Patent Publication Number: US-10310455-B2

Title: Combine harvester control and communication system

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     The present application is a divisional of and claims priority of U.S. patent application Ser. No. 15/626,967, filed Jun. 19, 2017, the content of which is hereby incorporated by reference in its entirety. 
    
    
     FIELD OF THE DESCRIPTION 
     The present description relates to a control interface for an agricultural machine. More specifically, the present description relates to a control interface for an operator of a combine harvester and/or for a remote operator. 
     BACKGROUND 
     There are a wide variety of different types of equipment, such as construction equipment, turf management equipment, forestry equipment, and agricultural equipment. These types of equipment are operated by an operator. For instance, a combine harvester (or combine) is operated by an operator, and it has many different mechanisms that are controlled by the operator in performing a harvesting operation. The combine may have multiple different mechanical, electrical, hydraulic, pneumatic, electromechanical (and other) subsystems, some or all of which can be controlled, at least to some extent, by the operator. The systems may need the operator to make a manual adjustment outside the operator&#39;s compartment or to set a wide variety of different settings and provide various control inputs in order to control the combine. Some inputs not only include controlling the combine direction and speed, but also threshing clearance and sieve and chaffer settings, rotor and fan speed settings, and a wide variety of other settings and control inputs. 
     Because of the complex nature of the combine operation, it can be very difficult to know how a particular operator or machine is performing in a given harvesting operation. While some systems are currently available that sense some operational and other characteristics, and make them available to reviewing personnel, those current systems are normally informational in nature. 
     The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter. 
     SUMMARY 
     A logic system aggregates metrics from a plurality of combine harvesters and controls a communication system to send aggregated metrics back to the combine harvesters and to send both machine performance metrics and aggregate metrics to a remote user computing system, for remote user control. 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. The claimed subject matter is not limited to implementations that solve any or all disadvantages noted in the background. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a partial pictorial, partial schematic illustration of a combine harvester. 
         FIG. 2  is a block diagram of one example of a computing system architecture that includes the combine harvester illustrated in  FIG. 1 . 
         FIG. 3  is a block diagram showing one example of performance metric generator logic in more detail. 
         FIG. 4  is a block diagram showing one example of a remote analytics logic in more detail. 
         FIG. 5  is a block diagram showing one example of display generator logic in more detail. 
         FIG. 6  is a block diagram showing one example of a remote user control interface. 
         FIGS. 7A-7D  show various examples of a remote user control interface. 
         FIG. 8  shows another example of a remote user control interface. 
         FIG. 9  shows another example of a remote user control interface. 
         FIG. 10  is a block diagram of one example of an operator control interface that can be generated on the combine harvester illustrated in  FIGS. 1 and 2 . 
         FIGS. 11A-11C  show various examples of an operator control interface. 
         FIG. 12  shows one example of user interaction with an operator control interface. 
         FIGS. 13A and 13B  (collectively referred to herein as  FIG. 13 ) show one example of the operation of the architecture shown in  FIG. 2  in generating control user interfaces for user interaction. 
         FIG. 14  shows one example of the architecture illustrated in  FIG. 2 , deployed in a remote server environment. 
         FIGS. 15-17  show examples of mobile devices that can be used in the architectures shown in the previous FIGS. 
         FIG. 18  is a block diagram showing one example of a computing environment that can be used in the architectures shown in the previous figures. 
     
    
    
