TW201437914A - User interface with visualization of real and virtual data - Google Patents

Abstract

First acquired data that represents past values of one or more parameters is displayed in a user interface through which a user can monitor, control and predict system operations. Second acquired data that represents present values of the one or more parameters is displayed in the user interface. Virtual data that represents predicted future values of the one or more parameters is displayed in the user interface, wherein the first acquired data, the second acquired data and the virtual data are presented with a unified visual appearance such that a relationship between the past values, present values and predicted future values is visually indicated.

Description

User interface with real and virtual data visibility

A particular embodiment of the invention relates to a user interface, and more particularly to a graphical user interface for visualizing real and virtual data.

In most manufacturing environments, reactive maintenance and quality control measures are used to repair problems as they occur. Examples of such reactive strategies include statistical process control (SPC) and advanced process control (APC). The downside of reactive strategies is that they respond to problems after they have occurred. Therefore, due to this problem, the product will be invalidated, the machine cannot be commanded, and the employee's working hours will be consumed.

In order to prevent problems before they occur, some manufacturing environments implement predictive strategies. This predictive strategy is to simulate future values of parameters. However, the visualization, software and/or hardware implementation of these future numerical simulations are independent and distinct from the past and present values of the manufacturing environment. Therefore, these simulations are not integrated into the information used in the plant and are different from the users in the plant. If the engineer wants to watch the forecast For future values, he must execute the first application that provides these predictions. However, if an engineer wants to view past or current values, he must execute a separate second application that provides this information. Typically, a simulation application will present data in a different manner and with different controls than the user of the application presenting the past and current values.

Analog solutions can also cause problems for static approximation patterns due to the use of such systems for simulation at a given point in time. Therefore, the results of these simulations in problem investigations are often based on outdated or old data. While these simulations still have value, they are often untrusted and therefore only used as supplemental information rather than true predictive use.

What is described herein is a method and apparatus for providing a user interface whereby a user can simultaneously view and interpret the acquired material (eg, from a sensor, test machine, input data, etc.) and virtual material. In one embodiment, the first retrieved data representing past values of one or more parameters is displayed on a user interface that the user can monitor, control, and predict system operations. A second acquired data representing the current value of the parameters is displayed on the user interface. Virtual data representing predicted future values of the one or more parameters are displayed on the same user interface. In a specific embodiment, a quality indicator indicating the accuracy of the virtual data is also displayed on the user interface. The virtual data and the quality indicator can be updated over time. In a specific embodiment, the first acquired data, the second obtained data, and the virtual data are presented in a unified visual appearance such that the relationship between the past values, current values, and predicted future values It can be indicated visually. The first acquired data, the second acquired data, and the virtual data may be represented by graphics, transparent maps, animations, and/or reports.

100‧‧‧Architecture

105‧‧‧Manufacture of information and control systems

110‧‧‧ Manufacturing Execution System

115‧‧‧Customer Database

120‧‧‧Supply Chain Database

125‧‧‧Network

130‧‧‧ Manufacturing Execution System Information Shop

135‧‧‧ Manufacturing information and control system information shop

140‧‧‧ Data Combiner

145‧‧‧User interface

150‧‧‧ predictor

155‧‧‧Decision support logic components

160‧‧‧Execution logic

165‧‧‧ Instant monitor

170‧‧‧Database outline

172‧‧‧Virtual Data Set

174‧‧‧Virtual Expansion Group

176‧‧‧First Relationship Group

178‧‧‧Second Relationship Group

200‧‧‧ first view

250‧‧‧ second view

300‧‧‧ third view

305‧‧‧Playback controls

700‧‧‧ computer system

702‧‧‧ processor

704‧‧‧ main memory

706‧‧‧ Static memory

708‧‧‧Network interface device

710‧‧‧Video display unit

712‧‧‧Text input device

714‧‧‧ cursor control device

716‧‧‧Signal generator

718‧‧‧ secondary memory

720‧‧‧Network

722‧‧‧Software

726‧‧‧ Processing logic

730‧‧ ‧ busbar

731‧‧‧ Machine readable storage media

The present invention is exemplified by the following examples, which are not intended to be limiting, and the accompanying drawings are: FIG. 1A illustrates an exemplary architecture of a manufacturing environment in which a specific embodiment of the present invention may be operated; FIG. 1B illustrates an embodiment in accordance with the present invention. An entity relationship diagram of a database profile of a specific embodiment; FIG. 2A illustrates a first view of a user interface in accordance with an embodiment of the present invention; and FIG. 2B illustrates the user interface in accordance with another embodiment of the present invention. Second view; FIG. 3 illustrates a third view of the user interface in accordance with yet another embodiment of the present invention; FIG. 4A illustrates a fourth view of the user interface in accordance with yet another embodiment of the present invention FIG. 4B illustrates a fifth view of the user interface in accordance with yet another embodiment of the present invention; FIG. 5 illustrates a flow chart of a specific embodiment of a method of dynamically generating virtual material; FIG. 6 illustrates an example of use A flowchart of a specific embodiment of a method for displaying acquired data and virtual data; and FIG. 7 illustrates exemplary electrical power according to an embodiment of the present invention Block diagram of the brain system.

