Grain flow measurement device, method, medium, yield measurement system and harvesting machinery

Abstract

The present invention relates to the technical field of intelligent agricultural machinery, and discloses a grain flow measurement device, method, medium, yield measurement system and harvesting machinery. The grain flow measurement device includes: an impulse grain flow sensor, installed at the grain outlet of the grain harvesting machinery, for collecting and outputting a grain impact signal that can characterize the grain flow; an elevator drive signal collector, arranged on the spiral elevator of the grain harvesting machinery, for collecting and outputting an elevator drive signal that can characterize the grain flow; and a data processing device, for converting the grain impact signal and the elevator drive signal into two channels of grain flow data respectively, and performing data fusion on the two channels of grain flow data to output a grain flow estimation value. The present invention performs data fusion on the measurement data of the impulse grain flow sensor and the elevator drive signal collector, thereby improving the grain flow measurement accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent agricultural machinery, and in particular to a grain flow measurement device, method, medium, yield measurement system and harvesting machinery. Background Art

[0002] At present, there are many types of grain flow sensors for yield measurement systems on grain harvesting machinery, but several commonly used types of sensors have the following shortcomings:

[0003] 1) Photoelectric volume flow sensors are easily affected by factors such as grain density, grain moisture content, and harvester inclination. In addition, the probe is easily contaminated by dust and needs to be cleaned and calibrated frequently, and the performance is not stable enough.

[0004] 2) Gamma-ray grain flow sensors are expensive and pose radiation hazards, so they are not currently used;

[0005] 3) The impulse flow sensor has the advantages of simple structure, low cost, and applicability to a variety of grain measurements. However, its measurement accuracy is easily affected by the elevator speed (sometimes a controller for controlling the speed and a sensor for measuring the speed are required), the position of the impact plate, the moisture content of the grain, and the vibration of the machine. It is especially affected by the calibration accuracy and has a large measurement error.

[0006] Moreover, these sensors are also affected by factors such as severe domestic land fragmentation, volatile working environment, and interference from rice straw, resulting in poor accuracy, which leads to low stability of the yield measurement system and large error fluctuations. Summary of the invention

[0007] The object of the present invention is to provide a grain flow measurement device, method, medium, yield measurement system and harvesting machinery to at least partially solve the above technical problems.

[0008] In order to achieve the above-mentioned purpose, the present invention provides a grain flow measurement device, including: an impulse grain flow sensor, installed at the grain outlet of a grain harvester, for collecting and outputting a grain impact signal that can characterize the grain flow; an elevator drive signal collector, arranged on a spiral elevator of the grain harvester, for collecting and outputting an elevator drive signal that can characterize the grain flow; and a data processing device, for converting the grain impact signal and the elevator drive signal into two channels of grain flow data respectively, and performing data fusion on the two channels of grain flow data to output a grain flow estimation value.

[0009] Preferably, the grain outlet is arranged on the spiral elevator, and the impulse grain flow sensor is arranged relative to the grain outlet to align with the center of the grain flow, and the elevator drive signal collector is installed on the top of the spiral elevator.

[0010] Preferably, the elevator drive signal is an instantaneous current signal of the elevator motor of the spiral elevator, and the elevator drive signal collector is a current sensor for collecting the instantaneous current signal.

[0011] Preferably, the data processing device is configured to have one or more data fusion systems for fusing two channels of grain flow data.

[0012] Preferably, the data fusion system includes a Kalman filter, which is configured to use the two-channel grain flow data as an initial estimated value of the Kalman filter algorithm, and use the covariance of the two-channel grain flow data as an initial variance estimate of the Kalman filter algorithm, and further derive the optimal grain flow estimate based on the Kalman filter algorithm.

[0013] Preferably, the grain flow measurement device further comprises: a display device, which is in communication with the data processing device and is used to display grain related information, and the grain related information includes the grain flow estimation value output by the data processing device.

[0014] On the other hand, the present invention also provides a grain flow measurement method, including: obtaining a grain impact signal at a grain outlet of a grain harvester, and converting the grain impact signal into first grain flow data; obtaining an elevator drive signal of a spiral elevator of the grain harvester, and converting the elevator drive signal into second grain flow data; and performing data fusion on the first grain flow data and the second grain flow data to output a grain flow estimation value.

