CN115062764B - Intelligent illuminance adjustment and environmental parameter Internet of things big data system - Google Patents
Intelligent illuminance adjustment and environmental parameter Internet of things big data system Download PDFInfo
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Abstract
The invention discloses an illuminance intelligent regulation and environmental parameter Internet of things big data system, which consists of an environmental parameter detection and regulation platform and an illuminance prediction subsystem, wherein the environmental parameter detection and regulation platform is responsible for detecting illuminance parameters, detecting, regulating and managing the illuminance parameters, storing the illuminance parameters into a cloud platform, and allowing a manager to view and regulate the illuminance parameters from a mobile terminal APP in real time; the illuminance prediction subsystem is used for detecting and predicting illuminance; the invention effectively solves the problems that the existing illuminance detection system has no nonlinear, large hysteresis, large illuminance area, complex change of parameters and the like according to illuminance parameter changes, has no influence on the safety of production and living environments, and has no prediction of illuminance parameters and no adjustment of illuminance, thereby greatly influencing the management of production and living environments.
Description
Technical Field
The invention relates to the technical field of automatic equipment for detecting and adjusting environmental parameters, in particular to an intelligent illuminance adjustment and environmental parameter Internet of things big data system.
Background
With the gradual maturation and perfection of modern communication and sensing technologies, environmental parameter detection systems have become a research hotspot in the industry of industry, agriculture, transportation, medical and health, construction and the like. The traditional method is to manually test and read, judge whether the environmental parameters deviate from normal values, and then take corresponding adjustment measures, so that a great deal of manpower and material resources are consumed. The system adopts the internet of things communication network platform to collect and control parameters such as temperature, humidity, wind speed, illumination intensity and the like of environmental parameters, has the advantages of convenient control, simple structure and high flexibility, and improves the convenience and the effectiveness of temperature, humidity, wind speed and light intensity control. With the rapid development of national economy, various industries and industry scale are continuously expanded, and the occasions of detecting and adjusting environmental parameters are increasingly increased. The traditional detection and control measures have shown great limitation, and with the development of the detection technology of the Internet of things, microcontrollers and various sensors, the difficult problems in the detection and transmission process of the environmental parameters are solved. Based on LoRa wireless communication technology, the intelligent illuminance regulation and environmental parameter Internet of things big data system is invented, the research of environmental parameter detection and regulation system expansion application based on the self-organizing Internet of things network and the cloud platform is constructed, the environmental parameter is monitored in real time, and especially the illuminance parameter is predicted so as to promote production and safety management, and the benefit is improved.
Disclosure of Invention
The invention provides an intelligent illuminance adjustment and environmental parameter Internet of things big data system, which effectively solves the problems that the existing illuminance detection system has no influence on production and living environment safety due to nonlinearity, large hysteresis, large illuminance area parameter change complexity and the like according to illuminance parameter change, and has no prediction and illuminance adjustment on illuminance parameters, so that production and living environment management are greatly influenced.
The invention is realized by the following technical scheme:
the intelligent illuminance regulation and environmental parameter Internet of things big data system consists of an environmental parameter detection and regulation platform and an illuminance prediction subsystem, wherein the environmental parameter detection and regulation platform is responsible for detecting illuminance parameters to be detected and regulated, the illuminance parameters are stored in the cloud platform, and a manager can view the illuminance parameters of the cloud platform from the mobile terminal APP in real time; the illuminance prediction subsystem consists of an illuminance detection module, a Vague set numerical fusion model and a beat delay line TDL and a Vague set HRFNN recurrent neural network prediction model, so that illuminance is predicted, and the illuminance intelligent regulation and environmental parameter Internet of things big data system realizes remote detection, regulation and intelligent management of environmental parameters.
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, a field monitoring end, a cloud platform and a mobile end App, wherein the gateway node is used for establishing a channel for information bidirectional transmission among the detection node, the control node, the gateway node, the field monitoring end, the mobile end APP and the cloud platform, and the cloud platform stores the environment information in a database of the cloud platform, so that the problem that a large amount of space is occupied due to the fact that a large amount of data is downloaded into intelligent mobile equipment is effectively solved. The structure of the environment parameter detection and adjustment platform is shown in fig. 1.
The invention further adopts the technical improvement scheme that:
the illumination prediction subsystem consists of an illumination detection module, a Vague set numerical fusion model, a beat delay line TDL and an HRFNN recurrent neural network prediction model of the Vague set, wherein the output of an illumination sensor is used as the input of the corresponding illumination detection module, the output of the plurality of illumination detection modules is used as the input of the Vague set numerical fusion model, 3 parameters output by the Vague set numerical fusion model are used as the input of the corresponding beat delay line TDL, the output of the 3 beat delay line TDL is used as the corresponding input of the HRFNN recurrent neural network prediction model of the Vague set, the three parameters output by the HRFNN recurrent neural network prediction model of the Vague set are respectively x, t and 1-f, 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 detected illumination set is [ x, (t, 1-f) ], and the predicted value of the Vague set is used as the predicted value of the HRFNN of the detected illumination. The structure and function of the illuminance 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 variation modal decomposition model, a subtractive clustering classifier, a CNN convolution-NARX neural network model and a Vague set fuzzy wavelet neural network model; the method comprises the steps that an illuminance sensor senses the time sequence illuminance value of a detected environment and respectively inputs the time sequence illuminance value as an LSTM neural network model and an ARIMA prediction model, the difference between the LSTM neural network model and the ARIMA prediction model is used as an illuminance fluctuation value of the detected environment, the time sequence illuminance fluctuation value is used as an input of a variable modal decomposition model, the variable modal decomposition model outputs a plurality of modal function IMF components, a plurality of IMF component energy entropies are used as an input of a subtractive clustering classifier, a plurality of types of IMF component energy entropies output by the subtractive clustering classifier are respectively input as a plurality of corresponding CNN convolution-NARX neural network models, the outputs of the ARIMA prediction model and the plurality of CNN convolution-NARX neural network models are respectively used as corresponding inputs of a Vague wavelet neural network model of a Vague set, three parameters of the Vague wavelet neural network model of the Vague set are y, z and 1-k are respectively, y is a real value of the illuminance of the detected, z is a reliability, 1-k-z is a degree of uncertainty, k is a degree of k is k, and k is a value of the luminance of the detected Vague wavelet set is a value, and 1-k is a Vague light value. The function and structure of the illuminance detection module are shown in fig. 3.
