Self-adaptive calibration integrated grain volume weight automatic detection device and method

Abstract

The invention discloses a self-adaptive calibration integrated grain volume weight automatic detection device and method, and belongs to the technical field of volume weight detection. The automatic detection device is located in a harvester granary, a slicking assembly is located at the top of a grain storage barrel, a weighing assembly is arranged at the bottom of the grain storage barrel, and the weighing assembly comprises a fixing base fixed to the inner wall of the harvester granary and a weighing sensor on the fixing base; a tilt angle sensor is arranged in the bottom plate at the top of the weighing sensor; the controller is in signal connection with the weighing sensor and the tilt angle sensor, and the controller is used for filtering an output value of the weighing sensor in real time to obtain a stable weight measurement value; a tilt angle compensation model is adopted to correct the filtered weight measurement value, and a corrected weight measurement value is obtained; the weight value of the constant-volume material is obtained through calculation according to the corrected weight measurement value and the weight value in the no-load state, the volume weight is calculated and output, and the volume weight automatic detection device can stably operate in the complex field environment and is high in detection accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of bulk density detection, and in particular to an integrated, adaptively calibrated, automatic grain bulk density detection device and method. Background Art

[0002] Bulk density refers to the mass of a material per unit volume and is commonly used to describe the bulk density of grain or other granular materials. Grain bulk density is one of the key parameters for measuring grain quality in agricultural production, and is closely related to processes such as grain flowability, storage, and transportation. Existing technologies use automated devices and electronic weighing equipment to rapidly measure grain bulk density, reducing manual intervention and improving measurement efficiency and safety. However, current bulk density measurement devices mostly perform measurements after grain harvest, during quality inspection and acceptance, and lack adaptability to the complex working conditions of dynamic field operations.

[0003] During the harvesting process, automatic detection of bulk density is crucial for yield monitoring and other agricultural tasks. For example, yield monitoring is usually based on volume measurement, where the volume of the grain pile is measured by photoelectric sensors, and the yield is calculated based on the bulk density. Due to the lack of devices that can directly measure the bulk density of grains during harvesting operations, moisture sensors are usually used to correct the default bulk density of the grain pile. This method is an indirect measurement, and the accuracy and stability of moisture sensors are poor, which reduces the accuracy of yield monitoring. Therefore, direct measurement of bulk density is particularly important in improving the accuracy of yield monitoring. However, the implementation of automatic bulk density detection in a harvesting operation environment faces many challenges. Existing research has mainly focused on indoor environments and has not yet effectively adapted to the actual needs of field harvesting operations. Therefore, the development of high-precision grain bulk density detection devices and methods that can operate stably in complex field environments to achieve automatic detection of grain bulk density and ensure the accuracy of bulk density measurement has become an urgent problem to be solved. Summary of the Invention

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

[0005] The present invention provides an integrated grain bulk density automatic detection device with adaptive calibration, which is located in the grain bin of the harvester and includes a grain storage barrel, a leveling assembly, a grain unloading assembly, a weighing assembly, and a controller. The leveling assembly is located on the top of the grain storage barrel, and the weighing assembly is located at the bottom of the grain storage barrel. The weighing assembly includes a fixing seat fixed to the inner wall of the grain bin of the harvester and a weighing sensor located on the fixing seat; a bottom plate is fixed on the top of the weighing sensor, and an inclination sensor is provided in the bottom plate; the grain unloading assembly is located on the bottom plate and is used to move the grain storage barrel upward, thereby generating a gap between the bottom of the grain storage barrel and the bottom plate for unloading grain; and the grain unloading assembly is further used to reset the grain storage barrel after unloading is completed; The controller is connected to the weighing sensor and the inclination sensor signal. The controller is used to obtain the output value of the weighing sensor when the grain storage barrel is fully loaded; 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 obtain the detection value of the inclination sensor, and use the inclination compensation model to correct the filtered weight measurement value to obtain a corrected weight measurement value; the controller is also used to obtain the weight value collected by the weighing sensor in the empty state after unloading the grain; and to calculate the weight value of the fixed volume material through the corrected weight measurement value and the weight value in the empty state, and then calculate and output the bulk density through the volume of the grain storage barrel.