What is described herein is a method and apparatus for providing a user interface whereby a user can simultaneously view and interpret the acquired material (eg, from a sensor, test machine, input data, etc.) and virtual material. In one embodiment, the first retrieved data representing past values of one or more parameters is displayed on a user interface that the user can monitor, control, and predict system operations. A second acquired data representing the current value of the parameters is displayed on the user interface. Virtual data representing predicted future values of the one or more parameters are displayed on the same user interface. In a specific embodiment, a quality indicator indicating the accuracy of the virtual data is also displayed on the user interface. The virtual data and the quality indicator can be updated over time. In a specific embodiment, the first acquired data, the second acquired data, and the virtual data are presented in a unified visual appearance, such that the relationship between the past values, current values, and predicted future values can be visually Instructions. The first acquired data, the second acquired data, and the virtual data may be represented by graphics, transparent maps, animations, and/or reports.

Many details will be presented in the description below. However, it will be apparent to those skilled in the art that the present invention may be practiced without these specific details. In some instances, well-known structures and devices are shown in block diagrams and not in detail, thereby avoiding obscuring the invention.

Some of these detailed descriptions are presented below in terms of algorithms and symbolic representations of operations on data bits in a computer memory. Now. Unless specifically stated otherwise, it is clear from the following discussion that terms used throughout the discussion, such as "display," "receive," "merge," "generate," "update," or the like, are representative. The operation and processing of a computer system or similar electronic computing device that can manipulate and convert the data representing the number of physical registers (electronics) in the temporary storage and memory of the computer system into other materials, which are similarly represented in the computer system The number of entities in a memory, or scratchpad, or other like information storage, transmission, or display device.

The invention also relates to apparatus for performing such operations herein. The device may be specially constructed for the required purposes, or the device may be a general purpose computer that is selectively activated or reconfigured by a computer program stored in the computer. The computer program can be stored in a computer readable storage medium such as, but not limited to, any type of disc, including floppy discs, optical discs, CD-ROMs and magnetic optical discs, read-only memory (Read- Only memory, ROM), random access memory (RAM), EPROM, EEPROM, magnetic or optical card, or any type of media suitable for storing electronic instructions, each coupled to a computer system Bus bar.

The algorithms and displays presented herein are not intrinsically related to any particular computer or other device. A variety of general purpose systems may be used in accordance with the teachings herein, or it may be convenient to construct a more specialized apparatus to perform the required method steps. The structure required for many of these systems will be in the following Presented in the description. Moreover, the invention has not been described with reference to any particular stylized language. It will be appreciated that the teachings of the invention described herein can be implemented using a variety of stylized languages.

The present invention can be provided as a computer program product or software, which can include a machine readable medium having instructions stored thereon that can be used to program a computer system (or other electronic device to perform a program in accordance with the present invention. Machine readable media includes any mechanism for storing or transmitting information that can be read by a machine (eg, a computer). For example, a machine readable (eg, computer readable) medium includes a machine (eg, a computer) readable. Take storage media (such as read only memory (ROM), random access memory (RAM), disk storage media, optical storage media, flash memory devices, etc.), a machine (such as a computer) can read the transmission media (Electronic, optical, acoustic or other forms of transmitted signals (eg carrier waves, infrared signals, digital signals, etc.)) and the like.

FIG. 1A illustrates an exemplary architecture 100 of a manufacturing environment in which a particular embodiment of the present invention may operate. The manufacturing environment can be a semiconductor manufacturing environment, an automotive manufacturing environment, or the like. In one embodiment, the architecture 100 includes one or more supply chain databases 120, one or more customer repositories 115, a Manufacturing Execution System (MES) 110, and a Manufacturing Information and Control System (MICS) 105.

The network 125 can be a public network (such as the Internet), a private network (such as an Ethernet or a local area network (Local area network, LAN)) or a combination thereof. Network 125 may include multiple private networks that may be directly connected or connected via a public network. For example, the supply chain database 120 can be connected to a first private network controlled by a vendor, the customer database 115 can be connected to a second private network controlled by a customer, and the MICS 105 and MES 110 can be connected to A third private network. Each of these private networks can be connected via a public network.

A supply chain database 120 includes information that can be used and/or provided by a supplier or distributor. Such information may include, for example, a supplier's order (eg, parts and merchandise orders), supplier inventory (eg, current inventory, expected inventory, etc.), expected delivery date, and the like. Architecture 100 may include multiple supply chain repositories 120 when materials are obtained from multiple resellers or suppliers. For example, a first supply chain database may include information of the original product, and a second supply chain database may include information for manufacturing the device.

A customer database 115 includes information that a customer can use and/or provide. Such information may include, for example, customer-specific manufacturing items, customer inventory, and the like. The architecture 100 can include a single customer database 115 of a plurality of customers, or a plurality of customer profiles 115, each of which provides information about a different customer.

A Manufacturing Execution System (MES) 110 is a system that can be used to measure and control production activities in a manufacturing environment. The MES 110 can control a group of manufacturing equipment (such as all lithography equipment in a semiconductor manufacturing facility), a manufacturing facility (such as a car manufacturing plant), part of the production activities of the entire company (such as key manufacturing activities) or all production Activities, etc. MES 110 may include manual and computerized offline and/or Online transaction processing system. Such systems may include manufacturing machines, metrology devices, client computing devices, server computing devices, databases, etc., which may perform functions such as process, device tracking, dispatching (eg, determining which materials are sent to those processes), Product systems, labor tracking (eg personal scheduling), inventory management, cost, electronic signature capture, defect and resolution monitoring, key performance indicator monitoring and alerting, maintenance scheduling, and more.

