CN109542146A - The long-range plant physiological ecology monitor control system in greenhouse based on big data analysis - Google Patents
The long-range plant physiological ecology monitor control system in greenhouse based on big data analysis Download PDFInfo
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- CN109542146A CN109542146A CN201810534198.XA CN201810534198A CN109542146A CN 109542146 A CN109542146 A CN 109542146A CN 201810534198 A CN201810534198 A CN 201810534198A CN 109542146 A CN109542146 A CN 109542146A
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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
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/0275—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using fuzzy logic only
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Abstract
The invention belongs to plant monitoring technical fields, a kind of long-range plant physiological ecology monitor control system in the greenhouse based on big data analysis is disclosed, the long-range plant physiological ecology monitor control system in the greenhouse based on big data analysis includes: humidity detecting module, temperature detecting module, illumination detection module, single chip control module, carbon dioxide control module, big data computing module, data memory module, display module.The present invention takes device using cloud by big data computing module and big data computing resource is concentrated to be analyzed and processed detection data, greatly improves monitoring velocity, and then promote monitoring efficiency；The utilization rate for effectively promoting carbon dioxide by carbon dioxide control module simultaneously, greatly promotes the photosynthetic efficiency of hothouse plants, is conducive to the growth of plant.
Description
Technical field
The invention belongs to the long-range plant of plant monitoring technical field more particularly to a kind of greenhouse based on big data analysis is raw
Manage ecological monitoring control system.
Background technique
Currently, the prior art commonly used in the trade is such that
Plant is one of Main Morphology of life, is contained such as trees, shrub, rattan class, green grass, fern and green alga, lichens
The biology Deng known to.In the plants such as seed plant, bryophyte, pteridophyte and quasi- fern, it is estimated that existing about 350
000 species.The most energy of green plants is via photosynthesis obtained in the sunlight, and temperature, humidity, light are
The primary demand of plant life.Seed plant shares six big organs: root, stem, leaf, flower, fruit, seed.Green plants has light
The ability of cooperation --- by luminous energy and chlorophyll, under the Catalysis work of enzyme, carried out using water, inorganic salts and carbon dioxide
Photosynthesis discharges oxygen, generates the organic matters such as glucose, utilizes for plant.However, existing to plant physiology monitoring data
Processing speed is slow；Monitoring efficiency is low；Photosynthetic efficiency is low in the greenhouse for existing plant simultaneously, is unfavorable for the growth of plant.
Light is natural resources for the survival of mankind, and no light all life can all terminate, therefore, raw in industrial or agricultural
It produces, in the building industries such as video display and health care, often requires that the intensity of measurement illumination.Develop high-precision intensity of illumination measurement
Instrument is paid close attention to by people, however, the illumination tester of most of companies production at present, all using " phototriode or photocell+
Signal amplification circuit+A/D conversion circuit " design structure, although these illumination testers are capable of measuring intensity of illumination, but these are tested
The disadvantages of instrument has system design complicated, and precision is not high, and debugging is difficult, and product price is more expensive.
In conclusion problem of the existing technology is:
It is existing slow to plant physiology monitoring data processing speed；Monitoring efficiency is low；Existing plant is photosynthetic in the greenhouse simultaneously
Functioning efficiency is low, is unfavorable for the growth of plant.
In the prior art, detection data poor robustness.
The illumination tester of most of company's productions at present, all uses " phototriode or photocell+signal amplification circuit
+ A/D conversion circuit " design structure, although these illumination testers are capable of measuring intensity of illumination, but these testers are set with system
The disadvantages of meter is complicated, and precision is not high, and debugging is difficult, and product price is more expensive.
Summary of the invention
In view of the problems of the existing technology, the present invention provides a kind of long-range plant in greenhouse based on big data analysis is raw
Manage ecological monitoring control system.