     DETAILED DESCRIPTION 
     Combine harvesters often have a wide variety of sensors that sense a variety of different variables, such as operating parameters, along with crop characteristics, environmental parameters, etc. The sensors can communicate this information over a controller area network (CAN) bus (or another network, such as an Ethernet network, etc.) to various systems that can process the sensor signals and generate output signals (such as control signals) based on the sensed variables. Given the complex nature of the control operations needed to operate a combine harvester, and given the wide variety of different types of settings and adjustments that an operator can make, and further given the widely varying different types of crops, terrain, crop characteristics, etc. that can be encountered by a combine harvester, it can be very difficult to determine how a particular machine, or operator, is performing. This problem is exacerbated when a particular organization has a plurality of different combine harvesters that are all operating at the same time. These combine harvesters are often referred to as a “fleet” of harvesters. 
     The operation of the fleet of harvesters is often overseen by a (remote or local) fleet manager (or farm manager) who is located remotely relative to at least some of the combine harvesters in the fleet. It can be extremely difficult for a farm manager or remote manager to determine how the various combine harvesters are operating in the fleet, how they are operating relative to one another, how they are operating relative to other similarly situated harvesters, etc. 
     It is also extremely difficult for a remote manager to identify performance criteria for the various operators and machines, and determine how they compare relative to one another, in near real time. Thus, it is very difficult for a remote manager to attempt to modify the settings on any combine harvester to increase the performance of that harvester. This is because the remote manager does not have access to the current settings of a particular machine, nor does the remote manager have access to an interface that allows the remote manager to view and interact with display elements that indicate how various machines and operators are performing relative to one another. 
     Instead, the remote manager often needs to review data after the harvesting season, and even then the task is difficult. The remote manager often needs to switch between different applications, between different views of data, for the different machines and operators, in an attempt to compare the data in this way. This results in a relatively large amount of bandwidth consumption, because the operator often needs to make many different calls from his or her device to a remote data store where the information is stored. 
     Some systems currently allow remote viewing of settings, to some extent. One drawback is the delay time involved. In current systems, there may be a delay of thirty minutes or more. 
       FIG. 1  is a partial pictorial, partial schematic, illustration of an agricultural machine  100 , in an example where machine  100  is a combine harvester (or combine). It can be seen in  FIG. 1  that combine  100  illustratively includes an operator compartment  101 , which can have a variety of different operator interface mechanisms, for controlling combine  100 , as will be discussed in more detail below. Combine  100  can include a set of front end equipment that can include header  102 , and a cutter generally indicated at  104 . It can also include a feeder house  106 , a feed accelerator  108 , and a thresher generally indicated at  110 . Thresher  110  illustratively includes a threshing rotor  112  and a set of concaves  114 . Further, combine  100  can include a separator  116  that includes a separator rotor. Combine  100  can include a cleaning subsystem (or cleaning shoe)  118  that, itself, can include a cleaning fan  120 , chaffer  122  and sieve  124 . The material handling subsystem in combine  100  can include (in addition to a feeder house  106  and feed accelerator  108 ) discharge beater  126 , tailings elevator  128 , clean grain elevator  130  (that moves clean grain into clean grain tank  132 ) as well as unloading auger  134  and spout  136 . Combine  100  can further include a residue subsystem  138  that can include chopper  140  and spreader  142 . Combine  100  can also have a propulsion subsystem that includes an engine that drives ground engaging wheels  144  or tracks, etc. It will be noted that combine  100  may also have more than one of any of the subsystems mentioned above (such as left and right cleaning shoes, separators, etc.). 
     In operation, and by way of overview, combine  100  illustratively moves through a field in the direction indicated by arrow  146 . As it moves, header  102  engages the crop to be harvested and gathers it toward cutter  104 . After it is cut, it is moved through a conveyor in feeder house  106  toward feed accelerator  108 , which accelerates the crop into thresher  110 . The crop is threshed by rotor  112  rotating the crop against concave  114 . The threshed crop is moved by a separator rotor in separator  116  where some of the residue is moved by discharge beater  126  toward the residue subsystem  138 . It can be chopped by residue chopper  140  and spread on the field by spreader  142 . In other implementations, the residue is simply dropped in a windrow, instead of being chopped and spread. 
     Grain falls to cleaning shoe (or cleaning subsystem)  118 . Chaffer  122  separates some of the larger material from the grain, and sieve  124  separates some of the finer material from the clean grain. Clean grain falls to an auger in clean grain elevator  130 , which moves the clean grain upward and deposits it in clean grain tank  132 . Residue can be removed from the cleaning shoe  118  by airflow generated by cleaning fan  120 . That residue can also be moved rearwardly in combine  100  toward the residue handling subsystem  138 . 
     Tailings can be moved by tailings elevator  128  back to thresher  110  where they can be re-threshed. Alternatively, the tailings can also be passed to a separate re-threshing mechanism (also using a tailings elevator or another transport mechanism) where they can be re-threshed as well. 
       FIG. 1  also shows that, in one example, combine  100  can include ground speed sensor  147 , one or more separator loss sensors  148 , a clean grain camera  150 , and one or more cleaning shoe loss sensors  152 . Ground speed sensor  146  illustratively senses the travel speed of combine  100  over the ground. This can be done by sensing the speed of rotation of the wheels, the drive shaft, the axel, or other components. The travel speed can also be sensed by a positioning system, such as a global positioning system (GPS), a dead reckoning system, a LORAN system, or a wide variety of other systems or sensors that provide an indication of travel speed. 
     Cleaning shoe loss sensors  152  illustratively provide an output signal indicative of the quantity of grain loss by both the right and left sides of the cleaning shoe  118 . In one example, sensors  152  are strike sensors which count grain strikes per unit of time (or per unit of distance traveled) to provide an indication of the cleaning shoe grain loss. The strike sensors for the right and left sides of the cleaning shoe can provide individual signals, or a combined or aggregated signal. It will be noted that sensors  152  can comprise only a single sensor as well, instead of separate sensors for each shoe. 
     Separator loss sensor  148  provides a signal indicative of grain loss in the left and right separators. The sensors associated with the left and right separators can provide separate grain loss signals or a combined or aggregate signal. This can be done using a wide variety of different types of sensors as well. It will be noted that separator loss sensors  148  may also comprise only a single sensor, instead of separate left and right sensors. 
     It will also be appreciated that sensor and measurement mechanisms (in addition to the sensors already described) can include other sensors on combine  100  as well. For instance, they can include a residue setting sensor that is configured to sense whether machine  100  is configured to chop the residue, drop a windrow, etc. They can include cleaning shoe fan speed sensors that can be configured proximate fan  120  to sense the speed of the fan. They can include a threshing clearance sensor that senses clearance between the rotor  112  and concaves  114 . They include a threshing rotor speed sensor that senses a rotor speed of rotor  112 . They can include a chaffer clearance sensor that senses the size of openings in chaffer  122 . They can include a sieve clearance sensor that senses the size of openings in sieve  124 . They can include a material other than grain (MOG) moisture sensor that can be configured to sense the moisture level of the material other than grain that is passing through combine  100 . They can include machine setting sensors that are configured to sense the various configurable settings on combine  100 . They can also include a machine orientation sensor that can be any of a wide variety of different types of sensors that sense the orientation of combine  100 . Crop property sensors can sense a variety of different types of crop properties, such as crop type, crop moisture, and other crop properties. They can also be configured to sense characteristics of the crop as they are being processed by combine  100 . For instance, they can sense grain feed rate, as it travels through clean grain elevator  130 . They can sense mass flow rate of grain through elevator  130 , or provide other output signals indicative of other sensed variables. Some additional examples of the types of sensors that can be used are described below. 
       FIG. 2  is a block diagram showing one example of an architecture  200  that includes combine harvester  100  coupled for communication with remote analytics computing system  202  and remote manager computing  204 , over network  206 . Network  206  can be any of a wide variety of different types of networks, such as a wide area network, a local area network, a near field communication network, a cellular network, or any of a wide variety of other networks or combinations of networks. As is discussed in greater detail below, combine harvester  100  can communicate with other systems using store-and-forward mechanisms as well.  FIG. 2  also shows that, in one example, combine harvester  100  can generate operator interface displays  208  with user input mechanisms  210  for interaction by operator  212 . Operator  212  is illustratively a local operator of combine  100 , in the operator&#39;s compartment of combine  100 , and can interact with user input mechanisms  210  in order to control and manipulate combine harvester  100 . In addition, as is described below, operator  212  can interact directly with other user interface mechanisms on combine harvester  100 . This is indicated by arrow  214 . 
       FIG. 2  also shows that, in one example, remote manager computing system  204  illustratively generates user interfaces  216 , with user input mechanisms  218 , for interaction by remote user  220  (who may be a farm manager, a remote manager, or other remote user that has access to data corresponding to combine  100 ). Remote user  220  illustratively interacts with user input mechanisms  218  in order to control and manipulate remote manager computing system  204 , and, in some examples, to control portions of combine harvester  100  and/or remote analytics computing system  202 . 
     Before describing the overall operation of architecture  200  in more detail, a brief description of some of the items in architecture  200 , and their operation, will first be provided. As shown in  FIG. 2 , in addition to the items described above with respect to  FIG. 1 , combine  100  can include computing system  222 , one or more control systems  224 , controllable subsystems  226 , application running logic  228 , user interface logic  230 , data store  232 , one or more communication systems  234 , user interface mechanisms  236 , and it can include a wide variety of other items  238 . Computing system  222 , itself, can include one or more processors or servers  240 , performance metric generator logic  242 , display generator logic  244 , a plurality of different sensors  246 , and it can include a wide variety of other items  248 . User interface mechanisms  236  can include one or more display devices  250 , one or more audio devices  252 , one or more haptic devices  254 , and it can include other items  256 , such as a steering wheel, joysticks, pedals, levers, buttons, keypads, etc. 
     As described above with respect to  FIG. 1 , sensors  246  can generate a wide variety of different sensor signals representing a wide variety of different sensed variables. Performance metric generator logic  242  (as is described in greater detail below with respect to  FIG. 3 ) illustratively generates performance metrics indicative of the operational performance of combine  100 . Display generator logic  244  illustratively generates a control interface display for operator  212 . The display can be an interactive display with user input mechanisms  210  for interaction by operator  212 . 
     Control system  224  can generate control signals for controlling a variety of different controllable subsystems  226  based on the sensor signals generated by sensors  246 , based on the performance metrics generated by performance score generator logic  244 , based upon user inputs received through user interface mechanisms  236 , based upon information received from remote manager computing system  204  or from remote analytics computing system  202 , or it can generate control signals a wide variety of other ways as well. Controllable subsystems  226  can include a variety of different systems, such as a propulsion system used to drive combine  100 , a threshing subsystem as described above with respect to  FIG. 1 , a cleaning subsystem (such as the cleaning fan, the chaffer, the sieve, etc.) and/or a variety of other controllable subsystems, some of which are discussed above with respect to  FIG. 1 . 
     Application running logic  228  can illustratively run any of a variety of different applications that may be stored in data store  232 . The applications can be used to control combine  100 , to aggregate information sensed and collected by combine  100 , to communicate that information to other systems, etc. Communication systems  234  illustratively include one or more communication systems that allow combine  100  to communicate with remote analytics computing system  202  and remote manager computing system  204 . Thus, they include one or more communication systems, that can communicate over the networks described above. 
     Display generator logic  244  illustratively generates an operator display and uses user interface logic  230  to display the operator display on one of display devices  250 . It will be noted that display devices  250  can include a display device that is integrated into the operator compartment of combine  100 , or it can be a separate display on a separate device that may be carried by operator  212  (such as a laptop computer, a mobile device, etc.). All of these architectures are contemplated herein. 
     In the example shown in  FIG. 2 , remote analytics computing system  202  illustratively includes one or more processors or servers  260 , remote analytics logic  262  which exposes an application programming interface (API)  263 , data store  264 , authentication system  265 , one or more communication systems  266  and it can include a wide variety of other items  268 . Remote analytics logic  262  illustratively receives the performance metrics generated by performance metric generator logic  242  in computing system  222 , from a plurality of different combines, including combine  100 . It can illustratively aggregate that data and compare it to reference sets of data to generate multi-machine performance metrics that are based on the performance information from a plurality of different machines. The data can be stored on data store  202 , along with a wide variety of other information, such as operator information corresponding to the operators of each of the combines, machine details identifying the particular machines being used, the current machine settings for each machine that are updated by the machines, and historical data collected from the various machines. The data store  202  can include authentication information used to authenticate remote user  220 , operator  212 , and others. It can include mappings between combines and the remote users they are assigned to. It can include a wide variety of other information as well. 
     Remote analytics computing system  202  illustratively uses one or more of the communication systems  266  to communicate with both combine  100  (and other combines) and remote manager computing system  204 . 
     Remote manager computing system  204  can be a wide variety of different types of systems, such as a mobile device, a laptop computer, etc. It illustratively includes one or more processors  270 , data store  272 , application running logic  274 , communication system  276 , and user interface logic  278  (which, itself, includes display generator logic  280 , interaction processing logic  282 , and it can include other items  284 ). Remote manager computing system  204  can, also include a wide variety of other items  286 . 
     Application running logic  274  illustratively runs an application that allows remote user  220  to access comparison information that compares the performance of various combines  100  and their operators on a near real time basis (such as within five seconds of real time or within another time value of real time). It also illustratively surfaces user control interfaces  216 , with user input mechanisms  218  so that remote user  220  can provide settings inputs, or other control information, and communicate it to one or more combines  100 . Again, as with communication systems  234  and  266 , communication system  276  allows remote manager computing system  204  to communicate with other systems over network  206 . Display generator logic  282  illustratively generates a display, with various interactive display elements on control user interface  216 . Interaction processing logic  282  illustratively detects user interaction with the display, from remote user  220 , and performs control operations based upon those user interactions. 
       FIG. 3  is a block diagram showing one example of performance metric generator logic  242 , in more detail. In the example shown in  FIG. 3 , performance metric generator logic  242  illustratively includes grain loss/savings metric generator logic  288 , grain productivity metric generator logic  290 , fuel economy metric generator logic  292 , power utilization metric generator logic  294 , overall metric generator logic  296 , machine benchmark generator logic  298 , performance trend generator logic  300 , and it can include a wide variety of other items  302 . Some ways of generating performance metrics are shown in more detail in co-pending US Patent Publication numbers 2015/0199637 A1, 2015/0199360 A1, 2015/0199630 A1, 2015/0199775 A1, 2016/0078391 A1 which are incorporated herein by reference. 
