An integrated automatic grain bulk density detection device and method with adaptive calibration

Abstract

This invention discloses an integrated automatic grain bulk density detection device and method with adaptive calibration, belonging to the field of bulk density detection technology. The automatic detection device of this invention is located inside the grain bin of a harvester. The leveling component is located at the top of the grain storage bin, and the weighing component is located at the bottom of the grain storage bin. The weighing component includes a fixed base fixed to the inner wall of the harvester's grain bin and a weighing sensor on the fixed base. A tilt sensor is installed in the bottom plate at the top of the weighing sensor. The controller is connected to the weighing sensor and the tilt sensor, and is used to filter the output value of the weighing sensor in real time to obtain a stable weight measurement value. A tilt compensation model is used to correct the filtered weight measurement value to obtain a corrected weight measurement value. The weight value of the material at a constant volume is calculated using the corrected weight measurement value and the weight value under no-load conditions. The bulk density is then calculated and output. The automatic bulk density detection device of this invention can operate stably in complex field environments and has high detection accuracy.

Description

Technical Field

[0001] This invention relates to the field of bulk density testing technology, specifically to an integrated automatic bulk density testing device and method for grains with adaptive calibration. Background Technology

[0002] Bulk density refers to the mass of a material per unit volume, commonly used to describe the bulk density of grains or other particulate materials. Grain bulk density is one of the important parameters for measuring grain quality in agricultural production, and it is closely related to processes such as grain flowability, storage, and transportation. However, while current technologies, through automated devices and electronic weighing equipment, enable rapid measurement of grain bulk density, reducing manual intervention and improving measurement efficiency and safety, most current bulk density measuring devices are used after grain harvesting, during quality inspection and acceptance, and lack adaptability to complex working conditions in dynamic field operations.

[0003] Automatic bulk density (BDC) detection is crucial for yield monitoring and other agricultural tasks during harvesting. For example, yield monitoring is typically based on volumetric measurement, using photoelectric sensors to measure the volume of the grain pile and then calculating the yield based on the BDC. Due to the lack of devices for directly measuring grain BDC during harvesting, moisture content sensors are often used to correct for the default BDC. This method is indirect, and the moisture content sensors have poor accuracy and stability, reducing the accuracy of yield monitoring. Therefore, direct BDC measurement is particularly important for improving yield monitoring accuracy. However, implementing automatic BDC detection in harvesting environments faces numerous challenges. Existing research mainly focuses on indoor environments and has not yet effectively adapted to the actual needs of field harvesting. Therefore, developing a high-precision grain BDC detection device and method that can operate stably in complex field environments to achieve automatic grain BDC detection and ensure the accuracy of BDC measurement has become an urgent problem to be solved. Summary of the Invention

[0004] The purpose of this invention is to overcome the problems in the prior art and provide an integrated automatic grain bulk density detection device and method with adaptive calibration.

[0005] This invention discloses an integrated automatic grain bulk density detection device with adaptive calibration, located inside the grain bin of a harvester. It includes a grain storage bin, a leveling assembly, a unloading assembly, a weighing assembly, and a controller. The leveling assembly is located at the top of the grain storage bin, and the weighing assembly is located at the bottom of the grain storage bin. The weighing assembly includes a fixed base fixed to the inner wall of the harvester's grain bin and a weighing sensor located on the fixed base. A base plate is fixed to the top of the weighing sensor, and an inclination sensor is installed inside the base plate. The unloading assembly is located on the base plate and is used to move the grain storage bin upwards, thereby creating a gap between the bottom of the grain storage bin and the base plate for unloading. The unloading assembly is also used to reset the grain storage bin after unloading is completed.

[0006] The controller is connected to the weighing sensor and the tilt sensor. The controller is used to acquire the output value of the weighing sensor when the grain storage tank is full; and to filter the output value of the weighing sensor in real time to obtain a stable weight measurement value. The controller is also used to acquire the detection value of the tilt sensor, and to correct the filtered weight measurement value using a tilt compensation model to obtain a corrected weight measurement value. The controller is also used to acquire the weight value of the unloaded state collected by the weighing sensor after unloading; and to calculate the weight value of the fixed-volume material using the corrected weight measurement value and the weight value under the unloaded state, and then calculate and output the bulk density based on the volume of the grain storage tank.

[0007] Preferably, the method for real-time filtering of the output value of the weighing sensor is as follows:

[0008] First, construct a linear discrete-time system expression based on the filtered estimate of the actual weight of the grain storage bin at time k, the gain control value of the weighing sensor at time k, random disturbances or noise, the state transition matrix α, and the control input matrix β. Then, based on the original weight value of the weighing sensor at time k, which includes noise, and the measurement matrix... The system's measured values ​​are obtained by taking into account the noise of the weighing sensor itself.