The invention further adopts the technical improvement scheme that:
vague set numerical fusion model
(1) The parameter detection module of a plurality of parameter measurement sensors outputs Vague set numbers to form a time sequence Vague set number array, and the quotient obtained by dividing the positive ideal value distance measure of the time sequence Vague set number of each parameter measurement sensor by the sum of the negative ideal value distance measure of the time sequence Vague set number of the parameter measurement sensor and the positive ideal value distance measure of the time sequence Vague set number of the parameter measurement sensor is the relative distance measure of the time sequence Vague set number of each parameter measurement sensor; dividing the relative distance measure of the time series Vague set values of each parameter measurement sensor by the sum of the relative distance measures of the time series Vague set values of all the parameter measurement sensors to obtain a quotient which is the distance measure fusion weight of the time series Vague set values of each parameter measurement sensor;
(2) The quotient obtained by dividing the similarity between the time series Vague set value of each parameter measurement sensor and the positive ideal value of the Vague set value array by the similarity between the time series Vague set value of the parameter measurement sensor and the positive ideal value of the Vague set value array adds the similarity between the time series Vague set value of the parameter measurement sensor and the negative ideal value of the Vague set value array is the similarity relative measure of the time series Vague set value of the parameter measurement sensor; dividing the similarity relative measure of the time series Vague set values of each parameter measurement sensor by the sum of the similarity relative measures of the time series Vague set values of all the parameter measurement sensors to obtain a quotient which is the similarity fusion weight of the time series Vague set values of the parameter measurement sensors;
(3) The distance measure fusion weight of the time sequence Vague set value of each parameter measurement sensor and the similarity fusion weight of the time sequence Vague set value of the parameter measurement sensor are used as the interval number fusion weight of the time sequence Vague set value of the parameter measurement sensor according to the interval number formed by sequencing from small to large; and obtaining a fusion value of the time series Vague set number of all the parameter measuring sensors as the time series interval Vague set number according to the sum of the products of the time series Vague set number of each parameter measuring sensor and the interval number fusion weight of the time series Vague set number of the parameter measuring sensor at the same moment. The structure and function of the Vague set numerical fusion model are shown in fig. 2.
Compared with the prior art, the invention has the following obvious advantages:
1. aiming at the uncertainty and the randomness of the problems of sensor precision error, interference, measurement abnormality and the like in the parameter measurement process, the invention converts the parameter value measured by the illuminance sensor into the numerical form of the detection parameter Vague set for representation through the illuminance detection module, effectively processes the ambiguity, the dynamic property and the uncertainty of the measurement parameter of the illuminance sensor, and improves the objectivity and the credibility of the illuminance parameter detected by the illuminance sensor.
2. The three parameters output by the HRFNN recurrent neural network prediction model of the Vague set are x, t and 1-f respectively, wherein x is the predicted value of the detected illuminance, 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 Vague set of the detected illuminance to be [ x, (t, 1-f) ], the internal variables are introduced in the feedback link, the output quantity of the rule layer is weighted and summed and then defuzzified to be output as the feedback quantity, the feedback quantity and the output quantity of the membership function layer are taken together as the input of the rule layer at the next moment, the network output contains the historical information of the activation intensity and the output of the rule layer, the capability of the HRFNN recurrent neural network prediction model of the Vague set for adapting to a nonlinear dynamic system is enhanced, the HRFNN recurrent neural network prediction model of the Vague set can accurately predict the light parameters of the detected environment, the function approaches arbitrarily and has the function of the nonlinear function with arbitrary precision, and has the functions of converging speed, the fast speed, the required quantity is less, the sample quantity is obtained, the calculation result is fast, and the result is good.
3. The LSTM neural network model is a cyclic neural network with 4 interaction layers in a repetitive network, and can not only extract information from illuminance sequence data output by an illuminance sensor like a standard cyclic neural network, but also can retain information of long-term correlation of illuminance parameters output by the illuminance sensor from a previous far step. In addition, because the sampling interval of the illuminance parameters output by the illuminance sensor is relatively smaller, the illuminance parameters output by the illuminance sensor have long-term space and time correlation, and the LSTM neural network model has enough long-term memory to process the space-time relationship between the illuminance parameters output by the illuminance sensor, so that the accuracy and the robustness of processing the illuminance parameters output by the illuminance sensor are improved.
4. The variation mode decomposition model can decompose the time sequence illuminance fluctuation value into a series of intrinsic mode functions IMF, continuously and iteratively update the center frequency and the frequency band bandwidth of each component, separate the self-adaptive frequency components of the original time sequence illuminance fluctuation value, extract the characteristic frequency components of the time sequence illuminance fluctuation value, effectively overcome the mode aliasing problem to realize denoising the time sequence illuminance fluctuation value, and the denoised peak-to-peak characteristics of the time sequence illuminance fluctuation value evolution curve disappear and become smooth gradually.
