CN107272788A - A kind of greenhouse intelligent control device controlled based on computer and control method - Google Patents
A kind of greenhouse intelligent control device controlled based on computer and control method Download PDFInfo
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- CN107272788A CN107272788A CN201710620506.6A CN201710620506A CN107272788A CN 107272788 A CN107272788 A CN 107272788A CN 201710620506 A CN201710620506 A CN 201710620506A CN 107272788 A CN107272788 A CN 107272788A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D27/00—Simultaneous control of variables covered by two or more of main groups G05D1/00 - G05D25/00
- G05D27/02—Simultaneous control of variables covered by two or more of main groups G05D1/00 - G05D25/00 characterised by the use of electric means
Abstract
The invention belongs to greenhouse intelligent control technical field, a kind of greenhouse intelligent control device controlled based on computer and control method are disclosed, including：Main control computer；Main control computer connects temperature adjustment module, detection of air quality module and dust extractor by circuit line.Temperature adjustment module includes temperature regulation controller and temperature sensor；Indoor temperature is detected in real time and is controlled, and temperature is reached setting value.Detection of air quality module includes Detection of Air Quality sensor and data storage；Detection air quality data is simultaneously stored, and lifts data backup memory performance；Dust extractor includes micronic dust detection sensor and dust catcher, is connected the carry out dust cleaning of intelligence by circuit line with main control computer.The present invention sets detection of air quality module, can carry out quality testing to room air in real time, provide the user with IAQ data message；Set dust catcher can koniology in process chamber in time, ensure the clean hygiene of interior.
Description
Technical field
The invention belongs to greenhouse intelligent control technical field, more particularly to a kind of greenhouse intelligent control controlled based on computer
Device processed and control method.
Background technology
Greenhouse intelligent control system, puts forward, particularly Top's agriculture in the case of Internet of Things application is gradually extensive
The appearance of industry Internet of Things, a set of Control management system for greenhouse irrigation environmental monitoring developed based on this.Greenhouse intelligence
Energy automation control system function is with soil moisture value, the soil moisture, time, air themperature, air humidity, illumination, titanium dioxide
Based on carbon etc., user can set the desired value of its parameter, and the desired value that program is set according to user is controlled and monitoring electromagnetism
Valve, water pump, fertilization system, skylight, side window, internal sunshade, external sunshade, blower fan, wet curtain, outer pivot frame, warming-up device, humidification apparatus,
The state of the equipment such as carbon-dioxide generator, the target range set with several parameters more than ensureing in greenhouse in user it
It is interior.However, existing greenhouse intelligent control, it is impossible to provide IAQ data message, it has not been convenient to which user defends to indoor environment
It is raw to judge；It is not simultaneously timely to handle house dust, influences indoor environment.
In summary, the problem of prior art is present be：Existing greenhouse intelligent control, it is impossible to which IAQ number is provided
It is believed that breath, it has not been convenient to which user is judged indoor environmental hygiene；It is not simultaneously timely to handle house dust, influences indoor environment.
The content of the invention
The problem of existing for prior art, the invention provides a kind of greenhouse intelligent control dress controlled based on computer
Put and control method.
