76 results about "Process dynamics" patented technology

The invention discloses a graph neural network scheduling method and system for aluminum rolling multi-process production scheduling, and belongs to the technical field of production scheduling, and the method comprises the steps: constructing an aluminum rolling process dynamic graph model based on a constructed aluminum rolling production line digital twin model in combination with a production process logic relation and constraint conditions; dynamic graph features are extracted by using a graph neural network, a state vector fusing local and global information is generated, and an initial scheduling scheme is generated in combination with a multi-objective optimization algorithm; when disturbance occurs in a production line, sensing and quantifying the influence in real time through a digital twinborn model, updating a dynamic graph model and optimizing an initial scheme by adopting an incremental graph neural network, and verifying feasibility in a digital twinborn environment; issuing the optimization scheme to a physical production line for execution, and dynamically calibrating the digital twin model and the process dynamic graph model through a feedback mechanism; dynamic intelligent scheduling of aluminum rolling multi-process production is achieved, and the production efficiency and the anti-disturbance capacity are improved.

The invention provides a dynamic risk prediction and optimization decision-making method and system for a deep foundation pit construction process. A deep foundation pit numerical simulation model is established, and the model is used for simulating soil mechanical behaviors and supporting system response parameters in the construction process; acquiring multi-dimensional real-time monitoring data of the foundation pit, comparing the monitoring data with a numerical simulation result, and calibrating parameters of the deep foundation pit numerical simulation model according to a comparison result; performing time series data modeling analysis on the real-time monitoring data by using a Transform algorithm, capturing possible risk modes in the construction process, and identifying potential risk signals in the data; on the basis of Transform algorithm analysis and numerical simulation feedback results, the foundation pit construction risk state is predicted in real time, and a real-time risk prediction result is obtained; and according to the prediction result, dynamically adjusting the construction scheme to complete decision optimization. According to the method, the risk management and control capability of the deep foundation pit construction process is remarkably improved, and the construction safety and efficiency are greatly improved.

The invention relates to the technical field of building design, and discloses an Internet-based building design dynamic process management system, which comprises a multi-factor identity verification module, a distributed data storage module, a process dynamic optimization engine, a behavior evaluation and intervention module and an intelligent audit tracking system. According to the building design dynamic process management system based on the Internet, through cooperative work of the process dynamic optimization engine and the behavior evaluation and intervention module, the problem that task optimization of a traditional process management system depends on a preset rule is solved; the flow dynamic optimization engine dynamically adjusts the task priority and resource allocation by using an improved ant colony algorithm based on the real-time task progress and the executor behavior data; meanwhile, the behavior evaluation and intervention module quantitatively evaluates the behavior of the executor according to a weighted scoring model, triggers an automatic intervention process when necessary, and can quickly adapt to sudden changes, so that the resource allocation efficiency is improved, and human errors are reduced.

The invention relates to the technical field of industrial production process control, and provides a production process dynamic scheduling method and system based on energy consumption optimization. The method comprises the steps that the power waveform of an internal mixer and temperature control energy consumption data of a vulcanizing machine are collected in real time, and a process chain topological relation is constructed based on a material circulation path; generating an energy consumption coupling coefficient through a partial derivative of the power change of the internal mixer to the energy consumption of the vulcanizing machine according to the topological relation; generating a decoupling instruction when the coupling coefficient exceeds a threshold value, modifying the starting time of the operation plan of the internal mixer according to the decoupling instruction, and determining an energy consumption peak time period needing to be avoided by the vulcanizing machine; and after it is verified that the power peak value and the energy consumption peak value of the internal mixer are staggered, cooperative control is conducted on equipment. The system comprises a data acquisition module, a coupling coefficient generation module, a decoupling instruction generation module, a peak time period determination module and an equipment cooperative control module. According to the method, the process chain topological relation and the energy consumption coupling dynamic model are constructed, so that accurate energy consumption optimization of multi-device cooperation is realized.