[0009] Perform time-domain recursive calculations and execute standard Kalman filter iterations, including state prediction, covariance prediction, Kalman gain calculation, state quantity update, and covariance update.

[0010] When performing gain adjustment based on standardized residuals, the standardized residuals and their covariance are first calculated to construct outlier detection conditions. When an outlier is detected, the Kalman gain is dynamically adjusted based on the standardized residuals to suppress the influence of abnormal measurements.

[0011] The dynamic measurement process of the weighing sensor is described as a discrete-time system. By iteratively updating the detection time and the measured value, the optimal estimate of the weighing signal is output.

[0012] Preferably, during the standard Kalman filter iteration, the covariance, measurement matrix H, and transpose H of the measurement matrix H are obtained by predicting the current state based on the previous state. T The covariance matrix of the weighing sensor's own noise and Calculate the Kalman filter gain using the inverse matrix;

[0013] Based on the prediction of the current state from the previous state, the measurement matrix H, the original weight value of the weighing sensor at time k including noise, and the Kalman filter gain, calculate the optimal estimate of the state at time k.

[0014] The covariance update is calculated based on the covariance of the prediction result obtained from the previous state, the measurement matrix H, and the Kalman filter gain.

[0015] Preferably, the difference between the weight measured by the weighing sensor and the predicted weight is first calculated. and residual covariance matrix In the residual feature extraction unit, the standardized residuals and their covariance are calculated;

[0016] Based on the threshold coefficient d = 2.5~3.5, preferably d = 3.0, the outlier detection conditions are constructed in the adaptive threshold judgment unit: ;according to In the gain adjustment execution unit, when an outlier is detected, the Kalman gain is dynamically corrected. .

[0017] Preferably, when using a tilt compensation model to correct the filtered weight measurement value, if the tilt angle between the weighing sensor and the base plate is 0°, no tilt compensation is needed, and the output value of the weighing sensor is the weight value of the grain; if the tilt angle between the weighing sensor and the base plate is 0°, no tilt compensation is needed, and the output value of the weighing sensor is the weight value of the grain. At that time, error compensation is performed according to the weighing error compensation formula. ,

[0018] in This is the output of the weighing sensor after filtering. This is the tilt compensation coefficient. These are the model correction values.

[0019] Preferably, the tilt compensation coefficient K and model correction values Given an inclination angle θ, and satisfying the requirement of covering a range of 0° to 20°, calibration points and actual weights are set at 1° intervals. Under the condition that the load cell's full-scale range is covered by calibration points set at 20% intervals, the filtered output of the load cell is collected. Based on the tilt compensation formula, the experimental data were fitted using the least squares method, and the result was optimized by minimizing the sum of squared residuals between the theoretical and actual values.

[0020] Preferably, there are three weighing sensors, which are evenly distributed along the circumference of a point on the central axis of the grain storage bin.

[0021] Preferably, the tilt sensor has a built-in triaxial accelerometer.

[0022] Preferably, the diameter of the base plate is smaller than the outer diameter of the grain storage bin and larger than the inner diameter of the grain storage bin; and / or the outer diameter of the fixing seat is smaller than the diameter of the base plate.

[0023] This invention also discloses a detection method for the aforementioned adaptively calibrated integrated automatic grain bulk density detection device, comprising the following steps:

[0024] During the harvesting operation, the grain in the grain bin is filled with grain and the storage bin is full. After the harvester is fully loaded and the grain is unloaded, the leveling component scrapes the grain on the storage bin so that the surface of the grain is flush with the top surface of the storage bin.

[0025] The initial weight signal is acquired by a weighing sensor and filtered in real time to obtain a stable weight measurement value.

[0026] Based on the tilt angle measured by the tilt sensor, the output of the weighing sensor is corrected by the tilt angle compensation model to eliminate the influence of the tilt angle on the weighing, and the corrected grain weight value is calculated.

[0027] After weighing is completed, the unloading assembly starts working, the gap between the grain storage hopper and the bottom plate is opened, the unloading is completed, and then the unloading assembly is reset.

[0028] The weight value under no-load conditions is collected by a weighing sensor;

[0029] Based on the corrected grain weight value and the weight value under no-load conditions, the weight value of the material at constant volume is obtained, and then the bulk density measurement value is obtained.

[0030] Compared with existing technologies, the advantages of this invention are as follows: The integrated automatic grain bulk density detection device of this invention integrates multiple functions such as automatic sampling, leveling and removing excess grain, and bulk density calculation into a compact structure through an integrated design. This design not only effectively solves the problem of limited installation space in the grain bins of combine harvesters, but also improves operational convenience. In particular, through the automatic leveling mechanism, excess grain can be accurately removed, ensuring volume consistency, and providing an efficient and reliable automatic grain bulk density measurement solution for complex harvesting operations.