The invention is realized in this way a kind of long-range plant physiological ecology monitoring control system in greenhouse based on big data analysis
System, the long-range plant physiological ecology monitor control system in the greenhouse based on big data analysis include:
Humidity detecting module is connect with single chip control module, for passing through the indoor humidity of humidity sensor detection temperature
Information；
The fractional lower-order ambiguity function of the digital modulation signals x (t) of humidity detecting module indicates are as follows:
Wherein, τ is delay skew, and f is Doppler frequency shift, 0 < a, b < α/2, x*(t) conjugation for indicating x (t), as x (t)
When for real signal, x (t)< p >=| x (t) |< p >sgn(x(t))；When x (t) is time multiplexed signal, [x (t)]< p >=| x (t) |p-1x*
(t)；
Temperature detecting module is connect with single chip control module, for passing through the indoor temperature of temperature sensor detection temperature
Information；
The signal model of the reception signal of temperature detecting module indicates are as follows:
R (t)=x1(t)+x2(t)+…+xn(t)+v(t)
Wherein, xiIt (t) is each signal component of time-frequency overlapped signal, each component signal is independently uncorrelated, and n is time-frequency weight
The number of folded signal component, θkiIndicate the modulation to each signal component carrier phase, fciFor carrier frequency, AkiBelieve for i-th
Amplitude number at the k moment, TsiFor Baud Length；
Illumination detection module, connect with single chip control module, for passing through the indoor illumination of optical sensor detection temperature
Strength information；Illumination detection module embeds 32 Nios II soft nucleus CPUs, SDRAM in programmable logic device gate array
The SDRAM memory chip of controller and periphery, constitutes the SDRAM storage system of this system, for storing NiosII soft nucleus CPU
Program when operation and at runtime caused by important data, JTAG UART controller passes through JTAG line and upper PC machine
It is connected, realizes the downloading and on-line debugging function of program；EPSC controller and its EPSC storage chip of periphery, constitute a string
The storage system of capable electric erasable is mainly used for storing FPGA preparation file and NiosII soft nucleus CPU executes program code；
LCD controller and its LCD display are for showing light intensity value；BH1750 illuminance sensor module passes through the total road of I2C data
Line is communicated with the NiosII in FPGA, and PIO1 mouthfuls provide SCL clock signal for BH1750 light intensity sensor module, and
POI2 provides SDA signal；
The application circuit structure of the BH1750 specifically: by numeric type light intensity sensor integrated circuit BH1750, outside
Two resistance R1, the R2 and periphery two capacitor C1, C2 composition, the 2nd pin ADD of integrated circuit BH1750 and the 3rd pin enclosed
GND is grounded 0 current potential；The 1st pin VCC of integrated circuit BH1750 and the 5th pin DVI connect power supply；Integrated circuit BH1750
The 1st pin VCC and the 5th pin DVI connect filter capacitor C1 and C2, the 4th pin SDA of integrated circuit BH1750 and respectively
6 pin SCL connect pull-up resistor R1 and R2 respectively, and fpga chip reads photometric data by 4 pin SDA and the 6th pin SCL；
Single chip control module is controlled with humidity detecting module, temperature detecting module, illumination detection module, carbon dioxide
Module, big data computing module, data memory module, display module connection, work normally for dispatching modules；
Single chip control module carries out nonlinear transformation to signal s (t) is received, and carries out as follows:
WhereinA indicates the amplitude of signal, a (m) table
Show that the symbol of signal, p (t) indicate shaping function, fcIndicate the carrier frequency of signal,It indicates the phase of signal, leads to
It is obtained after crossing the nonlinear transformation:
Carbon dioxide control module, connect with single chip control module, for carrying out to the indoor gas concentration lwevel of temperature
Control；
Big data computing module, connect with single chip control module, concentrates big data resource to carry out for taking device by cloud
Analysis processing is calculated to detection data；
The processing method of big data computing module includes: frequency-hopping mixing signal time-frequency domain matrixIt is pre-processed:
The first step is rightLow energy is carried out to pre-process, i.e., in each sampling instant p,
It willValue of the amplitude less than thresholding ε sets 0, obtains
The setting of thresholding ε is determined according to the average energy for receiving signal；
Second step finds out p moment (p=0,1,2 ... P-1)
The time-frequency numeric field data of non-zero is usedIt indicates, whereinWhen indicating the p moment
Frequency response is answeredCorresponding frequency indices when non-zero normalize pre- place to these non-zeros
Reason, obtains pretreated vector b (p, q)=[b1(p,q),b2(p,q),…,bM(p,q)]T, wherein
Data memory module is connect with single chip control module, for storing the data information of detection；
Display module is connect with single chip control module, for the data information by display display detection.
Further, the processing method of big data computing module specifically includes:
Step 1 takes the multiple synchronized orthogonal frequency hoppings of device from cloud using the array antenna received containing M array element
Frequency Hopping Signal samples, the road the M discrete time-domain mixed signal after being sampled to per reception signal all the way
Step 2 carries out overlapping adding window Short Time Fourier Transform to the road M discrete time-domain mixed signal, obtains M mixing letter
Number time-frequency domain matrix Wherein P
Indicate total window number, NfftIndicate FFT transform length；?