The invention relates to a numerical simulation method for multiphase fluid stratum seepage, which belongs to the technical field of numerical simulation and comprises the following steps: S1, generating an unstructured grid by adopting a mode of combining a leading edge propulsion method and Delaunay triangulation; s2, performing dynamic adjustment through an adaptive grid encryption technology according to the change of the seepage physical quantity; s3, establishing a dynamic phase change model, and obtaining a function of representing the phase change rate as temperature, pressure and concentration of each phase; s4, the mass and energy change of each phase in the phase change process is calculated in real time through a kinetic equation; s5, processing dynamic evolution of a phase interface by adopting a phase field method, and describing distribution conditions of different phases through phase field variables; and S6, coupling the grid system obtained by dividing the multi-scale unstructured grid with the dynamic phase change model through a numerical solution algorithm. According to the method, the complex geometrical shape of the stratum can be better fitted through multi-scale unstructured grid division, and then the change of the phase change process along with time and space can be tracked in real time through the dynamic phase change model.

The invention discloses a BIM-based building design process dynamic management method, and relates to the technical field of BIM. Spatial state information of a construction task is extracted from a BIM model and is uniformly expressed as a six-dimensional space body vector TBox, standardized processing of a task geometric boundary is realized, and a task time and space three-dimensional data set Cx is generated; and the spatial evolution trend between tasks can be perceived in advance. On the basis, the system carries out space overlapping detection on any task pair in the three-dimensional data set Cx and calculates a task conflict vector CVec, whether conflicts exist or not is judged, directionality and space centralization of the conflicts are quantized by combining task path direction vectors and overlapping volume information, conflict severity index CIdx values of all the task pairs are summarized, and the conflict severity index CIdx values of all the task pairs are calculated. Dynamic judgment and optimization triggering of system-level construction risks are achieved, and building design and construction scheduling can have the capabilities of feedforward perception, self-adaptive adjustment and feedback optimization.

The invention relates to a dynamic control method for an efficient assembly process of a high-precision driving mechanism, which improves the proficiency and the professionality of operators in a step-by-step station design mode and ensures the consistency of assembly quality. Data interconnection between measurement equipment and an MES system is broken through by establishing a driving assembly integration and application management system, a prediction model of part size and output precision is constructed based on assembly field actual measurement multi-source errors and by utilizing actual measurement key parameters, field dynamic optimization decision and intelligent regulation and control of assembly process parameters are achieved, and field assembly and adjustment are guided.

The invention provides a grouting process dynamic control method based on statistical characteristics of a water permeability rate and a grouting amount per meter, which comprises the following steps: collecting water pressure test data (pressure P, flow Q and segment length L) and grouting process data (dry material weight M and segment length L) through a system, and calculating the water permeability rate q and the grouting amount per meter m; sorting and grouping the permeable rate data and the grouting amount data per meter, then carrying out normality test, and respectively establishing a mapping relation table between permeable rate groups and standard deviation values of the grouting amount per meter or percentile values per meter according to test results; in the grouting process, the water permeability is monitored and calculated in real time, the water permeability is matched with the mapping table, and the grout proportion is adjusted in a stepped mode according to the statistical interval where the current grouting amount per meter is located; and after grouting is finished, the grouting effect is evaluated according to the statistical interval where the final grouting amount is located. According to the method, quantitative control over the grouting process is achieved, and the problem that grout is wasted or the diffusion range is insufficient due to the fact that the proportion of the grout is adjusted through experience judgment is solved.

The invention provides a chemical product quality multi-dimensional detection and process dynamic modeling system and method, and belongs to the technical field of chemical production quality detection. Comprising the steps of collecting multi-dimensional parameter data of chemical production equipment, adding a timestamp, storing the data to obtain an original multi-dimensional data set, extracting multi-sensor data, and performing data fusion through a weighted average algorithm or a neural network algorithm to generate a comprehensive quality evaluation result; taking the comprehensive quality evaluation result and historical window data as input, constructing and training a dynamic quality model, and outputting a predicted value; and comparing the predicted value with a product quality target value, and determining whether the operation parameters of the production process need to be adjusted or not. According to the method, the problems of insufficient multi-dimensional detection, lack of real-time monitoring and modeling method lag in traditional chemical product quality control are solved, and multi-dimensional data fusion, real-time updating of a dynamic model and closed-loop optimization control of a production process are realized.

