CN106292282A - Reading intelligent agriculture environmental monitoring systems based on big data - Google Patents

Abstract

The invention provides reading intelligent agriculture environmental monitoring system based on big data, including Cloud Servers based on big data, home server, sensing system and wireless sense network, described Cloud Server based on big data is connected with home server by wireless sense network, home server is connected with sensing system and wireless sense network, and described home server is also associated with device controller by wireless sense network.The present invention is with sensing system and various awareness apparatus as carrier, with application software technology as service core, realize the environmental information monitoring of real-time and pinpoint accuracy, reliable growing environment is provided for crops, and management personnel can inquire about each node current context information in each warmhouse booth and booth at any time, the data gathered are analyzed and process by monitor supervision platform, pass through device controller, system can automatically start and regulate each equipment, provides an optimum growing environment for crops.

Description

Reading intelligent agriculture environmental monitoring systems based on big data

Technical field

The present invention relates to agricultural monitoring technical field, be specifically related to reading intelligent agriculture environmental monitoring system based on big data.

Background technology

In correlation technique, the crops in greenhouse and flowers cultivate wait during understand and be affected by several factors, such as temperature The ambient parameters such as the temperature humidity of room, the soil moisture, soil moisture, intensity of illumination, gas concentration lwevel, therefore at agriculture in greenhouse Cultivating process in the control of ambient parameter is seemed increasingly important.And related data shows, indoor can automatically controlled Temperature, humidity, irrigate, ventilate, in the greenhouse of gas concentration lwevel and illumination, every square metre can produce in greenhouse one season Fructus Lycopersici esculenti 30kg～ 50kg, Fructus Cucumidis sativi 40kg, be equivalent to outdoor cropping yield more than 10 times.Other kinds crop yield in such a case is also incited somebody to action To being obviously improved.At present, the monitoring for most of agricultural greenhouse greenhouse-environments is passive, backward, information-based and intelligent water Flat the highest, and there is problems and unsafe factor, such as: use manual measurement, the mode of record, it is impossible to enough 24 hours are real Time, dynamically monitor；Unstable, error is greatly, be easily disturbed；Use manual monitoring, ageing very poor, especially for famous and precious agriculture Industry, it is sensitive to environmental change, and once environment changes and takes measures the most in time, it would be possible to cause the most economic damage Lose.

Summary of the invention

For solving the problems referred to above, it is desirable to provide reading intelligent agriculture environmental monitoring system based on big data.

The purpose of the present invention realizes by the following technical solutions:

Reading intelligent agriculture environmental monitoring systems based on big data, including Cloud Servers based on big data, home server, Sensing system and wireless sense network, described Cloud Servers based on big data are by wireless sense network with home server even Connecing, home server is connected with sensing system and wireless sense network, and described home server is also connected with by wireless sense network There is device controller.

Preferably, described device controller connects blower fan, shutter, air-conditioning, electric light and Irrigation Machine.

The invention have the benefit that with sensing system and various awareness apparatus as carrier, with application software technology be Service core, it is achieved the environmental information monitoring of real-time and pinpoint accuracy, provides reliable growing environment, and management for crops Personnel can inquire about each node current context information in each warmhouse booth and booth at any time, the monitor supervision platform data to gathering Being analyzed and process, by device controller, system can start and regulation blower fan, shutter, air-conditioning, illumination, filling automatically The equipment such as irrigate, provide an optimum growing environment for crops.

Accompanying drawing explanation

The invention will be further described to utilize accompanying drawing, but the embodiment in accompanying drawing does not constitute any limit to the present invention System, for those of ordinary skill in the art, on the premise of not paying creative work, it is also possible to obtain according to the following drawings Other accompanying drawing.

Fig. 1 is present configuration schematic diagram；

Fig. 2 is the schematic diagram of inventive sensor trouble-shooter.

Reference:

Signals collecting filtration module 1, fault signature extraction module 2, online characteristic extracting module 3, the preferred mould of characteristic vector Block 4, failure modes identification module 5, failure mode more new module 6, health records module 7.

Detailed description of the invention

The invention will be further described with the following Examples.

Application scenarios 1

See Fig. 1, Fig. 2, the reading intelligent agriculture environmental monitoring systems based on big data of an embodiment of this application scene, Including Cloud Servers based on big data, home server, sensing system and wireless sense network, described clouds based on big data Server is connected with home server by wireless sense network, and home server is connected with sensing system and wireless sense network, Described home server is also associated with device controller by wireless sense network.

The above embodiment of the present invention is with sensing system and various awareness apparatus as carrier, with application software technology for service Core, it is achieved the environmental information monitoring of real-time and pinpoint accuracy, provides reliable growing environment, and management personnel for crops Can inquire about each node current context information in each warmhouse booth and booth at any time, the data gathered are carried out by monitor supervision platform Analyze with process, by threshold range is previously set, system can automatically start with regulation blower fan, shutter, air-conditioning, illumination, The equipment such as irrigation, provide an optimum growing environment for crops.

Preferably, described sensing system includes aerial temperature and humidity sensor, soil temperature-moisture sensor, illumination sensing Device, CO2 sensor.

This preferred embodiment arranges aerial temperature and humidity sensor, soil temperature-moisture sensor, optical sensor, CO2 sensing Device, it is achieved the collection of agricultural environment data.

Preferably, described reading intelligent agriculture environmental monitoring system also includes that the sensor fault diagnosing each sensor is examined Disconnected device, described sensor malfunction diagnostic device includes signals collecting filtration module 1, fault signature extraction module 2, online feature Extraction module 3, characteristic vector preferred module 4, failure modes identification module 5, failure mode more new module 6 and health records module 7。

The above embodiment of the present invention arranges sensor malfunction diagnostic device and achieves the fast of sensor malfunction diagnostic device Speed is built, and is conducive to monitoring each sensor, it is ensured that the environmental data collecting of monitoring system.

Preferably, described signals collecting filtration module 1 is used for gathering historical sensor signal and on-line sensor test letter Number, and use combination form wave filter to be filtered signal processing；

This preferred embodiment arranges combination form wave filter, can effectively remove the various noise jamming of signal, preferably The primitive character information of stick signal.

Preferably, described fault signature extraction module 2 is for carrying out integrated experience to filtered historical sensor signal Mode decomposition (EEMD) processes, and extracts the Energy-Entropy of integrated empirical mode decomposition (EEMD) as training feature vector, including:

(1) the historical sensor signal by collection is divided into the fault-signal of nominal situation signal and plurality of classes；

(2) described historical sensor signal carries out integrated empirical mode decomposition (EEMD) process, it is thus achieved that described history passes The intrinsic mode function of sensor signal and remainder function；

(3) intrinsic mode function and the Energy-Entropy of remainder function of described historical sensor signal are calculated；

(4) Energy-Entropy of historical sensor signal is normalized, extracts the Energy-Entropy after normalization as instruction Practice characteristic vector；

Described online characteristic extracting module 3 is for carrying out integrated empirical modal to filtered on-line sensor test signal Decompose (EEMD) to process, and extract the Energy-Entropy of integrated empirical mode decomposition (EEMD) as characteristic vector to be measured, including:

(1) described on-line sensor test signal is carried out EEMD process, it is thus achieved that described on-line sensor test signal Intrinsic mode function and remainder function；

(2) intrinsic mode function and the Energy-Entropy of remainder function of described on-line sensor test signal are calculated；

(3) Energy-Entropy of on-line sensor test signal is normalized, extracts the Energy-Entropy after normalization and make For characteristic vector to be measured.

