CN110942146A - Method and device for measuring feeding quantity of self-propelled silage harvester - Google Patents

Method and device for measuring feeding quantity of self-propelled silage harvester Download PDF

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CN110942146A
CN110942146A CN201910485662.5A CN201910485662A CN110942146A CN 110942146 A CN110942146 A CN 110942146A CN 201910485662 A CN201910485662 A CN 201910485662A CN 110942146 A CN110942146 A CN 110942146A
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self
feeding
measuring
feeding roller
silage
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CN110942146B (en
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樊成孝
李志刚
赵博
刘阳春
李卓立
汪凤珠
王猛
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Shihezi University
Chinese Academy of Agricultural Mechanization Sciences
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Chinese Academy of Agricultural Mechanization Sciences
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01D—HARVESTING; MOWING
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    • A01D45/02—Harvesting of standing crops of maize, i.e. kernel harvesting
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    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
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    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P60/00—Technologies relating to agriculture, livestock or agroalimentary industries
    • Y02P60/80—Food processing, e.g. use of renewable energies or variable speed drives in handling, conveying or stacking
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Abstract

A self-propelled silage harvester feed amount measuring method and device, the method includes obtaining the feed roller displacement information of the self-propelled silage harvester in real time, and transmitting the feed roller displacement information to the vehicle computer; acquiring the rotating speed information of a feeding roller of the harvester in real time, and transmitting the rotating speed information of the feeding roller to the vehicle-mounted computer; the method comprises the steps of acquiring the moisture content information of harvested silage in real time, and transmitting the moisture content information of the silage to the vehicle-mounted computer; acquiring the forward traveling speed information of the harvester in real time, and transmitting the traveling speed information to the vehicle-mounted computer; and carrying out data processing on the input displacement information of the feeding roller, the rotating speed information of the feeding roller, the moisture content information of the silage and the walking speed information by the vehicle-mounted computer by adopting a feeding quantity neural network to obtain the current feeding quantity of the harvester. The invention also provides a measuring device adopting the measuring method.

Description

Method and device for measuring feeding quantity of self-propelled silage harvester
Technical Field
The invention relates to a detection and measurement technology of precision agricultural machinery equipment, in particular to a method and a device for measuring the feeding quantity of a self-propelled silage harvester.
Background
The feeding amount is important for the operation quality of the self-propelled silage harvester and the drawing of a silage yield graph. In the harvesting season, on one hand, due to the fact that the work of the super feeding amount can not only cause frequent blockage of a feeding part of the self-propelled silage harvester, but also reduce the operation quality, the qualified rate of the cutting length is not enough, and the fermentation of silage is influenced; on the other hand, the working performance of the corn ensilage harvester is reduced and the working efficiency is low because the rated feeding amount can not be reached. In addition, in the large background of precision agriculture, the measurement of the feeding amount is necessary for constructing an accurate corn silage yield map. The foreign research on the feeding amount of the self-propelled silage harvester starts earlier. Related researchers put forward a way of measuring the torque of a driving shaft of a blower for blowing materials and a driving shaft of a basic working component, a way of installing a radiation measuring device and a capacitance oscillation sensor at an outlet of a throwing barrel, a way of installing an impulse sensor on the wall of the throwing barrel, a way of reflecting the feeding amount through the momentum change of a bouncing plate at the outlet of the throwing barrel, and a way of measuring the feeding amount by utilizing the opening degree between feeding rollers in a feeding device, thereby obtaining certain results. Domestic research on the corn silage harvester also focuses on the mechanical structure design of the corn silage harvester, the feeding amount of the corn silage harvester is not analyzed systematically, and the corn silage harvester basically belongs to the experimental research stage due to the influence of various factors and cannot achieve the quantitative degree.
