CN115062764A - Big data system of illuminance intelligent regulation and environmental parameter thing networking - Google Patents
Big data system of illuminance intelligent regulation and environmental parameter thing networking Download PDFInfo
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Abstract
The invention discloses an intelligent illuminance regulation and environment parameter Internet of things big data system, which consists of an environment parameter detection and regulation platform and an illuminance prediction subsystem, wherein the environment parameter detection and regulation platform is used for detecting, regulating and managing illuminance parameters, the illuminance parameters are stored in a cloud platform, and managers can check and regulate the illuminance parameters from a mobile terminal APP in real time; the illuminance prediction subsystem realizes detection and prediction of illuminance; the invention effectively solves the problems that the existing illuminance detection system has no influence on the safety of production and living environments according to the nonlinearity and large hysteresis of the change of the illuminance parameter, the large parameter change of the illuminance area, the complexity and the like, and the production and living environment management is greatly influenced by predicting the illuminance parameter and adjusting the illuminance.
Description
Technical Field
The invention relates to the technical field of automatic equipment for detecting and adjusting environmental parameters, in particular to a big data system for intelligently adjusting illuminance and the Internet of things of the environmental parameters.
Background
With the gradual maturity and perfection of modern communication and sensing technologies, the environmental parameter detection system has become a research hotspot of industries such as current industry, agriculture, transportation, medical health and construction. The traditional method depends on manual test and reading, judges whether the environmental parameters deviate from normal values or not, and then takes corresponding adjustment measures, thereby consuming a large amount of manpower and material resources. The Internet of things communication network platform is adopted to collect and control the temperature, humidity, wind speed, illumination intensity and other parameters of the environmental parameters, so that the system not only has the advantages of convenience in control, simple structure and high flexibility, but also improves the convenience and effectiveness of temperature, humidity, wind speed and light intensity control. With the rapid development of national economy, the scale of various industries and industries is continuously enlarged, and the occasions for detecting and adjusting environmental parameters are increasingly increased. The traditional detection and control measures have shown great limitations, and with the development of the detection technologies of the internet of things, the microcontroller and various sensors, the problems in the detection and transmission processes of environmental parameters are solved. The intelligent illumination adjustment and environmental parameter Internet of things big data system is invented based on the LoRa wireless communication technology, application research is conducted on the environmental parameter detection and adjustment system based on the self-organizing Internet of things network and the cloud platform, the environmental parameters are monitored in real time, particularly, the illumination parameters are predicted to promote production and safety management, and benefits are improved.
Disclosure of Invention
The invention provides an intelligent illuminance regulation and environment parameter Internet of things big data system, which effectively solves the problems that the existing illuminance detection system does not influence the safety of production and living environments according to the nonlinearity, large hysteresis, complicated illuminance area large parameter change and the like of the illuminance parameter change, does not predict and regulate the illuminance parameter, and thus greatly influences the management of the production and living environments.
The invention is realized by the following technical scheme:
the intelligent illuminance and environmental parameter Internet of things big data system consists of an environmental parameter detection and adjustment platform and an illuminance prediction subsystem, wherein the environmental parameter detection and adjustment platform is responsible for detecting and adjusting environmental parameters, the environmental parameters are stored in a cloud platform, and managers can check the environmental parameters of the cloud platform in real time from a mobile terminal APP; the illuminance prediction subsystem is composed of an illuminance detection module, a figure set numerical fusion model, a time delay line TDL (time delay line) according to a beat and HRFNN (neural network) recurrent neural network prediction model of a figure set, so that the illuminance is predicted, and the remote detection, adjustment and intelligent management of environmental parameters are realized by the illuminance intelligent adjustment and environmental parameter Internet of things big data system.
The invention further adopts the technical improvement scheme that:
the environment parameter detection and adjustment platform comprises a detection node, a control node, a gateway node, an on-site monitoring terminal, a cloud platform and a mobile terminal App, wherein the gateway node is used for establishing a channel for bidirectional information transmission between the detection node, the control node, the gateway node, the on-site monitoring terminal, the mobile terminal APP and the cloud platform, the cloud platform stores environment information in a database of the cloud platform, and the problem that a large amount of data are downloaded to the intelligent mobile device to cause occupation of a large amount of space is effectively solved. The structure of the environmental parameter detecting and adjusting platform is shown in fig. 1.
The invention further adopts the technical improvement scheme that:
the illuminance prediction subsystem consists of an illuminance detection module, a figure of value fusion model and an HRFNN recurrent neural network prediction model of a figure of value set, wherein the output of an illuminance sensor is used as the input of the corresponding illuminance detection module, the outputs of a plurality of illuminance detection modules are used as the input of the figure of value fusion model, 3 parameters output by the figure of value fusion model are used as the corresponding input of the corresponding figure of value delay line TDL, 3 outputs by the FN delay line TDL are used as the corresponding inputs of the HRN recurrent neural network prediction model of the figure of value set, three parameters output by the HRFNN recurrent neural network prediction model of the figure of value set are x, t and 1-f respectively, x is the predicted value of the detected illuminance, t is credibility, 1-f-t is uncertain, f is credibility, and the predicted value of the figure of the detected illuminance set consisting of x, t and 1-f is [ x, (t, 1-f) ], the HRFNN recurrent neural network prediction model of the Vague set, outputs as a predicted value of the detected ambient light illuminance. The structure and function of the illumination prediction subsystem are shown in fig. 2.
The invention further adopts the technical improvement scheme that:
the illuminance detection module consists of an LSTM neural network model, an ARIMA prediction model, a variational modal decomposition model, a subtraction cluster classifier, a CNN convolution-NARX neural network model and a fuzzy wavelet neural network model of a figure set; the illuminance sensor senses a time sequence illuminance value of a detected environment and respectively serves as the input of an LSTM neural network model and an ARIMA prediction model, the difference between the output of the LSTM neural network model and the output of the ARIMA prediction model serves as the illuminance fluctuation value of the detected environment, the time sequence illuminance fluctuation value serves as the input of a variation modal decomposition model, the variation modal decomposition model outputs a plurality of modal function IMF components, a plurality of IMF component energy entropies serve as the input of a subtraction cluster classifier, a plurality of types of IMF component energy entropies output by the subtraction cluster classifier respectively serve as the input of a plurality of corresponding CNN convolution-NARX neural network models, the output of the ARIMA prediction model and the plurality of CNN convolution-NARX neural network models serves as the corresponding input of a Vague set of a Vague wavelet neural network model, three parameters output by the Vague set of the Vague wavelet neural network model are y, z and 1-k respectively, y is an actual value of the detected illuminance, z is the reliability, 1-k-z is the uncertainty, k is the uncertainty, y, z and 1-k form a value of a Vague set of the detected illuminance, which is [ y, (z, 1-k) ], and the output of the fuzzy wavelet neural network model of the Vague set is output as the illuminance detection module. The function and structure of the illuminance detection module are shown in fig. 3.
