CN114911185A - Security big data Internet of things intelligent system based on cloud platform and mobile terminal App - Google Patents

Security big data Internet of things intelligent system based on cloud platform and mobile terminal App Download PDF

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CN114911185A
CN114911185A CN202210696170.2A CN202210696170A CN114911185A CN 114911185 A CN114911185 A CN 114911185A CN 202210696170 A CN202210696170 A CN 202210696170A CN 114911185 A CN114911185 A CN 114911185A
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security
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张卫星
秦源汇
苏衍
冯凯宇
郭硕
尹太誉
马从国
周恒瑞
周红标
秦小芹
柏小颖
王建国
马海波
周大森
金德飞
黄凤芝
李亚洲
丁晓红
叶文芊
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Huaiyin Institute of Technology
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
    • G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
    • G05B19/0428—Safety, monitoring
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/24—Pc safety
    • G05B2219/24024—Safety, surveillance
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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Abstract

The invention discloses a security big data Internet of things intelligent system based on a cloud platform and a mobile terminal App, which consists of a security parameter acquisition platform and a security parameter big data processing subsystem, and is used for detecting security parameters and predicting security safety; the invention effectively solves the problem that the existing security parameters have no influence on the environmental safety according to the nonlinearity and large lag of the change of the security environmental parameters, the large parameter change of the security environmental area, the complexity and the like, and the security management problem of the security is greatly influenced by predicting the security parameters and early warning the security.

Description

Security big data Internet of things intelligent system based on cloud platform and mobile terminal App
Technical Field
The invention relates to the technical field of security big data detection and alarm automation equipment, in particular to a security big data Internet of things intelligent system based on a cloud platform and a mobile terminal App.
Background
The invention discloses a security big data internet of things intelligent system based on a cloud platform and a mobile terminal App, which realizes the real-time monitoring and automatic completion of the fire hazard and illegal intrusion information in the monitored environment, can realize the intelligent inspection monitoring of the fire hazard and illegal intrusion information in the monitored environment and realize linkage alarm, and a user only needs to observe the safety state of the monitored environment output by the system to master the safety state in the monitored environment and realize linkage alarm, when any monitoring node is abnormal, the linkage alarm prompt information is output, and more time is won for the disposal of the alarm event.
Disclosure of Invention
The invention provides a security big data Internet of things intelligent system based on a cloud platform and a mobile terminal App, and the security big data Internet of things intelligent system effectively solves the problem that the existing security parameters do not have the influence on environmental safety according to nonlinearity, large hysteresis, large security environment area parameter change complexity and the like of security environment parameter change, and the security parameters are not predicted and the security safety is not early warned, so that the security management is greatly influenced.
The invention is realized by the following technical scheme:
the security big data Internet of things intelligent system based on the cloud platform and the mobile terminal App is composed of a security parameter acquisition platform and a security parameter big data processing subsystem, wherein the security parameter acquisition platform comprises a measurement node, a gateway node, an on-site monitoring terminal, the cloud platform and the mobile terminal App, and detection and early warning of security parameters are realized; the security and protection parameter big data processing subsystem comprises a plurality of parameter detection modules and a security and protection safety classifier, so that the security and protection safety is predicted, and the safety and reliability management level of the building engineering is improved.
The invention further adopts the technical improvement scheme that:
the security parameter acquisition platform comprises a plurality of detection nodes of security parameters, a gateway node, an on-site monitoring terminal, a cloud platform and a mobile terminal App, wherein the detection nodes and the gateway node realize wireless communication between the detection nodes and the gateway node by constructing a LoRa communication network; the detection node sends the detected security parameters to an on-site monitoring terminal through an RS232 interface of the gateway node, and the on-site monitoring terminal manages the security parameters and predicts the security parameters; the gateway node realizes bidirectional transmission of security 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 security parameters between the gateway node and the field monitoring terminal is realized through an RS232 interface. The structure of the security parameter acquisition platform is shown in figure 1.