5. In the CNN convolutional-NARX neural network model adopted by the invention, the CNN convolutional neural network is a deep feed-forward neural network, the typical structure of the CNN convolutional neural network is composed of an input layer, a convolutional layer, a pooling layer and a full-connection layer, the CNN convolutional neural network is used for performing operations such as convolution and pooling on input data, and local characteristics of the data are extracted by establishing a plurality of filters, so that robust characteristics with translational rotation invariance are obtained. The NARX neural network model input comprises CNN convolutional neural network output and NARX neural network model output history feedback for a period of time, the feedback input can be considered to comprise history information of CNN convolutional neural network output for a period of time to participate in prediction, and the NARX neural network model is a dynamic neural network model capable of effectively predicting nonlinear and non-stationary time sequences of CNN convolutional neural network output and improving prediction accuracy of CNN convolutional neural network output of time sequences under the condition that the non-stationary time sequences are reduced; the NARX neural network model is characterized in that a dynamic recursion network of the model is built by introducing a delay module and output feedback, the CNN convolutional neural network input and NARX neural network model output vector delay feedback are introduced into network training, a new input vector is formed, the NARX neural network model input not only comprises original input data, but also comprises trained output data, the generalization capability of the network is improved, and the NARX neural network model has higher accuracy and robustness in the time sequence fluctuation value prediction of nonlinear detected parameters compared with the traditional static neural network.
Drawings
FIG. 1 is a schematic illustration of an environmental parameter detection and adjustment platform of the present invention;
FIG. 2 is a schematic diagram of an illuminance prediction subsystem according to the present invention;
FIG. 3 is a schematic diagram of an illuminance detection module according to the present invention;
FIG. 4 is a schematic diagram of a detection node according to 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 a view of the field monitoring software of the present invention.
Detailed Description
The technical scheme of the invention is further described with reference to the accompanying drawings 1-7:
1. design of overall system function
The system for detecting the environmental parameters comprises an environmental parameter detection and adjustment platform and an illuminance 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 wireless communication among the detection nodes, the control nodes and the gateway nodes is realized by constructing an ad hoc communication network of the Internet of things; the detection node sends the detected environmental parameters to a site monitoring end through an RS232 interface of the gateway node, and processes sensor data and predicts illuminance; the control node is responsible for adjusting the environmental parameter equipment, the gateway node realizes the 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 the gateway node and the field monitoring terminal realize the bidirectional transmission of environmental parameter information through an RS232 interface. The mobile App end provides real-time environment parameter data for management personnel and satisfies convenient visualization of environment parameter data information, all data acquired by sensors from detection nodes are uploaded to a database of the cloud platform, and the management personnel can remotely check and control current environment parameters through the mobile App end. The cloud platform is mainly responsible for processing, storing, analyzing and displaying the received environment parameters; the structure of the environment parameter detection and adjustment platform is shown in fig. 1.
2. Detection node design
The detection node consists of illuminance, temperature, humidity and wind speed sensors, 5 corresponding conditioning circuits, an STM32 singlechip and an SX127X radio frequency module interface, and is mainly used for collecting environmental parameter detection point data information which is transmitted to the cloud platform through the SX127X radio frequency module interface of the detection node and an NB-IoT module of the gateway node to realize real-time interaction with the mobile terminal APP; the detection node structure is shown in figure 4 and is transmitted to the site monitoring end through an SX127X radio frequency module interface and an RS232 interface of the gateway node.
3. Design of control nodes
The control node realizes information interaction with the gateway node through a self-organizing LoRa network, and comprises 4 digital-to-analog conversion circuits, an STM32 microprocessor, 4 external device controllers and an SX127X radio frequency module interface, which are corresponding to the control external devices; the 4 external device controllers are a temperature controller, a humidity controller, a wind speed controller and an illumination controller respectively. The control node structure is shown in fig. 5.
4. Gateway node design
The gateway node is composed of an SX127X radio frequency module interface, an NB-IoT module, an STM32 singlechip and an RS232 interface, the bidirectional transmission of data among the detection node, the control node and the site monitoring end is realized through the SX127X radio frequency module interface and the RS232 interface, and the bidirectional transmission among the cloud platform, the mobile end APP, the detection node and the site monitoring end is realized through the SX127X radio frequency module interface, the NB-IoT module and the RS232 interface, and the gateway node structure is shown in figure 6.
5. Design of field monitoring end software
The on-site monitoring end is an industrial control computer, and mainly realizes detection and illuminance prediction of environmental parameters and adjustment of the environmental parameters, realizes information interaction with detection nodes, control nodes and cloud platforms, and has the main functions of communication parameter setting, data analysis, data management and illuminance prediction subsystems. The management software selects Microsoft visual++6.0 as a development tool, and invokes an Mscomm communication control of the system to design a communication program, and the function of the field monitoring end software is shown in figure 7. The illuminance prediction subsystem is shown in fig. 2, and is composed of an illuminance detection module, a Vague set numerical fusion model and a beat delay line TDL and a Vague set HRFNN recurrent neural network prediction model, and the design process is as follows:
A. illuminance detection module design
1. LSTM neural network model design
The illuminance sensor senses a time series illuminance value component of the detected environmentThe difference between the LSTM neural network model and the ARIMA prediction model is used as the illuminance fluctuation value of the detected environment; the LSTM neural network model introduces a mechanism of Memory cells and hidden layer states (Cell states) to control information transfer between hidden layers. The memory unit of an LSTM neural network has 3 Gates (Gates) computing structures, namely an Input Gate (Input Gate), a Forget Gate (force Gate) and an Output Gate (Output Gate). The input gate can control the addition or filtering of new illuminance information; the forgetting door can forget the required lost illuminance detection information and retain the useful information in the past; the output gate can make the memory unit output only the information of the called illuminance detection related to the current time step. The 3 gate structures perform matrix multiplication, nonlinear summation and other operations in the memory unit, so that the memory is not attenuated in continuous iteration. The long-short-term memory unit (LSTM) structure unit consists of a unit (Cell), an Input Gate (Input Gate), an Output Gate (Output Gate) and a Forget Gate (Forget Gate). The LSTM neural network model is suitable for predicting the change of the illuminance input quantity of a time sequence by a long-term memory model, effectively prevents gradient disappearance during RNN training, and is a special RNN. The LSTM neural network model can learn the long-term detection illuminance dependency information, and meanwhile the gradient disappearance problem is avoided. LSTM adds a structure called a Memory Cell (Memory Cell) to the neural nodes of the hidden layer of the internal structure RNN of the neuron to memorize dynamic change information of the past called detected illuminance, and adds three gate (Input, forget, output) structures to control the use of the called detected illuminance history information. The time-series value inputted as the input quantity of the detected illuminance is set as (x 1 ,x 2 ,…,x T ) The hidden layer state is (h 1 ,h 2 ,…,h T ) Then the time t is:
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 is t 、f t 、o t Representing input gate, forward gate and output gate, c t Representing a cell, W h Weights representing recursive connections, W x The sigmoid and the tanh represent the weights from an input layer to an hidden layer, are two activation functions, and the LSTM neural network model is output as a nonlinear value of the illuminance of the detected environment.