The invention further adopts the technical improvement scheme that:
vague numerical fusion model
(1) The number of the figure sets output by the parameter detection module of a plurality of parameter measurement sensors in a period of time forms a time series figure set value array, and the quotient obtained by dividing the positive ideal value distance measure of the time series figure set value of each parameter measurement sensor by the sum of the negative ideal value distance measure of the time series figure set value of the parameter measurement sensor and the positive ideal value distance measure of the time series figure set value of the parameter measurement sensor is the relative distance measure of the time series figure set value of each parameter measurement sensor; dividing the relative distance measure of the time series value set values of each parameter measurement sensor by the sum of the relative distance measures of the time series value set values of all the parameter measurement sensors to obtain a quotient, and taking the quotient as the distance measure fusion weight of the time series value set values of each parameter measurement sensor;
(2) the quotient of the similarity between the time series value set of each parametric measurement sensor and the positive ideal value of the array of value sets divided by the similarity between the time series value set of the parametric measurement sensor and the positive ideal value of the array of value sets plus the similarity of the time series value set of the parametric measurement sensor and the negative ideal value of the array of value sets is the relative measure of similarity of the time series value set of the parametric measurement sensor; dividing the similarity relative measure of the time series value set values of each parameter measurement sensor by the sum of the similarity relative measures of the time series value set values of all the parameter measurement sensors to obtain a quotient, and taking the quotient as the similarity fusion weight of the time series value set values of the parameter measurement sensors;
(3) the distance measurement fusion weight of the time sequence figure set value of each parameter measurement sensor and the similarity fusion weight of the time sequence figure set value of the parameter measurement sensor are used as the interval number fusion weight of the time sequence figure set value of the parameter measurement sensor according to the interval number formed by sequencing from small to large; and the fused value of the time series value set numbers of all the parameter measurement sensors is the time series interval value set number obtained by adding the products of the time series value set number of each parameter measurement sensor and the interval number fusion weight of the time series value set number of the parameter measurement sensor at the same moment. The structure and function of the Vague numerical fusion model are shown in FIG. 2.
Compared with the prior art, the invention has the following obvious advantages:
aiming at the uncertainty and randomness of the problems of sensor precision error, interference, measurement abnormity and the like in the parameter measurement process, the invention converts the parameter value measured by the illuminance sensor into the numerical form of a detection parameter value set through the illuminance detection module to represent, effectively processes the ambiguity, the dynamic property and the uncertainty of the parameter measured by the illuminance sensor, and improves the objectivity and the reliability of the illuminance parameter detected by the illuminance sensor.
Secondly, the three parameters output by the HRFNN recurrent neural network prediction model of the figure set are x, t and 1-f respectively, x is the predicted value of the detected illumination, t is the reliability, 1-f-t is the uncertainty, f is the uncertainty, x, t and 1-f form the predicted value of the figure set of the detected illumination as [ x, (t, 1-f) ], the output quantity of the rule layer is subjected to weighted summation by introducing internal variables in a feedback link and then is subjected to defuzzification output as feedback quantity, the feedback quantity and the output quantity of the membership function layer are taken as the input of the next moment of the rule layer, the network output comprises the activation intensity of the rule layer and the historical information of the output, the capability of the HRFNN recurrent neural network prediction model of the figure set for adapting to a nonlinear dynamic system is enhanced, the HRFNN recurrent neural network prediction model of the figure set can accurately predict the light parameters of the detected environment, the method has the function of approximating any linear and nonlinear functions with any precision, and has the advantages of high convergence rate, less sample requirement, high model operation speed, reliable result and good effect.
The LSTM neural network model is a recurrent neural network with 4 interaction layers in a repetitive network, can extract information from illumination sequence data output by an illumination sensor like a standard recurrent neural network, and can retain information of long-term correlation of illumination parameters output by the illumination sensor from previous remote steps. In addition, because the sampling interval of the output light illumination parameters of the illumination sensor is relatively small, the output light illumination parameters of the illumination sensor have long-term spatial and temporal correlation, and the LSTM neural network model has enough long-term memory to process the space-time relationship between the output light illumination parameters of the illumination sensor, so that the accuracy and the robustness of processing the output light illumination parameters of the illumination sensor are improved.
The variational modal decomposition model can decompose the time series illuminance fluctuation value into a series of intrinsic modal functions IMF, continuously and iteratively update the central frequency and the frequency band bandwidth of each component, separate the self-adaptive frequency components of the original time series illuminance fluctuation value, extract the characteristic frequency component of the time series illuminance fluctuation value, effectively overcome the modal aliasing problem and realize the denoising of the time series illuminance fluctuation value, the peak thorn characteristics dense in the denoised time series illuminance fluctuation value evolution curve disappear and gradually become smooth, and the variational modal decomposition model improves the accuracy and the robustness of processing the time series illuminance fluctuation value.
In the CNN convolution-NARX neural network model adopted by the invention, the CNN convolution neural network is a deep feedforward neural network, the typical structure of the CNN convolution neural network is composed of an input layer, a convolution layer, a pooling layer and a full-connection layer, the CNN convolution neural network performs convolution, pooling and other operations on input data, and local features of the data are extracted by establishing a plurality of filters to obtain robust features with translation and rotation invariance. The NARX neural network model input comprises a CNN convolutional neural network output and NARX neural network model output historical feedback for a period of time, the feedback input can be considered to contain historical information of the CNN convolutional neural network output for a period of time to participate in prediction, the NARX neural network model is a dynamic neural network model capable of effectively predicting nonlinear and non-stationary time sequences of the CNN convolutional neural network output, and the prediction accuracy of the CNN convolutional neural network output of a time sequence can be improved under the condition that the non-stationary time of the time sequence is reduced; the NARX neural network model establishes the dynamic recursive network of the model by introducing the delay module and the output feedback, the CNN convolutional neural network input and the NARX neural network model output vector delay feedback are introduced into the network training to form a new input vector, the NARX neural network model has good nonlinear mapping capability, the input of the NARX neural network model not only comprises original input data, but also comprises output data after training, the generalization capability of the network is improved, and the NARX neural network model has higher accuracy and robustness in time sequence fluctuation value prediction of nonlinear detected parameters compared with the traditional static neural network.
