175 results about "Production model" patented technology

The method and system identifies and classifies and monetizes intangible asset (I.A.) unstructured digital data in a computer system by ownership characterizations and intellectual property (I.P.) classifications. Enterprise and non-enterprise data collections are searched based upon an expanded project specification (of descriptive functions, characteristics and objectives). Primary search terms from the project are expanded by content, contextual and taxonomic analysis to generate a plurality of search string parameters employed to search the data collections. The search engine returns data and documents, ownership characteristics are ascertained by comparison of the returns and I.P. classifications are made based upon the returns. This process also handles unstructured enterprise specific data not otherwise located in the data collections or unstructured data from other I.A. programs such as H.R. performance modules, supply chain modules or CRM modules. Unstructured data matching an organized data set from project and the search returns is integrated into the organized data set.

Improved real-time analytics systems are provided. An analytics system may be used to generate transaction scores for transactions. A method may comprise receiving a scoring request associated with a transaction, sending the scoring request to a plurality of scoring models including a production model, wherein each scoring model is operable to generate a transaction score in response to the scoring request, and wherein each scoring model may be implemented using a virtual machine, receiving a transaction score generated by the production model, and sending the transaction score to a server for approval or denial the transaction.

The invention relates to a noninvasive analyzer and a method of using information determined at least in part from in-vitro spectra of tissue phantoms or analyte solutions to aid in the development of a noninvasive glucose concentration analyzer and/or in the analysis of noninvasive spectra resulting in glucose concentration estimations in the body. The preferred apparatus is a spectrometer that includes a base module and a sample module that is semi-continuously in contact with a human subject and that collects spectral measurements which are used to determine a biological parameter in the sampled tissue, such as glucose concentration. Collection of in-vitro samples is, optionally, performed on a separate instrument from the production model allowing the measurement technology to be developed on a research grade instrument and used or transferred to a target product platform or production analyzer for noninvasive glucose concentration estimation.

The invention provides a rock seam hole type oil deposit tracer-agent-injection injection-production model and a manufacturing method and application of the rock seam hole type oil deposit tracer-agent-injection injection-production model. A carbonate rock is cut into a square plate, on the surface of the plate, the sizes of seam holes in the model are calculated according to a set proportional scale on the basis of the distribution condition of the seam holes on an actual oil deposit earthquake curvature graph, and the rock plate is etched to obtain cracks or caves. The two sides of the etched rock plate are respectively coated with adhesives and are then respectively covered with transparent organic glass, pressed and sealed, set screws are arranged on related portions, and the joints are coated with glue and sealed. Holes are drilled in different portions of the model according to the requirement for setting simulation well positions, valves are installed, different types of well position distribution of seam hole oil deposit is simulated, and then the rock seam hole type oil deposit tracer-agent-injection injection-production model is obtained. By means of the model, the test process of real seam hole oil deposit injection-production well network tracer agent injection can be well simulated, and guidance is provided for the research on the oil-water relation of seam hole oil deposit.

A spot color dictionary is fine tuned or updated. Localized color production models are determined for spot colors of interest to be produced by an associated document processing system or printer. Measurements are made of colors of produced spot colors. Optionally, measurements are made of colors of test patches that are based on perturbations from the colors of the spot colors. In determining a model for the production of a target spot color, measurement data related to colors that are closer in color space to a given target color is given a higher weight than is measurement data related to colors that are further in color space from the target color. Accordingly, the model is localized to the region of color space about the target color and therefore, more accurately predicts a colorant recipe for the target color than would interpolation based on a full gamut, or more general model.

The invention particularly relates to an intelligent scoring system for dairy cow body condition based on depth learning and remote video, comprises a cow walking channel, an ear tag reader, a 3D camera and a processing module, wherein the ear tag reader is used for reading cow ear tag information, the 3D camera is used for shooting a 3D image of the cow, and the processing module adopts a depth learning algorithm Faster R-CNN processes the received cow 3D image to obtain cow scoring data, associates the cow scoring data with cow ear tag information, and outputs and/or stores the cow ear tag information. The original 3D images of cow body were obtained by 3D camera and the feature database of cow BCS images was constructed. The sample set was trained by depth learning algorithm and the classification accuracy of BCS was 0.1 point. Then, 3D infrared camera and electronic ear tag are used to construct a model that can continuously track the healthy growth and production of dairy cows inthe life cycle, so that the system can identify and early warn the abnormal condition of dairy cows in advance.

The invention relates to a deep-concave strip mine production model system which comprises a deep-concave excavating plant, a refuse dump, ground transportation roads and a raw coal washing field. The deep-concave excavating plant is a coal excavating plant arranged on a coal seam in a simulating mode; as simulation, multiple bucket wheel excavators conduct continuous stripping work on the north working pit edge and conduct coal production work in a deep place, stripped objects in the excavating plant are conveyed to the refuse dump on the south part of the ground through a belt conveyor arranged on the east end pit edge to be discharged through a dumping plough, and run-of-mine coal in the excavating plant is conveyed to the raw coal washing field on the west side of the ground surface boundary through a belt conveyor arranged on the west end pit edge to be processed. By means of the deep-concave strip mine production model system, tests under various construction process conditions can be simulated, the improvement can be carried out in real time according to the mining requirements, and overall adjustment can be conducted on a mining site.