This preferred embodiment carries out integrated empirical mode decomposition (EEMD) to the sensor signal gathered and processes, it is possible to effectively Elimination modal overlap phenomenon, the effect of decomposition is preferable.

Preferably, training feature vector is carried out similar with characteristic vector to be measured by described characteristic vector preferred module 4 respectively Property tolerance, the characteristic vector high for similarity reject, including:

(1) two vector similarities function S (X, Y) are defined:

$S(X,Y)=\frac{\mathrm{cov}(X,Y)}{\sqrt{D\left(X\right)}\sqrt{D\left(Y\right)}}$

In formula, X, Y represent that two characteristic vectors, cov (X, Y) are the covariance of X and Y respectively,For X, Y standard deviation；

For any two training feature vector X₁、X₂, and any two characteristic vector D to be measured₁、D₂, it is respectively adopted similar Its similarity is measured by degree function, obtains S (X₁,X₂) and S (D₁,D₂)；

(2) for S (X₁,X₂) and S (D₁,D₂), if S is (X₁,X₂)>T₁, T₁∈ (0.9,1), only chooses X₁As training characteristics Vector, if S is (D₁,D₂)>T₂, T₂∈ (0.95,1), only chooses D₁As characteristic vector to be measured.

This preferred embodiment screens characteristic vector by measuring similarity, it is possible to reduce amount of calculation, improves efficiency.

Preferably, described failure modes identification module 5 is for using the least square method supporting vector machine of optimization to treat described Survey characteristic vector and carry out failure modes identification, select optimize submodule, training submodule and identify submodule, specifically including parameter For:

Described parameter selects the kernel function optimizing submodule for constructing least square method supporting vector machine, and to least square The structural parameters of support vector machine use multi-population to work in coordination with Chaos particle swarm optimization algorithm and are optimized；

Described training submodule, for using many classification sides of the least square support vector machines of the optimum binary tree structure of improvement Method, instructs the least square method supporting vector machine after structure parameter optimizing using the training feature vector obtained as training sample Practice, and build sensor fault diagnosis model；

Described identification submodule is used for using described sensor fault diagnosis model that described characteristic vector to be measured carries out event Barrier Classification and Identification；

Wherein, it is considered to Polynomial kernel function and the superiority of RBF kernel function, the core letter of described least square method supporting vector machine Number is configured to:

K=(1-δ) (xx_i+1)^p+δexp(-‖x-x_i‖²/σ²)

In formula, δ is the structure adjusting factor, and the span of δ is set as [0.45,0.55], and p is the rank of Polynomial kernel function Number, σ²For RBF kernel functional parameter.

Wherein, shown employing multi-population is worked in coordination with Chaos particle swarm optimization algorithm and is optimized, including:

(1) to main population and initialize from population respectively, randomly generate initial as particle of one group of parameter Position and initial velocity, definition fitness function is:

$S=\frac{1}{N}\underset{i=1}{\overset{N}{\Σ}}\left|\frac{q_{i}W}{q_{i}W+(1-q_i)T}\right|\×100\%$

In formula, N is the total number of training sample, and W is that bug is classified number, and T is that fault is correctly classified number, q_iFor certainly The weight coefficient set, q_iSpan be set as [0.4,0.5]；

(2) renewal from population is carried out, in every generation renewal process, according to fitness function, from population respectively The speed of more new particle and position, then will be experienced in its history adaptive optimal control angle value and main population body each particle The fitness value of desired positions compares, if more preferably, then as current global optimum position；

(3) described global optimum position is carried out the optimum particle position in chaos optimization, and iteration current sequence and speed Degree, generates optimal particle sequence；

(4) in the main population of every generation, choose from population optimum particle, and the position of more new particle and speed, Until reaching maximum iteration time or meeting the error requirements of fitness function.

Wherein, many sorting techniques of the least square support vector machines of the optimum binary tree structure of described improvement specifically include:

(1) calculate the standard variance of all training samples and two classifications j,Between Separatory measure；

(2) output minimum separation estimate correspondence j,

(3) after being optimized the structural parameters of least square method supporting vector machine, the least square setting up two classification props up Hold vector machine in order to train jth class andThe training sample of class, forms optimum two classification least square method supporting vector machines, output The parameter of discriminant function,The training sample of class is merged in j class, constitutes new j class training sample；

(4) all of classification is circulated training according to (1)-(3), until the optimum root node of output；

(5) according to the categorised decision tree of above output result composition least square method supporting vector machine, then to remaining instruction Practice sample and carry out classifying quality test.

This preferred embodiment is in order to improve the precision of fault diagnosis, and employing training speed is fast, generalization ability strong and robustness Preferably least square support vector machines is as grader, and proposes the many sorting techniques improving optimum binary tree structure, with between class Separatory measure substitutes the weights in binary tree structure, the nicety of grading that improve and classification speed；In view of RBF kernel function it is Karyomerite function, Polynomial kernel function is overall situation kernel function, and karyomerite function learning ability is strong, and Generalization Capability is relatively weak, and Overall situation kernel function Generalization Capability is strong, and learning capacity is relatively weak, carries out on the basis of the advantage of summary two class kernel function The Kernel of least square method supporting vector machine, optimizes classification performance and the Generalization Capability of least square method supporting vector machine； The multi-population of design works in coordination with Chaos particle swarm optimization algorithm, has preferable convergence rate, and has preferable global and local Optimizing performance, it is possible to jump out Local Extremum timely, finds the optimal value of the overall situation, thus uses multi-population to work in coordination with chaotic particle The structural parameters of least square method supporting vector machine are optimized by colony optimization algorithm, and effect of optimization is good.

Preferably, described failure mode more new module 6, for being updated training set, is continued to optimize sensor fault and is examined Disconnected model, including:

(1) when sensor fault diagnosis model cannot carry out effective failure modes to characteristic vector to be measured, by feature to be measured Vector is as new training feature vector；

(2) training sample is updated by new training feature vector, to the least square support after structure parameter optimizing Vector machine is trained, and builds the sensor fault diagnosis model made new advances；

(3) use new sensor fault diagnosis model that described characteristic vector to be measured is carried out failure modes identification, complete Failure mode updates.

This preferred embodiment arranges failure mode more new module 6, to improve adaptation ability and the range of application of model.

Preferably, described health records module 7 includes sub module stored and secure access submodule, described sub module stored Use storage model based on cloud storage, specifically, be encrypted after fault message is compressed, be uploaded to cloud storage, Described secure access submodule is for conducting interviews to information, specifically, corresponding to sub module stored, downloads data to this Ground, after using corresponding secret key to be unlocked, then carries out decompressing to read information.

This preferred embodiment arranges health records module 7, on the one hand ensure that information security, on the other hand can be the most right Fault conducts interviews, it is simple to search problem.