2. ARIMA predictive model design
The illuminance sensor senses the time sequence illuminance value of the detected environment and respectively uses the time sequence illuminance value as the input of an LSTM neural network model and an ARIMA prediction model, and the difference output by the LSTM neural network model and the ARIMA prediction model is used as the illuminance fluctuation value of the detected environment; the ARIMA predictive model is a method for modeling objects based on time series prediction, which can be extended to analyze the time series of the predicted objects. The patent researches the time series characteristics of the ARIMA prediction model, and adopts 3 parameters to analyze the time series of the illuminance change, namely an autoregressive order (p), a difference number (d) and a moving average order (q). The ARIMA prediction model is written as: ARIMA (p, d, q). The ARIMA predicted illuminance equation with p, d, q as parameters can be expressed as follows:
Δ d y t representing y t Sequence epsilon after d times of differential conversion t Is random error of time, is a mutually independent white noise sequence, obeys to be 0 as a mean value, and has 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 of the ARIMA prediction model, and p and q are orders of the ARIMA prediction illuminance model. The ARIMA dynamic prediction illuminance model essentially belongs to a linear model, and modeling and prediction comprise 4 steps of (1) and sequence stabilization treatment. If the sequence of illuminance data is non-stationary, if there is a certain trend of increase or decrease, etc., the data needs to be differentially processed. Common tools are autocorrelation function diagrams and partial autocorrelation function diagrams. If the autocorrelation function quickly goes to zero, the illuminance time series is a stationary time series. If the time sequence has a certain trend, differential processing is needed to be carried out on the illuminance data, if the season law exists, season differential is needed, and if the time sequence has heteroscedasticity, logarithmic conversion is needed to be carried out on the illuminance data. And (2) identifying the model. The order p, d and q of the ARIMA predictive illuminance model are determined mainly by autocorrelation coefficients and partial autocorrelation coefficients. (3), estimating parameters of the model and model diagnosis. Obtaining estimated values of all parameters in an ARIMA dynamic prediction illuminance model by maximum likelihood estimation, checking significance test of the parameters and randomness test of residual errors, judging whether the built illuminance model is advisable, and carrying out parameter prediction by using the ARIMA dynamic prediction illuminance degree model with proper parameters; and a check is made in the model to determine if the model is appropriate and if not, re-estimate the parameters. (4) And predicting the detected parameters by using an illuminance model with the proper parameters. The patent uses software to call an ARIMA module of a time sequence analysis function in an SPSS statistical analysis software package to realize the whole modeling process. The ARIMA prediction model performs linear prediction on the illuminance of the detected environment.
3. Model design for variational modal decomposition
The time sequence 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, and the energy entropy of the plurality of IMF components is used as the input of a subtraction cluster classifier; the variational modal decomposition model is a self-adaptive non-recursive signal time-frequency analysis method, and can decompose the time-series illuminance sensor output fluctuation value signal into several illuminance sensor output fluctuation value sub-signals, i.e. IMF componentsu k And minimizes the sum of bandwidths of all IMF components, u k The amplitude modulation and frequency modulation function can be expressed as:
u k (t)＝A k cos[φ k (t)] (7)
phi in k (t) is a non-decreasing function, A k (t) is an envelope curve, and a constraint variation problem is constructed to solve u k And introducing a quadratic penalty term and a Lagrange multiplier to the solving of the variation problem, so that the variation problem becomes an unconstrained problem. The variation modal decomposition model may decompose the time-series illuminance sensor output fluctuation value signal to be decomposed into a number of IMF components. The energy entropy value of the IMF component can measure the degree of regularity of the output fluctuation of the time series illuminance sensor, and represents the energy characteristics of the output fluctuation signal of the time series illuminance sensor in different frequency bands, the output fluctuation value of the time series illuminance sensor is suddenly changed, the energy also changes, and the energy defining the mth IMF component is as follows:
In which x is m (i) Outputting an mth component after the decomposition of a fluctuation signal sample for the time sequence illumination degree sensor, wherein n is the number of sampling points, and the energy entropy of the mth IMF component is as follows:
4. subtractive cluster classifier design
The IMF component energy entropies are used as the input of a subtractive clustering classifier, and the IMF component energy entropies of a plurality of types output by the subtractive clustering classifier are respectively used as the input of a plurality of corresponding CNN convolution-NARX neural network models; compared with other clustering methods, the IMF component energy entropy subtraction clustering has the advantages that the position and the clustering number of the IMF component energy entropy clustering center can be rapidly determined only according to the data density of the IMF component energy entropy sample without the need of determining the clustering number in advance, and each IMF component energy entropy data point is used as a characteristic of a potential clustering centerThe feature is such that the result of the IMF component energy entropy clustering 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 modeling IMF component energy entropy data. Setting N IMF component energy entropy data points (X) 1 ,X 2 ,…X N ) Each data point X i ＝(x i,1 ,x i,1 ,…,x i,m ) Are candidates for cluster centers, i=1, 2, …, N, data point X i Is defined as:
In which 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, and typically no, to the density index of the point. Calculate each point X i Selecting the density value with the highest density index D c1 As the first cluster center X c1 The method comprises the steps of carrying out a first treatment on the surface of the The density value is then corrected to eliminate the influence of the previous cluster center. The density values were corrected as follows:
wherein D is c1 Is the highest density value corresponding to the initial clustering center, and the radius r is corrected b Is set to avoid that the second cluster center point is too close to the previous center point, 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 ck And D c1 When the following formula is satisfied, 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 of (2) ck And D c1 Terminating the clustering when the following is satisfied:
D ck /D c1 ＜δ (12)
wherein delta is set in advance according to actual conditionsIs set to a threshold value of (2). The basic idea of the online clustering method provided by the invention is that if the distance from an IMF component energy entropy point to the center of a group is smaller than the clustering radius r a Then the point belongs to the group and when new data is obtained, the group and the center of the group change accordingly. Along 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 the steps are as follows:
Step 1: IMF component energy entropy data normalization processing, and clustering radius r of each dimension of input data a And setting parameters such as a threshold delta.
