hyperbolic tangent function

Current cell state c_tBy forgetting door f_tLast time cell state c_t-1And input gate i_tIs determined together with the current input unit state and has the calculation formula of

Wherein symbol |, indicates multiplication by element. Hidden layer output value h of LSTM at current moment_tFrom an output gate o_tAnd the current cell state c_tJointly determining, the calculation formula is as follows: h is_t＝o_t⊙tanh(c_t)

And LSTM neural network output The calculation formula is as follows:

the invention adopts Mean Square Error (MSE) as loss function (loss), and the calculation formula is as follows:

wherein N is the number of samples, x is the observed value, and x' is the predicted value.

In the data preprocessing stage of the model, when the environmental data is predicted by using sequence data consisting of a plurality of variable sequences, dimensions of different variables are different, and numerical values are also greatly different. Considering the input and output range of the nonlinear activation function in the model, in order to avoid neuron saturation, normalization processing needs to be performed on the variable time series.

The prediction model is based on a Keras deep learning framework, an LSTM network related module in the Keras framework is used, and the input dimensionality and the time step length of input data of the LSTM are set; LSTM input data reads batch size and window length LSTM model optimizer and learning rate; the number of the cryptomelanic ganglion points; the number of model iterations; and continuously adjusting parameters, checking the convergence degree of the model according to the model loss, and preferentially selecting high-convergence parameters to form an indoor environment prediction model based on the LSTM.

And S4, based on the result of the indoor environment prediction model in the previous step, based on the thermal comfort control model under different mode feature constraints, receiving the result of the indoor environment prediction model in the previous step, combining the indoor environment parameter change range and the PMV thermal sensing scale (shown in table 1) set under the scene mode constraints, calculating comfort and fuzzy algorithm by using a PMV formula (shown in the following) to obtain target temperatures of the indoor environment aiming at different modes, and generating an environment control signal according to the target temperatures.

TABLE 1

PMV＝[0.303e-0.036M+0.028]{M-W-3.0*^10-3[5.733-6.99(M-W)-p_a][0039]-0.42[(M-W)-58.15]-1.7*10^-5M(5867-p_a)-0.0014M(34-t_a)-[0040]3.96*10^-8f_cl[(t_cl+273)⁴-(t_s+273)⁴-f_clh_c(t_cl-t_a)]}

Wherein M is new generationThe rate of decline is W/m 2; w is human body power, and the unit is W/s; t is t_aIs the indoor air temperature in units of; t is t_sIs the mean radiant temperature in degrees Celsius_clThe average temperature of the outer surface of the dressed human body is expressed in unit;

p_athe unit is Pa for the water vapor partial pressure in the ambient air, and the specific calculation formula is shown as follows;

p_a＝6107.8×RH_a×exp[t_a/(t_a+238.2)×17.2694]

f_clthe specific calculation formula is shown as follows;

h_cthe convective heat exchange coefficient was calculated in units of W/(s. m2 ℃), and its concrete calculation formula was as follows.

8 variables in the formula: m (metabolism rate), W (power of human body), p_a(partial pressure of Water vapor in ambient air), t_a(indoor air temperature), f_cl(ratio of the surface area of the body to be dressed), t_s(mean radiation temperature), t_cl(average temperature of outer surface of human body wearing clothes), h_c(convective heat transfer coefficient). In fact, M (metabolic rate), p_a(partial pressure of Water vapor in ambient air), t_a(indoor air temperature), f_cl(ratio of the surface area of the body to be dressed), t_s(mean radiation temperature), t_clThe (average temperature of the outer surface of the dressed body) can be replaced by the average of the statistics over a period of time, W (power of the body) we consider here as 0.

From the above formula, it can be seen that there are 6 factors affecting human comfort: air temperature, air velocity (wind speed), relative humidity, average radiation temperature, human body metabolic rate and clothing thermal resistance. The human body metabolic rate and the clothing thermal resistance are fixed values and are adjusted according to different seasons and different weather. Secondly, as can be known from the analysis of the PMV formula, the influence of the relative humidity on the comfort level is small, so in the comfort level adjustment model, the adjustment of the indoor comfort level is realized mainly by adjusting two environmental variables, namely the temperature and the air flow rate (wind speed).

The thermal comfort adjustment adopts a fuzzy control algorithm, T_upIs the upper temperature limit, T, of the target temperature region_downIs the lower temperature limit; w_upIs the upper temperature limit of the target temperature region, W_downIs a lower limit of temperature T_upIs the upper temperature limit, T, of the target temperature region_downThe lower temperature limit. Firstly, an indoor temperature value T and a wind speed value W at the next moment are obtained from an indoor environment prediction model. And according to the difference of indoor thermal comfort degree corresponding to the PMV thermal sensing ruler, different adjusting amplitudes are adopted according to a fuzzy control algorithm to give a target temperature. The specific fuzzy control algorithm is shown in Table 2

TABLE 2

x represents a preset indoor temperature control return difference which can be set independently and is usually 0.5 ℃ or 1 ℃. y represents the preset indoor wind speed control return difference, and is usually 0.05 m/s.

And meanwhile, limiting the fuzzy algorithm according to the set range of the system mode parameters in the thermal comfort level adjusting module, and terminating the current adjusting operation of the environmental parameter when the adjusting range of a certain parameter exceeds the set range of the system mode parameter, so as to keep the previous value unchanged. And the final regulation result is fed back to the indoor prediction model, and the effect of the model is optimized to realize the regulation of the indoor environment.

In the embodiments provided by the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling, direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some interfaces, and may be in an electrical, mechanical or other form.

The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) or a Processor (Processor) to execute some steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

The above embodiments are only for illustrating the technical solutions of the present invention, and are not meant to limit the present invention, and it is obvious to those skilled in the art that various modifications and variations can be made to the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.