In step 2, (p, q) indicates time-frequency index, and specific time-frequency value isHere NfftTable
Show the length of FFT transform, p indicates adding window number, TsIndicate sampling interval, fsIndicate sample frequency, C is integer, indicates Fu in short-term
In leaf transformation adding window interval sampling number, C < Nfft, and Kc=Nfft/ C is integer, that is to say, that using overlapping adding window
Short Time Fourier Transform；
Step 3, to frequency-hopping mixing signal time-frequency domain matrix obtained in step 2
It is pre-processed；
Step 4 estimates the jumping moment of each jump using clustering algorithm and respectively jumps corresponding normalized hybrid matrix
Column vector, Hopping frequencies；It is right at p (p=0,1,2 ... the P-1) momentThe frequency values of expression are clustered, in obtained cluster
Heart numberIndicate carrier frequency number existing for the p moment,A cluster centre then indicates the size of carrier frequency, uses respectivelyIt indicates；To each sampling instant p (p=0,1,2 ... P-1), clustering algorithm pair is utilizedInto
Row cluster, it is same availableA cluster centre is usedIt indicates；To allIt averages and is rounded, obtain
To the estimation of source signal numberThat is:
It finds outAt the time of, use phIt indicates, to the p of each section of continuous valuehIntermediate value is sought, is usedTable
Show the l sections of p that are connectedhIntermediate value, thenIndicate the estimation at first of frequency hopping moment；It is obtained according to estimationAnd the 4th frequency hopping moment for estimating in step estimate it is each jump it is correspondingIt is a mixed
Close matrix column vectorSpecific formula are as follows:
HereIt is corresponding to indicate that l is jumpedA mixing
Matrix column vector estimated value；Estimate the corresponding carrier frequency of each jump, usesIt indicates that l is jumped to correspond to
'sA frequency estimation, calculation formula are as follows:
Step 5 estimates time-frequency domain frequency hopping source signal according to the normalization hybrid matrix column vector that step 4 is estimated；
Step 6 splices the time-frequency domain frequency hopping source signal between different frequency hopping points；It is corresponding to estimate that l is jumpedIt is a
Incident angle is usedIndicate that l jumps the corresponding incident angle of n-th of source signal,Calculation formula it is as follows:
Indicate that l jumps n-th of hybrid matrix column vector that estimation obtainsM-th of element, c indicate light
Speed, i.e. vc=3 × 108Meter per second；Judge that l (l=2,3 ...) jumps the source signal of estimation and first and jump between the source signal of estimation
Corresponding relationship, judgment formula is as follows:
Wherein mn (l)Indicate that l jumps the m of estimationn (l)A signal and n-th of signal of the first jump estimation belong to the same source
Signal；By different frequency hopping point estimation to the signal for belonging to the same source signal be stitched together, as final time-frequency domain source
Signal estimation, uses YnTime-frequency domain estimated value of n-th of the source signal of (p, q) expression on time frequency point (p, q), p=0,1,2 ...,
P, q=0,1,2 ..., Nfft- 1, it may be assumed that
Step 7 restores time domain frequency hopping source signal according to source signal time-frequency domain estimated value；To each sampling instant p (p=
0,1,2 ...) frequency domain data Yn(p, q), q=0,1,2 ..., Nfft- 1 is NfftThe IFFT transformation of point, obtains p sampling instant pair
The time domain frequency hopping source signal answered, uses yn(p,qt) (qt=0,1,2 ..., Nfft- 1) it indicates；The time domain obtained to above-mentioned all moment
Frequency hopping source signal yn(p,qt) processing is merged, final time domain frequency hopping source signal estimation is obtained, specific formula is as follows:
Here Kc=Nfft/ C, C are the sampling number at Short Time Fourier Transform adding window interval, NfftFor the length of FFT transform.
Further, the carbon dioxide control module includes gas concentration lwevel detection module, carbon dioxide supply module；
Gas concentration lwevel detection module, for detecting the indoor gas concentration lwevel data information of temperature；
Carbon dioxide supply module is supplied for generating carbon dioxide by carbon-dioxide generator to plant.
Further, the carbon dioxide control module control method is as follows:
Firstly, by the indoor gas concentration lwevel data of gas concentration lwevel detection module detection temperature, if concentration is low,
The signal that then will test is sent to single chip control module；
Then, single chip control module dispatches carbon dioxide supply module and generates carbon dioxide by carbon-dioxide generator
It is supplied；
Finally, then gas concentration lwevel detection module sends a signal to single-chip microcontroller when gas concentration lwevel data are normal
Control module closes carbon dioxide supply module.
It is raw equipped with the long-range plant in the greenhouse based on big data analysis that another object of the present invention is to provide a kind of
Manage the information data processing terminal of ecological monitoring control system.
Another object of the present invention is to provide the long-range plant physiologies in greenhouse described in a kind of realize based on big data analysis
The computer program of ecological monitoring control system.
Another object of the present invention is to provide a kind of computer readable storage mediums, including instruction, when it is in computer
When upper operation, so that computer executes the long-range plant physiological ecology monitor control system in the greenhouse based on big data analysis
Monitoring method.
Advantages of the present invention and good effect are as follows:
The present invention takes device using cloud by big data computing module and big data computing resource is concentrated to divide detection data
Analysis processing greatly improves monitoring velocity, and then promotes monitoring efficiency；Two are effectively promoted by carbon dioxide control module simultaneously
The utilization rate of carbonoxide greatly promotes the photosynthetic efficiency of hothouse plants, is conducive to the growth of plant.
Humidity detecting module, temperature detecting module, the detection data of single chip control module and processing method of the invention
Strong robustness.
In order to verify whether this measuring system meets design requirement, this system is tested.Hardware system uses
Programmable logic gate array (FPGA) EP1C6Q240C8 of altera corp is designed, and software systems use altera corp
QuartusII12.0 software developed.The test result of this model machine, during the test, to the long-range plant physiology in greenhouse
The intensity of illumination of ecology measures, and the every 5 seconds record one-shot measurement results.Test result shows this luminous intensity measurement system
System not only the operation is stable, but also measurement accuracy height, therefore have certain practical value.
Detailed description of the invention
Fig. 1 is the long-range plant physiological ecology monitoring control system in the greenhouse provided in an embodiment of the present invention based on big data analysis
System structural block diagram.
In figure: 1, humidity detecting module；2, temperature detecting module；3, illumination detection module；4, single chip control module；5,
Carbon dioxide control module；6, big data computing module；7, data memory module；8, display module.
Fig. 2 is the block diagram of illumination detection module provided in an embodiment of the present invention；
Fig. 3 is the design schematic diagram of illumination detection module provided in an embodiment of the present invention.
Specific embodiment
In order to further understand the content, features and effects of the present invention, the following examples are hereby given, and cooperate attached drawing
Detailed description are as follows.
