Generating ground truth field data and calibrating a harvester loss detection system

Abstract

[00101] A combine harvester has a loss detection system that detects losses and generates a signal indicating the detected losses. A metering system feeds in a known quantity of additive material, which is detected by the loss detection system. A calibration system compares the signal generated by the loss detection system with the known quantity of material fed in and calibrates the loss detection system based on the known quantity of material fed in and the signal from the loss detection system.

Description

DESCRIPTION

[0001] This description concerns agricultural equipment. In particular, it concerns the acquisition of field-measured loss values ​​("ground truth") and the calibration of a combine harvester loss detection system. STATE OF THE ART

[0002] There are many different types of agricultural harvesting vehicles. One example of an agricultural harvesting vehicle is a combine harvester.

[0003] Combine harvesters often include a header that engages with the crop in a field, cutting it and conveying it towards the harvester's processing units. The combine includes a threshing / separating system and a sorting / cleaning system. Once the crop has passed through the threshing / separating and sorting / cleaning systems, it is typically conveyed to a grain tank.

[0004] Many combine harvesters are equipped with loss sensors mounted on the machine to experimentally measure the amount of grain loss that occurs during the harvesting process. In some cases, one or more loss sensors are mounted to measure grain loss at the threshing / separator system. Other loss sensors may be mounted on the combine to measure loss within the sorting / cleaning system.

[0005] The above explanation is provided for general background information only and is not intended to be used as an aid in determining the scope of protection of the claimed subject matter. SUMMARY

[0006] A combine harvester has a loss detection system that records losses and generates a signal indicating the detected losses. A metering system feeds in a known quantity of additive material, which is detected by the loss detection system. A calibration system compares the signal generated by the loss detection system with the known quantity of material fed in and calibrates the loss detection system based on the known quantity of material fed in and the signal from the loss detection system.

[0007] This summary is provided to introduce, in simplified form, a selection of concepts that are further described in the detailed description below. This summary is not intended to identify key features or essential characteristics of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of protection of the claimed subject matter. The claimed subject matter is not limited to implementations that overcome some or all of the disadvantages noted in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS Fig. Figure 1 is a partly pictorial, partly schematic representation of an example of a combine harvester. Fig. Figure 2 is a block diagram of a section of the combine harvester. Fig. Figure 3 shows a flowchart illustrating an example of how a loss processing system works. Fig. Figure 4 is a block diagram that shows an example of a loss processing system in more detail. The Fig. 5A and Fig. 5B (here together as Fig. Figure 5 shows a flowchart that illustrates a more detailed operation of a loss processing system. Fig. Figure 6 shows a block diagram illustrating an example of a combine harvester used in a remote server architecture. The Fig. 7, Fig. 8 and Fig. Figure 9 shows examples of mobile devices that can be used in the architectures and systems shown in other figures. Fig. Figure 10 is a block diagram of an example of a computing environment that can be used in the architectures and systems shown in other figures. DETAILED DESCRIPTION

[0008] For a better understanding of the principles of this disclosure, reference is now made to the examples illustrated in the drawings, which are described in specific terms. It is understood, however, that these are not to be interpreted as limiting the scope of the disclosure. Any modifications and further adaptations to the described devices, systems, and methods, and any further application of the principles of this disclosure that are normally apparent to a person skilled in the art in the field to which the disclosure relates, are all included. In particular, it is fully taken into account that the features, components, and / or steps described in relation to one example can be combined with the features, components, and / or steps described in relation to other examples in this disclosure.As explained above, combine harvesters often have a loss detection system that includes one or more different loss sensors mounted on the combine to detect grain loss or other crop loss in the threshing / separator system and the sorting / cleaning system and to generate a loss sensor signal, as well as a loss calculation system that uses a transfer function or other logic to estimate or calculate losses based on the loss sensor signal. Although the present explanation is applicable to all types of loss sensors, in one example the loss sensors are impact sensors that detect grain impacts in the residue material blown out of the threshing / separator system and the sorting / cleaning system. The loss sensors generate a signal that identifies the detected loss.For example, impact sensors generate a signal that indicates the number of grain impacts per unit of time or per distance traveled. The transfer function or other algorithm processes the sensor signal to produce an output that indicates, for example, the loss in units of bushels per acre. Calibrating the loss monitoring system to produce an accurate loss signal can prove difficult. One method for calibrating loss sensors is to establish a "ground truth" loss value that is indicative of the actual grain loss in a field and compare this "ground truth" loss value to the output value produced by the loss monitoring system that is indicative of the detected or estimated loss. However, this creates significant problems.Current methods for determining a "ground truth" loss value are costly and time-consuming. These methods often require placing troughs or other collection containers in the field to gather lost crop as the harvester passes over the affected area. The amount of crop in the troughs must then be quantified to determine a loss value. Other methods for determining a "ground truth" loss value include using screen cleaners or driving a separate loss-monitoring machine behind a harvester. These methods are all time-consuming and expensive, and scaling the "ground truth" values ​​across fields and machines often proves very difficult.

[0009] Furthermore, many sources of error exist in loss detection. Environmental and system variables can affect the detected loss, so a "ground truth" loss value must be determined under many different environmental and system conditions to create a robust model for estimating losses based on loss sensor signals. Environmental variables that can affect the accuracy of a loss detection system include non-grain content (NPC) conditions, crop moisture, soil moisture, terrain (for example, whether the combine is traveling uphill, downhill, or at a side slope), ambient weather conditions, and crop grain properties.System variables that can affect the accuracy of a loss detection system include sensor variables such as variance between different sensors, deterioration of the sensor's detection performance over time, sensor saturation in high-loss environments, effects of overflow, and other factors.

[0010] Thus, the present description describes a system in which a known quantity of grain or a grain-like material (such as wood pellets, other pellets, or other substitute material) is fed into a material stream that is monitored by a loss detection system. The loss signal value is also monitored to determine how the signal value changes based on the injection of a known quantity of loss material. The change in the loss signal caused by the injection of the known quantity of material can be used to calibrate the sensor itself (sensor settings, sensitivity, and other parameters) and / or to calibrate the transfer function or logic to more accurately estimate the actual loss based on the loss sensor signal. In one example, the injection of the known quantity of material can occur while the combine harvester is outside the crop (or...(not harvesting) to establish a baseline value for sensor calibration. Feeding a known quantity of material can also be repeated under varying conditions while the combine is within the crop (i.e., harvesting) to calibrate the crop loss detection system to account for different conditions. In another example, the feeding of a known quantity of material can be repeated, and the amount of material fed can be changed (e.g., increased) during each repetition. This repetition and variation in the amount of material fed can also be used to calibrate the loss detection system. Furthermore, the material can be fed directly into the loss sensor itself or into a material stream reaching the loss sensor.The calibration data can also be stored for use in training estimation models or for the design of sensor systems, among other things.

[0011] Fig. Figure 1 is a partly pictorial, partly schematic representation of a self-propelled agricultural harvesting vehicle 100. In the example shown, the agricultural harvesting vehicle 100 is a combine harvester.