Drawings
FIG. 1 is an environmental parameter sensing and conditioning platform of the present invention;
FIG. 2 illustrates an illumination prediction subsystem according to the present invention;
FIG. 3 is a block diagram of an illuminance detection module according to the present invention;
FIG. 4 is a detection node of the present invention;
FIG. 5 is a control node of the present invention;
FIG. 6 is a gateway node of the present invention;
fig. 7 is the site monitoring software of the present invention.
Detailed Description
The technical solution of the present invention is further described with reference to the accompanying drawings 1-7:
design of overall system function
The system is composed of an environment parameter detection and adjustment platform and a light illumination prediction subsystem. The environment parameter detection and adjustment platform comprises detection nodes, control nodes, gateway nodes, a field monitoring end, a cloud platform and a mobile end App of environment parameters, and the detection nodes and the gateway nodes realize wireless communication among the detection nodes, the control nodes and the gateway nodes through a self-organizing communication network networked by a constructed object; the detection node sends the detected environmental parameters to a field monitoring terminal through an RS232 interface of the gateway node and processes the sensor data and predicts the illumination intensity; the control node is responsible for adjusting the environmental parameter equipment, the gateway node realizes bidirectional transmission of the environmental parameters between the NB-IoT module and the cloud platform and between the cloud platform and the mobile terminal App through the 5G network, and bidirectional transmission of the environmental parameter information between the gateway node and the field monitoring terminal is realized through the RS232 interface. The mobile App terminal provides real-time environment parameter data for management personnel and meets convenient visualization of environment parameter data information, all data collected by sensors from detection nodes are uploaded to a database of a cloud platform, and the management personnel can remotely check and control current environment parameters through the mobile terminal APP. The cloud platform is mainly responsible for processing, storing, analyzing and displaying the received environment parameters; the structure of the environmental parameter detection and adjustment platform is shown in fig. 1.
Second, design of detection node
The detection node consists of illuminance, temperature, humidity and wind speed sensors, 5 corresponding conditioning circuits, an STM32 single chip microcomputer and an SX127X radio frequency module interface, and is mainly used for collecting environmental parameter detection point data information, and the environmental parameter data information of the detection point is transmitted to the cloud platform through the SX127X radio frequency module interface of the detection node and the NB-IoT module of the gateway node to realize real-time interaction with the mobile terminal APP; and transmitting the data to a field monitoring terminal through an SX127X radio frequency module interface and an RS232 interface of the gateway node, wherein the structure of the detection node is shown in FIG. 4.
Design of control node
The control node realizes mutual information interaction with the gateway node through a self-organized LoRa network, and comprises 4 digital-to-analog conversion circuits corresponding to control external equipment, an STM32 microprocessor, 4 external equipment controllers and an SX127X radio frequency module interface; the 4 external equipment controllers are respectively a temperature controller, a humidity controller, a wind speed controller and an illumination controller. The control node structure is shown in fig. 5.
Fourth, gateway node design
The gateway node is composed of an SX127X radio frequency module interface, an NB-IoT module, an STM32 single chip microcomputer and an RS232 interface, bidirectional transmission of data among the detection node, the control node and the field monitoring end is achieved through the SX127X radio frequency module interface and the RS232 interface, bidirectional transmission among the cloud platform, the mobile end APP, the detection node and the field monitoring end is achieved through the SX127X radio frequency module interface, the NB-IoT module and the RS232 interface, and the structure of the gateway node is shown in figure 6.
Design of site monitoring terminal software
The field monitoring terminal is an industrial control computer, mainly realizes detection and illumination prediction of environmental parameters and adjustment of the environmental parameters, realizes information interaction with the detection node, the control node and the cloud platform, and mainly has the functions of a communication parameter setting subsystem, a data analysis subsystem, a data management subsystem and an illumination prediction subsystem. The management software selects Microsoft Visual + +6.0 as a development tool, calls the Mscomm communication control of the system to design a communication program, and the functions of the field monitoring end software are shown in the attached figure 7. The illuminance prediction subsystem is shown in fig. 2, and consists of an illuminance detection module, a value set numerical fusion model and an HRFNN recurrent neural network prediction model according to a beat delay line TDL and a value set, and is designed in the following process:
A. illuminance detection module design
1. LSTM neural network model design
The illuminance sensor senses time sequence illuminance values of the detected environment and respectively serves as the input of an LSTM neural network model and an ARIMA prediction model, and the difference between the output of the LSTM neural network model and the output of the ARIMA prediction model serves as the illuminance fluctuation value of the detected environment; the LSTM neural network model introduces mechanisms of Memory cells (Memory cells) and hidden layer states (CellState) to control information transfer between hidden layers. The memory unit of an LSTM neural network has 3 Gates (Gates) as Input Gate, forgetting Gate and Output Gate. Wherein, the input gate can control the adding or filtering of the new information of the illumination intensity; the forgetting door can forget the detection information of the illumination intensity which needs to be lost and retain useful information in the past; the output gate enables the memory unit to output only the nominal illumination detection information associated with the current time step. The 3 gate structures carry out operations such as matrix multiplication, nonlinear summation and the like in the memory unit, so that the memory still cannot be attenuated in continuous iteration. The long-short term memory unit (LSTM) structure unit is composed of a unit (Cell), an Input Gate (Input Gate), an Output Gate (Output Gate) and a forgetting Gate (Forget Gate). The LSTM neural network model is suitable for predicting the change of the illumination input quantity of the time sequence by a long-lasting short-term memory model, the LSTM neural network model effectively prevents the gradient disappearance during RNN training, and a long-term short-term memory (LSTM) network is a special RNN. The LSTM neural network model can learn long-term detection illuminance dependence information, and meanwhile, the problem of gradient disappearance is avoided. Internal structure of LSTM in neuronA structure called a Memory Cell (Memory Cell) is added to a neural node of a hidden layer of the RNN to memorize dynamic change information called detected illuminance in the past, and three gate structures (Input, form, Output) are added to control use of history information called detected illuminance. Let the time-series value of the input as the input amount of the detected illuminance be (x) 1 ，x 2 ，…，x T ) The hidden layer state is (h) 1 ，h 2 ，…，h T ) Then, time t has:
i t ＝sigmoid(W hi h t-1 +W xi X t ) (1)
f t ＝sigmoid(W hf h t-1 +W hf X t ) (2)
c t ＝f t ⊙c t-1 +i t ⊙tanh(W hc h t-1 +W xc X t ) (3)
o t ＝sigmoid(W ho h t-1 +W hx X t +W co c t ) (4)
h t ＝o t ⊙tanh(c t ) (5)
wherein i t 、f t 、O t Stands for input, forget, and output doors, c t Representing a cell, W h Representing the weight of the recursive connection, W x Representing the weight from the input layer to the hidden layer, sigmoid and tanh are two activation functions, and the output of the LSTM neural network model is a nonlinear value of the detected ambient light illumination.