In this application scenarios, set threshold value T₁Value be 0.96, the monitoring velocity phase of sensor malfunction diagnostic device To improve 10%, the monitoring accuracy of sensor malfunction diagnostic device improves 12% relatively.

Application scenarios 2

See Fig. 1, Fig. 2, the reading intelligent agriculture environmental monitoring systems based on big data of an embodiment of this application scene, Including Cloud Servers based on big data, home server, sensing system and wireless sense network, described clouds based on big data Server is connected with home server by wireless sense network, and home server is connected with sensing system and wireless sense network, Described home server is also associated with device controller by wireless sense network.

The above embodiment of the present invention is with sensing system and various awareness apparatus as carrier, with application software technology for service Core, it is achieved the environmental information monitoring of real-time and pinpoint accuracy, provides reliable growing environment, and management personnel for crops Can inquire about each node current context information in each warmhouse booth and booth at any time, the data gathered are carried out by monitor supervision platform Analyze with process, by threshold range is previously set, system can automatically start with regulation blower fan, shutter, air-conditioning, illumination, The equipment such as irrigation, provide an optimum growing environment for crops.

Preferably, described sensing system includes aerial temperature and humidity sensor, soil temperature-moisture sensor, illumination sensing Device, CO2 sensor.

This preferred embodiment arranges aerial temperature and humidity sensor, soil temperature-moisture sensor, optical sensor, CO2 sensing Device, it is achieved the collection of agricultural environment data.

Preferably, described reading intelligent agriculture environmental monitoring system also includes that the sensor fault diagnosing each sensor is examined Disconnected device, described sensor malfunction diagnostic device includes signals collecting filtration module 1, fault signature extraction module 2, online feature Extraction module 3, characteristic vector preferred module 4, failure modes identification module 5, failure mode more new module 6 and health records module 7。

The above embodiment of the present invention arranges sensor malfunction diagnostic device and achieves the fast of sensor malfunction diagnostic device Speed is built, and is conducive to monitoring each sensor, it is ensured that the environmental data collecting of monitoring system.

Preferably, described signals collecting filtration module 1 is used for gathering historical sensor signal and on-line sensor test letter Number, and use combination form wave filter to be filtered signal processing；

This preferred embodiment arranges combination form wave filter, can effectively remove the various noise jamming of signal, preferably The primitive character information of stick signal.

Preferably, described fault signature extraction module 2 is for carrying out integrated experience to filtered historical sensor signal Mode decomposition (EEMD) processes, and extracts the Energy-Entropy of integrated empirical mode decomposition (EEMD) as training feature vector, including:

(1) the historical sensor signal by collection is divided into the fault-signal of nominal situation signal and plurality of classes；

(2) described historical sensor signal carries out integrated empirical mode decomposition (EEMD) process, it is thus achieved that described history passes The intrinsic mode function of sensor signal and remainder function；

(3) intrinsic mode function and the Energy-Entropy of remainder function of described historical sensor signal are calculated；

(4) Energy-Entropy of historical sensor signal is normalized, extracts the Energy-Entropy after normalization as instruction Practice characteristic vector；

Described online characteristic extracting module 3 is for carrying out integrated empirical modal to filtered on-line sensor test signal Decompose (EEMD) to process, and extract the Energy-Entropy of integrated empirical mode decomposition (EEMD) as characteristic vector to be measured, including:

(1) described on-line sensor test signal is carried out EEMD process, it is thus achieved that described on-line sensor test signal Intrinsic mode function and remainder function；

(2) intrinsic mode function and the Energy-Entropy of remainder function of described on-line sensor test signal are calculated；

(3) Energy-Entropy of on-line sensor test signal is normalized, extracts the Energy-Entropy after normalization and make For characteristic vector to be measured.

This preferred embodiment carries out integrated empirical mode decomposition (EEMD) to the sensor signal gathered and processes, it is possible to effectively Elimination modal overlap phenomenon, the effect of decomposition is preferable.

Preferably, training feature vector is carried out similar with characteristic vector to be measured by described characteristic vector preferred module 4 respectively Property tolerance, the characteristic vector high for similarity reject, including:

(1) two vector similarities function S (X, Y) are defined:

$S(X,Y)=\frac{\mathrm{cov}(X,Y)}{\sqrt{D\left(X\right)}\sqrt{D\left(Y\right)}}$

In formula, X, Y represent that two characteristic vectors, cov (X, Y) are the covariance of X and Y respectively,For X, Y standard deviation；

For any two training feature vector X₁、X₂, and any two characteristic vector D to be measured₁、D₂, it is respectively adopted similar Its similarity is measured by degree function, obtains S (X₁,X₂) and S (D₁,D₂)；

(2) for S (X₁,X₂) and S (D₁,D₂), if S is (X₁,X₂)>T₁, T₁∈ (0.9,1), only chooses X₁As training characteristics Vector, if S is (D₁,D₂)>T₂, T₂∈ (0.95,1), only chooses D₁As characteristic vector to be measured.

This preferred embodiment screens characteristic vector by measuring similarity, it is possible to reduce amount of calculation, improves efficiency.

Preferably, described failure modes identification module 5 is for using the least square method supporting vector machine of optimization to treat described Survey characteristic vector and carry out failure modes identification, select optimize submodule, training submodule and identify submodule, specifically including parameter For:

Described parameter selects the kernel function optimizing submodule for constructing least square method supporting vector machine, and to least square The structural parameters of support vector machine use multi-population to work in coordination with Chaos particle swarm optimization algorithm and are optimized；

Described training submodule, for using many classification sides of the least square support vector machines of the optimum binary tree structure of improvement Method, instructs the least square method supporting vector machine after structure parameter optimizing using the training feature vector obtained as training sample Practice, and build sensor fault diagnosis model；

Described identification submodule is used for using described sensor fault diagnosis model that described characteristic vector to be measured carries out event Barrier Classification and Identification；

Wherein, it is considered to Polynomial kernel function and the superiority of RBF kernel function, the core letter of described least square method supporting vector machine Number is configured to:

K=(1-δ) (xx_i+1)^p+δexp(-‖x-x_i‖²/σ²)

In formula, δ is the structure adjusting factor, and the span of δ is set as [0.45,0.55], and p is the rank of Polynomial kernel function Number, σ²For RBF kernel functional parameter.

Wherein, shown employing multi-population is worked in coordination with Chaos particle swarm optimization algorithm and is optimized, including:

(1) to main population and initialize from population respectively, randomly generate initial as particle of one group of parameter Position and initial velocity, definition fitness function is:

$S=\frac{1}{N}\underset{i=1}{\overset{N}{\Σ}}\left|\frac{q_{i}W}{q_{i}W+(1-q_i)T}\right|\×100\%$

In formula, N is the total number of training sample, and W is that bug is classified number, and T is that fault is correctly classified number, q_iFor certainly The weight coefficient set, q_iSpan be set as [0.4,0.5]；

(2) renewal from population is carried out, in every generation renewal process, according to fitness function, from population respectively The speed of more new particle and position, then will be experienced in its history adaptive optimal control angle value and main population body each particle The fitness value of desired positions compares, if more preferably, then as current global optimum position；

(3) described global optimum position is carried out the optimum particle position in chaos optimization, and iteration current sequence and speed Degree, generates optimal particle sequence；

(4) in the main population of every generation, choose from population optimum particle, and the position of more new particle and speed, Until reaching maximum iteration time or meeting the error requirements of fitness function.