[0012] As in Fig. As shown in Figure 1, the agricultural harvesting vehicle 100 includes, for illustrative purposes, an operator's cab 101, which can have a variety of different operator interface mechanisms for controlling the agricultural harvesting vehicle 100. The agricultural harvesting vehicle 100 comprises front equipment, e.g., a harvesting header 102 (with an associated spindle 164), and a cutting unit, generally designated 104. The agricultural harvesting vehicle 100 also includes an inclined conveyor 106, a feed accelerator 108, and a threshing unit, generally designated 110. The inclined conveyor 106 and the feed accelerator 108 are part of a material handling subsystem 125. The harvesting header 102 is pivotally coupled to a frame 103 of the agricultural harvesting vehicle 100 along a pivot axis 105.One or more actuators 107 drive the movement of the harvesting header 102 around the axis 105 in the direction generally indicated by the arrow 109. Thus, a vertical position of the harvesting header 102 (the harvesting header height) above the ground 111 over which the harvesting header 102 travels can be controlled by actuating the actuator 107. Although in . Fig. Not shown in Figure 1, the agricultural harvesting vehicle 100 may also include one or more actuators that are operated to apply a tilt angle, a roll angle, or both to the header 102 or sections of the header 102. The tilt angle refers to the angle at which the cutting unit 104 engages the crop. The tilt angle is increased, for example, by controlling the header 102 so that a remote edge 113 of the cutting unit 104 is directed closer to the ground. The tilt angle is decreased by controlling the header 102 so that the remote edge 113 of the cutting unit 104 is further away from the ground. The roll angle refers to the orientation of the header 102 about the longitudinal axis of the agricultural harvesting vehicle 100 from front to back.

[0013] The threshing unit 110 includes, for illustrative purposes, a threshing rotor 112 and a series of threshing concaves 114. The agricultural harvesting vehicle 100 further includes a separator 116. The agricultural harvesting vehicle 100 also includes a cleaning subsystem or sieve box (collectively referred to as cleaning subsystem 118), which includes a cleaning blower 120, a chaff sieve 122, and a fine sieve 124. The material handling subsystem 125 further includes an unloading drum 126, a return elevator 128, a clean grain elevator 130, as well as an unloading auger 134 and a discharge spout 136. The clean grain elevator conveys clean grain into a clean grain tank 132. The agricultural harvesting vehicle 100 also includes a residue handling system 138, which can include a chopper 140 and a spreader 142. The agricultural harvesting vehicle 100 also includes a drive system with a motor that powers ground-contacting components 144, such as...B. drives wheels or tracks. In some examples, a combine harvester within the scope of the present disclosure may have more than one of the aforementioned subsystems. In some examples, the agricultural harvesting vehicle 100 may have a left and right cleaning subsystem, separators, etc., which are located in . Fig. 1 are not shown.

[0014] In operation and in overview, the agricultural harvesting vehicle 100 is shown moving through a field in the direction indicated by arrow 147. As the agricultural harvesting vehicle 100 moves, the harvesting header 102 (and the associated spindle 164) grasps the crop to be harvested and transports it towards the cutting unit 104. An operator of the agricultural harvesting vehicle 100 can be a local human operator, a remote human operator, or an automated system. An operator command is a command issued by an operator. The operator of the agricultural harvesting vehicle 100 can set one or more height, tilt, or roll angle settings for the harvesting header 102. For example, the operator enters one or more settings into a control system that controls the actuator 107.The control system can also receive a setting from the operator to define the tilt and roll angles of the harvesting header 102 and implement the entered settings by controlling associated actuators (not shown) that serve to change the tilt and roll angles of the harvesting header 102. The actuator 107 holds the harvesting header 102 at a height above the ground 111, which is based on a height setting, and, if necessary, at desired tilt and roll angles.

[0015] To return to the description of the operation of the agricultural harvesting vehicle 100, after the crop material has been cut by the cutting unit 104, the separated crop material is conveyed via a conveyor belt in the inclined conveyor 106 to the feed accelerator 108, which accelerates the crop material into the threshing unit 110. The crop material is threshed by the rotor 112, which rotates the crop against threshing concaves 114. The threshed crop material is moved by a separator rotor in the separator 116, with some of the residue being moved by the discharge drum 126 towards the residue sorting system 138. The portion of the residue that is conveyed to the residue sorting system 138 is chopped by the residue chopper 140 and distributed on the field by the spreader 142. In other configurations, the residue is released in a swath from the agricultural harvesting vehicle 100.In other examples, the residue part system may include 138 weed seed eliminators (not shown), such as seed baggers or other seed collectors, or seed crushers or other seed destroyers.

[0016] The grain falls into the cleaning subsystem 118. The chaff screen 122 separates some larger material particles from the grain, and the fine screen 124 separates some finer material particles from the clean grain. The clean grain falls onto a screw conveyor, which moves the grain to the inlet end of the clean grain elevator 130. The clean grain elevator 130 moves the clean grain upwards and stores it in the clean grain tank 132. The residue is removed from the cleaning subsystem 118 by an airflow generated by the cleaning blower 120. The cleaning blower 120 directs air upwards along an airflow path through the fine screens and chaff screens. The airflow transports residues in the agricultural harvester 100 to the rear of the residue handling subsystem 138.

[0017] The return elevator 128 directs the returned material back to the threshing unit 110, where it is threshed again. Alternatively, the returned material can also be directed to a separate secondary threshing mechanism using a return elevator or other transport device, where it is also threshed again.

[0018] Fig. Figure 1 also shows that the agricultural harvesting vehicle 100 in an example includes a ground velocity sensor 146, one or more separation loss sensors 148, a clean grain camera 150 and one or more loss sensors 152, which are provided in the cleaning subsystem 118.

[0019] The ground speed sensor 146 detects the ground speed of the agricultural harvesting vehicle 100. The ground speed sensor 146 can detect the ground speed of the agricultural harvesting vehicle 100 by measuring the rotational speed of the ground-contacting components (such as wheels or tracks), a drive shaft, an axle, or other components. In some cases, the ground speed can be detected using a positioning system, such as a global positioning system (GPS), a dead reckoning system, a long-range navigation system (LORAN), or a variety of other systems or sensors that provide a ground speed indicator.

[0020] The loss sensors 152 provide an output signal indicating the amount of grain loss occurring at any point where material is ejected from the agricultural harvester 100, for example, on both the right and left sides of the cleaning subsystem 118. In some examples, the sensors 152 are impact sensors that count the grain impacts per unit of time or per distance traveled to indicate the grain loss occurring in the cleaning subsystem 118. The impact sensors for the right and left sides of the cleaning subsystem 118 can provide individual signals or a combined or aggregated signal. In some examples, the sensors 152 may comprise a single sensor, unlike separate sensors provided for each cleaning subsystem 118. The sensor configuration can vary further.For example, the agricultural harvesting vehicle 100 can have three or more shafts (left, right, and center) with two loss sensors per shaft, for a total of six sensors. Other configurations are also being considered.

[0021] The separator loss sensor 148 provides a signal that indicates the grain loss in the left and right separators (in Fig. 1 not shown separately). The separator loss sensors 148 can be assigned to the left and right separators and provide separate grain loss signals or a combined or aggregated signal.

[0022] In some cases, grain loss can also be measured using a wide variety of different sensors. For example, loss sensors can be cameras and image processing systems that capture images of the material flow (still images or video images) and process these images to identify the amount of grain loss. The loss sensors can also be used in the residue processing system 138 or elsewhere.