2. ARIMA predictive model design
The illuminance sensor senses time sequence illuminance values of the detected environment and respectively serves as the input of an LSTM neural network model and an ARIMA prediction model, and the difference between the output of the LSTM neural network model and the output of the ARIMA prediction model serves as the illuminance fluctuation value of the detected environment; the ARIMA predictive model is a method of modeling objects based on time series predictions and extends to the analysis of time series of predicted objects. According to the study on the time series characteristics of the ARIMA prediction model, 3 parameters are adopted to analyze the time series of the illumination change, namely, the autoregressive order (p), the difference times (d) and the moving average order (q). The ARIMA prediction model is written as: ARIMA (p, d, q). The ARIMA predicted illuminance equation with p, d, and q as parameters can be expressed as follows:
Δ d y t denotes y t Sequence after d differential conversions,. epsilon t Is a random error of time, is a white noise sequence which is independent of each other, and has a mean value of 0 and a variance of a constant sigma 2 Normal distribution of phi i (i ═ 1, 2, …, p) and θ j (j ═ 1, 2, …, q) are parameters to be estimated for the ARIMA predictive model, and p and q are orders of the ARIMA predictive illuminance model. The ARIMA dynamic prediction illuminance model belongs to a linear model in nature, and the modeling and prediction comprise 4 steps: (1) and carrying out sequence stabilization treatment. If the sequence of the illumination data is not stable, if there is a certain trend of increase or decrease, the data needs to be differentially processed. Common tools are autocorrelation function maps and partial autocorrelation function maps. If the autocorrelation function rapidly goes to zero, the illuminance time series is a stationary time series. If the time sequence has a certain trend, the difference processing needs to be carried out on the illumination data, if seasonal regularity exists, seasonal difference needs to be carried out, and if the time sequence has heteroscedasticity, the logarithmic conversion needs to be carried out on the illumination data. (2) And identifying the model. The orders p, d and q of the ARIMA predictive illuminance model are mainly determined by autocorrelation coefficients and partial autocorrelation coefficients. (3) Estimating parameters of the model and diagnosing the model. Obtaining estimated values of all parameters in the ARIMA dynamic prediction illumination model by using maximum likelihood estimation, checking the estimated values including parameter significance check and residual randomness check, judging whether the constructed illumination model is available, and performing parameter prediction by using the ARIMA dynamic prediction illumination degree model with proper parameters; and checks are made in the model to determine if the model is adequate and if not, the parameters are re-estimated. (4) By using a suitable ginsengAnd predicting the detected parameters by the digital illumination model. The ARIMA module of the time sequence analysis function in the SPSS statistical analysis software package is called by software to realize the whole modeling process. The ARIMA prediction model carries out linear prediction on the illuminance of the detected environment.
3. Variational modal decomposition model design
The time series illuminance fluctuation value is used as the input of a variational modal decomposition model, the variational modal decomposition model outputs a plurality of modal function IMF components, and the energy entropies of the IMF components are used as the input of a subtraction clustering classifier; the variational modal decomposition model is a self-adaptive non-recursive signal time-frequency analysis method, and can decompose the fluctuation value signal output by the time-series illuminance sensor into sub-signals of the fluctuation values output by a plurality of illuminance sensors, namely IMF (intrinsic mode function) components u k And minimizes the sum of the bandwidths of all IMF components, u k Is that the am-fm function can be expressed as:
u k (t)＝A k cos[φ k (t)] (7)
in the formula k (t) is a non-decreasing function, A k (t) is envelope curve, structure constraint variation problem solution u k And solving the variation problem, and introducing a secondary penalty term and a Lagrange multiplier to change the variation problem into an unconstrained problem. The variation modal decomposition model can decompose the time series illumination sensor output fluctuation value signal to be decomposed into a plurality of IMF components. The IMF component energy entropy value can measure the regularity degree of output fluctuation of the time series illuminance sensor, represents the energy characteristics of output fluctuation signals of the time series illuminance sensor in different frequency bands, and when the output fluctuation value of the time series illuminance sensor is suddenly changed, the energy is also changed, and the energy of the mth IMF component is defined as:
in the formula x m (i) Outputting an m-th component after the fluctuation signal sample is decomposed for the time series illumination degree sensor, wherein n is the number of sampling points, and the energy entropy of the m-th IMF component is as follows:
4. subtractive clustering classifier design
The IMF component energy entropies are used as the input of a subtraction clustering classifier, and the IMF component energy entropies of multiple types output by the subtraction clustering classifier are respectively used as the input of multiple corresponding CNN convolution-NARX neural network models; compared with other clustering methods, the IMF component energy entropy subtraction clustering method does not need to determine the clustering number in advance, can quickly determine the position and the clustering number of the IMF component energy entropy clustering center only according to the IMF component energy entropy sample data density, and uses each IMF component energy entropy data point as the characteristic of a potential clustering center, so that the IMF component energy entropy clustering result is independent of the dimension of the problem. Therefore, the IMF component energy entropy subtraction clustering algorithm is a rule automatic extraction method suitable for IMF component energy entropy data modeling. Setting N IMF component energy entropy data points (X) in m-dimensional space 1 ，X 2 ，…X N ) Each data point X i ＝(x i，1 ，x i，1 ，…，x i，m ) Are all candidates for cluster centers, i-1, 2, …, N, data point X i The density function of (a) is defined as:
in the formula, the radius r a Is a positive number, r a An influence neighborhood of the point is defined, and data points outside the radius contribute very little to the density index of the point and are generally ignored. Calculate each point X i Selecting the density value with the highest density index D c1 As the first cluster center X c1 (ii) a And then correcting the density value to eliminate the influence of the existing cluster center. The density value is corrected according to the following formula:
wherein D is c1 Is the highest density value corresponding to the initial clustering center, and the corrected radius r b Is set to avoid the second cluster center point being too close to the previous one, and is generally set to r b ＝ηr a Eta is more than or equal to 1.25 and less than or equal to 1.5. After correcting the density index of each data point, when D is ck And D c1 And if the following formula is met, the clustering center corresponding to the density index is the Kth clustering center. This process is repeated until a new cluster center X ck Corresponding density index D ck And D c1 Terminating clustering when the following equation is satisfied:
D ck /D c1 ＜δ (12)
in the formula, δ is a threshold value set in advance according to actual conditions. The basic idea of the online clustering method provided by the invention is as follows: if the distance from an IMF component energy entropy point to the center of a group is less than the cluster radius r a Then the point belongs to this group and when new data is obtained, the group and the center of the group are changed accordingly. With the continuous increase of the input IMF component energy entropy space data, the algorithm of the invention obtains better input space division by dynamically adjusting the IMF component energy entropy clustering center and the clustering number in real time, and comprises the following steps:
step 1: IMF component energy entropy data normalization processing, input data each dimension clustering radius r a And setting parameters such as a threshold value delta.
