Patent Application
20180329372
This disclosure relates generally to industrial process control and automation systems. More specifically, this disclosure relates to an apparatus and method for predictive time-based control of batch or sequential operations.
Many types of industrial processes involve sequential operations during the manufacture or production of products. Common examples are found in food and beverage, specialty chemical, and pharmaceutical industries, where discrete steps are typically performed in series on a batch of input materials to create a batch of output product. Sequential operations are also often performed in continuous process industries such as energy production, where pieces of equipment are periodically taken through sequences of operations to start, stop, or clean the equipment or to switch between different pieces of equipment.
This disclosure provides an apparatus and method for predictive time-based control of batch or sequential operations.
In a first embodiment, a method includes identifying multiple planned sequences of operations involving industrial equipment. The planned sequences of operations include one or more sequences of operations currently being performed using the industrial equipment and/or one or more sequences of operations to be performed using the industrial equipment. The method also includes identifying information associated with prior executions of the sequences of operations. The information includes timing information associated with the prior executions of the sequences of operations. The method further includes generating timing predictions for operations in the planned sequences of operations using the identified information. In addition, the method includes generating an interface for presentation to an operator. The interface identifies the planned sequences of operations and the timing predictions for the operations in the planned sequences of operations.
In a second embodiment, an apparatus includes at least one memory and at least one processing device. The at least one processing device is configured to identify multiple planned sequences of operations involving industrial equipment. The planned sequences of operations include one or more sequences of operations currently being performed using the industrial equipment and/or one or more sequences of operations to be performed using the industrial equipment. The at least one processing device is also configured to identify information associated with prior executions of the sequences of operations stored in the at least one memory. The information includes timing information associated with the prior executions of the sequences of operations. The at least one processing device is further configured to generate timing predictions for operations in the planned sequences of operations using the identified information. In addition, the at least one processing device is configured to generate an interface for presentation to an operator. The interface identifies the planned sequences of operations and the timing predictions for the operations in the planned sequences of operations.
In a third embodiment, a non-transitory computer readable medium contains instructions that when executed cause at least one processor to identify multiple planned sequences of operations involving industrial equipment. The planned sequences of operations include one or more sequences of operations currently being performed using the industrial equipment and/or one or more sequences of operations to be performed using the industrial equipment. The medium also contains instructions that when executed cause the at least one processor to identify information associated with prior executions of the sequences of operations stored in the at least one memory. The information includes timing information associated with the prior executions of the sequences of operations. The medium further contains instructions that when executed cause the at least one processor to generate timing predictions for operations in the planned sequences of operations using the identified information. In addition, the medium contains instructions that when executed cause the at least one processor to generate an interface for presentation to an operator. The interface identifies the planned sequences of operations and the timing predictions for the operations in the planned sequences of operations.
Other technical features may be readily apparent to one skilled in the art from the following figures, descriptions, and claims.
For a more complete understanding of this disclosure, reference is now made to the following description, taken in conjunction with the accompanying drawings, in which:
As noted above, many types of industrial processes involve sequential operations, such as during the manufacture or production of products or in continuous process industries. There are a wide variety of automation levels and degrees of human operator involvement in these types of batch or sequential processes. In some cases, sequences of operations are defined outside of a process control system and are performed manually, which could be done according to a documented list of instructions. In other cases, sequences of operations are implemented within a process control system, but periodic interactions with human operators are needed, such as to answer questions or confirm that physical actions have been completed by personnel at various stages. In still other cases, sequences of operations are completely automated. Human operators may only need to know about the sequences to perform various tasks, like starting the sequences, maintaining situation awareness, or responding to unforeseen or problematic situations.
For all of these various levels of automation, operators and other personnel involved in the sequences of operations often need to coordinate their daily tasks and oversight responsibilities with these sequential operations in mind. The times at which questions need to be answered, manual tasks need to be performed, specific equipment needs to be available, or critical process phases need to occur can be important. Any operator delays or distractions at certain times could result in lower plant throughput, equipment damage, or lost product. This challenge is compounded as the scope of responsibility for an operator increases, since the operator has more sequences running in different pieces of equipment with independent demands and timeframes. As a result, the scope of responsibility that an operator can handle without delay and without error is limited because time-based information that the operator needs in order to prepare is non-existent, fragmented, inaccurate, or expensive to engineer.