As shown in Figure 1, the long-range plant physiological ecology monitoring in the greenhouse provided in an embodiment of the present invention based on big data analysis
Control system includes: humidity detecting module 1, temperature detecting module 2, illumination detection module 3, single chip control module 4, titanium dioxide
Carbon control module 5, big data computing module 6, data memory module 7, display module 8.
Humidity detecting module 1 is connect with single chip control module 4, for indoor wet by humidity sensor detection temperature
Spend information；
Temperature detecting module 2 is connect with single chip control module 4, for passing through the indoor temperature of temperature sensor detection temperature
Spend information；
Illumination detection module 3 is connect with single chip control module 4, for passing through the indoor light of optical sensor detection temperature
According to strength information；
Single chip control module 4, with humidity detecting module 1, temperature detecting module 2, illumination detection module 3, carbon dioxide
Control module 5, big data computing module 6, data memory module 7, display module 8 connect, for dispatching the normal work of modules
Make；
Carbon dioxide control module 5 is connect with single chip control module 4, for the indoor gas concentration lwevel of temperature into
Row science control；
Big data computing module 6 is connect with single chip control module 4, for by cloud take device concentrate big data resource into
Row calculates analysis processing to detection data；
Data memory module 7 is connect with single chip control module 4, for storing the data information of detection；
Display module 8 is connect with single chip control module 4, for the data information by display display detection.
Carbon dioxide control module 5 provided by the invention includes gas concentration lwevel detection module, carbon dioxide supply mould
Block；
Gas concentration lwevel detection module, for detecting the indoor gas concentration lwevel data information of temperature；
Carbon dioxide supply module is supplied for generating carbon dioxide by carbon-dioxide generator to plant.
5 control method of carbon dioxide control module provided by the invention is as follows:
Firstly, by the indoor gas concentration lwevel data of gas concentration lwevel detection module detection temperature, if concentration is low,
The signal that then will test is sent to single chip control module；
Then, single chip control module dispatches carbon dioxide supply module and generates carbon dioxide by carbon-dioxide generator
It is supplied；
Finally, then gas concentration lwevel detection module sends a signal to single-chip microcontroller when gas concentration lwevel data are normal
Control module closes carbon dioxide supply module.
When the invention works, the indoor humidity information of temperature is detected by humidity detecting module 1；Pass through temperature detecting module 2
The indoor temperature information of thermometric；Pass through the indoor illumination intensity information of 3 thermometric of illumination detection module；Single chip control module 4 is adjusted
It spends the indoor gas concentration lwevel of 5 pairs of temperature of carbon dioxide control module and carries out scientific control；It is concentrated by big data computing module 6
Big data resource carries out calculating detection data analysis processing；Then, believed by the data that data memory module 7 stores detection
Breath；Finally, passing through the data information of the display detection of display module 8.
Below with reference to concrete analysis, the invention will be further described.
Humidity detecting module is connect with single chip control module, for passing through the indoor humidity of humidity sensor detection temperature
Information；
The fractional lower-order ambiguity function of the digital modulation signals x (t) of humidity detecting module indicates are as follows:
Wherein, τ is delay skew, and f is Doppler frequency shift, 0 < a, b < α/2, x*(t) conjugation for indicating x (t), as x (t)
When for real signal, x (t)< p >=| x (t) |< p >sgn(x(t))；When x (t) is time multiplexed signal, [x (t)]< p >=| x (t) |p-1x*
(t)；
Temperature detecting module is connect with single chip control module, for passing through the indoor temperature of temperature sensor detection temperature
Information；
The signal model of the reception signal of temperature detecting module indicates are as follows:
R (t)=x1(t)+x2(t)+…+xn(t)+v(t)
Wherein, xiIt (t) is each signal component of time-frequency overlapped signal, each component signal is independently uncorrelated, and n is time-frequency weight
The number of folded signal component, θkiIndicate the modulation to each signal component carrier phase, fciFor carrier frequency, AkiBelieve for i-th
Amplitude number at the k moment, TsiFor Baud Length；
Illumination detection module, connect with single chip control module, for passing through the indoor illumination of optical sensor detection temperature
Strength information；Illumination detection module embeds 32 Nios II soft nucleus CPUs, SDRAM in programmable logic device gate array
The SDRAM memory chip of controller and periphery, constitutes the SDRAM storage system of this system, for storing NiosII soft nucleus CPU
Program when operation and at runtime caused by important data, JTAG UART controller passes through JTAG line and upper PC machine
It is connected, realizes the downloading and on-line debugging function of program；EPSC controller and its EPSC storage chip of periphery, constitute a string
The storage system of capable electric erasable is mainly used for storing FPGA preparation file and NiosII soft nucleus CPU executes program code；
LCD controller and its LCD display are for showing light intensity value；BH1750 illuminance sensor module passes through the total road of I2C data
Line is communicated with the NiosII in FPGA, and PIO1 mouthfuls provide SCL clock signal for BH1750 light intensity sensor module, and
POI2 provides SDA signal；
The application circuit structure of the BH1750 specifically: by numeric type light intensity sensor integrated circuit BH1750, outside
Two resistance R1, the R2 and periphery two capacitor C1, C2 composition, the 2nd pin ADD of integrated circuit BH1750 and the 3rd pin enclosed
GND is grounded 0 current potential；The 1st pin VCC of integrated circuit BH1750 and the 5th pin DVI connect power supply；Integrated circuit BH1750