The invention further adopts the technical improvement scheme that:
the security parameter big data processing subsystem consists of an LSTM neural network model, an Adaline neural network model, a variation modal decomposition model, a K-means cluster classifier, a CNN convolution-NARX neural network model, an ANFIS fuzzy neural network model of a figure set, 2 parameter detection modules and a recursion fuzzy wavelet neural network model of a figure set according to a beat delay line TDL; the method comprises the steps that a flame sensor senses a time sequence flame value of a detected environment and respectively serves as the input of an LSTM neural network model and an Adaline neural network model, the difference between the output of the LSTM neural network model and the output of the Adaline neural network model serves as the flame fluctuation value of the detected environment, the time sequence flame fluctuation value serves as the input of a variational modal decomposition model, the variational modal decomposition model outputs a plurality of modal function IMF components, a plurality of IMF component energy entropies serve as the input of a K-means cluster classifier, a plurality of types of IMF component energy entropies output by a subtractive cluster classifier respectively serve as the input of a plurality of corresponding CNN convolution-NARX neural network models, the output of the Adaline neural network model and the CNN convolution-NARX neural network models serve as the corresponding input of an ANFIS fuzzy neural network model of a figure, and the three parameters output by the ANFIS fuzzy neural network model of the figure are respectively x, y and the output of the combination of the model, t and 1-f, x is the real value of the detected flame, t is the credibility, 1-f-t is the uncertainty, f is the credibility, and x, t and 1-f form the value of the Vague set of the detected flame as [ x, (t, 1-f) ]; the outputs of a plurality of temperature sensors and a plurality of humidity sensors are respectively used as the input of corresponding parameter detection modules, the outputs of 2 parameter detection modules and ANFIS fuzzy neural network models of a Vague set are used as the input of corresponding beat delay lines TDL, the outputs of 3 beat delay lines TDL are used as the corresponding input of fuzzy wavelet neural network models of the Vague set, three parameters output by the fuzzy wavelet neural network models of the Vague set are respectively y, s and 1-z, y is the predicted value of the detected flame, s is the credibility, 1-z-s is the uncertainty, z is the incredibility, and y, s and 1-z form the predicted value of the Vague set of the detected flame which is [ y, (s, 1-z) ]. The structure of the security parameter big data processing subsystem is shown in figure 2.
The invention further adopts the technical improvement scheme that:
the parameter detection module consists of an LSTM neural network model, an ARIMA prediction model, a variation modal decomposition model, a subtraction cluster classifier, an NARX neural network model and an ANFIS fuzzy neural network model of a Vague set; the parameter sensor senses time series parameter values of a detected environment to be respectively used as 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 is used as a parameter fluctuation value of the detected environment, the time series parameter fluctuation value is used as 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 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 input of a plurality of corresponding NARX neural network models, the output of the ARIMA prediction model and the plurality of NARX neural network models are used as corresponding input of an ANFIS fuzzy neural network model of a figure set, three parameters output by the ANFIS fuzzy neural network model of the figure set are respectively x, t and 1-f, and x is a real value of the detected parameter, t is credibility, 1-f-t is uncertainty, f is incredibility, x, t and 1-f form a value [ x, (t, 1-f) ] of a Vague set of detected parameters, and the ANFIS fuzzy neural network model output of the Vague set is used as the output of the parameter detection module. The structure of the parameter detection module is shown in figure 3.
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 values measured by the sensor into the numerical form of a detection parameter value set through a parameter detection module to represent, effectively processes the ambiguity, the dynamic property and the uncertainty of the measurement parameters of the sensor, and improves the objectivity and the reliability of the detection parameters of the sensor.
The LSTM neural network model is a recurrent neural network with 4 interaction layers in a repetitive network. The method not only can extract information from sequence data output by the detected parameter sensor like a standard recurrent neural network, but also can retain information of long-term correlation output by the detected parameter sensor from a previous remote step. In addition, because the sampling interval of the output of the detected parameter sensor is relatively small, the output of the detected parameter sensor has 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 outputs of the detected parameter sensor, so that the accuracy and the robustness of processing the output of the detected parameter sensor are improved.
The variational modal decomposition model can decompose the time series flame 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 time series flame fluctuation value, extract the characteristic frequency component containing the time series flame fluctuation value, effectively overcome the modal aliasing problem and realize the denoising of the time series flame fluctuation value, the dense peak spike characteristics of the denoised time series flame 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 flame fluctuation value.
According to the characteristic of the difference of a plurality of IMF component energy entropy samples of input detected parameters, a K-means cluster classifier is constructed to classify the plurality of IMF component energy entropy sample parameters, a plurality of CNN convolution-NARX neural network models are designed to predict the detected parameters, and in the process of predicting the detected parameters, a subtraction cluster classifier extracts a plurality of IMF component energy entropies of the detected parameters with similar causes from time-space characteristic data so as to establish a more targeted prediction method, and the detected environment parameters can be predicted by adopting the corresponding CNN convolution-NARX neural network models according to different characteristics of the plurality of IMF component energy entropies of the detected parameters to improve the prediction precision.