Conventional approaches often provide little or no useful information about when an operator will need to perform tasks or focus on particular pieces of equipment to support sequences of operations. For example, an operator may only have access to a sequential function chart, which in some cases can be defined according to a standard (such as the ANSI/ISA-88 standard). A sequential function chart is a flowchart that conveys the order of steps in a sequence but not the timing of those steps. In other cases, an application or customized display may attempt to visualize countdowns against “engineered” timing expectations for parts of a sequence. However, this requires additional effort to build and maintain, and it does not incorporate real-world variances that occur, such as those that occur due to changed product formulas or seasonal and equipment variations. An operator could load a historical trend of process data values for a particular “golden” batch of product and use that to estimate the timeline of a current process. However, the selected batch might not be similar to the current sequence, and using process trends for multiple batches can show too much data to clearly highlight upcoming operations in the current sequence and when they will occur. This visual overload problem is also true (or exacerbated) by simply displaying past and current trend data overlaid or beside each other. In addition, existing visualizations of sequences often give only fragmented views of what an operator is responsible for, and they typically focus on one sequence or unit of equipment at a time with only hints at when important events will occur.
This disclosure describes techniques for predictive time-based control of batch or sequential operations. In accordance with this disclosure, appropriate historical timing data from past sequence executions can be identified automatically and used to generate a graphical display. The graphical display, which could take the form of a consolidated Gantt-style chart, shows predictions of when important sequence phases and interactions are expected to occur across multiple units or equipment that an operator is managing or overseeing. Alongside this visualization, planned interactions, messages, or other information could optionally be displayed as a merged “to do” list that concisely lists each important event or action coming up in time order.
In this way, these techniques bring together the information that an operator needs for planning his or her upcoming tasks and focus areas. The operator can accomplish this with little or no additional mental effort and without being distracted away from core control system operational environments. This functionality could also be used in other ways. For example, accurate sequence timing expectation data can be used in the core process control system with no additional engineering to generate ancillary displays. This data could also be used in controller logic and for advanced applications used to perform control optimization or to monitor business key performance indicators (KPIs).
Moreover, organizations today often need to compensate for a lack of distilled timing information by limiting the scope of responsibility for operators and relying on greater operator experience to “fill in the gaps.” This is an undesirable strategy because it increases staffing costs and is inherently unreliable. The sustained mental effort required from operators to plan and prepare for their upcoming sequential tasks can become too great to perform indefinitely without error. With the techniques provided in this patent document, an organization can expand its operators' scopes of responsibility and decrease delays and errors that result in lower plant throughput, equipment damage, or lost product, which can impact profitability significantly. These techniques can also be used to train new operators in a faster manner because the experiences required to estimate when actions will occur based on fragmented data is not required, and a clear view of how a sequence will be executed can be presented automatically.
The industrial processes 102 to be controlled and the sequence of operations 104a-104n in each industrial process 102 can vary widely depending on the application. For example, in the food and beverage industry, the industrial processes 102 can include processes for converting raw materials into different food or beverage items, and the sequences of operations 104a-104n for the processes 102 can vary depending on the food or beverage items being manufactured. In the specialty chemical and pharmaceutical industries, the industrial processes 102 can include processes for converting raw materials into different chemicals or drugs, and the sequences of operations 104a-104n for the processes 102 can vary depending on the chemicals or drugs being manufactured. In continuous process industries such as energy production, the industrial processes 102 can involve specific pieces of equipment, and the sequences of operations 104a-104n for the processes 102 can vary depending on the equipment being used.
The system 100 in this example includes one or more sensors 106a and one or more actuators 106b. The sensors 106a and actuators 106b represent components that may perform any of a wide variety of functions. For example, the sensors 106a could measure a wide variety of characteristics in the industrial processes 102, such as pressure, temperature, or flow rate. Also, the actuators 106b could alter a wide variety of characteristics in the industrial processes 102. Each of the sensors 106a includes any suitable structure for measuring one or more characteristics in at least one industrial process. Each of the actuators 106b includes any suitable structure for operating on or affecting one or more conditions in at least one industrial process.