[0006] Preferably, when filtering the output value of the weighing sensor in real time, the method is as follows: First, a linear discrete-time system expression is constructed based on the filtered estimated value of the true weight of the grain storage barrel at time k, the load cell gain control value at time k, random disturbance or noise, the state transfer matrix α, and the control input matrix β; then, a linear discrete-time system expression is constructed based on the original weight value of the load cell at time k containing noise, the measurement matrix and the load cell's own noise to derive the system's measured value; 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; Perform gain adjustment based on standardized residuals. When performing gain adjustment based on standardized residuals, first calculate the standardized residuals and their covariance to construct the outlier detection conditions; when an outlier is detected, dynamically modify the Kalman gain based on the standardized residuals to suppress the influence of abnormal measurement values; The dynamic measurement process of the weighing sensor is expressed as a discrete time system. The optimal estimated value of the weighing signal is output through the alternating iteration of detection time update and measurement value update.

[0007] Preferably, when performing the standard Kalman filter iteration, the current state is predicted based on the previous state to obtain the corresponding covariance, measurement matrix H, and transpose H of the measurement matrix H. T , the covariance matrix of the load cell's own noise and The inverse matrix of is used to calculate the Kalman filter gain; The current state is predicted based on the previous state, the measurement matrix H, the original weight value of the weighing sensor containing noise at time k, and the Kalman filter gain, and the optimal estimated value of the state at time k is calculated; The covariance of the result, measurement matrix H and Kalman filter gain are obtained by predicting the current state based on the previous state, and the covariance update is calculated.

[0008] Preferably, the difference between the weight measured by the weighing sensor and the predicted weight is first obtained. and the residual covariance matrix ,In the residual feature extraction unit, the standardized residual and its covariance are calculated; According to the threshold coefficient d = 2.5 to 3.5, preferably d = 3.0, in the adaptive threshold judgment unit, the outlier detection condition is constructed: ;according to , in the gain adjustment execution unit, when an outlier is detected, the Kalman gain is dynamically modified: .

[0009] Preferably, when the tilt compensation model is used to correct the filtered weight measurement value, when the tilt angle between the weighing sensor and the bottom plate is 0°, no tilt compensation is required, and the output value of the weighing sensor is the grain weight value; when the tilt angle between the weighing sensor and the bottom plate is 0°, no tilt compensation is required, and the output value of the weighing sensor is the grain weight value; When the error is compensated, the error is compensated according to the weighing error compensation formula. , in is the output of the weighing sensor after filtering, is the tilt compensation coefficient, is the model correction value.

[0010] Preferably, the tilt compensation coefficient K and model correction values The inclination angle θ is known, covering the range of 0°~20°, and the calibration points and true weight are set at intervals of 1°. , under the condition of covering 10%-100% of the full scale of the weighing sensor and setting the calibration points at intervals of 20%, the output of the weighing sensor after filtering is collected ,Based on the inclination compensation formula, the experimental data are fitted using the least squares method, and the optimization is obtained by minimizing the sum of squares of the residuals between the theoretical value and the actual value.

[0011] Preferably, there are three weighing sensors in total, and they are evenly distributed along the circumferential direction with the point on the centerline axis of the grain storage barrel as the center.

[0012] Preferably, a three-axis acceleration sensor is embedded in the tilt sensor.

[0013] Preferably, the diameter of the bottom plate is smaller than the outer diameter of the grain storage barrel and larger than the inner diameter of the grain storage barrel; and / or the outer diameter of the fixing seat is smaller than the diameter of the bottom plate.

[0014] The present invention also discloses a detection method of the above-mentioned integrated grain bulk density automatic detection device with adaptive calibration, comprising the following steps: During the harvesting process, grains accumulate in the grain bin and fill the grain storage barrel. After the harvester is fully loaded and unloading is completed, the scraping assembly scrapes the grains on the grain storage barrel to make the grain surface flush with the top surface of the grain storage barrel. The initial weight signal is collected by the weighing sensor and filtered in real time to obtain a stable weight measurement value; Based on the inclination angle measured by the inclination sensor, the output of the weighing sensor is corrected through the inclination compensation model to eliminate the influence of the inclination angle on the weighing, and the corrected grain weight value is calculated; After weighing is completed, the grain unloading assembly starts working, the gap between the grain storage barrel and the bottom plate leaks out, the grain is unloaded, and then the grain unloading assembly is reset; The weight value in the no-load state is collected through the weighing sensor; According to the corrected grain weight value and the weight value in the empty state, the weight value of the fixed volume material is obtained, and then the bulk density measurement value is obtained.