[0031] In calculating bulk density, this invention addresses noise interference caused by mechanical vibration, uneven ground, and external impacts during field operations. It optimizes the weight allocation between state estimation and measured values ​​by real-time adjustment of the filter gain and incorporates adaptive covariance matrix optimization to enhance the algorithm's robustness against nonlinear interference, effectively suppressing Gaussian white noise and eliminating outliers. Based on recursive calculation characteristics, it achieves low-latency dynamic weighing processing, ensuring system real-time performance. Furthermore, the tilt compensation model of this invention corrects tilt errors in real-time through gravity decomposition and triaxial accelerometer calibration, further enhancing the system's comprehensive suppression capabilities against dynamic vibration, tilt changes, and external impacts. This achieves high-precision, high-stability weighing measurement under complex conditions, significantly improving the reliability of grain bulk density measurement in field environments. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the structure of an integrated automatic grain bulk density detection device with adaptive calibration according to the present invention.

[0033] Figure 2 This is a schematic diagram of the scraping component structure of the present invention.

[0034] Figure 3 This is a schematic diagram of the unloading assembly structure of the present invention.

[0035] Figure 4 This is a schematic diagram of the weighing component structure of the present invention.

[0036] Figure 5 This is a front view and a partial enlarged view of the adaptive calibration integrated automatic grain bulk density detection device of the present invention.

[0037] Figure 6 This is a flowchart of the integrated automatic grain bulk density detection method with adaptive calibration according to the present invention.

[0038] Figure 7 This is a flowchart illustrating how the present invention obtains stable weight measurement values.

[0039] Figure 8 This is a flowchart illustrating how the corrected weight measurement values ​​are obtained in this invention.

[0040] Explanation of reference numerals in the attached figures:

[0041] 3. Grain storage bin; 2. Leveling assembly; 201. Scraper; 202. Cross-shaped fixing frame; 203. DC motor; 204. Motor bracket; 3. Grain unloading assembly; 301. Linear electric push rod; 302. Frustum base; 4. Weighing assembly; 401. Fixing seat; 402. Weighing sensor; 5. Base plate; 6. Tilt sensor. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0043] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms “first,” “second,” and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as “comprising” or “including” indicate that the elements or objects preceding “comprising” or “including” encompass the elements or objects listed following “comprising” or “including” and their equivalents, and do not exclude other elements or objects. Terms such as “connected” or “linked” are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as “upper,” “lower,” “left,” and “right” are used only to indicate relative positional relationships; when the absolute position of the described objects changes, the relative positional relationship may also change accordingly.

[0044] This invention discloses an adaptively calibrated integrated automatic grain bulk density detection device, located inside the grain bin of a harvester. It includes a grain storage bin 1, a leveling assembly 2, a unloading assembly 3, a weighing assembly 4, and a controller. The leveling assembly 2 is located at the top of the grain storage bin 1, and the weighing assembly 4 is located at the bottom of the grain storage bin 1. The weighing assembly 4 includes a fixed base 401 fixed to the inner wall of the harvester's grain bin and a weighing sensor 402 located on the fixed base 401. A base plate 5 is fixed to the top of the weighing sensor 402, and an inclination sensor 6 is provided inside the base plate 5. Preferably, a triaxial accelerometer is embedded in the inclination sensor 6. The unloading assembly 3 is located on the base plate 5 and is used to move the grain storage bin 1 upwards, thereby creating a gap between the bottom of the grain storage bin 1 and the base plate 5 for unloading. The unloading assembly 3 is also used to reset the grain storage bin 1 after unloading. As an example, such as... Figure 1 and Figure 2 As shown, the leveling assembly 2 includes a brush scraper 201, a cross-shaped fixing frame 202, a connection hole for the grain storage hopper 1, a mounting hole for the motor bracket 204, a DC motor 203, an upper pin hole, and the motor bracket 204. The scraper 201 is connected to the output shaft of the DC motor 203. Driven by the DC motor 203, it can perform a circular motion above the cross-shaped fixing frame 202, thereby leveling excess grain on the surface of the cross-shaped fixing frame 202, removing accumulation, and ensuring that the volume of the target grain is consistent with the volume of the grain storage hopper 1. The cross-shaped fixing frame 202 is fixed to the grain storage hopper 1 by bolts through the connection hole. Its surface has multiple mounting holes, allowing the motor bracket 204 to be fixed to the cross-shaped fixing frame 202 via the mounting holes. The DC motor 203 is located inside the motor bracket 204 and is also fixed using the mounting holes on the surface of the cross-shaped fixing frame 202.

