CN108990521A - One kind being based on Embedded harvester header integrated control system - Google Patents
One kind being based on Embedded harvester header integrated control system Download PDFInfo
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01D—HARVESTING; MOWING
- A01D41/00—Combines, i.e. harvesters or mowers combined with threshing devices
- A01D41/12—Details of combines
- A01D41/14—Mowing tables
- A01D41/141—Automatic header control
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Abstract
The invention belongs to harvester technical fields, one kind is disclosed based on Embedded harvester header integrated control system and control method, includes: power monitoring module, feed quantity monitoring modular, cereal composition monitoring modular, main control module, locating module, blower module, data memory module, display module based on Embedded harvester header integrated control system.The present invention is conducive to engine high-efficiency fuel consumption, improves effects of energy saving and emission reduction；It realizes under the echo interferences such as rainy days, greasy weather, harvester header integrated control system obtains precise positioning；The temperature-compensating for realizing air blower, so that air blower avoids the influence of ambient temperature；The present invention is used for unsupervised multiple target radar emitter signal feature selection issues；Both the fast convergence of multi-objective particle had been remained, and disaggregation is made to have preferable diversity.
Description
Technical field
The invention belongs to harvester technical fields, more particularly to one kind to be based on Embedded harvester header integrated control system
System.
Background technique
Harvester it be integration harvesting crops machinery.It is disposable to complete harvesting, conveying, threshing, clean, and by paddy
Grain focuses on tanker, and then grain is being transported on transport vehicle by unloading screw conveyor.Hand harvest can also be used, by paddy and wheat etc.
The straw of crop is laid in field, then carries out pickup threshing with cereal harvesting machinery again.Harvest the cereal crops seeds such as paddy and wheat
With the crop harvesting machine of stalk.Including harvester, cutter-rower, reaper binder, grain combine and grain separator etc..Paddy
Object harvest machinery is grown up on the basis of various harvestings, threshing tool；However, existing harvester can not be measured accurately
Harvester is unfavorable for the accurate grasp to harvester amount to the feed quantity of crops；Simultaneously cannot to crops component content into
Row measurement needs to complete in laboratory, time-consuming, laborious and waste.
In conclusion problem of the existing technology is:
Existing harvester can not accurately measure harvester to the feed quantity of crops, be unfavorable for the accurate palm to harvester amount
It holds；Crops component content cannot be measured simultaneously, need to complete in laboratory, time-consuming, laborious and waste.
Raising energy-saving and emission-reduction cannot be done in the prior art；Under the echo interferences such as rainy days, greasy weather, the integrated control of harvester header
System processed cannot obtain precise positioning；Temperature-compensating cannot be carried out to air blower, so that the influence of the easy ambient temperature of air blower.
In the prior art, monitoring engine power data information has many methods, and in existing Feature Extraction Technology, all it is
On the basis of existing single object optimization technology, the scale of character subset will be minimized as another optimization aim, but it is special
The scale for levying subset is a dispersive target, and the solution usually acquired, which is concentrated, can only correspond to a solution under each Feature-scale, this makes
Obtaining the other feature subset that scale is identical but specific features are different can not be found.And these character subset dimensions are also for signal
Feature extraction is also useful.In addition, it is a series of compromise solution that multiple target feature selecting algorithm is finally obtained, need therefrom
The solution of function admirable is chosen, but is currently available that unsupervised approaches are also less.Main difficulty is: designed character subset
Evaluation function and search strategy fail to consider the redundancy and correlation of character subset；Interpretational criteria does not also consider character subset
Influence of the selection of dimension to classification validity；The Pareto solution of multi-objective optimization algorithm concentrates unsupervised mode to extract feature
The importance sorting of dimension and character subset is still unresolved.It causes engine power data monitoring error rate high, cannot effectively protect
Demonstrate,prove service life of aeroengine.
Summary of the invention
In view of the problems of the existing technology, the present invention provides one kind to be based on Embedded harvester header integrated control
System.