[0015] Compared with existing technologies, the present invention offers the following advantages: The integrated automatic grain bulk density detection device combines multiple functions, including automatic sampling, scraping and removing excess grain, and bulk density calculation, into a compact structure. This design not only effectively addresses the limited installation space in combine harvester silos but also improves operational convenience. In particular, the automatic scraping mechanism precisely removes excess grain, ensuring consistent volume. This provides an efficient and reliable automatic grain bulk density measurement solution for complex harvesting operations.

[0016] When calculating bulk density, the present invention targets the noise interference caused by mechanical vibration, uneven ground and external impact during field operations by adjusting the filter gain in real time, optimizing the weight distribution of state estimation and measurement values, and combining adaptive covariance matrix optimization to enhance the robustness of the algorithm to nonlinear interference, effectively suppress Gaussian white noise and eliminate abnormal wild values; based on the recursive calculation characteristics, low-latency dynamic weighing processing is achieved to ensure the real-time performance of the system. On this basis, the inclination compensation model of the present invention corrects the inclination error in real time through gravity decomposition and three-axis acceleration sensor calibration, further improving the system's comprehensive suppression ability for dynamic vibration, inclination change and external impact, achieving high-precision and high-stability weighing measurement under complex working conditions, and significantly improving the reliability of grain bulk density measurement in field operation environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a structural schematic diagram of an integrated grain bulk density automatic detection device with adaptive calibration according to the present invention.

[0018] Figure 2 It is a schematic structural diagram of the scraping assembly of the present invention.

[0019] Figure 3 It is a structural schematic diagram of the grain unloading component of the present invention.

[0020] Figure 4It is a schematic structural diagram of the weighing component of the present invention.

[0021] Figure 5 This is a front view and a partially enlarged view of the integrated grain bulk density automatic detection device with adaptive calibration of the present invention.

[0022] Figure 6 This is a flow chart of the integrated grain bulk density automatic detection method with adaptive calibration of the present invention.

[0023] Figure 7 Flowchart for obtaining stable weight measurements for the present invention.

[0024] Figure 8 Flowchart for obtaining corrected weight measurements according to the present invention.

[0025] Description of reference numerals: 3. Grain storage barrel; 2. Scraping assembly; 201. Scraper; 202. Cross fixing bracket; 203. DC motor; 204. Motor bracket; 3. Grain unloading assembly; 301. Linear electric push rod; 302. Cone base; 4. Weighing assembly; 401. Fixing seat; 402. Weighing sensor; 5. Bottom plate; 6. Inclination sensor. DETAILED DESCRIPTION

[0026] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning understood by persons of ordinary skill in the art to which the invention pertains. The terms "first", "second" and similar terms used in the present invention do not indicate any order, quantity or importance, but are merely used to distinguish different components. The terms "include" or "comprise" and similar words mean that the elements or objects appearing before "include" or "comprise" encompass the elements or objects listed after "include" or "comprise" and their equivalents, and do not exclude other elements or objects. The terms "connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms "upper", "lower", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0028] The present invention discloses an integrated grain bulk density automatic detection device with adaptive calibration, which is located in the grain bin of the harvester and includes a grain storage barrel 1, a leveling component 2, a grain unloading component 3, a weighing component 4 and a controller. The leveling component 2 is located on the top of the grain storage barrel 1, and the weighing component 4 is arranged at the bottom of the grain storage barrel 1. The weighing component 4 includes a fixing seat 401 fixed to the inner wall of the grain bin of the harvester and a weighing sensor 402 located on the fixing seat 401; a bottom plate 5 is fixed on the top of the weighing sensor 402, and an inclination sensor 6 is provided in the bottom plate 5. As a preferred embodiment, a three-axis acceleration sensor is embedded in the inclination sensor 6. The grain unloading component 3 is located on the bottom plate 5 and is used to move the grain storage barrel 1 upward, thereby generating a gap between the bottom of the grain storage barrel 1 and the bottom plate 5 for unloading grain. The grain unloading component 3 is also used to reset the grain storage barrel 1 after unloading is completed. As an example, if Figure 1 and Figure 2 As shown, the scraping assembly 2 includes a brush scraper 201, a cross-fixing frame 202, a connection hole for the grain storage barrel 1, a mounting hole for the motor bracket 204, a DC motor 203, an upper pin hole, and a 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 make a circular motion above the cross-fixing frame 202, thereby scraping off excess grain on the surface of the cross-fixing frame 202, removing accumulation, and ensuring that the target grain volume is consistent with the volume of the grain storage barrel 1. The cross-fixing frame 202 is fixed to the grain storage barrel 1 as a whole by bolts connected to the connection hole of the grain storage barrel 1. A plurality of mounting holes are opened on its surface, and the motor bracket 204 can be fixed to the cross-fixing frame 202 through the mounting holes of the motor bracket 204. The DC motor 203 is located inside the motor bracket 204 and is also fixed by means of the mounting holes on the surface of the cross-fixing frame 202.