The present invention is achieved in that a kind of greenhouse intelligent control device controlled based on computer, described based on calculating
The greenhouse intelligent control device of machine control includes：
Main control computer, for by temperature adjustment module, detecting indoor temperature in time and adjusting to design temperature, keeps
Indoor temperature is stable；
The main control computer is according to formulaTraining sequence and the circulation of itself are moved
Position result carries out conjugation related operation by sign bit, obtains correlation function M (m), wherein c (k) is by sign bit to local sequence
The complex result mapped out, c ((k+m))NIn k=1,2 ... represent to carry out c (k) result of cyclic shift during N；Search out
Correlation function main peak value and corresponding frequency domain sequence under submaximum value ratio maximum case, it is then determined that the corresponding instruction of the frequency domain sequence
Practice sequence；FormulaRepresent that training sequence is entered with the cyclic shift result of itself by sign bit
Row conjugation related operation, wherein c (k) is the complex result mapped out to local sequence C (k) by sign bit, and mapping equation is c
(k)=sign (Re (C (k)))+j*sign (Im (C (k)))；c((k))NExpression carries out periodic extension by the cycle of N to c (k)
As a result, thus c ((k+m))NIn k=1,2 ... represent to carry out c (k) result of cyclic shift during N, m ＞ 0 represent that circulation is left
M are moved, m ＜ 0 represent ring shift right | m | position, understood according to the structure of sequence when cyclic sequence circulation moves 0, correlation function
Value occurs one than larger main peak, and peak value is M (0), and if during to training sequence or so cyclic shift NFFT, correlation function
Less submaximum can all occur in value, and two peak values are respectively M (NFFT), M (- NFFT)；The purpose for searching for frequency-domain data sequence is to make
Training sequence moves the main peak value occurred at 0 and the submaximum value that NFFT appearance are moved in circulation left and right is with the biggest gap, is ensured with this
Receive the gap of timing slip estimation function main peak value and submaximum value that data are obtained with training sequence by sign bit related operation
It is as big as possible, increase the span of dynamic threshold, because two submaximum value sizes of correlation function are essentially identical, take its main peak value
M (0) and the ratio of one of submaximum value M (NFFT) are used as criterion；
Temperature adjustment module, is connected in advance with main control computer, for detecting indoor temperature in real time and being controlled, makes temperature
Degree reaches setting value；
The temperature adjustment module does Hough transform to the Short Time Fourier Transform spectrum of linear domain of holomorphy and is carried out as follows：
2.1) polar equation is ρ=tcos θ+fsin θ, wherein, (t, f) is the point on time-frequency domain, and ρ is the point to origin
Distance, θ was the straight line and the angle of x-axis of the point and origin, and polar coordinate space (ρ, θ) is quantified as into (ρu,θv), u=
1 ..., M, v=1 ..., N obtains M × N two-dimensional matrix M (ρ, θ)；M (ρ, θ) is an accumulator, and initial value is 0；
2.2) each point (t, f) on correspondence time-frequency domain, its spectral amplitude is | LA(t,f)|2, to improve calculating speed, setting
When spectral amplitude of some point be more than spectral amplitude a little maximumWhen then carry out Hough transform, otherwise neglect this
Point；
2.3) to meet spectral amplitude be more than spectral amplitude a little maximumPoint (t, f), by θ all quantized values
Polar equation is substituted into, corresponding ρ is obtained, and accumulator is added | LA(t,f)|2, i.e. M (ρ, θ)=M (ρ, θ)+| LA(t,f)
|2, obtain Hough transform matrix M (ρ, θ)；
Detection of air quality module, is connected in advance with main control computer, for detecting air quality data and being stored；
The detection of air quality module inter-signal interference relationship analysis method comprises the following steps：
Step one, some characteristic parameter CPs of the interference signal on wireless signal field, and feature based parameter shape are determined
Into corresponding interference space model, the interference space model based on foundation determines interference signal characteristic vector to be analyzedWith
Contrast signal characteristic vector
Step 2, based on interference space model, for interference signal characteristic vectorDefinition is to contrast signal characteristic vectorDisplacement vector
Step 3, defines displacement vectorIt is interference signal to the projection of some latitude coordinates axle in interference space
Characteristic vectorTo contrast signal characteristic vectorDistance in the CP dimensions, that is, have：
Wherein PRJ () operator representation is directed to the project of a certain CP dimensions；
Step 4, it is S to the disturbance state of contrast signal to define interference signal, to represent interference signal to contrast signal
Interference relationships；
Step 5, on the premise of interference has been formed, it is necessary first to choose and determine interference effect parameter EP, for dry
Disturb for signal, parameter is usually signal power p or energy e；
Step 6, it is G to the annoyance level of contrast signal to define interference signal, to weigh interference signal to contrast signal
Interference effect degree；
Dust extractor, is connected in advance with main control computer, for carrying out dust cleaning；
The dust extractor is to frequency-hopping mixing signal time-frequency domain matrixLocated in advance
Reason, specifically includes following two step：
The first step is rightProgress goes low energy to pre-process, i.e., in each sampling instant
P, willThe value that amplitude is less than thresholding ε is set to 0, and is obtained
Thresholding ε setting can be determined according to the average energy of signal is received；
Second step, finds out the time-frequency numeric field data of p moment (p=0,1,2 ... P-1) non-zero, usesRepresent, whereinRepresent the response of p moment time-frequency
These non-zeros are normalized and pre-processed, obtain pretreated vectorial b (p, q)=[b by corresponding frequency indices when non-zero1
(p,q),b2(p,q),…,bM(p,q)]T, wherein
Further, the temperature adjustment module includes temperature regulation controller and temperature sensor.