In the CNN convolution-NARX neural network model, 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 comprise prediction of a historical information parameter of the CNN convolutional neural network output for a period of time, the NARX neural network model is a dynamic neural network model capable of effectively predicting nonlinear and non-stationary time sequences output by the CNN convolutional neural network, and prediction accuracy of the CNN convolutional neural network output time sequences can be improved under the condition that time sequence non-stationarity is reduced; the NARX neural network model has good nonlinear mapping capability because the NARX neural network model establishes the dynamic recursive network of the model by introducing the delay module and the output feedback, and the CNN convolutional neural network output and the NARX neural network model output vector delay feedback are introduced into network training to form a new input vector.
Drawings
FIG. 1 is a security parameter acquisition platform of the present invention;
FIG. 2 is a security big data processing subsystem of the present invention;
FIG. 3 is a parameter detection module of the present invention;
FIG. 4 is a detection node of the present invention;
FIG. 5 is a gateway node of the present invention;
fig. 6 shows the site monitoring software according to the present invention.
Detailed Description
The technical scheme of the invention is further described by combining the attached drawings 1-6:
design of overall system function
The invention realizes the detection and the processing of security parameters, and the system consists of a security parameter acquisition platform and a security parameter big data processing subsystem. The security parameter acquisition platform comprises a detection node of a security parameter, a gateway node, a field monitoring terminal, a cloud platform and a mobile terminal App, wherein the detection node and the gateway node realize wireless communication between the detection node and the gateway node by constructing a LoRa communication network; the detection node sends the detected security parameters to a field monitoring terminal through an RS232 interface of the gateway node, and processes the sensor data and predicts the security parameters; the gateway node realizes bidirectional transmission of security parameter information 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 security parameter information between the gateway node and the field monitoring terminal is realized through an RS232 interface. The structure of the security parameter acquisition platform is shown in figure 1.
Design of detection node
A large number of detection nodes are adopted as security parameter sensing terminals, and information is transmitted between the detection nodes and the gateway nodes in a two-way mode through an LoRa communication network. The detection node comprises a temperature sensor, a humidity sensor, a flame sensor and a smoke sensor for acquiring security parameters, a corresponding signal conditioning circuit, a human body sensing module, an MSP430 microprocessor and an SX1278 radio frequency module; the software of the detection node mainly realizes the collection and pretreatment of communication and security parameters. The software is designed by adopting a C language program, so that the compatibility degree is high, the working efficiency of software design and development is greatly improved, and the reliability, readability and transportability of program codes are enhanced. The structure of the detection node is shown in fig. 4.
Third, gateway node design
The gateway node comprises an SX1278 radio frequency module, an NB-IoT module, an alarm module, an MSP430 microprocessor and an RS232 interface, the SX1278 radio frequency module is used for realizing an LoRa communication network between the gateway node and the detection node, the NB-IoT module is used for realizing data bidirectional interaction between the gateway and the cloud platform, and the RS232 interface is connected with the field monitoring terminal to realize information interaction between the gateway and the field monitoring terminal. The gateway node structure is shown in figure 5.
