228 results about "Process modeling" patented technology

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.

The invention discloses a compliance verification method and device for medical process data, and relates to the field of medical data, and the method comprises the steps: constructing a medical process knowledge graph, and defining the data dependence relation of clinical test, production and declaration stages; dynamically activating the rule subsets based on the current process stage, and calculating and loading association rules through dependency to form a composite rule set; converting the structured data, the medical image and the handwritten text into a unified vector space, calculating a cross-modal semantic consistency score, and resolving conflicts according to a preset priority or a cross-modal attention fusion mechanism; and when the flow stage is switched or the historical data is modified, full-link backtracking verification is triggered. The device comprises a process modeling module, a rule dynamic generation module, a multi-modal alignment module and a visual proof module. According to the invention, the compliance verification efficiency is greatly improved through the dynamic rule engine, and the supervision risk and operation cost caused by data defects of pharmaceutical enterprises are effectively reduced.

The invention relates to the technical field of ocean data interpolation filling, in particular to a space-time deficiency filling method and system based on context association and physical guidance. The method comprises the following steps: acquiring seawater dissolved oxygen data and context data; multivariable space-time dependence extraction is carried out based on the obtained seawater dissolved oxygen data and context data; gaussian noise diffusion is carried out based on the obtained seawater dissolved oxygen data; noise prediction is carried out based on double-view space-time correlation; and the prediction error is constrained based on the joint loss function. According to the method, a physical consistency constraint mechanism based on a partial differential equation is introduced in a model training process, so that model output better conforms to a physical coupling rule among variables in a marine environment. The constraint effectively inhibits non-physical fluctuation possibly occurring in the interpolation result, enhances the physical credibility and interpretability of the result, and provides a more reliable data basis for subsequent scientific analysis and process modeling.

The invention belongs to the technical field of iron phosphate preparation, and discloses an energy-saving control system for iron phosphate preparation based on an energy consumption scheduling model. The system is composed of a data acquisition module, an energy consumption sensing module, a preparation process modeling module, an energy consumption prediction module, an energy-saving scheduling module, an intelligent execution module, a feedback correction module, a man-machine interaction module and a remote operation and maintenance module. The energy consumption sensing module intelligently senses an energy consumption state, the preparation process modeling and energy consumption prediction module accurately predicts energy consumption, the energy-saving scheduling module generates an optimal scheduling strategy, the intelligent execution module accurately executes an instruction, and the feedback correction module realizes closed-loop adaptive regulation and control; all the modules cooperatively operate, process parameters are adjusted in real time according to actual working conditions of iron phosphate preparation, energy consumption in the preparation process is remarkably reduced, the energy utilization rate is increased, and energy-saving optimization of iron phosphate preparation is achieved.

The invention relates to the field of constructional engineering, and discloses an intelligent construction management and control method for constructional engineering, which comprises the steps of collecting multi-dimensional data of a building construction site, including construction progress data, personnel positioning information, an equipment operation state and site environment parameters, and constructing a construction behavior feature data set in combination with a multi-source data fusion algorithm; performing automatic structured analysis on the construction behavior feature data set, constructing a construction process modeling framework based on a hierarchical feature clustering method, and introducing a dynamic feedback learning mechanism to perform adaptive optimization on a modeling result; judging whether the optimization process is stable or not according to the evolution trend of the process model, and if so, recording the stage state of the current construction process feature; and based on the corrected construction plan path, performing disturbance factor correction on the prediction progress by applying a multi-scale construction simulation algorithm, extracting corresponding control parameters, and performing intelligent adjustment on prediction nodes. The method has the advantage of improving intelligent construction management.

The invention discloses a flexible job shop scheduling method based on a graph neural network and deep reinforcement learning, and relates to the technical field of flexible job shop scheduling. The method at least comprises the following steps: S1, firstly, carrying out Markov Decision Process (MDP) on a flexible job shop scheduling problem, namely, FJSP, and initializing a scheduling state; and S2, representing a complex relationship between a job and a machine by using a heterogeneity graph, and effectively mapping different entities (the job, the machine, the operation and the like) of the problem and the relationship between the different entities into a graph structure, wherein the different entities (the job, the machine, the operation and the like) of the problem and the relationship between the different entities (the job, the machine, the operation and the like) of the problem are represented by the heterogeneity graph. According to the method, the graph neural network based on the meta-relationships is provided, different graph convolution modes are innovatively adopted for different meta-relationships to extract features, original semantic information is reserved, the global information capturing capability is enhanced, and a reinforcement learning agent is more accurate when making a scheduling decision.