[0015] Preferably, converting the grain impact signal into the first grain flow data includes: establishing a first relationship model between the grain impact signal and the real-time grain flow; and matching the first grain flow data corresponding to the current grain impact signal based on the first relationship model.

[0016] Preferably, converting the elevator drive signal into second grain flow data includes: establishing a second relationship model between the elevator drive signal and the real-time grain flow; and matching the second grain flow data corresponding to the current elevator drive signal based on the second relationship model.

[0017] Preferably, the first relationship model is configured as shown in the following formula:

[0018]

[0019] Among them, a1 and b1 are calibration coefficients, x k It is a grain shock signal. for real-time grain flow.

[0020] Preferably, when the elevator driving signal is an instantaneous current signal of the elevator motor of the spiral elevator, the second relationship model is configured as shown in the following formula:

[0021]

[0022] Wherein, a2 and b2 are calibration coefficients, U is the voltage of the lifting motor, I k is the instantaneous current of the lifting motor, The grain flow rate is measured based on the spiral lifting principle.

[0023] Preferably, the data fusion of the first grain flow data and the second grain flow data includes: taking the first grain flow data and the second grain flow data as initial estimated values ​​of a Kalman filter algorithm; taking the covariance of each of the first grain flow data and the second grain flow data as an initial variance estimate of the Kalman filter algorithm; and starting from the initial estimated value and the initial variance estimate, further deriving an optimal grain flow estimate based on the Kalman filter algorithm.

[0024] On the other hand, the present invention also provides a grain flow measurement device, comprising: a memory storing a program that can be run on a processor; and the processor configured to implement any of the above-mentioned grain flow measurement methods when executing the program.

[0025] On the other hand, the present invention also provides a yield measurement system, comprising any of the above-mentioned grain flow measurement devices.

[0026] On the other hand, the present invention also provides a grain harvesting machine equipped with any of the above-mentioned yield measurement systems.

[0027] On the other hand, the present invention further provides a machine-readable storage medium having instructions stored thereon, the instructions being used to enable a machine to execute any of the above-mentioned grain flow measurement methods.

[0028] Through the above technical scheme, the present invention proposes a scheme for fusing the grain flow measurement data of the impulse grain flow sensor and the elevator drive signal collector based on the spiral lifting principle, which can significantly improve the grain flow measurement accuracy.

[0029] Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present invention but do not constitute a limitation of the present invention. In the accompanying drawings:

[0031] Figure 1 is a schematic structural diagram of a grain flow measurement device according to a first embodiment of the present invention;

[0032] Figure 2 It is a schematic diagram of the installation of the impulse grain flow sensor and the elevator drive signal collector in the example;

[0033] Figure 3 is a structural diagram of an exemplary Kalman filter algorithm according to an embodiment of the present invention;

[0034] Figure 4 is a flow chart of a method for measuring grain flow rate according to a second embodiment of the present invention; and

[0035] Figure 5 is a flow chart of an implementation of an exemplary grain flow measurement method.

[0036] Description of Reference Numerals

[0037] 100. Impulse grain flow sensor; 200. Elevator drive signal collector; 300. Data processing device; 400. Spiral elevator; 500. Grain outlet; 600. Scraper; 700. Display device. DETAILED DESCRIPTION

[0038] The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.

[0039] It should be noted that, in the embodiments of the present invention, unless otherwise specified, the directional words used, such as "top end, top", can refer to the top of the corresponding contour in the figure, and can also be understood in combination with the actual structure of the engineering machinery.

[0040] In addition, in the embodiments of the present invention, the "precision agriculture" mentioned refers to an integrated agricultural system based on information and production, which aims to improve long-term, location-specific and entire farm production efficiency, productivity and profitability, while minimizing secondary impacts on wildlife and the environment; the key to "precision agriculture" is to use information technology to apply the concept of precision agriculture with real-time variable management to planting systems, improve production management, and minimize the impact on the environment.

[0041] In addition, in the embodiment of the present invention, the "Kalman filter" mentioned is a method for estimating the system state quantity according to the input and output through the linear system state equation; it is based on the state space, the sensor data is the input of the filter, the state parameter of the system is the output of the filter, and the system prediction equation and the measurement equation are used to establish the input-output association; it uses the minimum mean square error as the estimation criterion and designs the filter in the time domain. The Kalman filter algorithm does not need to decompose the signal power spectrum to remove the interference signal. It is a set of real-time filtering algorithms with simple calculations and low hardware requirements. It is quite applicable in the field of multivariable systems and has strong application value. In addition, it should be noted that the "grain quality", "grain yield" and "grain flow" involved in the embodiment of the present invention can be used interchangeably.