[0029] As an example, the grain unloading component 3 of this embodiment is as follows Figure 3 As shown, the grain unloading assembly 3 includes a linear electric push rod 301; the linear electric push rod 301 is connected to the motor bracket 204 through the upper end pin hole, and is connected to the truncated cone base 302 through the lower end pin hole. The linear electric push rod 301 can perform linear telescopic movement in the vertical direction. When the grain weighing is completed, the linear electric push rod 301 extends upward, lifts the pile and leveling assembly 2, and drives the grain storage barrel 1 to lift up, so that a gap appears between the grain storage barrel 1 and the bottom plate 5, thereby realizing grain unloading. The inclination sensor 6 is installed on the bottom plate 5 and is used to measure the inclination angle of the device based on the bottom plate 5. The bottom plate 5 is fixed to the truncated cone base 302 through a circular array of bottom plate 5 fixing threaded holes.

[0030] As an example, the weighing component 4 of this embodiment is as follows Figure 4 As shown, the weighing assembly 4 includes a weighing sensor 402 and a fixed base. Figure 4As 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, while the lower surface is fixedly connected to the fixed base. Four symmetrical mounting holes are provided on the outer circumference of the fixed base. These holes are used to secure the capacity detection device to the profile bracket, which in turn secures the profile bracket to the inner wall of the harvester's grain bin. The fixed base also includes multiple through-holes for wiring.

[0031] like Figure 5 As shown, the diameter of the bottom plate 5 is smaller than the outer diameter of the grain storage barrel 1 and larger than its inner diameter. On the one hand, it acts as a limiter when the linear electric push rod 301 drives the grain storage barrel 1 back to its original position; on the other hand, it ensures that no grain remains at this position and does not affect the return of the grain storage barrel 1. The fixed base is equipped with an outer baffle to protect the weighing sensor 402. There is a gap between the upper edge of the fixed base and the bottom plate 5 to avoid affecting the weighing value. At the same time, the diameter of the outer baffle is smaller than the diameter of the bottom plate 5 and the height is less than the height of the weighing sensor 402, forming an "inward concave" design, which can effectively prevent grains from getting stuck in the gap and affecting the weighing.

[0032] The controller of this embodiment is connected to the signals of the weighing sensor 402 and the inclination sensor 6. The controller is used to obtain the output value of the weighing sensor 402 when the grain storage barrel 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 obtain the detection value of the inclination sensor 6, and uses the inclination compensation model to correct the filtered weight measurement value to obtain the corrected weight measurement value; the controller is also used to obtain the weight value of the weighing sensor 402 in the empty state after unloading the grain; and calculates the weight value of the fixed volume material through the corrected weight measurement value and the weight value in the empty state, and then calculates and outputs the bulk density through the volume V of the grain storage barrel 1.

[0033] As a preferred embodiment, when filtering the output value of the weighing sensor 402 in real time, the method is as follows: First, a linear discrete-time system expression is constructed based on the filtered estimated value of the true weight of the grain storage barrel 1 at time k, the gain control value of the weighing sensor 402 at time k, random disturbance or noise, the state transfer matrix α and the control input matrix β; then, a linear discrete-time system expression is constructed based on the original weight value of the weighing sensor 402 at time k containing noise, the measurement matrix and the load cell 402's own noise to obtain the system's measured value; 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; Perform gain adjustment based on standardized residuals. When performing gain adjustment based on standardized residuals, first calculate the standardized residuals and their covariance to construct the outlier detection conditions; when an outlier is detected, dynamically modify the Kalman gain based on the standardized residuals to suppress the influence of abnormal measurement values; The dynamic measurement process of the weighing sensor 402 is expressed as a discrete time system, and the optimal estimated value of the weighing signal is output through alternating iterations of detection time update and measurement value update.

[0034] As another preferred embodiment, when performing the standard Kalman filter iteration, the covariance corresponding to the result, the measurement matrix H, and the transpose H of the measurement matrix H are obtained by predicting the current state according to the previous state. T , the covariance matrix of the load cell 402's own noise and The inverse matrix of is used to calculate the Kalman filter gain; The current state is predicted based on the previous state, the measurement matrix H, the original weight value of the weighing sensor 402 including noise at time k, and the Kalman filter gain to calculate the optimal estimated value of the state at time k; The covariance of the result, measurement matrix H and Kalman filter gain are obtained by predicting the current state based on the previous state, and the covariance update is calculated.

