CN114839881A - Intelligent garbage cleaning and environmental parameter big data internet of things system - Google Patents
Intelligent garbage cleaning and environmental parameter big data internet of things system Download PDFInfo
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Abstract
The invention discloses an intelligent garbage cleaning and environmental parameter big data internet of things system, which consists of an environmental parameter acquisition and control platform and an environmental parameter processing and garbage cleaning subsystem, wherein the environmental parameter acquisition and control platform realizes the detection, regulation and monitoring of environmental parameters; the environmental parameter big data processing subsystem realizes the control of environmental parameter processing and garbage cleaning; the invention effectively solves the problems that the environmental quality is not influenced by nonlinearity, large hysteresis, large and complex environmental area and the like of the existing environmental garbage cleaning according to the change of environmental parameters, the environmental parameters are not predicted, the environmental parameters are accurately detected, and the garbage cleaning device is adjusted, so that the environmental parameter prediction and the environmental management are greatly influenced.
Description
Technical Field
The invention relates to the technical field of automatic equipment for detecting and processing environmental parameters and cleaning garbage, in particular to an intelligent garbage cleaning and environmental parameter big data Internet of things system.
Background
The ecological environment is a prerequisite for human survival and social production, and is the foundation for promoting economic civilization construction. Under the situation that natural resources are increasingly tense and the ecological environment is gradually deteriorated, all mankind should establish environmental protection consciousness of protecting nature, respecting nature and conforming to nature. The environmental awareness of residents is poor, the cognition of garbage classification is lagged, and the environment is seriously polluted and the ecological environment is seriously damaged. Key monitoring is carried out to the environmental pollution problem that awaits solving urgently, a large amount of discarded object are the important factor that causes ecological environment pollution, but the excrement and urine discarded object that has a large amount of mankind that can supply to retrieve and utilize and livestock can directly be clear away, reduce the pollution that excrement and urine caused the environment, the villager who undertakes livestock-raising can be managed at ease, needn't worry the influence that causes for environment and peripheral resident, realize the recovery and the reuse to rubbish, construct environment automatic monitoring and rubbish clearance system, ensure the authenticity, objectivity and the ageing of environmental detection data. Environmental monitoring and garbage removal are cooperated, so that environment-friendly work can be promoted to be smoothly and efficiently developed, and the environment monitoring and garbage removal device is designed by applying a network and an intelligent control technology, so that the intelligent garbage removal and environmental parameter big data Internet of things system is invented to improve the environment monitoring and environment purification level.
Disclosure of Invention
The invention provides an intelligent garbage cleaning and environmental parameter big data Internet of things system, which effectively solves the problems that the environmental quality is not influenced by nonlinearity, large hysteresis, large and complex environmental area and the like of environmental parameter change in the existing environmental garbage cleaning, environmental parameters are not predicted, the environmental parameters are accurately detected and a garbage cleaning device is adjusted, and therefore environmental parameter prediction and environmental management are greatly influenced.
The invention is realized by the following technical scheme:
the intelligent garbage cleaning and environmental parameter big data Internet of things system consists of an environmental parameter acquisition and control platform and an environmental parameter processing and garbage cleaning subsystem, wherein the environmental parameter acquisition and control platform realizes the detection and management of environmental parameters; the environmental parameter processing and garbage cleaning subsystem realizes processing of environmental parameters and adjustment of the garbage cleaning device, and improves environmental management efficiency and benefits.
The invention further adopts the technical improvement scheme that:
the environment parameter acquisition and control platform consists of a detection node, a control node, a gateway node, an on-site monitoring terminal, a cloud platform and a mobile terminal APP, the detection node acquires environment parameters and uploads the environment parameters to the cloud platform through the gateway node, the mobile terminal APP is provided with data by the cloud platform, the mobile terminal APP can monitor the environment parameters and adjust external equipment of the control node in real time through environment information provided by the cloud platform, the detection node and the control node are responsible for acquiring the environment parameter information and controlling the environment adjusting equipment, and bidirectional communication among the detection node, the control node, the on-site monitoring terminal, the cloud platform and the mobile terminal APP is realized through the gateway node, so that the environment parameter acquisition and the environment equipment control are realized; the structure of the environmental parameter acquisition and control platform is shown in fig. 1.