[0045] As an example, the grain unloading component 3 in this embodiment is as follows: Figure 3As shown, the grain unloading assembly 3 includes a linear electric actuator 301. The linear electric actuator 301 is connected to the motor bracket 204 through the upper pin hole and to the frustum base 302 through the lower pin hole. The linear electric actuator 301 can perform linear extension and retraction in the vertical direction. After the grain weighing is completed, the linear electric actuator 301 extends upward, lifting the stacking and leveling assembly 2, which in turn lifts the grain storage bin 1, creating a gap between the grain storage bin 1 and the base plate 5, thus achieving grain unloading. The tilt sensor 6 is installed on the base plate 5 to measure the tilt angle of the device relative to the base plate 5. The base plate 5 is fixed to the frustum base 302 as a whole through a circumferential array of fixed threaded holes.

[0046] As an example, the weighing component 4 in this embodiment is as follows: Figure 4 As shown, the weighing assembly 4 includes a weighing sensor 402 and a fixed base. Figure 4 As shown, there are three load cells 402, evenly distributed along the circumference to ensure uniform force distribution. Both the upper and lower surfaces of the load cells 402 have threaded holes; the upper surface is fixedly connected to the base plate 5, and the lower surface is fixedly connected to the fixed base. The fixed base has four symmetrical mounting holes on its outer circumference. These holes are used to fix the capacity detection device to the profile bracket, further securing the profile bracket to the inner wall of the harvester's grain bin. The fixed base also includes multiple through holes for cable routing.

[0047] like Figure 5 As shown, the diameter of the base plate 5 is smaller than the outer diameter of the grain storage bin 1 but larger than its inner diameter. This serves two purposes: firstly, it limits the movement of the grain storage bin 1 during the return process driven by the linear electric actuator 301; secondly, it ensures that no grain residue remains at this position, thus not affecting the return of the grain storage bin 1. The fixed base is equipped with an outer baffle to protect the load cell 402. A gap exists between the upper edge of the fixed base and the base plate 5 to prevent it from affecting the weighing value. Furthermore, the diameter of the outer baffle is smaller than the diameter of the base plate 5, and its height is smaller than the height of the load cell 402, forming a concave design that effectively prevents grain from getting stuck in the gap and affecting the weighing.

[0048] In this embodiment, the controller is connected to the weighing sensor 402 and the tilt sensor 6. The controller is used to acquire the output value of the weighing sensor 402 when the grain storage tank 1 is fully loaded. When there are multiple weighing sensors 402, the controller uses the sum of the output values ​​of the multiple weighing sensors 402 as the output value of the weighing sensor 402, and filters the output value of the weighing sensor 402 in real time to obtain a stable weight measurement value. The controller is also used to acquire the detection value of the tilt sensor 6, and use the tilt compensation model to correct the filtered weight measurement value to obtain the corrected weight measurement value. The controller is also used to acquire the weight value collected by the weighing sensor 402 under the empty state after unloading the grain, and calculate the weight value of the fixed-volume material by using the corrected weight measurement value and the weight value under the empty state, and then calculate and output the bulk density by using the volume V of the grain storage tank 1.

[0049] As a preferred embodiment, the method for real-time filtering of the output value of the weighing sensor 402 is as follows:

[0050] First, construct a linear discrete-time system expression based on the filtered estimate of the actual weight of grain storage bin 1 at time k, the gain control value of weighing sensor 402 at time k, random disturbances or noise, state transition matrix α, and control input matrix β; then, based on the original weight value of weighing sensor 402 at time k, including noise, and the measurement matrix... The system's measured values ​​are obtained by taking into account the noise of the weighing sensor 402 itself.

[0051] Perform time-domain recursive calculations and execute standard Kalman filter iterations, including state prediction, covariance prediction, Kalman gain calculation, state quantity update, and covariance update.

[0052] When performing gain adjustment based on standardized residuals, the standardized residuals and their covariance are first calculated to construct outlier detection conditions. When an outlier is detected, the Kalman gain is dynamically adjusted based on the standardized residuals to suppress the influence of abnormal measurements.

[0053] The dynamic measurement process of the weighing sensor 402 is described as a discrete-time system. By iteratively updating the detection time and the measured value, the optimal estimate of the weighing signal is output.

[0054] In another preferred embodiment, when performing standard Kalman filtering iteration, the covariance, measurement matrix H, and transpose H of the measurement matrix H are obtained by predicting the current state based on the previous state. T The covariance matrix of the noise of the weighing sensor 402 and Calculate the Kalman filter gain using the inverse matrix;

[0055] The current state is predicted based on the previous state, the measurement matrix H, the original weight value of the weighing sensor 402 at time k including noise, and the optimal estimated value of the state at time k is calculated by Kalman filter gain.

[0056] The covariance update is calculated based on the covariance of the prediction result obtained from the previous state, the measurement matrix H, and the Kalman filter gain.