[0023] The loss processing system 170 can be mounted on the agricultural harvesting vehicle 100 or on other systems, as described elsewhere herein. In one example, the loss processing system 170 receives sensor signals from the loss sensors 148 and 152 and also controls a grain dosing device, described elsewhere herein, to periodically feed a known quantity of loss material into the material stream detected by the loss sensors 148 and 152. The loss processing system 170 then monitors the sensor signals to identify any change in the value of the sensor signals caused by the injection of the known quantity of loss.Based on the response of the loss sensors 148, 152 to the supplied loss material, the loss processing system 170 can generate calibration outputs to calibrate the loss sensors 148, 152 and / or the algorithms used to generate a loss indicator output based on the loss sensor signals. The operation of the loss processing system 170 is described below with reference to the following. Fig. 2-5 described in more detail.

[0024] The agricultural harvesting vehicle 100 may also include other sensors and measuring mechanisms. For example, the agricultural harvesting vehicle 100 may include one or more of the following sensors: a header height sensor that detects the height of the header 102 above the ground 111; stability sensors that detect any vibration or bouncing motion (and amplitude) of the agricultural harvesting vehicle 100; a residue setting sensor designed to detect whether the agricultural harvesting vehicle 100 is set to shred the residue, create a swath, etc.; a sieve box blower speed sensor that detects the speed of the blower 120; a threshing concave clearance sensor that detects a distance between the rotor 112 and the threshing concaves 114; a threshing rotor speed sensor that detects a speed of the rotor 112; a chaff sieve clearance sensor that detects the size of openings in the chaff sieve 122; a sieve clearance sensor that detects the size of openings in the sieve 124; a non-grain (NPK) moisture sensor that detects the moisture content of the NKB passing through the agricultural harvesting vehicle 100; one or more machine setting sensors designed to detect various configurable settings of the agricultural harvesting vehicle 100; a machine alignment sensor that detects the alignment of the agricultural harvesting vehicle 100; and crop property sensors that detect a variety of different types of crop properties, such asThe crop type, moisture content, and other crop properties are recorded. The crop property sensors can also be designed to record characteristics of the separated crop material while it is being processed by the agricultural harvester 100. For example, in some cases, the crop property sensors can record properties such as grain quality, for example, broken kernels or nitrate content; grain components, for example, starch and protein; and grain feed rate as the grain is transported through the inclined conveyor 106, the pure grain elevator 130, or elsewhere in the agricultural harvester 100. The crop property sensors can also record the feed rate of biomass through the inclined conveyor 106, the separator 116, or elsewhere in the agricultural harvester 100.The crop property sensors can also detect the feed rate as mass flow of the grain through the elevator 130 or through other sections of the agricultural harvesting vehicle 100, or provide other output signals indicating other detected variables.

[0025] Fig. Figure 2 is a block diagram of an agricultural loss calibration architecture or system 180, which includes parts of the agricultural harvesting vehicle 100 (shown in Fig. 1) shows more details. Some in Fig. The two elements shown are those in Fig. The loss calibration architecture 180 is similar to the one shown in Figure 1 and is similarly numbered. It also includes a sample source 182, a loss feed system 183 (which includes a grain / proxy dosing device 184 and dosing actuators or valves 186-188), and a loss calculation system 190. In the case of the one shown in Figure 1, the loss calibration architecture 180 is similar to the loss calibration architecture 180. Fig. In the example shown in 2, the sample source 182 can include the pure grain tank 192, a substitute material source 194 or another source 196. Fig. Figure 2 further shows that the flow of harvested material into the threshing / separating systems 110 and 116 is represented by block 198, while the flow of residual material from the threshing / separating systems 110 and 116 is represented by block 200. The flow of threshed material plus fines into the cleaning / material handling systems 118 and 125 is represented by block 202, while the flow of residual material from the cleaning / material handling systems 118 and 125 is represented by block 204. The material flow from the cleaning / material handling systems 118 and 125 into the clean grain tank is represented by arrow 206.

[0026] Before describing the functionality of the Loss Calibration Architecture 100 in more detail, some of the elements of Architecture 100 and their functionality will first be described. As mentioned above regarding Fig. As described in Figure 1, the harvested material stream 198 enters the threshing / separating systems 110 and 116, and the threshed grain and fines 202 are fed to the cleaning / material handling systems 118 and 125. Separation loss sensors 148 are designed to detect grain loss in the residual material stream 200 exiting the threshing / separating systems 110 and 116. Separation loss sensors 148 generate one or more sensor signals 210, which are provided to the loss calculation system 190. Sieve box loss sensors 152 are designed to detect grain loss in the residual material stream 204 exiting the cleaning / material handling systems 118 and 125. Sieve box loss sensors 152 generate a sensor signal 212 that indicates the detected loss.

[0027] Sensor signals 210 and 212 are provided to the loss calculation system 190, which executes an algorithm based on sensor signals 210-212 to generate an Identified Loss Signal 214. The Identified Loss Signal 214 indicates the grain loss detected by the separation loss sensors 148 and the sieve box loss sensors 152 (and any additional loss sensors that may be provided to detect losses in crop residues or other material streams). Thus, the Identified Loss Signal 214 can be provided to an operator interface or other system where the identified loss can be displayed to the operator, or it can be made available to other processing systems or stored elsewhere. The identified loss can be displayed as an aggregated loss, loss per sensor or per system, or in some other format.

[0028] The identified loss signals 214 (and / or sensor signals 210, 212) can be made available again to the loss processing system 170. The loss processing system 170 generates sample control signals 216 at regular intervals to control the grain / proxy dosing device 184 and valves or actuators 186-188 to feed a known quantity of material either into the residual material streams 200, 204 or directly to the sensors 148, 152. The fed-in material can be grain, for example from the pure grain tank 132, or a substitute material (for example, wood pellets or other pellets or other material that can simulate grain in the material stream or upon impact with the sensors 148, 152). The grain / proxy metering device 184 can be a metering system such as those found on a planting row unit, a weight or volume distribution system such as on a seed drill, or any other metering device.Thus, the loss processing system 170 can control the grain / proxy dosing device 184 and the valves or actuators 186, 188 to feed a known quantity of source material from a sample source 182 and then process the response from the sensors 148, 152 by monitoring the sensor signals 210, 212 and / or the identified loss signals 214. Based on the response of the sensors 148, 152 and the loss calculation system 190 to the fed-in material, the loss processing system 170 generates calibration signals 218, which can be used to calibrate the sensors 148, 152 and / or the algorithm used by the loss calculation system 190 to generate the identified loss signals 214. Fig. Figure 3 is a flowchart that shows an example of how the loss calibration architecture 180 works, with more details. It is initially assumed that the agricultural harvesting vehicle 100 is equipped with loss sensors, as represented by block 220 in the flowchart in Figure 3. Fig. 3 is indicated. In one example, several loss sensors are located in different positions on the harvesting vehicle, as indicated by block 222. The loss sensors detect a variable (for example, grain allowances or other variable) that indicates a loss, and a loss calculation system 190 includes a transfer function or other logic or algorithm that converts the detected variable into identified loss signals 214, as indicated by block 224 in the flowchart in Fig. 3 is marked. The harvesting vehicle and the recording systems may also be designed differently, as indicated by block 226.

[0029] The loss processing system 170 can be configured to first determine a baseline value that identifies how the loss sensors 148 and 152 and the loss calculation system 190 operate. This baseline value can be determined, for example, when the harvester 100 is not harvesting any crop. The loss processing system 170 thus first recognizes that the harvester 102 is in a state outside of the crop (or unloaded), as indicated by block 228.