The invention is realized in this way a kind of be based on Embedded harvester header integrated control method, comprising:
Engine power data information is monitored by power monitoring module；
Using the objective function of the degree of correlation and one group of minimum of concept definition of redundancy, to engine evaluated power number
It is believed that the quality of number character subset；Wherein degree of correlation tendency retain it is all be associated with close feature with data structure, and redundancy
The feature that can then exclude and select the feature degree of correlation high；Both as the fitness function of particle swarm optimization；
Degree of correlation target uses entropy Measure Indexes, as follows:
sij=exp (- aDij),
Wherein, N is the number of engine power data signal data sample；A is weight coefficient, DijIt is sample i and sample j
The Euclidean distance under character subset represented by x；DaIndicate the average value of all samples Euclidean distance under the total space.Sij's
Value must normalize to [0,1]；When the character subset of selection is reasonable, if sample i and sample j belong to similar, SijTake
It is worth very little, on the contrary it is bigger；To f1(x) minimum value is chosen；
Redundancy target then utilizes related coefficient, and when related coefficient absolute value is smaller, the redundancy that character subset is included is got over
It is small；Objective function is as follows:
Wherein, nxIndicate the number of engine power data-signal character subset；D is total Characteristic Number；xjAnd xkRespectively
Indicate the value of j-th and k-th element in x；bijIndicate value of i-th of sample in j-th of feature, bajIndicate all
Mean value of the sample in j-th of feature；When character subset scale determines, the corresponding objective function of the small character subset of redundancy
f2(x) smaller；
Harvester crops feed quantity data information is measured by feed quantity monitoring modular；The measurement of feed quantity monitoring modular
Signal model 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；
Crops component content data information is measured by cereal composition monitoring modular；
Main control module obtains the position data of harvester according to locating module；
Removal is blowed to harvesting crops sundries using air blower by blower module；
Pass through the data information of data memory module storage monitoring；
Pass through the data information of display module display monitoring.
Further, in power monitoring module monitoring engine power data information, further comprise:
Using PID closed-loop control, the parameter of PID is adjusted accordingly according to different wave modes；
Δ n=n0-n
In formula: Δ n is actual speed and desired rotating speed difference；
kP1、TI1、TD1、kP2、TI2、TD2For two groups of setting values of P, I, D parameter adjustment；K is desired tachometer value.
Further, load estimation optimization revolving speed control is introduced on the basis of PID control；Using auxiliary hydraulic motor dynamic
Higher dynamic response coefficient in force-transmitting ring section, quickly compensates change in torque, and engine speed is avoided to be mutated situation
It generates, meanwhile, in conjunction with the swashplate angle signal that PID control method generates, obtain final auxiliary hydraulic motor swashplate angle letter
Number, formula is
In formula: β is the final displacement signal of auxiliary hydraulic motor；
β 1 is the displacement signal that obtains after load estimation；
β 2 is the displacement signal that PID control obtains；
Locating module obtains in the position data of harvester, establishes back the lower harvester header integrated control system of interference
GPS positioning model, specifically include:
Step 1: GPS positioning system state-transition matrix is established:
The vehicle GPS positioning system state equation and observational equation under rainy day echo interference are indicated using following formula:
In formula,State vector is tieed up for the n at K moment；
Z (K) is that the m at k moment ties up observation vector；
W (K-1) is the noise at system K-1 moment；
V (K) is that the m at system k moment ties up observation noise；
Φ (K, K-1) is the system Matrix of shifting of a step at K-1 to k moment；
H (K)+be the k moment observing matrix；
Γ (K-1) is system noise matrix, is expressed as the K-1 moment system mode affected by noise to the k moment；
The state variable for choosing vehicle GPS positioning system, is indicated using following formula are as follows:
In formula, X, y, z are the position on 3 change in coordinate axis direction, speed, acceleration component, and ε x, ε y, ε z are rainy days, mist
Echo-signal under the weather such as it interferes the various errors to be formed total location error caused by 3 change in coordinate axis direction, can use one
Rank Markov process is equivalent,
The associated time constant of Markov process is respectively represented, υ x, υ y, υ z then represent the height of system
This white noise；
Using dispersion Kalman Filter Technology handling the state variable of 3 axial directions successively, indicated using following formula
System state variables after processing by x-axis for
According to the upward adaptive extended kalman filtering equation of the available x-axis of the above method, it is expressed as using following formula
In formula, Φ1X(K) Kalman filtering gain is represented,
Represent the system discrete state at K-1 moment；
The vehicle GPS positioning system state under the weather such as rainy days, greasy weather under echo interference, which is stated, using following formula shifts square
Battle array
In formula, τ is represented and vehicle acceleration associated time constant；
Step 2: the realization of harvester header GPS positioning modeling:
By under echo interference under rainy days, greasy weather weather vehicle GPS positioning performance and error analysis, acquisition
Based on the upward adaptive extended kalman filtering equation of x-axis, the position detection matrix of vehicle GPS positioning system is established, is utilized
Following formula is expressed as
In formula, δPe、δPn、δPh3 tangent plane location errors of east orientation, north orientation, south orientation are respectively corresponded,
The accelerometer bias for representing 3 localities is poor, δ Ve, δ Vn, δ VhThe gyroscope constant value being then expressed as on 3 directions misses
Difference；
It is pre-processed using residual error amount of the following formula to each locality, obtains EKThe covariance value of representative andGeneration
The residual error amount average value of table:
In formula, n represents the quantity of residual error observation.