Site monitoring end software
The on-site monitoring end is an industrial control computer, mainly realizes acquisition of security parameters and processing and prediction of the security parameters, realizes information interaction with a gateway node through an RS232 interface, and mainly has the functions of a communication parameter setting subsystem, a data processing subsystem, a security alarm subsystem and a security parameter big data processing subsystem. The structure of the security parameter big data processing subsystem is shown in figure 2, and the security parameter big data processing subsystem consists of an LSTM neural network model, an Adaline neural network model, a variational modal decomposition model, a K-means cluster classifier, a CNN convolution-NARX neural network model, an ANFIS fuzzy neural network model of a figure set, 2 parameter detection modules and a recursive fuzzy wavelet neural network model of a figure set according to a beat delay line TDL and a figure set; 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 figure 5. The security parameter big data processing subsystem is designed as follows:
1. LSTM neural network model design
The method comprises the following steps that a flame sensor senses a time sequence flame value of a detected environment and respectively serves as the input of an LSTM neural network model and an Adaline neural network model, and the difference of the output of the LSTM neural network model and the output of the Adaline neural network model serves as a flame fluctuation value of the detected environment; the LSTM neural network model introduces a mechanism of a Memory Cell and a hidden layer State (Cell State) to control information transmission between hidden layers, and a Memory Cell of the LSTM neural network model is internally provided with 3 Gates (Gates) of which the computing structures are an Input Gate, a forgetting Gate and an Output Gate. Wherein, the input door can control the flame sensor to output the addition or filtration of new information; forget to gate can forgetRecording the output information of the flame sensor which needs to be discarded and keeping useful information in the past; the output gate enables the memory unit to output only the detection information output by the flame sensor related to 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 output and input quantity of the flame sensor in a 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 flame sensor output dependence information, and meanwhile, the problem of gradient disappearance is avoided. The LSTM adds a structure called a Memory Cell (Memory Cell) to a neural node of a hidden layer of a neuron internal structure RNN to memorize dynamic change information of a flame sensor Output in the past, and adds three gate structures (Input, form, Output) to control use of the flame sensor Output history information. The time-series value of the input as the output input quantity of the flame sensor is (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 Representing input, forget and output doors, c t Represents 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 the nonlinear value output by the flame sensor of the detected environment.
2. Adaline neural network model design
The method comprises the following steps that a flame sensor senses a time sequence flame value of a detected environment and respectively serves as the input of an LSTM neural network model and an Adaline neural network model, and the difference between the output of the LSTM neural network model and the output of the Adaline neural network model serves as a flame fluctuation value of the detected environment; the Adaptive Linear Element (Adaptive Linear Element) of the Adaline neural network model is one of the early neural network models, and the input signal of the model can be written in the form of a vector: x (k) ═ x 0 (K)，x 1 (K)，…x n (K)] T Each set of input signals corresponds to a set of weight vectors expressed as: w (k) ═ k 0 (K)，k 1 (K)，…k(K)]，x 0 (K) When the bias value of the Adaline neural network model is equal to minus 1, the excitation or inhibition state of the neuron is determined, and the network output can be defined as follows according to the input vector and the weight vector of the Adaline neural network model:
Figure BDA0003700373080000061
in the Adaline neural network model, a special input, namely an ideal response output d (K), is sent into the Adaline neural network model, then the output y (K) of the network is compared, a difference value is sent to a learning algorithm mechanism to adjust a weight vector until an optimal weight vector is obtained, the y (K) and the d (K) tend to be consistent, the adjusting process of the weight vector is the learning process of the network, the learning algorithm is a core part of the learning process, a weight optimization searching algorithm of the Adaline neural network model adopts a least square method of an LMS algorithm, and the Adaline neural network model outputs a flame linear value of a detected environment.
3. Variational modal decomposition model design
The time series flame 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 entropies of the IMF components are used as the input of a K-means cluster classifier; the variational modal decomposition model is a self-adaptive non-recursive signal time-frequency analysis method, and can decompose the time series flame fluctuation value signal into several flame fluctuation value sub-signals, i.e. IMF component 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 flame fluctuation value signal to be decomposed into a plurality of IMF components. The energy entropy value can measure the regular degree of flame fluctuation of the time series, represents the energy characteristics of flame fluctuation signals of the time series in different frequency bands, and when the flame fluctuation value of the time series is mutated, the energy is also changed, and the energy of the mth IMF component is defined as:
Figure BDA0003700373080000071
in the formula x m (i) The method is characterized in that the method is an mth component after a time series flame fluctuation signal sample is decomposed, n is the number of sampling points, and the energy entropy of the mth IMF component is as follows:
Figure BDA0003700373080000072
4. k-means cluster classifier design
The IMF component energy entropies are used as the input of a K-means cluster classifier, and the IMF component energy entropies of multiple types output by the K-means cluster classifier are respectively used as the input of multiple corresponding CNN convolution-NARX neural network models; the K-means clustering algorithm core idea divides n data objects into K classes, and enables the sum of squares from all data objects in each class to a clustering center point of each class to be minimum, but the clustering time is long, in order to realize the rapid clustering of data, the efficiency of a K-means clustering classifier is reserved, and simultaneously the application range of the K-means clustering classifier is expanded to discrete data, and the calculation process of the K-means clustering classifier is as follows:
(1) from the whole sample X, let I equal to 1, randomly pick K data objects as initial clustering centers m in X j (I) Where j is 1, 2, …, K.