[0057] In a more preferred embodiment, when performing gain adjustment based on standardized residuals, the difference between the weight measured by the weighing sensor 402 and the predicted weight is first calculated. and residual covariance matrix In the residual feature extraction unit, the standardized residuals and their covariance are calculated;

[0058] Based on the threshold coefficient d = 2.5~3.5, preferably d = 3.0, the outlier detection conditions are constructed in the adaptive threshold judgment unit: ;according to In the gain adjustment execution unit, when an outlier is detected, the Kalman gain is dynamically corrected. .

[0059] When using a tilt compensation model to correct the filtered weight measurement value, if the tilt angle between the weighing sensor 402 and the base plate 5 is 0°, no tilt compensation is needed, and the output value of the weighing sensor 402 is the grain weight value; if the tilt angle between the weighing sensor 402 and the base plate 5 is 0°, no tilt compensation is needed, and the output value of the weighing sensor 402 is the grain weight value. At that time, error compensation is performed according to the weighing error compensation formula.

[0060] ;

[0061] in, This is the output of the filtered weighing sensor 402. This is the tilt compensation coefficient. For model correction values, This is the error value of the weighing sensor 402; It is a secant function.

[0062] The tilt compensation coefficient K and the model correction value Given an inclination angle θ, and satisfying the requirement of covering a range of 0° to 20°, calibration points and actual weights are set at 1° intervals. Under the condition that the load cell 402's full-scale range is covered by calibration points set at 20% intervals, the output of the load cell 402 is collected. Based on the tilt compensation formula, the experimental data were fitted using the least squares method, and the result was optimized by minimizing the sum of squared residuals between the theoretical and actual values.

[0063] The present invention also discloses a detection method for an integrated automatic grain bulk density detection device based on the above-mentioned adaptive calibration, comprising the following steps:

[0064] Install the aforementioned adaptive calibration integrated automatic grain bulk density detection device in a suitable position inside the harvester's grain bin, ensuring that the measurement directions of the weighing sensor 402 and the tilt sensor 6 are aligned with the direction of gravity.

[0065] During the harvesting operation, the grain accumulates in the grain bin and gradually covers the detection device, with some grain grains falling into the grain storage bin 1. After the harvester is fully loaded and has finished unloading, the detection device is activated, and the DC motor 203 drives the upper scraper 201 to rotate, scraping the grain pile on the upper surface of the grain storage bin 1 and sweeping off the excess grain to ensure that the volume of grain in the grain storage bin 1 is constant.

[0066] The initial weight signal is acquired by the weighing sensor 402, and the signal is filtered in real time to obtain a stable weight measurement value. ;

[0067] Tilt Compensation and Weight Calculation: Tilt Angle Measured by Tilt Sensor 6 θ The output value of the weighing sensor 402 is corrected by using an adaptive tilt compensation model to eliminate the influence of tilt angle on the weighing accuracy, and the corrected weight measurement value is calculated. ;

[0068] Unloading and Return: After weighing is completed, the unloading component 3 starts to work. The DC electric push rod drives the grain storage tank 1 to lift, and the grains leak out from the gap between the grain storage tank 1 and the bottom plate 5 to complete the unloading. Then the DC electric push rod retracts back to its original position.

[0069] No-load weighing: The weight signal under no-load conditions is collected by the weighing sensor 402. ;

[0070] Calculate the output bulk density: Based on the fusion calculation of the output signals detected by the weighing sensor 402 and the tilt sensor 6, the weight value of the material at a constant volume is obtained. Further, the bulk density measurement value was obtained. Unit: kg / m 3 .in, This is a filtering and noise reduction algorithm. This is the tilt angle compensation algorithm, where V is the volume of the constant-volume material.

[0071] In this embodiment, an initial weight signal is acquired through a weighing sensor 402, and the signal is filtered in real time to obtain a stable weight measurement value. The specific methods are as follows:

[0072] S1.1 System Modeling;

[0073] First, construct the linear discrete-time system expression based on the filtered estimate of the actual weight of the grain storage bucket 1 at time k, the gain control quantity of the weighing sensor 402 at time k, random disturbances or noise, the state transition matrix α, and the control input matrix β. (1);

[0074] Where: For the system in k The state at any given moment; For the system in k-1 The state at any given moment, For the system in k-1 The amount of control at any given moment; for k-1 System process noise at any given time; and These are system parameters.

[0075] According to the weighing sensor 402 k Raw weight values ​​including noise at each moment, measurement matrix The system's measured values ​​are obtained by taking into account the noise of the weighing sensor 402 itself.

[0076] (2);

[0077] Where: Let k be the system's measured value at time k; For measurement matrix; Measure the noise of the system.

