121 results about "Predictive simulation" patented technology

The invention provides an intelligent rehabilitation training method and system based on artificial intelligence and virtual reality, a user wears an intelligent wearable device to collect multi-modal data such as electroencephalogram, myoelectricity, physiological features and motion signals, the multi-modal data is preprocessed and then input into an artificial intelligence training model, and key features are extracted and fused by using a graph convolutional network and an attention mechanism algorithm. And constructing a digital twinborn model by using the fusion features, performing real-time dynamic mapping and predictive simulation, and generating a customized training scheme by means of a reinforcement learning algorithm in combination with a rehabilitation target and a physical state of the user. A user is trained in the virtual reality interaction model, the system monitors actions and physiological states in real time, the digital twin model synchronously acts, and the scene is dynamically adjusted. After training, the rehabilitation effect is evaluated according to the physiological indexes, the motion data and the twinning optimization analysis result, and an optimization training scheme and a digital twinning model are fed back. Precision, individuation and intelligentization of rehabilitation training are achieved, and the training effect and quality are improved.

The invention discloses a BIM-based traffic engineering construction progress and resource management optimization system, and the system comprises a three-dimensional cognitive interaction body which adjusts the construction progress and resource configuration in real time, and eliminates the physical difference between a virtual model and an actual environment; the self-adaptive calculation engine is used for realizing butt joint between the virtual model and an actual construction site by utilizing real-time sensing data and a cognitive correction mechanism; the progress prediction simulation module is used for realizing dynamic prediction and optimization of the construction progress through deep learning and a self-adaptive feedback mechanism; the virtual-reality synchronous perception grid is constructed based on the obtained progress dynamic prediction and optimization result, and a virtual model and a construction site are synchronized and dynamically corrected; and the self-optimization module is used for realizing self-adaptive optimization and long-period evolution through a genetic algorithm and multi-objective optimization by utilizing a self-optimization and ecological optimization mechanism. According to the method, the problem of virtual reality distortion is solved, and the difference between virtuality and reality in the construction process is accurately corrected.

The invention relates to the technical field of port planning and construction, and particularly discloses a port planning and construction environment feasibility assessment method and system, and the method comprises the following steps: S1, obtaining planning parameters, real-time environment monitoring data, a historical ecological data set and social economic data of a target port; s2, constructing a dynamic coupling model of environmental bearing-ecological disturbance-pollution diffusion-social economic influence; and S3, based on the dynamic coupling model, adopting multi-scene space-time prediction simulation, and outputting an environmental feasibility comprehensive index, a key constraint factor and a social economic influence evaluation result. A dynamic coupling model of environment bearing, ecological disturbance, pollution diffusion and social economic influence is constructed, the four sub-models interact in real time through bidirectional data flow, full-dimension dynamic coupling evaluation of natural environment, ecological system, pollutant migration and social economic factors is achieved, and compared with traditional single-factor evaluation, the comprehensive evaluation accuracy is improved.

The invention discloses a cable sheath ring current fault positioning in-loop simulation method and system based on LABVIEW. The method comprises the following steps: collecting ring current data of cable sheath monitoring points in real time under the control of LABVIEW, and carrying out digital display and graphical display; decomposing circulation data to obtain a wavelet coefficient and calculating features, and inputting a classification model to obtain a fault type when the features are abnormal; calculating a compensation coefficient considering the influence of the electromagnetic field, and calculating the position of a fault point in combination with a double-end traveling wave method; the operation model switches a typical working condition simulation mode, prediction of a Bayesian reasoning correction model is carried out, and the full-life-cycle operation state of the cable is simulated; and performing fault early warning through the LSTM model and risk assessment. The method can effectively improve the cable operation and maintenance efficiency and reliability, and is widely applied to cable fault detection and guarantee of safe and stable operation of a power system.

The invention relates to the technical field of forest fire prediction and simulation, in particular to a forest fire spreading prediction simulation system under complex mountain terrain conditions, which comprises a data acquisition module, a data preprocessing module, a terrain analysis generation module, a vegetation analysis module, a meteorological analysis module, a fire spreading simulation module and a result display module. According to the forest fire spreading prediction simulation system under the complex mountainous terrain condition, the influence of multiple factors such as the complex mountainous terrain, vegetation and weather on forest fire spreading is comprehensively considered, the prediction accuracy and reliability are improved, a user can visually know the forest fire spreading condition through visual result display, and the forest fire spreading prediction simulation system has the advantages of being convenient to use and high in practicability. According to the invention, scientific and reasonable fire prevention and extinguishing strategies can be formulated conveniently, latest data can be acquired and processed in time, powerful support is provided for monitoring and early warning of forest fire, scientific decision basis can be provided for forest management departments, fire departments and the like, and improvement of the prevention and control capability of forest fire is facilitated.