Wherein, many sorting techniques of the least square support vector machines of the optimum binary tree structure of described improvement specifically include:

(1) calculate the standard variance of all training samples and two classifications j,Between Separatory measure；

(2) output minimum separation estimate correspondence j,

(3) after being optimized the structural parameters of least square method supporting vector machine, the least square setting up two classification props up Hold vector machine in order to train jth class andThe training sample of class, forms optimum two classification least square method supporting vector machines, output The parameter of discriminant function,The training sample of class is merged in j class, constitutes new j class training sample；

(4) all of classification is circulated training according to (1)-(3), until the optimum root node of output；

(5) according to the categorised decision tree of above output result composition least square method supporting vector machine, then to remaining instruction Practice sample and carry out classifying quality test.

This preferred embodiment is in order to improve the precision of fault diagnosis, and employing training speed is fast, generalization ability strong and robustness Preferably least square support vector machines is as grader, and proposes the many sorting techniques improving optimum binary tree structure, with between class Separatory measure substitutes the weights in binary tree structure, the nicety of grading that improve and classification speed；In view of RBF kernel function it is Karyomerite function, Polynomial kernel function is overall situation kernel function, and karyomerite function learning ability is strong, and Generalization Capability is relatively weak, and Overall situation kernel function Generalization Capability is strong, and learning capacity is relatively weak, carries out on the basis of the advantage of summary two class kernel function The Kernel of least square method supporting vector machine, optimizes classification performance and the Generalization Capability of least square method supporting vector machine； The multi-population of design works in coordination with Chaos particle swarm optimization algorithm, has preferable convergence rate, and has preferable global and local Optimizing performance, it is possible to jump out Local Extremum timely, finds the optimal value of the overall situation, thus uses multi-population to work in coordination with chaotic particle The structural parameters of least square method supporting vector machine are optimized by colony optimization algorithm, and effect of optimization is good.

Preferably, described failure mode more new module 6, for being updated training set, is continued to optimize sensor fault and is examined Disconnected model, including:

(1) when sensor fault diagnosis model cannot carry out effective failure modes to characteristic vector to be measured, by feature to be measured Vector is as new training feature vector；

(2) training sample is updated by new training feature vector, to the least square support after structure parameter optimizing Vector machine is trained, and builds the sensor fault diagnosis model made new advances；

(3) use new sensor fault diagnosis model that described characteristic vector to be measured is carried out failure modes identification, complete Failure mode updates.

This preferred embodiment arranges failure mode more new module 6, to improve adaptation ability and the range of application of model.

Preferably, described health records module 7 includes sub module stored and secure access submodule, described sub module stored Use storage model based on cloud storage, specifically, be encrypted after fault message is compressed, be uploaded to cloud storage, Described secure access submodule is for conducting interviews to information, specifically, corresponding to sub module stored, downloads data to this Ground, after using corresponding secret key to be unlocked, then carries out decompressing to read information.

This preferred embodiment arranges health records module 7, on the one hand ensure that information security, on the other hand can be the most right Fault conducts interviews, it is simple to search problem.

In this application scenarios, set threshold value T₁Value be 0.95, the monitoring velocity phase of sensor malfunction diagnostic device To improve 11%, the monitoring accuracy of sensor malfunction diagnostic device improves 11% relatively.

Application scenarios 3

See Fig. 1, Fig. 2, the reading intelligent agriculture environmental monitoring systems based on big data of an embodiment of this application scene, Including Cloud Servers based on big data, home server, sensing system and wireless sense network, described clouds based on big data Server is connected with home server by wireless sense network, and home server is connected with sensing system and wireless sense network, Described home server is also associated with device controller by wireless sense network.

The above embodiment of the present invention is with sensing system and various awareness apparatus as carrier, with application software technology for service Core, it is achieved the environmental information monitoring of real-time and pinpoint accuracy, provides reliable growing environment, and management personnel for crops Can inquire about each node current context information in each warmhouse booth and booth at any time, the data gathered are carried out by monitor supervision platform Analyze with process, by threshold range is previously set, system can automatically start with regulation blower fan, shutter, air-conditioning, illumination, The equipment such as irrigation, provide an optimum growing environment for crops.

Preferably, described sensing system includes aerial temperature and humidity sensor, soil temperature-moisture sensor, illumination sensing Device, CO2 sensor.

This preferred embodiment arranges aerial temperature and humidity sensor, soil temperature-moisture sensor, optical sensor, CO2 sensing Device, it is achieved the collection of agricultural environment data.

Preferably, described reading intelligent agriculture environmental monitoring system also includes that the sensor fault diagnosing each sensor is examined Disconnected device, described sensor malfunction diagnostic device includes signals collecting filtration module 1, fault signature extraction module 2, online feature Extraction module 3, characteristic vector preferred module 4, failure modes identification module 5, failure mode more new module 6 and health records module 7。

The above embodiment of the present invention arranges sensor malfunction diagnostic device and achieves the fast of sensor malfunction diagnostic device Speed is built, and is conducive to monitoring each sensor, it is ensured that the environmental data collecting of monitoring system.

Preferably, described signals collecting filtration module 1 is used for gathering historical sensor signal and on-line sensor test letter Number, and use combination form wave filter to be filtered signal processing；

This preferred embodiment arranges combination form wave filter, can effectively remove the various noise jamming of signal, preferably The primitive character information of stick signal.

Preferably, described fault signature extraction module 2 is for carrying out integrated experience to filtered historical sensor signal Mode decomposition (EEMD) processes, and extracts the Energy-Entropy of integrated empirical mode decomposition (EEMD) as training feature vector, including:

(1) the historical sensor signal by collection is divided into the fault-signal of nominal situation signal and plurality of classes；

(2) described historical sensor signal carries out integrated empirical mode decomposition (EEMD) process, it is thus achieved that described history passes The intrinsic mode function of sensor signal and remainder function；

(3) intrinsic mode function and the Energy-Entropy of remainder function of described historical sensor signal are calculated；

(4) Energy-Entropy of historical sensor signal is normalized, extracts the Energy-Entropy after normalization as instruction Practice characteristic vector；

Described online characteristic extracting module 3 is for carrying out integrated empirical modal to filtered on-line sensor test signal Decompose (EEMD) to process, and extract the Energy-Entropy of integrated empirical mode decomposition (EEMD) as characteristic vector to be measured, including:

(1) described on-line sensor test signal is carried out EEMD process, it is thus achieved that described on-line sensor test signal Intrinsic mode function and remainder function；

(2) intrinsic mode function and the Energy-Entropy of remainder function of described on-line sensor test signal are calculated；

(3) Energy-Entropy of on-line sensor test signal is normalized, extracts the Energy-Entropy after normalization and make For characteristic vector to be measured.

This preferred embodiment carries out integrated empirical mode decomposition (EEMD) to the sensor signal gathered and processes, it is possible to effectively Elimination modal overlap phenomenon, the effect of decomposition is preferable.

