24 results about "Virtual process" patented technology

The application relates to a rapid matching positioning optical projection system and method, wherein the optical projection system comprises a projector, a camera, an information editing module, a rapid positioning module and a projection operation module; the information editing module is used for compiling virtual process information required by each working procedure according to an assembly process flow, and generating a guide information set file; the rapid positioning module is used for determining two-dimensional coordinates of a preset target ball mark reference point in a camera image after movement according to projected workpiece images before and after movement of the optical projection system, and corresponding points of the target ball mark reference point in a projector image, and determining pose information of the projected workpiece relative to a projector space coordinate system in combination with measurement data of three-dimensional coordinates of the target ball obtained by a measuring device; the projection operation module is used for generating a working procedure guide image through a three-dimensional space projection algorithm according to the guide information set file and the pose information; and the projector is used for projecting the working procedure guide image on a surface of the projected workpiece.

The application discloses an ETL-based welding line virtual process environment building method and device and a storage medium, belongs to the technical field of software development, and comprises the following steps: preparation: establishing a two-dimensional plane legend library and a PD three-dimensional model resource library with corresponding names; calling legends in the two-dimensional plane legend library to create a two-dimensional plane map; data extraction: filling in the work station information of the equipment in the two-dimensional plane map and generating an automatic layout table; data conversion: reading the work station information in the automatic layout table to automatically generate a resource tree Input table; data loading: generating a corresponding resource tree according to the information in the resource tree Input table, inserting an equipment node under the resource tree and modifying the spatial position of the equipment when the PD-ID in the automatic layout table is empty, and completing the virtual process environment building; the method optimizes the working method, improves the working efficiency and accuracy, and can realize the traceability of data changes.

A solution for transforming and managing data in a data lake environment is disclosed. A definition of a transformation stream is received via a graphical user interface. The transform stream specifies an operation to transform source data into target data stored in the first format object store. And generating a virtual process corresponding to the transformation stream and storing the virtual process in a memory cloud database. A virtual process is then executed such that metadata is retrieved from the first format object store and a transform job is triggered on the compute cluster. The transform job reads the source data from the first format object store, applies one or more transforms specified in the definition of the transform stream, and writes the result to the target data in the first format object store.

The invention relates to a method for calibrating a physical unit (12) and a system (10) for calibrating and adjusting a physical unit. The physical unit is arranged in the real process environment (14). A virtual component (26) disposed in a virtual process environment (24) is provided. The virtual process environment is formed to correspond to the real process environment such that the physical units arranged in the real process environment are represented in a manner corresponding to the virtual components (26) in the virtual process environment. Measurements are performed with a physical unit to obtain true measurements. The measurement is simulated with a virtual component to obtain a virtual measurement value. The offset is determined by a verification unit (30) based on a comparison of the real measurement and the virtual measurement. The real measurement value corresponds to the virtual measurement value. The physical unit is calibrated by the verification unit using at least the offset such that the compensation data based on the offset takes into account future measurements for the physical unit.

A Multi-Purpose Dynamic Simulation and run-time Control platform includes a virtual process environment coupled to a physical process environment, where components / nodes of the virtual and physical process environments cooperate to dynamically perform run-time process control of an industrial process plant and / or simulations thereof. Virtual components may include virtual run-time nodes and / or simulated nodes. The MPDSC includes an I / O Switch which delivers I / O data between virtual and / or physical nodes, e.g., by using publish / subscribe mechanisms, thereby virtualizing physical I / O process data delivery. Nodes serviced by the I / O Switch may include respective component behavior modules that are unaware as to whether or not they are being utilized on a virtual or physical node. Simulations may be performed in real-time and even in conjunction with run-time operations of the plant, and / or simulations may be manipulated as desired (speed, values, administration, etc.). The platform simultaneously supports simulation and run-time operations and interactions / intersections therebetween.

The invention discloses a thermal control comprehensive practical training system based on DCS control, and relates to the technical field of thermal control comprehensive practical training, and the system comprises a DCS control practical training unit which is used for building an intelligent DCS control skill practical training room and is developed based on a domestic autonomous controllable DCS control system from the four aspects of decoration layout, a control system, an entity process object and a virtual process object. The thermal control maintenance training unit realizes the multifunctional operation of DCS hardware and system maintenance, DCS software configuration and debugging, control system design and optimization, on-site actual equipment adjustment and calibration and fault diagnosis and analysis, and is used for constructing a professional thermal control maintenance training room covering all thermal control maintenance contents. On the basis of practical training room construction, the intelligent level of the practical training room is comprehensively improved, the training function is expanded, the artificial intelligence technology is introduced into the practical training room construction process, deep recognition and learning of training equipment are completed through the intelligent technology, and intelligentization, real-time performance and scoring autonomy of the training process are achieved.