The invention discloses a waterproof composite material production intelligent control system, and relates to the technical field of control systems, the system comprises a data acquisition layer, a standard template generation module, a data processing layer, a Transform-based model core layer and an output layer, the data acquisition layer acquires production data in real time; a data processing standard template is generated through a standard template generation module, standardization processing is conducted on real-time data through a data processing layer, production process modeling and production state analysis are conducted through a Transform model core layer, an analysis result is decoded into a control instruction through an output layer, and production line process parameters are adjusted. The production process is intelligently and optimally controlled by applying the artificial intelligence technology, the intelligent level, the operation efficiency and the product quality of the production line are remarkably improved, meanwhile, the production goals of energy conservation, efficiency improvement and intrinsic safety are achieved through refined management and control and whole-process safety protection, and intelligent and green development of the waterproof composite material industry is promoted.

The invention relates to a control method for navigation obstacle avoidance of a surgical robot. Performing dynamic construction by adopting a continuous state space of intra-operative process modeling; tissue, tool and robot pose data are collected in real time, and a continuous change tensor field is calculated; establishing a process field atlas, and inputting a state space into a subsequent navigation control model to realize prospective modeling of an environment trend; carrying out adaptive evolution by adopting an obstacle avoidance strategy of a risk threshold drift model; environment change is monitored in real time, and a risk trend is evaluated through a time window sliding mechanism; path fine tuning constraint of navigation task integrity protection is adopted; collaborative navigation optimization based on man-machine strategy intention fusion is adopted; recording micro-control behaviors, including small-range fine tuning and path locking, of the doctor on the operation terminal; establishing an intention preference model by using the operation behavior, and taking the intention preference model as a dynamic navigation strategy adjustment factor; when the obstacle avoidance strategy of the robot conflicts with the intention of the doctor, triggering the collaborative optimizer to realize soft fusion of the navigation strategy; and meanwhile, safety and control consistency are guaranteed.

The invention relates to the field of communication service support systems, and particularly provides a service provisioning process oriented anomaly identification and automatic compensation method and device, service provisioning process modeling is an ordered process chain composed of a plurality of processing nodes, and each node corresponds to a specific service system operation; a unique flow identifier Flow ID is generated for each service instance and used for whole flow state tracking, execution results, state codes and response time information of all flow nodes are recorded in a flow tracking database in real time, and after execution of each node is completed, events are published for other modules to subscribe to, so that an asynchronous observable link is formed. Compared with the prior art, the method can achieve the closed-loop processing of the abnormal process, remarkably reduces the manual intervention rate, improves the business activation success rate and customer service experience, and improves the stability and reliability of a service opening link.

The invention relates to the technical field of process management, in particular to a sealing integrated collaborative process optimization method and system. The method comprises the following steps: obtaining sealing process data; performing sensor data acquisition according to the sealing process data to obtain a multi-modal sensor acquisition data set; performing data preprocessing on the multi-modal sensor acquisition data set to generate a standard multi-modal sensor acquisition data set; performing process flow analysis on the sealing process data to generate sealing process flow data, the sealing process flow data including a sealing flow, a cover closing flow and a cover pressing flow; and performing intelligent process modeling on the sealing process, the cover closing process and the cover pressing process to generate a process procedure mathematical model. According to the invention, through multi-modal data acquisition, detailed process flow analysis, real-time monitoring and dynamic adjustment strategies, the flexibility and adaptability of sealing process production process management are improved.

The invention relates to the technical field of metal material processing engineering, discloses a full-automatic full-process modeling method and system of crystal plasticity finite element model for laser welding weld, which adopts MATLAB to call ABAQUS and PYTHON for co-simulation programming. In this text, a full-automatic modeling method of the crystal plasticity finite element model for laser welding weld is proposed, and a one-key crystal plastic finite element model for laser welding weld is established based on EBSD experimental data. In addition, the full-process full-automatic modeling method of crystal plastic finite element for laser welding weld is universal, and can be used for the establishment of the crystal plasticity finite element model for the microstructure of the base material of aluminum alloy, titanium alloy, magnesium alloy, stainless steel and other materials.

