METHOD FOR OPTIMIZING THE SETTINGS OF MACHINE PARAMETERS OF AN AGRICULTURAL TEAM

Description

[0001] The present invention relates to a method for optimizing the setting of machine parameters of an agricultural team according to the preamble of claim 1 and a cloud control arrangement according to claim 12.

[0002] Agricultural machinery, such as tractors, can be combined with various implements. These implements are attached to the agricultural machinery via an interface. The focus here is on agricultural combinations consisting of a tractor and an agricultural implement. The implement is preferably pulled by the agricultural machinery.

[0003] These types of implements are generally used to carry out agricultural tasks. The primary focus here is on soil cultivation, such as plowing or cultivating.

[0004] Agricultural vehicles are known to have a driver assistance system that adjusts and optimizes various machine parameters. These optimizations take into account many different influencing factors. They are based on a model that maps the dependencies between operating data such as engine speed, driving speed, sensor-acquired data concerning the field or the vehicle, and the machine parameters to be set.

[0005] This model is subject to regular adjustments to the current situation. In particular, the model is typically designed for a specific range of operational data and must be updated when this range is exceeded. These updates require significant computing power, which must be provided by the system. Current models are becoming increasingly complex.

[0006] Furthermore, the hardware required for optimizing the model is expensive, needs to be supplied with and kept up-to-date with data from a large number of implements, and is often idle for most of the time. For example, the tractor or implement sits idle somewhere overnight, and model updates often only account for a fraction of the time during a work process. Nevertheless, the hardware must be sized to allow for rapid model adjustments.

[0007] Such methods are known from EP 3 340 130 A1, EP 3 626 041 A1 and US 2021 / 068334 A1.

[0008] The invention is based on the problem of designing and further developing the known methods in such a way that further optimization is achieved with regard to the aforementioned problems.

[0009] The above problem is solved by the features of the characterizing part of claim 1.

[0010] The essential consideration is the fundamental idea of ​​outsourcing the adaptation of the model to the cloud.

[0011] Specifically, it is proposed that when the driver assistance system leaves the primary working area of ​​the model, it initiates an update routine; that in the update routine, the driver assistance system sends at least some of the operating data to a cloud control unit; that the cloud control unit determines an updated or newly determined model and update data based on the operating data; that the cloud control unit sends the update data to the driver assistance system; and that the driver assistance system determines machine parameter settings from the updated or new model depending on the operating data of the vehicle combination and adjusts them on the vehicle combination as part of the control and / or regulation of the machine parameters.

[0012] The model is a characteristic map model. This is based on characteristic maps that define the relationships between the operating data and the machine parameter settings. Characteristic map models are particularly suitable for controlling and / or regulating the vehicle combination on-site by making movements along the characteristic maps.

[0013] One advantage realized by the embodiment according to claim 2 is that optimization data remote from the tractor unit is also available in the cloud without the need for the complex transmission, storage, and processing of this data to the tractor unit. This allows for a further reduction in the hardware requirements of the tractor unit.

[0014] According to claim 3, the cloud control arrangement can also take into account functional data of the tractor-implement combination. This data can, in principle, encompass extensive technical relationships between the functions of the tractor and the implement.

[0015] The computing power available in the cloud and the ability to store functional data for many implements and tractors allows the complexity of the model and its calculation to increase further, thus improving the results of optimization using the model.

[0016] Claim 4 relates to the possibility that the operating data includes measurement data. In particular, a working height control is of interest here.

[0017] Soil cultivation implements as preferred attachments with the possibility of working depth control as a special case of working height control are then the subject of claim 5.

[0018] Claim 6 relates to the preferred speed of the update routine. This can be performed quickly overall during the work process and preferably without interrupting the work process.

[0019] Claim 7 relates to the period between initiating the update routine and its completion. A bridging routine may be provided for this purpose, in order to continue operating the agricultural unit in a targeted manner until the update routine is completed.

[0020] According to claim 8, it can be provided that the driver assistance system initiates the update routine before the present model can no longer be used or can no longer be used with good quality, so that a seamless transition to the updated model is expected to be achieved.

[0021] Claim 9 relates to preferred distributions of the hardware of the driver assistance system.

[0022] According to claim 10, a connection between the driver assistance system and the cloud control arrangement is preferably mobile network based, thereby enabling a fast connection.

[0023] In an embodiment according to claim 11, the transmitted operational data includes at least that operational data which has left the working area. This provides the cloud control arrangement with a good basis for a new or updated model.