     Grain loss/savings metric generator logic  288  illustratively generates a metric indicative of grain savings or grain loss that the combine  100  is experiencing. This can be generated by sensing and combining items such as the mass flow of crop through combine  100  sensed by a sensor  246 , tailings volume of tailings output by combine  100  using a volume sensor, crop type, the measured loss on combine  100  using various loss sensors (such as separator loss sensors, cleaning shoe loss sensors, etc.), among others. The metric can be generated by performing an evaluation of the loss using fuzzy logic components and an evaluation of the tailings, also using fuzzy logic components. Based upon these and/or other considerations, grain loss/savings metric generator logic  288  illustratively generates a grain loss/savings metric indicative of the performance of combine  100 , under the operation of operator  212 , with respect to grain loss/savings. 
     Grain productivity metric generator logic  290  illustratively uses the sensor signals generated by sensors  246  on the combine to sense vehicle speed, mass flow of grain through combine  100 , and the machine configuration of combine  100  and generates an indication of crop yield and processes the crop yield to evaluate it against a productivity metric. For instance, a productivity metric plotted against a yield slope provides an output indicative of grain productivity. This is only one example. 
     Fuel economy metric generator logic  292  illustratively generates a fuel economy metric, based upon the throughput versus fuel consumption rate sensed by sensors on the combine  100 , a separator efficiency metric and also, based upon sensed fuel consumption that is sensed by a sensor  246 , vehicle state, vehicle speed, etc. The fuel economy metric can be based on a combination of a harvest fuel efficiency and a non-productive fuel efficiency. These metrics may indicate, respectively, the efficiency of combine  100  during harvesting operations, and in other, non-harvesting operations (such as when idling, etc.). Again, fuzzy logic components are illustratively applied to generate a metric indicative of fuel economy, although this is only one example. 
     Power utilization generator logic  294  illustratively generates a power utilization metric based on sensor signals from sensors  246  (or based on derived engine power used by combine  100 , that is derived from sensor signals) under the control of operator  212 . The sensors may generate sensor signals indicative of engine usage, engine load, engine speed, etc. The power utilization metric may indicate whether the machine could be more efficiently run at higher or lower power levels, etc. 
     Overall metric generator logic  296  illustratively generates a metric that is based upon a combination of the various metrics output by logic  288 - 294 . It illustratively provides a metric indicative of the overall operational performance of combine  100 , under the operation of operator  212 . 
     Machine benchmark generator logic  298  illustratively generates a machine benchmark metric for each of the metrics generated by items of logic  288 - 296 . The machine benchmark metric can, for instance, reflect the operation of combine  100 , under the control of operator  212 , for each of the particular metrics, over a previous time period. For instance, the machine benchmark metric for grain loss/savings may be an average of the value of the grain loss/savings metric generated by logic  288  over the prior 10 hours (or over another time period). In one example, machine benchmark generator logic  298  generates such a benchmark metric for each of the categories or metrics generated by items of logic  288 - 296 . 
     Performance trend generator logic  300  illustratively generates a metric indicative of the performance of machine  100 , under the operation of operator  212 , over a shorter period of time than is considered by machine benchmark generator logic  298 . For instance, performance trend generator logic  300  illustratively generates a trend metric indicating how combine  100  has performed over the previous 30 minutes, in each of the performance categories addressed by items of logic  288 - 296 . In one example, it saves periodically-generated values so that it can generate a trace or continuous depiction of the value of that particular metric over the previous 30 minutes (or other time period). This is described in more detail below with respect to  FIGS. 7A and 12 . 
       FIG. 4  is a block diagram showing one example of remote analytics logic  262  in more detail.  FIG. 4  shows that, in one example, remote analytics logic  262  includes multi-machine aggregation logic  304 , fleet benchmark generator logic  306 , group (e.g., location-based group or other group) benchmark generator logic  308 , global benchmark generator logic  310 , performance distribution and range generator logic  312 , and it can include a wide variety of other items  314 . Multi-machine aggregation logic  304  illustratively aggregates performance information received from a plurality of different combines (including combine  100 ) and aggregates that information so that it can be stored or retrieved for comparison or other processing. Fleet benchmark generator logic  306  illustratively generates a fleet benchmark metric based upon the multi-machine information aggregated by logic  304 . The fleet benchmark metric is illustratively indicative of the performance of a fleet of combines  100  corresponding to a particular organization that are currently harvesting the same crop as combine  100 , over the last 10 hours (or other time period). Thus, in one example, fleet benchmark generator logic  306  illustratively generates an average metric indicating the average performance metric, in each of the performance categories discussed above with respect to  FIG. 3 , for all combines currently operating in the fleet. The average may be calculated based upon the particular performance metric values aggregated for all such combines over the last 10 hours. 
     Group (e.g., location-based group or other group) benchmark generator logic  308  illustratively generates a similar benchmark metric, except that the number of combines that the metric is generated over is larger than that used by fleet benchmark generator logic  306 . Instead, combines from which data is obtained to generate the group benchmark metric may include data from multiple fleets or other groups. 
     Global benchmark generator logic  310  generates a similar set of metrics (one for each of the performance categories discussed above with respect to  FIG. 3 ), except that the number of combines from which data is obtained to generate the metric is larger than that used by group benchmark generator logic  308 . For instance, in one example, global benchmark generator logic  310  may generate a performance metric based upon the performance data obtained from all combines (which are accessible by remote analytics computing system  202 ) that are harvesting globally in a particular crop. The metric may be generated based on the data aggregated from those combines over the past 10 hours (or other time period). 
     Performance distribution and range generator logic  312  illustratively identifies a statistical distribution of observed performance values for combines  100 . The statistical distribution may be generated in terms of a bell curve so that the performance values are divided into ranges corresponding to a high performance operating range, an average performance operating range and a low performance operating range. These are examples only. 
       FIG. 5  is a block diagram showing one example of display generator logic  280  on remote manager computing system  204  in more detail. In the example illustrated in  FIG. 5 , display generator logic  280  illustratively includes bar graph display element generator  316 , numeric display element generator  318 , machine benchmark display element generator  320 , fleet benchmark display element generator  322 , group benchmark display element generator  324 , global benchmark display element generator  326 , multi-range performance distribution display element generator  328 , trend display generator  330 , display device controller  331 , and it can include a wide variety of other items  332 . Each of the generators  316 - 330  generate a display element corresponding to the various performance metrics described above with respect to  FIGS. 3 and 4 . Therefore, generator  316  illustratively generates a bar graph display element corresponding to each of the metrics described above. It illustratively generates a bar graph display element corresponding to each of the performance metrics indicative of grain loss/savings, grain productivity, fuel efficiency, power utilization, and the overall metric described above with respect to  FIG. 3 . Generator  318  illustratively generates a numeric display corresponding to the bar graphs generated by generator  316 , and also corresponding to the fleet benchmark metric generated by fleet benchmark generator  306 . Machine benchmark display element generator  320  illustratively generates a display element corresponding to the machine benchmarks generated by machine generator logic  298 . Fleet benchmark display element generator  322  generates a display element corresponding to the fleet benchmarks generated by fleet benchmark generator logic  306 . Group benchmark display element generator  324  and global benchmark display element generator  326  generate display elements corresponding to each of the group benchmark metrics and global benchmark metrics described above with respect to logic  308  and  310  in  FIG. 4 , and multi-range performance distribution display element generator  328  generates a display element corresponding to the performance distribution and ranges generated by logic  312  in  FIG. 4 . Trend display generator  330  illustratively generates display elements indicative of the trends identified by performance trend generator logic  300 . Display device controller  331  controls the display device on which the display is generated to place elements, relative to one another, as described in more detail below. 
       FIG. 6  is a block diagram showing one example of a remote manager (or remote user) control interface  216 . Interface  216  illustratively includes field selection and display element  334 . Element  334  illustratively includes a user actuatable element that allows user  220  to select a particular field being harvested so that user  220  can see and compare the operation of the various combines  100  harvesting in the selected field. Element  334  can also include a description or identifier corresponding to the selected field. 
     Performance display section  336  illustratively displays the display elements discussed above, that identify the performance of various machines, with respect to different performance categories or performance criteria, so that they can be compared relative to one another. Thus, the user control interface  216  illustratively includes multi-range performance distribution display elements  338  that are visually correlated to a pillar display and comparison section  340 . A number of examples of this are described below with respect to  FIGS. 7A-7D . 
     Pillar display and comparison section  340  illustratively includes multi-machine pillar metric display and comparison section  342  that displays the various pillar metrics (also referred to as performance metrics) for the plurality of combines so that they can be compared relative to one another. It can include bar graph display elements  344 , numeric display elements  346 , machine benchmark display elements  348  for each pillar metric, fleet benchmark graphic and numeric display elements  350  for each pillar metric, group (e.g., dealer) benchmark display elements  352  for each pillar metric, global benchmark display elements  354  for each pillar metric, and it can include other items  356 . Pillar display and comparison section  340  also illustratively includes legend  358 . 
     The performance display section  336  can also include trend section  360  which includes a user actuatable machine selector display element  362 , a user actuatable performance pillar selector display element  364 , and performance trend display section  366 . User  220  illustratively actuates machine selector display element  362  in order to select one of the multiple combines that the user  220  has access to, for display. User  220  illustratively actuates performance pillar selector display element  364  to select a particular performance pillar or performance metric for which user  220  wishes to see a trend, corresponding to the selected combine. Performance trend display section  336  then illustratively displays trend information for the selected combine that includes machine benchmark display element  368 , machine performance display element  370 , and multi-range distribution display element  372 . Element  368  illustratively displays the machine benchmark display element, for the machine selected by selector  362 , and for the particular performance pillar (or performance criteria) selected by selector  364 . Machine performance display element  370  illustratively displays the value of the performance pillar selected by display element  364 , to show how it has varied over a predetermined period of time (such as the prior 30 minutes, 60 minutes, etc.). Both display elements  368  and  370  are plotted relative to display element  372  so user  220  can quickly see how they compare to the performance distribution represented by display elements  372 . Performance trend display element  366  can include other items as well. This is indicated by block  374 . 
     In one example, remote user control interface  216  illustratively includes machine detail actuator  376 , actuatable machine setting display section  378 , and it can include other items  380 . Machine detail actuator  376  can illustratively be actuated by user  220 . This controls communication system  276  to communicate with combine  100  (the selected combine) and/or system  202  to retrieve information from the combine  100  and/or system  202 . The retrieval information, which can be displayed can include machine details, such as the identifying information corresponding to the machine, the operator information corresponding to the operator that is currently operating the machine, the service record of the machine, the machine settings, near real time sensor signal values being generated by sensors  246  on the selected machine, etc. The communication is illustratively provided over a secure link after authenticating remote user  220 . 
     Machine settings display section  378  illustratively displays the current settings of the selected machine. For instance, it may display the fan speed settings, the rotor speed settings, the sieve and chaffer clearance settings, the thresher clearance settings, among other settings. In one example, when user  220  actuates the actuatable machine settings display element  378 , user  220  is navigated through a user experience that allows the user to request or recommend adjustments to the displayed settings. This is described in greater detail below. 
       FIG. 7A  shows one example of a more detailed view of remote manager user interface  216 , shown in block diagram form in  FIG. 6 . Some of the items illustrated in  FIG. 6 , are shown in more detail in  FIG. 7A .  FIG. 7B  is an enlarged view of a portion of  FIG. 7  A, and it is enlarged for the sake of clarity.  FIGS. 7A and 7B  will now be described in conjunction with one another. Similar items to those shown in  FIG. 6  are similarly numbered in  FIGS. 7A and 7B . 
     It can be seen in  FIGS. 7A and 7B  that the fleet selector/display element  334  includes a written description  390  of the field that has been selected, and the crop planted in the field. It can also be seen that pillar display and comparison section  340  illustratively displays bar graphs, for each performance metric, for two different combines. While a similar display can be generated for a single machine, and that is expressly contemplated herein, the present description proceeds with respect to displaying data for multiple machines. The bar graphs for the two different combines are displayed adjacent one another, so that each performance metric can be compared, in a relatively straight forward way, between the two combines. The bar graph for the first combine is designated M 1  while the bar graph for the second combine is designated M 2 . In addition, numeric display elements  346  are shown toward the bottom of each bar graph so that the remote operator  220  need not translate the meaning of the bar graph, but may instead identify a particular numeric value corresponding to each performance pillar (or performance metric) for each machine (or combine). 
     In one example, the bar graphs  344  can be color-coded. For instance, they may be color coded based on the score level. There may be multiple different colors to code different score levels. In another example, if the machine is operating above a particular threshold value (such as the machine benchmark, the fleet benchmark, etc.), then the bar graph may be colored a first color. However, if the machine is operating below the threshold value, then the bar graph may be colored a second color. It can also be seen in  FIGS. 7A and 7B  that the fleet benchmark graphic element  350  is represented by both a line and a numeric value. Display device controller  331  controls the display device so they are placed on the bar graph at a position that corresponds to the numeric value, assuming that the bar graph corresponds to the numeric value  346  assigned to it. Thus, as shown in  FIGS. 7A and 7B , the bar graphs showing the overall performance metric for machine  1  and machine  2  have numeric values of 82 and 67, respectively. Therefore, display device controller  331  places the fleet benchmark display element  350 , which corresponds to a numeric value of 72, above the top of the bar graph for machine M 2  but below the top of the bar graph for machine M 1 . 
     The machine benchmark display elements are represented by dashed lines segments shown at  348 . They are also placed on the bar graph corresponding to the machine that has that benchmark value. The group benchmark display elements and global benchmark display elements are also shown by horizontal line segments (dashed and solid, respectively), which may be colored differently or have a thickness different than the other benchmarks. All of the values are illustratively visually distinguishable from one another. 
       FIGS. 7A and 7B  also show that display device controller  331  controls the display device to place the performance distribution display elements  338  along the left side of the multi-machine pillar display and comparison section  340 . They are shown in the form of a bell curve which represents high, average, and low performance, respectively. It can be seen that the remainder of display portion  342  is shaded with shading indicated by lines  339  and  341  that are projected over from the bell curve portion of display element  338 , across the bar graphs, to distinguish the high, average and low performance ranges on the performance distribution, from the other ranges. Therefore, the performance distribution is projected across all of the bar graphs (with the shading) so that it can easily be seen whether the machines are performing in the high, average, or low performance ranges. 