Step 2: subtracting and clustering the IMF component energy entropy data set to obtain c clustering centers and storing v i (i=1, 2, …, c) and its corresponding density value D (v i )。
Step 3: when the kth data in the newly added online IMF component energy entropy data set comes, calculating x k (k=1, 2, …, M) to i cluster centers v i Distance d of (2) ki ＝||x k -v i I and d, if ki ＞r a Turning to step 4; if d ki ≤r a Go to step 5.
Step 4: calculating x from (10) k Density value D (x) k ) And D (x) k ) > ε, then describe the detected parameter data x k If the clustering does not belong to any existing cluster, a cluster is newly created, the number of clusters c=c+1 in the input space is returned to the step 3.
Step 5: determining data point x based on minimum distance criterion k Belonging to the nearest cluster subset, further comparing new data x k And the density value of the cluster center, if D (x k )＞D(v i ) Data x k Closer to its nearest cluster center, x k Replacing the original cluster center as a new cluster center of the subset; if D (x) k )≤D(v i ) And (3) keeping the clustering result unchanged, and judging whether the newly added data set is ended. If so, turning to step 6; otherwise, returning to step 3.
Step 6: calculating a clustering center v i And v j The distance between them if min v i -v j ||≤(0.5-0.7)r a And D (v) i )＞D(v j ) Then describe cluster subset v i And v j Can be combined into a cluster, and the center of the cluster is v i The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, the clustering result is kept unchanged.
IMF component energy entropy subtraction clustering is used for classifying IMF component energy entropy historical data, and each type of IMF component energy entropy is input into a CNN convolution-NARX neural network model corresponding to each type of IMF component energy entropy to predict future values of illuminance fluctuation.
5. CNN convolution-NARX neural network model design
The IMF component energy entropy of a plurality of types output by the subtractive clustering classifier is respectively input as a plurality of corresponding CNN convolution-NARX neural network models, and the outputs of the ARIMA prediction model and the plurality of CNN convolution-NARX neural network models are respectively input as the corresponding inputs of a fuzzy wavelet neural network model of a Vague set; the CNN convolution-NARX neural network model is used for taking the output of the CNN convolution neural network as the input of the NARX neural network model, the CNN convolution neural network model can directly extract the sensitive space characteristic representing the output fluctuation value of the time-series illuminance sensor from the IMF component energy entropy values of the output fluctuation value of the time-series illuminance sensor, and the CNN convolution neural network model structure mainly comprises 4 parts: (1) an Input layer (Input). The input layer is the input of the CNN convolutional neural network model, and generally, IMF component energy entropy of the fluctuation value output by the time sequence illuminance sensor is directly input. (2) Convolutional layer (Conv). Because the dimension of the input layer data is larger, the CNN convolutional neural network model is difficult to directly and comprehensively sense the IMF component energy entropy input information of the output fluctuation value of the time sequence illuminance sensor, the input data is required to be divided into a plurality of parts for local sensing, global information is obtained through weight sharing, meanwhile, the complexity of the structure of the CNN convolutional neural network model is reduced, the process is the main function of the convolutional layer, and the specific flow is to traverse the IMF component energy entropy input signal of the output fluctuation value of the time sequence illuminance sensor by utilizing the convolution kernel with a specific size in a fixed step length And convolution operation, so that the mining and extraction of the sensitive characteristics of the IMF component energy entropy input signal of the output fluctuation value of the time sequence illuminance sensor are realized. (3) Pooling layer (Pool, also called downsampling layer). Because the dimension of the data sample obtained after the convolution operation is still large, the data volume needs to be compressed and key information needs to be extracted to avoid overlong model training time and over fitting, and therefore a pooling layer is connected behind the convolution layer to reduce the dimension. Taking the peak value characteristics of the defect characteristics into consideration, adopting a maximum value pooling method to carry out downsampling. (4) And a full connection layer. After all convolution operations and pooling operations, the IMF component energy entropy characteristic extraction data of the output fluctuation value of the time-series illuminance sensor enters a fully-connected layer, each nerve layer in the layer is fully connected with all the nerve cells of the previous layer, and local characteristic information of the IMF component energy entropy value of the output fluctuation value of the time-series illuminance sensor extracted by the convolution layer and the pooling layer is integrated. Meanwhile, in order to avoid the over-fitting phenomenon, a missing data (dropout) technology is added in the layer, the output value of the last layer of all-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 serve as the input of an NARX neural network model, the NARX neural network model is a dynamic recursion neural network with output feedback connection, the topology connection relationship can be equivalent to a BP neural network with input delay and delay feedback connection from the output to the input, the structure of the NARX neural network is composed of the input layer, a delay layer, a hidden layer and the output layer, wherein the input layer node is used for inputting signals, the delay layer node is used for time delay of the input signals and the output feedback signals, the hidden layer node is used for performing nonlinear operation on the signals after time delay 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 The method comprises the following steps:
node output o of jth output layer of NARX neural network j The method comprises the following steps:
6. fuzzy wavelet neural network model design of Vague set
The outputs of ARIMA predictive model and multiple CNN convolution-NARX neural network models are used as the corresponding inputs of Vague set fuzzy wavelet neural network model, the three parameters of Vague set fuzzy wavelet neural network model output 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 numerical value of Vague set of the detected illuminance as [ y, (z, 1-k)]The fuzzy wavelet neural network model output of the Vague set is output as the illuminance detection module. The fuzzy neural network is used for fuzzy reasoning, and the characteristics of multi-resolution analysis of wavelets are combined, and a wavelet function is used as an excitation function of neurons of the neural network to construct a fuzzy wavelet network (Fuzzy Wavelet Network, FWNN) of a Vague set. The fuzzy wavelet neural network based on the FWNN has good intelligence, robustness, stability and index tracking rapidity, and the Vague set comprises a Fuzzy Neural Network (FNN) and a Wavelet Neural Network (WNN). The Vague set fuzzy neural network comprises 4 basic layers, wherein the first layer is an input layer, and each input vector corresponds to