The invention discloses a process management and control method and system for a battery module, and belongs to the technical field of battery modules. In each process node, a corresponding assembly stage is determined according to analysis of process information, and the use progress of associated materials is determined according to the assembly stage and a manufacturing process database; therefore, the assembly process table of the battery module is updated, so that the process management and control effect of the battery module is improved. Meanwhile, determining an abnormal process part based on anomaly detection of the process dynamic graph of the battery module; and determining a corresponding abnormal process node according to the identification of the abnormal process part, and determining a process jump event of the battery module based on an abnormal event of the abnormal process node and the process execution content corresponding to the battery module so as to trigger a process optimization measure corresponding to the process jump event. The recognition accuracy of the process jump event is improved, the accuracy of the process optimization measure is realized, and the process management and control effect of the battery module is further improved.

The invention relates to the technical field of digital twinning, in particular to a construction process digital twinning online deduction method and device.The method comprises the steps that a structured knowledge base covering safety risks, potential quality hazards and a process dynamic interaction mechanism is constructed, and a core knowledge basis is provided for generation of scheduling responses; aiming at the uniqueness of construction process scheduling, a reinforcement learning network special for dynamic scheduling of the construction process is designed, and intelligent and near-real-time construction scheduling decision is realized; aiming at the problem that a reinforcement learning model is difficult to effectively learn and respond to sudden construction disturbance, an adaptive model training method is researched, so that the model can learn how to dynamically adjust a process network when disturbance occurs, and the influence caused by process adjustment is reduced as much as possible while an engineering construction target is ensured. Therefore, the problems that most of dynamic change elements of the construction site considered in the prior art are resource change, weather change and the like, and response to events such as safety problems or potential quality hazards of the construction site is not achieved are solved.

The invention discloses a BIM-based building design process dynamic management method, which belongs to the technical field of building design process management, and comprises the following steps: collecting basic information related to current project process dynamic management by using a dynamic collection module, receiving the basic information by using a calculation and evaluation module, and calculating and outputting a design efficiency evaluation value SL and a quality control parameter K according to the basic information; the design quality adjustment value ZT and the design cost optimization value CBY are calculated and output according to the calculation result, the trend analysis module draws and analyzes the trend change of the design quality adjustment value ZT, the visualization module displays the drawing and analysis result and makes a decision, and the calculation evaluation module comprises a current design process efficiency evaluation unit. According to the method, data integration and sharing, quantitative evaluation and optimization, real-time feedback of quality control parameters, collaboration of cost and time optimization and visual monitoring and decision support are realized.

The invention is suitable for the technical field of commercial vehicle driving control, and provides a commercial vehicle acceleration process dynamic response consistency control method, which comprises the following steps: setting expected dynamic response characteristics by designing a reference model, and estimating slow time-varying parameters such as vehicle mass and equivalent resistance coefficient by using an extended Kalman filter; the static gain difference is eliminated through feedforward compensation; meanwhile, a model with an L1 filter is adopted for reference adaptive control to ensure closed-loop dynamic response, and a disturbance observer is used for compensating the influence of transient gradient and wind resistance disturbance. According to the method, the acceleration dynamic response in the acceleration process can be kept consistent under the condition that the mass, the gradient and the wind resistance of the whole vehicle change, and therefore the driving quality is remarkably improved.

The invention discloses a molded case circuit breaker on-off process dynamic simulation method based on a heterogeneous simulation system, and relates to the technical field of low-voltage electric appliance design and simulation. Loop current calculated by the first simulation subsystem is transmitted to the second simulation subsystem in real time and used for calculating electromagnetic force for driving a mechanism to move, and contact displacement generated by mechanism movement is immediately fed back to the first subsystem and used for dynamically updating resistance characteristics of an arc model, so that calculation of arc voltage is affected, and the simulation effect is achieved. The arc voltage is directly fed back to the circuit model to determine the current value at the next moment, so that a simulation chain for complete dynamic interaction of the circuit current, the electromagnetic force, the mechanical displacement, the arc resistance, the arc voltage and the circuit current is formed; therefore, the simulation model can reflect the dynamic evolution process of each physical quantity in the real on-off process in a self-consistent manner.

