Device and method for detecting moisture content of plant groups in a combine harvester

Abstract

The present invention provides a device and method for detecting the moisture content of a plant group in a combine harvester, comprising a moisture content detection mechanism, at least two acceleration sensors, and a signal processing system. The moisture content detection mechanism is in direct contact with the plant group entering the combine harvester's conveyor trough, and is used to detect the current flowing through the plant group entering the conveyor trough and transmit the current to the signal processing system. The acceleration sensor is used to detect vibration signals of the combine harvester's conveyor trough under different feed rates and transmit the signals to the signal processing system. The signal processing system is connected to the moisture content detection mechanism and the acceleration sensor, respectively, and the signal processing system determines the moisture content of the plant group in the combine harvester based on the vibration signals and the current changes in the plant group in the conveyor trough under different feed rates. The present invention can relatively accurately detect the moisture content of the stem group entering the conveyor trough of the combine harvester, providing a reference for adjusting the parameters of subsequent working components, and significantly improving the operating quality of the combine harvester.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent agricultural machinery systems, and in particular relates to a device and method for detecting the moisture content of a plant group in a combine harvester. Background Art

[0002] Due to variations in field water, fertilizer, and soil conditions, crop plant moisture content varies across the field, directly impacting the combine harvester's feed rate. Fluctuations in feed rate during field operation can lead to uneven load and material flow within the combine, directly impacting overall machine performance. Therefore, real-time monitoring of plant moisture content feeding the combine, building a feed rate prediction model, and subsequently adjusting operating parameters such as the combine's forward speed, the angle of the threshing drum's guide strips, and the opening of the fish-scale screen are effective means of ensuring efficient combine harvester operation.

[0003] Existing research on crop moisture measurement has focused on measuring grain moisture content within grain bins using methods such as capacitance, neutron, resistance, spectroscopy, near-infrared, and microwave methods. However, due to the long path from the harvester to the combine before the grain enters the harvester, the grain moisture information obtained within the grain bin has a significant lag for the control system. This prevents the control system from taking timely regulatory measures based on changes in crop moisture content entering the combine. Consequently, existing control systems developed based on grain moisture content within the grain bin have limited effectiveness. A literature search revealed no studies directly monitoring the moisture content of plant populations in real time. Summary of the Invention

[0004] The present invention aims to address at least one of the aforementioned technical problems to a certain extent. To this end, the present invention provides a device and method for detecting the moisture content of a plant group within a combine harvester. This device and method can accurately detect the moisture content of plants entering the combine harvester's conveyor trough, providing a reference for subsequent parameter adjustments of operating components and significantly improving the combine harvester's operating quality.

[0005] Note that the inclusion of these objectives does not preclude the existence of other objectives. One embodiment of the present invention does not necessarily achieve all of the above objectives. Objectives other than the above objectives may be extracted from the description of the specification, drawings, and claims.

[0006] The present invention achieves the above technical objectives through the following technical means.

[0007] A device for detecting moisture content of a plant group in a combine harvester, comprising a moisture content detection mechanism, at least two acceleration sensors and a signal processing system;

[0008] The moisture content detection mechanism is in direct contact with the plant group entering the conveying trough of the combine harvester, and is used to detect the current of the plant group entering the conveying trough of the combine harvester and transmit it to the signal processing system;

[0009] The acceleration sensor is used to detect the vibration signal of the combine harvester conveyor trough under different feeding amounts and transmit it to the signal processing system;

[0010] The signal processing system is connected to the moisture content detection mechanism and the acceleration sensor respectively. The signal processing system obtains the moisture content of the plant group in the combine harvester according to the vibration signal and the current change of the plant group in the conveying trough of the combine harvester under different feeding amounts.

[0011] In the above scheme, the signal processing system analyzes the vibration signal and obtains the feed amount W through the established feed amount prediction model. The signal processing system fits the relationship between the correction coefficient K and the feed amount W based on the current changes of the plant group in the combine harvester conveyor trough under different feed amounts measured by the moisture content detection mechanism, and calculates the moisture content of the plant group in the combine harvester through the established plant group moisture content detection model.

[0012] In the above solution, the moisture content detection mechanism is installed on the inner side of the bottom plate of the conveying trough and is in direct contact with the plant group entering the combine harvester;

[0013] The acceleration sensor includes a first acceleration sensor and a second acceleration sensor; the first acceleration sensor and the second acceleration sensor are located on the outside of the bottom plate of the combine harvester conveyor trough and on the center line of the conveyor trough perpendicular to the width direction, and the first acceleration sensor is located behind the moisture content detection mechanism, and the second acceleration sensor is located in front of the moisture content detection mechanism.

[0014] In the above solution, the moisture content detection mechanism includes at least two moisture content detection modules with the same structure;

[0015] Each moisture content detection module includes a rubber insulating plate, a metal plate, a plastic sleeve, a first metal nut, a second metal nut, a wire and a metal screw; the rubber insulating plate is located on the inner side of the lower bottom plate of the conveying trough, and the metal plate is located above the rubber insulating plate. After one end of the metal screw is inserted into the plastic sleeve, it passes through the metal plate, the rubber insulating plate and the lower bottom plate of the conveying trough together with the plastic sleeve in sequence, and is fixed on the outer side of the lower bottom plate of the conveying trough by the first metal nut. The first metal nut and the lower bottom plate of the conveying trough are insulated by the step of the plastic sleeve. The wire is wrapped around the metal screw below the first metal nut and is tightened by the second metal nut.