Although the mode of installing related sensors on the outlet of the material throwing and conveying cylinder or the wall of the cylinder to measure the feeding amount is convenient to install and accurate in measurement, the measured value can only be used for drawing an ensiling yield graph, the working state of the feeding device of the self-propelled ensiling feed harvester is difficult to show, and the impending blockage of the feeding device when the self-propelled ensiling feed harvester works cannot be predicted. The method for measuring the displacement of the feeding roller and the feeding amount can be taken into consideration for both methods. When the silage harvester is operating, the main factor influencing the feed quantity is the field crop density (kg/m)3) The larger the field crop density is, the wider the cutting width is, the higher the harvester running speed is, the larger the feeding amount is, and the larger the feeding roller displacement is. The field crop density is limited by the crop water content, the harvest time is different, the crop water content can also change, and the higher the crop water content is, the higher the field crop density is under the condition that the number of the plants of the crops in unit area is the same. The prior art uses the following formula to calculate the feed amount according to the displacement of the feed roller:
m＝δωνfrcηnom
wherein: m is the feed amount (kg/s), delta is the feed roll displacement value (m), omega is the feed roll width (m), vfrFor the feed roll rotation speed (m/s), ηnomThe material enters the feeding device, the compacted density of the material after being extruded by the feeding roller is influenced by the characteristics of the silage material, and c is the compression coefficient.
From the above formula, it can be seen that when the model of the silage harvester is determined and the characteristics of the silage material are determined, the displacement of the feeding roller and the feeding amount have a one-to-one correspondence relationship, and a theoretical equation can better reflect the feeding amount, but ηnomThe determination of the size is influenced by various factors, different materials havingDifferent ηnomIt is difficult to determine by experiment, which limits the practical application of using feed roll displacement to determine feed.
Disclosure of Invention
The invention aims to solve the technical problem of the prior silage harvester feeding amount testing technology, and provides a self-propelled silage harvester feeding amount measuring method and a self-propelled silage harvester feeding amount measuring device.
In order to achieve the purpose, the invention provides a method for measuring the feeding quantity of a self-propelled silage harvester, which comprises the following steps:
s100, measuring the displacement of a feeding roller, acquiring the displacement information of the feeding roller of the self-propelled silage harvester in real time, and transmitting the displacement information of the feeding roller to a vehicle-mounted computer;
s200, measuring the rotating speed of a feeding roller, acquiring the rotating speed information of the feeding roller of the self-propelled silage harvester in real time, and transmitting the rotating speed information of the feeding roller to the vehicle-mounted computer;
s300, measuring the moisture content of the silage, acquiring the moisture content information of the harvested silage in real time, and transmitting the moisture content information of the silage to the vehicle-mounted computer;
s400, measuring the traveling speed of the harvester, acquiring the forward traveling speed information of the self-propelled silage harvester in real time, and transmitting the traveling speed information to the vehicle-mounted computer; and
s500, obtaining the feeding amount, and performing data processing on the input displacement information of the feeding roller, the input rotating speed information of the feeding roller, the input silage water content information and the input walking speed information by the vehicle-mounted computer by adopting a feeding amount neural network model to obtain the current feeding amount of the self-propelled silage harvester.
The feeding quantity measuring method of the self-propelled silage harvester is characterized in that the feeding quantity neural network is a BP neural network and comprises an input layer, an output layer and an implied layer, wherein the input layer comprises three neurons which are respectively a feeding roller displacement delta, a feeding roller rotating speed v and silage water content omicron; the hidden layer packetComprises a plurality of neurons; the output layer comprises a neuron, and the input of the output layer is the output a of the hidden layer1The output of the output layer is a real-time feeding amount a2The feed neural network model is shown in fig. 8;
wherein IW1,1Representing a connection weight matrix between each neuron of the input layer and the hidden layer; b1A neuron bias value for the hidden layer; s is the number of neurons in the hidden layer; LW2,1Is a1With the connection weight matrix between the neurons of the output layer, b2Is a neuron bias value of the output layer; f1, f2 are transfer functions of the hidden layer and the output layer, respectively, (P1) T ═ δ v omicron]，n1，n2Representing the net inputs to the implicit and output layer transfer functions f1, f2, respectively.
The feeding amount measuring method of the self-propelled silage harvester is characterized in that the number S of the neurons of the hidden layer is determined by a simulation experiment result, and S is larger than or equal to 5.