[0030] In an unloaded state, the loss processing system 170 controls the grain / proxy metering device 184 and the valves or actuators 186 and 188 to feed a known loss quantity into the loss detection systems of the harvester 100. The feeding of a known loss quantity is represented by block 230 in the flowchart in Fig. 3. The use of a metering device 184 for feeding in the known loss quantity is characterized by block 232. The known loss quantity can originate from a variety of different sample sources 182, for example the pure grain tank 132, or from a proxy source (e.g., wood pellets, etc.) or another source, as shown by block 234 in the flowchart in Fig. 3 is indicated. The known loss quantity can be fed into the material flow upstream of sensors 148, 152 or directly to sensors 148, 152, as indicated by block 235. The known loss quantity can also be fed in by other means, as indicated by block 236.

[0031] Sensors 148 and 152 then detect the loss, as shown by block 238 in the flowchart in Fig. 3 is marked. As explained above, the sensors can be impact sensors 240 or other sensors, for example image acquisition devices with image processing systems, or other sensors as marked by block 242.

[0032] The loss processing system 170 then compares the detected loss with the known loss quantity fed into the loss detection system to identify any deviation in the detected loss based on the response of the loss sensors 148, 152. The comparison of the detected loss with the known loss quantity is represented by block 244 in the flowchart in Fig. 3 marked.

[0033] In one example, the loss processing system 170 compares the known loss quantity with the sensor signals 210, 212 generated by sensors 148, 152, as shown by block 246 in the flowchart in Fig. 3 is marked. In another example, the loss processing system 170 compares the known loss quantity with the identified loss signals 214 generated by the loss calculation system 190, as shown by block 248 in the flowchart in Fig. The comparison can also be made in other ways, as indicated by block 250.

[0034] Based on the comparison, the loss processing system 170 now has baseline information to identify how the sensor systems (e.g., sensors 148, 152 and loss calculation system 190) responded to the known amount of loss injected, and can calibrate the sensor system (sensors 148, 152 themselves, the transfer function, and all other algorithms executed by the loss calculation system 190) based on the deviation between the detected loss and the known amount of loss injected into the sensor systems. The generation of calibration signals 218 for calibrating the sensor systems is described by block 250 in the flowchart in Fig. 3 marked. In one example, the loss processing system 170 can also generate calibration signals 218 to compensate for or correct the sensor systems with respect to other conditions such as environmental conditions (temperature, humidity, etc.) or other conditions that affect the sensor systems. A correction with respect to such conditions is shown by block 252 in the flowchart in Fig. 3 marked. Calibrating the sensor systems based on the baseline information can also be done in other ways, as indicated by block 254.

[0035] After performing the baseline correction and calibration, the loss processing system 170 can proceed to calibrate the loss detection systems during harvesting. The loss processing system 170 thus detects that the harvester 100 is within the crop and in the harvesting process or is being loaded. The detection that the harvester is in the harvesting process is represented by block 256 in the flowchart in Fig. 3 marked.

[0036] The loss processing system 170 can then generate the sample control signals 216 to feed an increase in the known loss rate into the loss detection systems of the harvesting vehicle 100 at regular intervals. The feeding of a known loss rate increase is represented by block 258 in the flowchart in Fig. 3. For example, the loss processing system 170 can generate sample control signals 216 to control the grain / proxy metering device 184 to meter a known quantity of loss material into the residual material stream 200 from the threshing / separating systems 110, 116, wherein the residual material stream 200 includes non-grain components (NPC) plus unthreshed grain and free loss, etc. The feeding of the known quantity of loss material (e.g., a known loss rate increase) into the material stream 200 is represented by block 260 in the flowchart in Fig. 3 marked. The loss processing system 170 can also generate sample control signals 216 to control the grain / proxy dosing device 184 and valve or actuator 188 such that it feeds a known loss rate increase into the residual material stream 204 from the cleaning / material handling subsystems 118, 125, as shown by block 262 in the flow diagram in Fig. 3 is marked. In another example, the loss processing system can generate 170 sample control signals 216 to directly dose the known loss rate increase to the sensors 148, 152, as shown by block 264 in the flowchart in Fig. 3 is marked. The known increase in the loss rate can also be fed into the loss detection systems in other ways, as indicated by block 266.

[0037] The loss sensors 148 and 152 then detect the loss and provide sensor signals 210 and 212 to the loss calculation system 190, which calculates the identified loss based on the sensor signals 210 and 212 and generates an output signal 214 that indicates the identified loss. The detection of the loss by the loss detection systems is shown in block 268 in the flowchart in Fig. 3 marked.

[0038] The sensor signals 210, 212 and / or the identified loss signals 214 are provided to the loss processing system 170, which compares the response of the loss detection systems with the applied loss rate increase to generate calibration signals 218. The calibration signals 218 can be provided to calibrate the loss sensors 148, 152 and / or to calibrate the loss calculation system 190 (e.g., to calibrate the models or algorithms executed by the system 190). Calibrating the sensor systems based on the known loss rate increase applied to the loss detection systems and the known loss is described by block 270 in the flowchart in [reference missing]. Fig. 3 marked.

[0039] Harvesting vehicle 100 then proceeds to carry out the harvesting process using the calibrated sensor systems, as described by block 272 in the flowchart in Fig. 3 is marked. The calibration data can be saved or sent to other machines or systems, as shown in block 274 in the flowchart of Fig. is marked 3.

[0040] It should also be noted that the loss processing system 170 can perform a calibration at regular intervals during the operation of the harvesting vehicle 100, as explained in more detail below. The calibration can be performed by feeding several different known loss quantities to the sensor systems, or it can be performed in another way.

[0041] Fig. Figure 4 is a block diagram showing an example of the Loss Processing System 170 with more details. In which... Fig. In the example shown, the loss processing system 170 includes one or more processors or servers 276, a communication system 278, a harvest condition detector 280, a data storage system 282, a sample duration timer 284, a sample feeding control system 286, a data quality analysis system 288, a calibration system 290, and other elements 292. The data storage system 282 may include environmental correction values ​​294, correction / calibration curves, models, algorithms, etc. 296, N-increment values ​​298, and other elements 300. The sample feeding control system 286 may include an increment selector 302, a dosing device control 304, a valve control 306, and other elements 308. The data quality analysis system 288 includes noise level processor 310, data consistency processor 312 and other data quality analysis functionalities 314.The calibration system 290 can include a loss aggregation system 316, a loss comparison system 318, a calibration value recognition system 320 (which can include a sensor calibration processor 322, a logic / algorithm calibration processor 324, and other elements 326), a calibration value output system 328, and other calibration functionalities 330. Before the general operation of the loss processing system 170 is described in more detail, some of the elements of the loss processing system 170 and their operation will first be described.

[0042] Environmental correction values ​​294 can be values ​​used to correct the loss sensors or the loss calculation system based on environmental factors. For example, sensors 148, 152, or system 190 may function differently under varying humidity or temperature conditions, or under other environmental conditions. This difference in functionality can be captured by environmental correction values ​​294, which are used to correct the sensor conditions or the variables used in the algorithm executed by the loss calculation system 190, or to make other corrections.