[0016] Furthermore, the metal plate is connected to the positive pole of the power supply through a wire, and the current is transmitted to the plant group through the metal plate. The plant group then transmits the current to the frame, monitors the changes in the current flowing through the frame, and the signal processing system fits the relationship between the correction coefficient K and the feed amount W based on the current changes, and calculates the moisture content of the plant group in the combine harvester through the plant group moisture content detection model.

[0017] In the above scheme, the feed amount prediction model is:

[0018] W=β0+β1Y1+β2Y2+β3Y3+β4Y4

[0019] Where β0 is the intercept, β1, β2, β3, and β4 are regression coefficients, Y1 is the time-domain RMS value of the first accelerometer measurement point, Y2 is the time-domain RMS value of the second accelerometer measurement point, Y3 is the 1 / 3 octave total RMS value of the first accelerometer measurement point, and Y4 is the 1 / 3 octave total RMS value of the second accelerometer measurement point.

[0020] In the above scheme, the plant group moisture content detection model is:

[0021] Q=M1×V×f1(W,V)

[0022] Among them, Q is the moisture content of the plant group, M1 is the average moisture content of the plants in the experimental field, V is the current of the plant group inside the conveying trough, and f1(W, V) is the correction coefficient K.

[0023] A detection method according to the device for detecting moisture content of a plant group in a combine harvester comprises the following steps:

[0024] The moisture content detection mechanism detects the current entering the plant group inside the conveying trough of the combine harvester and transmits it to the signal processing system;

[0025] The first acceleration sensor and the second acceleration sensor detect vibration signals of the conveying trough of the combine harvester under different feeding amounts and transmit the signals to the signal processing system;

[0026] The signal processing system analyzes the vibration signal and obtains the feed amount W through the established feed amount prediction model. The signal processing system fits the relationship between the correction coefficient K and the feed amount W based on the current changes of the plant group in the combine harvester conveyor trough under different feed amounts measured by the moisture content detection mechanism, and calculates the moisture content of the plant group in the combine harvester through the established plant group moisture content detection model.

[0027] In the above scheme, the steps for establishing the feed amount prediction model are as follows:

[0028] S1: Changing the forward speed of the combine harvester to change the feed rate of the combine harvester, collecting vibration signals of the first measuring point of the conveyor trough under different feed rate inputs through a first acceleration sensor, and transmitting the signals to a signal processing system, collecting vibration signals of the second measuring point of the conveyor trough under different feed rate inputs through a second acceleration sensor, and transmitting the signals to the signal processing system;

[0029] S2: Analyze the characteristic value change trend of the vibration signal collected by the first acceleration sensor and the second acceleration sensor in the time domain, including the average value, peak-to-peak value, standard deviation and root mean square;

[0030] S3: Use fast Fourier transform to convert the time domain signal into a frequency domain signal, draw a spectrum, and display the amplitude of each frequency component in the signal, including the center frequency and the root mean square value. Perform 1 / 3 octave analysis on the vibration signal corresponding to each spectrum to obtain the 1 / 3 octave band, obtain the center frequency and amplitude of each sub-band, and calculate the average, maximum value, and total root mean square value of the two measurement points in each test group.

[0031] S4: By analyzing the characteristic value change trend of the vibration signal collected in the time domain and frequency domain, the optimal characteristic value is selected to characterize the change of the feed amount of the combine harvester;

[0032] S5: Using the optimized vibration signal characteristic value, a feed amount prediction model is established to predict the feed amount of the combine harvester. The feed amount prediction model is:

[0033] W=β0+β1Y1+β2Y2+β3Y3+β4Y4

[0034] Where β0 is the intercept, β1, β2, β3, and β4 are regression coefficients, Y1 is the time-domain RMS value of the first accelerometer measurement point, Y2 is the time-domain RMS value of the second accelerometer measurement point, Y3 is the 1 / 3 octave total RMS value of the first accelerometer measurement point, and Y4 is the 1 / 3 octave total RMS value of the second accelerometer measurement point.

[0035] In the above scheme, the steps for establishing the plant group moisture content detection model are as follows:

[0036] The average water content of the plants in the test plot was detected and recorded as M1;

[0037] The current changes of the plant groups in the trough of the combine harvester under different feeding rates were measured, and then the correlation between the current V changes of the plant groups in the trough of the combine harvester under different feeding rates and the feeding rate W was obtained through analysis:

[0038] K=f1(W,V)

[0039] It is obtained that the plant group moisture content detection model is:

[0040] Q=M1×V×f1(W,V)

[0041] Among them, Q is the feed rate of the combine harvester, M1 is the average moisture content of the plants in the experimental field, and the current f1 (W, V) of the plant group inside the conveyor trough is the correction coefficient K.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] By analyzing the changes in current flow as plants with varying moisture contents flow through the trough monitoring area and analyzing the correlation between the monitored current and the feed rate, the impact of feed rate changes on the monitored current values ​​can be eliminated. This allows for the establishment of a plant moisture content monitoring model, enabling accurate characterization of the moisture content of plants entering the combine harvester's trough. Promptly capturing changes in plant moisture content as plants pass through the trough provides a pre-processing signal to the combine harvester's control system, enabling timely adjustments to relevant operating parameters. This eliminates the control lag associated with existing methods and is crucial for improving overall machine performance and efficiency.

[0044] Note that the description of these effects does not preclude the existence of other effects. One embodiment of the present invention does not necessarily have all of the above effects. Effects other than the above can be clearly seen and extracted from the description of the specification, drawings, claims, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 The figure shows the position of the conveying trough on the combine harvester according to one embodiment of the present invention.

[0046] Figure 2 The figure is a schematic diagram of the composition of a plant moisture content detection device in a combine harvester according to one embodiment of the present invention.

[0047] Figure 3 A schematic diagram of the composition of a plant moisture content detection unit according to one embodiment of the present invention.