The method for measuring the feeding quantity of the self-propelled silage harvester, wherein b1Is 1, b2Is 1.
The method for measuring the feeding quantity of the self-propelled silage harvester is characterized in that the transfer function f of the hidden layer and the output layer1、f2Are hyperbolic tangent transfer functions.
The method for measuring the feeding quantity of the self-propelled silage harvester, wherein the input of the output layer is a1＝tagsig(IW1,1+b1) (ii) a The output of the output layer is a2＝purelin(LW2,1+b2)。
The feeding amount measuring method of the self-propelled silage harvester is characterized in that the connection weight IW between each neuron of the input layer and each neuron of the hidden layer1,1Determined for network training.
The feeding amount measuring method of the self-propelled silage harvester is characterized in that the connection weight IW1,1Normalizing the sample data to the interval [ -1,1]In the method, N groups of sample data are sorted according to the feeding amount, three parts of sample data are extracted at equal intervals to be respectively used as a training sample, a verification sample and a test sample, and the training sample is used for calculating the gradient value of a network model and updating the weight IW of the network model1,1And LW2,1(ii) a The verification sample is used for terminating training, when errors on a verification set rise in a plurality of iterations, the training is terminated, and the weight and the offset value which generate minimum errors on the verification set are used as the finally trained network connection weight and neuron offset value; the test samples are used to compare different models and data packets of the test.
The method for measuring the feeding quantity of the self-propelled silage harvester can further comprise the following steps:
s600, controlling the advancing speed of the self-propelled silage harvester according to the feeding amount displayed by the vehicle-mounted computer in real time to obtain the optimal feeding amount.
In order to better achieve the above object, the present invention further provides a feeding amount measuring device for a self-propelled silage harvester, wherein the feeding amount measuring method for a self-propelled silage harvester is adopted to detect the feeding amount, and the method comprises:
the feeding roller displacement measuring mechanism is used for measuring the displacement information of a feeding roller of the self-propelled silage harvester in real time and comprises a connecting spring and a linear displacement sensor, the connecting spring is sleeved on the linear displacement sensor, one end of the connecting spring is connected with one end of the linear displacement sensor and is connected with a rotary shaft head of a front feeding floating roller of the self-propelled silage harvester, and the other end of the connecting spring is connected with the other end of the linear displacement sensor and is connected with a rotary shaft head of a front feeding fixed roller of the self-propelled silage harvester;
the feeding roller rotating speed measuring mechanism is used for acquiring rotating speed information of a feeding roller in real time and comprises a rotating speed measuring encoder and a coupler, wherein one end of the rotating speed measuring encoder is connected with a feeding roller shaft of the self-propelled silage harvester through the coupler;
the silage moisture measuring mechanism is used for acquiring silage moisture content information in real time and is arranged at the front part of the cooperative transport vehicle of the self-propelled silage harvester;
the walking speed measuring mechanism is used for acquiring the forward walking speed information of the self-propelled silage harvester in real time and comprises a proximity switch and a speed measuring gear, the speed measuring gear is installed on a walking driving shaft of the self-propelled silage harvester, and the proximity switch is arranged corresponding to the speed measuring gear; and
and the vehicle-mounted computer is respectively connected with the feeding roller displacement measuring mechanism, the feeding roller rotating speed measuring mechanism, the silage moisture measuring mechanism and the walking speed measuring mechanism, receives the feeding roller displacement information, the feeding roller rotating speed information, the silage moisture content information and the walking speed information, performs data fusion through a feeding quantity neural network model, takes the feeding roller displacement information, the feeding roller rotating speed information and the silage moisture content information as network input, takes the real-time feeding quantity as network output, establishes a nonlinear mapping relation between the input and the output to reflect the real-time feeding quantity, and adjusts the real-time feeding quantity by controlling the walking speed of the self-propelled silage harvester to advance.
