15 results about "Process dynamics" patented technology

A kind of zirconium alloy water environment corrosion microscopic mechanism analysis method based on ab initio molecular dynamics, 1, construct the multi-phase interface model of metal substrate model and corrosion medium layer model combination, obtain the size information of simulation box;2, set simulation parameters for multi-phase interface model;3, carry out geometry structure optimization to multi-phase interface model and eliminate local internal stress, then carry out ab initio molecular dynamics simulation, obtain atomic evolution track and corrosion product structure;4, carry out data post-processing and statistical analysis to ab initio molecular dynamics simulation result, obtain corrosion elementary reaction path and calculate reaction energy barrier, the interaction mechanism of corrosion group and surface is analyzed by DFT method.The method can complete accurate zirconium alloy corrosion process dynamic simulation under periodic boundary condition, has higher calculation precision and is more reasonable to the description of interatomic force in system, and can also analyze the interaction mechanism between alloy and corrosion medium from atomic electronic scale.

This invention relates to a dynamic simulation and optimization decision-making method for low-carbon policies in industrial parks based on energy-carbon-economic synergy. It falls within the technical field of low-carbon policy formulation and evaluation for industrial parks, and includes the following steps: park boundary identification and carbon emission responsibility unit division; multi-source data collection and construction of an energy-carbon-economic synergy database; construction of a dynamic coupling simulation model of energy flow, carbon flow, and economic flow; construction of an energy-carbon-economic synergy evaluation model; construction of a policy net economic effect model; establishment of a policy tool variable set and policy scenario library; and construction of a multi-objective optimization model to solve for the Pareto optimal frontier. This invention establishes a multi-dimensional synergistic evaluation index system and a full-lifecycle policy net economic effect accounting model that includes the systemic cost of the power grid. Combined with a multi-objective optimization algorithm and a policy sandbox intelligent recommendation mechanism, it achieves full-process dynamic simulation, multi-dimensional effect evaluation, multi-constraint compliance verification, and global optimization decision-making for low-carbon policies in industrial parks.

This application provides an intelligent management and control method and system for the construction progress of power engineering, relating to the field of power engineering technology. The method includes: acquiring dynamic time-series data, extracting standard torque-angle relationship curves and standard acoustic vibration characteristic sequences; real-time monitoring of the tightening operation process of the target bolts; generating a real-time torque-angle relationship curve based on the real-time tightening torque sequence and the real-time tightening angle sequence, and performing full-process dynamic matching with the standard torque-angle relationship curve; analyzing the real-time operation acoustic vibration sequence and the standard acoustic vibration characteristic sequence; and determining the tightening qualification based on the process matching degree and acoustic vibration anomaly index, generating a qualification judgment result. This application solves the technical problem in the prior art where the bolt tightening process is unobservable, leading to a process black box and affecting the construction efficiency of power engineering. By introducing a dynamic fingerprint database and intelligent tightening tools, the quality of the bolt tightening process is ensured, and the construction efficiency of power engineering is improved.

The application discloses an AI-based conveying belt quality control method and system, and belongs to the technical field of industrial process control, which comprises the following steps: constructing a dynamic coupling knowledge graph; analyzing requirements and environmental parameters, and utilizing a dynamic coupling knowledge reasoning benchmark process and floating rules; collecting multi-physical field data in production for cross-process dynamic compensation; driving a digital twin model to perform in-process performance simulation and instruction calibration; performing virtual testing after production to generate a digital final inspection report; and finally realizing knowledge graph closed-loop iteration. The method can realize full-process dynamic closed-loop quality control from pre-production design, in-production compensation to performance prediction and calibration by means of establishing a dynamic coupling knowledge graph for feedforward collaborative design and cross-process compensation control, and simultaneously utilizing a product digital twin model for in-process performance simulation and calibration.

The present application relates to the technical field of hydrological simulation, and more particularly to a rainfall-runoff-concentration-evolution whole-process dynamic simulation system and method. The method comprises the following steps: obtaining basin topographic information and extracting basin boundaries; dividing grid cells according to the basin boundaries and setting river nodes using the grid cells; collecting rainfall data using a rain measurement radar; calculating runoff using the rainfall data; inputting the runoff into a pre-trained distributed concentration model for concentration analysis and recording the concentration flow; inputting the concentration flow and the river nodes into a river flood evolution model to perform river evolution simulation and generate river concentration data; determining the amount of water entering the main river according to the river concentration data; generating a flow time series according to the amount of water entering the main river; and predicting the flood peak flow based on the flow time series. The present application realizes accurate simulation of the hydrological process of a basin based on hydrological simulation technology, and improves the accuracy and timeliness of flood prediction and early warning.