The invention relates to the technical field of power grid fault positioning, in particular to a rapid power grid fault positioning system based on digital twinning and AI, which obtains multi-dimensional operation data of each node of a power distribution network, constructs a structured data set, reflects the state of the system, and performs joint modeling on node features and a topological structure through a graph neural network. And reasoning to generate candidate fault nodes. And further constructing a digital twinborn model containing load flow calculation and disturbance injection, performing analogue simulation on candidate nodes, extracting an electrical quantity change rate, performing fuzzy similarity matching with operation data in a fault window, and accurately positioning a fault node. Through cooperation of three stages of prediction, simulation and matching, the problems of weak complex topology perception, poor perturbation identification, positioning offset and the like of the existing method are effectively solved, and the fault positioning precision and robustness are improved.

The invention provides an urban water network drainage joint scheduling method and system, and relates to the technical field of urban flood control and drainage, and the method comprises the steps: carrying out the fusion processing of multi-source monitoring data, and obtaining a feature vector; inputting the feature vector into a runoff prediction model for runoff prediction to obtain a comprehensive runoff prediction result; based on the comprehensive runoff prediction result, pipe network water flow and surface accumulated water simulation is conducted through a drainage hydraulic model, and a target simulation result is obtained; performing waterlogging risk assessment based on the target simulation result and the monitoring data to obtain a waterlogging risk level; selecting a corresponding scheduling generation mode based on the weather forecast data, the monitoring data and the waterlogging risk level; and determining a corresponding scheduling strategy based on the selected scheduling generation mode and the waterlogging risk level. According to the urban water network drainage joint scheduling method and system provided by the invention, accurate prediction simulation and scientific scheduling are realized, and the drainage efficiency is further improved.

Hardware and software system solutions provide real-time, interactive predictive simulation of an eye (e.g., a human eye or animal eye). A predictive simulation of an individual eye may be created using finite element models (FEM) of ocular structures involved in optical biomechanics, including ocular adjustments. Unlike previous ocular models, the present FEM described herein is a three-dimensional FEM of an adjustment mechanism, including but not limited to the anterior of the human eye that simulates ciliary muscle fiber contraction actions, to predict the dynamic deformation of the crystalline lens required to adjust visual power variations, referred to herein as' Central Visual Power '(COP). The present FEM further describes aqueous / vitreous outflow and fluid flow mechanisms referred to herein as "hydrodynamics". The virtual FEM further describes posterior ocular biomechanics that simulates the actions of the Bruch membrane, retina, vitreous and sieve plates during pre-stretching, non-regulatory and regulatory actions of the eye.

The invention relates to the field of underground water pollution monitoring, in particular to a dynamic intelligent monitoring method and system for underground water pollution. The method comprises the following steps: constructing and updating an underground water solute transport model; generating a comprehensive pollution feature vector by using the obtained multi-source data which is related to the groundwater pollution and comprises a plurality of pollution indexes; respectively constructing a pollution source positioning model and a pollution diffusion model; triggering a pollution alarm according to the comprehensive pollution feature vector; according to the multi-source data and the current hydrogeological data, utilizing the pollution source positioning model to obtain a pollution source position; respectively utilizing the pollution diffusion model and the groundwater solute transport model to carry out prediction simulation to obtain first pollution distribution changing along with time and second pollution distribution changing along with time; and carrying out weighted fusion on the first pollution distribution and the second pollution distribution to generate predicted pollution distribution changing along with time. The problem that underground water pollution monitoring is not timely and accurate enough in the prior art is solved.