[0024] According to a further teaching as claimed in claim 12, which has independent significance, a cloud control arrangement is set up for use in the proposed method.

[0025] Reference may be made to all descriptions of the proposed method. The invention is explained in more detail below with reference to a drawing that illustrates only one embodiment. The drawing shows Fig. 1 shows an agricultural team for use in the proposed procedure.

[0026] In this case, the tractor 1 has a rear linkage designed as a three-point linkage. An implement 2 attached to the rear linkage can be, in particular, a tillage implement such as a plow 3, a cultivator, or a harrow. Together, the tractor 1 and the agricultural implement 2 form an agricultural vehicle combination 4.

[0027] The embodiment shown in the figures, which is preferred in this respect, relates to a method for optimizing the setting of machine parameters of an agricultural vehicle combination 4, wherein the vehicle combination 4 comprises a tractor 1 and an agricultural implement 2.

[0028] The vehicle combination 4 also features a driver assistance system 5 that controls and / or regulates machine parameters of the vehicle combination 4 in real time during a work process.

[0029] The machine parameters can encompass a wide variety of adjustable parameters of the agricultural vehicle combination 4. This includes parameters of the engine control, the rear linkage, hydraulic cylinders, the driving speed, etc. The machine parameters can be direct settings of a technical component, such as the opening duration of a valve, or more abstract parameters such as driving speed and engine pressure settings.

[0030] The driver assistance system 5 includes a model for controlling and / or regulating the machine parameters of the vehicle combination 4. The driver assistance system 5 uses this model to determine machine parameter settings based on the operating data of the vehicle combination 4. In an execution routine, the driver assistance system 5 determines these machine parameter settings within the framework of controlling and / or regulating the machine parameters and sets them on the vehicle combination 4.

[0031] The model is optimized for a single operating point, and preferably for a specific operating point. It is designed to have a primary operating range for the operating data. This primary operating range can, for example, include a predefined engine speed of tractor 1 and a predefined range of the tractive force of the rear linkage's tractive force control. Preferably, the primary operating range includes ranges of many such parameters.

[0032] When operational data causes the model to leave its primary working range, the driver assistance system 5 triggers an update or recalculation of the model. A single operational parameter leaving the model may be sufficient, but several may also be necessary.

[0033] The term "operating data" encompasses all relevant processes, settings, measurement data, and the like for machine combination 4 and its environment. Settings and measured values ​​of machine parameters are also considered operating data. This includes both actively configured and dependently configured operating data. The latter, in particular, can deviate from the operating range.

[0034] It is essential that the driver assistance system 5 initiates an update routine when the model leaves its primary working area, that the driver assistance system 5 sends at least some of the operating data to a cloud control unit 6 in the update routine, that the cloud control unit 6 determines an updated or newly determined model and update data for it based on the operating data, that the cloud control unit 6 sends the update data to the driver assistance system 5, and that the driver assistance system 5 determines machine parameter settings from the updated or new model depending on the operating data of the vehicle combination 4 and sets these settings on the vehicle combination 4 as part of the control and / or regulation of the machine parameters.

[0035] Here, and preferably, a copy of the model is stored in the cloud control arrangement, which can then update it. The term "cloud" is representative of all suitable server architectures and should be understood broadly. It can also refer to dedicated servers.

[0036] The update data can fully comprise the updated or newly determined model or a condensed representation thereof, from which the updated or newly determined model can be derived. Preferably, the updated or new model can be determined from the update data and the current model. The update data can therefore include an update of the current model. Thus, the driver assistance system 5 can determine or extract the updated or new model from the update data.

[0037] The term "model" is to be interpreted broadly. Here, it refers to a characteristic map model, but it also encompasses models underlying conventional control systems and the like. The updating or recalculation of the model is also to be interpreted broadly. It is essential to distinguish between a purely application-oriented, arbitrarily configured model and a modification of the model itself. The application itself is, in this context, and preferably, significantly less computationally intensive than the updating or recalculation.

[0038] Furthermore, it is provided here that the model is a characteristic map model, which has characteristic maps that depict the dependencies of the machine parameter settings on the operating data; preferably, that in the update routine the characteristic maps are updated or recalculated by the cloud control arrangement 6.

[0039] Characteristic maps are equations or combinations of equations and / or inequalities or otherwise depicted dependencies between machine parameter settings, measurement data, influencing factors and the like.

[0040] The characteristic curves are preferably designed to maintain relative constancy with respect to some influencing factors while remaining flexible with respect to others. They comprise input parameters and output parameters, where the input parameters are, for example, measurement data and the output parameters are settings for machine parameters.