a 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 the 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 translating the wavelet function, so that the wavelet neural network generated by different scale functions can capture the characteristics of different time domains and frequency domains, and different fuzzy reasoning selects the corresponding wavelet network. The wavelet has the characteristic of multi-resolution analysis, if the wavelet function is used as the excitation function of the nerve network nerve cells, the expansion and translation of each nerve cell can be regulated, the smooth function can be learned by selecting low-scale parameters, the scale can be improved, and the local odd can be learned with higher precision The different function is more accurate than an ANN 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 of each layer is n, n multiplied by M, M, M and 3 respectively. Once the number of inputs n and rules M are determined, the structure of the FWNN model is determined. Wherein the input of the Vague set of fuzzy wavelet neural networks is x= [ X ] 1 ,x 2 ,…x n ]，T i Is the number of wavelets corresponding to the ith rule; w (w) ik Is a weight coefficient;
is a wavelet function, +.>
The output value of the linear combination of the local model wavelet network corresponding to the rule i is:
the first layer is an input layer: each node of the layer is directly connected with each component x of the input vector j Connection is performed, and the input value X= [ X ] 1 ,x 2 ,…x n ]Pass on to the next layer; the second layer calculates membership function values corresponding to each input variable; the third layer calculates the applicability of each rule; the fourth layer is the output of the wavelet network layer and is mainly used for output compensation; the fifth layer is a control signal output layer, also called an anti-ambiguity layer, and the anti-ambiguity calculation is carried out on the layer, three parameters output by the Vague set fuzzy wavelet neural network model are respectively y, z and 1-k, y is a real value of the illuminance to be detected, z is a reliability degree, 1-k-z is an uncertainty degree, k is an uncertainty degree, and the numerical value of the Vague set formed by y, z and 1-k is [ y, (z, 1-k) ]The fuzzy wavelet neural network model output of the Vague set is output as the illuminance detection module.
B. Vague set numerical fusion model design
(1) Time series Vague set numerical array for constructing parameter measuring sensor
The number of Vague sets output by the parameter detection models of a plurality of parameter measurement sensors in a period of time forms a time series Vague set value array, the Vague set values of n parameter measurement sensors and nm parameter measurement sensors at m times form a time series Vague set value array of parameter measurement sensors in n rows and m columns, and the Vague set values at different times of the same parameter measurement sensor are set as A ij (t),A ij (t+1),…,A ij (m) the time series Vague set value array of all parameter measurement sensors is:
(2) calculating distance fusion weights of time series Vague set values of parameter measurement sensors
The average value of the Vague set values of all the parameter measurement sensors at the same time constitutes a positive ideal value of the Vague set value array, which is:
the Vague set values of all parameter measuring sensors at the same time and the Vague set values with the largest distance measure between the Vague set values and the positive ideal values of the Vague set value array form negative ideal values of the Vague set value array, wherein the negative ideal values of the Vague set value array are as follows:
The positive ideal distance measure of the time series Vague set values of each parameter measurement sensor is the distance measure of the time series Vague set values of each parameter measurement sensor from the positive ideal values of the Vague set value array as:
wherein: pi ij ＝1-t ij -f ij And
the negative ideal distance measure of the time series Vague set values for each parameter measurement sensor is the distance measure of the time series Vague set values from the negative ideal values of the Vague set value array for each parameter measurement sensor is:
wherein: pi ij ＝1-t ij -f ij And
/>
the quotient obtained by dividing the positive ideal value distance measure of the time series Vague set value of each parameter measurement sensor by the sum of the negative ideal value distance measure of the time series Vague set value of the parameter measurement sensor and the positive ideal value distance measure of the time series Vague set value of the parameter measurement sensor is the relative distance measure of the time series Vague set value of each parameter measurement sensor, and the formula is as follows:
as can be seen from the formula calculation of (21), the greater the relative distance measure of the time series Vague set value of each parameter measurement sensor, the closer the time series Vague set value of the parameter measurement sensor is to the corresponding positive ideal value, otherwise the farther the time series Vague set value of the parameter measurement sensor is to the corresponding positive ideal value, and according to this principle, the distance measure fusion weight of the quotient obtained by dividing the relative distance measure of the time series Vague set value of each parameter measurement sensor by the sum of the relative distance measures of the time series Vague set values of all parameter measurement sensors is:
(3) Calculating similarity fusion weights of time series Vague set values of parameter measurement sensors
The similarity between the time series Vague set value and the positive ideal value of the Vague set value array of each parameter measurement sensor is as follows:
the similarity between the time series Vague set values and the negative ideal values of the Vague set value array of each parameter measurement sensor is as follows:
the quotient obtained by dividing the similarity between the time series Vague set value of each parameter measurement sensor and the positive ideal value of the Vague set value array by the similarity between the time series Vague set value of the parameter measurement sensor and the positive ideal value of the Vague set value array adds the similarity between the time series Vague set value of the parameter measurement sensor and the negative ideal value of the Vague set value array is the relative measure of the similarity between the time series Vague set values of the parameter measurement sensor:
as can be seen from the formula calculation (25), the greater the similarity relative measure of the time series Vague set values of each parameter measurement sensor, the greater the shape similarity of the time series Vague set values of the parameter measurement sensor to the positive ideal values of the Vague set value array, otherwise, the smaller the shape similarity of the time series Vague set values of the parameter measurement sensor to the positive ideal values of the Vague set value array, and according to this principle, it is determined that the quotient obtained by dividing the similarity relative measure of the time series Vague set values of each parameter measurement sensor by the sum of the similarity relative measures of the time series Vague set values of all parameter measurement sensors is the similarity fusion weight of the time series Vague set values of the parameter measurement sensor:
Obtaining interval number fusion weight w of time sequence Vague set value of the parameter measurement sensor according to formula (22) and 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 Vague set value of each parameter measurement sensor and the similarity fusion weight of the time series Vague set value of the parameter measurement sensor can be known, wherein the interval number formed by sequencing the time series Vague set values from small to large is used as the interval number fusion weight of the time series Vague set value of the parameter measurement sensor; according to the product of the time series Vague set value of each parameter measuring sensor and the interval number fusion weight of the time series Vague set value of the parameter measuring sensor at the same moment, the fusion value of the time series Vague set values of all parameter measuring sensors is the interval Vague set value of the time series, and the interval Vague set value of the time series is:
C. HRFNN recurrent neural network prediction model design of Vague set
3 parameters output by the Vague set numerical fusion model are used as input of a corresponding beat delay line TDL, and 3 output of the beat delay line TDL are used as HRFNN recurrent neural network of the Vague setCorresponding input of the complex prediction model, three parameters output by the HRFNN recurrent neural network prediction model of the Vague set are respectively x, t and 1-f, wherein x is a predicted value of the illuminance to be detected, t is the credibility, 1-f-t is the uncertainty, f is the uncertainty, and x, t and 1-f form the predicted value of the Vague set of the illuminance to be detected as [ x, (t, 1-f) ]The HRFNN recurrent neural network prediction model of the Vague set outputs as a predicted value of the detected ambient 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 the rule number, nm rule nodes and 3 output nodes. Layer I directs inputs to the network; the II layer fuzzifies the input, and the adopted membership function is a Gaussian function; the third layer corresponds to fuzzy reasoning; the fourth layer corresponds to a defuzzification operation. By using
Representing the input and output of the ith node of the kth layer, respectively, the signaling process within the network and the input-output relationship between the layers can be described as follows. Layer I: an input layer, each input node of the layer is directly connected with an input variable, and the input and output of the network are expressed as:
in the middle of
And->
For the input and output of the i-th node of the network input layer, N represents the number of iterations. Layer II: and a member function layer, wherein the nodes of the member function layer blur input variables, each node represents a membership function, a Gaussian basis function is adopted as the membership function, and the input and output of the network are expressed as follows:
M is in ij Sum sigma ij And respectively representing the mean center and the width value of the jth Gaussian basis function of the ith language variable of the IIth layer, wherein m is the number of all the language variables corresponding to the input node. Layer III: the fuzzy reasoning layer, namely the rule layer, adds dynamic feedback to enable the network to have better learning efficiency, and the feedback link introduces an internal variable h k And selecting a sigmoid function as an activation function of an internal variable of the feedback link. The inputs and outputs of the network are expressed as:
omega in jk Is the connection weight of the recursion part, the neurons of the layer represent the front part of the fuzzy logic rule, the layer nodes perform the 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, m represents the regular number when fully connected. The feedback link is mainly used for calculating the value of the internal variable and the activation intensity of the corresponding membership function of the internal variable. The activation strength is related to the level 3 regular node matching. The internal variables introduced by the feedback link include two types of nodes: and the receiving node and the feedback node. The receiving node calculates internal variables by using weighted summation to realize the defuzzification function; fuzzy inference results of hidden rules of internal variable representation. And the feedback node adopts a sigmoid function as a fuzzy membership function to realize fuzzification of internal variables. And IV layer: the defuzzification layer, the output layer. The layer node sums the input quantities. The inputs and outputs of the network are expressed as:
Lambda in the formula j Is the connection weight of the output layer. The HRFNN recurrent neural network prediction model of the Vague set has the performance approaching to a highly nonlinear dynamic system, the training error and the test error of the recurrent neural network added with internal variables are respectively obviously reduced, and the HRFNN recurrent neural network prediction model of the Vague set of the patent trains the weight of the neural network by adopting a gradient descent algorithm added with cross verification. The HRFNN recurrent neural network prediction model of the Vague set takes the output quantity of the rule layer as the feedback quantity after the weighted summation of the output quantity of the rule layer and takes the feedback quantity and the output quantity of the membership function layer as the input of the next moment of the rule layer through introducing internal variables in a feedback link. The network output contains the rule layer activation intensity and the output history information, so that the capability of the HRFNN recurrent neural network prediction model of the Vague set for adapting to a nonlinear dynamic system is enhanced, and the HRFNN recurrent neural network prediction model of the Vague set can accurately predict the illuminance parameters of the detected environment. The three parameters output by the HRFNN recurrent neural network prediction model of the Vague set are x, t and 1-f respectively, wherein x is the predicted value of the illuminance to be detected, t is the credibility, 1-f-t is the uncertainty, f is the uncertainty, and x, t and 1-f form the predicted value of the Vague set of the illuminance to be detected as [ x, (t, 1-f) ]The HRFNN recurrent neural network prediction model of the Vague set outputs as a predicted value of the detected ambient illuminance.
6. Design example of intelligent illuminance adjustment and environmental parameter Internet of things big data system
According to the actual condition of an environment parameter detection and adjustment system based on the Internet of things, the system is provided with a plane arrangement installation diagram of detection nodes, control nodes, gateway nodes and field monitoring ends of an environment parameter detection and adjustment platform, wherein sensors of the detection nodes are uniformly arranged in all directions of a pipe network according to detection requirements, and the system is used for collecting pipe network parameters.
The technical means disclosed by the scheme of the invention is not limited to the technical means disclosed by the embodiment, and also comprises the technical scheme formed by any combination of the technical features. It should be noted that modifications and adaptations to the invention may occur to one skilled in the art without departing from the principles of the present invention and are intended to be within the scope of the present invention.