At least one network 108 is coupled to the sensors 106a and actuators 106b. The network 108 facilitates interaction with the sensors 106a and actuators 106b. For example, the network 108 could transport measurement data from the sensors 106a and provide control signals to the actuators 106b. The network 108 could represent any suitable network or combination of networks. As particular examples, the network 108 could represent at least one Ethernet network, electrical signal network (such as a HART network), pneumatic control signal network, or any other or additional type(s) of network(s).
The system 100 also includes various controllers 110. The controllers 110 can be used in the system 100 to perform various functions in order to control one or more industrial processes. For example, a first set of controllers 110 may use measurements from one or more sensors 106a to control the operation of one or more actuators 106b. A second set of controllers 110 could be used to optimize the control logic or other operations performed by the first set of controllers. A third set of controllers 110 could be used to perform additional functions. The controllers 110 can communicate via one or more networks 112 and associated switches, firewalls, and other components.
Each controller 110 includes any suitable structure for controlling one or more aspects of an industrial process. At least some of the controllers 110 could, for example, represent proportional-integral-derivative (PID) controllers or multivariable controllers, such as controllers implementing model predictive control or other advanced predictive control. As a particular example, each controller 110 could represent a computing device running a real-time operating system, a WINDOWS operating system, or other operating system. However, as noted above, there may be some industrial processes that are not controlled automatically but rather manually, and actuators and controllers may not be needed for those industrial processes.
Operator consoles 114 can be used to provide information to an operator and receive information from an operator. For example, each operator console 114 could provide information identifying a current state of an industrial process to the operator, such as values of various process variables, alarms associated with the industrial process, and tasks to be performed for the industrial process. Each operator console 114 could also receive information from the operator, such as by receiving setpoints or control modes for process variables controlled by the controllers 110 or acknowledgements that certain tasks have been performed. Each operator console 114 includes any suitable structure for displaying information to and interacting with an operator. For instance, each operator console 114 could represent a computing device running a WINDOWS operating system or other operating system.
Multiple operator consoles 114 can be grouped together and used in one or more control rooms 116. Each control room 116 could include any number of operator consoles 114 in any suitable arrangement. In some embodiments, multiple control rooms 116 can be used to control an industrial plant, such as when each control room 116 contains operator consoles 114 used to manage a discrete part of the industrial plant.
The control and automation system 100 here also includes at least one historian 118 and one or more servers 120. The historian 118 represents a component that stores various information about the system 100. The historian 118 could, for instance, store information that is generated by the various controllers 110 during the control of one or more industrial processes. The historian 118 includes any suitable structure for storing and facilitating retrieval of information. Although shown as a single component here, the historian 118 could be located elsewhere in the system 100, or multiple historians could be distributed in different locations in the system 100. Each server 120 denotes a computing device that executes applications for users of the operator consoles 114 or other applications. The applications could be used to support various functions for the operator consoles 114, the controllers 110, or other components of the system 100. Each server 120 could represent a computing device running a WINDOWS operating system or other operating system.
As described in more detail below, at least one component of the system 100 includes or supports a platform 122 for predictive time-based control of batch or sequential operations. The platform 122 uses data associated with current or future batch or sequential operations and stored data related to prior batch or sequential operations in order to make predictions about the timing of the current or future batch or sequential operations. These predictions are used to generate graphical user interfaces (GUIs), which could be presented on display screens of the operator consoles 114, mobile devices 124 used by personnel, or other devices. A GUI can include multiple predictions associated with multiple processes or equipment being managed by an operator, and different GUIs can be tailored for different operators.
Additional details regarding the functionality of the platform 122 are provided below. The platform 122 could be implemented in any suitable manner using hardware or a combination of hardware and software/firmware. For example, the platform 122 could be implemented using software or firmware instructions that are executed by one or more processors of a computing device, such as a desktop, laptop, server, or tablet computer. As a particular example, the platform 122 could be executed by the server 120, the operator consoles 114, or other devices within the system 100 (such as another device coupled to the network 108 or 112 or to another network). The platform 122 could also be implemented outside of the system 100, such as in a remote server, a cloud-based environment, or any other computing system or environment communicatively coupled to the system 100.