Further, the detection of air quality module includes Detection of Air Quality sensor and data storage.
Further, the dust extractor includes micronic dust detection sensor and dust catcher.
Another object of the present invention is to provide a kind of base of the greenhouse intelligent control device controlled based on computer
The greenhouse intelligent control method controlled in computer, the greenhouse intelligent control method controlled based on computer includes following step
Suddenly：
Step one, indoor temperature is too high or too low, and main control computer can detect Indoor Temperature in time by temperature adjustment module
Spend and adjust to design temperature, keep indoor temperature stable；
Step 2, detection of air quality module can send the air quality data of detection to main control computer, for user
Inquiry；
Step 3, house dust is excessive, and main control computer can start dust extractor in time and carry out purification air, keeps indoor
Clean hygiene.
Further, the temperature sensor in the step one in temperature adjustment module is by the indoor temperature data transfer of detection
To main control computer, main control computer operational control temperature regulation controller is carried out to indoor temperature control；
Detection of Air Quality sensor in the step 2 in detection of air quality module will detect air quality data
Store in memory, be then transferred to main control computer, lifting data storage holds backup, prevents transmit process loss of data；
The step of main control computer data aggregation method, is as follows：
(1) area for=× deployment region in, the wireless sensor node of random distribution isomorphism, sink nodes
Outside deployment region, the data being collected into the whole wireless sensor network of node processing；
(2) non-homogeneous cluster
Sink nodes are located at the top of deployment region；Deployment region X-axis is divided into S swimming lane first, and all swimming lanes have phase
Same width w, and the length and the equal length of deployment region of each swimming lane；With the ID from 1 to s as swimming lane, high order end
The ID of swimming lane be 1, then each swimming lane along y-axis be divided into each grid in multiple rectangular mesh, each swimming lane by
A level is defined, the level of the grid of bottom is 1, and each grid and each swimming lane have identical width w；In each swimming lane
Distance dependent of number, length and the swimming lane of grid to sink；The size of grid is adjusted by setting the length of grid；For
Different swimming lanes, the lattice number that the swimming lane more remote apart from sink contains is smaller；For same swimming lane, the net more remote apart from sink
The length of lattice is bigger；Assuming that containing S element, the number of k-th of element representation grid in k-th of swimming lane in A；Each grid
With an array (i, j) as ID, represent that i-th of swimming lane has horizontal j；Define the length of S array representation grid, v-th of number
Group HvRepresent the length of grid in v-th of swimming lane, and HvW-th of element hvwRepresent the length of grid (v, w)；Grid (i,
J) border is：
O_x+ (i-1) × w ＜ x≤o_x+i × w
Non-uniform grid carries out the cluster stage after dividing；Algorithm, which is divided into many wheels, to be carried out, and chooses each in each round
The maximum node of dump energy is as cluster head node in grid, and remaining node adds cluster according to nearby principle, line number is then entered again
According to polymerization；
(3) Grubbs are pre-processed
Sensor node needs to pre-process the data of collection, then transmits data to cluster head node again；Using lattice
The data that this pre- criterion of granny rag is collected to sensor node carry out pretreatment and assume that some cluster head node contains a sensor
Node, the data that sensor node is collected into are x1,x2,…,xn, Normal Distribution, and set：
According to order statistics principle, Grubbs statistic is calculated：
After given significance (α=0.05), measured value meets gi≤g0(n, α), then it is assumed that measured value effectively, is surveyed
Value participates in the data aggregate of next level；It is on the contrary, then it is assumed that measured value is invalid, it is therefore desirable to reject, that is, it is not involved in down
The data aggregate of one level；
(4) adaptive aggregating algorithm
The unbiased estimator of each node measurement data is obtained by iteration, the measurement data of each sensor node is asked for
Euclidean distance between value and estimate, adaptive weighted warm weights are used as using normalized Euclidean distance；From in cluster
The average value of the maxima and minimas of data that collects of sensor node as centre data；
There is individual sensor node in some cluster, with dimensional vector D=(d1,d2,…,dn) represent respective nodes measured value,
By calculating the deviation between the different node datas of Euclidean distance reaction of each node data and centre data and centre data
Size, wherein liCalculation formula be：
According to the corresponding weights size of Euclidean distance adaptive setting, the bigger weights of distance are smaller, got over apart from smaller weights
Greatly；
WhereinwiFor corresponding weights；
Micronic dust detection sensor in the step 3 in dust extractor sends the micronic dust data of indoor detection to master control
Computer, main control computer control dust catcher carries out dust-collecting indoor operation, purifies the air of a room.