The invention further adopts the technical improvement scheme that:
the environmental parameter processing and garbage cleaning subsystem consists of a parameter detection module, an ANFIS neural network model, a parameter self-adjusting factor fuzzy controller, a PID controller, an LSTM neural network model, an NARX neural network controller and a fuzzy wavelet neural network model, wherein the outputs of a plurality of groups of ammonia, hydrogen sulfide and carbon dioxide sensors are used as the inputs of corresponding parameter detection modules, the outputs of a plurality of groups of temperature, humidity and wind speed sensors are used as the inputs of corresponding parameter detection modules, the outputs of 2 parameter detection modules and the fuzzy wavelet neural network model are used as the corresponding inputs of the ANFIS neural network model, the attitude errors and the error change rates of the garbage cleaning device output by the ANFIS neural network model and the fuzzy wavelet neural network model are respectively used as the inputs of the parameter self-adjusting factor fuzzy controller and the PID controller, and the output of the PID controller is used as the input of the LSTM neural network model, the output of the LSTM neural network model and the output of the parameter self-adjusting factor fuzzy controller are respectively used as the corresponding input of the NARX neural network controller, the output of the NARX neural network controller is respectively used as the yaw angle, pitch angle and roll angle control quantity of the garbage removal device, the time series yaw angle, pitch angle and roll angle output by the MPU6050 attitude sensor are used as the input of the corresponding parameter detection module, and the output of the parameter detection module is used as the input of the fuzzy wavelet neural network model. The structure of the environmental parameter processing and garbage cleaning subsystem is shown in figure 2.
The invention further adopts the technical improvement scheme that:
the parameter detection module consists of an NARX neural network model, an Adaline neural network model, a K-means cluster classifier, a CNN convolution-LSTM neural network model, an AANN self-association neural network model of a figure set and a time delay line TDL; the time sequence parameter values of the sensed environment sensed by a plurality of groups of parameter sensors are respectively used as the input of corresponding NARX neural network models and Adaline neural network models, the difference between the output of the NARX neural network models and the Adaline neural network models is used as the fluctuation value of the level of the sensed parameter, the output of a plurality of time sequence parameter fluctuation values and a plurality of Adaline neural network models are respectively used as the input of corresponding K-means cluster classifiers, the output of a plurality of types of time sequence parameter fluctuation values and the output of the Adaline neural network models output by 2K-means cluster classifiers are respectively used as the input of corresponding CNN convolution-LSTM neural network models, the output of the CNN convolution-LSTM neural network models is used as the corresponding input of the AANN self-coupling neural network models of the figure set, the three parameters output by the AANN self-coupling neural network models of the figure set are respectively x, t and 1-f, x is the real value of the detected parameter, t is the credibility, f is the incredibility, 1-f-t is the uncertainty, the values of the detected parameter value set formed by x, t and 1-f are [ x, (t, 1-f) ], the AANN self-association neural network model output of the value set is used as the input of the beat-to-beat delay line TDL, and the output of the beat-to-beat delay line TDL is used as the output of the parameter detection module. The parameter detection module is shown in fig. 3.
Compared with the prior art, the invention has the following obvious advantages:
1. 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 the 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.
2. The invention relates to an NARX neural network controller, which is a dynamic recursive network for establishing the NARX neural network controller by introducing an LSTM neural network model, a delay module of output characteristic parameters of a parameter self-adjusting factor fuzzy controller and the output feedback realization of the NARX neural network controller, and realizes the data relevance modeling idea of a function simulation function along a plurality of time LSTM neural network models of which the output characteristic parameters of the LSTM neural network models and the parameter self-adjusting factor fuzzy controller are expanded in the time axis direction and the sequence of the output characteristic parameters of the parameter self-adjusting factor fuzzy controller and the output of the NARX neural network controller, the method establishes a combined model of the output value of the NARX neural network controller by the output characteristic parameters of the LSTM neural network models and the parameter self-adjusting factor fuzzy controller and the output of the NARX neural network controller within a period of time, the output parameters of the NARX neural network controller are used as input in the feedback action and closed-loop training improves the calculation accuracy and robustness of the output of the NARX neural network controller.