The weight matrix of harvester header GPS positioning system is obtained, then eliminates rainy days, greasy weather weather next time using following formula
The abnormal data of harvester header positioning under wave interference, is established by μkAnd μpThe weight square of the vehicle GPS positioning system of representative
Battle array；
Establish the harvester header GPS positioning Optimized model under rainy days, greasy weather weather under echo interference:
In formula, μpIt (k/k-1) is the global best estimates of system whole state,
For the relationship of systematic perspective measurement and state variable；
Blower module, Embedded harvester header integrated control system is by air blower temperature-compensating Optimization Modeling to environment
Variation compensates；
By similarity law, when blower air-suction state changes, condition of similarity meets D1=D2、n1=n2、ρ1≠ρ2, wherein D be
Impeller diameter, m；N is revolving speed, r/min；Q is volume flow, m3/h；P is blower press, pa；ρ is atmospheric density, kg/m3；Phase
It is like three laws
It is located in the case where not considering ambient humidity, if the air blower total pressure of standard air-suction state is P, atmospheric density is
ρ0；Atmospheric density under off-rating is ρS, air blower total pressure is PS, then total pressure relationship has
The air-suction state of air blower is exactly local atmospheric condition, according to The Ideal-Gas Equation
P=ρ RT has
P in formulaK, ρS, TSIt is local atmospheric pressure, atmospheric density and the temperature of blower under conditions of use；?
According to the theory of similarity by use condition (PK, TS) under blower press P be transformed to standard air-suction state (P0, T0) under
Blower press PS；KSThe referred to as wind pressure correction factor of air blower, under real atmosphere state, relative humidity of atomsphere influences blower
The blow rate required, air quantity adjusted coefficient K S' are
φ-relative humidity of atomsphere, %；
PH- temperature TSWhen saturated vapor pressure, pa；
Somewhere air pressure change is little, and above-mentioned wind pressure adjusted coefficient K S and air quantity adjusted coefficient K S' are solid in practical applications
Definite value.
Another object of the present invention is to provide a kind of computer program, based on embedding described in the computer program operation
Enter the harvester header integrated control method of formula.
Another object of the present invention is to provide a kind of terminal, it is described based on Embedded that the terminal at least carries realization
The controller of harvester header integrated control method.
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 based on Embedded harvester header integrated control method described in computer execution.
Another object of the present invention is to provide Embedded harvester header integrated control side is based on described in a kind of realization
Method based on Embedded harvester header integrated control system, it is described to be based on Embedded harvester header integrated control system
Include:
Power monitoring module is connect with main control module, for monitoring engine power data information；
Feed quantity monitoring modular, connect with main control module, for measuring harvester crops feed quantity data information；
Cereal composition monitoring modular, connect with main control module, for measuring crops component content data information；
Main control module, with power monitoring module, feed quantity monitoring modular, cereal composition monitoring modular, locating module, air blast
Module, data memory module, display module connection, work normally for controlling modules；
Locating module is connect with main control module, for obtaining the position data of harvester by GPS locator；
Blower module is connect with main control module, for removal to be blowed to harvesting crops sundries by air blower；
Data memory module is connect with main control module, for storing the data information of monitoring；
Display module is connect with main control module, for the data information by display display monitoring.
Another object of the present invention is to provide one kind to be based on Embedded harvester, described to be based on Embedded harvester
It at least carries described based on Embedded harvester header integrated control system.
Another object of the present invention is to provide a kind of information processing terminal, at least integrated right of the information processing terminal
It is required that based on Embedded harvester header integrated control system described in 8.
Advantages of the present invention and good effect are as follows:
The present invention passes through the peripheral speed and power, cereal seed of feed quantity monitoring modular real-time monitoring cutting table auger line shaft
Grain flow and straw outlet stalk flow using multi-sensor Fusion Algorithm extrapolate the feeding of current Joint harvester
Amount.Car-carried monitor controller can keep driver accurate to real-time feed quantity size effectively to driver with intuitive information
Understand；Near infrared light spectrum sensor is used by cereal composition monitoring modular simultaneously, sample is not required to pre-process and be realized
Non-destructive testing has many advantages, such as that analysis speed is fast, signal-to-noise ratio is high.Prediction model is first completed in laboratory, is solved existing
The problem of conventional method can only be completed in laboratory, time-consuming, laborious and waste, can be harvested in conjunction with combine harvester with real-time detection
The information such as the protein and moisture content of crop；And near infrared spectrum sensor cereal is mounted on putting out cereal for combine harvester and is stirred
At dragon or grain outlet, the prescription letter to form protein and moisture content can be docked with geography information position systems such as existing survey productions
Breath figure, eliminates intermediate link, improves the accuracy of the corn protein and moisture content information in geography information position system
And real-time.