The invention discloses a cultural relic three-dimensional dynamic scene display system based on a virtual-real fusion technology, and relates to the technical field of mixed reality, and the system comprises a data collection module which obtains original signals of a to-be-displayed cultural relic and a surrounding environment, and generates an environment feature vector and an environment fusion index; the twinborn simulation module is used for generating health state feature vectors of the cultural relics in combination with finite element grids and carrying out prediction simulation on the deterioration state of cultural relic materials; the model creating module is used for creating a three-tense model resource package and generating time axis metadata by adopting a vertex interpolation and mapping mixing technology; the story layout module is used for constructing an environment event mapping rule base, obtaining a ranked trigger event list through priority ranking, and generating a scene script sequence; according to the method, the health state and the degradation process of the cultural relics under different environmental conditions can be dynamically reflected, and three-dimensional scene display changing along with time evolution is provided.

The invention discloses a method for simulating and predicting ablation of silicon carbide by femtosecond laser based on a dual-temperature model, and relates to the technical field of femtosecond laser processing. According to the method, the local carrier density of a silicon carbide material is corrected based on a Fokker-Plank equation; an improved dual-temperature model suitable for interaction of femtosecond laser and silicon carbide is constructed by combining a heat source model of the femtosecond laser; and then, after spatial discretization and time discretization in the depth direction are carried out on the material spatial three-dimensional model, a finite difference method is adopted to calculate the improved dual-temperature model to obtain a physical quantity evolution process in each grid unit, so that simulation prediction is carried out on the interaction process of femtosecond laser and silicon carbide under different laser parameters. The simulation prediction result can reflect the removal mechanism of thermally modified and non-thermally modified materials in the process of processing the silicon carbide by the femtosecond laser, and is favorable for guiding the practical process of processing the silicon carbide by the femtosecond laser, so that the processing quality and efficiency of the silicon carbide by the femtosecond laser can be improved.

The invention discloses a public facility reachability evaluation method and system based on geographic multiple agents, and belongs to the field of urban calculation and geographic information. According to the method, a multi-agent system comprising a user agent and a facility agent is firstly constructed, and then a dynamic utility function combined with an evolutionary game theory is introduced to simulate the dynamic decision and congestion avoidance behavior of a user; and user strategies are balanced through multiple rounds of iterative evolution, so that a macroscopic reachability pattern emerging from a microcosmic game is obtained. According to the method, starting from the microscopic individual level, by simulating the dynamic game process of a large number of agents, the defects of a static calculation model are overcome, and dynamic evaluation and predictive simulation of reachability of public facilities are achieved. Compared with the prior art, the evaluation result is more accurate, dynamic feedback of the real world can be reflected, and a scientific decision support tool is provided for urban planning.

The invention relates to the technical field of slope instability data analysis, and discloses a toppling deformation body forming process and fracture development mode analysis method and system.The method comprises the steps that a toppling deformation physical model is constructed according to geographic data, and deformation characteristics of an anti-tilting rock slope under different working conditions are simulated; building a rock stratum fracture development prediction model based on the dumping deformation physical model, inputting a simulation result into the rock stratum fracture development prediction model, and predicting rock stratum dumping deformation bodies and fracture development under different climate conditions; analyzing the prediction result to obtain a fracture development mode; carrying out stability partitioning on the toppling deformation body based on a fracture development mode, and evaluating a stability coefficient; and evaluating the stability coefficient and the real-time monitoring data, and adjusting the rock stratum fracture development prediction model according to an evaluation result. The system comprises a rock stratum data collection module, a prediction simulation module and a prediction result analysis module. According to the invention, the precision of evaluating the stability of the counter-tilting rock slope is improved.

The invention discloses a method for performing extreme precipitation simulation by using a CNN-LSTM model based on multi-source data fusion. The method comprises the following steps: firstly, acquiring multi-source data of a research area, calculating the extreme precipitation of the area, and screening the most important key atmospheric circulation index influencing the extreme precipitation of a target area by using an extreme gradient lifting model; performing unified preprocessing on the multi-source data; then constructing a space-time prediction model fusing a convolutional neural network and a long-short-term memory network, and generating a deep learning prediction model through multi-feature joint training; and carrying out space-time prediction on the extreme precipitation by utilizing the preprocessed multi-source data and a deep learning model. According to the method, the extreme rainfall data, the atmospheric circulation index and the terrain DEM data are fused in a unified input framework, so that the model can perform joint prediction on rainfall by simultaneously utilizing the space-time rainfall distribution characteristics, the atmospheric circulation mode and the terrain information, and the prediction and simulation precision is high.

