75 results about "Procedural modeling" patented technology

The invention discloses a construction method and device of a multi-component data agent, and the construction method specifically comprises the following steps: obtaining sample data, and constructing a vector knowledge base; extracting a knowledge service flow according to the sample data, determining an ontology through knowledge representation, and forming a knowledge graph; training an initial language model based on the vector knowledge base, and giving a data processing model; and performing modularization arrangement and linking on the knowledge graph and the data processing model in combination with a logic tool chain and a thinking graph of the data intelligent agent, and constructing to obtain the multi-component data intelligent agent. According to the method, the knowledge graph is fused to construct the business rule engine, training data support is provided, the thinking chain process is modeled into the thinking graph of a graph structure, and the data processing model and the knowledge graph associated with the workflow node are executed, so that the accuracy of result reasoning of the data agent is enhanced, and the knowledge boundary is expanded; and particularly for financial auditing data, key information can be better extracted, and the working efficiency of power grid auditing personnel is improved.

The invention provides a circuit board production yield root cause tracing method, which comprises the following steps of: acquiring process parameters, equipment states, environment variables and quality detection results of a whole production process, and constructing a multi-dimensional time sequence database; extracting a typical manufacturing process modeling unit through a sliding time window and dynamic time warping; establishing a cross-process dynamic causal relationship graph in combination with nonlinear Granger causal test, a structural equation model and a dynamic Bayesian network; an intervention and anti-factual reasoning method is applied, the causal effect and path stability under parameter disturbance of each process are evaluated, and the influence of a key causal path is quantified; according to the method, the accuracy of defect rate root cause positioning can be improved, and powerful support is provided for circuit board production process optimization and quality improvement.

Disclosed herein are system, method, and computer program product embodiments for machine-assisted process modeling and validation. An embodiment operates by receiving, by at least one processor, a process document describing a process in a user locale. The embodiment then generates the model notation in accordance with a model notation format by processing the process document with a deep learning technique based on a prompt for modeling the process document. The embodiment then outputs the model notation.

An unsupervised time series anomaly detection method based on multi-scale reconstruction processes a time series anomaly detection task from the aspect of different scales of a time series, and comprises the following steps: firstly, inputting time series data into a multi-scale decoupling module (MTD) to separate the time series data into components with different scales; and the reconstruction process is modeled as parallel full-scale time sequence reconstruction. This is because the normal time point and the abnormal time point have significant differences in different scales. In consideration of the correlation among different scales, the joint weights of different generators in the network are controlled by adaptively calculating the joint weights by reconstructing a residual error collaborative learning module (RRCL), so that interaction among different scales is realized. The model is designed, and multi-scale features of the time sequence are deeply extracted on the premise that an abnormal sample is not needed, so that the accuracy of time sequence anomaly detection is improved, and the robustness of the model is improved.

The invention discloses a whole-process modeling method and device for a foundation pit and a storage medium, and relates to the technical field of capital construction data analysis, and the method comprises the steps: building an initial foundation pit-surrounding BIM three-dimensional space model, setting model grid conditions with different capture capabilities according to the characteristics of the foundation pit and the influence of surrounding buildings, so as to achieve the matching of analysis demands, and achieving the whole-process modeling of the foundation pit. And the simulation prediction capability of the model is improved. According to the method, the degree of influence between the foundation pit and surrounding buildings is determined, engineering evaluation factors are assisted to screen excavation schemes, a unified scheme evaluation standard with data support is established, a basis is provided for adaptive adjustment of subsequent schemes, and the accuracy and adaptability of the foundation pit scheme are improved. In the excavation stage, input parameters of the model are continuously updated, the excavation scheme can be adjusted in time, in the foundation construction stage and the backfill stage, foundation construction information and backfill information can be simulated to guide construction, and the integrity of foundation pit engineering is guaranteed.

The invention discloses a strip steel hot continuous rolling loop system modeling and fault detection method and system, and relates to the technical field of industrial steel rolling process modeling and fault detection, and the method comprises the steps: building a state space model of a strip steel hot continuous rolling loop system at a plurality of stable working points based on least square identification; fuzzifying the state space model based on a preset fuzzy rule to obtain a T-S fuzzy model of the strip steel hot continuous rolling loop system; optimizing the T-S fuzzy model based on a neural network, and establishing a target system model in which the neural network and fuzzy kernel representation are combined; and performing fault detection on the strip steel hot continuous rolling loop system based on the sum of residual signals between the model output of the target system model in the preset time period and the real output of the strip steel hot continuous rolling loop system. The technical problems that in the prior art, an accurate mathematical model of a loop is difficult to establish in a model-based fault detection method, and a data-driven fault detection method is low in accuracy and difficult in fault interpretation are solved.