Step 2: c clustering centers are obtained by carrying out subtractive clustering on the IMF component energy entropy data set and v is stored i (i ═ 1, 2, …, c) and its corresponding density value D (v) i )。
And step 3: when the k-th data in the newly added online IMF component energy entropy data set arrives, x is calculated k (k-1, 2, …, M) to i cluster centers v i Distance d of ki ＝||x k -v i If d | | ki ＞r a Go to step 4; if d is ki And (5) turning to the step 5.
And 4, step 4: calculating x from equation (10) k Density value of D (x) k ) And D (x) k ) If is greater than epsilon, the detected parameter data x is indicated k And if the cluster does not belong to any existing cluster, newly creating a cluster, inputting the number c of the clusters in the space to be c +1, and returning to the step 3.
And 5: determining a data point x according to a minimum distance criterion k Belonging to the nearest cluster subset, and further comparing the new data x k The density value of (2) and the density value of the cluster center, if D (x) k )＞D(v i ) Then data x k Closer to its nearest cluster center, x k Replacing the original clustering center as a new clustering center of the subset; if D (x) k )≤D(v i ) If so, keeping the clustering result unchanged, and judging whether the newly added data group is finished. If yes, go to step 6; otherwise, returning to the step 3.
Step 6: calculating a clustering center v i And v j If min | | v i -v j ||≤(0.5-0.7)r a And D (v) i )＞(v j ) Then, the cluster subset v is indicated i And v j Can be combined into a cluster with v as the center i (ii) a Otherwise, keeping the clustering result unchanged.
And classifying IMF component energy entropy historical data by IMF component energy entropy subtraction clustering, and inputting each type of IMF component energy entropy into a corresponding CNN convolution-NARX neural network model to predict a future value of the fluctuation of the illuminance.
5. CNN convolution-NARX neural network model design
The IMF component energy entropies of a plurality of types output by the subtraction clustering classifier are respectively used as the input of a plurality of corresponding CNN convolution-NARX neural network models, and the output of the ARIMA prediction model and the output of the CNN convolution-NARX neural network models are used as the corresponding input of the fuzzy wavelet neural network model of the Vague set; the CNN convolution-NARX neural network model is that the output of the CNN convolution neural network is used as the input of the NARX neural network model, and the CNN convolution neural network model can directly and automatically mine and extract the sensitivity representing the output fluctuation value of the time sequence illuminance sensor from a large number of IMF component energy entropy values of the output fluctuation value of the time sequence illuminance sensorThe CNN convolution neural network model structure mainly comprises 4 parts: input layer (Input). The input layer is the input of the CNN convolutional neural network model, and the IMF component energy entropy of the fluctuation value output by the time series illuminance sensor is generally directly input. ② a convolutional layer (Conv). Because the data dimension of the input layer is large, the CNN convolutional neural network model is difficult to directly and comprehensively sense IMF component energy entropy input information of all time series illuminance sensor output fluctuation values, the input data needs to be divided into a plurality of parts for local sensing, then the global information is obtained through weight sharing, and meanwhile the complexity of the structure of the CNN convolutional neural network model is reduced. And a pooling layer (Pool, also known as a down-sampling layer). Because the dimensionality of the data samples obtained after the convolution operation is still large, the data size needs to be compressed and key information needs to be extracted to avoid overlong model training time and overfitting, and therefore a pooling layer is connected behind the convolution layer to reduce the dimensionality. And taking the peak characteristic of the defect characteristic into consideration, performing down-sampling by adopting a maximum pooling method. And fourthly, a full connection layer. After all convolution operations and pooling operations, IMF component energy entropy feature extraction data of the time series illuminance sensor output fluctuation values enter a full-connection layer, each nerve layer in the layer is in full connection with all neurons in the previous layer, and local feature information of the IMF component energy entropy features of the time series illuminance sensor output fluctuation values extracted by the convolution layer and the pooling layer is integrated. Meanwhile, in order to avoid the over-fitting phenomenon, a lost data (dropout) technology is added in the layer, the output value passing through the last layer of full connection layer is transmitted to the output layer, the pooling result of the last layer is connected together in an end-to-end mode to form the output layer and used as the input of an NARX neural network model, the NARX neural network model is a dynamic recurrent neural network with output feedback connection, and in topology, the NARX neural network model is connected with the output feedbackThe connection relation can be equivalent to a BP neural network with input time delay and a time delay feedback connection from output to input, and the structure of the BP neural network is composed of an input layer, a time delay layer, a hidden layer and an output layer, wherein an input layer node is used for signal input, a time delay layer node is used for time delay of an input signal and an output feedback signal, the hidden layer node performs nonlinear operation on the delayed signal by using an activation function, and the output layer node is used for performing linear weighting on the hidden layer output to obtain final network output. Output h of ith hidden layer node of NARX neural network model i Comprises the following steps:
output O of j output layer node of NARX neural network j Comprises the following steps:
6. fuzzy wavelet neural network model design of Vague set
The output of the ARIMA prediction model and the CNN convolution-NARX neural network models is used as the corresponding input of the fuzzy wavelet neural network model of the Vague set, the three parameters of the output of the fuzzy wavelet neural network model of the Vague set are y, z and 1-k respectively, y is the real value of the detected light illumination, z is the credibility, 1-k-z is the uncertainty, k is the uncertainty, and y, z and 1-k form the value of the Vague set of the detected light illumination, y (z, 1-k)]And outputting the fuzzy wavelet neural network model of the figure set as the illuminance detection module. The Fuzzy inference method applies a Fuzzy neural Network to carry out Fuzzy inference, combines the characteristics of multi-resolution analysis of wavelets, takes a Wavelet function as an excitation function of neural Network neurons, and constructs a Fuzzy Wavelet Network (FWNN) of a figure set. The FWNN-based fuzzy wavelet neural network has good intelligence, robustness, stability and index tracking rapidity, and the fuzzy wavelet neural network of the Vague set comprises two parts: fuzzy Neural Networks (FNNs) and Wavelet Neural Networks (WNNs). The fuzzy neural network of the Vague set comprises 4 basic layers:the first layer is an input layer, and each input vector corresponds to one neuron; each neuron of the second layer represents a linguistic variable value; each neuron of the third layer represents a fuzzy rule; the fourth layer is a normalization layer. Meanwhile, the input of the fuzzy neural network is used as the input of the wavelet