[0042] Embodiment 1

[0043] Figure 1 Schematic diagram of the structure of the grain flow measurement device according to the first embodiment of the present invention. Figure 1 As shown, the grain flow measurement device may include: an impulse grain flow sensor 100, installed at the grain outlet of the grain harvester, for collecting and outputting a grain impact signal that can characterize the grain flow; an elevator drive signal collector 200, arranged on the spiral elevator 400 of the grain harvester, for collecting and outputting an elevator drive signal that can characterize the grain flow; and a data processing device 300, for converting the grain impact signal and the elevator drive signal into two channels of grain flow data respectively, and performing data fusion on the two channels of grain flow data to output a grain flow estimation value.

[0044] For this grain flow measurement device, Figure 2 The figure below is the installation diagram of the impulse grain flow sensor and elevator drive signal collector in the example. Figure 2 As shown, the grain outlet 500 is arranged on the spiral elevator 400, and the impulse grain flow sensor 100 is arranged relative to the grain outlet 500 to align with the center of the grain flow, and the elevator drive signal collector 200 is installed on the top of the spiral elevator 400. In addition, a scraper 600 adapted to the impulse grain flow sensor 100 is installed on the spiral top of the spiral elevator 400, which can perform spiral motion with the spiral elevator 400. The spiral elevator 400 includes but is not limited to a vertical spiral elevator.

[0045] The impulse-type grain flow sensor 100 , the elevator drive signal collector 200 and the data processing device 300 are further described in detail below.

[0046] 1. Impulse type grain flow sensor 100.

[0047] The impulse grain flow sensor is a sensor for measuring mass flow. Figure 2 , the grains are transported to the top of the granary through the spiral elevator 400, and the grains with a certain speed are thrown to the impact plate of the impulse grain flow sensor 100. Among them, the impact plate has a built-in conventional signal processing circuit including a resistance strain gauge, an amplifier and an AD converter. Accordingly, when the grains periodically hit the impact plate, the resistance value of the resistance strain gauge built into the impact plate will change as the impact plate deforms, thereby causing the voltage signal corresponding to the strain gauge to change. The voltage signal is then transmitted to the AD converter as a force measurement signal through the amplifier, and then converted into a digital signal and sent to the impulse grain flow sensor 100 to be stored in the memory built into the impulse grain flow sensor 100.

[0048] The corresponding force measurement digital signal is an impulse signal (or grain impact signal), and according to the definition of impulse in physics, when the speed of grain impact is known, the real-time measurement of grain quality can be achieved by measuring the change in momentum before and after the grain collision. That is, the impulse signal finally stored by the impulse grain flow sensor 100 in the example can be converted into grain flow (quality) data.

[0049] For example, grains are hit on the impact plate of the impulse grain flow sensor 100 at a certain stable speed at the outlet of the spiral elevator 400 to complete the measurement process. The grain impact signal collected by the impulse grain flow sensor 100 is linearly related to the actual grain flow, and the relationship model is established as shown in formula (1):

[0050]

[0051] Among them, a1 and b1 are calibration coefficients, x k is the grain impact signal collected by the impulse grain flow sensor 100, is the real-time grain flow value finally obtained by the impulse grain flow sensor 100. The calibration coefficients a1 and b1 can be calculated through field harvesting tests.

[0052] Accordingly, the grain impact signal measured by the impulse grain flow sensor 100 is converted as shown in formula (1) to obtain the corresponding grain flow value. The conversion process can be performed by the data processing device 300, or directly by a simple processor built into the impulse grain flow sensor 100, which enables the impulse grain flow sensor 100 to directly output the real-time grain flow.

[0053] 2. Elevator drive signal collector 200.

[0054] The impulse grain flow sensor 100 is currently a commonly used grain flow sensor, but as mentioned in the background technology of this application, it has defects such as poor accuracy. Based on this, the embodiment of the present invention uses, for example, Figure 2 The installation relationship between the impulse grain flow sensor 100 and the spiral elevator 400 shown in the figure considers further arranging the elevator drive signal collector 200 to form redundant detection of the grain flow. The following specifically introduces the principle that the elevator drive signal collector 200 can detect the grain flow, that is, introduces the characteristics of the "elevator drive signal" found by the inventor of the present application in the process of realizing the present application that can characterize the grain flow.