Preferably, training feature vector is carried out similar with characteristic vector to be measured by described characteristic vector preferred module 4 respectively Property tolerance, the characteristic vector high for similarity reject, including:

(1) two vector similarities function S (X, Y) are defined:

$S(X,Y)=\frac{\mathrm{cov}(X,Y)}{\sqrt{D\left(X\right)}\sqrt{D\left(Y\right)}}$

In formula, X, Y represent that two characteristic vectors, cov (X, Y) are the covariance of X and Y respectively,For X, Y standard deviation；

For any two training feature vector X₁、X₂, and any two characteristic vector D to be measured₁、D₂, it is respectively adopted similar Its similarity is measured by degree function, obtains S (X₁,X₂) and S (D₁,D₂)；

(2) for S (X₁,X₂) and S (D₁,D₂), if S is (X₁,X₂)>T₁, T₁∈ (0.9,1), only chooses X₁As training characteristics Vector, if S is (D₁,D₂)>T₂, T₂∈ (0.95,1), only chooses D₁As characteristic vector to be measured.

This preferred embodiment screens characteristic vector by measuring similarity, it is possible to reduce amount of calculation, improves efficiency.

Preferably, described failure modes identification module 5 is for using the least square method supporting vector machine of optimization to treat described Survey characteristic vector and carry out failure modes identification, select optimize submodule, training submodule and identify submodule, specifically including parameter For:

Described parameter selects the kernel function optimizing submodule for constructing least square method supporting vector machine, and to least square The structural parameters of support vector machine use multi-population to work in coordination with Chaos particle swarm optimization algorithm and are optimized；

Described training submodule, for using many classification sides of the least square support vector machines of the optimum binary tree structure of improvement Method, instructs the least square method supporting vector machine after structure parameter optimizing using the training feature vector obtained as training sample Practice, and build sensor fault diagnosis model；

Described identification submodule is used for using described sensor fault diagnosis model that described characteristic vector to be measured carries out event Barrier Classification and Identification；

Wherein, it is considered to Polynomial kernel function and the superiority of RBF kernel function, the core letter of described least square method supporting vector machine Number is configured to:

K=(1-δ) (xx_i+1)^p+δexp(-‖x-x_i‖²/σ²)

In formula, δ is the structure adjusting factor, and the span of δ is set as [0.45,0.55], and p is the rank of Polynomial kernel function Number, σ²For RBF kernel functional parameter.

Wherein, shown employing multi-population is worked in coordination with Chaos particle swarm optimization algorithm and is optimized, including:

(1) to main population and initialize from population respectively, randomly generate initial as particle of one group of parameter Position and initial velocity, definition fitness function is:

$S=\frac{1}{N}\underset{i=1}{\overset{N}{\Σ}}\left|\frac{q_{i}W}{q_{i}W+(1-q_i)T}\right|\×100\%$

In formula, N is the total number of training sample, and W is that bug is classified number, and T is that fault is correctly classified number, q_iFor certainly The weight coefficient set, q_iSpan be set as [0.4,0.5]；

(2) renewal from population is carried out, in every generation renewal process, according to fitness function, from population respectively The speed of more new particle and position, then will be experienced in its history adaptive optimal control angle value and main population body each particle The fitness value of desired positions compares, if more preferably, then as current global optimum position；

(3) described global optimum position is carried out the optimum particle position in chaos optimization, and iteration current sequence and speed Degree, generates optimal particle sequence；

(4) in the main population of every generation, choose from population optimum particle, and the position of more new particle and speed, Until reaching maximum iteration time or meeting the error requirements of fitness function.

Wherein, many sorting techniques of the least square support vector machines of the optimum binary tree structure of described improvement specifically include:

(1) calculate the standard variance of all training samples and two classifications j,Between Separatory measure；

(2) output minimum separation estimate correspondence j,

(3) after being optimized the structural parameters of least square method supporting vector machine, the least square setting up two classification props up Hold vector machine in order to train jth class andThe training sample of class, forms optimum two classification least square method supporting vector machines, output The parameter of discriminant function,The training sample of class is merged in j class, constitutes new j class training sample；

(4) all of classification is circulated training according to (1)-(3), until the optimum root node of output；

(5) according to the categorised decision tree of above output result composition least square method supporting vector machine, then to remaining instruction Practice sample and carry out classifying quality test.

This preferred embodiment is in order to improve the precision of fault diagnosis, and employing training speed is fast, generalization ability strong and robustness Preferably least square support vector machines is as grader, and proposes the many sorting techniques improving optimum binary tree structure, with between class Separatory measure substitutes the weights in binary tree structure, the nicety of grading that improve and classification speed；In view of RBF kernel function it is Karyomerite function, Polynomial kernel function is overall situation kernel function, and karyomerite function learning ability is strong, and Generalization Capability is relatively weak, and Overall situation kernel function Generalization Capability is strong, and learning capacity is relatively weak, carries out on the basis of the advantage of summary two class kernel function The Kernel of least square method supporting vector machine, optimizes classification performance and the Generalization Capability of least square method supporting vector machine； The multi-population of design works in coordination with Chaos particle swarm optimization algorithm, has preferable convergence rate, and has preferable global and local Optimizing performance, it is possible to jump out Local Extremum timely, finds the optimal value of the overall situation, thus uses multi-population to work in coordination with chaotic particle The structural parameters of least square method supporting vector machine are optimized by colony optimization algorithm, and effect of optimization is good.

Preferably, described failure mode more new module 6, for being updated training set, is continued to optimize sensor fault and is examined Disconnected model, including:

(1) when sensor fault diagnosis model cannot carry out effective failure modes to characteristic vector to be measured, by feature to be measured Vector is as new training feature vector；

(2) training sample is updated by new training feature vector, to the least square support after structure parameter optimizing Vector machine is trained, and builds the sensor fault diagnosis model made new advances；

(3) use new sensor fault diagnosis model that described characteristic vector to be measured is carried out failure modes identification, complete Failure mode updates.

This preferred embodiment arranges failure mode more new module 6, to improve adaptation ability and the range of application of model.

Preferably, described health records module 7 includes sub module stored and secure access submodule, described sub module stored Use storage model based on cloud storage, specifically, be encrypted after fault message is compressed, be uploaded to cloud storage, Described secure access submodule is for conducting interviews to information, specifically, corresponding to sub module stored, downloads data to this Ground, after using corresponding secret key to be unlocked, then carries out decompressing to read information.

This preferred embodiment arranges health records module 7, on the one hand ensure that information security, on the other hand can be the most right Fault conducts interviews, it is simple to search problem.

In this application scenarios, set threshold value T₁Value be 0.94, the monitoring velocity phase of sensor malfunction diagnostic device To improve 12%, the monitoring accuracy of sensor malfunction diagnostic device improves 10% relatively.

Application scenarios 4

See Fig. 1, Fig. 2, the reading intelligent agriculture environmental monitoring systems based on big data of an embodiment of this application scene, Including Cloud Servers based on big data, home server, sensing system and wireless sense network, described clouds based on big data Server is connected with home server by wireless sense network, and home server is connected with sensing system and wireless sense network, Described home server is also associated with device controller by wireless sense network.