After the present invention selectes revolving speed, the stabilization of rotating speed of target is maintained, is conducive to engine high-efficiency fuel consumption, improves energy conservation
Emission reduction effect；It realizes under the echo interferences such as rainy days, greasy weather, harvester header integrated control system obtains precise positioning；It realizes
The temperature-compensating of air blower, so that air blower avoids the influence of ambient temperature.
The present invention is used for unsupervised multiple target radar emitter signal feature selection issues.Both multi-target particle is remained
The fast convergence of colony optimization algorithm, and disaggregation is made to have preferable diversity.Then, using KUT and ZDT series of tests letter
Algorithm and MOPSO, SPEA2, PESA2 algorithm are compared test by number.In time complexity O (N2+ NR) in the case where, with
IGD index is as each algorithm performance is evaluated, and MOPSO convergence precision improves 8%, while the present invention can quickly converge on really
The forward position Pareto, the algorithm proposed are feasible and effective.Experiment shows to change to 20dB every 2dB from 4dB in SNR, select
The seven kinds of signals of BPSK, LFM and NLFM for taking different parameters, after multiple target feature selecting selected character subset with it is optimal
Intrinsic dimensionality is 3, is 98% with the average cluster accuracy that FCM is carried out.
The extracted important feature subset of particle swarm algorithm multiple target feature selecting of the invention is in SNR=5dB or more table
Reveal good cluster property, can obviously divide between signal, sharpness of border no overlap, can simplify the design of sorter, improves
Discrimination is sorted, engineer application is conducive to.Finally signal characteristic subset (is chosen most important using traditional FCM clustering algorithm
First 5) carry out independent repeatedly test, algorithm proposed by the present invention with higher 98.25% sorting discrimination.
Harvester crops feed quantity data information is measured by feed quantity monitoring modular；The measurement of feed quantity monitoring modular
Signal model indicates are as follows:
R (t)=x1(t)+x2(t)+…+xn(t)+v(t)
It can get harvester crops feed quantity data, just for equipment
Often operation provides condition.
Detailed description of the invention
Fig. 1 is provided in an embodiment of the present invention based on Embedded harvester header integrated control system structural block diagram.
In figure: 1, power monitoring module；2, feed quantity monitoring modular；3, cereal composition monitoring modular；4, main control module；5,
Locating module；6, blower module；7, data memory module；8, display module.
Fig. 2 locating module localization method flow chart 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.
Prior art progress can be used in the undocumented method of the present invention.
Structure of the invention is explained in detail with reference to the accompanying drawing.
As shown in Figure 1, it is provided by the invention based on Embedded harvester header integrated control system include: power monitoring
Module 1, feed quantity monitoring modular 2, cereal composition monitoring modular 3, main control module 4, locating module 5, blower module 6, data are deposited
Store up module 7, display module 8.
Power monitoring module 1 is connect with main control module 4, for monitoring engine power data information；
Feed quantity monitoring modular 2 is connect with main control module 4, for measuring harvester crops feed quantity data information；
Cereal composition monitoring modular 3, connect with main control module 4, for measuring crops component content data information；
Main control module 4, with power monitoring module 1, feed quantity monitoring modular 2, cereal composition monitoring modular 3, locating module
5, blower module 6, data memory module 7, display module 8 connect, and work normally for controlling modules；
Locating module 5 is connect with main control module 4, for obtaining the position data of harvester by GPS locator；
Blower module 6 is connect with main control module 4, for removal to be blowed to harvesting crops sundries by air blower；
Data memory module 7 is connect with main control module 4, for storing the data information of monitoring；
Display module 8 is connect with main control module 4, for the data information by display display monitoring.
When the invention works, engine power data information is monitored by power monitoring module 1；Mould is monitored by feed quantity
Block 2 measures harvester crops feed quantity data information；Crops component content data are measured by cereal composition monitoring modular 3
Information；Main control module 4 obtains the position data of harvester according to locating module 5；Air blower pair is utilized by blower module 6
Removal is blowed in harvesting crops sundries；The data information of monitoring is stored by data memory module 7；Pass through display module 8
Show the data information of monitoring.