[0041] Such characteristic maps have the advantage of being optimized for a specific operating point or primary operating range, and changes in machine parameters within this primary operating range can be represented with minimal computing power. Changes in machine parameters can thus be understood as movements on the characteristic maps, which usually do not force a change in the maps themselves. Only when fundamental assumptions or significant changes occur are the characteristic maps adjusted to react accordingly. As long as the characteristic maps themselves do not change, one can therefore speak of control; changes to the characteristic maps correspond to a type of regulation.

[0042] It may also be provided that the cloud control arrangement 6 in the update routine continues to determine the updated or newly determined model based on optimization data from outside the field, preferably that the optimization data from outside the field is selected from the list including weather data, soil data, preferably laboratory data, in particular nutrient data and / or soil type data, optimization objectives, cost data, satellite data, field inventory data, preferably crop type, crop rotations and / or biomass data, and / or planning data of the work process and / or other work processes.

[0043] The term "remote from team" refers to data that is not determined by team 4.

[0044] The laboratory data can be derived from soil samples. Soil type can be, for example, clay soil or sandy soil, etc.

[0045] The optimization goals can be defined by a user or determined automatically. These goals include objectives such as cost efficiency, maximum processing speed, etc. In essence, the optimization goals are strategic specifications.

[0046] Furthermore, it is preferably provided that the cloud control arrangement 6 in the update routine continues to determine the updated or newly determined model based on functional data of the tractor 1 and / or the implement 2.

[0047] The functional data includes technical dependencies and functions of the tractor 1 and / or the implement 2, data such as weight, engine power, and the like. Here, and preferably comprehensively, they provide information about the tractor-implement combination 4 and form the technical basis for determining the model.

[0048] Furthermore, it is possible that the operating data includes measurement data from a sensor 7 of the vehicle combination 4, preferably that the measurement data relates to the environment of the vehicle combination 4 and / or machine parameters of the vehicle combination 4, and / or that the measurement data relates to a working height 8 of the implement 2. The measurement data may also include data from a drive train of the tractor 1.

[0049] Here, and preferably, at least one characteristic map includes the dependence of the working height 8 on machine parameters and / or field parameters. For example, the working height 8 depends on settings of the device interface. The working height 8 itself, in turn, influences fuel consumption, work quality, etc.

[0050] Furthermore, it is preferably provided here that the implement 2 is a soil cultivation implement, preferably that the soil cultivation implement is a plow 3 or a cultivator or a harrow, and more preferably that the measurement data relate to a working depth of the soil cultivation implement.

[0051] The primary working range can encompass a working depth range that is to be maintained. If maintaining the working depth is no longer possible, for example, because the tractor's control system cannot maintain a target range specified by the model, the model can be updated. Updating the model can also simply involve setting a new working depth. Preferably, however, updating the model includes changing the dependencies between operating data and machine parameter settings, in the form of characteristic maps.

[0052] Controlling working depth is inherently complex, as a multitude of factors and measurement data must be coordinated and considered. Controlling the rear linkage, such as position control or draft control, must be aligned with parameters of tractor 1, such as tire compression and tractor 1's inclination, which affect the position of the rear linkage, as well as with machine parameters of implement 2. The proposed cloud solution offers sufficient computing power for this purpose.

[0053] Furthermore, it is preferably provided that the agricultural implement 2 has a sensor 7 for determining an absolute working depth.

[0054] The term "absolute" does not necessarily refer to a highly precise measurement, but rather generally to a measurement relative to the ground. It is theoretically possible for the measurement to be taken relative to a component whose height relative to the ground is known; however, the absolute working depth is preferably measured directly relative to the ground.

[0055] The sensor 7 can be part of a sensor assembly 9. The sensor assembly 9 can have at least one sensor holder 10, wherein the sensor 7 is reversibly mounted at a mounting position 11 on different attachments 2 by means of the at least one sensor holder 10.

[0056] The sensor 7 is mounted at mounting position 11 using the sensor holder 10 in such a way that the mounting is non-destructive and reversible. In particular, it is not carried out during the manufacture of the attachment 2, but is preferably possible on-site in the field with simple tools or even without any tools at all.

[0057] The sensor 7 can therefore be used with various implements 2 and is thus modular. An advantageous application of this modular sensor technology also concerns the measurement of the working depth with an implement 2 that does not have its own electronics. As will be shown below, the sensor arrangement 9 can therefore be independent of the implement 2. Alternatively, the sensor 7 can be designed not to communicate with the implement 2. Likewise, the sensor 7 can be integrated into or communicate with the electronics of the implement 2. Even with implements 2 that have electronics, the sensor 7 can still not communicate directly with the implement 2. Preferably, the sensor arrangement 9 can be used with implements 2 both with and without electronics. Ultimately, it is even possible for the determination of the working depth to be completely independent of the agricultural implement 2.