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 relates to the technical field of data optimization, in particular to an industrial product design process modeling optimization method and system. The method comprises the following steps: acquiring industrial product multi-source heterogeneous data; performing data preprocessing on the industrial product multi-source heterogeneous data, and performing hypergraph construction to obtain industrial design hyperedge associated data; performing hyperbolic space embedding by using industrial design hyperedge associated data, and establishing product line source domain-product target domain feature data; performing multi-modal data modeling by using the product line source domain-product target domain feature data to obtain an industrial product design preliminary model; therefore, by constructing a multi-source data driven closed-loop optimization mechanism oriented to the industrial design process, the problems of data splitting, model feedback lagging and insufficient working condition adaptability in the traditional design process are solved, and the intelligent level and manufacturing adaptability of industrial product design are improved.

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 a natural gas station process modeling simulation system based on artificial intelligence, and relates to the technical field of modeling simulation. Comprising the steps that a simulation construction module recognizes geometric features and constructs a simulation data set by conducting global point cloud collection on the gas transmission pipe network; the grid division module is used for acquiring a simulation data set, setting boundary conditions, establishing a fluid simulation field and performing multi-scale grid division; the reconstruction extraction module identifies a torsional weight error region through a clustering algorithm by constructing a grid feature group; a working condition reproduction module performs working condition coupling analysis on the torsion weight error region, judges working condition applicability and splits a grid feature group; the simulation optimization module identifies the linkage effect, extracts a linkage optimization group and optimizes an adjustment strategy, simulation precision and efficiency are improved, and an optimized solution is provided for process modeling and simulation of a natural gas station.

The invention provides an energy complex flexible dynamic aggregation method and system based on reinforcement learning, and the method comprises the steps: collecting the real-time operation parameters of electric, gas, heat and multi-energy coupling equipment in an energy complex through an industrial Ethernet protocol, and constructing a continuous time dynamic system model through employing a Shenchang differential equation process modeling technology; and the nonlinear dynamic characteristics of various energy resources are accurately described. Modeling an aggregation process into a Markov decision process, and training and optimizing by adopting an improved twin delay depth deterministic strategy gradient algorithm to generate a reinforcement learning aggregation strategy model. The system realizes self-adaptive dynamic optimization through an online learning updating mechanism, and the renewable energy consumption rate and the system operation efficiency are remarkably improved.

The invention discloses an intelligent construction site management system based on big data analysis, and relates to the field of construction engineering management. Comprising a construction logic diagram construction module which is used for carrying out function partitioning on construction components, constructing a component node set and establishing a construction logic diagram; the construction risk propagation modeling module is used for constructing a risk propagation matrix and calculating a stable risk heat value of each component node; the scheduling priority calculation module is used for calculating a scheduling priority index of each component node; the construction logic diagram reconstruction module is used for identifying and temporarily removing the high-risk component sub-diagrams to obtain residual schedulable construction diagrams and form a reconstructed construction logic diagram; and the scheduling linkage module is used for updating the topological structure of the risk propagation matrix and the construction logic diagram and feeding back parameters to the scheduling priority calculation module. Through construction logic diagram construction, risk propagation modeling and scheduling priority calculation, whole-process modeling of construction risks, dynamic scheduling optimization and intelligent identification and isolation of high-risk areas are realized.

The invention relates to the field of computer application, in particular to a method, a device, equipment and a medium for integrating a heterogeneous business system and a workflow system.The method comprises the steps that business entity modeling is conducted on business receipts of the heterogeneous system, and a business table structure, a field type and a business entity referenced by associated fields are described in a setting form; binding a corresponding heterogeneous service system data query API (Application Program Interface) for the created service entity; performing approval form modeling, binding a mapping relationship between form control display contents and business entity fields, generating an approval form, and mapping business form data in real time by the approval form; performing flow modeling, defining a flow chart and binding a flow event, and calling a heterogeneous service system data query API to write back service form data at the event execution time; and modifying a submission button response event of the business form, and calling an open standard API of the workflow system to initiate an approval process. And flow centralized management and control are realized, approval entrances are unified, and consistent approval experience is provided.

The invention discloses an evaporation process multi-parameter trend analysis and anomaly recognition method and system, and the system comprises a process data collection and preprocessing module, a process modeling and mode recognition module, and a closed-loop anomaly detection and response module. According to the system, a plurality of key parameters such as coating rate, heating power, film thickness, vacuum degree, electron gun current and temperature are collected in real time through a sensor array, data alignment and multi-dimensional feature vector construction are carried out based on a unified time reference, and a normal working condition parameter distribution model is established by adopting a clustering modeling method; multi-parameter coupling type anomaly detection and grading early warning and real-time recording of abnormal events and parameter states are achieved through a mahalanobis distance mode and the like, a historical report is generated for tracing and analysis, the anomaly recognition accuracy and response speed of the technological process are improved, and the system is suitable for evaporation equipment quality monitoring in the fields of semiconductors, optics, photovoltaics and the like.