[0055] In the example, the elevator driving signal is an instantaneous current signal of the elevator motor of the spiral elevator 400, and the elevator driving signal collector 200 is a current sensor for collecting the instantaneous current signal.

[0056] Combination Figure 2 , the elevator drive signal collector 200 is installed on the top of the vertical spiral elevator. It is easy to know that the elevator drive signal has a direct relationship with the weight of the grain on the elevator, but is less affected by other vibration noises, and is suitable for detecting grain flow. Based on this, in the example, a relationship model between the elevator drive signal and the real-time grain quality can be established first. For example, a polynomial is selected to fit the relationship model, and the fitting relationship between the instantaneous grain quality transported by the elevator and the elevator drive signal can be obtained as shown in formula (2):

[0057]

[0058] Among them, a2 and b2 are calibration coefficients, which can be obtained from calibration experiments; U is the voltage of the screw lifting motor (can be a fixed value), I k is the instantaneous current of the spiral lifting motor, It is the instantaneous grain flow rate measured based on the spiral lifting principle.

[0059] According to this, through formula (2), the conversion between the elevator drive signal (such as the elevator motor current signal) and the grain flow rate is realized, and the conversion can also be performed by the data processing device 300 or the processor built into the elevator drive signal collector 200. The discovery of this conversion enables the elevator drive signal collector 200 and the impulse grain flow sensor 100 to form a redundant detection of grain flow.

[0060] In addition, the embodiment of the present invention can be specially equipped with an elevator drive signal collector 200 for the grain harvester. However, if the grain harvester itself has a current sensor for the elevator motor, there is no need to install an additional collector to reduce costs and installation steps.

[0061] It should be noted that, whether it is formula (1) or formula (2), the calibration parameters can be obtained through calibration tests. In the example, in order to obtain the calibration parameters, at least two sets of grain flow measurement values ​​and true values ​​are required, and the calibration method is: after completing a certain distance of harvesting and yield measurement, unload the grain and weigh it to obtain the true weight of the harvested grain in this distance (i.e., the true value of the grain flow); in the data processing device 300, for example, the grain flow measurement value and the true value are analyzed, and the calibration coefficients of the two measurement methods are calculated by combining formula (1) and formula (2). This calibration method has high versatility and can adapt to the complex farmland conditions in China, reducing the workload of the conventional method of multiple experimental calibration of the sensor.

[0062] 3. Data processing device 300.

[0063] In the embodiment of the present invention, the data processing device 300 can be configured using a conventional processor, etc. Figure 1 It mainly completes the data fusion of two channels of grain flow data corresponding to the grain impact signal and the elevator driving signal.

[0064] It should be noted that, based on the above-mentioned first and second parts (especially equations (1) and (2)), the basic idea of ​​the data processing device 300 of the embodiment of the present invention for data fusion is: 1) Based on the impulse principle, the output signal of the impulse grain flow sensor is positively correlated with the grain flow, and the dynamic measurement accuracy is high but it is easily affected by the vibration of the fuselage; 2) Based on the principle of spiral conveying technology, the grain moves axially under the thrust and centrifugal force of the spiral blades, wherein the mass of the lifted grain is linearly related to the lifting motor drive signal (such as the motor current), and the real-time measurement of the grain flow can be completed, and the grain flow calculated by the elevator drive signal can effectively eliminate the influence of adverse factors such as manual errors and fuselage vibration, and the measurement is simple and low in cost; 3) By fusing the impulse grain flow sensor and the elevator drive signal collector based on the spiral lifting principle, the multi-sensor data fusion method is used to estimate and filter the grain flow, thereby effectively suppressing the signal fluctuation and improving the output accuracy of the grain flow.

[0065] Furthermore, in the embodiment of the present invention, the data processing device 300 may be configured to have one or more data fusion systems for fusing the two-way grain flow data, that is, to achieve data fusion through one or more data fusion algorithms. The data fusion algorithms include but are not limited to weighted average algorithms, Bayesian estimation algorithms, Kalman filter algorithms, and algorithms based on neural networks, feature extraction or search, etc.