[0043] Correction / calibration curves, models, algorithms, etc. 296 can be used to perform the processing used by the calibration system 290 to generate calibration signals 218 for calibrating the loss sensors 148, 152, the loss calculation system 190, or other elements. For example, correction / calibration curves, models, algorithms, etc. 296 can include an optimization algorithm that is executed based on the values ​​obtained from the loss processing system 170 to identify sensor settings, transfer function values, etc., as calibration data.

[0044] N increment values ​​298 can be predefined or can be default values ​​used to identify the amount of grain or surrogate material fed in during a calibration process. In one example, the N increment values ​​N are standalone values ​​that can be fed in during a calibration process that occurs over several different sample time intervals, as explained in more detail below.

[0045] Communication systems 278 can simplify communication between elements in the loss processing system 170 and the loss calibration architecture 180, as well as with other elements. Thus, the communication system 278 can be an ECU network (CAN) bus and bus control, and / or any other communication system used to communicate over a wide area network, a local area network, a cellular network, a near field communication network, a WLAN or Bluetooth network, or other networks or combinations of networks.

[0046] The harvest condition detector 280 detects whether the harvester 100 is within the crop and in the harvesting process, or outside the crop and not in the harvesting process. For example, the harvest condition detector 280 detects whether crop is flowing through one or more different areas of the harvester 100 to determine if the harvester 100 is harvesting. In another example, the harvest condition detector 280 can detect whether the header 102 is engaged with crop or in operation, or detect other characteristics of the header 102 to determine if the harvester 100 is harvesting. In yet another example, the harvest condition detector 280 can detect the presence or flow of crop through the harvester 100, or detect whether other harvesting functionalities are in operation to determine if the harvester is within the crop and harvesting.The harvest condition detector 280 can also detect the location of the harvester 100 and access coverage information (such as a coverage map) to determine whether the harvester 100 is in an area with unharvested crop or in an area where the crop has already been harvested. The harvest condition detector 280 can also determine the status of the harvester 100 (e.g., whether the harvester 100 is in the harvesting process) in other ways.

[0047] The sample duration timer 284 can be a timing circuit or logic that defines and controls a sample duration interval. For example, the loss processing system 170 might continue to feed a known loss quantity into the loss detection systems of the harvesting vehicle 100 during one sample duration, and then suspend the loss feed during another sample duration. The loss detected over the two time periods is then aggregated to determine if calibration is required. In such a scenario, the sample duration timer 284 can be programmed with a sample duration, or the sample duration can be a standard duration, an operator-defined duration, or any other duration. The sample duration timer 284 is used to generate a signal that indicates when the sample duration begins and when it ends.

[0048] The sample feed control system 286 controls the feeding of grain / proxy material by controlling the grain / proxy metering device 184 and / or the valves or actuators 186-188. The increment selector 302 determines how much material must be fed into the loss detection systems to perform a calibration. In one example, the increment selector 302 accesses the N increment values ​​298 in the data memory 282 to identify the quantity of material to be fed into the detection systems. The increment selector 302 can also identify the quantity to be fed in other ways.

[0049] The dosing device controller 304 generates control signals 216 to control the grain / proxy dosing device 184 to feed the desired material increment into the loss detection systems. For example, the dosing device 184 can be controlled to feed a desired quantity (volume, weight, etc.) of material from one of the sample sources 182 based on time, velocity, or other control criteria. The dosing device controller 304 generates a control signal to feed the desired increment of increased loss (selected by the increment selector 302) into the loss detection systems for calibration. The valve control 306 controls the valves or actuators 186, 188 to determine where the material is fed in (e.g. into one or more of the residual material streams 200, 204 or directly to the sensors 148, 152 or otherwise).The valve control 306 can control the various valves 186, 188 independently, sequentially or simultaneously in order to feed a known loss quantity into several material streams or to several sensors at the same time.

[0050] The data quality analysis system 288 analyzes the data collected during the sample durations to determine whether the data are of sufficient quality to be used for calibration. For example, the data may be too noisy or inconsistent and therefore unsuitable for calibrating the loss detection systems. The noise level processor 310 thus detects the noise level in the data (for example, by recognizing the extent of data variation over a specified time period or other criteria that characterize noise). The data consistency processor 312 can process the data to determine whether it varies consistently or varies as expected based on a model, algorithm, or other mechanisms. Additional data quality analysis functionalities 314 can perform further operations to identify data quality in other ways.Based on the noise level in the data, the consistency of the data and / or any other criteria, the data quality analysis system 288 provides an output for the calibration system 290 indicating whether the calibration system 290 should perform a calibration based on the collected data or whether the data are of insufficient quality (e.g. because the noise level is too high, the data are unexpectedly inconsistent, etc.), so that a calibration should not be performed based on this data.

[0051] In the calibration system 290, the loss aggregation system 316 aggregates the loss value outputs (e.g., from the sensor signals 210, 212, or as identified loss signals 214, or both) over the sample durations, which were time-controlled by the sample duration timer 284. The loss comparison system 318 compares the aggregated loss with the value of the known loss that was fed into the sensor systems during the sample duration intervals. Based on this comparison, the loss comparison system 318 generates an output indicating how the sensor systems reacted to the injected increase in loss. The calibration value recognition system 320 identifies calibration values ​​that can be output as calibration signals 218 to calibrate the sensors 148, 152 themselves and / or the loss calculation system 190.The sensor calibration processor 322 generates outputs that identify sensor settings or other calibration values ​​that can be used to calibrate the sensors 148 and 152 themselves. The logic / algorithm calibration processor 324 generates outputs that can be used to calibrate the transfer function or other algorithms or prediction models executed by the loss calculation system 190 in order to generate the identified loss signals 214 that indicate the detected loss.

[0052] The calibration value output system 328 generates an output that identifies the calibration values. The output can be in the form of calibration signals 218, which can be used to configure the sensors 148, 152 based on the calibration values ​​and / or to configure the transfer function or other algorithms or models used by the loss calculation system 190 based on the calibration values.

[0053] The Fig. 5A and Fig. 5B (herein collectively referred to as Fig. Figure 5) shows an example of a flowchart that illustrates the operation of the loss processing system 170 in more detail.

[0054] In explaining Fig. 5 It is assumed that the loss processing system 170 has already generated the baseline value, which, referring above, Fig. 3 explained that the harvesting vehicle 100 was outside the crop and not in the harvesting process. Therefore, for the purposes of this description, it is assumed that the harvesting condition detector 280 recognizes that the harvesting vehicle 100 is inside the crop and harvesting crop, as shown by block 340 in the flowchart in Fig. is marked 5.

[0055] It can also be assumed that the loss processing system 170 can feed a known loss value into several sensor systems simultaneously, or that the processes can be performed for individual detection systems. For the purposes of this description, it is assumed that the loss processing system 170 attempts to calibrate a single detection system (e.g., the loss sensors 148 and the loss calculation system 190). Therefore, before feeding in the loss value, the loss aggregation system 316 accumulates or aggregates the loss detected by the loss detection system (sensor 148 and system 190) during a sample time interval controlled by the sample duration timer 284. The accumulation of the loss detected by the loss sensor system during a sample time interval is represented by block 342 in the flowchart in Fig. 5. The loss aggregation system 316 can aggregate the loss indicated by the sensor signals 210 and / or the identified loss signals 214 for the sample time interval. The aggregated loss can be recorded and stored, as indicated by block 344, or the loss can be accumulated in other ways, as indicated by block 346.