In one embodiment, the MES 110 is coupled to one or more MES data stores 130. MES Data Shop 130 can be a database, a file system, or other data management, volatile memory (such as random access memory) on non-volatile memory (such as hard drives, tape drives, CD players, etc.) RAM)), or a combination thereof. Each MES data store 130 can store, for example, historical process information for manufacturing prescriptions (eg, temperature, pressure, chemicals used, process time, etc.), equipment maintenance history, inventory, and the like.

The Manufacturing Information and Control System (MICS) 105 combines different information from different sources, such as data shops, and presents this information on a single interface. The MICS 105 can be used to gain an understanding of the manufacturing environment and can enable a user to determine the efficiency of the manufacturing environment and/or how to improve all or parts of the manufacturing environment. The MICS 105 can also derive inferences, report and/or act on the combined information. For example, the MICS 105 can be used as an early warning system (eg, predicting scrap, starting product rework, etc.), providing bottleneck analysis, providing asset management (eg, reducing unscheduled equipment downtime), and improving the implementation of poor returns. In a specific embodiment, the MICS 105 includes a data combiner 140, a user interface 145, and a decision support module 155. An execution module 160, a predictor 150 and an instant monitor 165.

The data combiner 140 combines the acquired data of past parameter values and/or presents parameter values from a plurality of different sources (eg, data stores) and presents the resulting data as if it were from a single source of data. The manner in which the acquired information from such sources is combined may be based on the relationship between the acquired information. These relationships can be defined by the user. Furthermore, the source of the data being merged, and the way in which the data is combined, can be configured by the user. Thus, because the new material store and/or the old data store are removed, the data combiner 140 can be adjusted to accommodate the change.

In one embodiment, the data combiner 104 combines data from a plurality of MES data stores 130 (eg, an inventory data shop, a maintenance data shop, a measurement data shop, a process data shop, etc.). In another embodiment, data combiner 140 merges data from supply chain database 120 and/or customer database 115. In yet another embodiment, data combiner 140 combines immediacy data (eg, from manufacturing machines and metrology machines) when the data is collected by instant monitor 165. In yet another embodiment, the data combiner 140 merges the virtual material that has been generated by the predictor 150. The data combiner 140 may also incorporate manually entered data (e.g., data entered by a device operator, maintenance personnel, etc.).

In one embodiment, the data combiner 140 stores the merged fetched data in the MICS data store 135. In addition, the data combiner 140 can store a subset of all merged acquired data in the MICS data store 135. For example, the data combiner 140 can store the merged material needed to generate the virtual material in the MICS data store 135.

The instant monitor 165 collects real-time data of current values of one or more parameters. This instant data can be collected from the sensors and systems to which the MICS 105 is connected via the network 125. The instant monitor 165 can collect data from the manufacturing device and the metrology device, for example, when the material is generated. In one embodiment, the instant monitor 165 provides the instant data to the data combiner 140.

The predictor 150 uses the combined acquisition data of the past parameter values (or one or more components of the execution system 110) and/or the current parameter values to generate virtual data that predicts future values of the parameters. The predictor 115 can then store the generated virtual data in the MICS data store 135. Virtual materials can be generated and stored without affecting the historical data of the information obtained. Virtual data can be generated based on trend extrapolation, interpolation, simulation (eg, model simulation), and the like.

The methods, models, and/or algorithms used to generate the virtual data may be based on the predicted parameters. For example, a first simulation model can be used to predict future values of a first parameter, and a second simulation model can be used to predict future values of a second parameter.

In a specific embodiment, the predicted future value of a particular parameter requires access to past values of the parameter. It also requires access to past values that affect the relevant parameters of the parameters to be predicted. For example, predicting a buffer length parameter value for a particular manufacturing device may include collecting parameter values for the received batch, dispatching a batch, maintenance of a schedule similar to the manufacturing device, health statistics of the manufacturing device, and the like.

Predicting future values for a particular parameter may also include predicting relevant parameters The future value of the number. For example, to accurately predict the pressure of a device, a model first needs to predict the future power and gas flow rate of the device. Related future predictions are referred to herein as virtual expansions. The virtual extension has a one-to-many relationship with the non-virtual component (data obtained), and each of the non-virtual components can be associated with one or more virtual components, which can be applied to more than one data parameter. Therefore, a single virtual extension (or combination of virtual extensions) can be used for prediction of multiple different parameters. A single virtual extension can also be associated with multiple predicted values for a single virtual data parameter. Applying a virtual extension to multiple predicted values minimizes the consumption of data storage.

In one embodiment, the predictor 150 generates data quality indicators and associates the data quality indicators to the virtual data. A data quality indicator can be implemented as a virtual extension that does not have a special type of current or past counterparts. The data quality indicator represents the accuracy of the virtual data. Data quality indicators may include, for example, error bars, standard deviations, variances, averages, and the like. These data quality indicators allow the user to understand the confidence of the forecast and use the forecast in a quantitative manner.

The prediction extends further into the future, and the accuracy of the virtual data is lower. Therefore, the quality of the data generally decreases with time, and this is transmitted using these data quality indicators. As with other virtual extensions, data quality metrics can be associated with multiple parameters. For example, if a data pass includes upper and lower limits, and these limits are valid for some data points over a period of time, the method allows all of these data points to be linked to a single upper and lower combination.