In a method of predicting an optimal process condition model for a semiconductor fabrication process, process parameter information of a unit process in the semiconductor fabrication process may be collected. First characteristics information of objects to be processed before the unit process and second characteristic information of processed objects after the unit process may be extracted. Process global uniformity (PGU) may be calculated using the first characteristic information and the second characteristic information. A data set of the unit process may be created using the process parameter information and the PGU. A virtual process environment function of the unit process may be created using the data set. The optimal process condition model of the unit process may be created using the virtual process environment function.

Even in fields where it is difficult to determine the target variable with high accuracy solely through simulation, there has been a need for technology that can efficiently acquire data for machine learning using simulation and generate trained models with high predictive accuracy. [Solution] Actual process data measured when the manufacturing equipment manufactures a product under first manufacturing conditions, actual quality data measuring the quality of the product, and first virtual process data generated by simulating the manufacturing process in which the manufacturing equipment manufactures the product are acquired. A process data prediction unit and a quality prediction unit are generated by machine learning. Second virtual process data is acquired by simulating the manufacturing process in which the manufacturing equipment manufactures a product under second manufacturing conditions. The second virtual process data is input to the process data prediction unit to output virtual process data for educational purposes. The second virtual process data is input to the quality prediction unit to output virtual quality data for educational purposes. A product quality prediction model is generated by machine learning.

A method of calibrating a physical unit and a system for calibrating and adjusting a physical unit. The physical unit is arranged in a real process environment. A virtual component is provided in a virtual process environment formed to correspond to the real process environment so that the physical unit behaves in a manner corresponding to the virtual component. A measurement is performed with the physical unit to obtain a real measured value. A measurement is simulated with the virtual component to obtain a virtual measured value. An offset is determined by a verification unit based on a comparison of the real and virtual measured values. The real and virtual measured values are corresponding. The physical unit is calibrated by the verification unit using the offset at least such that compensation data based on the offset is taken into account for future measured values of the physical unit.

Aspects of the present disclosure relate to automatic virtual process completion assistance. Embodiments include receiving data associated with a given virtual process. Embodiments further include retrieving, based on a semantic similarity comparison involving the received data and embedding representations of a set of rules, one or more rules. Embodiments further include retrieving one or more knowledge graphs of a set of knowledge graphs based on the received data, wherein each respective knowledge graph of the set of knowledge graphs represents a respective historical virtual process instance. Embodiments further include providing an input based on the received data, the retrieved rules, and the retrieved knowledge graphs to a machine learning model that is configured to evaluate virtual processes. Embodiments further include generating, using the machine learning model, an evaluation of the given virtual process based on the received data, the retrieved rules, and the retrieved knowledge graphs.

The application discloses a kind of based on process fusion's number real fusion prediction method, belong to intelligent manufacturing, industrial big data and digital twin technical field. Including: acquisition equipment state, process parameter and quality detection etc. Multi-source heterogeneous data, construct unified benchmark time series process characteristic sample;Second, based on deep time sequence framework process fusion prediction model is built, long time sequence dependence and process coupling are jointly modeled using time coding mechanism, and virtual process parameters and disturbance factors are introduced, and the collaborative fusion of virtual and real process information is realized through feature fusion or cross attention mechanism;Finally, based on the model, the future manufacturing quality is forecasted, and the batch deduction of virtual scene is carried out, and the quality result and its uncertainty distribution are output.According to the application, decision support can be provided for line optimization and virtual debugging.

An information processing system includes: a physical sensor data acquisition unit configured to acquire physical sensor data measured by a semiconductor manufacturing apparatus that executes a process according to a process parameter; a simulation execution unit configured to execute a simulation by a simulation model of the semiconductor manufacturing apparatus according to the process parameter and calculate virtual sensor data and virtual process result data; and a display controller that visualizes a process state of the semiconductor manufacturing apparatus and displays the process state on a display unit during the execution of the process using the physical sensor data, the virtual sensor data, and the virtual process result data.

Methods and apparatus consistent with the present disclosure may be used in environments where multiple different virtual sets of program instructions are executed by shared computing resources. These methods may allow actions associated with a first set of virtual software to be paused to allow a second set of virtual software to be executed by the shared computing resources. In certain instances, methods and apparatus consistent with the present disclosure may manage the operation of one or more sets of virtual software at a point in time. Apparatus consistent with the present disclosure may include a memory and one or more processors that execute instructions out of the memory. At certain points in time, a processors of a computing system may pause a virtual process while allowing instructions associated with another virtual process to be executed.

