194 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 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 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 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 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.

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 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.

The invention provides a multi-agent collaborative task process arrangement method and system, and the method comprises the steps: carrying out the processing of a task demand through a large language model by employing a demand analysis agent, and obtaining a structured task description; determining a workflow topological graph based on the structured task description by using a process modeling agent; scheduling and operating a task execution agent corresponding to each subtask by utilizing an execution engine according to the workflow topological graph; determining an actual execution path of each sub-task based on the task execution log of each sub-task by using a deviation identification agent, comparing the expected execution path with the actual execution path by using the deviation identification agent, and comparing the expected index data with the task execution index data to obtain a comparison result; and optimizing the workflow topological graph based on the comparison result by using a process modeling agent. Therefore, a complete closed loop of demand analysis, flow generation, execution, monitoring, diagnosis, optimization and regeneration of flow arrangement can be realized.

The invention provides a code-free SaaS application rapid development method and system based on data source driving, and the method comprises the steps: constructing a forward business process directed acyclic graph, defining compensation association and reality binding for a forward operation node of the forward business process directed acyclic graph, and generating a visual transaction model; analyzing the transaction model to generate a transaction step sequence, constructing a finite state set and a state transfer function based on the sequence, and packaging the finite state set and the state transfer function into an SAGA process state machine definition object; generating a global transaction ID when a transaction life cycle is started, constructing a transaction context object in combination with the state machine definition object, and serializing to generate an initial transaction context instance; and loading and deserializing the instance, starting a state-driven execution loop, terminating by iterating single-step state transition when the current state belongs to a termination state set, judging a final result of the transaction, and archiving an audit log. According to the method, transaction process modeling and formalized state driving execution are fused, and the method is suitable for SaaS application development of cross-heterogeneous data sources.

The invention discloses a process development platform, a process development method, equipment and a storage medium, and relates to the technical field of data processing, and the platform comprises a visual configuration module which is used for obtaining a process construction demand of a target user; the process modeling layer is used for constructing a target process structure through a process structure construction model based on the process construction requirement, receiving a process adjustment operation of the target user, and modeling the target process structure to obtain a target process modeling model; the third-party docking module is used for acquiring process access data corresponding to the target process modeling model by connecting a third-party standard interface; and the process development module is used for performing process development based on the target process modeling model and the process access data to generate a target process. According to the method, real-time data synchronization and standardized docking of the visual process platform and the third-party heterogeneous system can be realized, and interoperability and expansibility of the platform system are remarkably improved.

The invention provides a fermentation process modeling method based on a generative fuzzy system and expert knowledge evaluation. The generation type fuzzy rule can mine own specific information for the fermentation process. The modeling method supports evaluation and optimization of fermentation control parameters by utilizing expert knowledge. Therefore, the fermentation process optimization model can be effectively suitable for a fermentation process optimization scene, and the experiment frequency and the optimization time can be remarkably reduced, so that the cost is reduced and the efficiency is improved. According to the method, the rolling learning method is innovatively designed for the fermentation process optimization model, so that the fermentation process optimization model has excellent adaptive capacity. The adaptive characteristic enables the model to flexibly respond to the dynamic change and uncertainty in the fermentation process, and the robustness of the system is remarkably improved. In addition, automatic optimization and control are achieved through the intelligent characteristic, dependence on artificial experience is reduced, and operation convenience and accuracy are improved.

The invention discloses a container terminal storage yard management and control rehearsal method and system and a storage medium thereof. The method comprises the steps of data acquisition and preprocessing; carrying out 3D physical modeling on the layout of the storage yard; the operation process rule logic modeling comprises operation process modeling and resource scheduling logic modeling, and the resource scheduling logic modeling comprises stockpiling prediction, dynamic path planning and reinforcement learning of dynamic optimization scheduling rules; the reinforcement learning dynamically optimizes a scheduling rule, defines three sub-award functions of efficiency award, energy consumption penalty and safety penalty, and dynamically adjusts the weights of the three sub-award functions according to a scene; the state of the storage yard is visually displayed through a 3D dynamic billboard in combination with a thermodynamic diagram; and regularly updating the model and the rule base to adapt to wharf business changes. The storage yard bottleneck is pre-judged in advance through deep learning, and passive response is reduced; meanwhile, the scheduling rule is dynamically optimized in combination with reinforcement learning, so that the model is dynamically adjusted along with data, and the complexity of wharf operation is adapted.

The invention discloses a galvanized steel pipe surface characteristic prediction method based on process modeling, and relates to the technical field of intelligent manufacturing process optimization, and the galvanized steel pipe surface characteristic prediction method comprises the following steps: basic data acquisition, process state characteristic modeling, dynamic process diagram modeling, physical data dual-drive mixing and surface characteristic prediction. Adopting a derived feature construction method based on an embedded mechanism kernel equation to form process intermediate state variable enhanced data; by establishing a dynamic process diagram model, the state transfer and evolution process between steel pipe procedures is represented; a residual learning mechanism of a physical model and a data-driven model is combined, physical consistency constraint is introduced, a dual-drive fusion prediction framework is constructed, and multi-target surface characteristic hybrid prediction is realized; according to the method, high-precision and multi-target prediction of the characteristics such as the surface thickness, the adhesive force and the smoothness of the galvanized steel pipe is achieved, and the prediction stability and the physical interpretability under the complex galvanizing process are effectively improved.

The invention provides a structured modeling and intelligent sorting decision-making method and device for feeding and discharging visual detection data of a metal-based composite material, and is applied to the technical field of data processing. The full-process technical system of data acquisition, threshold setting, processing modeling, strategy making and transmission execution is constructed according to the feeding and discharging sorting requirements of the metal-based composite material. The method comprises the following steps: firstly, collecting visual detection core data and working condition associated data, and setting a multi-dimensional threshold according to sorting precision, defect detection sensitivity and production efficiency; generating a standardized data set through data noise reduction, calibration, alignment and feature extraction, and sorting the standardized data set; determining a sorting decision strategy in combination with characteristics such as material specifications and computing power resources, and matching a sensor acquisition frequency; and after the data set is divided into control batches for ordered transmission, executing parameters are optimized through a multi-modal feature fusion algorithm, equipment and scenes are dynamically adapted, and finally accurate sorting control signals are generated, so that efficient and accurate sorting is realized.

The invention provides a business workflow dynamic arrangement and optimization method based on a large model agent, and the method comprises the steps: (1) analyzing the features and operation logic of a multi-element business workflow, and defining a business workflow description language; and (2) decomposing a complex business process construction process, and providing a layered process modeling system to guide a large model agent to construct a workflow model. And (3) constructing a task execution queue based on the task dependency relationship, and dynamically executing and monitoring the workflow. And (4) according to the execution log of the business workflow, discovering the bottleneck, redundancy and abnormity of the workflow model by adopting a process mining algorithm, and proposing a process improvement and optimization strategy. And (5) constructing a collaboration framework of a plurality of large model agents, and realizing dynamic arrangement, monitoring and optimization of the business workflow. According to the method, the defects that the current business workflow construction depends on experience of designers, and effectiveness and function requirements are difficult to guarantee are overcome; the defect that the business workflow is difficult to adapt to dynamic demand changes is overcome.