[0078] S1.2, Time-domain recursive calculation;

[0079] Perform standard Kalman filter iteration.

[0080] The system state update expression is:

[0081] (3);

[0082] Where: To use the previous state to predict the current state and obtain the result; This is the optimal result of the previous state. For the system in k The amount of control at any given moment.

[0083] Covariance prediction is:

[0084] (4);

[0085] Where: for The corresponding covariance; for The corresponding covariance; Let A be the transpose of Aa; Q Let be the covariance of the system process.

[0086] Kalman filter gain calculation:

[0087] (5);

[0088] in, H For measurement matrix, H T To measure the transpose of matrix H, R The covariance matrix of the measurement noise for the weighing sensor 402. for The inverse matrix.

[0089] The most estimated value of the state at time k is:

[0090] (6);

[0091] Where, This represents the raw weight value of the weighing sensor 402 at time k, including noise. To predict the current state based on the previous state and obtain the result, This represents the Kalman filter gain.

[0092] The covariance update expression is:

[0093] (7);

[0094] Where, For a single measurement model, the identity matrix is ​​used. , for The corresponding covariance.

[0095] S1.3, enhanced suppression of outliers;

[0096] Perform gain adjustment based on standardized residuals.

[0097] In the residual feature extraction unit, the standardized residuals and their covariance are calculated:

[0098] (8);

[0099] (9);

[0100] Where, Let be the observation residual at time k; Let be the residual covariance matrix at time k. Let k be the system's measured value at time k. R This is the covariance matrix of the noise measured by the weighing sensor 402.

[0101] In the adaptive threshold judgment unit, outlier detection conditions are constructed as follows:

[0102] (10);

[0103] In the formula, the threshold coefficient d = 2.5 ~ 3.5, preferably d = 3.0.

[0104] In the gain adjustment execution unit, the Kalman gain is dynamically corrected when an outlier is detected:

[0105] (11);

[0106] Where, This is the proportionality coefficient. .

[0107] S1.4 Iterative optimization outputs the optimal value;

[0108] The dynamic measurement process of the weighing sensor 402 is described as a discrete-time system, and the optimal estimation of the weighing signal is achieved through iterative optimization. Preferably, the detection time update and measurement update are iterated alternately.

[0109] Time update process:

[0110] (12);

[0111] (13);

[0112] Measurement update process:

[0113] (14);

[0114] (15);

[0115] (16);

[0116] Through the above iterative process, the optimal estimate of the weighing signal is output:

[0117] (17);

[0118] In the formula, C is the output matrix, and C=[1,0] is used to extract the weight component. As input to the tilt compensation module.

[0119] It should be clearly pointed out that by assigning values ​​based on observation or querying sensor parameters, the following can be obtained: , .

[0120] This invention improves the accuracy of the measurement system by adding outlier judgment (outlier refers to erroneous data detected by the weighing sensor 402 during the weighing process due to vibration or noise) and eliminating outlier.

[0121] The algorithm described above in this invention processes the output value of the symmetric weighing sensor 402, effectively suppressing vibration noise and improving signal stability. The filtered output value is input into the adaptive tilt compensation module for real-time compensation calculation.

[0122] The tilt compensation model in this embodiment is based on a gravity decomposition model. The tilt angle is calibrated using a triaxial accelerometer and constructed using the Taylor expansion method. Details are as follows:

[0123] S2.1, Establishment of the output model of weighing sensor 402;

[0124] When the tilt angle between the weighing sensor 402 and the mounting plate is 0°, the weight of the grain in the grain storage bin 1 is... When the load cell 402 is applied perpendicularly to the mounting plate, the output of the filtered load cell 402 is... and If the values ​​are the same, the expression is:

[0125] (18);

[0126] When the plane where the weighing sensor 402 is located has an angle with the horizontal plane At this point, the scale is tilted. Introducing a gravity decomposition model, the weight of the grain... Decomposed into:

[0127] (19);

[0128] Where, The tilt angle between the load cell 402 and the horizontal plane; Stress is in the vertical direction; For horizontal stress, W m This is the corrected weight measurement.

[0129] From the perspective of force analysis, it can be seen that, This is the main force direction of the sensor, exhibiting a good linear relationship, while The force direction is parallel to the load cell 402, and its output is affected by the tilt direction of the load cell 402 and the weight of the grain. and tilt angle Due to factors such as [missing information], the output of the weighing sensor 402 is nonlinear, therefore its output expression is:

[0130] (20);

[0131] Where, for The resulting nonlinear output function.

[0132] S2.2 Establishment of the weighing error model;

[0133] Considering the presence of an angle, the error expression for the load cell 402 is:

[0134] (twenty one);

[0135] S2.3, Inclination calibration method based on triaxial accelerometer;

[0136] As a preferred embodiment, the tilt sensor 6 used in this embodiment has an embedded triaxial accelerometer that can measure the acceleration in the X, Y, and Z axes, thereby calculating the angle between each axis and the direction of gravity.

