320 results about "Static model" patented technology

System and method for modeling a nonlinear process. A combined model for predictive optimization or control of a nonlinear process includes a nonlinear approximator, coupled to a parameterized dynamic or static model, operable to model the nonlinear process. The nonlinear approximator receives process inputs, and generates parameters for the parameterized dynamic model. The parameterized dynamic model receives the parameters and process inputs, and generates predicted process outputs based on the parameters and process inputs, where the predicted process outputs are useable to analyze and/or control the nonlinear process. The combined model may be trained in an integrated manner, e.g., substantially concurrently, by identifying process inputs and outputs (I/O), collecting data for process I/O, determining constraints on model behavior from prior knowledge, formulating an optimization problem, executing an optimization algorithm to determine model parameters subject to the determined constraints, and verifying the compliance of the model with the constraints.

The invention relates to a method of performing a gas operation of an oilfield having a subterranean formation with at least one reservoir positioned therein. The method steps include modeling the gas operation of the oilfield using a multi-domain simulator by coupling a static model of the subterranean formation, a dynamic model of the subterranean formation, and a well model, wherein the multi-domain simulator comprises the static model, the dynamic model, and the well model, defining a development plan for the gas operation based on the modeling, and performing gas injection according to the development plan.

The invention discloses an intelligent workshop transparent monitoring method. The method comprises the following steps: step A: constructing an intelligent workshop transparent monitoring platform, comprising: step A1: establishing an interconnection and interworking system of a virtual model and a real object; step A2: static-modeling a workshop; step A3: dynamic-modeling the workshop; and stepA4: integrating the model and a device; and step B: realizing the intelligent workshop transparent monitoring method, comprising: step B1: specifically implementing intelligent workshop three-dimensional simulation; step B2: associating the virtual model and a real object model; step B3: downloading instructions and feeding back data acquisition; and step B4: displaying data visually. The invention further discloses an intelligent workshop transparent monitoring system. The intelligent workshop transparent monitoring system comprises an MES module, a unit control module, an SCADA module and abut control network module.

A computerized method of offering a comprehensive control system for varied kinds of manufactures, including: providing a computer graphical user interface having the static model of hardware systems, information and status of the control system and context of the automation procedure commands; providing a hardware database containing 1) port database including all ports of a controller connecting to devices, 2) a device database including information, operation methods, events of all devices connected to the controller, 3) a system database including information and configuration of all systems that controller controls; providing a dictionary containing word pairs that define operations and status of every device; providing a background worker responsible for refreshing data from port database. Computer systems configured to perform the method.

The disclosed embodiments relate to prediction of traffic dynamics. A descriptive model is provided that uses historical probe data to create “tidal-like” patterns for the usual dynamics on the road network and creates a framework for taking a future time, e.g. in terms of month, day, time, and suggesting a typical speed for the specified road network link at that specific time. With this model, better predictions for estimated time of arrival will be derived. As opposed to blindly extrapolating from a static model, the disclosed embodiments dynamically adapt to current conditions using real time data to adapt, based on current conditions, the model from which a predicted speed may be determined.

The invention belongs to the technical field of power systems and discloses a system for monitoring a secondary circuit model on line in real time based on a protection device. The system comprises a network communication module, a model resolution module, a graph generation module, a model storage module and a real-time comparison module, wherein the model resolution model is connected with the graph generation module and the model storage module and outputs a substation configuration description (SCD) static model and a resolved configured IED description (CID) on-line model respectively; the real-time comparison module is connected with the network communication module and the model storage module, receives a generic object oriented substation event (GOOSE) real-time model, a verified SCD static model and a resolved CID on-line model, compares and outputs a comparison result to the network communication module, and the comparison result is uploaded to a background system to realize real-time on-line monitoring. By the system, a phenomenon that a virtual terminal and a secondary circuit of the protection device make mistakes due to SCD file modification, CID configuration errors, secondary circuit real-time connection obstacles corresponding to GOOSE messages and the like can be timely found, so that operational risks of a substation are reduced.

The invention provides a secondary circuit closed loop monitoring system based on a GOOSE input end mapping mechanism in the technical field of power systems. The secondary circuit closed loop monitoring system comprises a network communication module, a model analyzing module, a pattern generating module, a real-time comparing module and a data storage module. The model analyzing module analyzes SCD files from the network communication module, outputs an obtained static model of an IED device virtual terminal/virtual circuit to the pattern generating module, maps a GOOSE message feature value and a GOOSE input terminal to mapping definitions of a corresponding MMS model object, and outputs the mapping definitions to the real-time comparing module. According to an IED device standard drawing template prestored in the data storage module and the static model of an IED device virtual terminal/virtual circuit in the model analyzing module, by means of a template matching technology, the pattern generating module carries out matching on the IED device standard drawing template to generate an IED device secondary circuit diagram, and output the IED device secondary circuit diagram to the data storage module for visual display. According to the system, abnormal conditions of a secondary circuit and abnormal situations of a GOOSE control block, a dataset, goIDs and other key content can be found timely according to abnormality of a GOOSE message. Secondary circuit closed loop monitoring based on the GOOSE input end mapping mechanism is achieved, and reliable technical guarantee is provided for safe operation of an intelligent substation.