neural network, and each fuzzy rule corresponds to one wavelet network. The wavelet basis function is a wavelet basis group obtained by shifting the wavelet function, so that wavelet neural networks generated by different scale functions can capture the characteristics of different time domains and frequency domains, and different fuzzy reasoning selects corresponding wavelet networks. The wavelet has the characteristic of multi-resolution analysis, if the wavelet function is used as the excitation function of the neural network neurons, the expansion and the translation of each neuron can be adjusted, smooth functions can be learned by selecting low-scale parameters, local singular functions can be learned with higher precision by increasing the scale, and the ANN precision is higher than that of the same neuron number and parameters. The fuzzy wavelet network is realized by 5 basic layers of input, fuzzification, reasoning, wavelet network layer and de-fuzzification layer, and the number of the neural network nodes in each layer is n, nxM, M, M and 3 respectively. Once the number of inputs n and rules M is decided, the structure of the FWNN model is decided. The fuzzy wavelet neural network with the figure set has the input of X ═ X 1 ，x 2 ，… x n ]，T i Is the number of wavelets corresponding to the ith rule; w ik Is the weight coefficient;
is a function of the wavelet, and is,
the output value of the linear combination of the local model wavelet network corresponding to the rule i is as follows:
the first layer is an input layer: each node of the layer is directly connected to each component x of the input vector j Connecting, converting the input value X to [ X ] 1 ，x 2 ，… x n ]Transfer to the next layer; the second layer calculates the membership function value corresponding to each input variable; the third layer calculates the applicability of each rule; the fourth layer is wavelet network layer output and is mainly used for output compensation; the fifth layer is a control signal output layer, also called a defuzzification layer, the defuzzification calculation is carried out on the layer, three parameters output by the fuzzy wavelet neural network model of the Vague set are y, z and 1-k respectively, y is the real value of the detected illuminance, z is the credibility, 1-k-z is the uncertainty, k is the uncertainty, y, z and 1-k form the value of the Vague set of the detected illuminance as [ y, (z, 1-k)]And outputting the fuzzy wavelet neural network model of the figure set as the illuminance detection module.
B. Vague numerical fusion model design
Firstly, constructing a time sequence figure set numerical array of the parameter measurement sensor
The value set numbers output by the parameter detection models of a plurality of parameter measurement sensors at a period of time form a time sequence value set numerical array, the value set numerical array provided with n parameter measurement sensors and m time nm parameter measurement sensors forms a time sequence value set numerical array of n rows and m columns of parameter measurement sensors, and the value set numerical values at different times of the same parameter measurement sensor are set as A ij (t)，A ij (t+1)，…，A ij (m), then the time series Vague set numerical array for all parameter measurement sensors is:
② calculating the distance fusion weight of time sequence figure set value of parameter measurement sensor
The average value of the value sets of all the parameter measurement sensors at the same time constitutes the positive ideal value of the value set numerical array, and the positive ideal value of the value set numerical array is:
the value set value of all the parametric measurement sensors at the same time and the value set value with the largest distance measure from the positive ideal value of the value set value array form a negative ideal value of the value set value array, and the negative ideal value of the value set value array is:
the positive ideal distance measure of the time series value set values of each parametric measurement sensor is the distance measure of the time series value set values of each parametric measurement sensor from the positive ideal values of the value set value array:
wherein: pi ij ＝1-t ij -f ij And
the negative ideal value distance measure of the time series of value sets of each parametric measurement sensor is a distance measure of the time series of value sets of each parametric measurement sensor from the negative ideal value of the array of value sets of:
wherein: pi ij ＝1-t ij -f ij And
dividing the positive ideal value distance measure of the time series value set value of each parameter measurement sensor by the sum of the negative ideal value distance measure of the time series value set value of the parameter measurement sensor and the positive ideal value distance measure of the time series value set value of the parameter measurement sensor to obtain a quotient, wherein the quotient is the relative distance measure of the time series value set value of each parameter measurement sensor, and the formula is as follows:
as can be known from formula (21), the greater the relative distance measure of the time-series Vague set values of each parametric measurement sensor, the closer the time-series Vague set values of the parametric measurement sensor are to the corresponding positive ideal value, and otherwise, the farther the time-series Vague set values of the parametric measurement sensor are from the corresponding positive ideal value, and according to this principle, the distance measure fusion weight of the time-series Vague set values of each parametric measurement sensor divided by the sum of the relative distances of the time-series Vague set values of all the parametric measurement sensors is determined as:
thirdly, calculating the similarity fusion weight of the time sequence figure set values of the parameter measurement sensor
The time series of the value set of each parameter measurement sensor is similar to the positive ideal value of the value set value array by the following similarity:
the similarity of the time series value set value of each parameter measurement sensor and the negative ideal value of the value set value array is as follows:
the quotient of the similarity between the time-series value set values of each parametric measurement sensor and the positive ideal value of the array of value sets divided by the similarity between the time-series value set values of the parametric measurement sensor and the positive ideal value of the array of value sets added to the similarity between the time-series value set values of the parametric measurement sensor and the negative ideal value of the array of value sets is the relative measure of similarity between the time-series value set values of the parametric measurement sensor and the value sets:
as can be known from (25) formula calculation, the greater the relative similarity measure of the time-series value set values of each parametric measurement sensor, the greater the shape similarity of the time-series value set values of the parametric measurement sensor with the positive ideal value of the value set value array, otherwise, the smaller the shape similarity of the time-series value set values of the parametric measurement sensor with the positive ideal value of the value set value array, and according to this principle, the similarity fusion weight of the time-series value set values of each parametric measurement sensor obtained by dividing the relative similarity measure of the time-series value set values of all parametric measurement sensors by the sum of the relative similarity measures of the time-series value set values of all parametric measurement sensors is determined as:
obtaining the interval number fusion weight of the time sequence figure set value of the parameter measurement sensor as w according to the formula (22) and the formula (26) i ：
