94 results about "Process modeling" patented technology

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 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 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 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 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 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.

The invention belongs to the technical field of intelligent manufacturing and production scheduling, and particularly discloses and provides a cabinet flexible production line intelligent scheduling and material collaborative optimization MES system, which comprises a dynamic process modeling module for constructing a dynamic process route model based on order configuration, BOM and equipment capability; the real-time state sensing module forms a production line operation state data set through multi-source data acquisition; the rolling scheduling optimization module generates an initial production scheduling scheme according to a multi-objective function; the material collaborative distribution module performs material alignment verification based on the schedule and generates a synchronous distribution instruction; the disturbance response rescheduling module triggers local re-optimization for events such as equipment faults. According to the system, a dynamic process route is constructed based on order configuration and equipment capability, multi-target scheduling in a rolling window is driven by fusing multi-source real-time data, accurate cooperation of material delivery and process rhythm is realized, and local rapid rescheduling under disturbance events is supported.

The invention discloses a steel plate heat treatment model parameter correction method, device and equipment and a storage medium, and relates to the technical field of industrial process modeling and optimization control, the steel plate heat treatment model parameter correction method comprises the steps that a to-be-optimized steel plate heat treatment model is obtained, and the steel plate heat treatment model comprises a radiation coefficient and a convection coefficient; constructing a target function according to the actual furnace temperature data and the actual plate temperature data, wherein the target function is used for quantifying a prediction error of the steel plate heat treatment model; performing iterative optimization on the radiation coefficient and the convection coefficient through a covariance matrix adaptive evolutionary strategy algorithm until the value of the target function is smaller than a preset function threshold value, and obtaining a target parameter; and the target parameters are used for updating the steel plate heat treatment model, and parameter correction is completed. According to the method, the parameters of the steel plate heat treatment model can be efficiently and accurately corrected, and low efficiency and inaccuracy of manual trial and error are avoided.

The application relates to a sandstone processing system gradation and energy consumption double-target optimization method and system, and relates to the technical field of sandstone processing. The method comprises the following steps: acquiring sandstone processing units, processing unit processes and unit mapping relationships; analyzing the sandstone processing units, the processing unit processes and the unit mapping relationships to determine general process modeling; acquiring project unit parameters and project optimization requirements; inputting the project unit parameters into the general process modeling to determine a double-target optimization model and equipment operation boundaries; analyzing the double-target optimization model and the equipment operation boundaries according to a preset double-target optimization model to determine requirement weight groups; searching in the requirement weight groups according to the project optimization requirements to determine target optimization weights; and analyzing the double-target optimization model according to the target optimization weights and the double-target optimization model to determine an optimal configuration scheme. The application has the effect of improving sandstone processing efficiency.

A flexible production line and mixed line production process modeling and anomaly prediction method relates to the field of production line anomaly prediction, and comprises the following steps: S1, constructing a final flexible production line operation simulation model driven by real-time monitoring data; s2, constructing an anomaly detection model of the monitoring data of the whole manufacturing cycle of the product; and S3, integrating the flexible production line operation simulation model established in the S1 and the anomaly detection model established in the S2. Operation behavior modeling of the flexible reentrant production line and whole production process anomaly prediction of multi-variety product mixed line production are achieved, support can be provided for manufacturing process parameter optimization, and the product percent of pass and reliability are improved.

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.

The invention discloses a dynamic modeling method and system for a three-dimensional model, and the method comprises the steps: recognizing an interactive operation, and obtaining coordinate point data in a three-dimensional scene in real time according to the interactive operation, so as to complete vertex assignment and form a vertex list; calculating a geometric center point of the vertex list based on the vertex list; connecting the vertexes in the vertex list with the geometric center point in a clockwise sequence to generate a planar triangular surface index array, and forming an initial plane; copying a vertex list of the initial plane and offsetting a target numerical value along a specified direction to generate a target plane; and connecting the vertex of the initial plane and the vertex of the target plane in sequence to generate a side triangular surface so as to complete modeling of the three-dimensional model. According to the method, the user interaction track is directly converted into the vertex data and the geometric calculation logic, so that the user can independently complete the whole-process modeling from plane to three-dimensional without relying on a modeler, the Unity scene development efficiency is remarkably improved, and a low-cost and high-flexibility solution is provided for interactive three-dimensional application.