[0058] Here, and preferably, it is provided that the sensor holder 10 can be mounted separately from the sensor 7 on the different attachments 2. The sensor 7 can then be reversibly mounted on the sensor holder 10. It is generally possible for the sensor holder 10 to be a relatively inexpensive, mass-produced component, while the sensor 7 itself is relatively expensive. The proposed method allows for the reuse of the expensive sensor 7.

[0059] For convenience, however, the sensor bracket 10 may be left attached to the implement 2. This allows for a more stable and complex mounting of the sensor bracket 10, while the mounting of the sensor 7 to the sensor bracket 10 itself is preferably relatively simple. This also allows the same mounting position 11 to be reused when the sensor 7 is reattached.

[0060] To determine the working height 8, a reference height can be stored that specifies the working height 8 at a known distance of the sensor 7 from the ground. The working height 8 can then be calculated as the difference between this reference height and the working height 8.

[0061] This combination enables the control of the working depth using the measurement data from sensor 7 and the adaptation of this control via the update routine. It is particularly interesting that the working depth depends on many machine parameters. If these parameters collectively deviate from the working range, this indicates a significant deviation of the current situation from the model's operating point. The cloud control arrangement 6 enables the analysis of the situation and the adaptation of the model accordingly.

[0062] Furthermore, the update routine should preferably take no more than 5 minutes, preferably no more than 2 minutes, and preferably no more than 30 seconds.

[0063] Thus, control and / or regulation of the vehicle combination 4 based on the model in real time, i.e. with a delay of a few seconds or less, is provided for, and the adaptation of the model is also possible relatively quickly.

[0064] Furthermore, it is preferably provided that the driver assistance system 5 performs a bridging routine during the update routine, in which the driver assistance system 5 controls or regulates the machine parameters of the vehicle combination 4 according to a bridging rule.

[0065] It may be provided that the driver assistance system 5, in particular depending on the operating data, resorts to an emergency model in the bridging routine and / or makes emergency settings of the machine parameters and / or continues to use the current model and / or updates or recalculates it to a reduced extent.

[0066] The emergency model might be designed, for example, to reach or remain in a safe operating range, particularly without considering optimization goals or similar factors. It might also be designed to minimize any noticeable deviations for the user. Therefore, continuing to use the current model might also be a sensible option.

[0067] An emergency stop or emergency deceleration of the vehicle combination 4 can also be provided, for example, in the event of excessive changes in the operating data.

[0068] Furthermore, it may be provided that the model has a real working area that is larger than the primary working area, so that the update routine is expected to be completed before the real working area is left, preferably that the driver assistance system 5 takes into account a latency of the connection to the cloud control arrangement 6 and / or an expected computation time of the cloud control arrangement 6 and triggers the update routine in such a way that the update routine is expected to be completed before the real working area is left.

[0069] Furthermore, it is preferably provided here that the driver assistance system 5 comprises a tractor unit 12 and an implement unit 13, preferably that the hardware of the driver assistance system 5 is arranged on the tractor 1 and charges the implement unit 13 as required, or that the hardware tractor unit 12 is arranged on the tractor 1 and the hardware of the implement unit 13 is arranged on the implement 2.

[0070] Preferably, the driver assistance system 5 communicates with the cloud control arrangement 6 via mobile communication, in particular 4G or 5G, and / or satellite-based communication.

[0071] Additionally, it is preferably provided here that the operational data sent by the driver assistance system 5 to the cloud control arrangement 6 includes at least the operational data that has left the primary working area.

[0072] Furthermore, in accordance with another doctrine which has independent significance, it is proposed that a Cloud Control Arrangement 6 be established for use in the proposed procedure.