The invention has the technical effects that:
the measuring device is convenient to install, the vehicle-mounted computer can simultaneously obtain a plurality of parameters, so that a driver can know the current feeding amount, the driving speed and the feed water content in real time, the driving speed is timely adjusted according to the change of the feeding amount, the harvester is enabled to work in a stable and efficient state, the operation efficiency is improved, the chopping quality of the feed is ensured, the change of the material humidity in practice can influence the friction coefficient, the density and the compression coefficient, and the compaction density of the material after being extruded by the feeding roller is influenced, so that the detection method selects the material water content as the influence ηnomThe method is characterized in that a predicted value of the feeding amount is automatically given through a trained artificial neural network by taking a feeding roller displacement value delta, a feeding roller rotating speed v and material water content omicron as input factors.
The method obtains the real-time feeding amount by using the artificial neural network, breaks through the limitation that the displacement of the feeding roller is converted into the feeding amount by using the traditional formula in the prior art, takes the moisture content of the silage into account, ensures that the method for converting the displacement of the feeding roller into the feeding amount can be applied to the harvest of various silage, has more accurate measurement result, and also ensures that the feeding amount measurement method can adapt to the field operation environment with variable moisture content.
The invention is described in detail below with reference to the drawings and specific examples, but the invention is not limited thereto.
Drawings
FIG. 1 is a schematic view of an installation position of a measuring device according to an embodiment of the present invention;
FIG. 2 is a schematic view of a feed roll displacement measuring mechanism according to an embodiment of the present invention;
FIG. 3 is a schematic view of a feeding roller rotation speed measuring mechanism according to an embodiment of the present invention;
FIG. 4 is a schematic view of a walking speed measuring mechanism according to an embodiment of the present invention;
FIG. 5 is a right side view of FIG. 4;
FIG. 6 is a schematic diagram of a feeding amount neural network model according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a measuring device according to an embodiment of the present invention;
FIG. 8 is a model of a feed neural network, in accordance with an embodiment of the present invention.
Wherein the reference numerals
1 feeding roller displacement measuring mechanism
11 front feeding floating roller
12 front feeding fixed roller
13 linear displacement sensor
14 connecting spring
2 feeding roller rotating speed measuring mechanism
21 speed measuring encoder
22 coupling
23 feeding roll shaft
24 feeding roller
3 silage moisture measuring mechanism
31 microwave moisture measuring sensor
4 walking speed measuring mechanism
41 proximity switch
42 speed measuring gear
43 brake pad
44 walking driving shaft
5 vehicle computer
6 transport vechicle in coordination
7 self-propelled silage harvester
Detailed Description
The invention will be described in detail with reference to the following drawings, which are provided for illustration purposes and the like:
referring to fig. 1 and 7, fig. 1 is a schematic view of an installation position of a measuring device according to an embodiment of the present invention, and fig. 7 is a schematic view of an operation of the measuring device according to the embodiment of the present invention. The self-propelled silage harvester 7 to which the invention is applicable comprises at least one pair of feed rollers 24 with variable relative displacement. The invention discloses a feeding quantity measuring device of a self-propelled silage harvester, which comprises a feeding roller displacement measuring mechanism 1, a feeding roller rotating speed measuring mechanism 2, a silage moisture measuring mechanism 3, a traveling speed measuring mechanism 4 and an on-board computer 5. And the vehicle-mounted computer 5 is respectively connected with the feeding roller displacement measuring mechanism 1, the feeding roller rotating speed measuring mechanism 2, the silage moisture measuring mechanism 3 and the walking speed measuring mechanism 4, receives the feeding roller displacement information, the feeding roller rotating speed information, the silage moisture content information and the walking speed information, performs data fusion through a feeding quantity neural network model, takes the feeding roller displacement information, the feeding roller rotating speed information and the silage moisture content information as network input, takes the real-time feeding quantity as network output, establishes a nonlinear mapping relation between the input and the output to reflect the real-time feeding quantity, and adjusts the real-time feeding quantity by controlling the walking speed of the self-propelled silage harvester 7 to advance.