Advantages of the present invention and good effect are：The present invention sets detection of air quality module, can be in real time to Interior Space
Gas carries out quality testing, provides the user with IAQ data message；The dust catcher of setting can handle Interior Space in time
Gas dust, ensures indoor clean hygiene.
Brief description of the drawings
Fig. 1 is the greenhouse intelligent control device provided in an embodiment of the present invention controlled based on computer and control method structure
Schematic diagram.
Fig. 2 is the greenhouse intelligent control method flow chart provided in an embodiment of the present invention controlled based on computer.
In figure：1st, main control computer；2nd, temperature adjustment module；3rd, detection of air quality module；4th, dust extractor.
Embodiment
In order to further understand the content, features and effects of the present invention, hereby enumerating following examples, and coordinate accompanying drawing
Describe in detail as follows.
The structure to the present invention is explained in detail below in conjunction with the accompanying drawings.
As shown in figure 1, the greenhouse intelligent control device that should be controlled based on computer is included：Main control computer 1, temperature adjustment
Module 2, detection of air quality module 3, dust extractor 4；The main control computer 1 and temperature adjustment module 2, Detection of Air Quality
Module 3 and the wired connection of dust extractor 4.
The temperature adjustment module 2 includes temperature regulation controller and temperature sensor；Indoor temperature can be detected in real time
And be controlled, temperature is reached setting value.
The detection of air quality module 3 includes Detection of Air Quality sensor and data storage；Air can be detected
Qualitative data is simultaneously stored, and lifts data backup memory performance.
The dust extractor 4 includes micronic dust detection sensor and dust catcher, and being connected with main control computer by circuit line can
With the carry out dust cleaning of intelligence.
The main control computer 1 is according to formulaBy training sequence and the circulation of itself
Shift result carries out conjugation related operation by sign bit, obtains correlation function M (m), wherein c (k) is by symbol to local sequence
The complex result that bit mapping goes out, c ((k+m))NIn k=1,2 ... represent to carry out c (k) result of cyclic shift during N；Search
Go out correlation function main peak value and corresponding frequency domain sequence under submaximum value ratio maximum case, it is then determined that the frequency domain sequence is corresponding
Training sequence；FormulaRepresent that training sequence presses sign bit with the cyclic shift result of itself
Conjugation related operation is carried out, wherein c (k) is the complex result mapped out to local sequence C (k) by sign bit, and mapping equation is c
(k)=sign (Re (C (k)))+j*sign (Im (C (k)))；c((k))NExpression carries out periodic extension by the cycle of N to c (k)
As a result, thus c ((k+m))NIn k=1,2 ... represent to carry out c (k) result of cyclic shift during N, m ＞ 0 represent that circulation is left
M are moved, m ＜ 0 represent ring shift right | m | position, understood according to the structure of sequence when cyclic sequence circulation moves 0, correlation function
Value occurs one than larger main peak, and peak value is M (0), and if during to training sequence or so cyclic shift NFFT, correlation function
Less submaximum can all occur in value, and two peak values are respectively M (NFFT), M (- NFFT)；The purpose for searching for frequency-domain data sequence is to make
Training sequence moves the main peak value occurred at 0 and the submaximum value that NFFT appearance are moved in circulation left and right is with the biggest gap, is ensured with this
Receive the gap of timing slip estimation function main peak value and submaximum value that data are obtained with training sequence by sign bit related operation
It is as big as possible, increase the span of dynamic threshold, because two submaximum value sizes of correlation function are essentially identical, take its main peak value
M (0) and the ratio of one of submaximum value M (NFFT) are used as criterion；
The Short Time Fourier Transform spectrum of the linear domain of holomorphy of 2 pairs of temperature adjustment module is done Hough transform and is carried out as follows：
2.1) polar equation is ρ=tcos θ+fsin θ, wherein, (t, f) is the point on time-frequency domain, and ρ is the point to origin
Distance, θ was the straight line and the angle of x-axis of the point and origin, and polar coordinate space (ρ, θ) is quantified as into (ρu,θv), u=
1 ..., M, v=1 ..., N obtains M × N two-dimensional matrix M (ρ, θ)；M (ρ, θ) is an accumulator, and initial value is 0；
2.2) each point (t, f) on correspondence time-frequency domain, its spectral amplitude is | LA(t,f)|2, to improve calculating speed, setting