[0066] In a preferred embodiment, the data fusion system includes a Kalman filter, that is, the Kalman filter is configured to use the two-channel grain flow data as the initial estimated value of the Kalman filter algorithm, and use the covariance of the two-channel grain flow data as the initial variance estimate of the Kalman filter algorithm, and further obtain the optimal grain flow estimate based on the Kalman filter algorithm. That is, the Kalman filter algorithm is used to solve the two problems of filtering and parameter estimation at the same time: the minimum mean square error is used as the best estimation criterion, a state space model including signals and noise is used, and the observation values ​​from different sensors at the current moment are used to correct the estimated value at the previous moment, thereby updating the estimate of the state quantity and obtaining the estimated value at the current moment. Accordingly, the Kalman filter algorithm can make an optimal estimate that satisfies the minimum mean square error for the signal to be processed based on the established system equation and observation equation.

[0067] Combine the following Figure 3 , the application of the Kalman filter algorithm to the data fusion involved in the embodiments of the present invention is specifically described by examples.

[0068] Figure 3 is a diagram showing the structure of an example Kalman filter algorithm according to an embodiment of the present invention. Figure 3 As shown, the impulse grain flow sensor and the elevator drive signal collector each correspond to a channel for filtering and parameter estimation. Among them, the deviation prediction equation involved in the Kalman filter algorithm is:

[0069]

[0070] Where D k-1 is the optimal filtering deviation value at the previous moment, is the uncertainty, A k The predicted deviation value for the current moment.

[0071] Furthermore, the filter gain weights of the two grain flow signals can be expressed as:

[0072]

[0073] In the formula, is the gain weight of the grain flow data corresponding to the impulse grain flow sensor 100, is the gain weight of the grain flow data corresponding to the elevator drive signal collector 200, is the covariance of the grain flow data corresponding to the impulse grain flow sensor 100 at the current moment, is the covariance of the grain flow data corresponding to the elevator drive signal collector 200.

[0074] Furthermore, the filter estimation equation can be expressed as:

[0075]

[0076]

[0077] In the formula, is the optimal grain flow estimate after fusion, is the predicted value at the previous moment, is the grain flow value converted by the impulse grain flow sensor at the current moment, is the grain flow value converted by the elevator drive signal collector at the current moment. The conversion process can refer to equations (1) and (2).

[0078] Furthermore, in combination with the above equations (3)-(5), this example also involves the following filtering deviation equation:

[0079]

[0080] in D k It is the optimal filtering deviation value at the current moment.

[0081] Back to Figure 1 In a more preferred embodiment, the grain flow measurement device further includes: a display device 700, which communicates with the data processing device 300 and is used to display grain-related information, wherein the grain-related information may include the grain flow estimation value output by the data processing device, and may also include information such as the total grain yield, yield distribution map and operation trajectory from other components of the grain harvesting machinery. It should be noted that the grain-related information includes but is not limited to this information. It should also be noted that the communication method between the display device 700 and the data processing device 300 is, for example, a CAN bus communication method.

[0082] In addition, the display device 700 can be an intelligent display terminal, preferably an intelligent handheld terminal, such as a smart phone, which can even communicate remotely with the grain harvesting machinery, so that relevant personnel can grasp data such as grain flow estimation in real time, which helps to realize unmanned driving of grain harvesting machinery on site.

[0083] In addition, the data processing device 300 and the display device 700 may be integrated together, for example, on the same server.

[0084] In summary, the grain flow measurement device according to the embodiment of the present invention can have the following effects:

[0085] First, redundant detection effect. Specifically, for the same object to be tested (grain flow), two types of sensors (impulse grain flow sensor and elevator drive signal collector) are actually used for detection, which can expand the test dimension, avoid the inherent defects of a single sensor, and make the monitored grain flow information contain redundancy, which can then be used to reduce measurement errors and improve measurement accuracy. In addition, if one type of sensor fails, it will not have a significant impact on the final measurement result, which can ensure the stability of the production measurement system.

[0086] Second, the multi-sensor data fusion effect. Specifically, the impulse-type grain flow sensor is seriously affected by vibration and noise interference, and because the cumulative yield is obtained by signal integration, the longer the harvesting time, the greater the cumulative error. However, the elevator drive signal collector based on the spiral lifting principle may have low measurement sensitivity due to its structural characteristics. Therefore, it is difficult to obtain the true grain yield with these two grain flow measurement methods. Accordingly, the embodiment of the present invention performs data fusion on the measurement results of the two, breaking the existing conventional concept of calibrating the sensor flow value with one measurement result, which can effectively avoid the integral drift of the impulse-type grain flow sensor due to error integration, and avoid the defect of low sensitivity of the elevator drive signal collector, thereby improving the measurement accuracy and reliability.