Below with reference to concrete analysis, the invention will be further described.
It is provided in an embodiment of the present invention to be based on Embedded harvester header integrated control method, comprising:
Engine power data information is monitored by power monitoring module；
Using the objective function of the degree of correlation and one group of minimum of concept definition of redundancy, to engine evaluated power number
It is believed that the quality of number character subset；Wherein degree of correlation tendency retain it is all be associated with close feature with data structure, and redundancy
The feature that can then exclude and select the feature degree of correlation high；Both as the fitness function of particle swarm optimization；
Degree of correlation target uses entropy Measure Indexes, as follows:
sij=exp (- aDij),
Wherein, N is the number of engine power data signal data sample；A is weight coefficient, DijIt is sample i and sample j
The Euclidean distance under character subset represented by x；DaIndicate the average value of all samples Euclidean distance under the total space.Sij's
Value must normalize to [0,1]；When the character subset of selection is reasonable, if sample i and sample j belong to similar, SijTake
It is worth very little, on the contrary it is bigger；To f1(x) minimum value is chosen；
Redundancy target then utilizes related coefficient, and when related coefficient absolute value is smaller, the redundancy that character subset is included is got over
It is small；Objective function is as follows:
Wherein, nxIndicate the number of engine power data-signal character subset；D is total Characteristic Number；xjAnd xkRespectively
Indicate the value of j-th and k-th element in x；bijIndicate value of i-th of sample in j-th of feature, bajIndicate all
Mean value of the sample in j-th of feature；When character subset scale determines, the corresponding objective function of the small character subset of redundancy
f2(x) smaller；
Harvester crops feed quantity data information is measured by feed quantity monitoring modular；The measurement of feed quantity monitoring modular
Signal model 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；
Crops component content data information is measured by cereal composition monitoring modular；
Main control module obtains the position data of harvester according to locating module；
Removal is blowed to harvesting crops sundries using air blower by blower module；
Pass through the data information of data memory module storage monitoring；
Pass through the data information of display module display monitoring.
Power monitoring module monitors in engine power data information, further comprises:
Using PID closed-loop control, the parameter of PID is adjusted accordingly according to different wave modes；
Δ n=n0-n
In formula: Δ n is actual speed and desired rotating speed difference；
kP1、TI1、TD1、kP2、TI2、TD2For two groups of setting values of P, I, D parameter adjustment；K is desired tachometer value.
Load estimation optimization revolving speed control is introduced on the basis of PID control；It is transmitted using auxiliary hydraulic motor in power
Higher dynamic response coefficient in link, quickly compensates change in torque, avoids engine speed mutation situation from generating, together
When, in conjunction with the swashplate angle signal that PID control method generates, obtain final auxiliary hydraulic motor swashplate angle signal, formula
For
In formula: β is the final displacement signal of auxiliary hydraulic motor；
β 1 is the displacement signal that obtains after load estimation；
β 2 is the displacement signal that PID control obtains；
Such as Fig. 2, locating module is obtained in the position data of harvester, establishes back the lower integrated control of harvester header of interference
The GPS positioning model of system processed, specifically includes:
S101: GPS positioning system state-transition matrix is established；
S102: the realization of harvester header GPS positioning modeling.
In step S101: establish GPS positioning system state-transition matrix:
The vehicle GPS positioning system state equation and observational equation under rainy day echo interference are indicated using following formula:
In formula,State vector is tieed up for the n at K moment；
Z (K) is that the m at k moment ties up observation vector；
W (K-1) is the noise at system K-1 moment；
V (K) is that the m at system k moment ties up observation noise；
Φ (K, K-1) is the system Matrix of shifting of a step at K-1 to k moment；
H (K)+be the k moment observing matrix；
Γ (K-1) is system noise matrix, is expressed as the K-1 moment system mode affected by noise to the k moment；
The state variable for choosing vehicle GPS positioning system, is indicated using following formula are as follows:
In formula, X, y, z are position on 3 change in coordinate axis direction, speed, acceleration component,
ε x, ε y, ε z are that the echo-signal under the weather such as rainy days, greasy weather interferes the various errors to be formed in 3 reference axis
Total location error caused by direction, can be equivalent with first-order Markov process,
The associated time constant of Markov process is respectively represented, υ x, υ y, υ z then represent the height of system
This white noise；
Using dispersion Kalman Filter Technology handling the state variable of 3 axial directions successively, indicated using following formula
System state variables after processing by x-axis for
According to the upward adaptive extended kalman filtering equation of the available x-axis of the above method, it is expressed as using following formula
In formula, Φ1X(K) Kalman filtering gain is represented,
Represent the system discrete state at K-1 moment；
The vehicle GPS positioning system state under the weather such as rainy days, greasy weather under echo interference, which is stated, using following formula shifts square
Battle array
In formula, τ is represented and vehicle acceleration associated time constant；
In step S102: the realization of harvester header GPS positioning modeling:
By under echo interference under rainy days, greasy weather weather vehicle GPS positioning performance and error analysis, acquisition
Based on the upward adaptive extended kalman filtering equation of x-axis, the position detection matrix of vehicle GPS positioning system is established, is utilized
Following formula is expressed as
In formula, δPe、δPn、δPh3 tangent plane location errors of east orientation, north orientation, south orientation are respectively corresponded,
The accelerometer bias for representing 3 localities is poor, δ Ve, δ Vn, δ VhThe gyroscope constant value being then expressed as on 3 directions misses
Difference；
It is pre-processed using residual error amount of the following formula to each locality, obtains EKThe covariance value of representative andIt represents
Residual error amount average value:
In formula, n represents the quantity of residual error observation.