The invention discloses a forging heat synergistic material structure and performance integrated intelligent regulation and control method and system, and belongs to the technical field of material processing and intelligent manufacturing. The method comprises the following steps: 1) performing parameter calibration on a multi-module coupled physical model by using a small amount of real experimental data; 2) in the process parameter space, generating a large amount of virtual process combination parameter data by adopting a Latin hypercube sampling strategy, inputting the parameter data into a calibrated physical model for calculation to obtain output results, and screening the output results by taking physical constraints as conditions to obtain an enhanced data set meeting the physical constraints; 3, the enhanced data set is utilized to train a cascade prediction physical model.According to the cascade prediction physical model, integrated and high-precision prediction of multiple macroscopic performance and microstructure indexes of a material is achieved, the physical consistency of prediction results is ensured, physical derivative characteristics contribute to more than 70% of prediction capacity, and the interpretability of the model is ensured.

The invention relates to the cross technical field of intelligent manufacturing, industrial artificial intelligence, a big data technology and high-end material engineering, in particular to a neodymium iron boron production process management method based on artificial intelligence and a big data technology. According to the method, a'big data platform + AI algorithm engine 'double-wheel-driven intelligent management system is constructed, artificial intelligence technologies such as machine learning, deep learning, knowledge graph and digital twinning are deeply fused with a mass multi-source heterogeneous big data processing technology, and the method runs through the whole process of neodymium iron boron material production. The core comprises the steps of constructing a unified process data lake based on a big data technology, and realizing fusion treatment of multi-source data; establishing a quantitative correlation model of process parameters and product performance based on a machine learning algorithm; digital expression and intelligent reasoning of process knowledge are realized based on the knowledge graph; constructing a virtual process simulation optimization platform based on a digital twinning technology; and finally, a continuous evolution closed loop of data acquisition, feature extraction, model training, intelligent decision making and feedback optimization is formed. The technical problems that in traditional neodymium iron boron production, value mining of mass data is insufficient, process decision depends on artificial experience, quality control lags behind, and the process optimization period is long are effectively solved, and fundamental transformation of the production process from experience driving to data and intelligent double-wheel driving is achieved.

The invention discloses a process queue dynamic arrangement and parameter automatic issuing intelligent control system and method based on the Internet of Things, and belongs to the technical field of flexible manufacturing. The system comprises a production line control unit, an Internet of Things unit, an intelligent control unit, a manufacturing execution system unit and an equipment control unit. Global moving line information of the mixed production line composed of the asynchronous shifting line body and the synchronous shifting line body is collected in real time through the Internet of Things unit; and the intelligent control unit dynamically constructs and maintains a virtual process queue synchronized with the physical production line in real time based on the information, intelligently judges the opportunity according to the virtual process queue, obtains corresponding assembly parameters from the manufacturing execution system unit, and issues the corresponding assembly parameters to the equipment control unit for execution. According to the method, zero-waiting accurate issuing of production parameters is realized, the problems of information isolation, poor adaptability, dependence on single-point identification and the like of a traditional RFID system are effectively solved, the production takt, the flexibility and the system robustness are remarkably improved, and a data basis is provided for intelligent collaborative optimization of the whole line.

The present application relates to a kind of fenton process reagent intelligent control method, comprising: S1: sewage data are collected and preprocessed, obtain current state;S2: according to the current state, state space and action space are constructed;The state space includes current water index, process index and current each reagent dosing state;S3: closed loop virtual process flow network according to the state space predicts the corresponding fenton pH of each dosing combination, effluent COD and effluent pH;S4: the comprehensive evaluation index of the prediction result of each dosing combination is calculated respectively;S5: according to the comprehensive evaluation index, select optimal dosing combination, and each processing flow in fenton oxidation treatment is dosed.The fenton process reagent intelligent control method of the present application can accurately predict the dosing effect, and automatically regulate the reaction environment according to the prediction result.

The application provides a virtualized log vulnerability repair method and device, and relates to the technical field of computer security. The method comprises the following steps: in response to detecting that a target process uses a Log4j2 framework, obtaining a process identifier; based on the process identifier, creating an independently running virtual process in user memory through an eBPF module of a kernel, and starting a Java virtual machine and an associated network service in the virtual process; constructing a string containing a JNDI protocol identifier as an attack payload in the target process, which is used to simulate Log4j2 remote vulnerability exploitation behavior; sending the attack payload from the target process to the virtual process, and judging whether the Log4j2 remote vulnerability exists according to the response of the virtual process to the attack payload; if the Log4j2 remote vulnerability exists, mounting the eBPF module to the kernel network driver layer to intercept related network traffic. The application combines memory virtualization with kernel layer traffic interception, and realizes accurate detection and effective repair of the Log4j2 remote vulnerability.

