60 results about "Process dynamics" patented technology

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 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 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.

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 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 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.

The invention discloses a method and system for automatically achieving dynamic adjustment of a cold rolling process based on steelmaking components and a medium. The method comprises the following steps that a dynamic adjustment table of the cold rolling process is established; comparing a steelmaking smelting component test result of the material with data in the cold rolling process dynamic adjustment table to obtain adjusted annealing and leveling process adjustment values; and the adjusted annealing and leveling process numerical values and the random group production plan are issued to a production execution system, and automatic dynamic adjustment of the cold rolling annealing and leveling process along with steelmaking components is achieved. When the steelmaking smelting component result deviates from the quality design target value, the process control parameters of the material in the cold rolling annealing and leveling processes are dynamically adjusted, and the dynamic design result is issued to the corresponding production process through the operation plan, so that the quality control problem caused by the fluctuation of the raw material is avoided, and the production efficiency is improved. The problems of standard and actual control are effectively solved, the performance stability of finished products is improved, and the workload of technicians is reduced.

The invention discloses a kinetic parameter prediction method, system and equipment in the mirror image milling process of a thin-walled workpiece, a medium and a product, and relates to the technical field of machining.The method comprises the steps that in the mirror image milling process of the thin-walled workpiece, for the thin-walled workpiece in any milling state, any milling state serves as the current milling state, and the current milling state serves as the current milling state; taking the thin-wall part in any milling state as a to-be-predicted thin-wall part in the current milling state; discretizing the to-be-predicted thin-wall part in the current milling state into a plurality of to-be-measured shell units in the current milling state by adopting a finite element analysis method, and determining the thickness value of each to-be-measured shell unit in the current milling state; and inputting the thickness values of all the to-be-measured shell units in the current milling state into the thin-wall part mirror image milling kinetic parameter prediction model to obtain predicted values of kinetic parameters of the to-be-predicted thin-wall part in the current milling state. According to the invention, the prediction precision and prediction efficiency of the time-varying kinetic parameters are improved.

The application is suitable for the technical field of commercial vehicle driving control, and provides a commercial vehicle acceleration process dynamic response consistency control method, which sets expected dynamic response characteristics through designing a reference model, estimates slow time-varying parameters such as vehicle mass and equivalent resistance coefficient by using an extended Kalman filter, and eliminates static gain difference through feedforward compensation; meanwhile, model reference adaptive control with an L1 filter is adopted to ensure closed-loop dynamic response, and a disturbance observer is used to compensate the influence of transient slope and wind resistance disturbance. The method can keep acceleration dynamic response consistent in the acceleration process under the condition of vehicle mass, slope and wind resistance change, thereby significantly improving driving quality.

The application provides a process dynamic control method, device and system of a dividing wall column and a storage medium, and applies a chemical engineering rectification process. The method comprises the following steps: obtaining first parameter information of the dividing wall column, including a first heat load of a reboiler, a second heat load of a condenser, a temperature difference of a feed section and a temperature difference of a side line production section; performing joint processing on the first parameter information, an adaptive neuro-fuzzy inference system and an MPC model to obtain control information, wherein the control information comprises liquid phase flow allocated to the feed section by a common rectification section, a first production flow of the side line production section and reflux flow of a column top; and controlling the dividing wall column according to the control information. By adopting the joint control of the adaptive neuro-fuzzy inference system and the MPC model and taking the temperature difference as a control point, the control problems in the rectification process, such as difficult real-time measurement of components and strong coupling of variables, are solved, the dynamic control performance is improved, and disturbances of feed flow and feed composition are effectively resisted.

The invention 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, and the method comprises the steps: obtaining production operation data and process mechanism knowledge, and forming a high-quality time series data set through data cleaning and automatic working condition division; on the basis of the data and knowledge, constructing a lightweight dynamic model according to working conditions and equipment, and integrating a whole-process dynamic relation network; receiving an operation instruction, performing quantitative safety evaluation and generating a standardized control sequence; the sequence drives the network to perform layered dynamic simulation, and real-time feedback correction is combined to obtain a future prediction trajectory of the key variable; and finally, converting the trajectory into a system comprehensive energy consumption prediction result according to an equipment-level energy consumption model. The method is used for solving the problem that in the prior art, the energy consumption dynamic change process caused by operation adjustment is difficult to predict in a high-precision mode, and conversion from static result prediction to dynamic process simulation is achieved.