When spectral amplitude of some point be more than spectral amplitude a little maximumWhen then carry out Hough transform, otherwise neglect this
Point；
2.3) to meet spectral amplitude be more than spectral amplitude a little maximumPoint (t, f), by θ all quantized values
Polar equation is substituted into, corresponding ρ is obtained, and accumulator is added | LA(t,f)|2, i.e. M (ρ, θ)=M (ρ, θ)+| LA(t,f)
|2, obtain Hough transform matrix M (ρ, θ)；
The inter-signal interference relationship analysis method of detection of air quality module 3 comprises the following steps：
Step one, some characteristic parameter CPs of the interference signal on wireless signal field, and feature based parameter shape are determined
Into corresponding interference space model, the interference space model based on foundation determines interference signal characteristic vector to be analyzedWith
Contrast signal characteristic vector
Step 2, based on interference space model, for interference signal characteristic vectorDefinition is to contrast signal characteristic vectorDisplacement vector
Step 3, defines displacement vectorIt is interference signal to the projection of some latitude coordinates axle in interference space
Characteristic vectorTo contrast signal characteristic vectorDistance in the CP dimensions, that is, have：
Wherein PRJ () operator representation is directed to the project of a certain CP dimensions；
Step 4, it is S to the disturbance state of contrast signal to define interference signal, to represent interference signal to contrast signal
Interference relationships；
Step 5, on the premise of interference has been formed, it is necessary first to choose and determine interference effect parameter EP, for dry
Disturb for signal, parameter is usually signal power p or energy e；
Step 6, it is G to the annoyance level of contrast signal to define interference signal, to weigh interference signal to contrast signal
Interference effect degree；
The dust extractor 4 is to frequency-hopping mixing signal time-frequency domain matrixCarry out pre-
Processing, specifically includes following two step：
The first step is rightProgress goes low energy to pre-process, i.e., in each sampling instant p,
WillThe value that amplitude is less than thresholding ε is set to 0, and is obtained
Thresholding ε setting can be determined according to the average energy of signal is received；
Second step, finds out the time-frequency numeric field data of p moment (p=0,1,2 ... P-1) non-zero, usesRepresent, whereinRepresent the response of p moment time-frequency
These non-zeros are normalized and pre-processed, obtain pretreated vectorial b (p, q)=[b by corresponding frequency indices when non-zero1
(p,q),b2(p,q),…,bM(p,q)]T, wherein
As shown in Fig. 2 the greenhouse intelligent control method provided in an embodiment of the present invention controlled based on computer is including following
Step：
Step S101：Indoor temperature is too high or too low, and main control computer can be in time by temperature adjustment module, and detection is indoor
Temperature is simultaneously adjusted to design temperature, keeps indoor temperature stable.
Step S102：Detection of air quality module can send the air quality data of detection to main control computer, for
Family is inquired about.
The step of main control computer data aggregation method, is as follows：
(1) in area for in=x deployment region, the wireless sensor node of random distribution isomorphism, sink nodes
Outside deployment region, the data being collected into the whole wireless sensor network of node processing；
(2) non-homogeneous cluster
Sink nodes are located at the top of deployment region；Deployment region X-axis is divided into S swimming lane first, and all swimming lanes have phase
Same width w, and the length and the equal length of deployment region of each swimming lane；With the ID from 1 to s as swimming lane, high order end
The ID of swimming lane be 1, then each swimming lane along y-axis be divided into each grid in multiple rectangular mesh, each swimming lane by
A level is defined, the level of the grid of bottom is 1, and each grid and each swimming lane have identical width w；In each swimming lane
Distance dependent of number, length and the swimming lane of grid to sink；The size of grid is adjusted by setting the length of grid；For
Different swimming lanes, the lattice number that the swimming lane more remote apart from sink contains is smaller；For same swimming lane, the net more remote apart from sink
The length of lattice is bigger；Assuming that containing S element, the number of k-th of element representation grid in k-th of swimming lane in A；Each grid
With an array (i, j) as ID, represent that i-th of swimming lane has horizontal j；Define the length of S array representation grid, v-th of number
Group HvRepresent the length of grid in v-th of swimming lane, and HvW-th of element hvwRepresent the length of grid (v, w)；Grid (i,
J) border is：