[0048] Figure 4 Schematic diagram of the installation position of a plant moisture content detection unit according to one embodiment of the present invention.

[0049] Figure 5 A cross-sectional view of the installation of a plant moisture content detection unit according to one embodiment of the present invention.

[0050] Figure 6 The time domain signals of each test group according to one embodiment of the present invention, wherein Figure 6 (a) is the time domain signal of experimental group 1, Figure 6 (b) is the time domain signal of experimental group 2, Figure 6 (c) is the time domain signal of experimental group 3, Figure 6 (d) is the time domain signal of experimental group 4.

[0051] Figure 7 Spectrum diagram of each test group according to one embodiment of the present invention, wherein Figure 7 (a) is the spectrum of experimental group 1, Figure 7 (b) is the spectrum of experimental group 2, Figure 7 (c) is the spectrum of experimental group 3, Figure 7 (d) is the spectrum diagram of experimental group 4.

[0052] Figure 8 The 1 / 3 octave frequency diagram of each test group according to one embodiment of the present invention, wherein Figure 8 (a) is the 1 / 3 octave frequency diagram of experimental group 0, Figure 8 (b) is the 1 / 3 octave diagram of experimental group 1, Figure 8 (c) is the 1 / 3 octave diagram of experimental group 2. Figure 8 (d) is the 1 / 3 octave diagram of experimental group 3.

[0053] In the figure: 1-conveying trough, 2-rubber insulating plate, 3-conductive metal plate, 4-moisture content detection mechanism, 5-first moisture content detection module, 6-second moisture content detection module, 7-plastic sleeve, 8-first metal nut, 9-second metal nut, 10-conducting wire, 11-metal screw, 12-first acceleration sensor, 13-second acceleration sensor. DETAILED DESCRIPTION

[0054] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0055] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "front", "back", "left", "right", "up", "down", "axial", "radial", "vertical", "horizontal", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0056] In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "connect," "fixed," etc. should be understood broadly. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediary; or internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0057] like Figure 1 As shown, the plant group moisture content detection device in the combine harvester mainly characterizes the change of plant moisture content by detecting the change of plant group current in the conveying trough 1. The conveying trough 1 is used to connect the cutting table and the threshing and separation device of the combine harvester, that is, the plants entering the cutting table are transmitted through the conveying trough 1 and then enter the threshing device to complete the threshing and separation operation of the grains.

[0058] like Figure 2 As shown in the figure, a preferred embodiment of the device for detecting the moisture content of a plant group in a combine harvester according to the present invention is shown, and the device for detecting the moisture content of a plant group in a combine harvester includes a moisture content detection mechanism 4, a first acceleration sensor 12, a second acceleration sensor 13 and a signal processing system; the moisture content detection mechanism 4 is in direct contact with the plant group entering the conveying trough 1 of the combine harvester, and is used to detect the current of the plant group entering the conveying trough 1 of the combine harvester, and transmit it to the signal processing system; the first acceleration sensor 12 and the second acceleration sensor 13 are used to detect the vibration signal of the conveying trough 1 of the combine harvester under different feeding amounts, and transmit it to the signal processing system; the signal processing system is connected to the moisture content detection mechanism 4, the first acceleration sensor 12 and the second acceleration sensor 13 respectively, and the signal processing system obtains the moisture content of the plant group in the combine harvester according to the vibration signal and the current change of the plant group in the conveying trough 1 of the combine harvester under different feeding amounts.

[0059] The signal processing system analyzes the vibration signal and obtains the feed amount W through the established feed amount prediction model. The signal processing system fits the relationship between the correction coefficient K and the feed amount W based on the current changes of the plant group in the combine harvester conveyor trough 1 under different feed amounts measured by the moisture content detection mechanism 4, and calculates the moisture content of the plant group in the combine harvester through the established plant group moisture content detection model.

[0060] The moisture content detection mechanism 4 is installed on the inner side of the bottom plate of the conveying trough 1 and is in direct contact with the plant group entering the combine harvester; in a specific embodiment of the present invention, the first acceleration sensor 12 and the second acceleration sensor 13 are on the outside of the bottom plate 1 of the conveying trough of the combine harvester and are located on the center line of the conveying trough 1 perpendicular to the width direction, and the first acceleration sensor 12 is located behind the moisture content detection mechanism 4, and the second acceleration sensor 13 is located in front of the moisture content detection mechanism 4; in a specific embodiment of the present invention, the distance between the first acceleration sensor 12 and the second acceleration sensor 13 is 200 mm; the first acceleration sensor 12 and the second acceleration sensor 13 are respectively adsorbed on the outside of the bottom plate of the conveying trough by magnetic seats.

[0061] like Figure 3-5 As shown, the moisture detection mechanism 4 includes at least two moisture detection modules with identical structures. In one embodiment of the present invention, the moisture detection modules include a first moisture detection module 5 and a second moisture detection module 6. Each moisture detection module comprises a rubber insulation plate 2, a metal plate 3, a plastic sleeve 7, a first metal nut 8, a second metal nut 9, a wire 10, and a metal screw 11. The rubber insulation plate 2 is located inside the lower base plate 1 of the conveyor trough, and the metal plate 3 is located above the rubber insulation plate 2. One end of the metal screw 11 is inserted into the plastic sleeve 7, and then, together with the plastic sleeve 7, passes through the metal plate 3, the rubber insulation plate 2, and the lower base plate 1 of the conveyor trough in sequence. It is then secured to the outside of the lower base plate 1 by the first metal nut 8. The first metal nut 8 is insulated from the lower base plate 1 by a step in the plastic sleeve 7. The wire 10 is wrapped around the metal screw 11 below the first metal nut 8 and is tightened by the second metal nut 9.