The application is suitable for the technical field of vehicle acceleration control, and provides a commercial vehicle acceleration closed-loop control method considering load and running resistance, which comprises the following steps: step 1, driving system dynamics modeling; step 2, driving process dynamic response transfer function modeling; step 3, acceleration dynamic response control parameter determination; and step 4, acceleration adaptive closed-loop control. The method derives the dynamic response transfer function of the driving process by constructing the driving system dynamics model; after the key parameters most sensitive to the acceleration dynamic response are determined through analysis, the method of combining feedforward control and closed-loop feedback adaptive control is adopted to realize the closed-loop adaptive control of the acceleration dynamic response. When the vehicle mass and the running resistance change, the method can effectively guarantee the consistency of the acceleration dynamic response, and further improve the driving quality.

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.

A computer-implemented method, computer program product, and computer system for automated model predictive control. The computer system trains multiple step look-ahead regression models, using historical states and historical actions for a to-be-optimized system, for each timestep of a past time horizon. Regression models may be either linear or nonlinear in order to capture process dynamics and nonlinearity. The computer system generates optimization constraints for each timestep of a future time horizon. The computer system generates optimization variables, based on the multiple step look-ahead regression models, for each timestep of the future time horizon. The computer system constructs a mixed integer linear programming based optimization model that includes an objective function, the optimization constraints, and the optimization variables. Nonlinear regression models are converted into piecewise linear approximation functions. The computer system solves the optimization model to produce actions for the to-be-optimized system, over the future time horizon, and recommend commitment-look-ahead actions.

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.

1. The name of the design product: missile simulation launching platform. 2. The use of the design product: the design product is used for missile launching whole process dynamic simulation and state simulation, overall function demonstration, carrying unmanned sensing suite, and realizing automatic driving of missile launching vehicle in cooperation with wire control chassis. 3. The design points of the design product: in shape. 4. The picture or photo best indicating the design points: perspective view 1.

The present application relates to the technical field of industrial process dynamic characteristic monitoring, and discloses a kind of injection molding process real-time monitoring method based on multi-sensor data fusion, comprising: firstly, the dynamic response characteristics of drive source itself are identified and compensated to obtain corrected process output signal;Further, process input signal and the corrected process output signal are cooperatively processed to generate process gain and time constant capable of overall representing process dynamic transfer characteristics, and process stability is judged accordingly, the present application separates the disturbance source of drive system performance fluctuation from the monitoring signal, realizes the direct quantitative characterization of the dynamic transfer process connecting input and output itself, converts a complex industrial process stability problem into the monitoring of process gain and time constant two-dimensional coordinate point, and improves the reliability of monitoring.

The invention discloses a dynamic simulation method for the ultrasonic welding process of multiple layers of thin plates. According to the method, simulation model combinations of different dimensions are provided, so that the problem of simulation of the mechanical property non-uniformity of the welded joint is effectively solved. The entity unit detailed model can finely present the microstructure characteristics of the welding joint, including the interface reaction area and the welding seam fusion state of each layer of plate, and the shell unit simplified model greatly improves the calculation efficiency on the premise of ensuring the engineering precision; the part impact test simulation model further combines the material failure behavior with the actual working condition. The three models support one another through a unified material failure setting system, the performance difference between a welding seam area and base metal is captured, the dynamic response rule under the impact load is reflected, and the simulation result can truly reproduce the mechanical behavior of a welding joint in the complex stress state.

The present invention provides a method for managing a crystallization process in a process control plant. The method includes capturing process parameters of an operating reactor unit (102) in the process control plant (100). The method includes predicting desired process parameters based on a first set of parameters and the captured process parameters. The first set of parameters includes information related to process dynamics and process disturbances associated with the operating reactor unit (102). Furthermore, the method includes controlling a process control loop associated with the operating reactor unit (102) based on the desired process parameters and the first set of parameters.

The invention belongs to the technical field of air separation plant intelligent control, and discloses an air separation plant intelligent variable load control method and system based on mechanism-data fusion deep learning. Comprising the steps of constructing a space division process dynamic model; training and generating a control sequence based on the operation strategy and the physical constraint; the control sequence is applied to the space division process dynamic model, and a model prediction value is generated; acquiring operation data of an actual system, comparing the operation data with a model predicted value, calculating deviation, importing historical data to carry out feedback correction and rolling optimization on the space division process dynamic model, and continuously correcting the space division process dynamic model until the deviation reaches a set threshold range; and inputting the corrected space division process dynamic model and historical data into a neural network model, training to obtain a mechanism-data fusion learning model, and connecting a control system through a data stream. The load of the air separation equipment is controlled based on a mechanism-data fusion learning model, and the problems of more energy waste and unstable product quality of variable load operation can be solved.