Referring to fig. 2, fig. 2 is a schematic view of a feed roller displacement measuring mechanism according to an embodiment of the present invention. The feeding roller displacement measuring mechanism 1 can measure the relative displacement change of a feeding roller 24 in real time, is used for measuring the displacement information of the feeding roller of a self-propelled silage harvester 7 in real time, and comprises a connecting spring 14 and a linear displacement sensor 13, wherein the connecting spring 14 is installed between a front feeding floating roller 11 and a front feeding fixed roller 12, the connecting spring 14 is sleeved on the linear displacement sensor 13, one end of the connecting spring 14 is connected with one end of the linear displacement sensor 13 and is connected with a rotating shaft head of the front feeding floating roller 11 of the self-propelled silage harvester 7, and the other end of the connecting spring 14 is connected with the other end of the linear displacement sensor 13 and is connected with the rotating shaft head of the front feeding fixed roller 12 on the same side of the self-propelled silage harvester 7. When the feeding amount changes, the relative displacement of the feeding roller, namely the relative displacement between the front feeding floating roller 11 and the front feeding fixed roller 12, changes, and then the connecting spring 14 deforms, and the deformation can be measured by the linear displacement sensor 13.
Referring to fig. 3, fig. 3 is a schematic view of a feeding roller rotation speed measuring mechanism according to an embodiment of the present invention. The feeding roller rotating speed measuring mechanism 2 of the embodiment is used for acquiring feeding roller rotating speed information in real time and comprises a rotating speed measuring encoder 21 and a coupler 22, wherein one end of the rotating speed measuring encoder 21 is connected with a feeding roller shaft 23 of the self-propelled silage harvester 7 through the coupler 22. When the feeding roller 24 rotates, the rotating speed measuring encoder 21 can obtain the rotating speed value of the feeding roller 24.
Silage moisture measuring mechanism 3 of this embodiment can acquire the fodder moisture change in real time for acquire silage moisture content information in real time, including microwave moisture measuring sensor 31, install the cooperation transport vechicle 6 front portion of self-propelled silage harvester 7. When the chopped feed is thrown to the cooperative transport vehicle 6 by the throwing barrel of the self-propelled silage harvester 7, the feed covers the upper surface of the silage moisture measuring mechanism 3, the microwave moisture measuring sensor 31 can immediately measure the moisture content of the silage at the moment, and data are transmitted to the vehicle-mounted computer 5 in a wireless transmission mode.
Referring to fig. 4 and 5, fig. 4 is a schematic view of a walking speed measuring mechanism according to an embodiment of the present invention, and fig. 5 is a right side view of fig. 4. The traveling speed measuring mechanism 4 of this embodiment is configured to obtain traveling speed information of the self-propelled silage harvester 7 in real time, and includes a proximity switch 41 and a speed measuring gear 42, where the speed measuring gear 42 is installed on a traveling driving shaft 44 of the self-propelled silage harvester 7, the speed measuring gear 42 may be arranged in parallel to a brake pad 43 installed on the traveling driving shaft 44, and the proximity switch 41 is arranged corresponding to a gear tooth of the speed measuring gear 42. When the self-propelled silage harvester 7 travels, the travel driving shaft 44 rotates, the speed measuring gear 42 rotates synchronously with the travel driving shaft 44, the proximity switch 41 counts the number of teeth of the speed measuring gear 42, that is, the number of pulses, and the current travel speed value of the self-propelled silage harvester 7 is obtained through the number of pulses. And finally, transmitting the measured values of all the measuring mechanisms to the vehicle-mounted computer 5 in a serial port communication or wireless communication mode, and performing data fusion on the measured values in the vehicle-mounted computer 5 through a trained artificial neural network model to finally obtain the real-time feeding amount of the self-propelled silage harvester 7.