Claims (4)
1. Illuminance intelligent regulation and environmental parameter thing networking big data system, its characterized in that: the system consists of an environment parameter detection and adjustment platform and an illuminance prediction subsystem, wherein the environment parameter detection and adjustment platform is responsible for detecting illuminance parameters, detecting, adjusting and managing the illuminance parameters, storing the illuminance parameters into the cloud platform, and allowing a manager to view and adjust the illuminance parameters from the mobile terminal APP in real time; the illuminance prediction subsystem is used for detecting and predicting illuminance;
The illuminance prediction subsystem consists of an illuminance detection module, a Vague set numerical fusion model and a beat delay line TDL and a Vague set HRFNN recurrent neural network prediction model;
the output of the illuminance sensor is used as the input of a corresponding illuminance detection module, the output of the illuminance detection modules is used as the input of a Vague set numerical fusion model, 3 parameters output by the Vague set numerical fusion model are used as the input of a corresponding beat delay line TDL, 3 beat delay lines TDL are output as the corresponding input of an HRFNN recurrent neural network prediction model of the Vague set, the three parameters output by the HRFNN recurrent neural network prediction model of the Vague set are respectively x, t and 1-f, x is the predicted value of the illuminance to be detected, t is the credibility, 1-f is the sum of credibility and uncertainty, 1-f-t is the uncertainty, f is the uncertainty, x, t and 1-f form the predicted value of the Vague set of the illuminance to be detected as [ x, (t, 1-f) ], and the HRFNN recurrent neural network prediction model of the Vague set is output as the predicted value of the illuminance to be detected;
the illuminance detection module consists of an LSTM neural network model, an ARIMA prediction model, a variation modal decomposition model, a subtractive clustering classifier, a CNN convolution-NARX neural network model and a Vague set fuzzy wavelet neural network model;
The method comprises the steps that an illuminance sensor senses the time sequence illuminance value of a detected environment and respectively inputs the time sequence illuminance value as an LSTM neural network model and an ARIMA prediction model, the difference between the LSTM neural network model and the ARIMA prediction model is used as the illuminance fluctuation value of the detected environment, the time sequence illuminance fluctuation value is used as the input of a variable modal decomposition model, the variable 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 subtractive clustering classifier, a plurality of types of IMF component energy entropies output by the subtractive clustering classifier are respectively input as a plurality of corresponding CNN convolution-NARX neural network models, the outputs of the ARIMA prediction model and the plurality of CNN convolution-NARX neural network models are respectively used as the corresponding inputs of a Vague wavelet neural network model of a Vague set, three parameters of the Vague wavelet neural network model output of the Vague set are y, z and 1-k are respectively the real values of the illuminance to be detected, z is the reliability, 1-k is the reliability and the uncertainty sum, 1-k is the uncertainty, and the Vague value is the luminance of the Vague wavelet set is output;
The method comprises the steps that a Vague set number output by a parameter detection module of a plurality of parameter measurement sensors in a period of time of a Vague set number fusion model forms a time sequence Vague set number array, and a quotient obtained by dividing a positive ideal value distance measure of a time sequence Vague set number of each parameter measurement sensor by a negative ideal value distance measure of the time sequence Vague set number of the parameter measurement sensor and a sum of positive ideal value distance measures of the time sequence Vague set number of the parameter measurement sensor is a relative distance measure of the time sequence Vague set number of each parameter measurement sensor; dividing the relative distance measure of the time series Vague set values of each parameter measurement sensor by the sum of the relative distance measures of the time series Vague set values of all the parameter measurement sensors to obtain a quotient which is the distance measure fusion weight of the time series Vague set values of each parameter measurement sensor;
the quotient obtained by dividing the similarity between the time series Vague set value of each parameter measurement sensor and the positive ideal value of the Vague set value array by the similarity between the time series Vague set value of the parameter measurement sensor and the positive ideal value of the Vague set value array adds the similarity between the time series Vague set value of the parameter measurement sensor and the negative ideal value of the Vague set value array is the similarity relative measure of the time series Vague set value of the parameter measurement sensor; dividing the similarity relative measure of the time series Vague set values of each parameter measurement sensor by the sum of the similarity relative measures of the time series Vague set values of all the parameter measurement sensors to obtain a quotient which is the similarity fusion weight of the time series Vague set values of the parameter measurement sensors;
The distance measure fusion weight of the time sequence Vague set value of each parameter measurement sensor and the similarity fusion weight of the time sequence Vague set value of the parameter measurement sensor are used as the interval number fusion weight of the time sequence Vague set value of the parameter measurement sensor according to the interval number formed by sequencing from small to large; and obtaining a fusion value of the time series Vague set number of all the parameter measuring sensors as the time series interval Vague set number according to the sum of the products of the time series Vague set number of each parameter measuring sensor and the interval number fusion weight of the time series Vague set number of the parameter measuring sensor at the same moment.
2. The illuminance intelligent regulation and environmental parameter internet of things big data system of claim 1, wherein: the environment parameter detection and adjustment platform comprises a detection node, a gateway node, a control node, a field monitoring end, a cloud platform and a mobile end App, wherein communication among the detection node, the control node and the gateway node is realized by constructing an ad hoc communication network based on the Internet of things.
3. The illuminance intelligent regulation and environmental parameter internet of things big data system of claim 2, wherein: the detection node sends the detected environmental parameters to a site monitoring end through an RS232 interface of the gateway node and processes sensor data, and the control node is responsible for adjusting the environmental parameters; the gateway node realizes the 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 a 5G network, and realizes the bidirectional transmission of illuminance information between the gateway node and the field monitoring terminal through an RS232 interface.
4. The intelligent illuminance adjustment and environmental parameter internet of things big data system according to claim 2 or 3, wherein: the mobile terminal App provides real-time environment parameter data for the manager, convenient visualization of environment information is met, all data acquired by the sensors from the detection nodes are uploaded to the database of the cloud platform, and the manager can remotely check the current environment parameter information through the mobile terminal APP.
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