The application discloses a high-speed rail second-level wind speed prediction and early warning method based on wind process dynamic division and physical guidance, comprising the following steps: in stage one, wind process dynamic division and offline construction of a knowledge base, historical wind field data are divided into wind process types with clear physical meaning through a graph attention network and adaptive spectral clustering; in stage two, hierarchical online wind speed prediction based on physical guidance, macro-, meso- and micro-scale decoupling deduction is carried out based on a hierarchical state space model with physical guidance, and a multi-physical constraint loss function is used to ensure that the prediction conforms to the fluid mechanics law; in stage three, dynamic adaptive envelope line online early warning, a dynamic safety envelope line is generated by using the extreme value theory and real-time scene information, and scene-adaptive accurate hierarchical early warning is realized. The method can significantly improve the accuracy of high-speed rail wind speed prediction and the reliability of early warning.

This invention discloses a load dispatching method and apparatus based on process dynamic analysis, belonging to the field of power dispatching. The method includes extracting load time-series data from real-time telemetry data, production plan data, and state time-series data, performing wavelet transform, matching the separated high-frequency transient components and low-frequency steady-state baseband components with real-time work order data according to preset process constraints, obtaining matching results; calculating the influence intensity value based on the process constraints, matching results, and identified target process links; searching within a decision space composed of production risk and production benefit indicators calculated based on the influence intensity value to generate a set of flexible dispatching plans; for each flexible dispatching plan, calculating the total entropy value of disturbance propagation based on the generated process influence chain, calculating the real-time safety entropy value based on the adjusted voltage amplitude and active power sequence, and determining the final flexible dispatching plan based on Pareto mapping. By implementing this invention, the problem of inaccurate dispatching in existing technologies is solved.

The present application provides a kind of based on physical data joint driving's excavator electric shovel excavating trajectory optimization method, it is related to engineering machinery intelligent control and digital twin technology field, it includes: S1, constructs the dynamic mechanism model of excavator and high-fidelity discrete element simulation dataset, weighted fusion obtains total torque data;S2, using excavator total torque data to physical information neural network agent model is supervised learning;S3, establish the energy consumption of shovel unit volume and single operation cycle excavating time excavating trajectory planning model, output optimal excavating trajectory.The present application is deep fusion by physical mechanism and multi-fidelity data, the dynamics of excavating process is analyzed, constructs high confidence total torque data;Total driving torque containing soil resistance is predicted by PINN agent model, with the minimum unit volume energy consumption and the shortest operation cycle time as the goal to carry out multi-objective optimization, so that the generated excavating trajectory completely meets the physical mechanical limit and process requirement of excavator.

The application discloses a drilling process dynamic full-freedom twin simulation method and system based on multi-parameter coupling, relates to the technical field of oil drilling simulation, and comprises the following steps: collecting geological structure, drilling tool assembly, drilling fluid performance and real-time engineering parameters, aligning time stamps, unifying formats and generating a standardized data set; constructing a geometric and physical model containing a wellbore trajectory, a drill string and the like and establishing kinematic and dynamic constraints; integrating four-freedom dynamics of the drill string, multiphase flow heat transfer of the drilling fluid and a drill bit-rock interaction equation to form a multi-physical field coupling control equation group. The real-time parameters are used as boundary conditions and excitation sources to synchronously solve the dynamic response and the formation state, update the model space attitude, stress deformation and temperature distribution, and realize dynamic synchronous mapping of a virtual space. The method breaks through the limitation of a single physical field, accurately depicts multi-field interaction dynamics, reflects the state of wellbore trajectory deviation, drill string buckling and the like in real time, improves the simulation accuracy and real-time performance of the drilling process, and supports accurate regulation and control.

The present application belongs to the technical field of industrial process control and energy management, and particularly relates to a chemical production energy consumption prediction method and system based on artificial intelligence, which comprises the following steps: obtaining production operation data and process mechanism knowledge, forming a high-quality time series data set through data cleaning and automatic working condition division; based on the data and knowledge, constructing a lightweight dynamic model for each working condition and each device, and integrating a whole-process dynamic relationship network; receiving operation instructions, performing quantitative safety assessment and generating a standardized control sequence; driving the network with the sequence to perform hierarchical dynamic simulation, and combining real-time feedback correction to obtain the future prediction trajectory of key variables; and finally converting the trajectory into a system comprehensive energy consumption prediction result according to the device-level energy consumption model. The present application is used to solve the problem that the prior art is difficult to predict the energy consumption dynamic change process caused by operation adjustment with high precision, and realizes the transition from static result prediction to dynamic process simulation.