     In the example shown in  FIG. 7A , machine selector display element  362  and performance pillar selector display element  364  are both shown as drop-down menu actuators that can be actuated by the remote user  220  to control the application to select a particular machine and a particular performance pillar. When one of the machines is selected, a machine identifier (such as a textual identifier) corresponding to that machine is displayed on actuator  362 . When one of the performance pillars is selected, an identifier (such as a textual description) of that pillar is displayed on actuator  364 . 
       FIG. 7A  also shows that, in one example, the performance trend display section  366  displays a performance distribution display element  372  and a continuous machine benchmark display element  368 , along with a continuous machine performance display element  370 . Display elements  368  and  370  reflect the value of the performance pillar selected by actuator  364  for an immediately prior time period. In the example shown in  FIG. 7A , the machine benchmark value and the performance value corresponding to the selected performance pillar over the previous 30 minutes. The performance distribution display element  372  can also have a shaded portion that extends across the performance trend display section  366  so it can easily be determined whether the machine benchmark or the machine performance value falls in the high, average or low performance distribution ranges. 
       FIG. 7A  also shows that the machine detail actuator  376  is displayed as an actuatable button, that can be actuated by user  220 . In response to detected user actuation of actuator  376 , a pop-up display, or another type of display, can be generated that shows the details of the machine being reviewed. As mentioned above, the displayed details can include machine and operator identifying information, live (or near real time) sensor signal values, current machine settings, trend data, etc. 
     Machine settings display section  378 , in the example shown in  FIG. 7A , shows display elements that reflect the value of current machine settings. These can be obtained by controlling communication system  276  to retrieve them from system  202  or from combine  100 , directly. They can include the threshing clearance, the threshing rotor speed, the cleaning fan speed, the chaffer clearance, and the sieve clearance. In addition, there may be settings that are made by the operator outside the operator&#39;s compartment. These settings may be sensed. If they are not sensed, they can be input by the operator. These are examples only. 
     In one example, an adjustment actuator  392  is also provided. When the user  220  actuates the adjustment actuator  392 , the application navigates the user to a display and user experience that allows the user to adjust the values of the displayed settings, and communicate those adjusted values to combine  100  and the operator  212  of the combine  100 . 
       FIG. 7C  shows user interface display  396 , that is similar to user interface display  216  shown in  FIGS. 7A and 7B . However, the user interface display  396  has a different scale. In  FIG. 7B , the bar graphs are scaled from 0-100, while in  FIG. 7C , the bar graphs are scaled from 0-200. 
       FIG. 7D  shows another user interface display  398 , which is similar to that shown in  FIGS. 7B and 7C . However, in  FIG. 7D , the numerical indicators for the performance pillars (and corresponding to the bar graphs) and those corresponding to the fleet benchmark values are removed. Thus,  FIG. 7D  provides a relative comparison of the two machines, as compared to one another, as compared to the fleet benchmark value, the machine benchmark value, the machine average value, the group benchmark value, the global benchmark value, and the performance distribution. However, unlike the other figures, no numeric values are provided. 
     It will be appreciated that all of the examples shown in  FIGS. 7A-7D  are contemplated herein, along with others. For instance, it may be that the bar graphs are neither color-coded, nor coded with shading. That is, the color or shading of the bar graphs need not change based on whether the value of the bar graph meets or fails to meet a threshold value. Similarly, the numerical indicators need not be provided for the values reflected on the user interface display. In addition, the fleet benchmark, machine benchmark, global benchmark, and/or group benchmark need not be displayed. However, in one example, the user interface display displays multiple machines with display elements corresponding to each performance pillar, so the performance of the multiple machines can be compared to one another, within each performance metric or performance pillar, relatively easily. In addition, in one example, the fleet benchmark reference value is indicated by a fleet reference display element, and the performance distribution is also indicated using the bell curve display element. It will be appreciated that all of the additional display elements discussed above can be provided as well. 
       FIGS. 8 and 9  show additional examples of a remote manager user interface display  216 . In  FIG. 8 , it can be seen that some of the items in the display are similar to those shown in FIGS.  7 A and  7 B. However, in  FIG. 8 , the display is not only comparing the performance of two machines, but is instead comparing the performance of seven machines. Thus, the bar graph display elements, and the various other display elements are all generated for the seven different machines. Because there are now a larger number of bar graphs being displayed,  FIG. 8  shows that the pillar display and comparison section  340  is horizontally pannable (or horizontally scrollable). Therefore, with a touch gesture (such as a swipe gesture) or other appropriate user input, the remote manager  220  can pan or scroll section  340  horizontally to view other performance pillars for the seven different machines. 
       FIG. 9  is similar to  FIG. 8 , except that the scrolling actuator is now represented by a series of selector elements  400 . When the user taps one of the selector elements, section  340  displays the bar graphs corresponding to a different performance pillar. Thus, instead of a continuous horizontally scrolling or panning pane, as illustrated in  FIG. 8 , the display section  340  is displayed with discrete, selectable display portions corresponding to different performance metrics. 
       FIG. 10  is a block diagram of one example of an operator control interface display  208 . Display  208  illustratively includes performance display section  402 , aggregation time span actuators  404 , and it can include a wide variety of other items  406 . Performance display section  402  illustratively includes multi-range performance distribution display elements  408  that are visually correlated to a pillar metric display section  410 . Pillar metric display section  410  illustratively includes, for each performance pillar, bar graph display elements  412 , numeric display elements  414 , machine performance display elements  416 , and fleet benchmark display elements  418 , for each pillar metric. It can include other items  420  as well. The display elements  408 ,  412 ,  414 ,  416 , and  418  are similar to those shown on the remote user control interface display  216 . However, instead of displaying the display elements for multiple different machines, the operator control interface display  208  only shows the display elements for the machine that the display  402  is displayed on. 
       FIGS. 11A-11C  show more detailed examples of this.  FIG. 11A  shows a display device  250  that can reside in the operator compartment of the combine  100 .  FIG. 11B  shows a portion of the display shown in  FIG. 11A  in an enlarged view, for the sake of clarity. The display device  250  generates a user interface display  208 . At least part of the user interface display  208  includes the multi-range performance distribution display elements  408  which, similar to display elements  338  shown in  FIGS. 7A and 7B , are shown as a bell curve divided into high, average, and low performance ranges. The display device is controlled so that shading is extended across the bar graphs in the remainder of the display so that the value of the bar graphs can easily be identified as being in the high, medium or low performance distribution ranges. 
     In the example shown in  FIG. 11A , aggregation time span actuators  404  illustratively include a field average actuator  422  and an instant actuator  424 . When the user actuates the field average actuator  422 , the display illustrated in  FIG. 11A  is generated. This shows the average values for a current field being harvested. However, when the user actuates the instant actuator  424 , a display such as display  426 , shown in  FIG. 11C  is generated. Display  426  is similar to display  208  shown in  FIGS. 11A and 11B , except that the values illustrated thereon for bar graphs  412 , numeric values  414 , fleet benchmark values  418  and machine benchmark display elements  416  are all the instantaneous values (or the values generated in near real time), instead of the average values over the entire field. 
       FIG. 12  shows one example of a trend user interface display  426 . Display  426  can be generated from display  208 , for instance, when the user taps or otherwise actuates one of the bar graph display elements for one of the performance metrics illustrated on display  208 . When that occurs, display  426  can be overlaid, or otherwise displayed on display device  250 . It can be seen in  FIG. 12 , that the user has actuated the bar graph corresponding to the “grain savings” performance category. In that case, display  426  shows a fleet benchmark value  418 , for the “grain savings” performance metric, along with a trend display element  428  that shows the values of the “grain savings” performance criteria generated for the machine on which the display is generated, over a recent time span. In the example shown in  FIG. 12 , it is over the last 30 minutes. Display  426  also includes an instant metric display section  430  that displays a bar graph and numerical value for an instant value (or near real time value) of the “grain savings” performance metric, along with the machine benchmark display element  432 . A set of zoom level actuators  434  include a field actuator  436  that allows the operator to view the trend display elements  418  and  428  that aggregate and show information over the entire field being harvested, and a last 30 minutes actuator  438  that allows the operator to view those values over the last 30 minutes. It will be noted of course that 30 minutes is just one example and other time spans can be used as well. 
       FIGS. 13A and 13B  (collectively referred to herein as  FIG. 13 ) illustrate a flow diagram showing one example of the operation of architecture  100 , illustrated in  FIG. 2 , in controlling display devices to generate the various user interface displays, detecting user interaction, and performing control operations and other processing based on that user interaction. 
     It is first assumed that the combine  100  has received performance distribution metrics that can be reflected by performance distribution display elements  408  to the operator  212  of the machine. In one example, this can be generated by remote analytics logic  262  and communicated to the application being run by application running logic  228  on combine  100  so that it can be displayed to operator  212 . Having the machine receive performance distribution metrics is indicated by block  440  in the flow diagram of  FIG. 13 . 
     It is also assumed, for the sake of describing  FIG. 13 , that the combine  100  is operating in a field on a crop of a known crop type (such as wheat, soybeans, etc.). This is indicated by block  442 . 
     Sensors  246  then sense a variety of different variables, such as operating characteristics of combine  100 , machine settings, environmental characteristics, crop characteristics, etc. Having the machine sensors  246  sense the variables and generate sensor data is indicated by block  444  in the flow diagram of  FIG. 13 . 
     Performance metric generator logic  242  then generates performance metrics for combine  100 . This is indicated by block  446 . In one example, the performance metrics correspond to the different performance categories or performance pillars described above. Thus, the performance metrics can reflect the performance of combine  100  along those different pillars (grain loss/savings, grain productivity, fuel economy, power utilization, and an overall metric). Others can be generated as well. The overall metric is indicated by block  448 . The grain loss/savings metric is indicated by block  450 . The grain productivity metric is indicated by block  452 . The fuel economy metric is indicated by block  454 . The power utilization metric is indicated by block  456 , and a variety of other performance metrics can be generated as well, as indicated by block  458 . 
     The application running on combine  100  then controls communication system  234  to send the performance metrics generated for combine  100  to the remote analytics computing system  202 , where they may be received through the exposed API  263  or in another way. This is indicated by block  460 . In one example, the performance metrics can be sent along with the current machine settings as indicated by block  462 . The machine settings can be sent ahead of time, or in other ways as well. This is indicated by block  464 . 
     Multi-machine aggregation logic  304  in analytics logic  262  then aggregates the metrics received from a plurality of different combines. This is indicated by block  466  in the flow diagram of  FIG. 13 . The data can be aggregated across different groups or different levels of groups. For instance, it can be aggregated across a plurality of machines from a fleet of machines owned or operated by a single organization. This is indicated by block  468 . It can be aggregated across another group of machines as indicated by block  470 . It can be aggregated across a global set of machines including all machines that access remote analytics logic  262 , across an entire geographic region, a country, or in the world, that are currently operating on the same type of crop. Aggregating across a global set of combines is indicated by block  472 . The data can be aggregated in other ways as well, and this is indicated by block  474 . 
     The analytics logic  262  then generates multiple machine-based metrics, that is, metrics based on data from multiple combines. This is indicated by block  476 . For instance, fleet benchmark generator logic  306  illustratively aggregates data from a fleet of combines (or filters the data to obtain that for the fleet) and generates the fleet benchmarks  478 . Group benchmark generator logic  308  illustratively aggregates data from a group of combines (or filters the data to obtain that for the groups) and generates the group benchmark metric  480 . Global benchmark generator logic  310  aggregates data from a global set of combines (or filters the data to obtain that for the global set) and generates the global benchmark metric  482 . Other metrics based on data from multiple combines can be generated as well, and this is indicated by block  484 . 
     The remote analytics logic  262  then illustratively controls communication system  266  to send the multiple machine-based metrics to combine  100  where it can be displayed to operator  212 , as described above. This is indicated by blocks  486  and  488 . The timespan actuators are indicated by block  490 . The metrics can be displayed in other ways as well, as indicated by block  492 . 
     There may illustratively be a plurality of different remote managers or remote users  220  that can access remote analytics computing system  202 . In one example, an application run by application running logic  274  generates a display that allows remote user  220  to access data from remote analytics computing system  202  so that remote user  220  can see a comparison among a variety of different combines to which the remote user  220  has access. In doing so, the application on remote user device  204  can generate a request for machine performance information. This is indicated by block  494  in the flow diagram of  FIG. 13 . The request can be to request performance information for multiple machines associated with remote user  220  (such as machines that the user is authorized to view). Thus, remote analytics computing system  202  may execute authentication processing to authenticate remote user  220  and to identify the particular set of machines for which the user  220  is authorized to access information. Requesting information for multiple machines is indicated by block  496 . The request can be made by calling exposed API  263  as indicated by block  498 , or in a wide variety of other ways, as indicated by block  550 . 
     Based on the request, remote analytics logic  260  aggregates information for the set of machines for which the request was received, and sends the multiple machine-based metrics for the request to the requesting user device  204 . This is indicated by block  552  in  FIG. 13 . The various communication systems  234 ,  266  and  276  can also be configured to establish a secure communication link between remote user  220  and operator  212 . This can go through system  202  or be direct between system  204  and combine  100 . 
     The remote user device  204  receives the metrics. In one example, the application run by application running logic  274  receives the metrics through API  263 . They can be received in other ways as well. Receiving the metrics is indicated by block  554  in the flow diagram of  FIG. 13 . 
     The application then controls user interface logic  278 , and display generator logic  280 , to generate display elements for the performance display section on user interface display  216 . This is indicated by block  556  in the flow diagram of  FIG. 13 . The display elements illustratively include graphic/numeric metric comparison data as indicated by block  558 . Bar graphs or other graphical elements can be visually distinguished (e.g., color-coded) based on how they relate to a particular threshold. This is indicated by block  560 . 
     The comparison display illustratively displays data from multiple machines in a direct machine-to-machine comparison. This is indicated by block  562 . For instance, in one example, the bar graphs or display elements corresponding to each performance pillar, and corresponding to each machine, are displayed adjacent one another. Therefore, the remote user  220  can quickly determine how the machines compare to one another, on each performance pillar. Display elements are generated reflecting a comparison with different groups of combines as indicated by block  564 , as compared to historical information for the same combine as indicated by block  556 , and as compared to a set of performance distribution ranges, as indicated by block  568 . The other sections of the display can be generated as well, and this is indicated by block  570 . 