O_x+ (i-1) × w ＜ x≤o_x+i × w
Non-uniform grid carries out the cluster stage after dividing；Algorithm, which is divided into many wheels, to be carried out, and chooses each in each round
The maximum node of dump energy is as cluster head node in grid, and remaining node adds cluster according to nearby principle, line number is then entered again
According to polymerization；
(3) Grubbs are pre-processed
Sensor node needs to pre-process the data of collection, then transmits data to cluster head node again；Using lattice
The data that this pre- criterion of granny rag is collected to sensor node carry out pretreatment and assume that some cluster head node contains a sensor
Node, the data that sensor node is collected into are x1,x2,…,xn, Normal Distribution, and set：
According to order statistics principle, Grubbs statistic is calculated：
After given significance (α=0.05), measured value meets gi≤g0(n, α), then it is assumed that measured value effectively, is surveyed
Value participates in the data aggregate of next level；It is on the contrary, then it is assumed that measured value is invalid, it is therefore desirable to reject, that is, it is not involved in down
The data aggregate of one level；
(4) adaptive aggregating algorithm
The unbiased estimator of each node measurement data is obtained by iteration, the measurement data of each sensor node is asked for
Euclidean distance between value and estimate, adaptive weighted warm weights are used as using normalized Euclidean distance；From in cluster
The average value of the maxima and minimas of data that collects of sensor node as centre data；
There is individual sensor node in some cluster, with dimensional vector D=(d1,d2,…,dn) represent respective nodes measured value,
By calculating the deviation between the different node datas of Euclidean distance reaction of each node data and centre data and centre data
Size, wherein liCalculation formula be：
According to the corresponding weights size of Euclidean distance adaptive setting, the bigger weights of distance are smaller, got over apart from smaller weights
Greatly；
WhereinwiFor corresponding weights；
Step S103：House dust is excessive, and main control computer can start dust extractor in time and carry out purification air, keep room
Interior clean hygiene.
The temperature sensor that temperature adjustment module is set in the step S101 gives the indoor temperature data transfer of detection
Main control computer, main control computer operational control temperature regulation controller is carried out to indoor temperature control.
The Detection of Air Quality sensor that detection of air quality module is set in the step S102 will detect air quality
In data Cun Chudao memories, main control computer is then transferred to, lifting data storage holds backup, prevents transmit process data from losing
Lose.
Claims (6)
1. a kind of greenhouse intelligent control device controlled based on computer, it is characterised in that the temperature controlled based on computer
Room intelligent controlling device includes：
Main control computer, for by temperature adjustment module, detecting indoor temperature in time and adjusting to design temperature, keeps indoor
Temperature stabilization；
The main control computer is according to formulaBy training sequence and the cyclic shift knot of itself
Fruit carries out conjugation related operation by sign bit, obtains correlation function M (m), wherein c (k) is by symbol bit mapping to local sequence
The complex result gone out, c ((k+m))NIn k=1,2 ... represent to carry out c (k) result of cyclic shift during N；Search out correlation
Function main peak value and corresponding frequency domain sequence under submaximum value ratio maximum case, it is then determined that the corresponding training sequence of the frequency domain sequence
Row；FormulaRepresent that training sequence is total to the cyclic shift result of itself by sign bit
Yoke related operation, wherein c (k) are the complex results mapped out to local sequence C (k) by sign bit, mapping equation be c (k)=
sign(Re(C(k)))+j*sign(Im(C(k)))；c((k))NExpression carries out the result of periodic extension to c (k) by the cycle of N,
Thus c ((k+m))NIn k=1,2 ... represent to carry out c (k) result of cyclic shift during N, m ＞ 0 represent ring shift left m,
M ＜ 0 represent ring shift right | m | position, and understand that correlation function value can go out when cyclic sequence circulation moves 0 according to the structure of sequence
Existing one than larger main peak, peak value is M (0), and if during to training sequence or so cyclic shift NFFT, correlation function value all can
There is less submaximum, two peak values are respectively M (NFFT), M (- NFFT)；The purpose for searching for frequency-domain data sequence is to make training sequence
Row move the main peak value occurred at 0 and the submaximum value that NFFT appearance are moved in circulation left and right is with the biggest gap, ensure to receive number with this