The weight matrix of harvester header GPS positioning system is obtained, then eliminates rainy days, greasy weather weather next time using following formula
The abnormal data of harvester header positioning under wave interference, is established by μkAnd μpThe weight square of the vehicle GPS positioning system of representative
Battle array；
Establish the harvester header GPS positioning Optimized model under rainy days, greasy weather weather under echo interference:
In formula, μpIt (k/k-1) is the global best estimates of system whole state,
For the relationship of systematic perspective measurement and state variable；
Blower module provided in an embodiment of the present invention, Embedded harvester header integrated control system is by air blower temperature
Compensation optimizing modeling compensates environmental change；
By similarity law, when blower air-suction state changes, condition of similarity meets D1=D2、n1=n2、ρ1≠ρ2, wherein D be
Impeller diameter, m；N is revolving speed, r/min；Q is volume flow, m3/h；P is blower press, pa；ρ is atmospheric density, kg/m3；Phase
It is like three laws
It is located in the case where not considering ambient humidity, if the air blower total pressure of standard air-suction state is P, atmospheric density is
ρ0；Atmospheric density under off-rating is ρS, air blower total pressure is PS, then total pressure relationship has
The air-suction state of air blower is exactly local atmospheric condition, is had according to The Ideal-Gas Equation P=ρ RT
P in formulaK, ρS, TSIt is local atmospheric pressure, atmospheric density and the temperature of blower under conditions of use；?
According to the theory of similarity by use condition (PK, TS) under blower press P be transformed to standard air-suction state (P0, T0) under
Blower press PS；KSThe referred to as wind pressure correction factor of air blower, under real atmosphere state, relative humidity of atomsphere influences blower
The blow rate required, air quantity adjusted coefficient K S' are
φ-relative humidity of atomsphere, %；
PH- temperature TSWhen saturated vapor pressure, pa；
Somewhere air pressure change is little, and above-mentioned wind pressure adjusted coefficient K S and air quantity adjusted coefficient K S' are solid in practical applications
Definite value.
In the above-described embodiments, can come wholly or partly by software, hardware, firmware or any combination thereof real
It is existing.When using entirely or partly realizing in the form of a computer program product, the computer program product include one or
Multiple computer instructions.When loading on computers or executing the computer program instructions, entirely or partly generate according to
Process described in the embodiment of the present invention or function.The computer can be general purpose computer, special purpose computer, computer network
Network or other programmable devices.The computer instruction may be stored in a computer readable storage medium, or from one
Computer readable storage medium is transmitted to another computer readable storage medium, for example, the computer instruction can be from one
A web-site, computer, server or data center pass through wired (such as coaxial cable, optical fiber, Digital Subscriber Line (DSL)
Or wireless (such as infrared, wireless, microwave etc.) mode is carried out to another web-site, computer, server or data center
Transmission).The computer-readable storage medium can be any usable medium or include one that computer can access
The data storage devices such as a or multiple usable mediums integrated server, data center.The usable medium can be magnetic Jie
Matter, (for example, floppy disk, hard disk, tape), optical medium (for example, DVD) or semiconductor medium (such as solid state hard disk Solid
State Disk (SSD)) etc..