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The memory 210 and a persistent storage 212 are examples of storage devices 204, which represent any structure(s) capable of storing and facilitating retrieval of information (such as data, program code, and/or other suitable information on a temporary or permanent basis). The memory 210 may represent a random access memory or any other suitable volatile or non-volatile storage device(s). The persistent storage 212 may contain one or more components or devices supporting longer-term storage of data, such as a read only memory, hard drive, Flash memory, or optical disc.
The communications unit 206 supports communications with other systems or devices. For example, the communications unit 206 could include a network interface card or a wireless transceiver facilitating communications over a wired or wireless network (such as the network 108 or 112). The communications unit 206 may support communications through any suitable physical or wireless communication link(s).
The I/O unit 208 allows for input and output of data. For example, the I/O unit 208 may provide a connection for user input through a keyboard, mouse, keypad, touchscreen, or other suitable input device. The I/O unit 208 may also send output to a display, printer, or other suitable output device. Note, however, that the platform 122 could operate in a device that does not require a local I/O unit for data exchange.
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A sequence history database 304 is used in the architecture 300 to collect and store data associated with past executions of the industrial process(es) 102. For example, the sequence history database 304 could store various historical sequence data for a number of past executions of the industrial processes 102. The sequence history database 304 can store any suitable information about prior executions of industrial processes. For example, the information could include batch or other process identifiers, sequence/recipe names, and start/stop/duration times and equipment used for all levels of a sequence (such as is described in a procedural model according to the ANSI/ISA-88 standard). The information could also include timing and information for all operator messages, manual instructions, and formula parameters/sets used during the industrial processes. The information could further include report parameters that describe the results of the industrial processes and a designation of report parameters or an entire record as being desirable by an engineer, manager, or other personnel. In addition, the information could include other data, such as a process history for key parameters of a sequence, but such information may not be needed for some embodiments of this disclosure.
The information in the sequence history database 304 can be used to identify those prior executions that are most similar to a current or future execution of one or more industrial processes 102. The data for those most similar prior executions can be used as described below to make predictions for the current or future execution. The sequence history database 304 may also contain information that identifies “desirable” prior executions or “desirable” traits of prior executions of a process. Report parameters representing results of a prior execution, such as quantity of product produced, quality of product produced, or cycle time of a batch, can be tagged by one or more users as being desirable traits to maximize or minimize. The similar prior executions that are most desirable according to these criteria can be selected for use in predictions of current and future executions. The sequence history database 304 includes any suitable structure configured to store and facilitate retrieval of information, such as one or more hard drives, Flash memories, optical discs, or other data storage devices.
A sequence engine 306 is responsible for managing the execution of sequential operations in the industrial process(es) and could exist in a number of forms and reside on a number of different computers, controllers, or other devices. The sequence engine 306 is also used here to collect information about past, current, and future executions of the industrial process(es) 102. For example, the sequence engine 306 could obtain the data described above for the prior executions of the industrial processes 102 and store that data in the sequence history database 304. The sequence engine 306 could also obtain data regarding the current or planned future execution of the industrial processes 102 and provide that data to the sequence history database 304 for storage and to a prediction engine 308 for use in making predictions about the current or future execution.
The sequence engine 306 could obtain the data about the executions of the industrial processes 102 in any suitable manner. For example, the sequence engine 306 could be configured to receive control and monitoring data from the control system 302, parse the received data, and identify desired information in the received data about process executions. The control system 302 could also be configured to provide specific data related to the process executions to the sequence engine 306. The sequence engine 306 includes any suitable logic for identifying information about sequence or batch operations. The sequence engine 306 could, for example, include software or firmware instructions that are executed by the processor 202 of the device 200.
The prediction engine 308 uses information from the sequence history database 304 about prior executions of the industrial process(es) 102 and information from the sequence engine 306 about the current or future execution of the industrial process(es) 102 to make predictions for the current or future execution. For example, the prediction engine 308 can select historical data from the sequence history database 304 and map the selected historical data to a prediction of the current or future execution. The results can then be used to generate graphical displays for one or more users.