The invention discloses a double-paddle stirring power prediction method and system based on residual neural network correction, and belongs to the technical field of chemical mixing process modeling and optimization control. The method comprises the following steps: acquiring geometric parameters and operation condition data of a stirring system; constructing a mixed feature set; calculating a reference power value based on dimensional analysis or a response surface method; a residual neural network is constructed, and network hyper-parameters are optimized by adopting a tree structure Parsenson estimator algorithm; predicting a power residual value by using the optimized network; and adding the reference power and the residual value to output a final predicted value. According to the method, the advantages of interpretability of a physical mechanism and high precision of data driving are fused, the problems that the power prediction precision of a complex double-layer paddle system is low and a pure neural network model lacks physical interpretability are effectively solved, high-precision and high-robustness power soft measurement is realized, a reliable basis is provided for model selection of a stirring motor, and the method is suitable for popularization and application. And the operation energy consumption of equipment is reduced.

The application discloses a data-driven noise control system sound-electricity coupling model and a construction method, and comprises the following specific contents: first step: creating a multivariate sound-electricity transmission characteristic data set; second step: on the basis of a large amount of data set, a deep learning sound-electricity coupling bidirectional prediction model is established; and third step: embedding the trained model into a sound-electricity coupling parameter centralized control interface. The application mainly solves the problems that the sound-electricity transmission coupling mechanism of a front-end control loading device in a noise virtual test system is difficult to determine, and fine modeling and prediction cannot be realized, which restricts the whole-process modeling of the virtual test system. Meanwhile, the application solves the problem that a digital sound-electricity model reverse prediction optimizes a real test driving setting, shortens a closed-loop debugging cycle, and eliminates the risk of over test.

The application provides a boiler-turbine coupling process modeling method based on an improved particle swarm optimization algorithm, and relates to the field of thermal power generation technology. The method first establishes a three-input three-output boiler-turbine coupling process transfer function matrix model; collects historical operation data of a thermal power generating unit and performs zero-mean preprocessing to construct a training data set and a test data set for verification; then uses the improved particle swarm optimization algorithm to identify the parameters to be identified in the boiler-turbine coupling process transfer function model; substitutes the identified parameters into the boiler-turbine coupling process transfer function matrix model to form a complete boiler-turbine coupling process transfer function matrix model, and uses the test set to verify the model precision. The boiler-turbine coupling process transfer function matrix model established based on actual operation data provides a reliable theoretical basis and technical support for the subsequent advanced control strategy design and optimized operation of ultra-supercritical units.

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.

This invention discloses an information-based monitoring and management method and system for galvanizing production processes. Utilizing a SCADA+SKF multi-parameter visualization monitoring platform, it acquires the internal and surface defect characteristics of production components corresponding to risk nodes where oxidized mixtures intrude at unit temperatures. Combining data from manually involved processes, impurity introduction, acid washing concentration, and temperature and humidity changes, it obtains an oxidized mixture distribution impact assessment model through an edge computing gateway, generates an oxidized mixture distribution matrix per unit time, and conducts impact degree analysis and impurity source tracing to calculate the galvanizing compliance rate. Furthermore, through a bidirectional categorical variable chi-square test of defects and compliance rate, it identifies key interference factors and stores the quality assessment results, achieving intelligent monitoring and optimized management of the entire galvanizing process. This invention realizes closed-loop intelligent control from data acquisition and process modeling to quality analysis, significantly improving the stability, automation level, and product compliance rate of the galvanizing process.

This application provides a method for controlling the ethylene glycol purification process based on deep learning and hybrid optimization. It involves real-time acquisition of multi-source time-series data from the entire ethylene glycol purification process to construct a high-dimensional tensor dataset. This multi-source time-series data is used as input to a pre-constructed multimodal deep hybrid network model to obtain predicted values ​​of the overall process performance indicators. Based on these predicted performance indicators, an adaptive chaotic multi-objective collaborative algorithm is used to solve the problem, yielding a refined Pareto front. A meta-learning adaptive model is then used to determine the target control strategy from this refined Pareto front, and the entire ethylene glycol purification process is controlled based on this target control strategy. Through this technical solution, end-to-end optimization from precise process modeling and multi-objective Pareto optimization to dynamic closed-loop control can be achieved.