A system for task modeling interactive sequential applications for one or more mobile devices, which comprises a software tool for defining a generic meta-model of the target applications, wherein the generic meta-model consist of a static model of application components, and a dynamic model of IDs of application screens and connections; a passive TM database for storing the generic meta-model for future reuse, which further comprises static instances of the generic meta-model specifically generated for a variety of mobile devices; a tracker module for real-time monitoring and creating unique ID for each application's screen visited by the user of the mobile device, wherein created IDs are used for generating active models of the user actual use; and an active TM database for storing the active models, wherein data transferring and storing is minimized to screen ID numbers.

The invention discloses a digital twinning body for a production line and a construction method and application thereof, and the digital twinning body comprises: a unit domain which comprises a staticmodel and a dynamic model of physical equipment, provides a data carrier for a data domain, and provides a model basis for the implementation of a behavior domain; the data domain which is used for acquiring operation data of physical equipment and products and instruction data of the digital management system, performing format conversion and storage on the acquired data according to a unified data structure, and supporting a behavior domain to access the data; and the behavior domain which is used for defining semantic description of each behavior in the operation process of the productionline, and driving a model of the unit domain to operate synchronously with the physical world according to the data acquired by the data domain. The digital twinning body can be quickly deployed in areal production environment, and operations such as monitoring, simulation and prediction are carried out on production from different dimensions.

The invention discloses a maneuvering target tracking method under constraint conditions, comprising the steps of: A, obtaining the position measurement information of a maneuvering target; B, converting the position measurement information into Cartesian coordinate measurement; C, establishing a constrained coordinate static model in a one-dimensional space, i.e., building the mileage S expression of the position of the maneuvering target in road x, y directions; D, setting motion models according to motion characteristics of the maneuvering target, and establishing a state equation and a measurement equation according to the established expression; and E, for each motion model, filtering converted Cartesian coordinate measurement to obtain a one-dimensional variable mileage S and speed filtering result. According to the embodiment, the tracking performance of a maneuvering target can be improved.

The invention relates to a method for designing a jointless track of a long and large bridge girder longitudinal butt plate type ballastless track for a high-speed railway. According to the method provided by the invention, ANSYS software is utilized to establish a latitude vertical space coupling statics model of the jointless track of the long and large bridge girder longitudinal butt plate type ballastless track for the high-speed railway, and ABAQUS software is utilized to establish a space coupling kinetic model. According to the method, the compositions of systems such as a steel rail, a fastener, track plates, a slurry placement layer, bed plates, a sliding layer, a consolidation mechanism, high-strength extruded sheets and L-shaped side check blocks, and structures such as supporting layers on a subgrade soil body and a road foundation and the action of high-speed vehicles are taken into full consideration, and friction factors of sliding layers on different bridge or friction plates, the longitudinal resistance of the fastener, the temperature difference of the bridge girder, the temperature difference of a ballastless track structure, the elastic modulus of the slurry placement layer and the like are calculated, so that the stress and deformation of the track and the bridge girder are obtained. The method provided by the invention is suitable for the design and computational check of the jointless track of the long and large bridge girder longitudinal butt plate type ballastless track for the high-speed railway, and providing service for the design calculation as well as conservation and maintenance of the high-speed railway.

The invention discloses an emulation system for a PaaS (Platform-as-a-service) cloud platform. A static model comprises a node model, a topology model, a link model, an application model and a user model, wherein the node model is used for determining computing resources, communication resources and application deployment conditions; the topology module is used for determining role definition of each node and topology connecting conditions; the link model is used for determining communication link attribute of the node; the application model is used for determining computing resource spending, communication resource spending and time delay for application logic and application processing; the user model is used for determining user requirement distribution and user requirement achieving conditions; the dynamic model comprises a control model, a protocol model and an event model, wherein the control model is used for determining operation logic and layout relationship of application and node as well as selecting a mode of processing nodes for business request after reaching the business request; the protocol model is used for determining an interacting framework of the PaaS cloud platform; and the event model is used for determining dynamic events with uncertainty generated in a PaaS cloud platform operating process. The invention discloses an emulation method based on the system. According to the emulation system for the PaaS cloud platform and the emulation method for the PaaS cloud platform disclosed by the invention, emulation load conditions of the node in the PaaS cloud platform can be determined.