     Interaction processing logic  282  then detects and processes any user interactions with the displayed interface. This is indicated by block  572  in  FIG. 13 . For instance, it may be that remote user  220  provides a scrolling or panning input to pan or scroll the display so that different performance pillars can be viewed as discussed above with respect to  FIGS. 8 and 9 . Scrolling and panning interaction processing is indicated by block  574  in the flow diagram of  FIG. 13 . 
     User  220  may provide an input indicating that the user wishes to review trend information. In that case, the application controls communication system  276  to obtain trend values, and generates the trend display elements based on the trend interactions detected. This is indicated by block  576 . 
     It may be that user  220  actuates the machine details actuator  376 . In that case, the application running on user device  204  illustratively accesses the machine details and navigates user  220  to a display (or generates a pop-up display) populated with the machine details. It can establish communication with combine  100  to obtain near real time sensor signal values, or other values as well. Processing machine detail interactions is indicated by block  578 . 
     The user  220  may actuate the machine settings actuator  392 . In that case, the application illustratively navigates the user through a user experience that allows user  220  to view current machine settings for combine  100  and to make changes to the machine settings for combine  100 . The application then controls the communication system  276  to send the adjusted machine settings to the operator  212  of combine  100  so they can be either accepted, and applied, or rejected by operator  212 . Performing processing based on machine settings interactions is indicated by block  580 . Detecting and processing user interactions can be performed in a wide variety of other ways as well, and this is indicated by block  582 . 
     It will be noted that, during the displaying of the performance metrics and comparison data, the sensors  246  on combine  100  continue to sense the variables and generate sensor signals indicative of the sensed variables. The display generator logic  280  then updates the performance metrics, and provides the updated performance metrics to remote analytics computing system  202 . Remote analytics logic  262  updates the various metrics that it generates, and provides the indications of those metrics back to combine  100 , and to remote manager computing system  204 . Display generator logic  280  updates the display elements, such as by modifying the height of the bar graphs, and the various metric display elements on display  216 , based on the modified information received. The modifications can be received in near real time, periodically or otherwise intermittently, or in a variety of other ways. Further, where combine  100  loses its communication link with computing system  202  and/or system  204 , communication system  234  illustratively stores data to be communicated in data store  232 . When communication is re-established, the stored data can then be sent and transmission of near real time data can be commenced as well. 
     The present discussion has mentioned processors and servers. In one embodiment, the processors and servers include computer processors with associated memory and timing circuitry, not separately shown. They are functional parts of the systems or devices to which they belong and are activated by, and facilitate the functionality of the other components or items in those systems. 
     Also, a number of user interface displays have been discussed. They can take a wide variety of different forms and can have a wide variety of different user actuatable input mechanisms disposed thereon. For instance, the user actuatable input mechanisms can be text boxes, check boxes, icons, links, drop-down menus, search boxes, etc. They can also be actuated in a wide variety of different ways. For instance, they can be actuated using a point and click device (such as a track ball or mouse). They can be actuated using hardware buttons, switches, a joystick or keyboard, thumb switches or thumb pads, etc. They can also be actuated using a virtual keyboard or other virtual actuators. In addition, where the screen on which they are displayed is a touch sensitive screen, they can be actuated using touch gestures. Also, where the device that displays them has speech recognition components, they can be actuated using speech commands. 
     A number of data stores have also been discussed. It will be noted they can each be broken into multiple data stores. All can be local to the systems accessing them, all can be remote, or some can be local while others are remote. All of these configurations are contemplated herein. 
     Also, the figures show a number of blocks with functionality ascribed to each block. It will be noted that fewer blocks can be used so the functionality is performed by fewer components. Also, more blocks can be used with the functionality distributed among more components. 
       FIG. 14  is a block diagram of the architecture  200 , shown in  FIG. 2 , except that it communicates with elements in a remote server architecture  500 . In an example, remote server architecture  500  can provide computation, software, data access, and storage services that do not require end-user knowledge of the physical location or configuration of the system that delivers the services. In various embodiments, remote servers can deliver the services over a wide area network, such as the internet, using appropriate protocols. For instance, remote servers can deliver applications over a wide area network and they can be accessed through a web browser or any other computing component. Software or components shown in  FIG. 2  as well as the corresponding data, can be stored on servers at a remote location. The computing resources in a remote server environment can be consolidated at a remote data center location or they can be dispersed. Remote server infrastructures can deliver services through shared data centers, even though they appear as a single point of access for the user. Thus, the components and functions described herein can be provided from a remote server at a remote location using a remote server architecture. Alternatively, they can be provided from a conventional server, or they can be installed on client devices directly, or in other ways. 
     In the example shown in  FIG. 14 , some items are similar to those shown in  FIG. 2  and they are similarly numbered.  FIG. 14  specifically shows that the architecture can include a plurality of combines  100 - 100 ′ each with its own local operator  212 - 212 ′.  FIG. 14  also shows that remote analytics computing system  202  can be located at a remote server location  502 . Therefore, combines  100 - 100 ′ and remote user computing system  204  access those systems through remote server location  502 . 
       FIG. 14  also depicts another example of a remote server architecture.  FIG. 14  shows that it is also contemplated that some elements of  FIG. 2  are disposed at remote server location  502  while others are not. By way of example, performance metric generator logic  242  can be disposed in system  202  instead of, or in addition to, being on the combines. It can communicate the performance metrics to the combines, to remote user computing system  204  or to other systems. Remote analytics logic  262  and data store  264  can be disposed at a location separate from location  502 , and accessed through the remote server at location  502 . Regardless of where they are located, they can be accessed directly by combine  100 , through a network (either a wide area network or a local area network), they can be hosted at a remote site by a service, or they can be provided as a service, or accessed by a connection service that resides in a remote location. Also, the data can be stored in substantially any location and intermittently accessed by, or forwarded to, interested parties. For instance, physical carriers can be used instead of, or in addition to, electromagnetic wave carriers. In such an example, where cell coverage is poor or nonexistent, another mobile machine (such as a fuel truck) can have an automated information collection system. As the combine comes close to the fuel truck for fueling, the system automatically collects the information from the harvester using any type of ad-hoc wireless connection. The collected information can then be forwarded to the main network as the fuel truck reaches a location where there is cellular coverage (or other wireless coverage). For instance, the fuel truck may enter a covered location when traveling to fuel other machines or when at a main fuel storage location. All of these architectures are contemplated herein. Further, the information can be stored on the combine until the combine enters a covered location. The combine, itself, can then send the information to the main network. 
     It will also be noted that the elements of  FIG. 2 , or portions of them, can be disposed on a wide variety of different devices. Some of those devices include servers, desktop computers, laptop computers, tablet computers, or other mobile devices, such as palm top computers, cell phones, smart phones, multimedia players, personal digital assistants, etc. 
       FIG. 15  is a simplified block diagram of one illustrative example of a handheld or mobile computing device that can be used as a user&#39;s or client&#39;s hand held device  16 , in which the present system (or parts of it) can be deployed. For instance, a mobile device can be deployed as remote user computing system  202  in the operator compartment of combine  100  for use in generating, processing, or displaying the information discussed herein and in generating the control interface.  FIGS. 16-17  are examples of handheld or mobile devices. 
       FIG. 15  provides a general block diagram of the components of a client device  16  that can run some components shown in  FIG. 2 , that interacts with them, or both. In the device  16 , a communications link  13  is provided that allows the handheld device to communicate with other computing devices and in some examples provide a channel for receiving information automatically, such as by scanning. Examples of communications link  13  include allowing communication though one or more communication protocols, such as wireless services used to provide cellular access to a network, as well as protocols that provide local wireless connections to networks. 
     In other examples, applications can be received on a removable Secure Digital (SD) card that is connected to an interface  15 . Interface  15  and communication links  13  communicate with a processor  17  (which can also embody processors or servers from previous FIGS.) along a bus  19  that is also connected to memory  21  and input/output (I/O) components  23 , as well as clock  25  and location system  27 . 
     I/O components  23 , in one embodiment, are provided to facilitate input and output operations. I/O components  23  for various embodiments of the device  16  can include input components such as buttons, touch sensors, optical sensors, microphones, touch screens, proximity sensors, accelerometers, orientation sensors and output components such as a display device, a speaker, and or a printer port. Other I/O components  23  can be used as well. 
     Clock  25  illustratively comprises a real time clock component that outputs a time and date. It can also, illustratively, provide timing functions for processor  17 . 
     Location system  27  illustratively includes a component that outputs a current geographical location of device  16 . This can include, for instance, a global positioning system (GPS) receiver, a LORAN system, a dead reckoning system, a cellular triangulation system, or other positioning system. It can also include, for example, mapping software or navigation software that generates desired maps, navigation routes and other geographic functions. 
     Memory  21  stores operating system  29 , network settings  31 , applications  33 , application configuration settings  35 , data store  37 , communication drivers  39 , and communication configuration settings  41 . Memory  21  can include all types of tangible volatile and non-volatile computer-readable memory devices. It can also include computer storage media (described below). Memory  21  stores computer readable instructions that, when executed by processor  17 , cause the processor to perform computer-implemented steps or functions according to the instructions. Processor  17  can be activated by other components to facilitate their functionality as well. 
       FIG. 16  shows one example in which device  16  is a tablet computer  600 . In  FIG. 16 , computer  600  is shown with user interface display screen  602 . Screen  602  can be a touch screen or a pen-enabled interface that receives inputs from a pen or stylus. It can also use an on-screen virtual keyboard. Of course, it might also be attached to a keyboard or other user input device through a suitable attachment mechanism, such as a wireless link or USB port, for instance. Computer  600  can also illustratively receive voice inputs as well. 
       FIG. 17  shows that the device can be a smart phone  71 . Smart phone  71  has a touch sensitive display  73  that displays icons or tiles or other user input mechanisms  75 . Mechanisms  75  can be used by a user to run applications, make calls, perform data transfer operations, etc. In general, smart phone  71  is built on a mobile operating system and offers more advanced computing capability and connectivity than a feature phone. 
     Note that other forms of the devices  16  are possible. 
       FIG. 18  is one example of a computing environment in which elements of  FIG. 2 , or parts of it, (for example) can be deployed. With reference to  FIG. 18 , an example system for implementing some embodiments includes a general-purpose computing device in the form of a computer  810 . Components of computer  810  may include, but are not limited to, a processing unit  820  (which can comprise processors or servers from previous FIGS.), a system memory  830 , and a system bus  821  that couples various system components including the system memory to the processing unit  820 . The system bus  821  may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. Memory and programs described with respect to  FIG. 2  can be deployed in corresponding portions of  FIG. 18 . 
     Computer  810  typically includes a variety of computer readable media. Computer readable media can be any available media that can be accessed by computer  810  and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media is different from, and does not include, a modulated data signal or carrier wave. It includes hardware storage media including both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computer  810 . Communication media may embody computer readable instructions, data structures, program modules or other data in a transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. 
     The system memory  830  includes computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM)  831  and random access memory (RAM)  832 . A basic input/output system  833  (BIOS), containing the basic routines that help to transfer information between elements within computer  810 , such as during start-up, is typically stored in ROM  831 . RAM  832  typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit  820 . By way of example, and not limitation,  FIG. 18  illustrates operating system  834 , application programs  835 , other program modules  836 , and program data  837 . 
     The computer  810  may also include other removable/non-removable volatile/nonvolatile computer storage media. By way of example only,  FIG. 18  illustrates a hard disk drive  841  that reads from or writes to non-removable, nonvolatile magnetic media, an optical disk drive  855 , and nonvolatile optical disk  856 . The hard disk drive  841  is typically connected to the system bus  821  through a non-removable memory interface such as interface  840 , and optical disk drive  855  are typically connected to the system bus  821  by a removable memory interface, such as interface  850 . 
     Alternatively, or in addition, the functionality described herein can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (e.g., ASICs), Application-specific Standard Products (e.g., ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc. 
     The drives and their associated computer storage media discussed above and illustrated in  FIG. 18 , provide storage of computer readable instructions, data structures, program modules and other data for the computer  810 . In  FIG. 18 , for example, hard disk drive  841  is illustrated as storing operating system  844 , application programs  845 , other program modules  846 , and program data  847 . Note that these components can either be the same as or different from operating system  834 , application programs  835 , other program modules  836 , and program data  837 . 
     A user may enter commands and information into the computer  810  through input devices such as a keyboard  862 , a microphone  863 , and a pointing device  861 , such as a mouse, trackball or touch pad. Other input devices (not shown) may include a joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit  820  through a user input interface  860  that is coupled to the system bus, but may be connected by 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 speakers  897  and printer  896 , which may be connected through an output peripheral interface  895 . 
     The computer  810  is operated in a networked environment using logical connections (such as a local area network—LAN, or wide area network WAN) to one or more remote computers, such as a remote computer  880 . 
     When used in a LAN networking environment, the computer  810  is connected to the LAN  871  through a network interface or adapter  870 . When used in a WAN networking environment, the computer  810  typically includes a modem  872  or other means for establishing communications over the WAN  873 , such as the Internet. In a networked environment, program modules may be stored in a remote memory storage device.  FIG. 18  illustrates, for example, that remote application programs  885  can reside on remote computer  880 . 
     It should also be noted that the different embodiments described herein can be combined in different ways. That is, parts of one or more embodiments can be combined with parts of one or more other embodiments. All of this is contemplated herein. 
     Example 1 is a computing system, comprising:
         multi-machine aggregation logic configured to obtain and aggregate a set of machine performance metrics indicative of performance of a plurality of remote harvesting machines;   group metric generator logic configured to generate a set of group performance metrics based on the aggregated machine performance metrics;   a communication system; and   analytics logic configured to control the communication system to send the set of group performance metrics to each of the plurality of remote harvesting machines from which the set of machine performance metrics are obtained, and to send the obtained machine performance metrics and the set of group performance metrics to a remote computing system corresponding to the plurality of remote harvesting machines.       