The embodiment of the invention discloses an adjustable capacity calculation method based on long-process steel production process modeling, which comprises the following steps: analyzing a long-process steel production process, and determining the material flow, equipment start-stop and energy consumption relationship of each link; drawing a resource task network block diagram by using a resource task network method, and depicting a coupling relationship among the links; based on the resource task network method, an optimization scheduling model is established, and the maximum adjustable capacity is calculated; the method can analyze the whole long-process steel production process, fully considers the incidence relation between links based on a resource task network method, determines key elements such as material flow, equipment start and stop, energy consumption and the like, considers various requirements, establishes a long-process steel production process model, can determine the adjustable maximum capacity of steel enterprises in each time period, and improves the production efficiency. And theoretical support is provided for iron and steel enterprises to participate in power grid dispatching.

In one aspect of the present disclosure, a method includes obtaining, by a processing device, input data indicative of a first set of process parameters. The method further includes providing the input data to a first process model. The method further includes obtaining, from the first process model, first predictive output indicative of performance of a first process operation in accordance with the first set of process parameters. The method further includes providing the first predictive output to a second process model. The method further includes obtaining, from the second process model, second predictive output indicative of performance of a second process operation, different than the first process operation or a repetition of the first process operation, in accordance with the first set of process parameters. The method further includes performing a corrective action in view of the second predictive output.

The invention provides an integrated circuit core process modeling method and system based on personalized federal learning, and the method comprises the steps: a server initializes a global model, and transmits the global model to a plurality of clients; after the client side receives the global model, cross-level local training is executed, and local models are generated and comprise a personalized local model and a universal local model; the client uploads the local model to the server side, and the server side generates an updated global model through model aggregation; and repeating the steps until a preset convergence condition is met, and obtaining a global model corresponding to each core process flow of the integrated circuit. The method is used for realizing efficient and accurate core process modeling on the premise of protecting data privacy, a personalized model and a local model are generated through cross-level local training so as to improve the generalization ability of the model, and model convergence is accelerated and oscillation behaviors are reduced through an integrated optimization method, so that the modeling efficiency is improved. And high precision can still be kept under the condition that the data volume is greatly reduced.

The invention discloses a paper tray production quality detection method and system based on visual processing, and belongs to the technical field of quality detection, and the method comprises the steps: collecting image and motion data, carrying out the alignment according to a time sequence, carrying out the risk classification, marking a high-risk region, and constructing a stress prediction distribution diagram. Calculating an angle residual error according to an existing image and a stress prediction distribution diagram, calculating the angle residual error diagram to obtain a regional average residual error value, taking the regional average residual error value as a weighting coefficient and a deviation distribution index to generate speed and angular speed regulation factors, and finally generating a mold execution instruction of the next period based on an adjustment strategy. According to the detection method, process modeling and structure perception are combined, the defects of a traditional paper support defect detection means based on a static image in the aspects of time hysteresis, structure invisibility, control inaccessibility and the like are effectively overcome, and the intelligent level and the detection coverage rate of paper support quality detection are remarkably improved.

The invention relates to the technical field of process modeling, in particular to an intelligent process management system based on process parameter modeling, which comprises a parameter acquisition module, a nested analysis module, a path construction module, a coupling deconstruction module and a structure compression module. According to the method, the hierarchy and expression accuracy of parameter organization are improved by collecting structured interface information and constructing a parameter mapping relation, dominant and attached parameters are set to form a control logic hierarchy, the clarity and controllability of process parameter response are enhanced, and the stability and anomaly adaptability of path combination are improved; the node frequency and sequence difference are compared to form an interference disassembly fragment, structural anomaly perception and local reconstruction efficiency are enhanced, a path tree structure is constructed through compression and recombination of a repeated path segment, the reusability and organization compactness of path information are improved, parameter logic nesting, path change tracking and interference analysis are achieved under multi-process cooperation, and the reliability of the system is improved. And the logic coupling identification capability, the structural adaptability and the expression precision of process modeling are enhanced.