The invention belongs to the field of cognitive radio CR and in particular relates to multicast routing protocols in a CR network. The method provides an energy optimization-based CR multicast routing method. The method comprises the following steps of: establishing a network model; establishing a Cournot game model; calculating a profit function; solving Nash equilibrium; and establishing a multicast tree. The invention provides an energy optimization-based multicast routing algorithm in the CR network and efficient spectrum allocation rate is achieved by establishing a spectrum allocation model in the CR network according to a classical Cournot static model. And simultaneously, the problem of dynamic spectrum selection is solved by utilizing a mode of routing selection and spectrum allocation cross-layer design according to energy optimization and the thought of minimum increment capacity MIC and on-demand drive, and the multicast routing algorithm which is suitable for the characteristics such as diversity, dynamic and differentiation of the frequency spectrum of the CR network is designed.

A method for providing independent static and dynamic models in a prediction, control and optimization environment utilizes an independent static model (20) and an independent dynamic model (22). The static model (20) is a rigorous predictive model that is trained over a wide range of data, whereas the dynamic model (22) is trained over a narrow range of data. The gain K of the static model (20) is utilized to scale the gain k of the dynamic model (22). The forced dynamic portion of the model (22) referred to as the b_i variables are scaled by the ratio of the gains K and k. The b_i have a direct effect on the gain of a dynamic model (22). This is facilitated by a coefficient modification block (40). Thereafter, the difference between the new value input to the static model (20) and the prior steady-state value is utilized as an input to the dynamic model (22). The predicted dynamic output is then summed with the previous steady-state value to provide a predicted value Y. Additionally, the path that is traversed between steady-state value changes.

The invention discloses an oilfield exploitation and development working platform system. The oilfield exploitation and development working platform system comprises an exploitation and development part, wherein the exploitation and development part comprises a data layer, an application service layer and a service research layer; and the data layer is quoted from external data, and is used for establishing various databases and providing support for the outputs of the application service layer and the service research layer. Deep fusion of surface and underground information systems is realized. Characterized by a multi-disciplinary data integration technology taking dynamic and static models as centers, a multi-dimensional visualization technology taking multi-disciplinary information fusing as a target, a geological research technology described with a fine reservoir and a reservoir dynamic analysis technology taking blocks, well groups and single wells as units, dynamic unification of reservoir geology and a reservoir and unification of exploitation and development information are realized. Oilfield digital construction is prompted, and the productivity construction and potential tapping of oilfields are greatly ensured.

The invention discloses a depth map generation method for plane video three-dimensional conversion, comprising the following steps of: segmenting a current frame image based on scene static model distribution to obtain segmented regions, marking pixels and clustering the pixels to different regions to generate a static model depth map of the current frame image; selecting characteristic points ofthe current frame image and a reference frame image, calculating a motion vector of a moving object, and performing motion analysis on the motion vector to obtain motion analysis result of the current frame image and the reference frame image; generating a dynamic model depth map of the current frame image according to the segmented regions of the current frame image and the motion analysis result of the current frame image and the reference frame image; and adaptively fusing the static model depth map and the dynamic model depth map of the current frame image. The invention further disclosesa depth map generation device for plane video three-dimensional conversion. With the method and the device, a static model and a dynamic model of a scene are fused, a stereoscopic video with more vivid effect can be obtained, and better stereoscopic effect can be achieved.

The invention provides a method and a device for generating a dynamic alarm threshold value of parameters of a refining process, and relates to the technical field of petroleum refining fault monitoring. The method comprises the following steps of obtaining historic data of the parameters of a refining process production device; determining initial historic training data of the parameters according to the length of a sliding window and the step length, and updating historic training data for once every other step length time from the initial historic training data; forming normalized historic training data; carrying out kernel density estimation on each parameter, and generating a probability density function of the parameters; determining a probability distribution function of each parameter; determining an alarm threshold value corresponding to each parameter according to the probability distribution function of each parameter and a preset alarm threshold confidence degree. According to the method for generating the dynamic alarm threshold value of the parameters of the refining process, provided by the invention, the dynamic alarm threshold value of the parameters can be obtained, and the problems that because a static model parameter threshold value method is adopted at present, and the alarm threshold value of the parameters is artificially regulated, false alarm or leakage alarm can be easily caused, and the sensitivity of abnormal monitoring can be easily reduced can be solved.

Disclosed is a method and system for static behavior-predictive malware detection. The method and system uses a transfer learning model from behavior prediction to malware detection based on static features. In accordance with an embodiment of the invention, machine learning is used to capture the relations between static features, behavior features, and other context information. For example, the machine learning may be implemented with a deep learning network model with multiple embedded layers is pre-trained with metadata gathered from various resources, including sandbox logs, simulator logs and context information. Synthesized behavior-related static features are generated by projecting the original static features to the behavior features. A final static model may then be trained using the combination of the original static features and the synthesized features as the training data. The detection stage may be performed in real time with static analysis because only static features are needed. Other embodiments and features are also disclosed.