[0035] As a more preferred embodiment, when performing gain adjustment based on the normalized residual, the difference between the weight measured by the weighing sensor 402 and the predicted weight is first calculated. and the residual covariance matrix ,In the residual feature extraction unit, the standardized residual and its covariance are calculated; According to the threshold coefficient d = 2.5 to 3.5, preferably d = 3.0, in the adaptive threshold judgment unit, the outlier detection condition is constructed: ;according to , in the gain adjustment execution unit, when an outlier is detected, the Kalman gain is dynamically modified: .

[0036] When the tilt compensation model is used to correct the filtered weight measurement value, when the tilt angle between the weighing sensor 402 and the bottom plate 5 is 0°, no tilt compensation is required, and the output value of the weighing sensor 402 is the grain weight value; when the tilt angle between the weighing sensor 402 and the bottom plate 5 is 0°, no tilt compensation is required, and the output value of the weighing sensor 402 is the grain weight value; When the error is compensated, the error is compensated according to the weighing error compensation formula. ; in, is the output of the weighing sensor 402 after filtering, is the tilt compensation coefficient, is the model correction value, is the error value of the weighing sensor 402; is the secant function.

[0037] The tilt compensation coefficient K and the model correction value The inclination angle θ is known, covering the range of 0°~20°, and the calibration points and true weight are set at intervals of 1°. , under the condition of covering 10%-100% of the full scale of the weighing sensor 402 and setting the calibration points at intervals of 20%, the output of the weighing sensor 402 is collected ,Based on the inclination compensation formula, the experimental data are fitted using the least squares method, and the optimization is obtained by minimizing the sum of squares of the residuals between the theoretical value and the actual value.

[0038] The present invention also discloses a detection method of the integrated grain bulk density automatic detection device based on the above-mentioned adaptive calibration, comprising the following steps: Install the above-mentioned integrated grain bulk density automatic detection device with adaptive calibration at a suitable location in the grain bin of the harvester, ensuring that the measuring directions of the weighing sensor 402 and the inclination sensor 6 are aligned with the direction of gravity; During the harvesting process, grain accumulates in the granary and gradually covers the detection device, and some grain kernels fall into the grain storage barrel 1. After the harvester is fully loaded and has finished unloading the grain, the detection device starts, and the DC motor 203 drives the upper scraper 201 to rotate, scraping the grain pile on the upper surface of the grain storage barrel 1 and sweeping away the excess grain to ensure a constant volume of grain in the grain storage barrel 1.

[0039] The initial weight signal is collected by the weighing sensor 402, and the signal is filtered in real time to obtain a stable weight measurement value. ; Tilt compensation and weight calculation: Based on the tilt angle measured by the inclination sensor 6 θ The output value of the weighing sensor 402 is corrected by the adaptive tilt compensation model to eliminate the influence of the tilt on the weighing accuracy, and the corrected weight measurement value is calculated. ; Unloading and returning to position: After weighing is completed, the grain unloading component 3 starts working, the DC electric push rod drives the grain storage barrel 1 to rise, and the grains leak out from the gap between the grain storage barrel 1 and the bottom plate 5, completing the unloading, and then the DC electric push rod retracts and returns to its position; No-load weighing: The weight signal in the no-load state is collected by the weighing sensor 402 ; Calculate the output volume weight: The output signal of the weighing sensor 402 and the inclination sensor 6 is combined and calculated to obtain the weight value of the fixed volume material. , further obtain the bulk density measurement value , unit kg / m 3 .in, is the filtering noise reduction algorithm, is the inclination compensation algorithm, and V is the volume of the constant volume material.

[0040] In this embodiment, the initial weight signal is collected by the weighing sensor 402, and the signal is filtered in real time to obtain a stable weight measurement value. The specific methods are as follows: S1.1, System Modeling; First, a linear discrete-time system expression is constructed based on the filtered estimated value of the true weight of the grain storage barrel 1 at time k, the gain control value of the weighing sensor 402 at time k, the random disturbance or noise, the state transfer matrix α and the control input matrix β; (1); Where: For the system k The state of the moment; For the system k-1 The state of the moment, For the system k-1 The amount of control at each moment; for k-1 System process noise at the moment; and is the system parameter.