1B is an entity relationship diagram of a repository outline 170 in accordance with an embodiment of the present invention. The database profile 170 optimizes the utilization of the database space and supports possible complex relationships in virtual expansion. The database profile 170 includes a virtual data set 172 that shares a relationship with a virtual extended set 174 via a first relationship set 176. Each virtual material parameter of virtual data set 172 can include a unique identifier (ID), a descriptor (name), a value (eg, 10 degrees), a time stamp, and/or one or more virtual extents. In a specific embodiment, the unique identification entry is a primary key of the virtual data set 172, and the virtual extended login is a foreign key of the virtual data set 172. As a foreign key of the virtual data set 172, the virtual extended entry can link a virtual data parameter to a different member of the virtual extended set 174. Each virtual extension of virtual expansion set 174 can include a unique identification (ID), a descriptor (name), one or more extended parameters, and/or one or more secondary expansions. In a specific embodiment, the unique identification entry is the primary key of the virtual expansion group 174, and the secondary expansion entry is the virtual expansion group 174 external key. As a foreign key to the virtual extent 174, the extended entry can be linked to a different virtual member of the virtual extent 174.

The first relationship group 176 shows that the plurality of virtual data parameters of the virtual data set 172 can be shared with a single virtual expansion of the virtual expansion group 174. The relationship characterized by the first relationship group 176 may cause the virtual data set 172 to be modified from and/or modified by the virtual expansion group 174. The database profile 170 also includes a second relationship group 178 that displays that multiple virtual extents of the virtual extent group 174 can be shared with another virtual extent of the virtual extent group 174. a relationship. The relationship characterized by the second relationship group 178 may cause the virtual extension group 174 to be modified according to and/or by the virtual expansion group 174. This allows for a recursive relationship between virtual extensions.

In one example, if the temperature parameter is predicted in a one-minute interval with a predicted granularity of one second, then 60 predicted virtual data parameter values will be stored in a virtual database. If a confidence indicator is calculated for each forecast, 60 confidence indicators are also stored. If the 3-sigma upper and lower limits are only calculated for each 10th data point, then 6 (not 60 per storage) will be stored for each upper and lower limit. Each of these extensions will pass the foreign key. Linked to 10 predicted values. If a single absolute upper bound is based on 3-sigma limited to calculations per minute, the single value of the absolute upper bound will be linked to the six 3-sigma upper bounds (and therefore linked to all 60 predicted values). This extension method also allows for simpler reconfiguration of predictive capabilities and subsequent updates to predicted extended information.

Returning to Figure 1A, the data combiner 140 can merge the virtual data with the merged acquired data. The virtual material and the obtained data can be combined such that the virtual data can be aligned to the relevant acquired data. Thus, for example, if the fetched data represents a database field of machine buffer length and this value is being predicted, the virtual data will represent the predicted machine buffer length. As explained with reference to Figure 1B, in the expansion of a data field into the future, additional qualifier parameters can be linked to the data field to provide a more complete description of the forecast data (eg data quality indicators, virtual extensions) Wait). In particular, at least one indication of the predicted quality (data quality indicator) can be linked to the predicted data.

In one embodiment, the data combiner 140 continuously merges the newly acquired data with the existing merged real and virtual data. The newly obtained data may be real-time data received from the instant monitor 165 and/or additional data added to the MES data store 130, the supply chain database 120, the customer database 115, and the like. The newly acquired data can replace part of the virtual data (for example, when the newly acquired data is marked with the time stamp of the virtual data). For example, once a predicted event occurs, the predicted value of the event can be replaced by a true value.

In a specific embodiment, predictor 150 continuously updates the virtual material using a dynamic prediction model. When new material is collected and merged by data combiner 140, the new data can be used to recalculate the virtual data using the specified prediction technique (eg, model). Therefore, the virtual data can always be based on the latest data, increasing the accuracy of the prediction. Each recalculation can provide a more accurate prediction of future values. In a specific embodiment, recalculating the virtual data includes recalculating the data quality indicators associated with the virtual data. In a specific embodiment, recalculating the virtual material includes replacing the original virtual material. In addition, the original virtual material will not be overwritten.

The user interface 145 provides an interactive display of the merged acquired data and the virtual material. Through these displays, a user can view past, current, and future values of the manufacturing environment. In one embodiment, the user interface 145 provides flexible visual capabilities, such as customized graphical curves, transparent maps, summary data, key performance metrics, and drill down into individual data point values. In another embodiment, the user interface 145 provides animated information visualization, so the dynamic nature of the information can be compressed. Interformat to watch. The user interface 145 can access and present virtual data and retrieved data in the same manner, thereby providing historically accessible data to the predicted data. The data quality indicator can provide additional information about the quality of the predicted virtual data, which is superimposed on the visualization of the virtual data. A specific embodiment of user interface 145 is described in greater detail below with reference to Figures 2A-4B.

Referring to FIG. 1A, the decision support logic component 155 makes recommendations and decisions based on historical and current operational status (eg, past and current values). Decision support logic component 155 can also provide recommendations and decisions based on future operational status (eg, virtual data for future values). Decision support logic component 155 can provide these suggestions and decisions based on the business logic of a set of values that match the results. The result may be, for example, such that the maintenance personnel are notified of a waiting machine failure, such that a process engineer is notified of an abnormal measurement result, and the like. The result can also suggest actions to take. For example, the result suggests specific maintenance to be performed on the machine.

Execution logic component 160 is responsible for taking action on the business system based on the output of decision support logic component 155. These actions are in the form of intelligent business rules that can be initiated via immediate system events, predicted events, or scheduled activities. For example, execution logic 160 automatically schedules maintenance of a machine when certain values are detected, automatically shuts down the machine, and the like.