(2) Let d (i, j) represent K cluster centers m j (I) With each object X in the big data sample X i The distance between them is:
Figure BDA0003700373080000081
searching the minimum Euclidean distance d in the Euclidean distances corresponding to all (i, j) values of d (i, j) by using a formula (10), and locating the minimum Euclidean distance d in a clustering center m j (I) Identical clusters S j In memory object x i . Let m j (I +1) represents the new cluster center point, and the calculation formula is as follows:
Figure BDA0003700373080000082
n in formula (11) j Representing the number of data objects in the jth class.
(4) And (3) setting a judgment criterion, judging whether the criterion is met, if so, carrying out the next step, and if not, turning to the step (2).
(5) And outputting a clustering result of the big data, and determining whether to terminate the loop by using a judgment criterion under the normal condition, namely when the partitioning results obtained by the I-th iteration and the I-1-th iteration are the same, considering that the partitioning is reasonable, and ending the iteration.
5. CNN convolution-NARX neural network model design
The IMF component energy entropies of multiple types output by the K-means cluster classifier are respectively used as the input of multiple corresponding CNN convolution-NARX neural network models, and the outputs of the Adaline neural network and the CNN convolution-NARX neural network models are used as the corresponding input of the ANFIS fuzzy neural network model of the Vague set; the CNN convolution-NARX neural network model is characterized in that the output of the CNN convolution neural network is used as the input of the NARX neural network model, the CNN convolution neural network model can directly and automatically mine and extract sensitive space characteristics representing flame fluctuation values of a time sequence from IMF component energy entropy values of a large number of flame fluctuation values of the time sequence, and the CNN convolution neural network model 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 time series flame fluctuation value 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 flame 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 CNN convolutional neural network model structure 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 flame fluctuation value enter a full connection layer, each nerve layer in the layer is in full connection with all neurons in the previous layer, and the time series flame extracted by the convolution layer and the pooling layer is subjected to full connectionAnd integrating local characteristic information of the IMF component energy entropy value of the fluctuation value. 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 pooled results of the last layer are connected together in an end-to-end mode to form the output layer and serve as the input of a NARX neural network model, the NARX neural network model is a dynamic recurrent neural network with output feedback connection, the topological connection relationship can be equivalent to a BP neural network with input delay and added with a delay feedback connection from the output to the input, the network output device is structurally 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 input signals and output feedback signals, the hidden layer node performs nonlinear operation on the delayed signals by using an activation function, and the output layer node is used for performing linear weighting on 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:
Figure BDA0003700373080000091
output O of jth output layer node of NARX neural network j Comprises the following steps:
Figure BDA0003700373080000092
6. ANGLE set ANFIS fuzzy neural network model
The output of the Adaline neural network model and the CNN convolution-NARX neural network models is used as the corresponding input of the ANFIS fuzzy neural network model of the Vague set, three parameters output by the ANFIS fuzzy neural network model of the Vague set are x, t and 1-f respectively, x is the real value of the detected flame, t is the credibility, 1-f-t is the uncertainty, f is the incredibility, and the values of the Vague set of the detected flame formed by x, t and 1-f are [ x, (t, 1-f) ]; an Adaptive Fuzzy Inference System (ANFIS) based on a neural network, also called an Adaptive neural-Fuzzy Inference System (Adaptive neural-Fuzzy Inference System), organically combines the neural network and the Fuzzy Inference System, can exert the advantages of the neural network and the Fuzzy Inference System, and can make up the respective defects. The fuzzy membership function and fuzzy rule in the adaptive neural network fuzzy system are obtained by learning a large amount of known data, and the maximum characteristic of ANFIS is a data-based modeling method instead of any given method based on experience or intuition. This is particularly important in systems where the characteristics are not yet fully understood or are very complex. The main operation steps of the ANGSE fuzzy neural network model of the Vague set are as follows:
layer 1: fuzzifying input data, wherein n is the number of each input membership function, the membership function adopts a Gaussian membership function, and the corresponding output of each node can be represented as:
Figure BDA0003700373080000093
layer 2: and realizing rule operation, outputting the applicability of the rule, and multiplying the rule operation of the ANTIS fuzzy neural network model of the figure set by multiplication.