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 (10)
1. one kind is based on Embedded harvester header integrated control system, which is characterized in that described to be based on Embedded harvesting
Machine cut platform integrated control system includes:
Power monitoring module is connect with main control module, for monitoring engine power data information；
Feed quantity monitoring modular, connect with main control module, for measuring harvester crops feed quantity data information；
Cereal composition monitoring modular, connect with main control module, for measuring crops component content data information；
Main control module, with power monitoring module, feed quantity monitoring modular, cereal composition monitoring modular, locating module, air blast mould
Block, data memory module, display module connection, work normally for controlling modules；
Locating module is connect with main control module, for obtaining the position data of harvester by GPS locator；
Blower module is connect with main control module, for removal to be blowed to harvesting crops sundries by air blower；
Data memory module is connect with main control module, for storing the data information of monitoring；
Display module is connect with main control module, for the data information by display display monitoring.
2. one kind is based on Embedded harvester, which is characterized in that described at least to carry right based on Embedded harvester and want
Based on Embedded harvester header integrated control system described in asking 1.
3. a kind of information processing terminal, which is characterized in that the information processing terminal at least integrates described in claim 1 be based on
Embedded harvester header integrated control system.
4. it is a kind of using described in claim 1 based on Embedded harvester header integrated control system based on Embedded receipts
Cutting mill ceding of Taiwan integrated control method, which is characterized in that described to include: based on Embedded harvester header integrated control method
Engine power data information is monitored by power monitoring module；
Using the objective function of the degree of correlation and one group of minimum of concept definition of redundancy, to engine evaluated power number it is believed that
The quality of number character subset；Wherein the reservation of degree of correlation tendency is all is associated with close feature with data structure, and redundancy then can
The feature for excluding and having selected the feature degree of correlation high；Both as the fitness function of particle swarm optimization；
Degree of correlation target uses entropy Measure Indexes, as follows:
sij=exp (- aDij),
Wherein, N is the number of engine power data signal data sample；A is weight coefficient, DijIt is sample i and sample j in x
Euclidean distance under represented character subset；DaIndicate the average value of all samples Euclidean distance under the total space.SijTake
Value must normalize to [0,1]；When the character subset of selection is reasonable, if sample i and sample j belong to similar, SijValue
Very little, on the contrary it is bigger；To f1(x) minimum value is chosen；
Redundancy target then utilizes related coefficient, and when related coefficient absolute value is smaller, the redundancy that character subset is included is smaller；Mesh
Scalar functions are as follows:
Wherein, nxIndicate the number of engine power data-signal character subset；D is total Characteristic Number；xjAnd xkIt respectively indicates
The value of j-th and k-th element in x；bijIndicate value of i-th of sample in j-th of feature, bajIndicate all samples
Mean value in j-th of feature；When character subset scale determines, the corresponding objective function f of the small character subset of redundancy2
(x) smaller；
Harvester crops feed quantity data information is measured by feed quantity monitoring modular；The signal of feed quantity monitoring modular measurement
Model is expressed as:
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；
Crops component content data information is measured by cereal composition monitoring modular；
Main control module obtains the position data of harvester according to locating module；
Removal is blowed to harvesting crops sundries using air blower by blower module；
Pass through the data information of data memory module storage monitoring；
Pass through the data information of display module display monitoring.
5. being based on Embedded harvester header integrated control method as claimed in claim 4, which is characterized in that
Power monitoring module monitors in engine power data information, further comprises:
Using PID closed-loop control, the parameter of PID is adjusted accordingly according to different wave modes；
Δ n=n0-n
In formula: Δ n is actual speed and desired rotating speed difference；
kP1、TI1、TD1、kP2、TI2、TD2For two groups of setting values of P, I, D parameter adjustment；K is desired tachometer value.
6. being based on Embedded harvester header integrated control method as claimed in claim 4, which is characterized in that
Load estimation optimization revolving speed control is introduced on the basis of PID control；Using auxiliary hydraulic motor in power transmission link
In higher dynamic response coefficient, change in torque is quickly compensated, avoid engine speed mutation situation generate, meanwhile,
In conjunction with the swashplate angle signal that PID control method generates, final auxiliary hydraulic motor swashplate angle signal is obtained, formula is
In formula: β is the final displacement signal of auxiliary hydraulic motor；
β1For the displacement signal obtained after load estimation；
β2The displacement signal obtained for PID control.