The invention discloses a residual agricultural film migration process dynamic simulation method based on a liquid neural network, and belongs to the technical field of environmental system simulation and prediction. The problem of accurate simulation of the residual agricultural film migration process is solved. The method comprises the following steps: establishing a regional feature data set, and constructing a residual agricultural film migration process simulation framework based on a liquid neural network; embedding the physical crushing process model, the biodegradation process model, the chemical oxidation model and the migration diffusion model in a soft constraint form to obtain a residual agricultural film migration process simulation model; constructing a comprehensive loss function containing data fitting errors and light degradation, biodegradation, chemical oxidation, physical fragmentation and migration diffusion mechanism constraints, and performing dynamic migration process simulation of the residual agricultural film on an output result of the model to obtain multi-medium residual quantity and cross-medium migration flux of the residual agricultural film; recognizing a migration path in combination with cumulative flux ridge extraction and streamline density analysis; the dominant environment factors and the key mechanism parameters are quantitatively identified by using differential gradient sensitivity analysis.

The invention discloses a numerical calculation method for high-frequency induction heating forming of a marine complex bent plate, which comprises the following steps: constructing a plate finite element model according to plate geometry and material parameters, and establishing a heat source model based on heat source parameters; according to the position of the heat source model, grid division is conducted on the plate finite element model, electromagnetic field and force field constraint and thermal boundary conditions are set for the plate finite element model, and technological parameters of sail-shaped plate machining are configured; defining a heat source moving track and a cooling water acting range; simulating a high-frequency induction heating forming process based on a multi-physics field coupling mechanism to obtain plate temperature field spatio-temporal evolution data; extracting forming deformation data in combination with the grid change distribution; and finally, the influence rule of geometric / process parameters on the temperature field and the deformation result is analyzed, and the process parameters are optimized. According to the method, the problem that the simulation precision of the complex bent plate forming process is low is solved, full-process dynamic simulation of heat source moving-cooling-deformation is achieved, and the machining precision and the process design efficiency of the sail-shaped plate are remarkably improved.

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.

The application discloses a kind of dynamic simulation methods of multiple physical processes of sea grass seed drift planting, belong to the field of sea grass seed drift prediction.The method comprises the following steps: a, determine the environmental suitability condition of sea grass seed, and judge whether sea grass seed is alive;B, establish a sea grass seed drift planting model;For the movement process of the sea grass seed that has not yet planted in marine environment is influenced by sea current, turbulent disturbance, and will also settle in the movement process due to its own nature;For the sea grass seed that has planted, because it has integrated into the bottom material, its movement state is not affected by marine environment;C, for the sea grass seed that is judged to be alive, the established sea grass seed drift planting model is used to carry out dynamic simulation prediction of multiple physical processes.The application can improve the dynamic simulation precision of sea grass seed in natural scene by establishing a sea grass seed drift planting model coupled with multiple elements, and integrating the movement characteristics of sea grass seed, environmental survival conditions and bottom material adaptability.

The invention discloses a whole-process dynamic characteristic simulation method and device of a wind turbine generator, a medium and equipment, and the method comprises the steps: constructing each sub-model needed by a whole-process dynamic characteristic simulation model of the wind turbine generator; comprising a wind field effect model, a tower shadow effect model, a fan aerodynamic model, a mechanical model, a circuit model, a variable pitch control system model, an induction generator model and a converter model. The sub-models are integrated to simulate the dynamic response of the wind turbine generator in actual operation, so that the characteristics of the whole process can be accurately simulated and analyzed.

The invention discloses a compound additive production process dynamic scheduling system, which belongs to the technical field of data processing, and comprises an acquisition and evaluation module for acquiring original data in a compound additive generation process in real time and generating a reaction stage evaluation value; the collaborative optimization module is used for receiving the response stage evaluation value and outputting an adjustment instruction set; the quality rehearsal module is used for receiving the adjustment instruction draft and generating a virtual working condition parameter set; the scheduling generation module is used for generating a first process execution sequence and a second process execution sequence according to the virtual working condition parameter set and the adjustment confidence factor; and the feedback module is used for collecting actual quality data produced by the split charging equipment, comparing the actual quality data with the virtual working condition parameter set, calculating a deviation value, generating an optimization instruction based on the deviation value, and feeding back the optimization instruction to the acquisition and evaluation module and the collaborative optimization module. According to the invention, a future production process is simulated in a virtual environment through the quality rehearsal module, and the production stability and the product consistency are enhanced.