Figure BDA0003700373080000101
Layer 3: normalizing the applicability of each rule:
Figure BDA0003700373080000102
layer 4: the transfer function of each node is a linear function and represents a local linear model, and the output of each self-adaptive node i is as follows:
Figure BDA0003700373080000103
layer 5: the single node of the layer is a fixed node, and the total output of the compensation estimation value of the ANFIS fuzzy neural network model for calculating the figure set is as follows:
Figure BDA0003700373080000104
the condition parameters determining the shapes of the membership functions and the conclusion parameters of the inference rules in the ANGES fuzzy neural network model of the figure set can be trained through a learning process. The parameters are adjusted by an algorithm combining a linear least square estimation algorithm and gradient descent. In each iteration of the ANGIE set ANFIS fuzzy neural network model, firstly, an input signal is transmitted along the forward direction of the network until reaching the layer 4, at the moment, the condition parameters are fixed, and conclusion parameters are adjusted by adopting a least square estimation algorithm; the signal continues to propagate forward along the network to the output layer. The ANFIS fuzzy neural network model of the figure set propagates the obtained error signals along the network in a backward direction, and the condition parameters are updated by a gradient method. By adjusting the given condition parameters in the ANFIS fuzzy neural network model of the figure set in this way, the global optimum point of the conclusion parameters can be obtained, thus not only reducing the dimensionality of the search space in the gradient method, but also improving the convergence speed of the ANFIS fuzzy neural network model parameters of the figure set. The three parameters output by the ANFIS fuzzy neural network model of the Vague set are x, t and 1-f respectively, x is the real numerical value of the detected flame, t is the credibility, 1-f-t is the uncertainty, f is the uncertainty, and x, t and 1-f form the Vague set numerical value of the detected flame as [ x, (t, 1-f) ].
7. Parameter detection module design
(1) LSTM neural network model design
The time sequence parameter values of the sensed environment sensed by the parameter sensor are respectively used 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 is used as the parameter fluctuation value of the sensed environment; the LSTM neural network model is designed according to the LSTM neural network model design process of step 1 of the patent.
(2) ARIMA prediction model design
The parameter sensor senses time series parameter values of the detected environment and respectively serves as the input of the LSTM neural network model and the 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 parameter 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 detected parameter 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 detected parameter equation with p, d and q as parameters can be expressed as follows:
Figure BDA0003700373080000111
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) is the parameter to be estimated for the ARIMA predictive model, and p and q are the orders of the ARIMA predictive detected parametric model. The detected parameter model for ARIMA dynamic prediction belongs to a linear model in nature, and the modeling and prediction comprise 4 steps: (1) and carrying out sequence stabilization treatment. If the detected parameter data sequence is not stable, if a certain increase or decrease trend exists, the data needs to be differentially processed. Common tools are autocorrelation function maps and partial autocorrelation function maps. If the autocorrelation function rapidly approaches zero, the detected parameter time series is a stationary time series. If the time sequence has a certain trend, the detected parameter data needs to be subjected to difference processing, if seasonal rules exist, seasonal differences also need to be carried out, and if the time sequence has heteroscedasticity, the detected parameter data needs to be subjected to logarithmic conversion firstly. (2) And identifying the model. The orders p, d and q of the ARIMA prediction detected parameter model are mainly determined through autocorrelation coefficients and partial autocorrelation coefficients. (3) Estimating parameters of the model and diagnosing the model. Using maximum likelihoodEstimating to obtain estimated values of all parameters in an ARIMA dynamic prediction detected parameter model, detecting the estimated values including parameter significance detection and residual error randomness detection, judging whether the established detected parameter model is available, and performing detected parameter prediction by using the ARIMA dynamic prediction detected parameter model with selected 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) And predicting the detected parameters by using the detected parameter model with the proper parameters. 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.
(3) Design of variational modal decomposition model
The time series parameter fluctuation value is used as the input of a variation modal decomposition model, the variation modal decomposition model outputs a plurality of modal functions IMF components, the energy entropies of the IMF components are used as the input of a subtraction clustering classifier, and the design of the variation modal decomposition model refers to the variation modal decomposition model design method in step 3 of the patent.
(4) Subtractive clustering classifier design
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 clustering classifier, and a plurality of types of IMF component energy entropies output by the subtraction clustering classifier are respectively used as the input of a plurality of corresponding 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 clusteredCandidate for center, i ═ 1, 2, …, N, data point X i The density function of (a) is defined as:
Figure BDA0003700373080000121
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:
Figure BDA0003700373080000122
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 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 ck And D c1 Terminating clustering when the following equation is satisfied:
D ck /D c1 ＜δ (22)
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 dynamically adjusts the IMF component energy entropy in real timeAnd the clustering center and the clustering number obtain better input space division, 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 corresponding NARX neural network model to predict the fluctuation future value of the detected parameter.