Although the exemplary architecture 100 described above is for a manufacturing environment, embodiments of the present invention may operate in other environments, such as an investment environment (eg, for trading stocks, bonds, foreign exchange, etc.), a research environment, and the like. In this other environment, there is no manufacturing execution system, and the manufacturing information and The control system can also be a research information and control system, investment information and control system. However, the functions of data combiner 140, user interface 145, decision support logic component 155, execution logic component 160, predictor 150, and instant monitor 165 remain the same regardless of the environment.

2A-4B is a diagrammatic view of a user interface in accordance with an embodiment of the present invention in which a user can monitor, control, and predict system operations. An exemplary view of the user interface can display the past values of the parameters and the current values. An exemplary view of the user interface can also display virtual data for future values of the parameters. Some or all of the exemplary views of the user interface may present the acquired data and virtual data using a unified visual appearance such that the relationship between the past values, current values, and predicted values is visually indicated. In a specific embodiment, the user interface corresponds to the user interface 145 of FIG. 1A.

Referring to FIG. 2A, the first view 200 of the user interface illustrates a dashboard view of an exemplary key performance indicator for a manufacturing facility (factory) at a specified point in time in accordance with an embodiment of the present invention. These key performance indicators include plant capacity, plant status, hourly work, first time quality, manufacturing time, downtime, and more. Each key performance indicator is displayed using a related value. For example, factory capacity is shown to be 87%, while hourly work shows about 60. The specified time point can be adjusted to display past key performance indicator values, display current key performance indicator values, or display future key performance indicator values. If a future key performance indicator value is displayed, the data quality indicator can be associated with the value display.

In another embodiment, the key performance indicator can be visualized For animation, the evolution of indicator values from past to present and future is dynamically displayed in a compressed time format. For example, the animation can be equivalent to one second of the display time being one hour of the factory time. These indicators can be animated to illustrate the data from the past week to the next week (with an animation of about 6 minutes). When the animation crosses the current time boundary (herein referred to as NOWTIME) into the prediction space, the visualization can select and display the data quality indicator along with the predicted values, for example by superimposing the values at a value greater than NOWTIME. Above all animations of time values.

These key performance indicators can be defined by the user and can be based on a system to which they are associated. For example, it can display an alternative dashboard view of key performance indicators for a particular department within a manufacturing facility. The key performance indicators included in this alternative dashboard view may include the status of a particular category of machines, traffic, downtime, and the like.

Figure 2B shows a second view 250 of the user interface in accordance with an embodiment of the present invention. In the second view 250, a plurality of exemplary superimposed windows display different real and/or virtual materials. As shown, a first window displays an operational data pattern, a second window displays a Univariate analysis (UVA) model result, a third window displays a factory status, and a fourth window displays a device status history. . More or fewer windows can be displayed. The material to be displayed in the window can be selected from a data selection list, and the visualization for displaying the data can be selected from a data view list. Examples of visualization options include drawing the material, providing a data transparency map, providing an animation of the material, and the like. Some or all of these windows may provide simultaneous visualization and visualization of the acquired data and virtual materials. Any window individually can be viewed in time or forward to view past values, current values or predicted future values. In addition, all windows can move forward or backward together in time.

Figure 3 shows a third view 300 of the user interface in accordance with an embodiment of the present invention. The third view 300 can be included as a different view of the second view 250. Additionally, the third view 300 can be separate and distinct from the second view 250.

The third view 300 includes a timeline perspective view of a set of specified parameters. The exemplary specified parameters shown include power down analysis of the manufacturing equipment. However, a timeline perspective of any specified single or multiple parameters can be displayed. The timeline perspective can display past values, current values, and future predicted values in a visually uniform manner. For example, the data points displayed on the dates 01/01/05 and 01/02/05 may represent the past values of the data obtained. The data points displayed on the date 01/03/05 may represent the current value of the data obtained. The data points displayed on date 01/04/05-01/10/05 can represent future values of the virtual material.

The third view 300 can include a plurality of play control items 305 that can move the timeline perspective forward and backward in time. The play control item 305 may include, for example, control items such as stop, forward, rewind, skip forward, and skip skip. The play control item 305 can be used to animate the material shown in the third view 300. In addition, the data can be displayed in a static format.

Figure 4A shows a fourth view 400 of the user interface in accordance with an embodiment of the present invention. The fourth view 400 includes a timeline perspective view of a specified parameter. In the fourth view 400, the past values of the parameter Displayed as time 0-24. The current value of this parameter is displayed at time 25. Future values for this parameter are shown after time 26. The vertical bar labeled NOWTIME_1 represents the current time. As shown, the parameter is visualized by the historical data, via NOWTIME_1 to the virtual data as a gap (eg, the visualization of the parameter value has not changed).

A data quality indicator is associated with future values of the parameter. The data quality indicator represents the degree of confidence in the predicted future value of the parameter. In the exemplary fourth view 400, the data quality indicator includes an upper control limit and a lower control limit. The upper control limit may represent the maximum possible predicted value of the parameter at a given time, and the lower control limit may represent the smallest possible predicted value of the parameter. Prediction of the value of this parameter In the further future, the confidence of the prediction is lower. For example, the upper control limit of the parameter at time 70 shows about 120, and the control limit at time 26 shows about 80. Similarly, the lower control limit of the parameter at time 70 shows about 40, while the control limit at time 26 shows a limit of about 50.