3. The invention adopts K-means to perform cluster analysis on a plurality of time sequence parameter fluctuation values and a plurality of Adaline neural network model output data, classifies the plurality of time sequence parameter fluctuation values and the plurality of Adaline neural network model output data by a cluster center obtained by the cluster analysis and respectively inputs the plurality of time sequence parameter fluctuation values and the plurality of Adaline neural network model output data as corresponding CNN convolution-LSTM neural network models, and respectively predicts the plurality of time sequence parameter fluctuation values and the plurality of Adaline neural network model output data of different types by adopting the corresponding CNN convolution-LSTM neural network models, thereby improving the accuracy of detecting and predicting the input data.
4. In the CNN convolution-LSTM neural network model adopted by the invention, the CNN convolution neural network is a deep feedforward neural network, the typical structure of the CNN convolution neural network is composed of an input layer, a convolution layer, a pooling layer and a full-connection layer, the CNN convolution neural network performs convolution, pooling and other operations on input data, and local features of the data are extracted by establishing a plurality of filters to obtain robust features with translation and rotation invariance. The LSTM neural network comprises an input layer, a hidden layer and an output layer, and as memory units are added into each neural unit of the hidden layer, the information on a time sequence can be controlled to be forgotten or output, the problems of gradient explosion and gradient disappearance in RNN are solved, the LSTM neural network is far superior to RNN in processing long sequence data, the characteristic information on the output time sequence of the CNN convolutional neural network can be effectively extracted by the LSTM neural network, the CNN convolutional-LSTM neural network model can fully mine the spatial characteristic relationship among all variables of the output data of the CNN convolutional neural network, and the time sequence characteristic information of the output historical data of the CNN convolutional neural network is extracted, so that the CNN convolutional-LSTM neural network model has strong learning and generalization capabilities.
5. The AANN self-association neural network model of the figure set extracts the most representative low-dimensional subspace of the input parameter change system structure in the high-dimensional parameter space reflecting the output values of a plurality of input CNN convolution-LSTM neural network models, meanwhile, noise and errors in input CNN convolution-LSTM neural network model data are effectively filtered, decompression of the input CNN convolution-LSTM neural network model data is achieved through a bottleneck layer, a demapping layer and an output layer, three parameters output by an AANN self-association neural network model of a Vague set are x, t and 1-f respectively, x is a real value of a detected parameter, t is credibility, f is uncertainty, 1-f-t is uncertainty, and the value of the detected parameter Vague set formed by x, t and 1-f is [ x, (t, 1-f) ], so that accuracy and robustness of prediction of the detected parameter are improved.
6. The ANFIS neural network model adopted by the invention is a fuzzy inference system based on a Takagi-Sugeno model, is a novel fuzzy inference system structure organically combining fuzzy logic and a neuron network, adopts a mixed algorithm of a back propagation algorithm and a least square method to adjust a precondition parameter and a conclusion parameter, and automatically generates an If-Then rule. The ANFIS neural network model is used as a very characteristic neural network, has the function of approximating any linear and nonlinear functions with any precision, and has the advantages of high convergence speed, less sample requirement, high ANFIS neural network model operation speed, reliable result and good effect.
Drawings
FIG. 1 is an environmental parameter acquisition and control platform of the present invention;
FIG. 2 is an environmental parameter processing and garbage disposal 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 control node of the present invention;
FIG. 6 is a gateway node of the present invention;
fig. 7 is the site monitoring software of the present invention.
Detailed Description
The technical solution of the present invention is further described with reference to the accompanying drawings 1-7:
first, the overall function design of the system
The intelligent garbage cleaning and environmental parameter big data Internet of things system realizes detection of environmental parameters and control of environmental regulation equipment, and comprises an environmental parameter acquisition and control platform and an environmental parameter processing and garbage cleaning subsystem. The environment parameter acquisition and control platform comprises a detection node, a control node, a gateway node, a field monitoring terminal, a cloud platform and a mobile terminal APP, wherein the detection node and the control node construct a LoRa monitoring network in a self-organizing manner to realize LoRa communication among the detection node, the control node and the gateway node; the detection node sends the detected environmental parameters to the field monitoring terminal and the cloud platform through the gateway node, and the gateway node and the cloud platform provide a bidirectional transmission channel for the environmental parameters and the related control information between the field monitoring terminal and the mobile terminal APP. Remove end APP and adopt the open source frame APP that the machine intelligence cloud provided to design, only need in removing the APP SDK that the integrated machine intelligence cloud provided in the end APP, just can connect cloud platform and realize based on removing the long-range detection and the regulation and control function of end APP. The structure of the environmental parameter acquisition and control platform is shown in figure 1.