The invention discloses an air-conditioning building system load modeling method and system based on a physical prior neural network, and relates to the field of air-conditioning building system loads, and the method comprises the steps: modeling a thermodynamic load dynamic evolution process of an air-conditioning building system as a discrete time Markov decision process; establishing a corresponding physical prior neural network architecture; simulating a room scene heated by a single heat source to generate training data, determining physical meanings of states and action spaces in the room scene, and updating a loss function form; training a physical prior neural network and carrying out hyper-parameter tuning, and verifying the prediction performance of the physical prior neural network on a test data set of a simulation scene and a test data set of an actual scene; according to the method, precise modeling and control over the temperature and power of the air conditioner building system are achieved, convergence is faster compared with a traditional neural network, the requirement for training data is lower, and better generalization performance and higher expandability deployment potential are achieved.

The invention discloses a scene generation method and device based on multi-source GIS data fusion, and relates to the technical field of digital twinning and programmed generation. The method comprises the following steps: acquiring GIS data, preprocessing the GIS data, and storing the preprocessed GIS data in a geographic information resource library; a structured resource library is constructed, semantic parameters are added to the three-dimensional model in the structured resource library through the configuration file, and three-dimensional model resources with structured semantics are constructed; on the basis of the three-dimensional model resources with structured semantics and GIS data in a geographic information resource library, building and road generation and terrain processing are carried out in a programmed modeling engine through a configuration file, and scene data are generated; and importing the generated scene data into a real-time rendering engine, carrying out dynamic environment interaction and biocenosis simulation, and generating a city scene. The problems that in the prior art, an urban three-dimensional modeling method is low in efficiency and insufficient in environment interaction reality sense are solved.

The application discloses a time capsule expression structure reservation method based on deep learning and a knowledge graph, and aims at solving the problems that the expression and organization structure of a user's sealed multi-modal content in a digital time capsule service is difficult to be identified and recorded, and the display according to the uploading time or the fixed template of the media type in the opening stage is easy to cause the distortion of the expression intention and the fragmentation of the receiving experience. The multi-modal content input in the sealing stage is normalized and time-aligned, the content representation is obtained by using multi-modal hierarchical coding, the segmentation boundary and hierarchical relationship are determined and the key weight and object information are extracted by combining span boundary detection and a pointer network, the narrative rhythm parameters are obtained by using a neural time point process modeling based on a media switching event sequence, a timing knowledge graph is fused and constructed, and the arrangement sequence and the presentation control parameters are generated under the sequence constraint and the segmentation continuous constraint by graph representation learning, and the presentation data for the display in the opening stage is generated, so that the technical effects of reserving the user's expression structure and rhythm and differentially arranging and presenting in the future are realized.

A system may use multi-path modeling to determine whether a process will complete on or before a specified time. For example, a system may access a process graph representing a process with multiple tasks, where each task is encoded as a node and each dependency between tasks is encoded as an edge. The system may traverse the process graph to discover pathway parameters. The system may decompose the process graph into multiple paths, each path including one or more tasks. The system may access a last known status for each task and, for each path, determine a probability of finishing on or before the specified time based on the pathway parameters and the last known status. The system may select a dynamic critical path based on the determined probabilities. The probability of completing the process on or before the specified time is determined based on the dynamic critical path.

This invention relates to a Text2SQL self-correction method based on error pattern awareness, belonging to the field of data processing technology. It includes: abstracting general error patterns from isolated error messages to enable the model to have generalizable diagnostic capabilities, and constructing a knowledge graph based on these error patterns; abandoning traditional single-step blind correction, constructing a dynamic feedback loop containing state memory to force the model to escape local optima; and performing adaptive iterative repair termination based on a comprehensive score. This invention models the SQL correction process as a closed-loop control flow of "diagnosis-retrieval-verification," breaking through the traditional "static verification + manual rules" paradigm. It transforms database execution feedback into a structured error pattern representation, guiding a Large Language Model (LLM) to establish generalizable SQL error diagnosis and repair capabilities.

The present invention provides a batch process modeling and final product quality prediction method based on big data, which belongs to the technical field of fine chemical and pharmaceutical batch process modeling and final product quality prediction, and specifically includes using an encoder to nonlinearly map process measurement variables and control input variables to higher-dimensional state variables, using a recurrent neural network to build a state variable dynamic model; mapping the state variables back to the measurement variables through a decoder to form nonlinear mapping parameters; using batch process historical operation big data to learn nonlinear mapping parameters and dynamic models; and combining an attention mechanism to establish a batch process final product quality prediction model. The present invention avoids staged modeling and learning, and the final product quality prediction model explicitly considers the impact of historical state variables on the final product quality, overcoming the problem that the recurrent neural network predicts the final product quality performance as the input length increases.