Figure 4B is a fifth view 450 of the user interface in accordance with another embodiment of the present invention. The fifth view 450 corresponds to the fourth view 400 shown in FIG. 4B after the time advances, so that the current time is 36. In the fifth view 450, the past values of the parameter are displayed as time 0-35. The current value of this parameter is displayed at time 36. Future values for this parameter are shown after time 37. The vertical bar labeled NOWTIME_2 represents the current time.

As shown, the data quality indicator is recalculated at NOWTIME_2. So for this parameter at NOWTIME_2 and at NOWTIME_1 It is displayed as different upper control limits and lower control limits. The tighter control limits displayed at NOWTIME_2 reflect the fact that these data quality indicators have been recalculated using additional data collected after NOWTIME_1. In a specific embodiment, the data quality indicators are continuously recalculated when the data is collected. In addition, data quality indicators can be recalculated periodically at specified time intervals (eg, every 5 seconds, every 10 minutes, daily, etc.).

FIG. 5 is a flow diagram of a particular embodiment of a method 500 of generating virtual data. The method can be performed by processing logic, which can include hardware (eg, circuitry, proprietary logic, stylized logic, microcode, etc.), software (eg, instructions that operate on a processing device), or a combination thereof. In one embodiment, method 500 is performed by manufacturing information and control system 105 of FIG. 1A.

Referring to Figure 5, method 500 includes merging acquisition data from past and current parameter values from a plurality of sources (block 505). The plurality of sources may include a customer database, a supply chain database, an MES material store, and/or an additional material store. In one embodiment, the fetched data is combined by data combiner 140 of FIG. 1A.

In block 510, the virtual data is generated by the application obtaining the data into a predictive model. The predictive model can be a simulated, extrapolated, interpolated or other kind of predictive model. The virtual data may represent predicted future values of the same parameters represented by the acquired data. In a specific embodiment, generating the virtual material includes generating a virtual extent that represents additional parameters associated with the predicted one or more parameters. In a specific embodiment The virtual data is generated by the predictor 150 of FIG. 1A.

In block 515, a data quality indicator is generated for the virtual material. Such data quality indicators may include, for example, error bars (eg, upper control limits and lower control limits), standard deviations, variances, and the like. In one embodiment, the data quality indicator is generated by predictor 150 of FIG. 1A.

In block 520, a data inventory is associated with virtual data and/or quality indicators. The database can be, for example, a manufacturing information and control system data store. In addition, the database can be a different database assigned to virtual materials, data quality indicators, and/or virtual extensions. Once the database already exists, the virtual data can be merged into the acquired data. In a specific embodiment, this occurs automatically when virtual material is generated.

In block 525, processing logic determines if additional fetched data has been received. The additionally obtained data may be received by, for example, an instant monitor (e.g., instant monitor 165 of Figure 1A), or one of a plurality of data stores and/or databases of processing data collected by the processing logic. If additional acquisition data has been received, the method proceeds to block 535. If no additional material has been received, the method proceeds to block 530.

In block 535, a portion of the virtual material is replaced by the additional acquired material. In a specific embodiment, all of the data is associated with a specified time period. The time period may be a historical time point at which the data was collected, or a raised future time point. This point in time can be represented by a time stamp. In a specific embodiment, the portion of the replaced virtual material is replaced by the obtained data having the same time stamp as the virtual material.

In block 540, the virtual data is applied by the additional data. Dynamically updated to this predictive model. In a specific embodiment, the virtual material is continuously updated as additional material is received. In addition, the virtual material can be updated in a regular manner. In block 545, the data quality indicators are dynamically updated.

In block 530, the retrieved data and virtual data are presented to a user via a user interface. In a specific embodiment, the user interface corresponds to the user interface shown in Figures 2A-4B. Then the method ends.

FIG. 6 is a flow chart of a specific embodiment of a method 600 of displaying acquired data and virtual data on a user interface. The method can be performed by processing logic, which can include hardware (eg, circuitry, proprietary logic, stylized logic, microcode, etc.), software (eg, instructions that operate on a processing device), or a combination thereof. In one embodiment, method 600 is performed by manufacturing information and control system 105 of FIG. 1A.

Referring to FIG. 6, method 600 includes first obtaining data showing a past value of one or more parameters in a user interface (block 605). The user interface enables a user to monitor, control, and predict system operations. In one embodiment, the parameters represent key performance indicators that may be displayed in the dashboard view of the user interface. In block 610, the second retrieved data of the current values of the one or more parameters is displayed in the user interface. The second acquired data can be presented in a continuous manner using the first acquired data.

In block 615, virtual data of future values of the one or more parameters are displayed in the user interface. The virtual material can utilize the The first rule data and the second acquired data are displayed in a continuous manner. In a specific embodiment, the first acquired data, the second acquired data, and the virtual data are presented in a unified visual appearance such that the relationship between the past values, current values, and future values can be visually indicated. In another embodiment, the user interface includes at least one of a graphic, a transparent image, an animation, or a report, which can provide simultaneous presentation and visualization of the first acquired data, the second acquired data, and the virtual data. . In another embodiment, the first fetched material, the second fetched material, and the virtual material are presented in a timeline perspective view.

In block 620, the data quality indicator for the virtual material is displayed in the user interface. The data quality indicator can represent the accuracy of the virtual data. In a specific embodiment, the data quality indicator changes as a function of time.