The above embodiment of the present invention is with sensing system and various awareness apparatus as carrier, with application software technology for service Core, it is achieved the environmental information monitoring of real-time and pinpoint accuracy, provides reliable growing environment, and management personnel for crops Can inquire about each node current context information in each warmhouse booth and booth at any time, the data gathered are carried out by monitor supervision platform Analyze with process, by threshold range is previously set, system can automatically start with regulation blower fan, shutter, air-conditioning, illumination, The equipment such as irrigation, provide an optimum growing environment for crops.

Preferably, described sensing system includes aerial temperature and humidity sensor, soil temperature-moisture sensor, illumination sensing Device, CO2 sensor.

This preferred embodiment arranges aerial temperature and humidity sensor, soil temperature-moisture sensor, optical sensor, CO2 sensing Device, it is achieved the collection of agricultural environment data.

Preferably, described reading intelligent agriculture environmental monitoring system also includes that the sensor fault diagnosing each sensor is examined Disconnected device, described sensor malfunction diagnostic device includes signals collecting filtration module 1, fault signature extraction module 2, online feature Extraction module 3, characteristic vector preferred module 4, failure modes identification module 5, failure mode more new module 6 and health records module 7。

The above embodiment of the present invention arranges sensor malfunction diagnostic device and achieves the fast of sensor malfunction diagnostic device Speed is built, and is conducive to monitoring each sensor, it is ensured that the environmental data collecting of monitoring system.

Preferably, described signals collecting filtration module 1 is used for gathering historical sensor signal and on-line sensor test letter Number, and use combination form wave filter to be filtered signal processing；

This preferred embodiment arranges combination form wave filter, can effectively remove the various noise jamming of signal, preferably The primitive character information of stick signal.

Preferably, described fault signature extraction module 2 is for carrying out integrated experience to filtered historical sensor signal Mode decomposition (EEMD) processes, and extracts the Energy-Entropy of integrated empirical mode decomposition (EEMD) as training feature vector, including:

(1) the historical sensor signal by collection is divided into the fault-signal of nominal situation signal and plurality of classes；

(2) described historical sensor signal carries out integrated empirical mode decomposition (EEMD) process, it is thus achieved that described history passes The intrinsic mode function of sensor signal and remainder function；

(3) intrinsic mode function and the Energy-Entropy of remainder function of described historical sensor signal are calculated；

(4) Energy-Entropy of historical sensor signal is normalized, extracts the Energy-Entropy after normalization as instruction Practice characteristic vector；

Described online characteristic extracting module 3 is for carrying out integrated empirical modal to filtered on-line sensor test signal Decompose (EEMD) to process, and extract the Energy-Entropy of integrated empirical mode decomposition (EEMD) as characteristic vector to be measured, including:

(1) described on-line sensor test signal is carried out EEMD process, it is thus achieved that described on-line sensor test signal Intrinsic mode function and remainder function；

(2) intrinsic mode function and the Energy-Entropy of remainder function of described on-line sensor test signal are calculated；

(3) Energy-Entropy of on-line sensor test signal is normalized, extracts the Energy-Entropy after normalization and make For characteristic vector to be measured.

This preferred embodiment carries out integrated empirical mode decomposition (EEMD) to the sensor signal gathered and processes, it is possible to effectively Elimination modal overlap phenomenon, the effect of decomposition is preferable.

Preferably, training feature vector is carried out similar with characteristic vector to be measured by described characteristic vector preferred module 4 respectively Property tolerance, the characteristic vector high for similarity reject, including:

(1) two vector similarities function S (X, Y) are defined:

$S(X,Y)=\frac{\mathrm{cov}(X,Y)}{\sqrt{D\left(X\right)}\sqrt{D\left(Y\right)}}$

In formula, X, Y represent that two characteristic vectors, cov (X, Y) are the covariance of X and Y respectively,For X, Y standard deviation；

For any two training feature vector X₁、X₂, and any two characteristic vector D to be measured₁、D₂, it is respectively adopted similar Its similarity is measured by degree function, obtains S (X₁,X₂) and S (D₁,D₂)；

(2) for S (X₁,X₂) and S (D₁,D₂), if S is (X₁,X₂)>T₁, T₁∈ (0.9,1), only chooses X₁As training characteristics Vector, if S is (D₁,D₂)>T₂, T₂∈ (0.95,1), only chooses D₁As characteristic vector to be measured.

This preferred embodiment screens characteristic vector by measuring similarity, it is possible to reduce amount of calculation, improves efficiency.

Preferably, described failure modes identification module 5 is for using the least square method supporting vector machine of optimization to treat described Survey characteristic vector and carry out failure modes identification, select optimize submodule, training submodule and identify submodule, specifically including parameter For:

Described parameter selects the kernel function optimizing submodule for constructing least square method supporting vector machine, and to least square The structural parameters of support vector machine use multi-population to work in coordination with Chaos particle swarm optimization algorithm and are optimized；

Described training submodule, for using many classification sides of the least square support vector machines of the optimum binary tree structure of improvement Method, instructs the least square method supporting vector machine after structure parameter optimizing using the training feature vector obtained as training sample Practice, and build sensor fault diagnosis model；

Described identification submodule is used for using described sensor fault diagnosis model that described characteristic vector to be measured carries out event Barrier Classification and Identification；

Wherein, it is considered to Polynomial kernel function and the superiority of RBF kernel function, the core letter of described least square method supporting vector machine Number is configured to:

K=(1-δ) (xx_i+1)^p+δexp(-‖x-x_i‖²/σ²)

In formula, δ is the structure adjusting factor, and the span of δ is set as [0.45,0.55], and p is the rank of Polynomial kernel function Number, σ²For RBF kernel functional parameter.

Wherein, shown employing multi-population is worked in coordination with Chaos particle swarm optimization algorithm and is optimized, including:

(1) to main population and initialize from population respectively, randomly generate initial as particle of one group of parameter Position and initial velocity, definition fitness function is:

$S=\frac{1}{N}\underset{i=1}{\overset{N}{\Σ}}\left|\frac{q_{i}W}{q_{i}W+(1-q_i)T}\right|\×100\%$

In formula, N is the total number of training sample, and W is that bug is classified number, and T is that fault is correctly classified number, q_iFor certainly The weight coefficient set, q_iSpan be set as [0.4,0.5]；

(2) renewal from population is carried out, in every generation renewal process, according to fitness function, from population respectively The speed of more new particle and position, then will be experienced in its history adaptive optimal control angle value and main population body each particle The fitness value of desired positions compares, if more preferably, then as current global optimum position；

(3) described global optimum position is carried out the optimum particle position in chaos optimization, and iteration current sequence and speed Degree, generates optimal particle sequence；

(4) in the main population of every generation, choose from population optimum particle, and the position of more new particle and speed, Until reaching maximum iteration time or meeting the error requirements of fitness function.

Wherein, many sorting techniques of the least square support vector machines of the optimum binary tree structure of described improvement specifically include:

(1) calculate the standard variance of all training samples and two classifications j,Between Separatory measure；

(2) output minimum separation estimate correspondence j,

(3) after being optimized the structural parameters of least square method supporting vector machine, the least square setting up two classification props up Hold vector machine in order to train jth class andThe training sample of class, forms optimum two classification least square method supporting vector machines, output The parameter of discriminant function,The training sample of class is merged in j class, constitutes new j class training sample；

(4) all of classification is circulated training according to (1)-(3), until the optimum root node of output；

(5) according to the categorised decision tree of above output result composition least square method supporting vector machine, then to remaining instruction Practice sample and carry out classifying quality test.