This invention relates to the field of molded case circuit breaker (MCCB) technology, and particularly to a method and system for configuring multi-stage tripping characteristics of MCCBs. The method analyzes the current switching and transmission process of the target circuit breaker, establishes current transmission paths, and determines multiple rated current levels. Based on the current level and path, it predicts the tripping action characteristics, constructs a multi-stage tripping characteristic distribution network, and defines the tripping characteristic constraint intervals for each operating stage. It obtains the tripping component parameter distribution network, combines the distribution network and constraint intervals to identify defects, accurately locates defective tripping components and their defect severity, and performs targeted configuration optimization on the defective components based on their defect severity, generating the final optimization result. This method, through full-process modeling and quantitative analysis, achieves a closed loop from precise defect location to automatic optimization, effectively solving the problems of traditional configuration relying on experience, high trial-and-error costs, and low accuracy. It significantly improves the tripping accuracy and protection reliability of MCCBs under different operating conditions.

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 LangGraph-based customer service system, which comprises an NLP module used for realizing a language interaction center function, and the NLP module has the following functions: receiving natural language input of a user side, the natural language input comprising character input and voice-to-character input; a LangGraph process engine, a knowledge base management module and an enterprise business system are linked; converting the user fuzzy natural language into a structured instruction which can be executed by a platform; dynamically judging a dialogue trend in combination with a LangGraph process node rule; and generating a natural language reply conforming to enterprise specifications based on a knowledge base retrieval result and business system data so as to complete user consultation response and business operation execution. The problem that the labor cost is increased due to lack of intelligentization of user consultation problems caused by lack of flow modeling and business handling capabilities in the prior art is solved.

This invention proposes a method for extracting parameters and performing reverse process modeling of ancient building roofs based on 3D point clouds. Belonging to the field of digital preservation and 3D modeling technology for ancient buildings, it solves the technical problem of automatically extracting geometric parameters of Chinese ancient building roofs from 3D point clouds and constructing BIM models of complex roof surfaces and upturned eaves through parametric modeling. The technical solution includes the following steps: Step 1: Preprocessing point cloud data; Step 2: Extracting section parameters; Step 3: Extracting wing angle parameters; Step 4: Parametric modeling. This invention can quickly extract parameters of ancient building roofs from laser point clouds and apply them to the digital twin modeling of ancient building roofs.

The present application relates to a kind of double-coated brake disc ultra-high speed laser cladding process method, comprising the following steps: according to brake disc surface area, the calculation formula of total energy output value of cladding efficiency, determine the total energy output value under the binding force qualified threshold;Set control factor, deduce the coupling formula of power, powder feed rate, efficiency and thickness;Using double-layer time-lapse cladding, establish radius time line speed kinematics model, accurately describe the second layer linear velocity variation law;Determine the compensation factor of second layer cladding, obtain the real-time compensation formula of power, powder feed rate changes with linear velocity in second layer cladding process.The present application is around the technical goal of high efficiency and high quality collaborative optimization, systematic design is carried out in process modeling, parameter coupling and dynamic control, on the premise of ensuring coating bond strength, forming quality and substrate deformation controllable, realize efficiency maximization, with stronger implementability and replicability, be conducive to standardization, large-scale development.

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.

The application discloses a kind of data and knowledge collaborative driving multivariate time delay nonlinear industrial process modeling method, comprising: step 1, the decision variable of definition industrial process multivariate time delay nonlinear industrial process modeling problem, optimization objective function, knowledge constraint condition;Step 2, the joint mixed integer programming solution is carried out to time delay parameter and network model parameter in decision variable, and the activation function in network is recursively self-adapting segmented linearization;Step 3, according to the multivariate time delay parameter of solution reconstruction industrial process data, and the calculated network model of the reconstructed process data is substituted into solution, whether it meets the preset condition is judged, meets the requirement and exports the multivariate time delay and network model parameter of solution, does not meet the requirement and adds new hidden layer node to network.The application accurately estimates the multiple time delays between process variables and quality indicators in process data, and simultaneously constructs a complex nonlinear soft-sensing model for difficult-to-measure quality indicators.