The invention discloses a method for measuring the feeding quantity of a self-propelled silage harvester, which comprises the following steps:
step S100, measuring the displacement of a feeding roller, acquiring the displacement information of the feeding roller of the self-propelled silage harvester 7 in real time, and transmitting the displacement information of the feeding roller to the vehicle-mounted computer 5; in the embodiment, the displacement value of the feeding roller in the vertical direction is obtained in real time through a linear displacement sensor 13 arranged at the feeding roller of the feeding device of the silage harvester, and the obtained displacement value is transmitted to the vehicle-mounted computer 5 through serial port communication;
step S200, measuring the rotating speed of a feeding roller, acquiring the rotating speed information of the feeding roller of the self-propelled silage harvester 7 in real time, and transmitting the rotating speed information of the feeding roller to the vehicle-mounted computer 5; the rotating speed of the feeding roller 24 is measured in real time through a rotating speed measuring encoder 21 arranged at the head of the feeding roller shaft 23, and the obtained rotating speed value is transmitted to the vehicle-mounted computer 5 through serial port communication;
step S300, measuring the moisture content of the silage, acquiring the moisture content information of the harvested silage in real time, and transmitting the moisture content information of the silage to the vehicle-mounted computer 5; the silage moisture content can be obtained in real time through a silage moisture measuring mechanism 3 arranged in the cooperative transport vehicle 6, and the obtained moisture content is transmitted to the vehicle-mounted computer 5 through serial port communication;
step S400, measuring the walking speed of the harvester, acquiring the forward walking speed information of the self-propelled silage harvester 7 in real time, and transmitting the walking speed information to the vehicle-mounted computer 5; the method comprises the steps that the travelling speed measuring mechanism 4 arranged on the self-propelled silage harvester 7 is used for acquiring the advancing speed value of the harvester in real time, and the measured travelling speed information of the harvester is transmitted to the vehicle-mounted computer 5 in a serial port communication mode; and
and S500, acquiring the feeding amount, and performing data processing on the input displacement information of the feeding roller, the input rotation speed information of the feeding roller, the input moisture content information of the silage and the input traveling speed information by the vehicle-mounted computer 5 by adopting a feeding amount neural network model to obtain the current feeding amount of the self-propelled silage harvester 7. The feeding roller displacement value, the feeding roller rotating speed value and the silage water content can be used as input layer data of an artificial neural network, the feeding amount is used as output layer data, the artificial neural network hidden layer and the output layer all adopt hyperbolic tangent transfer functions, the input layer data are subjected to normalization processing and then transmitted to the untrained artificial neural network, the network is trained, accordingly, internal parameters of the artificial neural network are determined, and a nonlinear mapping relation of input and output is established. And (3) randomly inputting a group of data by using the trained artificial neural network, and obtaining the real-time feeding amount under the input.
The method also comprises a step S600 of controlling the advancing speed of the self-propelled silage harvester 7 according to the feeding amount displayed by the vehicle-mounted computer 5 in real time to obtain an optimal feeding amount.
Referring to fig. 6, fig. 6 is a schematic diagram of a feeding amount neural network model according to an embodiment of the present invention. Wherein the feed neural network is a BP neural network and comprises an input layer, an output layer and a hidden layer, the input layer comprises three neurons which are respectively a feed rollerDisplacement delta, feed roller rotation speed v and silage water content omicron, namely (P1) T [ delta v ]](ii) a The hidden layer comprises a plurality of neurons, the number S of the neurons is determined by simulation experiment results and is generally not less than five, namely S is more than or equal to 5; the output layer comprises a neuron which is a feeding amount, the number of neurons of the hidden layer is determined by a simulation experiment result, and the input of the output layer is the output a of the hidden layer1The output of the output layer is a real-time feeding amount a2The feed neural network model is shown in fig. 8;
wherein IW1,1Representing a connection weight matrix between each neuron of the input layer and each neuron of the hidden layer, wherein the determination of the connection weight needs network training; b1Is the neuron bias value of the hidden layer, b1Preferably 1; s is the number of neurons in the hidden layer; LW2,1Is a1With the connection weight matrix between the neurons of the output layer, b2Is a neuron bias value of the output layer, b2Preferably 1; f1, f2 are the transfer functions of the hidden layer and the output layer, respectively, f1、f2Are preferably hyperbolic tangent transfer functions; n is1， n2Representing the net inputs to the implicit and output layer transfer functions f1, f2, respectively. The input of the output layer is a1＝tagsig(IW1,1+b1) (ii) a The output of the output layer is the real-time feeding amount a2，a2＝purelin(LW2,1+b2)。
Wherein, the connection weight IW between each neuron of the input layer and the hidden layer1,1Determined for network training. The connection weight IW1,1Adopting a method of stopping network training in advance, wherein the verification sample avoids overfitting, and the sample data is normalized to the interval [ -1,1 [ -1 [ ]]In the method, N groups (for example, 130 groups) of sample data are sorted according to the feeding amount, three parts of sample data are extracted at equal intervals to be respectively used as a training sample (60%), a verification sample (20%) and a test sample (20%), and the training sample is used for calculating the gradient value of the network model and updating the weight IW of the network model1,1And LW2,1(ii) a The above-mentionedThe verification sample is used for determining when to terminate training, the training is terminated when errors on a verification set rise in a plurality of iterations, and the weight and the bias value which generate minimum errors on the verification set are used as the final trained network connection weight and neuron bias value; the test samples are used to compare different models and to test whether bad data packets are present.