[0041] According to the weighing sensor 402 k The original weight value containing noise at the moment, the measurement matrix and the load cell 402's own noise to obtain the system's measured value; (2); Where: is the measured value of the system at time k; is the measurement matrix; Measure the noise for the system.

[0042] S1.2, time domain recursive calculation; Perform a standard Kalman filter iteration.

[0043] The system state update expression is: (3); Where: To use the previous state to predict the current state and get the result; is the optimal result of the previous state, For the system k The amount of control at any moment.

[0044] The covariance prediction is: (4); Where: for The corresponding covariance; for The corresponding covariance; is the transposed matrix of Aa; Q is the covariance of the system process.

[0045] Kalman filter gain calculation: (5); in, H is the measurement matrix, H T is the transpose of the measurement matrix H, R is the covariance matrix of the load cell 402 measurement noise, for The inverse matrix of .

[0046] The best estimated value of the state at time k is: (6); Where, is the original weight value of the weighing sensor 402 at time k including noise, To predict the current state based on the previous state, is the Kalman filter gain; The covariance update expression is: (7); Where, is the identity matrix, for a single measurement model , for The corresponding covariance.

[0047] S1.3, enhanced outlier suppression; Perform gain adjustment based on the normalized residual.

[0048] In the residual feature extraction unit, the standardized residual and its covariance are calculated: (8); (9); Where, is the observation residual at time k; is the residual covariance matrix at time k, is the measured value of the system at time k, R is the covariance matrix of the load cell 402 measurement noise.

[0049] In the adaptive threshold judgment unit, construct the outlier detection condition: (10); Wherein, the threshold coefficient d = 2.5 to 3.5, preferably d = 3.0.

[0050] In the gain adjustment execution unit, the Kalman gain is dynamically modified when an outlier is detected: (11); Where, is the proportionality coefficient, .

[0051] S1.4, iterative optimization outputs the optimal value; The dynamic measurement process of the weighing sensor 402 is expressed as a discrete time system, and the optimal estimation of the weighing signal is achieved through iterative optimization. Preferably, the detection time update and the measurement update are iterated alternately:

[0052] Time update process: (12); (13); Measurement update process: (14); (15); (16); Through the above iterative process, the optimal estimated value of the weighing signal is output: (17); Where C is the output matrix, C=[1,0] is used to extract the weight component, As input to the tilt compensation module.

[0053] It should be pointed out that by assigning values based on observing or querying sensor parameters, we can get , .

[0054] The present invention improves the accuracy of the measurement system by adding outlier judgment (outlier refers to erroneous data detected by the weighing sensor 402 due to the influence of vibration or noise during the weighing process) and eliminating the outlier. The algorithm of the present invention processes the output value of the weighing sensor 402 to effectively suppress vibration noise and improve signal stability. The filtered output value is input into the adaptive tilt compensation module for real-time compensation calculation.

[0055] The tilt compensation model of this embodiment is based on the gravity decomposition model, which is constructed by calibrating the tilt angle through a three-axis acceleration sensor and combining it with the Taylor expansion method.

[0056] S2.1, establishing the output model of the weighing sensor 402; When the inclination angle between the weighing sensor 402 and the mounting plate is 0°, the weight of the grain in the grain storage barrel 1 is Acting vertically on the mounting plate, the output of the filtered load cell 402 is and The value of is the same, and its expression is: (18); When the plane where the weighing sensor 402 is located has an inclination angle with the horizontal plane At this time, the scale is in a tilted state, and the gravity decomposition model is introduced. The grain weight Breaks down to: (19); Where, is the inclination angle of the weighing sensor 402 to the horizontal plane; is the stress in the vertical direction; is the horizontal stress, W m is the corrected weight measurement.

[0057] From the perspective of force analysis, is the main force direction of the sensor, which has a good linear relationship. The force direction is parallel to the load cell 402, and its output is affected by the tilt direction of the load cell 402, the weight of the grain, and the and inclination Due to the influence of factors such as , the output of the weighing sensor 402 is nonlinear, so its output expression is: (20); Where, for The nonlinear output function caused by .

[0058] S2.2, establishment of weighing error model; Considering the presence of an inclination state, the error expression of the weighing sensor 402 is: (twenty one); S2.3, Inclination Calibration Method Based on Triaxial Accelerometer; As a preferred solution, the tilt sensor 6 used in this embodiment has a built-in three-axis acceleration sensor, which can measure the acceleration in the X, Y, and Z axes, thereby calculating the angle between each axis and the direction of gravity.