[0087] In addition, conventional grain flow sensor signals are all unipolar signals, and when the sampling frequency is too high, the data redundancy is large, the software and hardware costs are high, and when the sampling frequency is too low, it is difficult to ensure the accuracy and stability of the yield measurement system. The embodiment of the present invention uses multi-sensor data fusion to automatically analyze and comprehensively analyze the collected data of different sensors according to certain criteria to complete the decision-making required for precision agriculture, which can achieve a balance between cost and accuracy.

[0088] Third, the effect of the Kalman filter method. The embodiment of the present invention is based on the Kalman filter method, and uses the sensor output data and covariance as the initial estimated value and initial variance estimate to obtain the optimal estimate. This iterative recursive calculation method using Kalman filtering requires very little storage space, is suitable for occasions where storage space and calculation speed are limited, and can effectively improve the accuracy of grain flow measurement.

[0089] Fourth, the effect of the display device. The embodiment of the present invention is equipped with a display device that can display data on-site or remotely, which is convenient for the staff to grasp the relevant grain harvesting data in real time, and has a guiding role in the on-site command and remote operation of the grain harvesting machinery.

[0090] Embodiment 2

[0091] Figure 42 is a flow chart of a method for measuring grain flow in accordance with the second embodiment of the present invention. The method for measuring grain flow is based on the same inventive concept as the device for measuring grain flow in accordance with the first embodiment. Figure 4 As shown, the grain flow measurement method may include the following steps S410-S430:

[0092] Step S410, obtaining a grain impact signal at a grain outlet of a grain harvesting machine, and converting the grain impact signal into first grain flow data;

[0093] Step S420, obtaining an elevator driving signal of a spiral elevator of the grain harvesting machine, and converting the elevator driving signal into second grain flow data; and

[0094] Step S430: performing data fusion on the first grain flow data and the second grain flow data to output a grain flow estimation value.

[0095] Preferably, for step S410, converting the grain impact signal into the first grain flow data includes: step S411, establishing a first relationship model between the grain impact signal and the real-time grain flow; and step S412, matching the first grain flow data corresponding to the current grain impact signal based on the first relationship model.

[0096] More preferably, the first relationship model is configured as described in the above formula (1), which will not be described in detail here.

[0097] Preferably, for step S420, converting the elevator drive signal into the second grain flow data includes: step S421, establishing a second relationship model between the elevator drive signal and the real-time grain flow; and step S422, matching the second grain flow data corresponding to the current elevator drive signal based on the second relationship model.

[0098] More preferably, when the elevator driving signal is the instantaneous current signal of the elevator motor of the spiral elevator, the second relationship model is configured as described in the above formula (2), which will not be repeated here.

[0099] Preferably, for step S430, the data fusion of the first grain flow data and the second grain flow data includes: step S431, using the first grain flow data and the second grain flow data as initial estimated values ​​of the Kalman filter algorithm; step S432, using the covariance of the first grain flow data and the second grain flow data as initial variance estimates of the Kalman filter algorithm; and step S433, starting from the initial estimated value and the initial variance estimate, further deriving the optimal grain flow estimate based on the Kalman filter algorithm. The specific data fusion process can be referred to the above formula (3)-formula (7), which will not be repeated here.

[0100] Combine the following Figure 5 , the implementation of the grain flow measurement method of the second embodiment is specifically introduced through an example, wherein the example is combined with the grain flow measurement device of the first embodiment. Figure 5 FIG. 1 is a flow chart of an exemplary grain flow measurement method. Figure 5 As shown, an example implementation of the grain flow measurement method may include the following steps:

[0101] Step S510, controlling the grain harvesting machine to start harvesting.

[0102] Step S520: The impulse grain flow sensor measures the real-time grain flow value.

[0103] For example, combining Figure 2 , the grains rise with the vertical spiral elevator, and finally rush out from the grain outlet and hit the impact plate of the impulse grain flow sensor. The impact plate is deformed after being stressed, so that the bridge of the internal strain gauge resistor generates a voltage signal. Grains with different flow rates generate different impact forces, and the obtained voltage signals are also different. The voltage signal (as the sensor signal value) has a strong linear correlation with the grain flow rate. The relationship coefficient between the two can be obtained through calibration tests, as shown in the above formula (1). Therefore, the impulse grain flow sensor collects the grain impulse signal at the grain outlet in real time and converts it into a real-time grain flow value.