Design of detection node
A large number of detection nodes 1 based on an LoRa sensor network are adopted as environment parameters and attitude sensing terminals of the garbage cleaning device, and the detection nodes realize mutual information interaction with gateway nodes through a self-organizing LoRa network. The detection node comprises a sensor for acquiring parameters of environment humidity, temperature, wind speed, ammonia gas, hydrogen sulfide and carbon dioxide, a corresponding signal conditioning circuit, an MPU6050 attitude sensor, an STM32 microprocessor and an LoRa communication module SX 1278; the software of the detection node mainly realizes LoRa communication and collection and pretreatment of environmental 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.
Design of control node
The control node realizes mutual information interaction with the gateway node through a self-organizing LoRa network, and comprises 4 digital-to-analog conversion circuits corresponding to control external equipment, an STM32 microprocessor, 4 external equipment controllers and a LoRa communication module SX 1278; the 4 external equipment controllers are respectively a temperature controller, a humidity controller, a wind speed controller and a garbage cleaning device controller. The control node structure is shown in fig. 5.
Fourth, gateway node design
The gateway node comprises an SX1278 module, an NB-IoT module, an STM32 microprocessor and an RS232 interface, the gateway node comprises an SX1278 module which realizes a self-organizing network communicated with the detection node and the control node, the NB-IoT module realizes bidirectional interaction of data 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 6.
Fifth, on-site monitoring terminal software
The field monitoring terminal is an industrial control computer, mainly realizes acquisition of environmental parameters and control of external equipment, realizes information interaction with the gateway node, and mainly has the functions of communication parameter setting, data analysis and data management, environmental parameter processing and garbage cleaning subsystems. 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 7. The design process of the environmental parameter processing and garbage cleaning subsystem is as follows:
1. parameter detection module design
The parameter detection module consists of an NARX neural network model, an Adaline neural network model, a K-means cluster classifier, a CNN convolution-LSTM neural network model, an AANN self-association neural network model of a figure set and a time delay line TDL;
(1) NARX neural network model design
The time sequence parameter values of the detected environment sensed by the parameter sensor are respectively used as the input of a corresponding NARX neural network model and an Adaline neural network model, and the difference between the output of the NARX neural network model and the output of the Adaline neural network model is used as the parameter fluctuation value of the detected environment; the NARX neural network model is a dynamic recurrent neural network with output feedback connection, which can be equivalent to a BP neural network with input time delay and added with time delay feedback connection from output to input on a topological connection relation, and the structure of the NARX neural network model is composed of an input layer, a time delay layer, a hidden layer and an output layer, wherein an input layer node is used for signal input, a time delay layer node is used for time delay of an input signal and an output feedback signal, the hidden layer node performs nonlinear operation on the delayed signal by using an activation function, and an 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:
output O of j output layer node of NARX neural network j Comprises the following steps:
(2) adaline neural network model design
The time sequence parameter values of the detected environment sensed by the parameter sensor are respectively used as the input of a corresponding NARX neural network model and an Adaline neural network model, and the difference between the output of the NARX neural network model and the output of the Adaline neural network model is used as the parameter fluctuation value of the detected environment; adaptive Linear Element (Adaptive Linear Element) of Adaline neural network model is an early stageThe 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:
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 linear value of a detected parameter.
(3) Design of K-means cluster classifier
The time series parameter fluctuation values and Adaline neural network model outputs are respectively used as the input of the corresponding K-means cluster classifier, and the time series parameter fluctuation values and Adaline neural network model outputs of multiple types output by the 2K-means cluster classifiers are respectively used as the input of the corresponding CNN convolution-LSTM neural network model; the K-means clustering algorithm core idea divides n data objects into K classes, the sum of squares of all data objects in each class to a clustering center point of each class is 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 kept, 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:
(a) 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.
(b) 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:
searching the minimum Euclidean distance d in the Euclidean distances corresponding to all (i, j) values of d (i, j) by using the formula (4), and locating the minimum Euclidean distance d in the clustering center m j (I) Identical clusters S j In which object x is stored i . Let m j (I +1) represents the new cluster center point, and the calculation formula is as follows:
n in formula (5) j Representing the number of data objects in the jth class.
(c) Setting a judgment criterion, judging whether the criterion is met, if so, carrying out the next step, and if not, turning to the step (b).
(d) 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.