A robot system (100) includes a robot (1) having an end effector (6), an operation section (8), and a control section (3) that moves the end effector (6) based on an operation of the operation section (8) and causes the end effector (6) to perform processing in a non-contact manner with respect to a processed surface of a workpiece. The control section (3) performs at least one of a first control that fixes a distance between the end effector (6) and a control processed surface that is the processed surface of the workpiece or a virtual processed surface of the workpiece and a second control that fixes an angle of the end effector with respect to the control processed surface when moving the end effector (6) based on the operation of the operation section (8).

This invention discloses a special materials process simulation optimization system based on few-shot learning, specifically involving the interdisciplinary fields of materials informatics and intelligent manufacturing. It extracts structured triples of "process parameters—microstructure—macro performance" to construct a knowledge graph in the field of special materials. Employing a relation-enhanced prototype network under the meta-learning paradigm, it utilizes a graph attention mechanism to transform the structured associations in the graph into attention priors for model training. An automated interaction interface is established between the agent and the physical simulation software. The output performance probability distribution is used as the agent's enhanced state observation. Through continuous action scheduling within the simulated virtual process space, it automatically identifies and outputs the process parameter combination that maximizes long-term cumulative rewards. Based on real-time production data streams and offline physical verification results, it dynamically calibrates the physical boundary conditions of the simulation model, and incrementally updates the knowledge graph nodes by online correction of the reward function.

The invention provides an AI inference service system based on heterogeneous resource isolation. The system comprises a reasoning process decoupling module used for responding to an AI reasoning request, decoupling a corresponding AI reasoning process into a CPU processing process and a GPU reasoning process, and deploying the CPU processing process and the GPU reasoning process on a CPU node and a GPU node respectively; the GPU resource scheduling module is used for adjusting a GPU node used for running a GPU reasoning process according to the obtained GPU resource state; the tenant-level resource isolation module is used for running each tenant or model in an independent container, namespace or virtual process group in a scene that multiple tenants access the same CPU processing process concurrently; and the sandbox configuration module is used for configuring a sandbox environment for the GPU reasoning process so as to limit the maximum video memory, the calculation time and the Batch Size of each reasoning task. According to the invention, the problems of tight CPU and GPU resource coupling, obvious performance bottleneck in a high-concurrency scene, low resource utilization rate and poor service isolation in the existing AI inference service can be solved.

The invention relates to the technical field of furniture intelligent manufacturing and production dispatching, and discloses a plate-type small home production dispatching system and method. According to the system, product composition and production requirements are extracted through a work order deep analysis module. And the dynamic resource coupling module performs matching operation on the analysis data and the production resource real-time data pool to generate a resource coupling scheme. And the process chain generation module traverses the process rule base based on the scheme, and dynamically constructs a customized virtual process chain. And the state prediction module performs simulation deduction in combination with historical data and outputs process prediction. And the dispatching optimization module iteratively optimizes the instruction sequence according to the instruction sequence to generate an optimized dispatching instruction set. And the execution tracking module issues an instruction, collects feedback data and updates the resource pool in real time. According to the system, the dynamic and accurate matching of the production resources and the process paths is realized, and the resource utilization efficiency and the flexible response capability of the production system are improved.

The invention relates to the technical field of computer three-dimensional animation simulation, in particular to an OpenCasCide-based plate bending simulation method, which comprises the following steps of: loading a machine tool model and recording bounding box data; inputting the size and processing data of the plate; generating a movement instruction of the plate and the machine tool part; establishing a form description structural body of a non-bending (central point + length, width and thickness) part and a bending (cylindrical surface parameter) part; plate center line form information is calculated, and animation key frame queues of plates and machine tools are generated; the animation time is updated, and linear interpolation is carried out on the form information; plate grid data and a bounding box are calculated; drawing a grid model based on OpenCasCad and updating the position of the machine tool; interference is compared and detected through a bounding box. According to the method, the three-dimensional bending process can be completely displayed, observation at any visual angle is supported, interference risks can be found in advance, the simulation precision is high, and the method is suitable for virtual process verification scenes of plate processing.

The invention discloses an additive and subtractive material forming quality control method based on digital twinning, which has the following beneficial effects that a virtual entity and interaction system is established based on digital twinning, and synchronous mapping of a virtual process model and an entity process model is realized; various data of the additive and subtractive machining system in the additive and subtractive composite manufacturing process are collected in real time through the data collection system, the initial milling position is further determined through the collected data, rapid tool setting is achieved, and therefore the problem that in the existing additive and subtractive composite manufacturing process, manual tool setting efficiency is low is solved; in addition, whether technological parameters of additive and subtractive composite manufacturing need to be modified or not is judged according to the collected data. By means of the mode, various data in the additive and subtractive composite forming process are collected, then the machining parameters of additive and subtractive materials are adjusted in real time, intelligent forming quality optimization, machining deformation prediction and rapid tool setting are achieved, and therefore the additive and subtractive machining efficiency and the quality of formed parts can be improved.