The example embodiments are directed to systems and methods which can generate a process graph with important process insights in a manner that is significantly faster and more scalable than related art approaches and answers questions most customers have through the use of standardization. In one example, a method may include querying a data store for document data of a process, identifying a plurality of milestones that occur within the process and blockers that prevent at least one of the milestones from completing based on the document data, generating a graph model of the process based on a predefined template, wherein the graph model comprises a plurality of nodes corresponding to the plurality of milestones and edges between the plurality of nodes based on the blockers, and displaying the graph model of the process via a user interface.

The application provides a kind of detection method and system of Internet of Things equipment credential trigger type backdoor, comprising: the binary code library is formed by decompressing device firmware;Executable file in code library is analyzed reversely, and backdoor verification key code is positioned using string comparison function and backdoor model feature;The code segment related to backdoor verification key code is stripped from original program, and complete credential generation process code is obtained;Each credential generation process code is subjected to data abstraction and process modeling processing, forming formal description of verification credential, and the semantics and uncertainty of each input variable in formal description are calculated;According to the semantics and uncertainty of each input variable, it is judged whether the verification credential is predictable, and whether the device has a backdoor that can be triggered by predictable credential.The application can automatically detect whether there is a backdoor triggered by credential in the device through static analysis of binary program in Internet of Things firmware.

The invention provides a machining process modeling method based on dimension reduction coding. The machining process modeling method comprises the steps of 1, obtaining discrete and analyzable machining paragraphs in a complex machining process; step 2, establishing a relative coordinate system based on a processing section corresponding to each section of processing after discretization in the complex processing process, and expressing each section of processing under the relative coordinate system; 3, calculating the vector difference of the local features of each processing section in the corresponding complex processing process under the relative coordinate system; and 4, according to each processing section, establishing a serialized expression of the processing process based on the vector difference and the processing parameters. According to the method, the vector difference under the relative coordinate system is adopted to reflect geometric or physical changes between complex machining sections, local features of complex machining are highlighted, different machining conditions are adapted through local changes, and the universality of the model is enhanced.

The application discloses a kind of broken screening process circulation steady-state solving method, system, medium and computer program product, belong to industrial process modeling and numerical solving technical field, comprising: obtaining the process configuration data of broken screening process;Directional graph model is constructed based on process configuration data;Establish equipment operating condition calculation model for each node;Cyclic structure identification is executed to directed graph;Linear pre-computation is executed to linear region, and the external input boundary of each cyclic subgraph is obtained;According to solving level, each cyclic subgraph is executed iteration solving, and the node output data of this round is obtained;In iteration solving process, iteration stabilization processing is executed;After reaching the minimum iteration number of pre-set, based on the error between the adjacent iteration output data of each node in cyclic subgraph, all node convergence determination is executed, when the error of all participating determination nodes meets pre-set convergence condition, determine that cyclic subgraph reaches steady state;Steady-state calculation result is output.

The application discloses a control-oriented sintering endpoint modeling method based on a coupling mapping framework, and belongs to the technical field of metallurgical industrial process modeling and process control. First, sintering endpoint time series data and multiple sintering data time series sequences are collected, and a sintering endpoint modeling model containing an attention gate cycle unit module and a bidirectional gate cycle unit is constructed; the sintering data is divided into manipulated variables, coupling disturbance variables and disturbance variables; after the various time series data are divided into time series segments, the coupling disturbance variables and the manipulated variables are respectively subjected to the attention gate cycle unit module to obtain state vector prediction values, and then the state vector prediction values are input into the bidirectional gate cycle unit together with the divided manipulated variables, disturbance variables and coupling disturbance variables to obtain sintering endpoint single-step prediction values; further, a loss function is calculated according to true values, and sintering endpoint modeling model parameters are iteratively updated. The method guarantees a simple model structure, and takes into account prediction accuracy and control availability.

The invention relates to the technical field of artificial intelligence and industrial process modeling, in particular to a time series data generation method and device based on a Gamma variational auto-encoder, and the method comprises the steps: carrying out the modeling of potential variables in the variational auto-encoder through Gamma distribution, and carrying out the modeling of the potential variables when the variational auto-encoder is trained, firstly, each variable value of original time sequence data in a training set is normalized according to a constraint condition to serve as training input, and then a micro-weight parameterization sampling mechanism is introduced to sample Gamma distribution constructed by encoder output parameters, so that the problem that training is unstable under the condition of small shape parameters in a traditional sampling method is solved. Therefore, the trained variational auto-encoder can significantly improve the constraint satisfaction rate and physical consistency of the generated time sequence data. According to the method, the high-quality time sequence data with good engineering availability can be stably generated.