According to the timing slip estimation function main peak value and the gap of submaximum value obtained with training sequence by sign bit related operation as far as possible
Greatly, increase dynamic threshold span, because two submaximum value sizes of correlation function are essentially identical, take its main peak value M (0) and
The ratio of one of submaximum value M (NFFT) is used as criterion；
Temperature adjustment module, is connected in advance with main control computer, for detecting indoor temperature in real time and being controlled, reaches temperature
To setting value；
The temperature adjustment module does Hough transform to the Short Time Fourier Transform spectrum of linear domain of holomorphy and is carried out as follows：
2.1) polar equation be ρ=tcos θ+fsin θ, wherein, (t, f) be time-frequency domain on point, ρ be the point to origin away from
From θ was the straight line and the angle of x-axis of the point and origin, and polar coordinate space (ρ, θ) is quantified as into (ρu,θv), u=1 ..., M,
V=1 ..., N obtains M × N two-dimensional matrix M (ρ, θ)；M (ρ, θ) is an accumulator, and initial value is 0；
2.2) each point (t, f) on correspondence time-frequency domain, its spectral amplitude is | LA(t,f)|2, to improve calculating speed, setting works as certain
The spectral amplitude of individual point be more than the maximum of spectral amplitude a littleWhen then carry out Hough transform, otherwise neglect the point；
2.3) to meet spectral amplitude be more than spectral amplitude a little maximumPoint (t, f), θ all quantized values are substituted into
Polar equation, obtains corresponding ρ, and accumulator is added | LA(t,f)|2, i.e. M (ρ, θ)=M (ρ, θ)+| LA(t,f)|2, obtain
To Hough transform matrix M (ρ, θ)；
Detection of air quality module, is connected in advance with main control computer, for detecting air quality data and being stored；
The detection of air quality module inter-signal interference relationship analysis method comprises the following steps：
Step one, some characteristic parameter CPs of the interference signal on wireless signal field are determined, and feature based parameter is formed pair
The interference space model answered, the interference space model based on foundation determines interference signal characteristic vector to be analyzedWith reference
Character vector of signals
Step 2, based on interference space model, for interference signal characteristic vectorDefinition is to contrast signal characteristic vector's
Displacement vector
Step 3, defines displacement vectorIt is interference signal feature to the projection of some latitude coordinates axle in interference space
VectorTo contrast signal characteristic vectorDistance in the CP dimensions, that is, have：
Wherein PRJ () operator representation is directed to the project of a certain CP dimensions；
Step 4, it is S to the disturbance state of contrast signal to define interference signal, to represent that interference signal is done to contrast signal
Disturb relation；
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Step 5, on the premise of interference has been formed, it is necessary first to choose and determine interference effect parameter EP, believes for interference
For number, parameter is usually signal power p or energy e；
Step 6, it is G to the annoyance level of contrast signal to define interference signal, and contrast signal is done to weigh interference signal
Disturb influence degree；
Dust extractor, is connected in advance with main control computer, for carrying out dust cleaning；
The dust extractor is to frequency-hopping mixing signal time-frequency domain matrixPre-processed, had
Body includes following two step：
The first step is rightProgress goes low energy to pre-process, i.e., in each sampling instant p, willThe value that amplitude is less than thresholding ε is set to 0, and is obtained
Thresholding ε setting can be determined according to the average energy of signal is received；
Second step, finds out the time-frequency numeric field data of p moment (p=0,1,2 ... P-1) non-zero, uses
Represent, whereinRepresent the response of p moment time-frequencyCorresponding frequency indices, right when non-zero
The normalization pretreatment of these non-zeros, obtains pretreated vectorial b (p, q)=[b1(p,q),b2(p,q),…,bM(p,
q)]T, wherein
2. the greenhouse intelligent control device as claimed in claim 1 controlled based on computer, it is characterised in that the temperature is adjusted
Saving module includes temperature regulation controller and temperature sensor.
3. the greenhouse intelligent control device as claimed in claim 1 controlled based on computer, it is characterised in that the air matter
Measuring detection module includes Detection of Air Quality sensor and data storage.
4. the greenhouse intelligent control device as claimed in claim 1 controlled based on computer, it is characterised in that the dust suction mould
Block includes micronic dust detection sensor and dust catcher.