7. being based on Embedded harvester header integrated control method as claimed in claim 4, which is characterized in that
Locating module obtains in the position data of harvester, establishes back and interferes lower harvester header integrated control system
GPS positioning model, specifically includes:
Step 1: GPS positioning system state-transition matrix is established:
The vehicle GPS positioning system state equation and observational equation under rainy day echo interference are indicated using following formula:
In formula,State vector is tieed up for the n at K moment；
Z (K) is that the m at k moment ties up observation vector；
W (K-1) is the noise at system K-1 moment；
V (K) is that the m at system k moment ties up observation noise；
Φ (K, K-1) is the system Matrix of shifting of a step at K-1 to k moment；
H (K)+be the k moment observing matrix；
Γ (K-1) is system noise matrix, is expressed as the K-1 moment system mode affected by noise to the k moment；
The state variable for choosing vehicle GPS positioning system, is indicated using following formula are as follows:
In formula, X, y, z are position on 3 change in coordinate axis direction, speed, acceleration component,
ε x, ε y, ε z are that the echo-signal under the weather such as rainy days, greasy weather interferes the various errors to be formed in 3 change in coordinate axis direction
Caused by total location error, can with first-order Markov process it is equivalent,
The associated time constant of Markov process is respectively represented, υ x, υ y, υ z then represent the white Gaussian of system
Noise；
Using dispersion Kalman Filter Technology handling the state variable of 3 axial directions successively, handled using following formula expression
System state variables by x-axis for afterwards
According to the upward adaptive extended kalman filtering equation of the available x-axis of the above method, it is expressed as using following formula
In formula, Φ1X(K) Kalman filtering gain is represented,
Represent the system discrete state at K-1 moment；
The vehicle GPS positioning system state-transition matrix under the weather such as rainy days, greasy weather under echo interference is stated using following formula
In formula, τ is represented and vehicle acceleration associated time constant；
Step 2: the realization of harvester header GPS positioning modeling:
Pass through the performance and error analysis to the vehicle GPS positioning under echo interference under rainy days, greasy weather weather, the x-axis of acquisition
Based on upward adaptive extended kalman filtering equation, the position detection matrix of vehicle GPS positioning system is established, following formula is utilized
It is expressed as
In formula, δPe、δPn、δPh3 tangent plane location errors of east orientation, north orientation, south orientation are respectively corresponded,Represent 3
The accelerometer bias of a locality is poor, δ Ve, δ Vn, δ VhThe gyroscope constant value error being then expressed as on 3 directions；
It is pre-processed using residual error amount of the following formula to each locality, obtains EKThe covariance value of representative andWhat is represented is residual
Residual quantity average value:
In formula, n represents the quantity of residual error observation.
The weight matrix of harvester header GPS positioning system is obtained, then it is dry to eliminate echo under rainy days, greasy weather weather using following formula
The abnormal data of harvester header positioning under disturbing, is established by μkAnd μpThe weight matrix of the vehicle GPS positioning system of representative；
Establish the harvester header GPS positioning Optimized model under rainy days, greasy weather weather under echo interference:
In formula, μpIt (k/k-1) is the global best estimates of system whole state,
For the relationship of systematic perspective measurement and state variable；
Blower module, Embedded harvester header integrated control system is by air blower temperature-compensating Optimization Modeling to environmental change
It compensates；
By similarity law, when blower air-suction state changes, condition of similarity meets D1=D2、n1=n2、ρ1≠ρ2, wherein D is impeller
Diameter, m；N is revolving speed, r/min；Q is volume flow, m3/h；P is blower press, pa；ρ is atmospheric density, kg/m3；Similar three
Law is
It is located in the case where not considering ambient humidity, if the air blower total pressure of standard air-suction state is P, atmospheric density ρ0；It is nonstandard
Atmospheric density under quasi- state is ρS, air blower total pressure is PS, then total pressure relationship has
The air-suction state of air blower is exactly local atmospheric condition, according to The Ideal-Gas Equation
P=ρ RT has
P in formulaK, ρS, TSIt is local atmospheric pressure, atmospheric density and the temperature of blower under conditions of use；?
According to the theory of similarity by use condition (PK, TS) under blower press P be transformed to standard air-suction state (P0, T0) under blower
Total pressure PS；KSThe referred to as wind pressure correction factor of air blower, under real atmosphere state, relative humidity of atomsphere influences the blower blow rate required,
Air quantity adjusted coefficient K S' is
φ-relative humidity of atomsphere, %；
PH- temperature TSWhen saturated vapor pressure, pa；
Somewhere air pressure change is little, and above-mentioned wind pressure adjusted coefficient K S and air quantity adjusted coefficient K S' are fixed value in practical applications.
8. a kind of computer program, which is characterized in that described in computer program operation claim 4~7 any one
Based on Embedded harvester header integrated control method.
9. a kind of terminal, which is characterized in that the terminal, which is at least carried, to be realized described in claim 4~7 any one based on embedding
Enter the controller of the harvester header integrated control method of formula.
10. a kind of computer readable storage medium, including instruction, when run on a computer, so that computer executes such as
Based on Embedded harvester header integrated control method described in claim 4-7 any one.
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