(4) CNN convolution-LSTM neural network model design
The time series parameter fluctuation values of a plurality of types output by the 2K-means cluster classifiers and the output of the Adaline neural network model are respectively used as the input of the corresponding CNN convolution-LSTM neural network model; the CNN convolution-LSTM neural network model is characterized in that the output of the CNN convolution neural network is used as the input of the LSTM neural network model, and the CNN convolution neural network model can directly and automatically mine and extract sensitivity representing time sequence input parameter information from a large amount of time sequence input parameter informationThe CNN convolutional neural network model structure mainly comprises 4 parts: input layer (Input). The input layer is the input of the CNN convolutional neural network model, and generally, the time series parameters are 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 all time sequence input parameter information, 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, time series input parameter feature extraction enters a full connection layer, each nerve layer in the layer is in full connection with all neurons in the previous layer, and local feature information of time series input parameter values extracted by the convolution layer and the pooling layer is integrated. Meanwhile, in order to avoid the over-fitting phenomenon, a lost data (dropout) technology is added in the layer, the output value passing through the last layer of full connection layer is transmitted to the output layer, the pooling result of the last layer is connected together in an end-to-end mode to form the output layer and serve as the input of an LSTM neural network model, and the LSTM neural network model introduces a Memory Cell (Memory Cell) and hidden layer State (Cell State) mechanism to control the information transmission between the hidden layers. The memory unit of an LSTM neural network has 3 Gates (Gates) as Input Gate, forgetting Gate and Output Gate. Wherein, the input gate can control the addition or filtration of new information input by the LSTM neural network model;the forgetting gate can forget the input information of the LSTM neural network model which needs to be lost and keep useful information in the past; the output gate enables the memory unit to output only the input information of the LSTM neural network model 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 consists of a unit (Cell), an Input Gate (Input Gate), an Output Gate (Output Gate) and a forgetting Gate (formula Gate). The LSTM neural network model is suitable for predicting the change of the input quantity of the time sequence LSTM neural network model 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 to detect the input dependence information of the LSTM neural network model for a long time, and meanwhile, the problem of gradient disappearance is avoided. The LSTM adds a structure called a Memory Cell (Memory Cell) in a neural node of a hidden layer of a neuron internal structure RNN for memorizing dynamic change information Input by a past LSTM neural network model, and adds three gate structures (Input, form and Output) for controlling the use of Input historical information of the LSTM neural network model. Let the time series value of input for detecting LSTM neural network model input quantity be (x) 1 ，x 2 ，…，x T ) The hidden layer state is (h) 1 ，h 2 ，…，h T ) Then, time t has:
i t ＝sigmoid(W hi h t-1 +W xi X t ) (6)
f t ＝sigmoid(W hf h t-1 +W hf X t ) (7)
c t ＝f t ⊙c t-1 +i t ⊙tanh(W hc h t-1 +W xc X t ) (8)
o t ＝sigmoid(W ho h t-1 +W hx X t +W co c t ) (9)
h t ＝o t ⊙tanh(c t ) (10)
wherein i t 、f t 、O t Representing input, forget and output doors, c t Representing a cell, W h Representing the weight of the recursive connection, W x Representing the weight from the input layer to the hidden layer, sigmoid and tanh are two activation functions, and the output of the LSTM neural network model is a nonlinear value of the parameter level of the detected region.