[0073] Reference may be made to all statements regarding the proposed procedure. Reference symbol list

[0074] 1 Tractor 2 Agricultural implement 3 Plow 4 Agricultural vehicle combination 5 Driver assistance system 6 Cloud control arrangement 7 Sensor 8 Working height 9 Sensor arrangement 10 Sensor bracket 11 Mounting position 12 Tractor unit 13 Implement unit

Claims

1. Method for optimizing the setting of machine parameters of an agricultural vehicle combination (4), wherein the vehicle combination (4) comprises a tractor (1) and an agricultural attachment (2), wherein the vehicle combination (4) comprises a driver assistance system (5) which controls and / or regulates machine parameters of the vehicle combination (4) in real time during a working process, wherein the driver assistance system (5) has a model for controlling and / or regulating the machine parameters of the vehicle combination (4), wherein the driver assistance system (5) determines settings of the machine parameters from the model on the basis of operating data relating to the vehicle combination (4), wherein the driver assistance system (5) determines the settings of the machine parameters when controlling and / or regulating the machine parameters in an implementation routine and sets them on the vehicle combination (4), wherein the model has a primary working area for the operating data, wherein the driver assistance system (5), when operating data leave the primary working area of the model, initiates an update or recalculation of the model, wherein the driver assistance system (5), when the primary working area of the model is left, initiates an update routine, wherein the driver assistance system (5) in the update routine sends at least a portion of the operating data to a cloud control arrangement (6), wherein the cloud control arrangement (6) determines an updated or newly determined model and update data therefor on the basis of the operating data, wherein the cloud control arrangement (6) sends the update data to the driver assistance system (5), and wherein the driver assistance system (5) determines settings of the machine parameters from the updated or new model on the basis of the operating data relating to the vehicle combination (4) and when controlling and / or regulating the machine parameters and sets them on the vehicle combination (4), characterized in that the model is a characteristic diagram model having characteristic diagrams representing the dependencies of the settings of the machine parameters on the operating data, and in that in the update routine the characteristic diagrams are updated or newly determined by the cloud control arrangement (6).

2. Method according to Claim 1, characterized in that the cloud control arrangement (6) in the update routine also determines the updated or newly determined model on the basis of optimization data remote from the vehicle combination, preferably in that the optimization data remote from the vehicle combination are selected from the list comprising weather data, soil data, preferably laboratory data, in particular nutrient data and / or soil type data, optimization goals, cost data, satellite data, field crop data, preferably crop type, crop rotations and / or biomass data, and / or planning data relating to the working process and / or other working processes.

3. Method according to Claim 1 or 2, characterized in that the cloud control arrangement (6) in the update routine also determines the updated or newly determined model on the basis of functional data relating to the tractor (1) and / or the attachment (2).

4. Method according to one of the preceding claims, characterized in that the operating data comprise measurement data from a sensor (7) of the vehicle combination (4), preferably in that the measurement data relate to an environment of the vehicle combination (4) and / or machine parameters of the vehicle combination (4), and / or in that the measurement data relate to a working height (8) of the attachment (2).

5. Method according to one of the preceding claims, characterized in that the attachment (2) is a soil cultivation device, preferably in that the soil cultivation device is a plough (3) or a cultivator or a harrow, further preferably in that the measurement data relate to a working depth of the soil cultivation device.

6. Method according to one of the preceding claims, characterized in that the update routine lasts at most 5 minutes, preferably at most 2 minutes, further preferably at most 30 s.

7. Method according to one of the preceding claims, characterized in that the driver assistance system (5) performs a bridging routine during the update routine, in which bridging routine the driver assistance system (5) controls or regulates the machine parameters of the vehicle combination (4) according to a bridging rule, in that the driver assistance system (5), in particular on the basis of the operating data, resorts to an emergency model in the bridging routine and / or makes emergency settings of the machine parameters and / or continues to use the current model and / or updates or recalculates it to a reduced extent.

8. Method according to one of the preceding claims, characterized in that the model has a real working area which is larger than the primary working area, with the result that the update routine is expected to be completed before the real working area is left, preferably in that the driver assistance system (5) takes into account a latency of the connection to the cloud control arrangement (6) and / or an expected computing time of the cloud control arrangement (6) and initiates the update routine such that the update routine is expected to be completed before the real working area is left.

9. Method according to one of the preceding claims, characterized in that the driver assistance system (5) comprises a tractor unit (12) and an attachment unit (13), preferably in that the hardware of the driver assistance system (5) is arranged on the tractor (1) and loads the attachment unit (13) as required, or in that the tractor hardware unit (12) is arranged on the tractor (1) and the hardware of the attachment unit (13) is arranged on the attachment (2).

10. Method according to one of the preceding claims, characterized in that the driver assistance system (5) communicates with the cloud control arrangement (6) via mobile radio.

11. Method according to one of the preceding claims, characterized in that the operating data sent by the driver assistance system (5) to the cloud control arrangement (6) comprise at least the operating data that have left the primary working area.

12. Cloud control arrangement configured for use in the method according to one of the preceding claims.