The selection of the historical data from the sequence history database 304 by the prediction engine 308 could be done in any suitable manner. For example, the prediction engine 308 could attempt to match as many attributes of stored data as possible (as described in the database description) with the current or future execution. The prediction engine 308 could also give preference to the historical data that has been marked as desirable in the sequence history database 304.
In some cases, there may not be an exact match of current or future execution characteristics with a historical record, such as when altered formula parameters are used or a different unit of equipment is executing an operation. In those cases, the prediction engine 308 could use regression analysis, “deep learning” techniques, or other data processing, mining, or analysis techniques to process multiple records and interpolate or extrapolate predicted timings based on learned patterns in the available prior execution records. Also, if there are several acceptable historical records to choose from and there is a desire to maximize/minimize one or more parameters, the prediction engine 308 could select one or more records based on optimization algorithms that maximize/minimize the parameters (such as by maximizing product yield, minimizing cycle time, or minimizing raw material or energy usage). The prediction engine 308 could further automatically select and combine the best parts of different historical records based on optimization algorithms, such as by choosing different operations from different records because different executions were good in some respects but not others. The prediction engine 308 could use any other or additional logic to select historical data for comparison to a current or future execution.
The prediction engine 308 uses the selected historical data to map timing information of the historical data to a current or future execution, thereby generating timing predictions for the current or future execution. For example, the prediction engine 308 could match the names of one or more sequences in the historical records to the names of one or more sequences in the current or future execution. The timing of the sequences in the historical records can then be used to estimate the timing of the sequences in the current or future execution. The prediction engine 308 could also perform any translations to different equipment as needed. This may be necessary, for example, if different equipment with different characteristics is currently being used compared to the historical data. As a current execution proceeds, the time predictions are continually updated based on the actual time(s) taken for the current execution. Delays and accelerations are propagated forward through each sequence (such as per an engineered structure of that sequence), incorporating parallel and alternate branches. In addition, unit or other equipment acquisition requirements can be incorporated into predictions, such as by showing and propagating a delay when it is predicted that required equipment will be in use by another sequence of operations.
The prediction engine 308 includes any suitable logic for selecting and using historical information about one or more industrial processes in order to make predictions for a current or future execution of the industrial processes. The prediction engine 308 could, for example, include software or firmware instructions that are executed by the processor 202 of the device 200.
Information is provided from the prediction engine 308 to a visualizer 310, which generates one or more graphical displays for presentation to one or more users. For example, the visualizer 310 could obtain information identifying the actual execution timing of one or more current sequences up to a current time, as well as predicted timings for the one or more current sequences for a future time period. This information could be provided in various forms to operators, such as based on the interfaces being used by the operators. As particular examples, the graphical displays could vary based on whether the operators are using personal computers, tablets, mobile smartphones, smart watches, augmented reality devices, or text streams.
In some embodiments, the presented information in a graphical display uses color, animation, or other indicators to highlight important aspects to operators. For example, certain parts of the future time period could be identified as “busy periods” when an operator will be performing multiple tasks. As another example, certain portions of time could be identified as “slow” periods when an executing sequence is or was running slower than expected. The graphical display can also provide contextual linkage to relevant process data, such as active messages and alarms, process graphics, and sequential function charts for detailed troubleshooting. Drill-down features in the presented graphical display may allow the operator to view timing information for sub-procedures that contribute to the overall timing information and status of an operation or sequence of operations.
The visualizer 310 includes any suitable logic for generating graphical displays and interacting with operators through the graphical displays. The visualizer 310 could, for example, include software or firmware instructions that are executed by the processor 202 of the device 200.
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The rows 402 also include various icons 410, and a current time indicator 412 identifies a current time along a time scale 413. The icons 410 are visual indicators identifying the operations that have been, are being, or will be performed by equipment in the various sequences monitored or controlled by the operator. In this example, the icons 410 graphically represent different operations in the sequences, although other types of indicators could also be used. Icons 410 to the left of the current time indicator 412 represent operations that have been completed, icons 410 on the current time indicator 412 represent operations that are currently being performed, and icons 410 to the right of the current time indicator 412 represent operations to be performed in the future. The icons 410 could be modified in various ways to provide status information about the associated sequences. For instance, icons 410 in uncompleted sequences could be brighter, have one color (like white), or identified in other ways. Icons 410 in completed sequences could be dimmer, have another color (like grey), or identified in other ways.