(5) AANN self-association neural network model design of figure set
The CNN convolution-LSTM neural network models are output as corresponding input of an AANN self-association neural network model of a figure set, three parameters output by the AANN self-association neural network model of the figure set are x, t and 1-f respectively, x is a real value of a detected parameter, t is credibility, f is unreliability, 1-f-t is uncertainty, the value of the detected parameter figure set formed by the x, the t and the 1-f is [ x, (t, 1-f) ], the AANN self-association neural network model of the figure set is output as TDL input according to a beat delay line, and output as output of a parameter detection module according to the TDL. The AANN self-associative neural network model is a feed-forward self-associative neural network (AANN) with a special structure, and the AANN self-associative neural network model structure includes an input layer, a number of hidden layers, and an output layer. The input parameter decompression method includes the steps of firstly compressing input data information through an input layer, a mapping layer and a bottleneck layer of input parameters, extracting a most representative low-dimensional subspace reflecting an input parameter system structure from a high-dimensional parameter space of the input parameters, effectively filtering noise and measurement errors in the input parameter data, decompressing the input parameters through the bottleneck layer, the demapping layer and an output layer, and restoring the previously compressed information to all parameter values, so that reconstruction of all input parameter data is achieved. In order to achieve the purpose of compressing input parameter information, the number of nodes of a bottleneck layer of an AANN self-association neural network model is obviously smaller than that of an input layer, and in order to prevent the formation of simple single mapping between input and output layers of input parameters, except that an excitation function of the output layer adopts a linear function, other layers all adopt nonlinear excitation functions. In essence, the first layer of the hidden layer of the AANN auto-associative neural network model is called as a mapping layer, and the node transfer function of the mapping layer can be an S-shaped function or other similar nonlinear functions; the second layer of the hidden layer is called a bottleneck layer, the dimension of the bottleneck layer is the minimum in the network, the transfer function of the second layer can be linear or nonlinear, the bottleneck layer avoids the mapping relation that the one-to-one output and input are equal, which is easy to realize, the bottleneck layer enables the network to encode and compress the input parameter signals to obtain the relevant models of the input data, and input parameter decoding and decompression are carried out behind the bottleneck layer to generate the estimated values of the input parameter signals; the third layer or the last layer of the hidden layer is called a demapping layer, the node transfer function of the demapping layer is a generally nonlinear S-shaped function, and the self-associative neural network is trained by an error back propagation algorithm.
2. ANFIS neural network model design
The outputs of the 2 parameter detection modules and the fuzzy wavelet neural network model are used as corresponding inputs of the ANFIS neural network model; the ANFIS neural network model is an Adaptive Fuzzy Inference System based on a neural network, also called an Adaptive neural-Fuzzy Inference System (Adaptive neural-Fuzzy Inference System), and organically combines the neural network and the Adaptive Fuzzy Inference System, thereby not only playing the advantages of the neural network and the Adaptive Fuzzy Inference System, but also making up the respective defects. The fuzzy membership function and the fuzzy rule in the ANFIS neural network model are obtained by learning known historical data of a large amount of input parameter information, and the ANFIS neural network model is mainly characterized by a data-based modeling method instead of being given arbitrarily based on experience or intuition. The main operation steps of the ANFIS neural network model are as follows:
layer 1: and fuzzifying the historical data of the input parameter information, wherein the corresponding output of each node can be represented as:
and the expression n is the number of the input membership functions of each network, and the membership functions adopt Gaussian membership functions.
Layer 2: realizing rule operation, outputting the applicability of the rule, and multiplying the rule operation of the ANFIS neural network model by the following multiplication:
layer 3: normalizing the applicability of each rule:
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:
layer 5: the single node of the layer is a fixed node, and the output of the ANFIS neural network model is calculated as follows:
the condition parameters determining the shape of the membership function and the conclusion parameters of the inference rule in the ANFIS neural network model 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 ANFIS neural network model, firstly, an input signal is transmitted to the layer 4 along the forward direction of the network, and a conclusion parameter is adjusted by adopting a least square estimation algorithm; the signal continues to propagate forward along the network to the output layer. The ANFIS neural network model reversely propagates the obtained error signals along the network, and the condition parameters are updated by a gradient method. By adjusting the given parameters in the ANFIS neural network model in this way, the global optimum point of the conclusion parameters can be obtained, so that the dimension of the search space in the gradient method can be reduced, and the convergence speed of the ANFIS neural network model parameters can be increased. And inputting parameter historical data of the ANFIS neural network model, and outputting the ANFIS neural network model as the attitude expected value of the garbage removal device.