(5) NARX neural network model design
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 NARX neural network models, the output of the ARIMA prediction model and the NARX neural network models are used as the corresponding input of the ANFIS fuzzy neural network model of the figure set, and the design process of the NARX neural network model refers to the NARX neural network model design method in the CNN convolution-NARX neural network model in the step 5 of the patent.
(6) ANFIS fuzzy neural network model design of Vague set
The outputs of the ARIMA prediction model and the NARX neural network models are used as corresponding inputs of the ANFIS fuzzy neural network model of the figure set, three parameters output by the ANFIS fuzzy neural network model of the figure set are x, t and 1-f respectively, x is a real numerical value of a detected parameter, t is credibility, 1-f-t is uncertainty, f is incredibility, the figure of the figure set of the detected parameters formed by x, t and 1-f is [ x, (t, 1-f) ], and the ANFIS fuzzy neural network model of the figure set is output as the output of the parameter detection module. The design of the angue set ANFIS fuzzy neural network model refers to the design process of the angue set ANFIS fuzzy neural network model in step 6 of this patent.
8. Fuzzy wavelet neural network model design of Vague set
The outputs of 2 parameter detection modules and the ANFIS fuzzy neural network model of the Vague set are used as the input of the corresponding beat delay line TDL, the outputs of 3 beat delay lines TDL are used as the corresponding input of the fuzzy wavelet neural network model of the Vague set, the three parameters output by the fuzzy wavelet neural network model of the Vague set are y, s and 1-z respectively, y is the predicted value of the detected flame, s is the credibility, 1-z-s is the uncertainty, z is the uncertainty, y is the predicted value of the Vague set which forms the detected flame, y, s and 1-z are [ y, (s, 1-z)]. 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) model of a figure set. The FWNN-based fuzzy wavelet neural network model has good intelligence, robustness, stability and index tracking rapidity, and comprises two parts: fuzzy Neural Networks (FNNs) and Wavelet Neural Networks (WNNs). The fuzzy neural network model 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 the wavelet neural networks generated by the functions with different scales can capture the characteristics of different time domains and frequency domains, and corresponding wavelet networks are selected by different fuzzy reasoning. 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 is a ik Is the weight coefficient;
Figure BDA0003700373080000141
is a function of the wavelet, and is,
Figure BDA0003700373080000142
the output value of the local model wavelet network linear combination corresponding to the rule i is:
Figure BDA0003700373080000143
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 control signal output layer, three parameters output by a fuzzy wavelet neural network model of a Vague set are y, s and 1-z respectively, y is a predicted value of the detected flame, s is credibility, 1-z-s is uncertainty, z is incredibility, and the predicted values of the Vague set of the detected flame formed by y, s and 1-z are [ y, (s, 1-z)]。
Design example of security parameter acquisition platform
According to the actual condition of the security parameter acquisition platform, a plane layout installation diagram of detection nodes, gateway nodes and a field monitoring end of the security parameter acquisition platform is arranged in the system, wherein sensors of the detection nodes are evenly arranged in all directions of a security environment according to detection requirements, and the security parameters are acquired 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 (5)

1. Security protection big data thing networking intelligent system based on cloud platform and removal end App, its characterized in that: the system consists of a security parameter acquisition platform and a security parameter big data processing subsystem, and is used for detecting security parameters and predicting security;
the security parameter big data processing subsystem consists of an LSTM neural network model, an Adaline neural network model, a variation modal decomposition model, a K-means cluster classifier, a CNN convolution-NARX neural network model, an ANFIS fuzzy neural network model of a figure set, a parameter detection module and a recursion fuzzy wavelet neural network model of a figure set according to a beat delay line TDL;