The 1st pin VCC and the 5th pin DVI connect filter capacitor C1 and C2, the 4th pin SDA of integrated circuit BH1750 and respectively
6 pin SCL connect pull-up resistor R1 and R2 respectively, and fpga chip reads photometric data by 4 pin SDA and the 6th pin SCL；
Single chip control module carries out nonlinear transformation to signal s (t) is received, and carries out as follows:
WhereinA indicates the amplitude of signal, a (m) table
Show that the symbol of signal, p (t) indicate shaping function, fcIndicate the carrier frequency of signal,It indicates the phase of signal, leads to
It is obtained after crossing the nonlinear transformation:
Big data computing module, connect with single chip control module, concentrates big data resource to carry out for taking device by cloud
Analysis processing is calculated to detection data；
The processing method of big data computing module includes: frequency-hopping mixing signal time-frequency domain matrixIt is pre-processed:
The first step is rightIt carries out low energy to pre-process, i.e., is adopted each
Sample moment p, willValue of the amplitude less than thresholding ε sets 0, obtainsThe setting of thresholding ε is determined according to the average energy for receiving signal；
Second step finds out p moment (p=0,1,2 ... P-1)
The time-frequency numeric field data of non-zero is usedIt indicates, whereinWhen indicating p
Carve time-frequency responseCorresponding frequency indices when non-zero, to these non-zero normalizings
Change pretreatment, obtains pretreated vector b (p, q)=[b1(p,q),b2(p,q),…,bM(p,q)]T, wherein
Data memory module is connect with single chip control module, for storing the data information of detection；
Display module is connect with single chip control module, for the data information by display display detection.
Further, the processing method of big data computing module specifically includes:
Step 1 takes the multiple synchronized orthogonal frequency hoppings of device from cloud using the array antenna received containing M array element
Frequency Hopping Signal samples, the road the M discrete time-domain mixed signal after being sampled to per reception signal all the way
Step 2 carries out overlapping adding window Short Time Fourier Transform to the road M discrete time-domain mixed signal, obtains M mixing letter
Number time-frequency domain matrix
Wherein P indicates total window number, NfftIndicate FFT transform length；?
In step 2, (p, q) indicates time-frequency index, and specific time-frequency value isHere NfftTable
Show the length of FFT transform, p indicates adding window number, TsIndicate sampling interval, fsIndicate sample frequency, C is integer, indicates Fu in short-term
In leaf transformation adding window interval sampling number, C < Nfft, and Kc=Nfft/ C is integer, that is to say, that using overlapping adding window
Short Time Fourier Transform；
Step 3, to frequency-hopping mixing signal time-frequency domain matrix obtained in step 2It is pre-processed；
Step 4 estimates the jumping moment of each jump using clustering algorithm and respectively jumps corresponding normalized hybrid matrix
Column vector, Hopping frequencies；It is right at p (p=0,1,2 ... the P-1) momentThe frequency values of expression are clustered, in obtained cluster
Heart numberIndicate carrier frequency number existing for the p moment,A cluster centre then indicates the size of carrier frequency, uses respectivelyIt indicates；To each sampling instant p (p=0,1,2 ... P-1), clustering algorithm pair is utilizedInto
Row cluster, it is same availableA cluster centre is usedIt indicates；To allIt averages and is rounded, obtain
The estimation of source signal numberThat is:
It finds outAt the time of, use phIt indicates, to the p of each section of continuous valuehIntermediate value is sought, is usedTable
Show the l sections of p that are connectedhIntermediate value, thenIndicate the estimation at first of frequency hopping moment；It is obtained according to estimationAnd the 4th frequency hopping moment for estimating in step estimate it is each jump it is correspondingIt is a
Hybrid matrix column vectorSpecific formula are as follows:
HereIt is corresponding to indicate that l is jumpedA mixing
Matrix column vector estimated value；Estimate the corresponding carrier frequency of each jump, usesIt indicates that l is jumped to correspond to
'sA frequency estimation, calculation formula are as follows:
Step 5 estimates time-frequency domain frequency hopping source signal according to the normalization hybrid matrix column vector that step 4 is estimated；
Step 6 splices the time-frequency domain frequency hopping source signal between different frequency hopping points；It is corresponding to estimate that l is jumpedIt is a
Incident angle is usedIndicate that l jumps the corresponding incident angle of n-th of source signal,Calculation formula it is as follows:
Indicate that l jumps n-th of hybrid matrix column vector that estimation obtainsM-th of element, c indicate the light velocity,
That is vc=3 × 108Meter per second；Judge that l (l=2,3 ...) jumps pair between the source signal of estimation and the source signal of the first jump estimation
It should be related to, judgment formula is as follows:
Wherein mn (l)Indicate that l jumps the m of estimationn (l)A signal and n-th of signal of the first jump estimation belong to the same source
Signal；By different frequency hopping point estimation to the signal for belonging to the same source signal be stitched together, as final time-frequency domain source
Signal estimation, uses YnTime-frequency domain estimated value of n-th of the source signal of (p, q) expression on time frequency point (p, q), p=0,1,2 ...,
P, q=0,1,2 ..., Nfft- 1, it may be assumed that
Step 7 restores time domain frequency hopping source signal according to source signal time-frequency domain estimated value；To each sampling instant p (p=
0,1,2 ...) frequency domain data Yn(p, q), q=0,1,2 ..., Nfft- 1 is NfftThe IFFT transformation of point, obtains p sampling instant pair
The time domain frequency hopping source signal answered, uses yn(p,qt) (qt=0,1,2 ..., Nfft- 1) it indicates；The time domain obtained to above-mentioned all moment
Frequency hopping source signal yn(p,qt) processing is merged, final time domain frequency hopping source signal estimation is obtained, specific formula is as follows:
Here Kc=Nfft/ C, C are the sampling number at Short Time Fourier Transform adding window interval, NfftFor the length of FFT transform.