The application discloses a kind of harness connection loose special prevention and control and whole-process dynamic management and control method, the method is by collecting harness full life cycle loose related data, constructs special pre-judgment model and control benchmark library;Optimized design scheme forms design-loose-pre-judgment integrated technical document;Manufacturing stage is in real time by linkage model pre-judgment risk and triggers compensation;Assembly stage uses three-level loose detection mode to trace back and deal with;Running stage dynamically updates benchmark library and executes graded compensation;Integrate data to build iterative optimization system to continuously update model and strategy.The application realizes harness full life cycle closed-loop management and control, solves the problem of fragmentation, no linkage of prior art, improves loose risk pre-judgment accuracy and compensation pertinence, guarantees harness connection reliability and equipment operation safety.

The application discloses a kind of limited space combustion consequence prediction methods based on gas leakage process dynamic parameter, collect every working condition from gas leakage starts every Δt time period concentration, velocity segmented time series data;Form three-dimensional primary ignition source array, according to the result of numerical simulation of combustion and explosion to simplify ignition source array;Get the maximum temperature field, static pressure field and dynamic pressure field data set on typical cross section in combustion process;Concentration, velocity segmented time series data set is mapped to maximum temperature field, static pressure field and dynamic pressure field data set, obtain combustible gas leakage explosion combustion consequence prediction data set, preliminary establish combustion consequence prediction model and train;Collect gas leakage concentration, velocity time series data in limited space input combustion consequence prediction model, obtain the maximum value field of combustion process temperature, static pressure and dynamic pressure.The application realizes real-time or super real-time combustible gas leakage in limited space after combustion process in the maximum overpressure, dynamic pressure and temperature field automatic prediction, and prediction result is accurate.

The present application relates to the technical field of nodular cast iron, and particularly relates to a nodular cast iron full-process dynamic regulation method, which comprises collecting real-time data of temperature, pressure, weight and element concentration in the steps of batching, smelting and pouring to form a unified data set; smelting temperature and composition uniformity data are fused by using a data fusion algorithm to obtain a comprehensive data set; after meeting the integrity threshold, a parameter correlation graph is generated by association rule mining; key quality influencing factors are identified based on a decision tree algorithm; real-time feedback data and the correlation graph are compared to generate an adjustment instruction sequence; the instruction is executed and the production parameters are optimized through a feedback loop; product stability indicators are monitored, and when deviating from the preset range, the key influencing factors are updated; difference information is iteratively processed to obtain a final dynamic regulation model. The present application realizes full-process data collaboration and dynamic closed-loop regulation, improves product quality stability and production intelligence level, and is suitable for high-end nodular cast iron continuous production scenarios.

The application belongs to the technical field of aero-engine icing simulation, and discloses a kind of ice crystal melting icing whole process simulation calculation method in aero-engine compressor passage, first, the fluid domain model of compressor is established and initial flow field is obtained;Subsequently, the movement, impact and phase change process of ice crystal are simulated by combining discrete phase model and user-defined function;Further, the ice accumulation is calculated based on the icing thermodynamic model, and the dynamic mesh technology is driven to update the grid node position of blade surface in real time, and the icing topography is reconstructed;Finally, the reconstructed ice model is calculated for flow field, and the aerodynamic performance loss caused by icing is analyzed.The application realizes the whole process dynamic coupling simulation from ice crystal transport, ice growth to aerodynamic influence evaluation, and provides accurate and effective numerical analysis means for compressor ice crystal icing risk assessment.

The application discloses a process engine-based adaptive OA approval process dynamic adjustment method and system, relates to the technical field of data analysis, and comprises the following steps: extracting a key business dimension from an approval form, generating a business scene characteristic hash value, searching a historical similar record, obtaining a standard approval chain node sequence, statistically screening a jump candidate set according to a historical idle rate of each node, extracting an intervention tendency vector and an intervention severity index of the candidate node; constructing a management and control ability vector of each node and the whole chain; mapping the intervention severity index to obtain a dynamic penalty coefficient, combining the intervention tendency vector and the management and control ability vector to calculate a dynamic stripping cost vector; using the total management and control ability vector of the whole chain to calculate a compliance redundancy vector and performing non-negative verification, and determining whether the candidate node is allowed to be skipped; rewriting a routing relationship table of the process engine according to the determination result and generating an audit log, and promoting the approval process by the process engine; and adaptive efficient flow and compliance guarantee of the approval process are realized.