This invention relates to a quality evaluation and automatic process control technology for PCBA welding scenarios, comprising: establishing a quantitative standard evaluation system for welding quality based on multiple performance indicators of the product; fusing multimodal sensor data with process parameters to construct a mathematical model; and optimizing processing parameters based on machine learning. Starting from the intelligent problem of PCBA welding process quality control, this invention proposes a quantitative evaluation system for PCBA welding quality, uses process modeling based on multi-sensor fusion of testing, innovatively introduces machine learning-based process parameter optimization methods, develops a PCBA welding prototype with online process control, achieves closed-loop automatic process optimization, and simultaneously solves the crucial data collection problem in big data analysis.

The embodiment of the present application relates to the technical field of computer-aided software engineering, and discloses a process modeling data processing method for human-computer collaboration and related equipment, which comprises: establishing a double-storage architecture for process modeling, including a business data storage module and a layout data storage module, the business data storage module storing entity data, and the layout data storage module storing relative coordinates of each node in a swimlane relative coordinate system and arrangement order of the swimlane on a canvas; in response to an adjustment operation of the swimlane arrangement order, obtaining the adjusted swimlane arrangement order and storing it in the layout data storage module; determining the offset of each swimlane on the canvas based on the adjusted swimlane arrangement order during rendering; and determining new absolute coordinates of the nodes in the canvas according to the relative coordinates and the offset of each swimlane and performing rendering update. Through the above method, the embodiment of the present application decouples business data and layout data, can improve modeling efficiency, reduce overhead and significantly improve performance.

The invention provides a hardened cement paste multi-scale direct coupling calculation method and device, and the method comprises the steps: carrying out the in-situ loading and computed tomography of hardened cement paste, generating multi-scale volume data of different stages, and carrying out the multi-phase segmentation to obtain a multi-valued volume data mask; the damage path prediction deep learning model is used to predict the damage path of the multi-valued volume data mask and divide a multi-scale grid, the finite element software is used to carry out direct simulation calculation on the multi-scale grid structure to generate a multi-scale damage result, and the high information density data obtained based on the image can be used to predict the damage path of the multi-valued volume data mask. A potential damage path is predicted through a deep learning model, modeling is performed on the basis, calculation complexity is simplified, on the premise that simulation precision is guaranteed, microstructure evolution characteristics and macroscopic mechanical property response of cement paste in the hydration process are considered, the numerical calculation cost of a multi-scale structure is effectively reduced, and the method is suitable for popularization and application. And an efficient and extensible technical path is provided for evolution process modeling of complex multi-phase materials.

The invention discloses a data sample screening and reconstruction method in high nickel matte flotation process modeling, relates to the technical field of high nickel matte flotation process data modeling, and solves the technical problem of data validity in an actual modeling process. The method comprises the following steps: selecting a system lag parameter p according to the actual process of a high nickel matte flotation system, and reconstructing data samples; marking invalid data; integrating discontinuous data in time, and constructing mutually independent sample data sets; carrying out matrix recombination on the extracted samples, and carrying out RNN modeling. According to the invention, data are preprocessed according to the actual working condition of high nickel matte, invalid data are removed, and some data blocks which are discontinuous in time are obtained.

This application relates to a method for allocating interference resources for a drone swarm based on a pre-trained attention encoder. The method includes: setting an objective function for the interference resource allocation problem using an interference effect evaluation model and the interference power consumed by the drone swarm; modeling the interference resource allocation problem as a constrained combinatorial optimization problem by considering the limitations on drone communication, energy, and flight speed under denial conditions; modeling the interference decision-making process in the combinatorial optimization problem as a distributed locally observable Markov game model; mapping drones as agents; and solving the model using the MAPPO algorithm based on a pre-trained attention encoder to obtain the interference resource allocation scheme for the drone swarm. This method can solve the problems of state space dimensionality explosion and environmental non-stationarity in multi-agent collaborative decision-making processes.