The invention discloses a circulation processing method and system based on digital process congestion degree analysis, and relates to the technical field of digital process management. The method comprises the following steps: constructing a network model containing nodes and an association relationship, and representing a task flow path and a weight; real-time data such as to-be-processed task queues, equipment states and environment parameters are collected in a multi-source mode and subjected to cleaning standardization processing; based on static load, dynamic change, equipment reliability and environmental interference characteristics, predicting a future congestion index through an LSTM sequential network; setting a dynamic threshold by combining node importance and a real-time state, and matching a strategy mapping table to generate adjustment strategies such as task shunting and resource redistribution; feedback is monitored after execution, and model parameters are optimized. The system comprises a process modeling module, a data acquisition module, a congestion prediction module, a strategy generation module and an execution feedback module. Through full-process intelligent management, the congestion risk is avoided, and the improvement efficiency and the resource utilization rate are improved.

The invention provides a Web workflow system and method based on JSON data analysis, and relates to the technical field of workflow design. A standardized JSON process definition file is generated by adopting a visual process designer, process semantic verification and BPMN conversion are realized through a three-level analysis architecture, and efficient management of the whole process of process modeling, execution and auditing is realized in combination with a plug-in task executor, a thread security context manager and a process monitoring module, so that the process modeling, execution and auditing efficiency is improved. The method has the advantages of fast analysis, strong expansion, easy collaboration, traceability and the like.

The invention discloses a process industry process modeling method under a cross-platform interaction condition. The process industry process modeling method under the cross-platform interaction condition covers standardized algorithm development logic, a fault injection mechanism and a unified API interface design. Specifically, a universal mechanism model base class is constructed based on the digital twinning characteristic of the technological process, and flexible development and application of the model are supported through inheriting and expanding; two fault injection mechanisms based on performance failure and environmental disturbance are designed, and dynamic behaviors of the system under different working conditions are simulated; and through the design based on a unified API interface, the consistency of data interaction and function calling among different platforms is ensured, and cross-platform efficient cooperation and data sharing are realized.

The invention relates to the technical field of flood disaster simulation, in particular to a watershed flood dynamic simulation system based on multi-source data fusion, which comprises a multi-source data preprocessing module, a multi-source data fusion module, a digital twin watershed modeling module, a flood dynamic simulation module, a flood risk assessment module and a visualization module. According to the scheme, a space-time cross-modal interaction fusion method is put forward to fuse multi-source data, a cross-modal interaction mechanism is introduced to capture dependency in a modal, high-dimensional interaction tensors are efficiently fused by means of low-rank decomposition, complementarity of space topology, time dynamic and multi-source heterogeneous data is considered, and multi-source data fusion efficiency is improved. The precision and robustness of hydrological process modeling are effectively improved; by constructing a distributed hydrological-hydrodynamic coupling model, whole-process dynamic simulation from runoff production to river channel-flood area flood routing is realized.

The invention discloses a business process automatic modeling method and device, electronic equipment and a medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: receiving and analyzing current multi-modal input description data in real time in a business process automatic modeling system architecture to obtain current multi-modal input intermediate representation description data; carrying out extraction through an entity relationship process extraction method, and generating a current process topological knowledge graph in combination with a process modeling standard adaptation method; verifying through a business rule atlas verification and identification method, and generating a current intermediate representation parameter with a semantic role; and through a dynamic model generation method, describing a current intermediate representation parameter with a semantic role, and generating and feeding back a current automatic modeling business process in combination with a historical business process modeling template. The problems that manual operation is highly relied on, and static characteristics and data input forms are rigid are solved, the flexibility and automation of business process modeling are improved, and the burden of manual processing is relieved.

The invention relates to the technical field of process industry process modeling and data-driven prediction, and discloses a process industry key index prediction method and device based on time-delay distribution learning. The method and the device are used for solving the problems of unreasonable historical information alignment and insufficient key index prediction precision and stability caused by difficulty in measurement of time-delay distribution attributes in process industry multivariable data. According to the method, a current window, a candidate lag historical window and a future target window are constructed by adopting a sliding window, time-lag weight distribution is learned on a candidate lag axis based on a state gating cross attention mechanism, time-lag distribution learning and a future window prediction task are jointly optimized through a time-lag modeling auto-encoder, and finally, time-lag prediction is performed on the candidate lag historical window and the future target window. And outputting a key index prediction value under the guidance of time-delay distribution. The method is suitable for online prediction of key indexes such as quality, yield and energy consumption of the process industry, and can be used for process monitoring and optimization control.