The rows 402 further include bars 414 that identify actual and predicted execution times for the industrial processes. For example, the lengths of the bars 414 to the left of the current time indicator 412 identify the lengths of time that the currently executing processes have actually been executing. The lengths of the bars 414 to the right of the current time indicator 412 identify the lengths of time that the currently executing processes are predicted to require for completion (based on the predictions by the prediction engine 308). As the processes continue to execute, the current time indicator 412 could move or the bars 414 can slide to the left in order to update the predictions or completion statuses of the operations. The lengths of the bars 414 to the left of the current time indicator 412 can be updated to account for actual execution times, and the lengths of the bars 414 to the right of the current time indicator 412 can be altered to account for updated predictions.
The bars 414 can also be colored-coded or use other indicators to identify different periods of time. For example, the bars 414 to the left of the current time indicator 412 could use one color (such as green) when the actual times needed to perform various operations were within the predicted times for those operations. The bars 414 to the left of the current time indicator 412 could use another color (such as yellow) when the actual times needed to perform various operations were excessive but within some threshold of the predicted times for those operations. The bars 414 to the left of the current time indicator 412 could use a third color (such as red) when the actual times needed to perform various operations were excessive and outside the threshold of the predicted times for those operations. Thus, the colors of the bars 414 (or other indicators) could be used to identify periods of slowness of the operations in the industrial processes compared to the predictions. If needed, controls or other information could be provided for the slow periods to help the operator complete those operations.
Various indicators 416-418 can be placed along the bars 414. The indicators 416 represent messages or upcoming tasks that are to be performed by the operator or by other personnel. The indicators 418 represent tasks that have been performed or that should have been performed by the operator or other personnel. These tasks could represent manual actions to be performed during execution of the industrial processes. As described above, batch or sequential processes can include varying degrees of automation, and some tasks can be performed manually by operators or other personnel. Example tasks could include manually adjusting a valve or manually capturing a sample of material from a tank.
The indicators 416 help to inform an operator of upcoming actions to be performed. Looking to the right of the current time indicator 412, highlighting 420 or other indicator could be used to identify upcoming time periods where at least a specified number of indicators 416 is present within some threshold amount of time. These time periods represent “busy” periods when the operator will be performing multiple actions. Looking to the left of the current time indicator 412, indicators 418 for tasks that have been performed could have one form, such as a circled checkmark. In contrast, indicators 418 for tasks that should have been (but were not) performed could have another form, such as a circled X.
A “to do” list 422 can optionally be presented to the operator in the GUI 400. The list 422 includes messages or other information to be read by the operator. The messages can identify currently active or predicted future tasks or other actions to be performed by the operator or by other personnel. The identified actions could include those tasks identified by the indicators 416-418. The messages can also identify alerts, warnings, or other notifications generated for the equipment being monitored or controlled by the operator. In some embodiments, the list 422 could be hidden by the user and represented when needed, such as when the user selects one of the indicators 408, 416, or 418 in the rows 402.
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In this way, the GUI 400 effectively provides information that an operator needs for planning his or her upcoming tasks and focus areas. The operator can easily view the current statuses of multiple processes, as well as prior behaviors and future predictions. The operator can also plan more effectively for performing specific tasks. This enables more effective control of the processes. The GUI 400 therefore supports an intelligent way to use data (such as timing predictions versus actual executions) and upcoming tasks to compress very complicated procedural hierarchies into single lines so an operator can see and control much more equipment and sequences of operations.
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One or more planned sequences of operations and their characteristics are identified at step 504. This could include, for example, the processor 202 executing the sequence engine 306 to identify one or more current sequences of operations being performed by the control system 302 and/or one or more planned sequences of operations to be performed by the control system 302 in the near future. This could also include the sequence engine 306 identifying various features of the planned sequences of operations, such as the equipment to be used and the recipes or formulas to be implemented.
A subset of the stored information is identified at step 506. This could include, for example, the processor 202 executing the prediction engine 308 to access the sequence history database 304 and identify the prior sequences of operations that are most similar to the planned sequences of operations. As noted above, the processor 202 could match as many attributes of a historical sequence to a current or future sequence, possibly giving preference to historical data identified as being more desirable. If no exact match is found, regression analysis, deep learning techniques, or other data processing, mining, or analysis techniques could be used to interpolate the historical data. Optimization could also be used to maximize or minimize one or more parameters.