[0137] (twenty two);

[0138] Where, , , These are the three-axis acceleration components; and These are the angles of inclination of the object to the horizontal planes along the X and Y axes, respectively; the angle between the Z axis and the acceleration due to gravity is... That is, theoretical tilt angle .

[0139] During actual installation, the accelerometer has an initial offset. After eliminating the initial value, the actual tilt angle of the load cell 402 is:

[0140] (twenty three);

[0141] In the formula: The initial tilt angle of the load cell 402. This is the actual tilt angle of the load cell 402.

[0142] S2.4 Establishment of tilt angle correction compensation value and tilt angle automatic compensation model;

[0143] In one embodiment, the tilt angle automatic compensation model includes the following steps:

[0144] at the angle Under these conditions, the actual weight of grain for:

[0145] (twenty four);

[0146] In the formula: This is the output of the filtered weighing sensor 402. It is a secant function.

[0147] at the angle Under the given conditions, the sensor error expression is linearized through Taylor decomposition. The expression is:

[0148] (25);

[0149] In the formula: express It is a second-order infinitesimal. But Grain weight value The impact is very small and usually negligible. The simplified model yields the weighing error compensation formula:

[0150] (26);

[0151] To account for weighing errors, a model correction value is introduced. The analysis model yielded The expression is:

[0152] (27);

[0153] Considering The load cell 402 is not designed for the primary force direction, and its sensitivity is far lower than that of other load cells. Further simplification of the model yields:

[0154] (28);

[0155] (29);

[0156] Introducing model correction values The final weighing error compensation formula is obtained as follows:

[0157] (30);

[0158] in, This is the tilt compensation coefficient. These are the model correction values.

[0159] Preferably, the measurement range of the tilt sensor 6 used is: The measurement accuracy is 0.1°, the data refresh rate is 100Hz, and based on the error range of tilt sensor 6, the secant function of the weighing error compensation formula is... Taylor decomposition yields an approximate formula:

[0160] (31);

[0161] in, .

[0162] Furthermore, as a preferred option, in the calibration module, calibration points are set at known tilt angles θ (covering a range of 0° to 20°, with calibration points at 1° intervals) and actual weights. (Under the condition of covering 10%-100% of the full scale of the load cell 402, with calibration points set at 20% intervals), the output of the filtered load cell 402 is collected. Based on the tilt compensation formula, the least squares method is used to fit the experimental data. By minimizing the sum of squared residuals between the theoretical and actual values, the tilt compensation coefficient is optimized. K and model correction values .

[0163] This invention provides a weighing device and method based on improved Kalman filtering and adaptive tilt compensation. The improved Kalman filtering algorithm is used to filter the output signal of the weighing sensor 402 in real time to suppress noise interference. Combined with the adaptive tilt compensation model, the weight compensation value is obtained, thereby accurately calculating the bulk density of the grain and significantly improving the measurement accuracy and stability under complex working conditions.

[0164] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An integrated automatic grain bulk density detection device with adaptive calibration, characterized in that, Located inside the grain bin of a harvester, the system includes a grain storage bin, a leveling assembly, a unloading assembly, a weighing assembly, and a controller. The leveling assembly is located at the top of the grain storage bin, and the weighing assembly is located at the bottom of the grain storage bin. The weighing assembly includes a fixed base fixed to the inner wall of the harvester's grain bin and a weighing sensor located on the fixed base. A base plate is fixed to the top of the weighing sensor, and an inclination sensor is installed inside the base plate. The unloading assembly is located on the base plate and is used to move the grain storage bin upward, thereby creating a gap between the bottom of the grain storage bin and the base plate for unloading. The unloading assembly is also used to reset the grain storage bin after unloading is completed. The controller is connected to the weighing sensor and the tilt sensor. The controller is used to acquire the output value of the weighing sensor when the grain storage tank is full; and to filter the output value of the weighing sensor in real time to obtain a stable weight measurement value; the controller is also used to acquire the detection value of the tilt sensor, and to correct the filtered weight measurement value using a tilt compensation model to obtain a corrected weight measurement value; the controller is also used to acquire the weight value collected by the weighing sensor under the empty state after unloading the grain. The weight of the material at constant volume is obtained by calculating the corrected weight measurement value and the weight value under no-load conditions. Then, the bulk density is calculated and output based on the volume of the grain storage tank. The method for real-time filtering of the output value of the weighing sensor is as follows: First, construct a linear discrete-time system expression based on the filtered estimate of the actual weight of the grain storage bin at time k, the gain control value of the weighing sensor at time k, random disturbances or noise, the state transition matrix α, and the control input matrix β. Then, based on the original weight value of the weighing sensor at time k, which includes noise, and the measurement matrix... The system's measured values ​​are obtained by taking into account the noise of the weighing sensor itself. Perform time-domain recursive calculations and execute standard Kalman filter iterations, including state prediction, covariance prediction, Kalman gain calculation, state quantity update, and covariance update. When performing gain adjustment based on standardized residuals, the standardized residuals and their covariance are first calculated to construct outlier detection conditions. When an outlier is detected, the Kalman gain is dynamically adjusted based on the standardized residuals to suppress the influence of abnormal measurements. The dynamic measurement process of the weighing sensor is described as a discrete-time system. By iteratively updating the detection time and the measured value, the optimal estimate of the weighing signal is output. When using a tilt compensation model to correct the filtered weight measurement, if the tilt angle between the weighing sensor and the base plate is 0°, no tilt compensation is needed, and the output value of the weighing sensor is the weight of the grain. At that time, error compensation is performed according to the weighing error compensation formula. ,in This is the output of the weighing sensor after filtering. This is the tilt compensation coefficient. These are the model correction values.