The invention discloses a process modeling and fault detection method and system for machine learning guided by a control theory, and the method comprises the steps: obtaining historical operation data of an industrial process, and extracting an operation dynamic state in the historical operation data through a neural network, approximation is carried out on an observer gain part of the kernel representation model to realize complete kernel representation model design; building an adjoint system of the nuclear representation model by using a Hamilton system theory, and performing guided optimization on the learning and training process of the nuclear representation model and the adjoint system thereof by using nondestructive constraint to realize the design of a regularized nuclear representation model; and a detection statistic is constructed by using the residual error of the kernel representation model and the regularization kernel representation model, and the maximum value of the detection statistic during normal operation of the industrial process is set as a fault detection threshold, so that fault detection of the industrial process is realized, and the safety of the industrial process is guaranteed. In addition, by designing an online fine tuning strategy, generalization and applicability of the model are improved, and the method has high practical value.

A scheduling strategy determination method, a terminal device and a computer readable storage medium, the method comprising: performing process modeling on a simulation model to obtain a process model, the simulation model being a model of a working environment where an automated guided vehicle is generated through simulation software, the process model being used for representing an action process of a dynamic object in the simulation model; and performing linear programming on the process model according to the scheduling task target to obtain a scheduling strategy. By means of the method, the optimal scheduling strategy is automatically generated, and scheduling optimization can be guaranteed; in addition, due to the fact that the scheduling strategy can be automatically generated in the mode, codes of the calling strategy do not need to be manually written / modified, the workload of program development is simplified, and the optimization efficiency of the calling strategy is improved.

A system and method for controlling the temperature setpoints in a furnace such that a random mixture of slabs with different compositions, sizes, initial temperatures, temperature requirements, and anticipated residence times are all discharged at an appropriate temperature, with emphasis upon ensuring that no slab is insufficiently heated (rejected) per rolling and quality requirements. This is to be accomplished with minimized fuel use. This system can be implemented in a graphical programming environment, where real-time tuning, configuration, logic changes, model replacement, model retraining and other programming changes can be made without interruption of control.

The embodiments described herein include a process for generating a hybrid model for modeling a process in a semiconductor processing equipment. In a particular embodiment, a method for creating a hybrid machine learning model includes identifying a first set of cases spanning a first range of process and / or hardware parameters and conducting laboratory experiments for the first set of cases. The method may further include collecting experimental outputs from the experiments and performing physics-based simulations for the first set of cases. In one embodiment, the method may further include collecting model outputs from the simulation and correlating the model outputs with the experimental outputs using a machine learning algorithm to provide a hybrid machine learning model.

The invention provides a multi-modal learning data acquisition and standardization method based on a smart writing system, and relates to the technical field of data processing. Disturbance signals are acquired through multi-modal sequences such as pressure, speed, stroke continuity, sight line coordinates and voice energy; nodes are generated in a node type mapping table according to the disturbance amplitude interval number, the change rate interval number and the duration interval number; calculating a disturbance amplitude difference, a change rate difference and a node time interval in a preset time window to form an edge parameter; compressing the node set and the edge set through the node difference degree and the edge parameter difference degree; and performing direction verification and parameter truncation on the structure according to the topology constraint table, and finally outputting a standardized causal graph structure comprising a node list area, an edge list area and a topology description area. According to the method, output structure consistency can be kept under the condition that the number of multiple modes is inconsistent or partial modes are missing, and the method is suitable for scenes such as learning behavior collection, writing process modeling and educational data processing.

The present application relates to the technical field of artificial intelligence and industrial process modeling, in particular to a time series data generation method and device based on Gamma variational autoencoder, comprising: modeling the latent variables by using Gamma distribution in the variational autoencoder, and when training the variational autoencoder, first, according to the constraint condition, normalizing each variable value of the original time series data in the training set as the training input, and then introducing a differentiable reparameterization sampling mechanism to sample the Gamma distribution constructed by the encoder output parameter, so as to solve the training instability problem existing in the traditional sampling method under the condition of small shape parameter, so that the trained variational autoencoder can significantly improve the constraint satisfaction rate and physical consistency of the generated time series data. The present application can stably generate high-quality time series data with good engineering usability.

The embodiment of the application relates to the technical field of software engineering and artificial intelligence, and discloses a process modeling method for human-machine cooperation and related equipment, the method comprising: constructing a hierarchical recursive process model, the process model comprising a process domain layer, an activity layer and a task layer; using an eight-tuple model to formally describe each task, the eight-tuple model comprising a task identifier, an input artifact set, an execution logic, an output artifact set, a role performer, a constraint set, a cognitive level and a deterministic classification, defining four types of role performers for the process model, including a human role, an agent role, a system role and a hybrid role, and based on the four types of role performers and the eight-tuple model, rendering each activity of the process model as an activity graph on a preset workbench interface through differentiated visual lanes, and in this way, the embodiment of the application effectively realizes modeling of AI tasks under the framework of organizational system constraints and standard specifications in a human-machine cooperation scenario.