w i ＝[min(α i ，β i )，max(α i ，β i )] (27)
From the formula (27), the distance measure fusion weight of the time series value set value of each parameter measurement sensor and the similarity fusion weight of the time series value set value of the parameter measurement sensor are used as the interval number fusion weight of the time series value set value of the parameter measurement sensor according to the interval numbers formed by sorting from small to large; the time series value set value of each parameter measurement sensor is the time series value set value obtained by adding the products of the time series value set value of each parameter measurement sensor and the interval number fusion weight of the time series value set value of the parameter measurement sensor, and the fusion value of the time series value set values of all the parameter measurement sensors is the time series interval value set value, and the time series interval value set value is as follows:
C. HRFNN recurrent neural network prediction model design of figure set
3 parameters output by the value fusion model of the Vague set are used as corresponding input of a beat delay line TDL, 3 output of the beat delay line TDL are used as corresponding input of an HRFNN recurrent neural network prediction model of the Vague set, three parameters output by the HRFNN recurrent neural network prediction model of the Vague set are x, t and 1-f respectively, x is a predicted value of the detected illumination intensity, t is the credibility, 1-f-t is the uncertainty, f is the incredibility, and x, t and 1-f form a predicted value of the Vague set of the detected illumination intensity as [ x, (t, 1-f)]The HRFNN recurrent neural network prediction model of the Vague set is output as a predicted value of the detected ambient light illuminance. The HRFNN recurrent neural network prediction model of the Vague set consists of 4 layers: the network comprises n input nodes, wherein each input node corresponds to m condition nodes, m represents a rule number, nm rule nodes and 3 output nodes. Layer I introducing input into the network; the second layer fuzzifies the input, and the adopted membership function is a Gaussian function; layer III corresponds to fuzzy reasoning; layer IV corresponds to the defuzzification operation. By using
Representing the input and output of the ith node of the kth layer, respectively, the signal transfer process inside the network and the input-output relationship between the layers can be described as follows. Layer I: an input layer, each input node of which is directly connected to an input variable, the input and output of the network being represented as:
in the formula
And
for the input and output of the ith node of the network input layer, N represents the number of iterations. Layer II: the membership function layer, the nodes of the layer fuzzify the input variables, each node represents a membership function, a Gaussian function is adopted as the membership function, and the input and output of the network are expressed as:
in the formula m ij And σ ij Respectively representing the mean center and the width value of the j-th Gaussian function of the ith linguistic variable of the II layer, wherein m is the number of all linguistic variables corresponding to the input nodes. Layer III: the fuzzy inference layer, namely the rule layer, adds dynamic feedback to ensure that the network has better learning efficiency, and the feedback link introduces an internal variable h k And selecting a sigmoid function as an activation function of the internal variable of the feedback link. The inputs and outputs of the network are represented as:
in the formula of omega jk Is the connecting weight value of the recursion part, the neuron of the layer represents the front-piece part of the fuzzy logic rule, the node of the layer performs pi operation on the output quantity of the second layer and the feedback quantity of the third layer,
is the output of the third layer, and m represents the number of rules in a full connection. The feedback link is mainlyThe values of the internal variables and the activation strengths of the corresponding membership functions of the internal variables are calculated. The activation strength is related to the rule node matching degree of the layer 3. The internal variables introduced by the feedback link comprise two types of nodes: and the receiving node and the feedback node. The carrying node calculates an internal variable by using weighted summation to realize the defuzzification function; the result of fuzzy inference of hidden rules represented by internal variables. And the feedback node adopts a sigmoid function as a fuzzy membership function to realize the fuzzification of the internal variable. Layer IV: the deblurring layer, i.e., the output layer. The layer node performs a summation operation on the input quantities. The inputs and outputs of the network are represented as:
in the formula lambda j Is the connection weight of the output layer. The HRFNN recurrent neural network prediction model of the figure set has the performance approaching to a highly nonlinear dynamic system, the training errors and the testing errors of the recurrent neural network added with internal variables are respectively obviously reduced, and the HRFNN recurrent neural network prediction model of the figure set trains the weight of the neural network by adopting a gradient descent algorithm added with cross validation. The HRFNN recurrent neural network prediction model of the figure set introduces internal variables in a feedback link, performs weighted summation on output quantities of the rule layer, then performs defuzzification output as feedback quantities, and uses the feedback quantities and the output quantities of the membership function layer as input of the rule layer at the next moment. The network output comprises the activation intensity of the rule layer and the output historical information, the capability of the HRFNN recurrent neural network prediction model of the figure set to adapt to a nonlinear dynamic system is enhanced, and the HRFNN recurrent neural network prediction model of the figure set can accurately predict the illuminance parameter of the detected environment. The three parameters output by the HRFNN recurrent neural network prediction model of the figure set are x, t and 1-f respectively, x is the predicted value of the detected illumination intensity, t is the credibility, 1-f-t is the uncertainty, f is the incredibility, and the predicted value of the figure set of the detected illumination intensity formed by x, t and 1-f is [ x, (t, 1-f)]Prediction model of HRFNN recurrent neural network of figure setAnd outputting the predicted value as the detected ambient light illumination.
Design example of intelligent illuminance adjustment and environment parameter Internet of things big data system
According to the actual condition of the environmental parameter detection and adjustment system based on the Internet of things, a plane layout installation diagram of detection nodes, control nodes, gateway nodes and a field monitoring end of an environmental parameter detection and adjustment platform is arranged in the system, sensors of the detection nodes are evenly arranged in all directions of a pipe network according to detection requirements, and the pipe network parameters are collected through the system.
The technical means disclosed in the invention scheme are not limited to the technical means disclosed in the above embodiments, but also include the technical scheme formed by any combination of the above technical features. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and such improvements and modifications are also considered to be within the scope of the present invention.