     Example 2 is the computing system of any or all previous claims wherein the multi-machine aggregation logic is configured to control the communication system to obtain the set of machine performance metrics from a first plurality of remote harvesting machines and wherein the analytics logic is configured to control the communication system to send machine performance metrics obtained from a subset of the remote harvesting machines to the remote computing system corresponding to the subset of remote harvesting machines. 
     Example 3 is the computing system of any or all previous claims and further comprising:
         a data store storing mappings that map remote harvesting machines to remote computing systems.       

     Example 4 is the computing system of any or all previous claims wherein the analytics logic is configured to access the mappings to identify the subset of remote harvesting machines corresponding to the remote computing system. 
     Example 5 is the computing system of any or all previous claims wherein the analytics computing system is configured to access the mappings to identify a plurality of subsets of remote harvesting machines that are each mapped to a different remote computing system. 
     Example 6 is the computing system of claim  5  wherein the analytics logic is configured to control the communication system to send the machine performance metrics from each of the plurality of subsets of remote harvesting machines to the different remote computing systems to which they are mapped. 
     Example 7 is the computing system of any or all previous claims wherein the group metric generator logic comprises:
         fleet benchmark generator logic configured to generate a fleet benchmark for each machine performance metric in the set of machine performance metrics, based on machine performance metrics aggregated by the multi-machine aggregation logic for a fleet of remote harvesting machines that are run by a same organization.       