the method comprises the steps that a flame sensor senses a time sequence flame value of a detected environment and respectively serves as the input of an LSTM neural network model and an Adaline neural network model, the difference between the output of the LSTM neural network model and the output of the Adaline neural network model serves as the flame fluctuation value of the detected environment, the time sequence flame fluctuation value serves as the input of a variational modal decomposition model, the variational modal decomposition model outputs a plurality of modal function IMF components, a plurality of IMF component energy entropies serve as the input of a K-means cluster classifier, a plurality of types of IMF component energy entropies output by a subtractive cluster classifier respectively serve as the input of a plurality of corresponding CNN convolution-NARX neural network models, the output of the Adaline neural network model and the CNN convolution-NARX neural network models serve as the corresponding input of an ANFIS fuzzy neural network model of a figure, and the three parameters output by the ANFIS fuzzy neural network model of the figure are respectively x, y and the output of the combination of the model, t and 1-f, x is the real value of the detected flame, t is the credibility, 1-f is the sum of the credibility and the uncertainty, 1-f-t is the uncertainty, f is the uncertainty, and x, t and 1-f form a value of a Vague set of the detected flame as [ x, (t, 1-f) ]; the outputs of a plurality of temperature sensors and a plurality of humidity sensors are respectively used as the input of corresponding parameter detection modules, the outputs of 2 parameter detection modules and the ANFIS fuzzy neural network model of the Vague set are used as the input of corresponding beat delay lines TDL, the outputs of 3 beat delay lines TDL are used as the corresponding input of the fuzzy wavelet neural network model of the Vague set, the three parameters of the outputs of the fuzzy wavelet neural network model of the Vague set are respectively y, s and 1-z, y is the predicted value of the detected flame, s is the credibility, 1-z is the sum of the credibility and the uncertainty, 1-z-s is the uncertainty, z is the uncertainty, and y, s and 1-z constitute the predicted value of the Vague set of the detected flame and is [ y, (s, 1-z) ].
2. The security big data internet of things intelligent system based on the cloud platform and the mobile terminal App as claimed in claim 1, wherein: the parameter detection module consists of an LSTM neural network model, an ARIMA prediction model, a variation modal decomposition model, a subtraction cluster classifier, an NARX neural network model and an ANFIS fuzzy neural network model of a Vague set.
3. The security big data internet of things intelligent system based on the cloud platform and the mobile terminal App as claimed in claim 2, wherein: the parameter sensor senses time series parameter values of a detected environment to be respectively used as 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 is used as a parameter fluctuation value of the detected environment, the time series parameter fluctuation value is used as 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 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 input of a plurality of corresponding NARX neural network models, the output of the ARIMA prediction model and the plurality of NARX neural network models are used as corresponding input of an ANFIS fuzzy neural network model of a figure set, three parameters output by the ANFIS fuzzy neural network model of the figure set are respectively x, t and 1-f, and x is a real value of the detected parameter, t is credibility, 1-f is sum of credibility and uncertainty, 1-f-t is uncertainty, f is uncertainty, x, t and 1-f form a value [ x, (t, 1-f) ] of a Vague set of detected parameters, and the ANFIS fuzzy neural network model output of the Vague set is used as the output of the parameter detection module.
4. The security big data internet of things intelligent system based on the cloud platform and the mobile terminal App as claimed in claim 1, wherein: the security parameter acquisition platform comprises a plurality of detection nodes for acquiring security parameters, a gateway node, a field monitoring end, a cloud platform and a mobile end App.
5. The security big data internet of things intelligent system based on the cloud platform and the mobile terminal App as claimed in claim 4, wherein: the wireless communication between the plurality of detection nodes and the gateway node is realized by constructing an LoRa communication network between the detection nodes and the gateway node; the detection node sends the detected security parameters to an on-site monitoring terminal through an RS232 interface of the gateway node, and the on-site monitoring terminal processes and predicts the security parameters; the gateway node realizes bidirectional transmission of security 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 security parameters between the gateway node and the field monitoring terminal is realized through an RS232 interface.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115511062A (en) * 2022-10-24 2022-12-23 淮阴工学院 Multi-parameter detection system of inspection robot
CN115905938A (en) * 2022-10-24 2023-04-04 淮阴工学院 Storage tank safety monitoring method and system based on Internet of things

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115511062A (en) * 2022-10-24 2022-12-23 淮阴工学院 Multi-parameter detection system of inspection robot
CN115905938A (en) * 2022-10-24 2023-04-04 淮阴工学院 Storage tank safety monitoring method and system based on Internet of things
CN115511062B (en) * 2022-10-24 2023-10-24 淮阴工学院 Multi-parameter detection system of inspection robot
CN115905938B (en) * 2022-10-24 2024-04-05 淮阴工学院 Storage tank safety monitoring method and system based on Internet of things

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