[0062] During the operation, the metal plate 3 is connected to the positive pole of the power supply through the wire 10, and the current is transmitted to the plant group through the metal plate 3. The plant group then transmits the current to the frame. If the moisture content of the plant group is high, more current will flow through the frame and into the earth. The change of the current flowing through the frame is monitored, and the signal processing system fits the correlation coefficient, that is, the correlation between the correction coefficient K and the feed amount W according to the current change, and calculates the moisture content of the plant group in the combine harvester through the plant group moisture content detection model, which can characterize the change of the moisture content of the plant group.

[0063] The feed rate prediction model is:

[0064] W=β0+β1Y1+β2Y2+β3Y3+β4Y4

[0065] Among them, β0 is the intercept, β1, β2, β3, and β4 are coefficients, Y1 is the time domain root mean square of the measuring point of the first acceleration sensor 12, Y2 is the time domain root mean square of the measuring point of the second acceleration sensor 13, Y3 is the 1 / 3 octave total root mean square of the measuring point of the first acceleration sensor 12, and Y4 is the 1 / 3 octave total root mean square of the measuring point of the second acceleration sensor 13.

[0066] The plant group moisture content detection model is:

[0067] Q=M1×V×f1(W,V)

[0068] Among them, Q is the moisture content of the plant group, M1 is the average moisture content of the plants in the experimental field, V is the current of the plant group inside the conveying trough 1, and f1(W, V) is the correction coefficient K.

[0069] A detection method according to the device for detecting moisture content of a plant group in a combine harvester comprises the following steps:

[0070] The moisture content detection mechanism 4 detects the current entering the plant group inside the combine harvester conveyor trough 1 and transmits it to the signal processing system;

[0071] The first acceleration sensor 12 and the second acceleration sensor 13 detect vibration signals of the combine harvester conveyor trough 1 under different feeding amounts and transmit them to the signal processing system;

[0072] The signal processing system analyzes the vibration signal and obtains the feed amount W through the established feed amount prediction model. The signal processing system fits the relationship between the correction coefficient K and the feed amount W based on the current changes of the plant group in the combine harvester conveyor trough 1 under different feed amounts measured by the moisture content detection mechanism 4, and calculates the moisture content of the plant group in the combine harvester through the established plant group moisture content detection model.

[0073] Furthermore, due to the different growth of field crops, the density of the plant groups to be harvested is different, and the volume of the plant groups entering the combine harvester conveyor trough will also change. The change in the volume of the plant groups will also affect the amount of current flowing through the plant groups. Therefore, it is necessary to eliminate the current changes caused by fluctuations in the feed amount in the feeding conveyor trough.

[0074] The steps for establishing the feed rate prediction model are as follows:

[0075] S1: Changing the forward speed of the combine harvester to change the feed rate of the combine harvester, collecting vibration signals of the first measuring point of the conveyor trough 1 under different feed rate inputs through the first acceleration sensor 12, and transmitting the signals to the signal processing system, collecting vibration signals of the second measuring point of the conveyor trough 1 under different feed rate inputs through the second acceleration sensor 13, and transmitting the signals to the signal processing system;

[0076] During the test, set the sampling mode of the test system, set the number of analysis points, number of spectral lines, and average number of average spectrum in the frequency domain analysis, and calculate the time domain data length T0 of each FFT transformation;

[0077] The combine harvester was harvested at full width with the header height unchanged. The forward speed of the combine harvester was set under the working conditions. The vibration information of different areas at the bottom of the conveyor trough was obtained under different feeding amounts during the operation of the combine harvester.

[0078] S2: Analyze the characteristic value change trend of the vibration signal collected by the first acceleration sensor 12 and the second acceleration sensor 13 in the time domain, including the average value (m / s 2 ), peak-to-peak value (m / s 2 ), standard deviation (m / s 2 ) and root mean square (m / s 2 );

[0079] S3: Use fast Fourier transform to convert the time domain signal into the frequency domain signal, draw a spectrum diagram, and show the amplitude of each frequency component in the signal including the center frequency (Hz), the root mean square value (m / s 2 ), perform 1 / 3 octave analysis on the vibration signal corresponding to each spectrum to obtain the 1 / 3 octave band, obtain the center frequency and amplitude of each sub-band, and calculate the average value (m / s) of the two measuring points of each test group. 2 ), maximum value (m / s 2 ), total root mean square (m / s 2 );

[0080] S4: By analyzing the characteristic value change trend of the vibration signal collected in the time domain and frequency domain, the optimal characteristic value is selected to characterize the change of the feed amount of the combine harvester;

[0081] S5: Using the optimized vibration signal characteristic value, a feed rate prediction model is established to predict the feed rate of the combine harvester and characterize the fluctuation of the feed rate of the combine harvester. The feed rate prediction model is:

[0082] W=β0+β1Y1+β2Y2+β3Y3+β4Y4

[0083] Among them, β0 is the intercept, β1, β2, β3, and β4 are coefficients, Y1 is the time domain root mean square of the measuring point of the first acceleration sensor 12, Y2 is the time domain root mean square of the measuring point of the second acceleration sensor 13, Y3 is the 1 / 3 octave total root mean square of the measuring point of the first acceleration sensor 12, and Y4 is the 1 / 3 octave total root mean square of the measuring point of the second acceleration sensor 13.