[0056] Next, the increment selector 302 selects an increment value 298, which identifies a known quantity of loss material that is either fed into the material stream 200 or directly to the sensors 148. The identification of the first of the N increment values ​​is shown by block 348 in the flowchart in Fig. 5 is indicated. In one example, the N increment values ​​identify 298 increments whose size increases from the first value to the Nth value, as indicated by block 350. The increments can also have other values, as indicated by block 352.

[0057] The dosing device control 304 and the valve control 306 then control the grain / proxy dosing device 184 and the valve 186 such that the identified increment of loss material is fed into the residual material stream 200 (or directly to the sensors 148) during the sample time interval, which is time-controlled by the sample duration timer 284. The feeding of the identified loss increment is shown by block 354 in the flowchart in Fig. 5 marked.

[0058] While the loss increment is fed into the detection system, the loss aggregation system 316 accumulates the detected loss, as shown by block 356 in the flowchart in Fig. 5 is marked. Again, the accumulated loss can be recorded or stored, as indicated by block 358, or the loss can be accumulated in other ways, as indicated by block 360.

[0059] After the sample duration has elapsed (as signaled by the sample duration timer 284), the dosing device control 304 and the valve control 306 generate control signals to stop feeding the loss material increment and to accumulate the loss detected during a different time period (without feeding additional loss into the loss detection system). Stopping the feeding of additional loss and resuming the detection and accumulation of loss over a sample duration interval is represented by block 362 in the flowchart in Fig. 5 marked.

[0060] In one example, the loss processing system 170 repeats this pattern (feeding in an increment value of loss during a sample time interval while the loss is aggregated) and then simply aggregating the loss without further feeding in loss, repeatedly for the N different increment values ​​of loss 298. Therefore, determined at block 364 in Fig. The increment selector 302 checks whether there are further N increment values ​​298 to be fed into the loss detection system for calibration. If there are further N increments to be fed, the increment selector 302 selects the next increment to be fed, as indicated by block 366, and processing returns to block 354, where this increment is fed into the detection system. It should be noted that the sample time intervals during a calibration process can all be the same or they can differ. Furthermore, the sample time intervals can be static or variable.

[0061] If block 364 determines that no further N-increment values ​​298 are to be fed into the loss detection system, the data quality analysis system 288 determines whether the recorded or stored loss data can be used for calibration, as shown by block 368 in the flowchart in Fig. 5 is indicated. In one example, the noise level processor 310 detects the noise level, as indicated by block 370, and / or the data consistency processor 312 detects the consistency of the data, as indicated by block 372. Any number of other quality criteria from a wide range can be analyzed to determine whether the aggregated loss data should be used to perform a calibration, as indicated by block 374. If the data are not used for calibration, as indicated by block 376, processing returns to block 340, where the calibration process can be repeated at regular intervals. However, if it is determined at block 376 that the aggregated loss data can be used to perform a calibration, the calibration system 290 calibrates the loss sensor system based on the aggregated loss.The calibration of the loss sensor system is described by block 378 in the flowchart in . Fig. 5 marked.

[0062] For example, the loss comparison system 318 can compare the detected loss with the input loss, as indicated by block 380, and the calibration value detection 320 can execute an optimization algorithm to modify the settings, logic, algorithms, or other parameters of the sensors (e.g., impact sensors 148) to calibrate the impact sensors based on the comparison. The execution of the optimization algorithm to modify the settings, logic, algorithms, or parameters corresponding to the sensors 148 is indicated at block 382 in the flowchart in Fig. 5. The logic / algorithm calibration processor 324 can also generate control signals to modify the transfer function or other algorithms or models executed by the loss calculation system 190 to generate the identified loss signals 214 based on the calibration data. Modifying the transfer function based on the calibration data is shown by block 384 in the flowchart in Fig. 5. Calibration of the loss sensor system can also be performed in other ways, as indicated by block 386. The calibration process can be repeated as often as specified by the configuration, for example, based on time criteria (e.g., regularly) or based on other criteria (e.g., based on a change in location, the crop being measured, terrain conditions, or other criteria), as indicated by block 387. The calibration data can also be saved and / or uploaded to other systems, other machines, etc., as indicated by block 388.

[0063] It is thus evident that the present description describes a system for calibrating loss sensors on a harvesting vehicle. The system feeds a known quantity of loss material into the material stream processed by the loss detection system or directly to the loss sensors. The response of the loss detection system to the injected loss material is used to determine whether calibration should be performed and, if so, to generate calibration values ​​for calibrating the sensors themselves and / or the transfer function or other algorithms used to output a loss value based on the sensor signals. This significantly reduces the time and complexity involved in calibrating the loss detection systems and also greatly increases their accuracy, particularly under fluctuating field or environmental conditions.

[0064] This discussion mentions processors and servers. In one example, processors and servers include computer processors with associated memory and a timing circuit, which are not shown separately. The processors or servers are functional parts of the systems or devices to which they belong and are activated by the other components or elements in those systems, thus supporting their functionality.

[0065] A number of user interfaces (UIs) were also discussed. UL displays can take a wide variety of forms and can feature a wide variety of user-operated input mechanisms. These user-operated input mechanisms can include text fields, checkboxes, icons, links, drop-down menus, search fields, and so on. Furthermore, the mechanisms can be operated in a variety of ways. For example, they can be operated using a point-and-click device (such as a trackball or mouse). They can also be operated with hardware buttons, switches, a joystick or keyboard, thumb switches, thumb pads, and so forth. Finally, they can be operated via a virtual keyboard or other virtual actuators.Furthermore, the mechanisms can be operated using touch gestures if the screen on which the mechanisms are displayed is a touchscreen. Additionally, the mechanisms can be operated using voice commands if the device displaying the mechanisms has speech recognition components.

[0066] Several data stores were also discussed. It should be noted that each data store can be subdivided into multiple data stores. All can be located locally within the systems accessing the data stores, all can be located remotely, or some can be local while others are located remotely. All of these configurations are considered here.

[0067] The figures also show a number of blocks, each with a specific functionality assigned to it. It should be noted that fewer blocks can be used, meaning the functionality is performed by fewer components. Conversely, more blocks can be used, distributing the functionality across more components.

[0068] It should be noted that the above descriptions encompass a variety of different systems, components, generators, selectors, detectors, and / or logics. It is understood that such systems, components, generators, selectors, detectors, and / or logics may consist of hardware elements (such as processors and associated memory or other processing components, some of which are described below) that perform the functions assigned to these systems, components, generators, and / or logics. Furthermore, the systems, components, generators, selectors, detectors, and / or logics may consist of software that is loaded into memory and subsequently executed by a processor, server, or other computing component, as described below.The systems, components, generators, selectors, detectors, and / or logics can also consist of various combinations of hardware, software, firmware, etc., some examples of which are described below. These are just a few examples of different structures that can be used to create the systems, components, generators, selectors, detectors, and / or logics described above. Other structures can also be used.

[0069] Fig. Figure 6 is a block diagram of the harvesting vehicle 100, which is in Fig. Figure 1 shows the harvesting vehicle 100 having an operator 504 and communicating with elements in a remote server architecture 500. In one example, the remote server architecture 500 can provide computing, software, data access, and storage services that do not require end-user knowledge of the physical location or configuration of the system providing the services. In various examples, remote servers can provide the services using appropriate protocols over a wide-area network such as the Internet. For example, remote servers can provide applications over a wide-area network, and these applications can be accessed via a web browser or any other computing component. The software or components shown in previous figures, as well as the corresponding data, can be stored on servers at a remote location.The computing resources in a remote server architecture can be consolidated at a remote data center location, or they can be distributed. Remote server infrastructures can deliver services across shared data centers, although they appear as a single access point to the user. Thus, the components and functions described herein can be delivered by a remote server at a remote location using a remote server architecture. Alternatively, the components and functions can be delivered from a traditional server, installed directly on client devices, or made available in some other way.