The present invention utilizes vehicle-mounted computer to display displacement of feeding roller, rotating speed of feeding roller, moisture content of silage and running speed in real time, and can automatically transfer the displacement value of feeding roller, rotating speed value of feeding roller and moisture content of silage into artificial neural network model to make real-time judgement of feeding quantity of the invented silage harvester. When the feeding amount is too large, the feeding mechanism is easy to block, and the vehicle-mounted computer immediately gives an alarm to remind a driver of reducing the vehicle speed; when the feeding amount is too small, the maximum working efficiency of the harvester is not exerted, and the vehicle-mounted computer gives a signal to remind a driver of increasing the speed. The harvester can achieve the best working efficiency and the best operation quality, and the driver of the harvester is helped to control the speed of the harvester without depending on experience.
The present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof, and it should be understood that various changes and modifications can be effected therein by one skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (10)

1. A method for measuring the feeding quantity of a self-propelled silage harvester is characterized by comprising the following steps:
s100, measuring the displacement of a feeding roller, acquiring the displacement information of the feeding roller of the self-propelled silage harvester in real time, and transmitting the displacement information of the feeding roller to a vehicle-mounted computer;
s200, measuring the rotating speed of a feeding roller, acquiring the rotating speed information of the feeding roller of the self-propelled silage harvester in real time, and transmitting the rotating speed information of the feeding roller to the vehicle-mounted computer;
s300, measuring the moisture content of the silage, acquiring the moisture content information of the harvested silage in real time, and transmitting the moisture content information of the silage to the vehicle-mounted computer;
s400, measuring the traveling speed of the harvester, acquiring the forward traveling speed information of the self-propelled silage harvester in real time, and transmitting the traveling speed information to the vehicle-mounted computer; and
s500, obtaining the feeding amount, and performing data processing on the input displacement information of the feeding roller, the input rotating speed information of the feeding roller, the input silage water content information and the input walking speed information by the vehicle-mounted computer by adopting a feeding amount neural network model to obtain the current feeding amount of the self-propelled silage harvester.
2. The self-propelled silage harvester feed amount measurement method of claim 1, wherein the feed amount neural network is a BP neural network comprising an input layer, an output layer, and an implied layer, the input layer comprising three neurons, respectively a feed roller displacement δ, a feed roller rotation speed ν, and silage water content omicron; the hidden layer comprises a plurality of neurons; the output layer comprises a neuron, and the input of the output layer is the output a of the hidden layer1The output of the output layer is a real-time feeding amount a2The feed neural network model is shown in fig. 8;
wherein IW1,1Representing a connection weight matrix between each neuron of the input layer and the hidden layer; b1A neuron bias value for the hidden layer; s is the number of neurons in the hidden layer; LW2,1Is a1With the connection weight matrix between the neurons of the output layer, b2Is a neuron bias value of the output layer; f1, f2 are transfer functions of the hidden layer and the output layer, respectively, (P1) T ═ δ v omicron]，n1，n2Representing the net inputs to the implicit and output layer transfer functions f1, f2, respectively.