3. Fuzzy wavelet neural network model design
The time sequence yaw angle, pitch angle and roll angle output by the MPU6050 attitude sensor are used as the input of a corresponding parameter detection module, the output of the parameter detection module is used as the input of a fuzzy wavelet neural network model, the output of an ANFIS neural network model and the output of a fuzzy wavelet neural network model, and the attitude error and the error change rate of the garbage removal device are respectively used as the input of a parameter self-adjusting factor fuzzy controller and a PID controller; the Fuzzy Wavelet Network (FWNN) based on the FWNN has good intelligence, robustness, stability and index tracking rapidity, and the Fuzzy Wavelet neural Network comprises two parts: fuzzy Neural Networks (FNNs) and Wavelet Neural Networks (WNNs). The fuzzy neural network comprises 4 basic layers: the first layer is an input layer, and each input vector corresponds to one neuron; each neuron of the second layer represents a linguistic variable value; each neuron of the third layer represents a fuzzy rule; the fourth layer is a normalization layer. Meanwhile, the input of the fuzzy neural network is used as the input of the wavelet neural network, and each fuzzy rule corresponds to one wavelet network. The wavelet basis function is a wavelet basis group obtained by shifting the wavelet function, so that wavelet neural networks generated by different scale functions can capture the characteristics of different time domains and frequency domains, and different fuzzy reasoning selects corresponding wavelet networks. The wavelet has the characteristic of multi-resolution analysis, if the wavelet function is used as the excitation function of the neural network neurons, the expansion and the translation of each neuron can be adjusted, smooth functions can be learned by selecting low-scale parameters, local singular functions can be learned with higher precision by increasing the scale, and the ANN precision is higher than that of the same neuron number and parameters. The fuzzy wavelet network is realized by 5 basic layers of input, fuzzification, reasoning, wavelet network layer and de-fuzzification layer, and the number of the neural network nodes in each layer is n, nxM, M, M and 3 respectively. Once the number of inputs n and rules M is decided, the structure of the FWNN model is decided. Wherein the input of the fuzzy wavelet neural network is 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;
is a function of the wavelet, and is,
the output value of the linear combination of the local model wavelet network corresponding to the rule i is as follows:
the first layer is an input layer: each node of the layer is directly connected to each component x of the input vector j Connecting, converting the input value X to [ X ] 1 ，x 2 ，…x n ]Transfer to the next layer; the second layer calculates the membership function value corresponding to each input variable; the third layer calculates the applicability of each rule; the fourth layer is wavelet network layer output and is mainly used for output compensation; the fifth layer is a control signal output layer, also called a defuzzification layer, and the defuzzification calculation is carried out on the control signal output layer. And outputting the fuzzy wavelet neural network model as an actual value of the attitude of the garbage removal device.
4. Parameter self-adjusting factor fuzzy controller design
The attitude error and the error change rate of the garbage removal device output by the ANFIS neural network model and the fuzzy wavelet neural network model are respectively used as the input of a parameter self-adjusting factor fuzzy controller and a PID controller, the output of the PID controller is used as the input of an LSTM neural network model, the output of the LSTM neural network model and the output of the parameter self-adjusting factor fuzzy controller are used as the corresponding input of an NARX neural network controller, and the output of the NARX neural network controller is respectively used as the yaw angle, pitch angle and roll angle control quantity of the garbage removal device; the parameter self-adjusting factor fuzzy controller is formed by connecting a fuzzy control part and an integral part in parallel, the control rule of the fuzzy controller is changed by adopting a self-adjusting factor, a more optimal control rule is used for controlling, the performance of the fuzzy controller is improved by adjusting the self-adjusting factor, when the attitude grade error of the garbage removing device is larger, the main task of the control system is to eliminate the error, and at the moment, the self-adjusting factor takes a larger value to eliminate the existence of the attitude grade error of the garbage removing device as soon as possible; when the error is small, the system is close to a steady state, the main control factor is to stabilize the system as soon as possible, the ascending speed of the system is accelerated, the control function of the attitude grade error change of the garbage removing device is highlighted for reducing the overshoot of the system, and the self-adjusting factor is selected to be small; when the system response is close to the desired value, both may take the same weight since the error and its variation are smaller at this time. The LSTM neural network model refers to the design process of the LSTM neural network model in the parameter detection module of the patent.
5. NARX neural network controller design
The output of the LSTM neural network model and the parameter self-adjusting factor fuzzy controller is used as the corresponding input of the NARX neural network controller, and the output of the NARX neural network controller is respectively used as the yaw angle, pitch angle and roll angle control quantity of the garbage removal device; the NARX neural network controller refers to the NARX neural network model design process in the parameter detection module of this patent.
Design example of environment parameter acquisition and control platform
According to the actual condition of the environment big data detection system, the system is provided with a plane layout installation diagram of detection nodes, gateway nodes and a field monitoring end of an environment parameter acquisition and control platform, wherein sensors of the detection nodes are arranged in all directions of the environment in a balanced manner according to the detection requirement, and the system is used for acquiring and adjusting environment parameters.