This preferred embodiment is in order to improve the precision of fault diagnosis, and employing training speed is fast, generalization ability strong and robustness Preferably least square support vector machines is as grader, and proposes the many sorting techniques improving optimum binary tree structure, with between class Separatory measure substitutes the weights in binary tree structure, the nicety of grading that improve and classification speed；In view of RBF kernel function it is Karyomerite function, Polynomial kernel function is overall situation kernel function, and karyomerite function learning ability is strong, and Generalization Capability is relatively weak, and Overall situation kernel function Generalization Capability is strong, and learning capacity is relatively weak, carries out on the basis of the advantage of summary two class kernel function The Kernel of least square method supporting vector machine, optimizes classification performance and the Generalization Capability of least square method supporting vector machine； The multi-population of design works in coordination with Chaos particle swarm optimization algorithm, has preferable convergence rate, and has preferable global and local Optimizing performance, it is possible to jump out Local Extremum timely, finds the optimal value of the overall situation, thus uses multi-population to work in coordination with chaotic particle The structural parameters of least square method supporting vector machine are optimized by colony optimization algorithm, and effect of optimization is good.

Preferably, described failure mode more new module 6, for being updated training set, is continued to optimize sensor fault and is examined Disconnected model, including:

(1) when sensor fault diagnosis model cannot carry out effective failure modes to characteristic vector to be measured, by feature to be measured Vector is as new training feature vector；

(2) training sample is updated by new training feature vector, to the least square support after structure parameter optimizing Vector machine is trained, and builds the sensor fault diagnosis model made new advances；

(3) use new sensor fault diagnosis model that described characteristic vector to be measured is carried out failure modes identification, complete Failure mode updates.

This preferred embodiment arranges failure mode more new module 6, to improve adaptation ability and the range of application of model.

Preferably, described health records module 7 includes sub module stored and secure access submodule, described sub module stored Use storage model based on cloud storage, specifically, be encrypted after fault message is compressed, be uploaded to cloud storage, Described secure access submodule is for conducting interviews to information, specifically, corresponding to sub module stored, downloads data to this Ground, after using corresponding secret key to be unlocked, then carries out decompressing to read information.

This preferred embodiment arranges health records module 7, on the one hand ensure that information security, on the other hand can be the most right Fault conducts interviews, it is simple to search problem.

In this application scenarios, set threshold value T₁Value be 0.93, the monitoring velocity phase of sensor malfunction diagnostic device To improve 13%, the monitoring accuracy of sensor malfunction diagnostic device improves 9% relatively.

Application scenarios 5

See Fig. 1, Fig. 2, the reading intelligent agriculture environmental monitoring systems based on big data of an embodiment of this application scene, Including Cloud Servers based on big data, home server, sensing system and wireless sense network, described clouds based on big data Server is connected with home server by wireless sense network, and home server is connected with sensing system and wireless sense network, Described home server is also associated with device controller by wireless sense network.

The above embodiment of the present invention is with sensing system and various awareness apparatus as carrier, with application software technology for service Core, it is achieved the environmental information monitoring of real-time and pinpoint accuracy, provides reliable growing environment, and management personnel for crops Can inquire about each node current context information in each warmhouse booth and booth at any time, the data gathered are carried out by monitor supervision platform Analyze with process, by threshold range is previously set, system can automatically start with regulation blower fan, shutter, air-conditioning, illumination, The equipment such as irrigation, provide an optimum growing environment for crops.

Preferably, described sensing system includes aerial temperature and humidity sensor, soil temperature-moisture sensor, illumination sensing Device, CO2 sensor.

This preferred embodiment arranges aerial temperature and humidity sensor, soil temperature-moisture sensor, optical sensor, CO2 sensing Device, it is achieved the collection of agricultural environment data.

Preferably, described reading intelligent agriculture environmental monitoring system also includes that the sensor fault diagnosing each sensor is examined Disconnected device, described sensor malfunction diagnostic device includes signals collecting filtration module 1, fault signature extraction module 2, online feature Extraction module 3, characteristic vector preferred module 4, failure modes identification module 5, failure mode more new module 6 and health records module 7。

The above embodiment of the present invention arranges sensor malfunction diagnostic device and achieves the fast of sensor malfunction diagnostic device Speed is built, and is conducive to monitoring each sensor, it is ensured that the environmental data collecting of monitoring system.

Preferably, described signals collecting filtration module 1 is used for gathering historical sensor signal and on-line sensor test letter Number, and use combination form wave filter to be filtered signal processing；

This preferred embodiment arranges combination form wave filter, can effectively remove the various noise jamming of signal, preferably The primitive character information of stick signal.

Preferably, described fault signature extraction module 2 is for carrying out integrated experience to filtered historical sensor signal Mode decomposition (EEMD) processes, and extracts the Energy-Entropy of integrated empirical mode decomposition (EEMD) as training feature vector, including:

(1) the historical sensor signal by collection is divided into the fault-signal of nominal situation signal and plurality of classes；

(2) described historical sensor signal carries out integrated empirical mode decomposition (EEMD) process, it is thus achieved that described history passes The intrinsic mode function of sensor signal and remainder function；

(3) intrinsic mode function and the Energy-Entropy of remainder function of described historical sensor signal are calculated；

(4) Energy-Entropy of historical sensor signal is normalized, extracts the Energy-Entropy after normalization as instruction Practice characteristic vector；

Described online characteristic extracting module 3 is for carrying out integrated empirical modal to filtered on-line sensor test signal Decompose (EEMD) to process, and extract the Energy-Entropy of integrated empirical mode decomposition (EEMD) as characteristic vector to be measured, including:

(1) described on-line sensor test signal is carried out EEMD process, it is thus achieved that described on-line sensor test signal Intrinsic mode function and remainder function；

(2) intrinsic mode function and the Energy-Entropy of remainder function of described on-line sensor test signal are calculated；

(3) Energy-Entropy of on-line sensor test signal is normalized, extracts the Energy-Entropy after normalization and make For characteristic vector to be measured.

This preferred embodiment carries out integrated empirical mode decomposition (EEMD) to the sensor signal gathered and processes, it is possible to effectively Elimination modal overlap phenomenon, the effect of decomposition is preferable.

Preferably, training feature vector is carried out similar with characteristic vector to be measured by described characteristic vector preferred module 4 respectively Property tolerance, the characteristic vector high for similarity reject, including:

(1) two vector similarities function S (X, Y) are defined:

$S(X,Y)=\frac{\mathrm{cov}(X,Y)}{\sqrt{D\left(X\right)}\sqrt{D\left(Y\right)}}$

In formula, X, Y represent that two characteristic vectors, cov (X, Y) are the covariance of X and Y respectively,For X, Y standard deviation；

For any two training feature vector X₁、X₂, and any two characteristic vector D to be measured₁、D₂, it is respectively adopted similar Its similarity is measured by degree function, obtains S (X₁,X₂) and S (D₁,D₂)；

(2) for S (X₁,X₂) and S (D₁,D₂), if S is (X₁,X₂)>T₁, T₁∈ (0.9,1), only chooses X₁As training characteristics Vector, if S is (D₁,D₂)>T₂, T₂∈ (0.95,1), only chooses D₁As characteristic vector to be measured.