3. The method for measuring the feeding quantity of the self-propelled silage harvester of claim 2, wherein the number S of neurons in the hidden layer is determined by simulation experiment results, and S is greater than or equal to 5.
4. The method for measuring the feed quantity of a self-propelled silage harvester according to claim 2 or 3, wherein b1Is 1, b2Is 1.
5. The method of measuring the feed rate of a self-propelled silage harvester of claim 4, wherein the transfer function f of the hidden layer and the output layer1、f2Are hyperbolic tangent transfer functions.
6. The method of measuring the feed rate of a self-propelled silage harvester of claim 5, wherein the input to the output layer is a1＝tagsig(IW1,1+b1) (ii) a The output of the output layer is a2＝purelin(LW2,1+b2)。
7. The method of claim 2 or 3, wherein the connection weights IW between the neurons of the input layer and the hidden layer are IW1,1Determined for network training.
8. The method of measuring the feed rate of a self-propelled silage harvester of claim 7, wherein the connection weight IW1,1Normalizing the sample data to the interval [ -1,1 ] by adopting a method of stopping network training in advance]In the method, N groups of sample data are sorted according to the feeding amount, three parts of sample data are extracted at equal intervals to be respectively used as a training sample, a verification sample and a test sample, and the training sample is used for calculating the gradient value of a network model and updating the weight IW of the network model1,1And LW2,1(ii) a The verification sample is used for terminating training, the training is terminated when errors on a verification set rise in a plurality of iterations, and the weight and the bias value which generate minimum errors on the verification set are used as the finally trained network connection weight and neuron biasA value; the test samples are used to compare different models and data packets of the test.
9. The method for measuring the feed quantity of a self-propelled silage harvester according to claim 8, further comprising the steps of:
s600, controlling the advancing speed of the self-propelled silage harvester according to the feeding amount displayed by the vehicle-mounted computer in real time to obtain the optimal feeding amount.
10. A feeding quantity measuring device of a self-propelled silage harvester, which is characterized in that the feeding quantity detection is carried out by the feeding quantity measuring method of the self-propelled silage harvester according to any one of the claims 1 to 9, and comprises the following steps:
the feeding roller displacement measuring mechanism is used for measuring the displacement information of a feeding roller of the self-propelled silage harvester in real time and comprises a connecting spring and a linear displacement sensor, the connecting spring is sleeved on the linear displacement sensor, one end of the connecting spring is connected with one end of the linear displacement sensor and is connected with a rotary shaft head of a front feeding floating roller of the self-propelled silage harvester, and the other end of the connecting spring is connected with the other end of the linear displacement sensor and is connected with a rotary shaft head of a front feeding fixed roller of the self-propelled silage harvester;
the feeding roller rotating speed measuring mechanism is used for acquiring rotating speed information of a feeding roller in real time and comprises a rotating speed measuring encoder and a coupler, wherein one end of the rotating speed measuring encoder is connected with a feeding roller shaft of the self-propelled silage harvester through the coupler;
the silage moisture measuring mechanism is used for acquiring silage moisture content information in real time and is arranged at the front part of the cooperative transport vehicle of the self-propelled silage harvester;
the walking speed measuring mechanism is used for acquiring the forward walking speed information of the self-propelled silage harvester in real time and comprises a proximity switch and a speed measuring gear, the speed measuring gear is installed on a walking driving shaft of the self-propelled silage harvester, and the proximity switch is arranged corresponding to the speed measuring gear; and
and the vehicle-mounted computer is respectively connected with the feeding roller displacement measuring mechanism, the feeding roller rotating speed measuring mechanism, the silage moisture measuring mechanism and the walking speed measuring mechanism, receives the feeding roller displacement information, the feeding roller rotating speed information, the silage moisture content information and the walking speed information, performs data fusion through a feeding quantity neural network model, takes the feeding roller displacement information, the feeding roller rotating speed information and the silage moisture content information as network input, takes the real-time feeding quantity as network output, establishes a nonlinear mapping relation between the input and the output to reflect the real-time feeding quantity, and adjusts the real-time feeding quantity by controlling the walking speed of the self-propelled silage harvester to advance.
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