5. a kind of temperature controlled based on computer of the greenhouse intelligent control device controlled as claimed in claim 1 based on computer
Room intelligent control method, it is characterised in that the greenhouse intelligent control method controlled based on computer is comprised the following steps：
Step one, indoor temperature is too high or too low, and main control computer can be in time by temperature adjustment module, and detection indoor temperature is simultaneously
Regulation keeps indoor temperature stable to design temperature；
Step 2, detection of air quality module can send the air quality data of detection to main control computer, be inquired about for user；
Step 3, house dust is excessive, and main control computer can start dust extractor in time and carry out purification air, keeps indoor and does
Net health.
6. the greenhouse intelligent control method controlled based on computer as claimed in claim 5, it is characterised in that the step
Temperature sensor in one in temperature adjustment module is by the indoor temperature data transfer of detection to main control computer, main control computer
Operational control temperature regulation controller is carried out to indoor temperature control；
Detection of Air Quality sensor in the step 2 in detection of air quality module will detect air quality data storage
Into memory, main control computer is then transferred to, lifting data storage holds backup, prevents transmit process loss of data；
The step of main control computer data aggregation method, is as follows：
(1) in area for in=x deployment region, the wireless sensor node of random distribution isomorphism, sink nodes are positioned at portion
Affix one's name to outside region, the data being collected into the whole wireless sensor network of node processing；
(2) non-homogeneous cluster
Sink nodes are located at the top of deployment region；Deployment region X-axis is divided into S swimming lane first, and all swimming lanes have identical
Width w, and the length and the equal length of deployment region of each swimming lane；With the ID from 1 to s as swimming lane, the swimming of high order end
The ID in road is 1, and each grid that then each swimming lane is divided into multiple rectangular mesh, each swimming lane along y-axis is defined
One level, the level of the grid of bottom is 1, and each grid and each swimming lane have identical width w；Grid in each swimming lane
Number, length and swimming lane to sink distance dependent；The size of grid is adjusted by setting the length of grid；For difference
Swimming lane, the lattice number that the swimming lane more remote apart from sink contains is smaller；For same swimming lane, the grid more remote apart from sink
Length is bigger；Assuming that containing S element, the number of k-th of element representation grid in k-th of swimming lane in A；Each grid uses one
Individual array (i, j) represents that i-th of swimming lane has horizontal j as ID；Define the length of S array representation grid, v-th of array Hv
Represent the length of grid in v-th of swimming lane, and HvW-th of element hvwRepresent the length of grid (v, w)；Grid (i, j)
Border is：
O_x+ (i-1) × w ＜ x≤o_x+i × w
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Non-uniform grid carries out the cluster stage after dividing；Algorithm, which is divided into many wheels, to be carried out, and each grid is chosen in each round
The maximum node of middle dump energy adds cluster as cluster head node, remaining node according to nearby principle, and data are then carried out again and are gathered
Close；
(3) Grubbs are pre-processed
Sensor node needs to pre-process the data of collection, then transmits data to cluster head node again；Using Ge Labu
The data that this pre- criterion is collected to sensor node carry out pretreatment and assume that some cluster head node contains a sensor node,
The data that sensor node is collected into are x1,x2,…,xn, Normal Distribution, and set：
vi=xi-x0,
According to order statistics principle, Grubbs statistic is calculated：
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After given significance (α=0.05), measured value meets gi≤g0(n, α), then it is assumed that measured value is effective, measured value
Participate in the data aggregate of next level；It is on the contrary, then it is assumed that measured value is invalid, it is therefore desirable to reject, that is, it is not involved in next layer
Secondary data aggregate；
(4) adaptive aggregating algorithm
The unbiased estimator of each node measurement data is obtained by iteration, ask for the measured data values of each sensor node with
Euclidean distance between estimate, adaptive weighted warm weights are used as using normalized Euclidean distance；From the biography in cluster
The average value of the maxima and minima for the data that sensor node is collected is as centre data；
There is individual sensor node in some cluster, with dimensional vector D=(d1,d2,…,dn) represent respective nodes measured value, pass through
The deviation size between the different node datas of the Euclidean distance reaction of each node data and centre data and centre data is calculated,
Wherein liCalculation formula be：
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According to the corresponding weights size of Euclidean distance adaptive setting, the bigger weights of distance are smaller, bigger apart from smaller weights；
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WhereinwiFor corresponding weights；
Micronic dust detection sensor in the step 3 in dust extractor sends the micronic dust data of indoor detection to master control and calculated
Machine, main control computer control dust catcher carries out dust-collecting indoor operation, purifies the air of a room.
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