Figure 7 shows a graphical representation of a machine in an exemplary form of a computer system 700 in which a set of instructions for causing the machine to perform any one or more of the methods described herein can be performed. In an alternate embodiment, the machine can be connected to (e.g., networked) a local area network (LAN), an intranet, an off-network, or other machine in the Internet. The machine can operate in the presence of a server or a client machine in a client-server network environment, or as a peer-to-peer machine in a peer-to-peer (or decentralized) network environment. The machine can be a personal computer (PC), a tablet PC, a set-top box (STB) 'Personal Digital Assistant (PDA), a cell phone, a network appliance, a server, A network router, switch, or bridge, or any machine capable of executing a set of instructions (sequence or other means) that specify actions to be taken by the machine. In addition, although only a single machine is illustrated, the term "machine" is also employed to include a collection of any machine (eg, a computer) that individually or jointly executes a set (or sets) of instructions to perform the methods described herein. Any one or more of the methods.

The exemplary computer system 700 includes a processor 702, a main memory 704 (eg, read only memory (ROM), flash memory, random access memory (DRAM), such as synchronous DRAM (SDRAM) or Rambus DRAM ( RDRAM), a static memory 706 (eg, flash memory, static random access memory (SRAM), etc.), and a primary memory 718 (eg, a data storage device) that are connected to each other via a bus 730 Communicate.

Processor 702 represents one or more general purpose processing devices such as a microprocessor, central processing unit or the like. More specifically, the processor 702 can be a Complex Instruction Set Computing (CISC) microprocessor, a Reduced Instruction Set Computing (RISC) microprocessor, or a very long instruction character (Very). Long instruction word, VLIW) A processor that implements a processor of other instruction sets or a combination of instruction sets. The processor 702 can also be one or more special purpose processing devices, such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), and a field programmable gate array (FPGA). A digital signal processor (DSP), a network processor, or the like. Processor 702 is configured to execute processing logic 726 to perform the operations and steps described herein.

Computer system 700 can additionally include a network interface device 708. The computer system 700 can also include a video display unit 710 (such as a liquid crystal display (LCD) or cathode ray tube (CRT)), an alphanumeric input device 712 (such as a keyboard), a cursor control device 714 (such as a mouse), and a Signal generating device 716 (eg, a speaker).

The secondary memory 718 can include a machine readable storage medium (or more specifically a computer readable storage medium) 731 having stored thereon one or more sets of instructions (eg, software 722) implemented herein. Describe one or more methods or functions. The software 722 may also be present entirely or at least partially within the main memory 704 and/or within the processing device 702 during execution by the computer system 700. The main memory 704 and the processing device 702 also constitute a machine readable storage medium. Software 722 can further be transmitted or received over network 720 via network interface device 708.

The machine readable storage medium 731 can also be used to store a data combiner, predictor and/or user interface 145 (eg, data combiner 140, predictor 150 and user interface 145 of FIG. 1A), and/or a A software library that contains methods for calling a data combiner, predictor, and/or user interface. The machine readable storage medium 731 can also be used to store one or more additional components of the Manufacturing Information and Control System (MICS), such as the Decision Support Logic Department. Pieces, instant monitors, and/or execution logic components. When the machine readable storage medium 731 is shown as a single medium in an exemplary embodiment, the term "machine readable storage medium" must be employed to include storing one of the one or more sets of instructions for a single medium or Multiple media (such as a centralized or decentralized repository, and/or associated caches and servers). The term "machine readable storage medium" must also be employed to include any medium that can store or encode a set of instructions executed by the machine and that cause the machine to perform any one or more of the methods of the present invention. The term "machine readable storage medium" must therefore be employed to include, but is not limited to, solid state memory and optical and magnetic media.

The above description is to be considered as illustrative and not restrictive. Numerous other specific embodiments will be apparent to those skilled in the <RTIgt; The present invention has been described with reference to the specific embodiments thereof, and it is understood that the present invention is not limited to the specific embodiments described, but may be practiced with modifications and variations within the spirit and scope of the appended claims. Accordingly, the specification and drawings are to be regarded as illustrative and not limiting. Therefore, the scope of the invention must be determined by reference to the scope of the appended claims and the full scope of the equivalents claimed.

100‧‧‧Architecture

105‧‧‧Manufacture of information and control systems

110‧‧‧ Manufacturing Execution System

115‧‧‧Customer Database

120‧‧‧Supply Chain Database

125‧‧‧Network

130‧‧‧ Manufacturing Execution System Information Shop

135‧‧‧ Manufacturing information and control system information shop

140‧‧‧ Data Combiner

145‧‧‧User interface

150‧‧‧ predictor

155‧‧‧Decision support logic components

160‧‧‧Execution logic

165‧‧‧ Instant monitor

Claims (22)