     Example 8 is the computing system of any or all previous claims wherein the group metric generator logic comprises:
         performance distribution and range generator logic configured to generate a performance distribution based on machine performance metrics aggregated by the multi-machine aggregation logic.       

     Example 9 is a computer implemented method, comprising:
         obtaining a set of machine performance metrics, from a plurality of different remote harvesting machines, indicative of performance of the plurality of different remote harvesting machines;   aggregating the sets of machine performance metrics to obtain aggregated machine performance metrics, aggregated across the plurality of different remote harvesting machines;   generating a set of group performance metrics based on the aggregated machine performance metrics;   controlling a communication system to send the set of group performance metrics to each of the plurality of different remote harvesting machines from which the set of performance metrics are obtained; and   controlling the communication system to send the obtained machine performance metrics and the set of group performance metrics to a remote computing system corresponding to the plurality of different remote harvesting machines.       

     Example 10 is the computer implemented method of any or all previous claims wherein obtaining the set of machine performance metrics comprises controlling the communication system to obtain the set of machine performance metrics from a first plurality of remote harvesting machines and wherein controlling the communication system to send the obtained machine performance metrics comprises:
         controlling the communication system to send machine performance metrics obtained from a subset of the remote harvesting machines to the remote computing system corresponding to the subset of remote harvesting machines.       