This preferred embodiment screens characteristic vector by measuring similarity, it is possible to reduce amount of calculation, improves efficiency.

Preferably, described failure modes identification module 5 is for using the least square method supporting vector machine of optimization to treat described Survey characteristic vector and carry out failure modes identification, select optimize submodule, training submodule and identify submodule, specifically including parameter For:

Described parameter selects the kernel function optimizing submodule for constructing least square method supporting vector machine, and to least square The structural parameters of support vector machine use multi-population to work in coordination with Chaos particle swarm optimization algorithm and are optimized；

Described training submodule, for using many classification sides of the least square support vector machines of the optimum binary tree structure of improvement Method, instructs the least square method supporting vector machine after structure parameter optimizing using the training feature vector obtained as training sample Practice, and build sensor fault diagnosis model；

Described identification submodule is used for using described sensor fault diagnosis model that described characteristic vector to be measured carries out event Barrier Classification and Identification；

Wherein, it is considered to Polynomial kernel function and the superiority of RBF kernel function, the core letter of described least square method supporting vector machine Number is configured to:

K=(1-δ) (xx_i+1)^p+δexp(-‖x-x_i‖²/σ²)

In formula, δ is the structure adjusting factor, and the span of δ is set as [0.45,0.55], and p is the rank of Polynomial kernel function Number, σ²For RBF kernel functional parameter.

Wherein, shown employing multi-population is worked in coordination with Chaos particle swarm optimization algorithm and is optimized, including:

(1) to main population and initialize from population respectively, randomly generate initial as particle of one group of parameter Position and initial velocity, definition fitness function is:

$S=\frac{1}{N}\underset{i=1}{\overset{N}{\Σ}}\left|\frac{q_{i}W}{q_{i}W+(1-q_i)T}\right|\×100\%$

In formula, N is the total number of training sample, and W is that bug is classified number, and T is that fault is correctly classified number, q_iFor certainly The weight coefficient set, q_iSpan be set as [0.4,0.5]；

(2) renewal from population is carried out, in every generation renewal process, according to fitness function, from population respectively The speed of more new particle and position, then will be experienced in its history adaptive optimal control angle value and main population body each particle The fitness value of desired positions compares, if more preferably, then as current global optimum position；

(3) described global optimum position is carried out the optimum particle position in chaos optimization, and iteration current sequence and speed Degree, generates optimal particle sequence；

(4) in the main population of every generation, choose from population optimum particle, and the position of more new particle and speed, Until reaching maximum iteration time or meeting the error requirements of fitness function.

Wherein, many sorting techniques of the least square support vector machines of the optimum binary tree structure of described improvement specifically include:

(1) calculate the standard variance of all training samples and two classifications j,Between Separatory measure；

(2) output minimum separation estimate correspondence j,

(3) after being optimized the structural parameters of least square method supporting vector machine, the least square setting up two classification props up Hold vector machine in order to train jth class andThe training sample of class, forms optimum two classification least square method supporting vector machines, output The parameter of discriminant function,The training sample of class is merged in j class, constitutes new j class training sample；

(4) all of classification is circulated training according to (1)-(3), until the optimum root node of output；

(5) according to the categorised decision tree of above output result composition least square method supporting vector machine, then to remaining instruction Practice sample and carry out classifying quality test.

This preferred embodiment is in order to improve the precision of fault diagnosis, and employing training speed is fast, generalization ability strong and robustness Preferably least square support vector machines is as grader, and proposes the many sorting techniques improving optimum binary tree structure, with between class Separatory measure substitutes the weights in binary tree structure, the nicety of grading that improve and classification speed；In view of RBF kernel function it is Karyomerite function, Polynomial kernel function is overall situation kernel function, and karyomerite function learning ability is strong, and Generalization Capability is relatively weak, and Overall situation kernel function Generalization Capability is strong, and learning capacity is relatively weak, carries out on the basis of the advantage of summary two class kernel function The Kernel of least square method supporting vector machine, optimizes classification performance and the Generalization Capability of least square method supporting vector machine； The multi-population of design works in coordination with Chaos particle swarm optimization algorithm, has preferable convergence rate, and has preferable global and local Optimizing performance, it is possible to jump out Local Extremum timely, finds the optimal value of the overall situation, thus uses multi-population to work in coordination with chaotic particle The structural parameters of least square method supporting vector machine are optimized by colony optimization algorithm, and effect of optimization is good.

Preferably, described failure mode more new module 6, for being updated training set, is continued to optimize sensor fault and is examined Disconnected model, including:

(1) when sensor fault diagnosis model cannot carry out effective failure modes to characteristic vector to be measured, by feature to be measured Vector is as new training feature vector；

(2) training sample is updated by new training feature vector, to the least square support after structure parameter optimizing Vector machine is trained, and builds the sensor fault diagnosis model made new advances；

(3) use new sensor fault diagnosis model that described characteristic vector to be measured is carried out failure modes identification, complete Failure mode updates.

This preferred embodiment arranges failure mode more new module 6, to improve adaptation ability and the range of application of model.

Preferably, described health records module 7 includes sub module stored and secure access submodule, described sub module stored Use storage model based on cloud storage, specifically, be encrypted after fault message is compressed, be uploaded to cloud storage, Described secure access submodule is for conducting interviews to information, specifically, corresponding to sub module stored, downloads data to this Ground, after using corresponding secret key to be unlocked, then carries out decompressing to read information.

This preferred embodiment arranges health records module 7, on the one hand ensure that information security, on the other hand can be the most right Fault conducts interviews, it is simple to search problem.

In this application scenarios, set threshold value T₁Value be 0.92, the monitoring velocity phase of sensor malfunction diagnostic device To improve 14%, the monitoring accuracy of sensor malfunction diagnostic device improves 8% relatively.

Last it should be noted that, above example is only in order to illustrate technical scheme, rather than the present invention is protected Protecting the restriction of scope, although having made to explain to the present invention with reference to preferred embodiment, those of ordinary skill in the art should Work as understanding, technical scheme can be modified or equivalent, without deviating from the reality of technical solution of the present invention Matter and scope.

Claims (3)

1. reading intelligent agriculture environmental monitoring systems based on big data, is characterized in that, including Cloud Servers based on big data, this locality Server, sensing system and wireless sense network, described Cloud Servers based on big data are by wireless sense network and local clothes Business device connects, and home server is connected with sensing system and wireless sense network, and described home server passes through wireless sense network It is also associated with device controller.

Reading intelligent agriculture environmental monitoring system based on big data the most according to claim 1, is characterized in that, described equipment control Device processed connects blower fan, shutter, air-conditioning, electric light and Irrigation Machine.

Reading intelligent agriculture environmental monitoring system based on big data the most according to claim 2, is characterized in that, described sensor System includes aerial temperature and humidity sensor, soil temperature-moisture sensor, optical sensor, CO2 sensor.