[0070] At the in Fig. In the example shown in Figure 6, some elements resemble those shown in previous figures, and they are labelled similarly. Fig. Figure 6 specifically shows that the calibration system 290 and the data storage 282 can be located at a remote server location 502. Therefore, the harvesting vehicle 100 accesses these systems via the remote server location 502.

[0071] Fig. Figure 6 also shows another example of a remote server architecture. Fig. Figure 6 shows that it is also conceivable that some elements of the previous figures are located at the remote server location 502, while others are not. For example, the data storage 282 or the data quality analysis system 288, as well as other systems, could be located at a site separate from location 502, and they could be accessed via the remote server at location 502. Regardless of where the elements are located, access to the elements could be directly from the harvester 100, via a network (either a wide area network or a local area network), the elements could be hosted at a remote location by a service, or the elements could be provided as a service or be accessible via a connection service located at a remote location.Furthermore, the data can be stored in virtually any location and either intermittently retrieved by or forwarded to participating parties. All of these architectures are considered here.

[0072] It should also be noted that the elements of previous figures or sections thereof can be arranged on a wide variety of different devices. Some of these devices include servers, desktop computers, laptop computers, tablet computers, or other mobile devices such as palmtop computers, mobile phones, smartphones, multimedia players, personal digital assistants, etc.

[0073] Fig. Figure 7 is a simplified block diagram of an illustrative example of a handheld or mobile computing device that can be used as a handheld device 16 by a user or client and in which the present system (or parts thereof) can be deployed. For example, a mobile device can be provided in the operator's cab of the harvester 100 for use in generating, processing, or displaying loss data. Fig. 8-9 are examples of handheld or mobile devices.

[0074] Fig. Figure 7 shows a general block diagram of the components of a client device 16, which can execute some of the components shown in the preceding figures, interact with them, or both. The device 16 provides a communication link 13, which enables the handheld device to communicate with other computing devices and, in some examples, provides a channel for automatically receiving information, such as by scanning. Examples of the communication link 13 include enabling communication via one or more communication protocols, such as wireless services used to provide cellular access to a network, and protocols that provide local wireless connections to networks.

[0075] In other examples, applications can be received on a removable SD (Secure Digital) card connected to an interface 15. The interface 15 and the communication links 13 communicate with a processor 17 (which can also represent processors or servers from previous figures) along a bus 19, which is also connected to the memory 21 and input / output (I / O) components 23, as well as a clock 25 and a location system 27.

[0076] In one example, I / O components 23 are provided to enable input and output operations. I / O components 23 for various examples of the device 16 can include input components, such as buttons, touch sensors, optical sensors, microphones, touchscreens, proximity sensors, accelerometers, and orientation sensors, and output components, such as a display device, a speaker, and / or a printer port. Other I / O components 23 can also be used.

[0077] For example, clock 25 includes a real-time clock component that outputs a time and date. This can also provide time control functions for processor 17.

[0078] The location determination system 27 includes, for illustrative purposes, a component that outputs a current geographic location of the device 16. This can include, for example, a receiver of a global positioning system (GPS receiver), a dead reckoning system, a cellular triangulation system, or another positioning system. The location determination system 27 can also include, for example, mapping software or navigation software that generates desired maps, navigation routes, and other geographic functions.

[0079] Memory 21 stores an operating system 29, network settings 31, applications 33, application configuration settings 35, data storage 37, communication drivers 39, and communication configuration settings 41. Memory 21 can include all types of physical volatile and non-volatile computer-readable storage devices. Memory 21 can also include computer storage media (described below). Memory 21 stores computer-readable instructions which, when executed by the processor 17, cause the processor to perform computer-implemented steps or functions according to the instructions. The processor 17 can be activated by other components to support their functionality as well.

[0080] Fig. Figure 8 shows an example where the device 16 is a Tablet Computer 600. In Fig. Figure 8 shows the Computer 600 with a user interface display screen 602. The screen 602 can be a touchscreen or a pen-operated surface that receives input from a pen or stylus. The Computer 600 can also use a virtual on-screen keyboard. Naturally, the Computer 600 could also be connected to a keyboard or other user input device via a suitable connection mechanism, such as a wireless connection or a USB port. For illustrative purposes, the Computer 600 can also receive voice input.

[0081] Fig. Figure 9 shows that the device can be a smartphone 71. The smartphone 71 has a touch-sensitive display 73 that shows icons or tiles or other user input mechanisms 75. The mechanisms 75 can be used by a user to run applications, make calls, perform data transfer operations, etc. In general, the smartphone 71 is built on a mobile operating system and offers more advanced computing power and connectivity than a feature phone.

[0082] It should be noted that other forms of devices 16 are possible.

[0083] Fig. Figure 10 is an example of a data processing environment in which elements from previous figures, or parts thereof, can be used (for example). According to Fig. Figure 10 comprises an example system for implementing some embodiments, comprising a computing device in the form of a computer 810 programmed to operate as described above. Components of the computer 810 may, but are not limited to, include a processing unit 820 (which may include processors or servers from the preceding figures), a system memory 830, and a system bus 821 coupling various system components, including the system memory, to the processing unit 820. The system bus 821 may be one of several types of bus structures, including a memory bus or memory controller, a peripheral bus, and a local bus, employing a variety of bus architectures. The memory and programs described with reference to the preceding figures may be, in corresponding parts of Fig. 10 will be used.

[0084] The Computer 810 typically includes a variety of computer-readable media. Computer-readable media can be any available media that the Computer 810 can access, and includes both volatile and non-volatile media, removable and non-removable media. By way of example, and without limitation, computer-readable media can include computer storage media and communication media. Computer storage media is distinct from and does not include a modulated data signal or carrier wave. Computer storage media includes hardware storage media, including both volatile and non-volatile, removable and non-removable media, implemented by any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data.Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory, or other storage technology; CD-ROM, DVD (Digital Versatile Discs), or other optical disc storage; magnetic cartridges, magnetic tapes, magnetic disk storage, or other magnetic storage devices; or any other medium that can be used to store the desired information and that the Computer 810 can access. Communication media can embody computer-readable instructions, data structures, program modules, or other data in a transfer mechanism and include any information delivery medium. The term "modulated data signal" describes a signal in which one or more of its properties are set or modified in such a way that information is encoded in the signal.

[0085] System memory 830 includes computer storage media in the form of volatile and / or non-volatile memory, such as ROM (read-only memory) 831 and RAM (random access memory) 832. A BIOS (basic input / output system) 833, containing the basic routines that assist in the transfer of information between elements within the computer 810, for example, during startup, is typically stored in ROM 831. RAM 832 usually contains data and / or program modules that are directly accessible to and / or currently being processed by the processing unit 820. This is an example and not an exhaustive description. Fig. 10 an operating system 834, application programs 835, other program modules 836 and program data 837.