[0104] Step S530, the elevator driving signal collector based on the spiral lifting principle measures the real-time grain flow value.

[0105] For example, combining Figure 2 , the grains rise with the elevator, and different grain weights cause the driving data of the elevator motor to change. The relationship coefficient between them can be obtained by calibration test, such as shown in formula (2). The data processing module 300 can obtain the real-time current signal of the elevator motor in real time, and obtain the real-time grain flow rate according to the function conversion relationship shown in formula (2).

[0106] Step S540: The Kalman filter performs data fusion to obtain the optimal estimate of the current grain flow rate.

[0107] For example, a Kalman filter is constructed, and the output data and covariance of two grain flow sensors are used as the estimated value and initial variance estimate. The above-mentioned schemes of equations (3) to (7) are used to fuse the data of the two sensors, thereby utilizing the statistical characteristics of the Kalman filter to recursively determine the optimal grain flow fusion estimate in a statistical sense.

[0108] Step S550, outputting grain flow.

[0109] For example, the output grain flow can be displayed via an intelligent display terminal.

[0110] Accordingly, Figure 5 The example shows that the grain flow measurement method of embodiment 2 of the present invention can utilize the grain flow measurement device of embodiment 1 to achieve redundant measurement and accurate measurement of grain flow. It is simple to operate, has small calculation amount, and strong real-time performance, and can effectively improve the measurement accuracy of the sensor and the stability of the production measurement system.

[0111] For other implementation details and effects of the grain flow measurement method of the second embodiment of the present invention, reference can be made to the aforementioned embodiment of the grain flow measurement device, and will not be repeated here.

[0112] Embodiment 3

[0113] A third embodiment of the present invention provides a grain flow measurement device, comprising: a memory storing a program that can be run on a processor; and the processor configured to implement the grain flow measurement method of any of the above embodiments when executing the program.

[0114] The grain flow measurement device includes a processor and a memory, and the data processing device 300 and the like can be stored in the memory as a program unit, and the processor executes the program unit stored in the memory to implement the corresponding function. The grain flow measurement device can utilize conventional devices with computing and data transmission capabilities, such as PLC, single-chip microcomputer, PC, controller, etc.

[0115] The processor includes a kernel, and the kernel retrieves the corresponding program unit from the memory. One or more kernels can be provided, and the grain measurement involved in the embodiment of the present invention can be realized by adjusting the kernel parameters.

[0116] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0117] Embodiment 4

[0118] A fourth embodiment of the present invention provides a yield measurement system, comprising the grain flow measurement device described in any of the above embodiments.

[0119] The structure of the production measurement system of the fourth embodiment of the present invention can be referred to Figure 2 , which also includes components such as grain outlet and spiral elevator.

[0120] The yield measurement system of the fourth embodiment of the present invention adopts the grain flow measurement device of the aforementioned embodiment. Compared with the existing yield measurement system that only uses a photoelectric volume flow sensor, a gamma-ray grain flow sensor or an impulse flow sensor, it overcomes the defect of poor accuracy of a single sensor due to factors such as severe domestic land fragmentation, variable working environment, and interference from straw residues, and can provide precision agriculture with grain flow measurement results with higher stability and smaller error fluctuations. Furthermore, the grain flow measurement result is an important part of precision agriculture. The precision agriculture solution can use the grain yield information obtained from different plots of farmland to analyze the spatial variation of crop yield, thereby providing support for intelligent agricultural machinery and smart agriculture.

[0121] Other embodiments of the present invention further provide a grain harvesting machine, which is equipped with the yield measurement system of the fourth embodiment.

[0122] Other embodiments of the present invention further provide a machine-readable storage medium, on which instructions are stored, and the instructions are used to enable a machine to execute the above-mentioned grain flow measurement method.

[0123] Other embodiments of the present invention also provide a computer program product, which, when executed on a data processing device, is suitable for executing a program for initiating the steps of the grain flow measurement method as described above.

[0124] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0125] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0126] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0127] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0128] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0129] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0130] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0131] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0132] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

[0133] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction, such as exchanging the execution order of some steps. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0134] In addition, various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the present invention, they should also be regarded as the contents disclosed by the present invention.