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. Intelligent rubbish clearance and environmental parameter big data thing networking system, its characterized in that: the garbage cleaning system is composed of an environmental parameter acquisition and control platform and an environmental parameter processing and garbage cleaning subsystem, wherein the environmental parameter acquisition and control platform is used for detecting, adjusting and monitoring environmental parameters; the environmental parameter big data processing subsystem realizes the control of environmental parameter processing and garbage cleaning;
the environmental parameter processing and garbage cleaning subsystem consists of a parameter detection module, an ANFIS neural network model, a parameter self-adjusting factor fuzzy controller, a PID controller, an LSTM neural network model, an NARX neural network controller and a fuzzy wavelet neural network model;
the outputs of a plurality of groups of ammonia gas, hydrogen sulfide and carbon dioxide sensors are used as the inputs of corresponding parameter detection modules, the outputs of a plurality of groups of temperature, humidity and wind speed sensors are used as the inputs of corresponding parameter detection modules, the outputs of the parameter detection modules and a fuzzy wavelet neural network model are used as the corresponding inputs of an ANFIS neural network model, attitude errors and error change rates of a garbage removal device output by the ANFIS neural network model and the fuzzy wavelet neural network model are respectively used as the inputs of a parameter self-adjusting factor fuzzy controller and a PID controller, the output of the PID controller is used as the input of an LSTM neural network model, the outputs of the LSTM neural network model and the parameter self-adjusting factor fuzzy controller are respectively used as the corresponding inputs of an NARX neural network controller, the outputs of the NARX neural network controller are respectively used as the yaw angle, pitch angle and roll angle control quantity of the garbage removal device, and the time sequence yaw angle, pitch angle and roll angle control quantity output by the sensors, The pitch angle and the roll angle are used as the input of the corresponding parameter detection module, and the output of the parameter detection module is used as the input of the fuzzy wavelet neural network model.
2. The intelligent garbage cleaning and environment parameter big data internet of things system according to claim 1, wherein: the parameter detection module consists of an NARX neural network model, an Adaline neural network model, a K-means cluster classifier, a CNN convolution-LSTM neural network model, an AANN self-association neural network model of a figure set and a beat delay line TDL.
3. The intelligent garbage cleaning and environment parameter big data internet of things system according to claim 2, characterized in that: the time sequence parameter values of the sensed environment sensed by a plurality of groups of parameter sensors are respectively used as the input of corresponding NARX neural network models and Adaline neural network models, the difference between the output of the NARX neural network models and the Adaline neural network models is used as the fluctuation value of the level of the sensed parameter, the output of a plurality of time sequence parameter fluctuation values and a plurality of Adaline neural network models are respectively used as the input of corresponding K-means cluster classifiers, the output of a plurality of types of time sequence parameter fluctuation values and the output of the Adaline neural network models output by 2K-means cluster classifiers are respectively used as the input of corresponding CNN convolution-LSTM neural network models, the output of the CNN convolution-LSTM neural network models is used as the corresponding input of the AANN self-coupling neural network models of the figure set, the three parameters output by the AANN self-coupling neural network models of the figure set are respectively x, t and 1-f, x is the real value of the detected parameter, t is the credibility, f is the uncertainty, 1-f is the sum of the credibility and the uncertainty, 1-f-t is the uncertainty, the values of the detected parameter value set formed by x, t and 1-f are [ x, (t, 1-f) ], the output of the AANN auto-associative neural network model of the value set is used as the input of the beat-to-beat delay line TDL, and the output of the beat-to-beat delay line TDL is used as the output of the parameter detection module.
4. The intelligent garbage cleaning and environment parameter big data internet of things system according to claim 1, wherein: the environment parameter acquisition and control platform is composed of a detection node, a control node, a gateway node, an on-site monitoring end, a cloud platform and a mobile end APP.
5. The intelligent garbage cleaning and environment parameter big data internet of things system according to claim 4, wherein: the detection node acquires environmental parameters and uploads the environmental parameters to the cloud platform through the gateway node, and data provided by the cloud platform are transmitted to the mobile terminal APP, the mobile terminal APP can monitor the environmental parameters and adjust the external equipment of the control node in real time through the cloud platform, the detection node and the control node are responsible for acquiring environmental parameter information and controlling the environmental conditioning equipment, bidirectional communication of the detection node, the control node, the field monitoring terminal, the cloud platform and the mobile terminal APP is realized through the gateway node, and environmental parameter acquisition, processing and environmental conditioning equipment control are realized.
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