[0084] The steps for establishing the plant group moisture content detection model are as follows:

[0085] The average moisture content of the plants in the test plot was detected and recorded as M1;

[0086] The current changes of the plant groups in the combine harvester conveyor trough 1 under different feeding amounts were measured, and then the correlation between the current V changes of the plant groups in the combine harvester conveyor trough 1 under different feeding amounts and the feeding amount W was obtained through analysis:

[0087] K=f1(W,V)

[0088] It is obtained that the plant group moisture content detection model is:

[0089] Q=M1×V×f1(W,V)

[0090] Where Q is the feed rate of the combine harvester, M1 is the average moisture content of the plants in the experimental field, V is the current of the plant group inside the conveyor trough 1, and f1(W, V) is the correction coefficient K.

[0091] In a specific embodiment of the present invention, the first acceleration sensor 12 and the second acceleration sensor 13 are used to monitor the change in the vibration characteristics of the conveyor trough of the combine harvester at different feed rates during field harvesting to characterize the change in the volume of the plants in the conveyor trough. The specific steps are as follows:

[0092] S1: Collection of conveyor trough vibration signals at different feeding rates

[0093] A plot with uniform growth was selected as the test plot. During the field test, the height of the harvesting platform remained unchanged, and the harvesting operation was carried out in the full-width state. The feed rate of the combine harvester was changed by changing the forward speed, and the vibration signal of the conveyor trough under different feed rate inputs was collected by the first acceleration sensor 12 and the second acceleration sensor 13. The sampling mode of the test system was set to continuous sampling, the sampling frequency was 2.56kHz, and the analysis frequency was equal to the sampling frequency divided by 2.56, which was 1000Hz. In addition, the number of analysis points in the frequency domain analysis was 4096, the number of spectral lines was 1600, and the average number of the average spectrum was 10 times. When the sampling frequency and the number of spectral lines are known, the time domain data length T0 of each FFT transformation can be calculated. The calculation formula is as follows:

[0094]

[0095] According to formula (1), T0 is equal to 1.6s at this time, that is, the length of the time domain data of each FFT transformation is 1.6 seconds, so the minimum length of 10 averages is 16s.

[0096] Each vibration signal acquisition test plan was conducted three times. In Test Group 1, the combine harvester's feed rate was zero, and the engine and working components were operating simultaneously. Vibration signals were collected at the trough under this no-load condition. Test Group 4 was the maximum speed group, maintained at 0.9 m / s. At this time, the feed rate was the highest, at 4.41 kg / s. The speed of Test Groups 3 and 2 was further reduced to 0.6 m / s and 0.3 m / s, respectively. The feed rates of these two groups also decreased accordingly, to 2.94 kg / s and 1.47 kg / s.

[0097] S2: Analyze the time domain characteristics of the vibration signals of each test group

[0098] Taking test groups 1 to 4 as an example, the time domain diagram of the vibration signal collected by the vibration acceleration sensor under different working conditions is drawn. The horizontal axis represents the sampling time and the vertical axis represents the acceleration. Figure 6 In order to more clearly observe the changing trend of the vibration signals collected by each test group, the characteristic values ​​were extracted from the time domain signals, including the average value (m / s 2 ), peak-to-peak value (m / s 2 ), standard deviation (m / s 2 ) and root mean square (m / s 2 ), these characteristic values ​​can be used to more specifically analyze the time domain variation trend of the vibration signal. The time domain characteristic parameters of the vibration signal collected by the first acceleration sensor 12 and the second acceleration sensor 13 are listed in Table 1.

[0099] Table 1 Time domain characteristic parameters of each experimental group

[0100]

[0101] Table 1 shows that across all test groups, the peak-to-peak value, standard deviation, and RMS value of second accelerometer 13 were consistently significantly greater than those of first accelerometer 12. This indicates that under the same vibration conditions, the vibration signal intensity measured at the second measuring point was consistently greater than that at the first measuring point. This is related to the sensor installation locations. The two sensors are located in different locations and are affected differently by the various vibration excitation sources, resulting in different vibration signals measured by the two sensors. Alternatively, the second measuring point may be closer to the conveyor trough's drive shaft, generating a greater vibration displacement, resulting in a greater vibration signal intensity than the first measuring point.

[0102] Next, we continue to analyze the changes in the time domain characteristic values ​​in Table 1 as the feed rate increases in each test group. From test group 1 to test group 4, the peak-to-peak value (the difference between the maximum and minimum amplitudes of the signal) shows a gradually increasing trend. The first measurement point increases from 303.92m / s to 2 Increased to 554.01m / s 2The second measuring point is 317.05m / s 2 Increased to 573.32m / s 2 Since the peak-to-peak value represents the amplitude range of the waveform, the amplitude range of the vibration signal waveform increases with feed rate, but not linearly. In particular, when the feed rate increases from 1.47 kg / s to 2.94 kg / s, the peak-to-peak value remains nearly constant, and even decreases slightly at the first measuring point. Furthermore, when the feed rate increases to 4.41 kg / s, the peak-to-peak value only increases slightly. This indicates that the peak-to-peak value only provides information about the extreme amplitude range of the vibration waveform and is insensitive to changes in the overall waveform shape. Unlike the peak-to-peak value, the RMS value considers the signal's direction and therefore more comprehensively reflects the amplitude of the vibration waveform. For complex waveforms, the RMS value more accurately represents the effective amplitude of the signal and is more sensitive to waveform changes. An increase in the RMS value can indicate a gradual increase in the vibration amplitude at the bottom of the conveyor trough, which is caused by increased force in the mechanical system, changes in the excitation source, or other external excitations. Furthermore, for vibration signals, the RMS value can be considered the average value of the signal's vibration energy, reflecting the signal's energy distribution. Therefore, a gradually increasing RMS value indicates a gradual increase in the vibration signal's energy. It can be clearly seen from Table 1 that as the feed rate gradually increases, the RMS value of the signal also gradually increases. The first measuring point increases from 31.91m / s to 2 Increased to 64.04m / s 2 The second measuring point is 37.31m / s 2 Increased to 68.09m / s 2 , and it increases gradually, but the increase is gradually slowing down. In this experiment, it shows that the vibration energy of the vibration signal collected by the two measuring points gradually increases with the increase of feed amount.