A computer-implemented method, the method comprising the steps of: combining acquisition data of at least one of a past value and a current value of a system from a plurality of sources; generating the data by applying the acquired data to a predictive model Virtual data of future values of the system; receiving additional data obtained; and dynamically updating the virtual data by applying the additional acquired data to the predictive model, wherein the additional obtained data replaces a portion of the generated virtual data The additional acquisition data includes a first time stamp that matches a second time stamp of the replaced portion of the generated virtual material. The method of claim 1, wherein the additional acquisition data reflects a real time operational value of a manufactured component, and wherein the virtual data will change with the immediate operational value of the manufactured component And dynamically updated. The method of claim 1, wherein the step of generating virtual data of a future value comprises (i) collecting information of parameters to be predicted, and (ii) collecting additional data that facilitate accurate prediction of the parameters. News. The method of claim 1, further comprising the step of: generating a data quality indicator indicating the accuracy of the virtual data by applying the obtained data to the predictive model; and The data quality indicator is dynamically updated based on the additional obtained data. The method of claim 1, wherein the plurality of sources comprise at least one component of a manufacturing execution system and at least one component of a manufacturing information system, and wherein the predictive model includes a simulation for a parameter ( At least one of simulation, extrapolation, or interpolation, the parameter being measured and controlled by the at least one component of the manufacturing execution system or the at least one component of the manufacturing information system. The method of claim 1, further comprising the steps of: storing the virtual data; and combining the obtained data with the virtual data to enable the monitoring and diagnostic tool to access and operate as the monitoring and diagnostic tools The virtual material is accessed, operated and presented in the same manner as the obtained data. The method of claim 6, wherein the virtual data and the obtained data are accessible by the monitoring and diagnostic tools in such a manner that the tools can be combined with past, present and future in an animated format. A seamless transition between the virtual data and the obtained data. The method of claim 1, further comprising the steps of: generating a virtual extension to the virtual data; storing the virtual extension and the virtual data in a database; and mapping the virtual extension to the Multiple values of virtual data, (i) reduced by The amount of space occupied by the virtual extension, and (ii) maximizing the reconfigurability of the virtual extension, The method of claim 1, wherein the virtual data comprises a virtual alert indicating that an alert condition is predicted to occur in the future. An arithmetic device comprising: one or more data stores, the one or more data stores storing data of at least one of a past value and a current value of a system; a data combiner The data combiner is coupled to the one or more data stores to merge the obtained data from the one or more data stores; and a predictor coupled to the data combiner to Generating the virtual data of the future value of the system by applying the merged acquired data to a predictive model, and dynamically updating the virtual data when the newly acquired data becomes available, wherein the newly acquired data replaces the generated data In one part of the virtual data, the newly acquired data includes a first time stamp that matches a second time stamp of the replaced portion of the generated virtual data. The computing device of claim 10, wherein the newly acquired data reflects an immediate operational value of a manufacturing component, and wherein the virtual data is to be changed with the immediate operational value of the manufacturing component. Stately updated. The computing device of claim 10, wherein the predictor generates a data quality indicator indicating the accuracy of the virtual data by applying the acquired data to the predictive model, and when the newly acquired data becomes This data quality indicator is dynamically updated when it is available. The computing device of claim 10, further comprising: an additional data store connected to the data combiner and the predictor to store the virtual data generated by the predictor; The data combiner combines the obtained data with the virtual data to enable the monitoring and diagnostic tool to access, operate and present the virtual data in the same manner as the monitoring and diagnostic tools access, operate and present the acquired data. . The computing device of claim 10, further comprising: the predictor configured to generate a virtual extension to the virtual data, and mapping the virtual expansion to a plurality of values of the virtual data to (i) The amount of space occupied by the virtual extension is reduced, and (ii) the reconfigurability of the virtual extension is maximized; and the additional data is stored to store the virtual extension. The computing device of claim 10, further comprising: a decision support module, wherein the decision support module is coupled to the combiner and the predictor to detect a virtual alert, the virtual alert indicating a The alarm condition is predicted to occur in the future; and An execution module that performs an action in response to the virtual alarm. A non-transitory computer readable medium, comprising instructions for causing the processing system to perform a method when executed by a processing system, the method comprising the steps of merging past values and current values of a system from a plurality of sources Acquiring information of at least one of the data; applying the obtained data to a predictive model to generate virtual data of future values of the system; receiving additional acquired data; and applying the additional acquired data to the predictive The model dynamically updates the virtual data, wherein the additional obtained data replaces a portion of the generated virtual data, the additional obtained data includes a first time stamp, the first time stamp conforming to the replacement of the generated virtual data Part of a second time stamp. The non-transitory computer readable medium of claim 16, wherein the additional obtained data reflects an immediate operational value of a manufactured component, and wherein the virtual data is to be immediately operated with the manufactured component The value is dynamically updated. The non-transitory computer readable medium of claim 16, wherein the step of generating virtual data of a future value comprises (i) collecting information of parameters to be predicted, and (ii) collecting Accurate prediction of parameters Additional information. The non-transitory computer readable medium according to claim 16, wherein the method further comprises the step of: generating a data indicating the accuracy of the virtual data by applying the obtained data to the predictive model. Quality indicators; and dynamically update the data quality indicators based on the additional data obtained. The non-transitory computer readable medium of claim 16, wherein the plurality of sources comprise at least one component of a manufacturing execution system and at least one component of a manufacturing information system, and wherein the predictive model Including at least one of simulation, extrapolation, or interpolation for a parameter measured and controlled by the at least one component of the manufacturing execution system or the at least one component of the manufacturing information system. The non-transitory computer readable medium according to claim 16, wherein the method further comprises the steps of: storing the virtual data; and combining the obtained data with the virtual data, so that the monitoring and diagnostic tool can be like The virtual data is accessed, operated, and presented in the same manner as the monitoring and diagnostic tools access, operate, and present the acquired data. The non-transitory computer readable medium of claim 21, wherein the virtual data and the obtained data are accessible by the monitoring and diagnostic tools in the following manner: those tools can be in an animated format Presenting the virtual material along with a seamless transition between the past, present and future And the information obtained.