     Example 11 is the computer implemented method of any or all previous claims and further comprising:
         accessing mappings that map remote harvesting machines to remote computing systems to identify the subset of remote harvesting machines corresponding to the remote computing system.       

     Example 12 is the computer implemented method of any or all previous claims wherein accessing the mappings comprises:
         accessing the mappings to identify a plurality of subsets of remote harvesting machines that are each mapped to a different remote computing system.       

     Example 13 is the computer implemented method of any or all previous claims wherein controlling the communication system to send the machine performance metrics comprises:
         controlling the communication system to send the machine performance metrics from each of the plurality of subsets of remote harvesting machines to the different remote computing systems to which they are mapped.       

     Example 14 is the computer implemented method of any or all previous claims generating a set of group performance metrics comprises:
         generating a fleet benchmark for each machine performance metric in the set of machine performance metrics, based on the aggregated machine performance metrics aggregated for a fleet of remote harvesting machines that are run by a same organization.       

     Example 15 is the computer implemented method of any or all previous claims generating a set of group performance metrics comprises:
         generating a performance distribution metric based on the aggregated machine performance metrics.       

     Example 15 is a computing system, comprising:
         multi-machine aggregation logic configured to obtain and aggregate a set of machine performance metrics, indicative of performance of a plurality of remote harvesting machines, from the plurality of remote harvesting machines;   group metric generator logic configured to generate a set of group performance metrics based on the aggregated machine performance metrics;   a communication system; and   analytics logic configured to control the communication system to send the set of group performance metrics to each of the plurality of remote harvesting machines from which the set of machine performance metrics are obtained, and to send the set of machine performance metrics obtained from a subset of the remote harvesting machines to a remote computing system corresponding to the subset of remote harvesting machines, and to send the set of group performance metrics to the remote computing system.       

     Example 17 is the computing system of any or all previous claims and further comprising:
         a data store storing mappings that map remote harvesting machines to remote computing systems, wherein the analytics logic is configured to access the mappings to identify the subset of remote harvesting machines corresponding to the remote computing system.       

     Example 18 is the computing system of any or all previous claims wherein the analytics computing system is configured to access the mappings to identify a plurality of subsets of remote harvesting machines that are each mapped to a different remote computing system and to control the communication system to send the machine performance metrics from each of the plurality of subsets of remote harvesting machines to the different remote computing systems to which they are mapped. 
     Example 19 is the computing system of any or all previous claims wherein the group metric generator logic comprises:
         fleet benchmark generator logic configured to generate a fleet benchmark for each machine performance metric in the set of machine performance metrics, based on machine performance metrics aggregated by the multi-machine aggregation logic for a fleet of remote harvesting machines that are run by a same organization.       

     Example 20 is the computing system of any or all previous claims wherein the group metric generator logic comprises:
         performance distribution and range generator logic configured to generate a performance distribution based on machine performance metrics aggregated by the multi-machine aggregation logic.       

     Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.