Timing predictions for the planned sequences of operations are identified using the selected subset of information at step 508. This could include, for example, the processor 202 executing the prediction engine 308 to map the timings contained in the historical data (or generated via interpolation or other processing) onto the planned sequences of operations. Various tasks associated with the planned sequences of operations are identified at step 510. This could include, for example, the processor 202 executing the prediction engine 308 to identify the tasks to be performed by an operator or other personnel during the planned sequences of operations.
A graphical display is generated for the operator at step 510 and presented to the operator at step 512. This could include, for example, the processor 202 executing the visualizer 310 to receive the timing predictions and the identified tasks. This could also include the visualizer 310 generating a GUI 400 with one or more rows 402 for each planned sequence of operations. Each row 402 could include one or more of the timing predictions.
The graphical display is updated as the planned sequences of operations progress through their executions at step 514. This could include, for example, the processor 202 executing the visualizer 310 to move the current time indicator 412 or shift the bars 414 in the GUI 400 to the left as the planned sequences of operations progress. This could also include the visualizer 310 changing the colors or other indicators of the bars 414 to identify sequences or portions thereof that were or are slower than predicted.
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In some embodiments, various functions described in this patent document are implemented or supported by a computer program that is formed from computer readable program code and that is embodied in a computer readable medium. The phrase “computer readable program code” includes any type of computer code, including source code, object code, and executable code. The phrase “computer readable medium” includes any type of medium capable of being accessed by a computer, such as read only memory (ROM), random access memory (RAM), a hard disk drive, a compact disc (CD), a digital video disc (DVD), or any other type of memory. A “non-transitory” computer readable medium excludes wired, wireless, optical, or other communication links that transport transitory electrical or other signals. A non-transitory computer readable medium includes media where data can be permanently stored and media where data can be stored and later overwritten, such as a rewritable optical disc or an erasable storage device.
It may be advantageous to set forth definitions of certain words and phrases used throughout this patent document. The terms “application” and “program” refer to one or more computer programs, software components, sets of instructions, procedures, functions, objects, classes, instances, related data, or a portion thereof adapted for implementation in a suitable computer code (including source code, object code, or executable code). The term “communicate,” as well as derivatives thereof, encompasses both direct and indirect communication. The terms “include” and “comprise,” as well as derivatives thereof, mean inclusion without limitation. The term “or” is inclusive, meaning and/or. The phrase “associated with,” as well as derivatives thereof, may mean to include, be included within, interconnect with, contain, be contained within, connect to or with, couple to or with, be communicable with, cooperate with, interleave, juxtapose, be proximate to, be bound to or with, have, have a property of, have a relationship to or with, or the like. The phrase “at least one of,” when used with a list of items, means that different combinations of one or more of the listed items may be used, and only one item in the list may be needed. For example, “at least one of: A, B, and C” includes any of the following combinations: A, B, C, A and B, A and C, B and C, and A and B and C.
The description in the present application should not be read as implying that any particular element, step, or function is an essential or critical element that must be included in the claim scope. The scope of patented subject matter is defined only by the allowed claims. Moreover, none of the claims invokes 35 U.S.C. § 116(f) with respect to any of the appended claims or claim elements unless the exact words “means for” or “step for” are explicitly used in the particular claim, followed by a participle phrase identifying a function. Use of terms such as (but not limited to) “mechanism,” “module,” “device,” “unit,” “component,” “element,” “member,” “apparatus,” “machine,” “system,” “processor,” or “controller” within a claim is understood and intended to refer to structures known to those skilled in the relevant art, as further modified or enhanced by the features of the claims themselves, and is not intended to invoke 35 U.S.C. § 116(f).
While this disclosure has described certain embodiments and generally associated methods, alterations and permutations of these embodiments and methods will be apparent to those skilled in the art. Accordingly, the above description of example embodiments does not define or constrain this disclosure. Other changes, substitutions, and alterations are also possible without departing from the spirit and scope of this disclosure, as defined by the following claims.