Fig. 2 is the block diagram of illumination detection module provided in an embodiment of the present invention；
Fig. 3 is the design schematic diagram of illumination detection module provided in an embodiment of the present invention.
Below with reference to concrete analysis, the invention will be further described.
Illumination detection module of the invention can compiled in order to effectively be communicated with illuminance sensor BH1750
In journey logical device gate array (FPGA), 32 Nios II soft nucleus CPUs have been embedded.The SDRAM of sdram controller and periphery
Memory chip constitutes the SDRAM storage system of this system, for storing program when NiosII soft nucleus CPU is run and transporting
Generated important data when row.JTAG UART controller is connected by JTAG line with upper PC machine, realizes the downloading of program
And on-line debugging function；EPSC controller and its EPSC storage chip of periphery, constitute the storage of a serial electric erasable
System is mainly used for storing FPGA preparation file and NiosII soft nucleus CPU executes program code；LCD controller and its LCD are shown
Device is for showing light intensity value；BH1750 illuminance sensor module is soft by I2C data bus and the NiosII in FPGA
Core CPU is communicated, and PIO1 mouthfuls provide SCL clock signal for BH1750 light intensity sensor module, and POI2 provides SDA letter
Number.
1Qsys hardware design
Qsys is the system integration tool of new generation of altera corp, is the said firm after Quartus II 11.0
It is proposed programmable system on chip developing instrument.Compared with pervious SOPC Builder, Qsys supports that layering is in all directions
System design, is greatly improved design flexibility, makes to obtain the raising in amplitude based on team's design efficiency, enhance design reuse function
Can, verifying speed is faster.When defining the above modules, NiosII is defined as standard type CPU, SCL is defined as
SDA is defined as " Bidir " type by " Output " type.When defining the parameter of modules, can click in Qsys developing instrument
" Generation " button, allow system to automatically generate various files.
The design of 2.BH1750 application circuit
In order to improve the measurement accuracy of this system, using BH1750 as illuminance sensor.BH1750 is collection optical signal
Acquisition, optical signal amplification, the powerful totally digitilized photo-sensing device IC of the one such as analog-to-digital conversion.This IC to light source according to
Rely property little, and has and spectral characteristic similar in human eye.In this IC, also built-in 16 analog-digital converters, can by pair
It is arranged to high, medium and low three kinds of resolution measurement modes by the instruction answered, and the resolution ratio of high resolution measurement mode is 0.5lx,
The resolution ratio of intermediate-resolution measurement pattern is 1lx, and the resolution ratio of low resolution measurement pattern is 4lx.Based on the above advantage, illumination
Degree sensor BH1750 has been obtained extensively in mobile phone, portable game machine, digital camera, DV, the equipment such as vehicle mounted guidance
General application.In order to reduce the volume of device, BH1750 additionally uses I2C data bus and is communicated with external CPU, such as schemes
3, it is its typical application circuit, wherein SCL is input clock signal, and SDA is data-signal.
3. software system design
Other than carrying out Hardware Design, the software program of this system is also designed.The software of this system utilizes
The 12.0 Software Build Tools for Eclipse Integrated Development Tool of Nios II of altera corp is set
Meter.Firstly, initializing to LCD, then, illuminance module is set as power-up operating mode with instruction 0x01 and 0x10
(power on) and the continuous measurement pattern of high-resolution (Continuously H-Resolution Mode) are delayed 180 milliseconds
And then the data in reading BH1750, it is converted into photometric data finally by formula, and result is shown with LCD display
Out.
4. test result
It is to be tested to this to verify whether this measuring system meets design requirement.Hardware system uses
Programmable logic gate array (FPGA) EP1C6Q240C8 of altera corp is designed, and software systems use altera corp
QuartusII12.0 software developed.Table 1 is the test result of this model machine, during the test, is carried out to intensity of illumination
Measurement, the every 5 seconds record one-shot measurement results.Test result shows this luminous intensity measurement system not only the operation is stable, but also
Measurement accuracy is high, therefore has certain practical value.
1 illuminance measuring test result of table
The above is only the preferred embodiments of the present invention, and is not intended to limit the present invention in any form,
Any simple modification made to the above embodiment according to the technical essence of the invention, equivalent variations and modification, belong to
In the range of technical solution of the present invention.