2. The adaptively calibrated integrated automatic grain bulk density detection device as described in claim 1, characterized in that, When performing a standard Kalman filter iteration, the covariance and measurement matrix corresponding to the result are obtained by predicting the current state based on the previous state. H Measurement matrix H transpose H T The covariance matrix of the weighing sensor's own noise and Calculate the Kalman filter gain using the inverse matrix; The result and measurement matrix are obtained by predicting the current state based on the previous state. H The original weight value of the weighing sensor at time k, including noise, and the Kalman filter gain are used to calculate the optimal estimate of the state at time k. The covariance and measurement matrix of the result obtained by predicting the current state based on the previous state. H The covariance update is calculated by taking the Kalman filter gain and the covariance gain.

3. The adaptively calibrated integrated automatic grain bulk density detection device as described in claim 1, characterized in that, First, calculate the difference between the weight measured by the weighing sensor and the predicted weight. and residual covariance matrix In the residual feature extraction unit, the standardized residuals and their covariance are calculated; Based on the threshold coefficient d =2.5~3.5, take d =3.0, in the adaptive threshold judgment unit, construct the outlier detection condition: ; In the gain adjustment execution unit, when an outlier is detected, the Kalman gain is dynamically corrected: In the formula This is the proportionality coefficient. , The Kalman gain before correction. This is the corrected Kalman gain.

4. The adaptively calibrated integrated automatic grain bulk density detection device as described in claim 1, characterized in that, The tilt compensation coefficient K and model correction values Given an inclination angle θ that covers a range of 0° to 20°, calibration points and actual weights are set at 1° intervals. Under the condition of covering 10%-100% of the full scale of the load cell and setting calibration points at 20% intervals, the output of the filtered load cell is collected. Based on the tilt compensation formula, the experimental data were fitted using the least squares method, and the result was optimized by minimizing the sum of squared residuals between the theoretical and actual values.

5. The adaptively calibrated integrated automatic grain bulk density detection device as described in claim 1, characterized in that, There are three weighing sensors in total, and they are evenly distributed along the circumference with the point on the central axis of the grain storage tank as the center.

6. The integrated automatic grain bulk density detection device with adaptive calibration as described in claim 1, characterized in that, The tilt sensor has a built-in triaxial accelerometer.

7. The adaptively calibrated integrated automatic grain bulk density detection device as described in claim 1, characterized in that, The diameter of the base plate is smaller than the outer diameter of the grain storage bin and larger than the inner diameter of the grain storage bin; and / or the outer diameter of the fixing seat is smaller than the diameter of the base plate.

8. The detection method of the adaptively calibrated integrated automatic grain bulk density detection device as described in any one of claims 1-7, characterized in that, Includes the following steps: During the harvesting operation, the grain in the grain bin is filled with grain and the storage bin is full. After the harvester is fully loaded and the grain is unloaded, the leveling component scrapes the grain on the storage bin so that the surface of the grain is flush with the top surface of the storage bin. The initial weight signal is acquired by a weighing sensor and filtered in real time to obtain a stable weight measurement value. Based on the tilt angle measured by the tilt sensor, the output of the weighing sensor is corrected by the tilt angle compensation model to eliminate the influence of the tilt angle on the weighing, and the corrected grain weight value is calculated. After weighing is completed, the unloading assembly starts working, the gap between the grain storage hopper and the bottom plate is opened, the unloading is completed, and then the unloading assembly is reset. The weight value under no-load conditions is collected by a weighing sensor; Based on the corrected grain weight value and the weight value under no-load conditions, the weight value of the material at constant volume is obtained, and then the bulk density measurement value is obtained.

Images