[0086] The Computer 810 can also include other removable / non-removable volatile / non-volatile computer storage media. This is purely an example. Fig. 10. A hard disk drive 841, which reads from or writes to non-removable non-volatile magnetic media, an optical disk drive 855, and a non-volatile optical disk 856. The hard disk drive 841 is usually connected to the system bus 821 through a non-removable memory interface such as the 840 interface, and the optical disk drive 855 is usually connected to the system bus 821 through a removable memory interface such as the 850 interface.

[0087] Alternatively or additionally, the functionality described here can be implemented, at least partially, by one or more hardware logic components. Examples of usable hardware logic components include, but are not limited to, field-programmable gate arrays (FPGAs), application-specific integrated circuits (e.g., ASICs), application-specific standard products (e.g., ASSPs), system-on-chip systems (SOCs), complex programmable logic devices (CPLDs), etc.

[0088] The drives and their associated computer storage media discussed above and in Fig. The ten illustrated examples provide a storage system for computer-readable instructions, data structures, program modules, and other data for the Computer 810. Fig. Figure 10, for example, illustrates the hard disk drive 841 as storing the operating system 844, the application programs 845, other program modules 846, and the program data 847. It should be noted that these components may be either the same as the operating system 834, the application programs 835, the other program modules 836, and the program data 837, or they may differ from them.

[0089] A user can input commands and information into the computer 810 via input devices such as a keyboard 862, a microphone 863, and a pointing device 861 such as a mouse, trackball, or touchpad. Other input devices (not shown) may include a joystick, gamepad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit 820 via a user input interface 860 coupled to the system bus, but they may be connected via other interface and bus structures. A visual display 891 or other type of display device is also connected to the system bus 821 via an interface such as a video interface 890. In addition to the monitor, computers may also include other peripheral output devices, such as loudspeakers 897 and a printer 896, which may be connected via an output peripheral interface 895.

[0090] The Computer 810 operates in a networked environment using logic connections (such as a control unit network - CAN, a local area network - LAN, or a wide area network - WAN) with one or more remote computers such as a Remote Computer 880.

[0091] When used in a LAN network environment, the computer 810 is connected to the LAN 871 via a network interface or adapter 870. When used in a WAN network environment, the computer 810 typically includes a modem 872 or other means for establishing communication over the WAN 873, such as the Internet. In a networked environment, program modules may be stored in a remote storage device. Fig. Figure 10 illustrates, for example, that the remote application programs 885 can be located on the remote computer 880.

[0092] It should also be noted that the various examples described herein can be combined in different ways. That is, parts of one or more examples can be combined with parts of one or more other examples. All of this is included here.

[0093] Although the subject matter is described in a language specific to structural features and / or methodological processes, it is understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or processes described above. Instead, the specific features and actions described above are disclosed as exemplary forms of implementing the claims.

Claims

[1] Computer-implemented method, comprising: Control of a loss feed-in system (183) for feeding (230, 258) a quantity of material into a loss recording system on an agricultural harvesting vehicle (100); Recording loss (238, 268) using the loss recording system (148, 152, 190); Generating a loss signal (210, 212, 214) that indicates the detected loss; Generating (250, 270) a calibration value (218) based on the amount of loss material fed in and the loss detected; and Generating (250, 270) a control signal (218) to configure the loss detection system (148, 152, 190) based on the calibration value. [2] Computer-implemented method according to claim 1, wherein the loss detection system comprises a loss sensor (148, 152) generating a loss sensor signal and a loss calculation system (190) generating an identified loss signal based on the loss sensor signal, and wherein controlling the loss feed system comprises: Controls (235, 260) of the loss feed system (183) such that it feeds the quantity of material into a material flow which is detected by the loss sensor. [3] Computer-implemented method according to claim 1, wherein the loss detection system comprises a loss sensor (148, 152) designed to generate a loss sensor signal and a loss calculation system (190) designed to generate an identified loss signal based on the loss sensor signal, and wherein controlling the loss feed system comprises: Controls (235, 264) of the loss feed system (183) such that it feeds the amount of material to the loss sensor. [4] Computer-implemented method according to claim 1, wherein the loss detection system comprises a loss sensor (148, 152) generating a loss sensor signal and a loss calculation system (190) generating an identified loss signal based on the loss sensor signal, and wherein controlling a loss feed-in system comprises: Controls (354) of the loss feed-in system (183) such that it feeds in the known quantity of material for a first sampling period; and Controls (362) of the loss feed-in system such that it stops feeding in material for a second sampling period. [5] Computer-implemented method according to claim 4, wherein controlling the loss feed-in system for feeding in a known quantity of material comprises: Selecting (348) a known quantity of material; and Controls (354) of the loss feed-in system such that it feeds in the selected known quantity of material. [6] Computer-implemented method according to claim 5, wherein controlling the loss feed system for feeding in a known quantity of material comprises: Repeat the following steps during a calibration process: Selecting (348, 366) a known quantity of material; Controls (354) of the loss feed-in system such that it feeds in the selected known quantity of loss material; and Controls (362) of the loss feed-in system such that it stops feeding in loss material for the second sampling period. [7] Computer-implemented method according to claim 6, wherein selecting a known quantity of material comprises: Selecting (350) a known value of material that changes with each repetition during the calibration process. [8] Computer-implemented method according to claim 4, wherein generating a calibration value comprises: Aggregating (356) the recorded loss during the first sampling period; Aggregating (362) the recorded loss during the second sampling period; and Generating (378) the calibration value based on the aggregated loss and the known amount of material fed in. [9] Computer-implemented method according to claim 8, wherein generating the calibration value comprises: (380) comparing the aggregated recorded loss during the first sampling period with the known quantity of material to obtain a comparative result; and Generating (378) the calibration value based on the comparison result. [10] Computer-implemented method according to claim 1, wherein generating a calibration value further comprises: Recognize (228, 256) whether the agricultural harvesting vehicle is in the harvesting process; Generating (228, 256) a harvester condition value based on whether the agricultural harvester is in the harvesting process; and Generating (250, 270) the calibration signal based on the harvester status value. [11] Computer-implemented method according to claim 4, wherein generating a control signal for configuring the loss detection system based on the calibration value comprises: Generating (218) a sensor calibration signal to configure the loss sensor based on the calibration value. [12] Computer-implemented method according to claim 4, wherein generating a control signal for configuring the loss detection system based on the calibration value comprises: Generating (218) a calculation system calibration signal to configure the loss calculation system (190) based on the calibration value. [13] Computer-implemented method according to claim 4, wherein the first and second sample time intervals have the same duration. [14] Agricultural system, comprehensive: a loss detection system (148, 152) designed to detect a crop loss on an agricultural harvesting vehicle (100) and to generate a loss signal (210, 212) indicating the detected crop loss; a loss feed system (183) designed to feed material into the loss detection system (148, 152); and a loss processing system (170) designed to control the loss feed system (183) to feed a known quantity of material into the loss detection system (148, 152), generate a calibration value based on the known quantity of material fed in and the detected crop loss, and generate a control signal to configure the loss detection system (148, 152) based on the calibration value. [15] Agricultural system according to claim 14, wherein the loss detection system comprises: a loss sensor (148, 152) designed to generate a loss sensor signal; and a loss calculation system (190) designed to generate an identified loss signal based on the loss sensor signal and wherein the loss processing system (170) is designed to control the loss feed system (183) to feed the known quantity of material into a material stream detected by the loss sensor (148, 152).

Images