Claims (7)
1. Big data system of illuminance intelligent regulation and environmental parameter thing networking, its characterized in that: the system consists of an environment parameter detection and adjustment platform and a illuminance prediction subsystem, wherein the environment parameter detection and adjustment platform is responsible for detecting, adjusting and managing illuminance parameters, the illuminance parameters are stored in a cloud platform, and managers can check and adjust the illuminance parameters in real time from a mobile terminal APP; the illuminance prediction subsystem realizes detection and prediction of illuminance;
the illuminance prediction subsystem consists of an illuminance detection module, a figure set numerical fusion model and an HRFNN recurrent neural network prediction model according to a beat delay line TDL and a figure set;
the output of the illuminance sensor is used as the input of the corresponding illuminance detection module, the outputs of the illuminance detection modules are used as the input of a figure set numerical fusion model, 3 parameters output by the figure set numerical fusion model are used as the input of a corresponding beat-to-beat delay line TDL, 3 parameters output by the beat-to-beat delay line TDL are used as the corresponding inputs of an HRFNN recurrent neural network prediction model of the figure set, three parameters output by the HRFNN recurrent neural network prediction model of the figure set are x, t and 1-f respectively, x is the predicted value of the detected illuminance, t is the reliability, and 1-f is the sum of the reliability and the uncertainty, 1-f-t is uncertainty, f is uncertainty, x, t and 1-f form a prediction value of a Vague set of the detected illuminance, which is [ x, (t, 1-f) ], and the HRFNN recurrent neural network prediction model of the Vague set outputs the prediction value as the predicted value of the detected ambient illuminance.
2. The system for intelligently adjusting illuminance and intelligently adjusting environmental parameters through internet of things as claimed in claim 1, wherein: the illuminance detection module consists of an LSTM neural network model, an ARIMA prediction model, a variation modal decomposition model, a subtraction cluster classifier, a CNN convolution-NARX neural network model and a fuzzy wavelet neural network model of a figure set.
3. The system for intelligently adjusting illuminance and intelligently adjusting the environment parameters through the internet of things big data according to claim 2, wherein the system comprises: the illuminance sensor senses a time series illuminance value of a detected environment as the input of an LSTM neural network model and an ARIMA prediction model respectively, the difference between the output of the LSTM neural network model and the output of the ARIMA prediction model is used as the illuminance fluctuation value of the detected environment, the time series illuminance fluctuation value is used as the input of a variation modal decomposition model, the variation modal decomposition model outputs a plurality of modal function IMF components, a plurality of IMF component energy entropies are used as the input of a subtraction cluster classifier, a plurality of types of IMF component energy entropies output by the subtraction cluster classifier are respectively used as the input of a plurality of corresponding CNN convolution-NARX neural network models, the output of the ARIMA prediction model and the plurality of CNN convolution-NARX neural network models are used as the corresponding input of a Vague set Vague wavelet neural network model, three parameters output by the Vague set Vague wavelet neural network model are y, z and 1-k respectively, y is the real value of the detected illuminance, z is the credibility, 1-k is the sum of the credibility and the uncertainty, 1-k-z is the uncertainty, k is the uncertainty, y, z and 1-k form the value of the Vague set of the detected illuminance, which is [ y, (z, 1-k) ], and the output of the fuzzy wavelet neural network model of the Vague set is used as the output of the illuminance detection module.
4. The system for intelligently adjusting illuminance and intelligently adjusting environmental parameters through internet of things as claimed in claim 1, wherein: the value set number output by the parameter detection module of a plurality of parameter measurement sensors in a period of time of the value fusion model forms a time series value set numerical array, and the quotient obtained by dividing the positive ideal value distance measure of the time series value set numerical value of each parameter measurement sensor by the sum of the negative ideal value distance measure of the time series value set numerical value of the parameter measurement sensor and the positive ideal value distance measure of the time series value set numerical value of the parameter measurement sensor is the relative distance measure of the time series value set numerical value of each parameter measurement sensor; dividing the relative distance measure of the time series value set values of each parameter measurement sensor by the sum of the relative distance measures of the time series value set values of all the parameter measurement sensors to obtain a quotient, and taking the quotient as the distance measure fusion weight of the time series value set values of each parameter measurement sensor;
the quotient of the similarity between the time-series value set value of each parametric measurement sensor and the positive ideal value of the value set value array divided by the similarity between the time-series value set value of the parametric measurement sensor and the positive ideal value of the value set value array added to the similarity between the time-series value set value of the parametric measurement sensor and the negative ideal value of the value set value array is a relative measure of similarity between the time-series value set values of the parametric measurement sensors; dividing the similarity relative measure of the time series value set values of each parameter measurement sensor by the sum of the similarity relative measures of the time series value set values of all the parameter measurement sensors to obtain a quotient, and taking the quotient as the similarity fusion weight of the time series value set values of the parameter measurement sensors;
the distance measurement fusion weight of the time sequence figure set value of each parameter measurement sensor and the similarity fusion weight of the time sequence figure set value of the parameter measurement sensor are used as the interval number fusion weight of the time sequence figure set value of the parameter measurement sensor according to the interval number formed by sequencing from small to large; and the fused value of the time series value set numbers of all the parameter measurement sensors is the time series interval value set number obtained by adding the products of the time series value set number of each parameter measurement sensor and the interval number fusion weight of the time series value set number of the parameter measurement sensor at the same moment.
5. The system for intelligently adjusting illuminance and intelligently adjusting environmental parameters through internet of things as claimed in claim 1, wherein: the environment parameter detection and adjustment platform comprises a detection node, a gateway node, a control node, an on-site monitoring terminal, a cloud platform and a mobile terminal App, wherein the detection node, the control node and the gateway node are communicated with one another by constructing an ad hoc communication network based on the Internet of things.
6. The system for intelligently adjusting illuminance and intelligently adjusting environmental parameters through internet of things as claimed in claim 5, wherein: the detection node sends the detected environmental parameters to a field monitoring terminal through an RS232 interface of the gateway node and processes the sensor data, and the control node is responsible for adjusting the environmental parameters; the gateway node realizes bidirectional transmission of environmental parameters between the NB-IoT module and the cloud platform and between the cloud platform and the mobile terminal App through the 5G network, and bidirectional transmission of illuminance information between the gateway node and the field monitoring terminal is realized through an RS232 interface.
7. The system for intelligently adjusting illuminance and intelligently adjusting the environment parameters through the internet of things big data according to claim 5 or 6, wherein the system comprises: remove end App and provide real-time environmental parameter data for managers, satisfy the convenient visualization of environmental information, all come from the sensor data of detecting node collection and have all uploaded to the database of cloud platform, managers looks over current environmental parameter information through removing end APP accessible long-range.
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