[0059] (twenty two); Where, 、 、 are the three-axis acceleration components; and are the inclination angles between the object and the horizontal plane of the X and Y axes respectively; the angle between the Z axis and the acceleration of gravity is , that is, the theoretical inclination .

[0060] During the actual installation process, the acceleration sensor has an initial offset , eliminating the initial value and obtaining the actual inclination angle of the weighing sensor 402: (twenty three); Where: is the initial inclination angle of the weighing sensor 402, is the actual inclination angle of the weighing sensor 402.

[0061] S2.4. Establishment of tilt correction compensation value and tilt automatic compensation model; In one embodiment, the tilt automatic compensation model includes the following steps: At the inclination Under the conditions of for: (twenty four); Where: is the output of the weighing sensor 402 after filtering, is the secant function.

[0062] At the inclination Under the condition of , the sensor error expression is linearly processed by Taylor decomposition. The expression is: (25); Where: express is a second-order infinitesimal. But Grain weight value The influence is very small and can usually be ignored. The weighing error compensation formula is obtained by simplifying the model:

[0063] (26); Taking into account the weighing error, the model correction value is introduced , the analytical model obtains The expression is: (27); Taking into account It is not the main force direction of the load cell 402, and its sensitivity is much lower than , further simplify the model to obtain: (28); (29); Introducing model correction values , and the final weighing error compensation formula is obtained: (30); in, is the tilt compensation coefficient, is the model correction value.

[0064] Preferably, the measuring range of the inclination sensor 6 used is The measurement accuracy is 0.1°, the data refresh rate is 100Hz, and according to the error range of the inclination sensor 6, the secant function of the weight error compensation formula is Perform Taylor decomposition to obtain the approximate formula: (31); in, .

[0065] Furthermore, as a preferred solution, in the calibration module, at a known inclination angle θ (covering the range of 0°~20°, with calibration points set at intervals of 1°) and the actual weight Under the condition of covering 10%-100% of the full scale of the weighing sensor 402, the output of the weighing sensor 402 after filtering is collected. Based on the tilt compensation formula, the least square method is used to fit the experimental data, and the tilt compensation coefficient is optimized by minimizing the residual square sum of the theoretical value and the actual value. K and model correction values .

[0066] The present 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. In combination with an adaptive tilt compensation model, a weight compensation value is obtained, thereby accurately calculating the bulk density of grains, significantly improving the measurement accuracy and stability under complex working conditions.

[0067] 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 grain bulk density automatic detection device with adaptive calibration, characterized in that: Located in the grain bin of the harvester, it includes a grain storage barrel, a leveling assembly, a grain unloading assembly, a weighing assembly and a controller. The leveling assembly is located on the top of the grain storage barrel, and the weighing assembly is located at the bottom of the grain storage barrel. The weighing assembly includes a fixing seat fixed to the inner wall of the grain bin of the harvester and a weighing sensor located on the fixing seat; a bottom plate is fixed on the top of the weighing sensor, and an inclination sensor is provided in the bottom plate. The grain unloading assembly is located on the bottom plate and is used to move the grain storage barrel upward, thereby creating a gap between the bottom of the grain storage barrel and the bottom plate for unloading grain. The grain unloading assembly is also used to reset the grain storage barrel after unloading is completed. The controller is connected to the weighing sensor and the inclination sensor signals. The controller is used to obtain the output value of the weighing sensor when the grain storage bucket is fully loaded; 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 obtain the detection value of the inclination sensor, and use the inclination compensation model to correct the filtered weight measurement value to obtain a corrected weight measurement value; the controller is also used to obtain the weight value of the weighing sensor in the empty state after unloading the grain; The weight value of the fixed volume material is calculated by the corrected weight measurement value and the weight value in the empty state, and then the bulk density is calculated and output through the volume of the grain storage barrel.

2. The integrated grain bulk density automatic detection device with adaptive calibration according to claim 1, characterized in that: When filtering the output value of the weighing sensor in real time, the method is as follows: First, a linear discrete-time system expression is constructed based on the filtered estimated value of the true weight of the grain storage barrel at time k, the load cell gain control value at time k, random disturbance or noise, the state transfer matrix α, and the control input matrix β; then, a linear discrete-time system expression is constructed based on the original weight value of the load cell at time k containing noise, the measurement matrix and the load cell's own noise to derive the system's measured value; 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; Perform gain adjustment based on standardized residuals. When performing gain adjustment based on standardized residuals, first calculate the standardized residuals and their covariance to construct the outlier detection conditions; when an outlier is detected, dynamically modify the Kalman gain based on the standardized residuals to suppress the influence of abnormal measurement values; The dynamic measurement process of the weighing sensor is expressed as a discrete time system. The optimal estimated value of the weighing signal is output through the alternating iteration of detection time update and measurement value update.