Claims

1. A grain flow measurement device, characterized in that: include: The impulse grain flow sensor is installed at the grain outlet of the grain harvester to collect and output a grain impact signal that can characterize the grain flow. an elevator drive signal collector, arranged on the spiral elevator of the grain harvesting machine, for collecting and outputting an elevator drive signal capable of representing the grain flow rate, wherein the elevator drive signal is an instantaneous current signal of the elevator motor of the spiral elevator; as well as The data processing device is used for converting the grain impact signal and the elevator driving signal into two paths of grain flow data respectively, and performing data fusion on the two paths of grain flow data to output a grain flow estimation value.

2. The grain flow measuring device according to claim 1, characterized in that: The grain outlet is arranged on the spiral elevator, and the impulse grain flow sensor is arranged relative to the grain outlet to align with the center of the grain flow, and the elevator driving signal collector is installed on the top of the spiral elevator.

3. The grain flow measurement device according to claim 1, characterized in that: The elevator drive signal collector is a current sensor used to collect the instantaneous current signal.

4. The grain flow measurement device according to claim 1, characterized in that: The data processing device is configured to have one or more data fusion systems for fusing two channels of grain flow data.

5. The grain flow measurement device according to claim 4, characterized in that: The data fusion system includes a Kalman filter, which is configured to use two channels of grain flow data as initial estimates of the Kalman filter algorithm, and use the covariance of the two channels of grain flow data as an initial variance estimate of the Kalman filter algorithm, and further derive an optimal grain flow estimate based on the Kalman filter algorithm.

6. The grain flow measurement device according to any one of claims 1 to 5, characterized in that: The grain flow measurement device also includes: A display device is in communication with the data processing device and is used to display grain related information, and the grain related information includes the grain flow estimation value output by the data processing device.

7. A method for measuring grain flow, characterized in that: include: Acquire a grain impact signal at a grain outlet of a grain harvesting machine, and convert the grain impact signal into first grain flow data; Acquire an elevator drive signal of a spiral elevator of the grain harvesting machine, and convert the elevator drive signal into second grain flow data, wherein the elevator drive signal is an instantaneous current signal of an elevator motor of the spiral elevator; as well as The first grain flow rate data and the second grain flow rate data are subjected to data fusion to output a grain flow rate estimation value.

8. The method for measuring grain flow according to claim 7, characterized in that: The converting the grain impact signal into the first grain flow data comprises: establishing a first relationship model between the grain impact signal and the real-time grain flow; and matching the first grain flow data corresponding to the current grain impact signal based on the first relationship model; And / or, converting the elevator drive signal into the second grain flow data includes: establishing a second relationship model between the elevator drive signal and the real-time grain flow; and matching the second grain flow data corresponding to the current elevator drive signal based on the second relationship model.

9. The method for measuring grain flow according to claim 8, characterized in that: The first relationship model is configured as shown below: Among them, a1 and b1 are calibration coefficients, x k It is a grain shock signal. for real-time grain flow.

10. The method for measuring grain flow according to claim 8, characterized in that: The second relational model is configured as shown below: Wherein, a2 and b2 are calibration coefficients, U is the voltage of the lifting motor, I k is the instantaneous current of the lifting motor, The grain flow rate is measured based on the spiral lifting principle.

11. The method for measuring grain flow according to claim 7, characterized in that: The data fusion of the first grain flow data and the second grain flow data comprises: Using the first grain flow data and the second grain flow data as initial estimated values ​​of a Kalman filter algorithm; using the covariance of each of the first grain flow rate data and the second grain flow rate data as an initial variance estimate for the Kalman filter algorithm; and Starting from the initial estimated value and the initial variance estimate, an optimal grain flow estimate is further derived based on the Kalman filter algorithm.

12. A grain flow measurement device, characterized in that: The grain flow measuring device comprises: a memory storing a program that can be executed on the processor; and The processor is configured to implement the grain flow measurement method according to any one of claims 7 to 11 when executing the program.

13. A production measurement system, characterized in that: A grain flow measuring device comprising the grain flow measuring device as described in any one of claims 1-6 and 12.

14. A grain harvesting machine, characterized in that: Equipped with the production measurement system according to claim 13.

15. A machine-readable storage medium, characterized in that: The machine-readable storage medium stores instructions for enabling a machine to execute the grain flow measurement method described in any one of claims 7 to 11.

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