[0103] S3: Analyze the frequency domain characteristics of the vibration signals of each test group

[0104] Time domain analysis is suitable for observing the changes of vibration signals over time, while frequency domain analysis provides important information about the frequency characteristics of the signal. Therefore, in order to analyze the different frequency components contained in the signal and their relative strength, the time domain signal is converted into a frequency domain signal using fast Fourier transform, and a spectrum is drawn to show the amplitude of each frequency component in the signal, such as Figure 7 As shown in Table 2, the frequency spectrum is plotted with frequency as the horizontal axis and acceleration as the vertical axis. The first four peaks of the amplitude at each measuring point and their corresponding vibration frequencies were extracted from the frequency spectrum of each test group.

[0105] Table 2 Amplitude frequency and peak value of each test group

[0106]

[0107] In order to analyze and observe the frequency characteristics of the vibration signals of each test group under variable feed rate more clearly, 1 / 3 octave analysis was performed on 2~1000hz of each spectrum to obtain 1 / 3 octave band. The specific data of each test group was exported from the dynamic signal test analysis software and plotted into a histogram, as shown in the figure. Figure 8 It is worth noting that the spectrum plotted here is the effective value spectrum, with the horizontal axis representing frequency and the vertical axis representing the root mean square. The first measurement point of test group 1 is in the 1 / 3 octave band of 2 to 1000 Hz, as shown in Table 3, which shows the center frequency and amplitude of each sub-band.

[0108] Table 3 1 / 3 octave analysis statistics for each experimental group

[0109]

[0110] Then the data were processed to calculate the average value, maximum value and total root mean square of the two measuring points of each test group, as shown in Table 4.

[0111] Table 4 Average, maximum and total RMS values ​​of two measuring points in each test group

[0112]

[0113] The calculation formula of the total root mean square is shown in formula (2), which is an expression method for evaluating the intensity and energy of the vibration signal. The total root mean square reflects the vibration intensity in the entire frequency range, while the root mean square of each sub-band provides more detailed frequency domain information, reflecting the energy distribution in different sub-bands.

[0114]

[0115] Where M is the number of one-third octave subbands, and the frequency range from 2 Hz to 1000 Hz is divided into 28 subbands in total; RMSj represents the root mean square value of the jth subband.

[0116] exist Figure 8 The height of each column in represents the root mean square value of each sub-band, which can be used to represent the energy intensity of all frequency components in this sub-band. Figure 8 As can be seen, from test group 1 to test group 4, it can be intuitively observed that the area of ​​the real area of ​​the first and second measuring points is gradually increasing, which is reflected in Table 4 as the average value increases. This shows that as the feed rate increases, the average energy of the two measuring points in the octave range of 2Hz to 1000Hz is gradually increasing, and the average energy at the second measuring point is always greater than that of the first measuring point. The significance of the RMS value of a certain sub-band in the octave analysis indicates the importance of this band in the entire signal. Figure 8It can be clearly observed that the height of the sub-band with a center frequency of 315Hz at the first measuring point of each test group has always been the largest, and the height of the sub-band with a center frequency of 63Hz and 315Hz at the second measuring point is more prominent, indicating that the sub-band with a center frequency of 315Hz has dominated the test process, and the frequency components within the sub-band always provide greater vibration intensity. Taking the sub-band amplitude at 315hz as the focus point, from test group 1 to test group 4, at the first measuring point, the amplitude increases from 17.93m / s 2 Gradually increases to 35.58m / s 2 The amplitude at the second measuring point is 13.49m / s 2 Increased to 25.93m / s 2 , which means that the vibration intensity of the dominant sub-band in the octave increases with the increase of feed rate. Finally, the change of the total RMS value is analyzed. It calculates the root sum of the squares of the amplitudes of all sub-bands, reflecting the vibration intensity in the entire frequency range. The increase of the total RMS value indicates that the overall vibration energy is increasing. From the change of the total RMS value in the last column of Table 4, the first measuring point has increased from 31.52m / s 2 Gradually increases to 62.46m / s 2 , the second measuring point is 37.19m / s 2 Gradually increases to 64.94m / s 2 This further demonstrates that an increase in feed rate leads to an increase in vibration intensity at the bottom of the conveyor trough, which is consistent with the results from the time-domain analysis, and the magnitude of the increase is also decreasing. It is also worth noting that in Experimental Group 2, the average vibration intensity at the first measuring point is lower than that at the second measuring point, but the overall vibration intensity is slightly higher. This is because the RMS value is more sensitive to extreme values ​​in the data. The data at the first measuring point has greater variability or extreme values ​​than the data at the second measuring point, which results in a higher RMS value without necessarily significantly affecting the average value.