3. The integrated grain bulk density automatic detection device with adaptive calibration according to claim 1, characterized in that: When performing standard Kalman filter iteration, the current state is predicted based on the previous state to obtain the corresponding covariance, measurement matrix H, and transpose H of the measurement matrix H. T , the covariance matrix of the load cell's own noise and The inverse matrix of is used to calculate the Kalman filter gain; The current state is predicted based on the previous state, the measurement matrix H, the original weight value of the weighing sensor containing noise at time k, and the Kalman filter gain, and the optimal estimated value of the state at time k is calculated; The covariance of the result, measurement matrix H and Kalman filter gain are obtained by predicting the current state based on the previous state, and the covariance update is calculated.

4. The integrated grain bulk density automatic detection device with adaptive calibration according to claim 1, characterized in that: First, the difference between the weight measured by the weighing sensor and the predicted weight and the residual covariance matrix ,In the residual feature extraction unit, the standardized residual and its covariance are calculated; According to the threshold coefficient d = 2.5 to 3.5, preferably d = 3.0, in the adaptive threshold judgment unit, the outlier detection condition is constructed: ;according to , in the gain adjustment execution unit, when an outlier is detected, the Kalman gain is dynamically modified: .

5. The integrated grain bulk density automatic detection device with adaptive calibration according to claim 1, characterized in that: When the tilt compensation model is used to correct the filtered weight measurement value, when the tilt angle between the weighing sensor and the bottom plate is 0°, no tilt compensation is required, and the output value of the weighing sensor is the grain weight value; when the tilt angle between the weighing sensor and the bottom plate is 0°, no tilt compensation is required, and the output value of the weighing sensor is the grain weight value; When the error is compensated, the error is compensated according to the weighing error compensation formula. ,in is the output of the weighing sensor after filtering, is the tilt compensation coefficient, is the model correction value.

6. The integrated grain bulk density automatic detection device with adaptive calibration according to claim 5, characterized in that: The tilt compensation coefficient K and model correction values The inclination angle θ is known, covering the range of 0°~20°, and the calibration points and true weight are set at intervals of 1°. , under the condition of covering 10%-100% of the full scale of the weighing sensor and setting the calibration points at intervals of 20%, the output of the weighing sensor after filtering is collected ,Based on the inclination compensation formula, the experimental data are fitted using the least squares method, and the optimization is obtained by minimizing the sum of squares of the residuals between the theoretical value and the actual value.

7. The integrated automatic grain bulk density detection device with adaptive calibration according to claim 1, characterized in that: There are three weighing sensors in total, and they are evenly distributed along the circumferential direction with the point on the centerline axis of the grain storage barrel as the center.

8. The integrated grain bulk density automatic detection device with adaptive calibration according to claim 1, characterized in that: The tilt sensor is embedded with a three-axis acceleration sensor.

9. The integrated grain bulk density automatic detection device with adaptive calibration according to claim 1, characterized in that: The diameter of the bottom plate is smaller than the outer diameter of the grain storage barrel and larger than the inner diameter of the grain storage barrel; and / or the outer diameter of the fixing seat is smaller than the diameter of the bottom plate.

10. The detection method of the integrated grain bulk density automatic detection device with adaptive calibration according to any one of claims 1 to 9, characterized in that: The following steps are involved: During the harvesting process, grains accumulate in the grain bin and fill the grain storage barrel. After the harvester is fully loaded and unloading is completed, the scraping assembly scrapes the grains on the grain storage barrel to make the grain surface flush with the top surface of the grain storage barrel. The initial weight signal is collected by the weighing sensor and filtered in real time to obtain a stable weight measurement value; Based on the inclination angle measured by the inclination sensor, the output of the weighing sensor is corrected through the inclination compensation model to eliminate the influence of the inclination angle on the weighing, and the corrected grain weight value is calculated; After weighing is completed, the grain unloading assembly starts working, the gap between the grain storage barrel and the bottom plate leaks out, the grain is unloaded, and then the grain unloading assembly is reset; The weight value in the no-load state is collected through the weighing sensor; According to the corrected grain weight value and the weight value in the empty state, the weight value of the fixed volume material is obtained, and then the bulk density measurement value is obtained.

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