Claims (7)
1. the long-range plant physiological ecology monitor control system in greenhouse based on big data analysis, which is characterized in that described based on big
Data analysis the long-range plant physiological ecology monitor control system in greenhouse include:
Humidity detecting module is connect with single chip control module, for passing through the indoor humidity information of humidity sensor detection temperature；
The fractional lower-order ambiguity function of the digital modulation signals x (t) of humidity detecting module indicates are as follows:
Wherein, τ is delay skew, and f is Doppler frequency shift, 0 < a, b < α/2, x*(t) conjugation for indicating x (t), when x (t) is real
When signal, x (t)< p >=| x (t) |< p >sgn(x(t))；When x (t) is time multiplexed signal, [x (t)]< p >=| x (t) |p-1x*(t)；
Temperature detecting module is connect with single chip control module, for passing through the indoor temperature information of temperature sensor detection temperature；
The signal model of the reception signal of temperature detecting module indicates are as follows:
R (t)=x1(t)+x2(t)+…+xn(t)+v(t)
Wherein, xiIt (t) is each signal component of time-frequency overlapped signal, each component signal is independently uncorrelated, and n is time-frequency overlapping letter
The number of number component, θkiIndicate the modulation to each signal component carrier phase, fciFor carrier frequency, AkiExist for i-th of signal
The amplitude at k moment, TsiFor Baud Length；
Illumination detection module, connect with single chip control module, for passing through the indoor intensity of illumination of optical sensor detection temperature
Information；Illumination detection module embeds 32 NiosII soft nucleus CPUs, sdram controller in programmable logic device gate array
With the SDRAM memory chip of periphery, the SDRAM storage system of this system is constituted, when for storing the operation of NiosII soft nucleus CPU
Program and at runtime caused by important data, JTAG UART controller is connected by JTAG line with upper PC machine, reality
The downloading and on-line debugging function of existing program；EPSC controller and its EPSC storage chip of periphery, constitute a serial electricity
Erasable storage system is mainly used for storing FPGA preparation file and NiosII soft nucleus CPU executes program code；LCD control
Device and its LCD display are for showing light intensity value；BH1750 illuminance sensor module passes through I2C data bus and FPGA
In NiosII communicated, PIO1 mouthfuls provide SCL clock signal for BH1750 light intensity sensor module, and POI2 is provided
SDA signal；
The application circuit structure of the BH1750 specifically: by numeric type light intensity sensor integrated circuit BH1750, periphery
Two resistance R1, R2 and periphery two capacitor C1, C2 composition, the 2nd pin ADD of integrated circuit BH1750 and the 3rd pin GND connect
0 current potential of ground；The 1st pin VCC of integrated circuit BH1750 and the 5th pin DVI connect power supply；The 1st of integrated circuit BH1750
Pin VCC and the 5th pin DVI connects filter capacitor C1 and C2, the 4th pin SDA of integrated circuit BH1750 and the 6th pin respectively
SCL connects pull-up resistor R1 and R2 respectively, and fpga chip reads photometric data by 4 pin SDA and the 6th pin SCL；
Single chip control module, with humidity detecting module, temperature detecting module, illumination detection module, carbon dioxide control module,
Big data computing module, data memory module, display module connection, work normally for dispatching modules；
Single chip control module carries out nonlinear transformation to signal s (t) is received, and carries out as follows:
WhereinA indicates the amplitude of signal, and a (m) indicates the code of signal
Metasymbol, p (t) indicate shaping function, fcIndicate the carrier frequency of signal,Indicate the phase of signal, it is non-linear by this
It is obtained after transformation:
Carbon dioxide control module, connect with single chip control module, for controlling the indoor gas concentration lwevel of temperature；
Big data computing module, connect with single chip control module, concentrates big data resource to carry out to inspection for taking device by cloud
Measured data calculates analysis processing；
The processing method of big data computing module includes: frequency-hopping mixing signal time-frequency domain matrixIt is pre-processed:
The first step is rightLow energy is carried out to pre-process, i.e., it, will in each sampling instant pValue of the amplitude less than thresholding ε sets 0, obtains
The setting of thresholding ε is determined according to the average energy for receiving signal；
Second step finds out p moment (p=0,1,2 ... P-1)
The time-frequency numeric field data of non-zero is usedIt indicates, whereinIndicate frequency response when the p moment
It answersCorresponding frequency indices when non-zero normalize these non-zeros and pre-process, obtain
To pretreated vector b (p, q)=[b1(p,q),b2(p,q),…,bM(p,q)]T, wherein
Data memory module is connect with single chip control module, for storing the data information of detection；
Display module is connect with single chip control module, for the data information by display display detection.