[0117] S4: Build a combine harvester feed rate prediction model

[0118] After time-domain and frequency-domain analysis of the signals collected from the bottom of the conveyor trough, the time-domain RMS value and the total RMS value of the octave band of the vibration signals collected at two measuring points were used as selected vibration signal characteristic values ​​to predict the combine harvester's feed rate. Let the time-domain RMS value of the first measuring point be Y1, the time-domain RMS value of the second measuring point be Y2, the total RMS value of the 1 / 3 octave band of the first measuring point be Y3, the total RMS value of the 1 / 3 octave band of the second measuring point be Y4, and the feed rate be W. The following relationship exists between them:

[0119] When W = 0, Y1 = 31.91, Y2 = 37.31, Y3 = 31.52, Y4 = 37.19;

[0120] When W = 1.47, Y1 = 49.50, Y2 = 50.31, Y3 = 45.81, Y4 = 44.48;

[0121] When W = 2.94, Y1 = 58.61, Y2 = 62.84, Y3 = 55.41, Y4 = 60.15;

[0122] When W = 4.41, Y1 = 64.04, Y2 = 68.09, Y3 = 62.46, Y4 = 64.94;

[0123] The multiple linear regression method is used to simultaneously consider the values ​​of Y1, Y2, Y3 and Y4 to predict the feed amount W. First, a prediction model equation is constructed as shown in Equation (3), where W is the dependent variable and Y1, Y2, Y3 and Y4 are independent variables.

[0124] W=β0+β1Y1+β2Y2+β3Y3+β4Y4 (3)

[0125] Among them, β0 is the intercept, and β1, β2, β3, and β4 are regression coefficients.

[0126] To simplify the calculation, we use the "LinearRegression" class of the scikit-learn library in Python to create a model and input the dataset into the model for fitting. The final calculation result is:

[0127] W=-4.73209-0.57295Y1-0.30436Y2+1.0809Y3+0.00808Y4 (4)

[0128] Furthermore, the process of establishing a calculation model for the moisture content of the plant group in the conveyor trough is as follows:

[0129] First, plants were selected from the experimental plot using a five-point sampling method. A portable moisture meter was used to determine the average moisture content of the plants in the experimental plot, denoted as M1. To ensure that the measured plant moisture content is not affected by fluctuations in feed rate, a correlation coefficient, or correction factor K, was introduced. K changes with the volume of the plants inside the combine harvester's trough, thereby eliminating the influence of feed rate on the current test results.

[0130] Furthermore, the correction coefficient K is determined as follows:

[0131] A certain amount of rice plants were harvested from a certain area of ​​the field and transported back to the laboratory. On an indoor threshing test bench, different feed rates were fed through the trough of a combine harvester. The current changes in the plant population in the trough of the combine harvester under different feed rates were measured, and the relationship between the correction coefficient K and the feed rate W was obtained by fitting:

[0132] K = f1(W, V) (5)

[0133] Establish a plant group moisture content detection model:

[0134] Q = M1 × V × f1(W, V) (6)

[0135] Among them, Q is the moisture content of the plant group, M1 is the average moisture content of the plants in the experimental field, V is the current of the plant group inside the conveyor trough 1, and f1(W, V) is the correction coefficient K, which changes with the change of the material quality inside the conveyor trough of the combine harvester, thereby eliminating the influence of the feed amount on the current test results.

[0136] The present invention establishes a plant group moisture content monitoring model based on the difference in current when plant groups with different moisture contents flow through the conveyor trough monitoring area, thereby achieving accurate characterization of the moisture content of the plant group. It can provide a pre-processed crop moisture content signal for the combine harvester control system, and the control system can adjust relevant working parameters in a timely manner, eliminating the control lag of the existing method, which is of great significance to improving the operating performance and efficiency of the entire machine.

[0137] It should be understood that although this specification is described according to various embodiments, not every embodiment contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

[0138] The series of detailed descriptions listed above are only specific descriptions of feasible embodiments of the present invention. They are not intended to limit the scope of protection of the present invention. Any equivalent embodiments or changes that do not deviate from the technical spirit of the present invention should be included in the scope of protection of the present invention.

Claims

1. A device for detecting moisture content of plant groups in a combine harvester, characterized in that: It includes a moisture content detection mechanism (4), at least two acceleration sensors and a signal processing system; The moisture content detection mechanism (4) is in direct contact with the plant group entering the combine harvester conveyor trough (1), and is used to detect the current of the plant group entering the combine harvester conveyor trough (1) and transmit it to the signal processing system; The acceleration sensor is used to detect vibration signals of the combine harvester conveyor trough (1) under different feeding amounts and transmit the signals to a signal processing system; the acceleration sensor comprises a first acceleration sensor (12) and a second acceleration sensor (13); the first acceleration sensor (12) and the second acceleration sensor (13) are located outside the bottom plate of the combine harvester conveyor trough and on a center line perpendicular to the width direction of the conveyor trough (1); The signal processing system is connected to the moisture content detection mechanism (4) and the acceleration sensor respectively, and the signal processing system obtains the moisture content of the plant group in the combine harvester according to the vibration signal and the current change of the plant group in the combine harvester conveying trough (1) under different feeding amounts; The signal processing system analyzes the vibration signal and obtains the feed amount W through the established feed amount prediction model. The signal processing system fits the relationship between the correction coefficient K and the feed amount W based on the current change of the plant group in the combine harvester conveyor trough (1) under different feed amounts measured by the moisture content detection mechanism (4), and calculates the moisture content of the plant group in the combine harvester through the established plant group moisture content detection model. The feed rate prediction model is: W=β0+β1Y1+β2Y2+β3Y3+β4Y4 Wherein, β0 is the intercept, β1, β2, β3, β4 are regression coefficients, Y1 is the time domain root mean square of the measurement point of the first acceleration sensor (12), Y2 is the time domain root mean square of the measurement point of the second acceleration sensor (13), Y3 is the 1 / 3 octave total root mean square of the measurement point of the first acceleration sensor (12), and Y4 is the 1 / 3 octave total root mean square of the measurement point of the second acceleration sensor (13).