The invention discloses a supervision system and method for industrial oleic acid production, and the system comprises a raw material reaction regulation and control module, a real-time parameter collection module, a phase equilibrium analysis module, a data intelligent reasoning module, a cloud centralized management module and a process dynamic adjustment module which are connected through an industrial data transmission protocol, an encrypted data channel and the like to form a complete closed-loop link. The raw material reaction regulation and control module sets reaction initial parameters, the real-time parameter acquisition module continuously captures data such as temperature and oleic acid concentration in a reaction kettle, the phase equilibrium analysis module combines a thermodynamic model to calculate and obtain phase component parameters, and the data intelligent reasoning module complements the parameters and deduces an abnormal trend based on an omission-Bayesian reasoning model. The cloud centralized management module synchronizes data with a related platform, and the process dynamic adjustment module performs stepped correction on the stirring rate and the like. The system realizes coherent operation of parameter acquisition, analysis, reasoning, management and adjustment through cooperation of all the modules, and improves the stability of oleic acid production and the product quality.

The embodiment of the specification provides a rail transit modeling process exception handling method and system based on multi-source data fusion, wherein the method comprises the following steps: collecting basic data of a rail transit line, wherein the basic data comprises but is not limited to line plane trend data, longitudinal section elevation data and track structure parameter data; initializing an initial framework of a single-side model of rail transit in a three-dimensional modeling environment according to the collected basic data, and determining a starting point, a direction and basic dimensions of the model; constructing various features of the single-side model on the initial framework according to design specifications and actual requirements of rail transit, performing refinement processing on the constructed single-side model, and adding materials, textures and detail features; and comparing and verifying the generated single-side model with a single-side condition of an actual rail transit line, and modifying and optimizing the model. Through the establishment of a whole-process dynamic feedback mechanism, the scheme forms a closed-loop control in three aspects of data fusion, model optimization and exception repair, and the accuracy of modeling is improved.

The invention discloses an RBFNN disturbance adaptive compensation control method for a multi-stage intermittent process, and the method comprises the following steps: S1, building a stage state updating model containing a lumped disturbance term according to the characteristics that the dynamic state of the multi-stage intermittent process has nonlinearity and inter-stage mechanism difference; s2, constructing a radial basis function neural network disturbance estimator taking a system state and control input as network input based on the lumped disturbance term, and calculating a disturbance estimation value; s3, constructing a composite control strategy fusing disturbance compensation and batch iterative learning based on the disturbance estimation value; convergence of errors along the batch direction is realized; according to the invention, disturbance estimation and multi-step prediction are realized; a physical information fusion loss function and a self-adaptive updating mechanism are introduced, so that the physical rationality and robustness of estimation are enhanced; and in combination with batch iterative learning and a stage switching smoothing strategy, convergence of errors along batches and smooth transition of the whole process are realized, and the control precision and stability of the multi-stage intermittent process are improved.

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 invention provides a metallurgy full-process dynamic collaborative optimization method and system based on digital twinning, which can realize metallurgy full-process, multi-dimensional and dynamic collaborative closed-loop optimization. The method specifically aims to construct a digital twinborn base capable of reflecting the complex state of the whole metallurgical process in real time, accurate prediction and diagnosis of key indexes and potential problems in the metallurgical process are achieved based on digital twinborn and artificial intelligence technologies, and collaborative optimization decisions of time dimensions (scheduling) and space dimensions (process parameters) based on the state of the whole process are achieved. The feasibility and effect of an optimization scheme are verified through virtual simulation, the trial and error cost is reduced, a closed-loop control mechanism from the virtual world to the physical world is established, online self-learning and self-adaption of the model are achieved, and finally the metallurgical production efficiency is improved, the energy consumption is optimized, the product quality is guaranteed and improved, the operation and maintenance cost is reduced, and the flexible production capacity is enhanced.

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.