2. the long-range plant physiological ecology monitor control system in greenhouse as described in claim 1 based on big data analysis, feature
It is, the processing method of big data computing module specifically includes:
Step 1 takes the frequency hopping of the multiple synchronized orthogonal frequency hoppings of device using the array antenna received containing M array element from cloud
Signal samples, the road the M discrete time-domain mixed signal after being sampled to per reception signal all the way
Step 2 carries out overlapping adding window Short Time Fourier Transform to the road M discrete time-domain mixed signal, obtains M mixed signal
Time-frequency domain matrixP=0,1 ..., P-1, q=0,1 ..., Nfft- 1, wherein P indicates total
Window number, NfftIndicate FFT transform length；In step 2, (p, q) indicates time-frequency index, and specific time-frequency value isHere NfftIndicate the length of FFT transform, p indicates adding window number, TsIndicate sampling interval, fsIt indicates
Sample frequency, C are integer, indicate the sampling number at Short Time Fourier Transform adding window interval, C < Nfft, and Kc=Nfft/ C is whole
Number, that is to say, that using the Short Time Fourier Transform of overlapping adding window；
Step 3, to frequency-hopping mixing signal time-frequency domain matrix obtained in step 2It carries out
Pretreatment；
Step 4, using clustering algorithm estimate each jump jumping moment and respectively jump corresponding normalized mixed moment array to
Amount, Hopping frequencies；It is right at p (p=0,1,2 ... the P-1) momentThe frequency values of expression are clustered, obtained cluster centre
NumberIndicate carrier frequency number existing for the p moment,A cluster centre then indicates the size of carrier frequency, uses respectivelyIt indicates；To each sampling instant p (p=0,1,2 ... P-1), clustering algorithm pair is utilizedInto
Row cluster, it is same availableA cluster centre is usedIt indicates；To allIt averages and is rounded, obtain
To the estimation of source signal numberThat is:
It finds outAt the time of, use phIt indicates, to the p of each section of continuous valuehIntermediate value is sought, is usedIndicate the
The l sections of p that are connectedhIntermediate value, thenIndicate the estimation at first of frequency hopping moment；It is obtained according to estimationAnd the 4th frequency hopping moment for estimating in step estimate it is each jump it is correspondingIt is a mixed
Close matrix column vectorSpecific formula are as follows:
HereIt is corresponding to indicate that l is jumpedA hybrid matrix
Column vector estimated value；Estimate the corresponding carrier frequency of each jump, usesIt is corresponding to indicate that l is jumped
A frequency estimation, calculation formula are as follows:
Step 5 estimates time-frequency domain frequency hopping source signal according to the normalization hybrid matrix column vector that step 4 is estimated；
Step 6 splices the time-frequency domain frequency hopping source signal between different frequency hopping points；It is corresponding to estimate that l is jumpedA incidence
Angle is usedIndicate that l jumps the corresponding incident angle of n-th of source signal,Calculation formula it is as follows:
Indicate that l jumps n-th of hybrid matrix column vector that estimation obtainsM-th of element, c indicate the light velocity, i.e. vc
=3 × 108Meter per second；It is corresponding between the source signal of estimation and the source signal of the first jump estimation to judge that l (l=2,3 ...) is jumped
Relationship, judgment formula are as follows:
Wherein mn (l)Indicate that l jumps the m of estimationn (l)A signal and n-th of signal of the first jump estimation belong to the same source and believe
Number；By different frequency hopping point estimation to the signal for belonging to the same source signal be stitched together, believe as final time-frequency domain source
Number estimation, use YnTime-frequency domain estimated value of n-th of the source signal of (p, q) expression on time frequency point (p, q), p=0,1,2 ..., P,
Q=0,1,2 ..., Nfft- 1, it may be assumed that
Step 7 restores time domain frequency hopping source signal according to source signal time-frequency domain estimated value；To each sampling instant p (p=0,1,
2 ...) frequency domain data Yn(p, q), q=0,1,2 ..., Nfft- 1 is NfftThe IFFT transformation of point, it is corresponding to obtain p sampling instant
Time domain frequency hopping source signal, uses yn(p,qt) (qt=0,1,2 ..., Nfft- 1) it indicates；The time domain frequency hopping obtained to above-mentioned all moment
Source signal yn(p,qt) processing is merged, final time domain frequency hopping source signal estimation is obtained, specific formula is as follows:
Here Kc=Nfft/ C, C are the sampling number at Short Time Fourier Transform adding window interval, NfftFor the length of FFT transform.
3. the long-range plant physiological ecology monitor control system in greenhouse as described in claim 1 based on big data analysis, feature
It is, the carbon dioxide control module includes gas concentration lwevel detection module, carbon dioxide supply module；
Gas concentration lwevel detection module, for detecting the indoor gas concentration lwevel data information of temperature；
Carbon dioxide supply module is supplied for generating carbon dioxide by carbon-dioxide generator to plant.
4. the long-range plant physiological ecology monitor control system in greenhouse as described in claim 1 based on big data analysis, feature
It is, the carbon dioxide control module control method is as follows:
Firstly, will if concentration is low by the indoor gas concentration lwevel data of gas concentration lwevel detection module detection temperature
The signal of detection is sent to single chip control module；
Then, single chip control module scheduling carbon dioxide supply module generates carbon dioxide by carbon-dioxide generator and carries out
Supply；
Finally, then gas concentration lwevel detection module sends a signal to single-chip microcontroller control when gas concentration lwevel data are normal
Module closes carbon dioxide supply module.
5. a kind of long-range plant physiological ecology in greenhouse equipped with described in Claims 1 to 4 any one based on big data analysis
The information data processing terminal of monitor control system.
6. a kind of long-range plant physiological ecology prison in greenhouse realized described in Claims 1 to 4 any one based on big data analysis
Survey the computer program of control system.
7. a kind of computer readable storage medium, including instruction, when run on a computer, so that computer is executed as weighed
Benefit requires the monitoring of the long-range plant physiological ecology monitor control system in the greenhouse described in 1-4 any one based on big data analysis
Method.
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