2. The device for detecting moisture content of plant groups in a combine harvester according to claim 1, characterized in that: The moisture content detection mechanism (4) is installed on the inner side of the bottom plate of the conveying trough (1) and is in direct contact with the plant group entering the combine harvester; The first acceleration sensor (12) is located behind the water content detection mechanism (4), and the second acceleration sensor (13) is located in front of the water content detection mechanism (4).

3. The device for detecting moisture content of plant groups in a combine harvester according to claim 1, wherein: The moisture content detection mechanism (4) comprises at least two moisture content detection modules with the same structure; Each moisture content detection module comprises a rubber insulating plate (2), a metal plate (3), a plastic sleeve (7), a first metal nut (8), a second metal nut (9), a wire (10) and a metal screw (11); the rubber insulating plate (2) is located on the inner side of the bottom plate of the conveying trough, the metal plate (3) is located above the rubber insulating plate (2), one end of the metal screw (11) is inserted into the plastic sleeve (7), and then passes through the metal plate (3), the rubber insulating plate (2) and the bottom plate of the conveying trough together with the plastic sleeve (7), and is fixed on the outer side of the bottom plate of the conveying trough by the first metal nut (8), the first metal nut (8) and the bottom plate of the conveying trough are insulated by the step of the plastic sleeve (7), the wire (10) is wound around the metal screw (11) below the first metal nut (8) and is pressed by the second metal nut (9).

4. The device for detecting moisture content of a plant group in a combine harvester according to claim 3, wherein: The metal plate (3) is connected to the positive electrode of the power supply through a wire (10), and the current is transmitted to the plant group through the metal plate (3), and the plant group then transmits the current to the frame. The change of the current flowing through the frame is monitored, and the signal processing system fits the relationship between the correction coefficient K and the feed amount W according to the current change, and calculates the moisture content of the plant group in the combine harvester through the plant group moisture content detection model.

5. The device for detecting moisture content of a plant group in a combine harvester according to claim 1, wherein: The plant group moisture content detection model is: Q = M 1× V × f 1(W, V) Among them, Q is the moisture content of the plant group, M 1 is the average moisture content of tubers in the experimental field, V is the current of the plant group inside the conveying trough (1), and f1 (W, V) is the correction coefficient K.

6. A detection method for the plant group moisture content detection device in a combine harvester according to any one of claims 1 to 5, characterized in that: The following steps are involved: The moisture content detection mechanism (4) detects the current entering the plant group inside the combine harvester conveyor trough (1) and transmits it to the signal processing system; The first acceleration sensor (12) and the second acceleration sensor (13) detect vibration signals of the combine harvester conveyor trough (1) under different feeding amounts and transmit the signals to the signal processing system; The signal processing system analyzes the vibration signal and obtains the feed amount W through the established feed amount prediction model. The signal processing system fits the relationship between the correction coefficient K and the feed amount W based on the current change of the plant group in the combine harvester conveyor trough (1) under different feed amounts measured by the moisture content detection mechanism (4), and calculates the moisture content of the plant group in the combine harvester through the established plant group moisture content detection model.

7. The detection method of the plant group moisture content detection device in a combine harvester according to claim 6, characterized in that: The steps for establishing the feed rate prediction model are as follows: S1: changing the forward speed of the combine harvester to change the feed rate of the combine harvester, collecting vibration signals of the first measuring point of the conveyor trough (1) under different feed rate inputs through the first acceleration sensor (12), and transmitting the signals to the signal processing system, collecting vibration signals of the second measuring point of the conveyor trough (1) under different feed rate inputs through the second acceleration sensor (13), and transmitting the signals to the signal processing system; S2: Analyzing the characteristic value change trend of the vibration signal collected by the first acceleration sensor (12) and the second acceleration sensor (13) in the time domain, including the average value, peak-to-peak value, standard deviation and root mean square; S3: Use fast Fourier transform to convert the time domain signal into a frequency domain signal, draw a spectrum, and display the amplitude of each frequency component in the signal, including the center frequency and the root mean square value. Perform 1 / 3 octave analysis on the vibration signal corresponding to each spectrum to obtain the 1 / 3 octave band, obtain the center frequency and amplitude of each sub-band, and calculate the average, maximum value, and total root mean square value of the two measurement points in each test group. S4: By analyzing the characteristic value change trend of the vibration signal collected in the time domain and frequency domain, the best characteristic value is selected to characterize the change of the feed amount of the combine harvester; S5: Using the selected vibration signal characteristic values, a feed rate prediction model is established to predict the feed rate of the combine harvester. The feed rate prediction model is: W=β0+β1Y1+β2Y2+β3Y3+β4Y4 Wherein, β0 is the intercept, β1, β2, β3, β4 are regression coefficients, Y1 is the time domain root mean square of the measurement point of the first acceleration sensor (12), Y2 is the time domain root mean square of the measurement point of the second acceleration sensor (13), Y3 is the 1 / 3 octave total root mean square of the measurement point of the first acceleration sensor (12), and Y4 is the 1 / 3 octave total root mean square of the measurement point of the second acceleration sensor (13).

8. The detection method of the plant group moisture content detection device in a combine harvester according to claim 7, characterized in that: The steps for establishing the plant group moisture content detection model are as follows: The average moisture content of tubers in the test field was detected and recorded as M1; The current change of the plant group in the conveyor trough (1) of the combine harvester under different feeding amounts was measured, and the correlation between the current V change of the plant group in the conveyor trough (1) of the combine harvester under different feeding amounts and the feeding amount W was obtained through analysis: K = f 1(W, V) It is obtained that the plant group moisture content detection model is: Q= M 1× V × f 1(W, V) Among them, Q is the feed amount of the combine harvester, M 1 is the average moisture content of the plants in the experimental field, and the current f1 (W, V) of the plant group inside the conveying trough (1) is the correction coefficient K.

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