The invention discloses a gas turbine blade geometry forging stock control methods and a testing model. The method includes the model of production and specific inspection, the model produced by the cross-sectional model making, model making and model axis combinations, axial cross-sectional model making production model are by cutaway model of the blade-shaped three-dimensional model of two-dimensional graphics, the graphics were produced by two-dimensional cross-sectional axis model and model both inside and outside the arc, and then combined to form inside and outside the arc than the fixed model with a model than by combining visual and feeler the methods were inside and outside the arc blades comparison checks. Model by a number of cross-section than the production model by forming an axis model fishbone-shaped cross-section and the axis of the model than the model on the side blade-shaped formation. The beneficial effect of the invention is a method which can detect the test results obtained in several parts of the blade, simple operation, high accuracy and high detection efficiency; check the model structure is simple, low labor intensity is detected, the detection of low skill requirements.

A method, comprising: indicating a leak of a first diameter on a canister side of a fuel system following applying a vacuum to the fuel system with a fuel tank isolation valve closed; and indicating a leak of a second diameter on a fuel tank side of the fuel system, based on a vacuum decay in an isolated fuel tank following applying a vacuum to the fuel system with the fuel tank isolation valve open. In this way, an evaporative leak check module with a single reference orifice may be used to determine leaks of multiple sizes in multiple sectors of a fuel system. This may allow current production models to meet more stringent emissions regulations in the future.

The invention discloses an urban rainstorm water accumulation assessment modeling method based on full space-time, and the method comprises the following steps: 1, obtaining DSM data containing surface information, employing a D8 algorithm to divide a water catchment area, and modifying the boundary of the water catchment area according to the connection and distribution conditions of a real pipenetwork, so as to obtain the divided water catchment area; 2, establishing a surface runoff production model, and calculating runoff production of each catchment area; Step 3, establishing a surface confluence model, and calculating confluence of each catchment area; And step 4, establishing a drainage experience model to carry out drainage calculation, and calculating the drainage capacity of each catchment area through the drainage experience model so as to obtain an urban inland inundation assessment result. The DSM is adopted to divide the urban catchment area, the influence of urban planning and construction on urban topography reconstruction can be well reflected, and the method is more practical; The problem that drainage calculation cannot be carried out due to lack of structural data of a drainage pipeline is solved, and the operation time of the model is remarkably shortened.

The recharging device for portable electronic devices includes at least one protective case for a portable electronic device in the process of being recharged, at least one electrical power supply for a battery of a portable electronic device in the case, a case management device capable of controlling on the one hand the power supply for the battery of a portable electronic device placed inside a case and, on the other hand, the access to each case. In production models, at least one protective case includes recharge connectors of different shapes that fit the recharge connectors of portable electronic devices. In production models, the management device includes a user interface which includes a user identification device.

A method for analyzing web visitors using anonymized behavior production models begins with a website visitor accessing a website. The website includes a web beacon, which redirects the website visitor's browser to the server of a marketing services provider (MSP). The MSP server reads a cookie previously set by the MSP that includes an anonymous link. The anonymous link may be used to match data related to the website visitor that is maintained by the MSP, and browsing activity for that visitor is stored in an activity database from which periodic reports may be constructed for the website owner. The anonymous link is not associated with any personally identifiable information (PII), and thus privacy for the visitor is maintained.

The invention discloses a metal part grafting printing method. The method comprises the following steps that CAD software is utilized to plan an area to be machined and an area to be subjected to selective laser melting machining on product production models, after a machining model adopts a traditional method to machine a finished product, a special method is adopted to carry out accurate positioning on the upper surface of the finished product and the area to be subjected to selective laser melting, and then grafting printing forming is carried out. The method belongs to composite manufacturing, the metal part grafting printing method based on the selective laser melting technology can fully play the advantages of 3D printing, the printing cost of a large-scale part including a part of complex structures is greatly reduced while the production efficiency is greatly improved, the cost can be reduced to be 30% or below of the original printing cost to the maximum, and the efficiency can be improved by 70% or above to the maximum; and the method is suitable for grafting and printing of any metal powder capable of being printed, so that the application field of metal 3D printing is expanded.

The invention discloses an intelligent manufacturing industry parameter optimization method based on machine learning and industrial Internet of Things, and the method comprises the following steps: S1, building an industrial Internet of Things system, bottom-layer equipment being connected into the industrial Internet of Things system, and extracting the operation data of the bottom-layer equipment; S2, analyzing process steps of the intelligent production line, building a digital production model of the intelligent production line, and the collected data corresponding to variables in the model and storing at different positions in a database; S3, building an association analysis model; S4, building a sequence model, and quantifying relevance rules; and S5, obtaining the relationship between different sections of the production line, and then building a gray model to calculate the change trend of the data between different sections. By importing the industrial Internet of Things system, centralized collection, centralized cleaning and centralized processing of production data are achieved, data support is provided for subsequent work, the optimal value of each part is calculated through a machine learning model, an engineer adjusts with the optimal value as the reference value, and a large amount of debugging workloads are reduced.

The invention provides a metallurgical enterprise converter gas scheduling method based on knowledge. The method comprises steps: firstly, as for a procedure variable which is needed in a scheduling decision process but can not be acquired directly, a neural network model is adopted for modeling analysis, and a neural network model based on data is formed; secondly, a production rule model based on fuzzy clustering is designed, through carrying out clustering analysis and association rule mining on historical data, knowledge information contained in the data is mined, fusion with expert prior knowledge is further carried out, and a production model based on knowledge is built; and finally, an online updating strategy for the production rule model is designed, if large deviation exists between a reasoning result and a practical solution, the output parameters of the production rule are updated. By using the method of the invention, the balance state of a converter gas system can be dynamically analyzed, a corresponding scheduling scheme can be obtained, and decision by the scheduling person can be reasonably guided.