The invention discloses an ozone pollution gridding traceability method. The method comprises the following steps: (1) acquiring a localized basic information data set and carrying out preprocessing; (2) carrying out meteorological field forecasting simulation by utilizing a numerical weather mode WRF and carrying out atmospheric pollutant concentration field forecasting simulation by utilizing an air quality mode CMAQ to obtain a high-resolution atmospheric pollutant spatial and temporal distribution data set; (3) forecasting result evaluation is carried out on a result obtained in the step (2) by comparing hourly ambient air quality monitoring data with meteorological observation network data; (4) constructing a cost model for performing reverse simulation on the adjoint mode to obtain adjoint sensitivity, namely emission source intensity; (5) verifying the adjoint sensitivity and calculating the contribution rate to obtain an emission source causing ozone pollution and a time-space distribution diagram of the contribution rate of the emission source; according to the method, the specific contribution rate of different emission source types of different grids in different time periods to ozone pollution can be obtained in a one-time reverse simulation calculation process.

The invention discloses a tumor radiotherapy prognosis prediction simulation method and system based on big data and a medium, and relates to the technical field of tumor radiotherapy prognosis, and the method comprises the following steps: collecting basic physiological information of a tumor patient, and collecting a color image and a temperature image of skin of a radiotherapy part of the tumor patient; skin color feature extraction and skin temperature feature extraction are carried out to obtain skin color feature data and skin temperature feature data; a radiodermatitis prediction model is constructed; performing prediction simulation on the skin of the radiotherapy part of the tumor patient based on the radiodermatitis prediction model, and performing prediction simulation correction to obtain a radiodermatitis prediction result of the tumor patient; the method is used for solving the problem that when an existing tumor radiotherapy prognosis technology is used for predicting and simulating disease development, the risk and time of radiodermatitis of a tumor patient cannot be objectively and accurately predicted and simulated according to physiological characteristics of skin of the tumor patient after radiotherapy.

The invention relates to the technical field of production decision management, and discloses a factory production decision management method and system based on an MES (Manufacturing Execution System), and the method comprises the steps: obtaining monitoring data through a sensing module, carrying out the analysis and classification, generating a production monitoring information flow, calling a production decision record, and carrying out the multi-dimensional analysis with an overall production target, so as to carry out the prediction simulation; the method comprises the steps of obtaining a reference decision chain, carrying out preliminary material scheduling and task allocation, analyzing decision computing power requirements, identifying idle computing power time periods, positioning abnormal feedback information, carrying out bidirectional equilibrium analysis, generating an expansion decision chain, constructing an expected decision tree and optimizing production scheduling and task allocation. According to the invention, resource allocation is optimized, the capability of dealing with abnormities of factories is improved, intelligent production management is promoted, and the problem of low efficiency of production decision making in the prior art is solved.

The embodiment of the invention provides a pollution plume grading method, system and equipment and a storage medium, and belongs to the technical field of underground water pollution intelligent management. The pollution plume grading method comprises the following steps: acquiring point pollution data; dividing a plurality of independent pollution plumes based on the point pollution data; carrying out pollution migration simulation on the pollution plume to obtain a corresponding simulated pollution plume; and predicting the duration when the factory boundary area concentration of the simulated pollution plume is greater than the preset concentration to obtain a duration prediction result, and grading the pollution plume based on the duration prediction result. According to the embodiment of the invention, the change of the pollution plume along with time is considered, migration simulation is performed on the divided pollution plume, the time when the concentration of the factory boundary region exceeds the standard is predicted, and the future pollution trend of the pollution plume is simulated and predicted, so that the pollution plume is divided, the accuracy and precision of pollution level division are improved, and the user experience is improved. The key area of enterprise pollution management is effectively reduced, and the enterprise pollution management efficiency is improved.

A computer-implemented method of predicting, simulating, or forecasting values of one or more specified subject-related attributes during a clinical trial comprises: receiving input data comprising: a medical history of a subject, the medical history comprising values of a plurality of subject-related attributes of a subject; and data specifying a requested output, the data comprising: the one or more specified subject-related attributes of the subject and a time frame; and applying a trained generative machine-learning model to the received input data, the trained generative machine-learning model configured to generate output data based on the input data, the output data comprising: respective values of the one or more specified subject-related attributes of the subject in the specified time frame.

To propose a mechanism for predicting demographic information in a specific time zone in a predetermined region based on demographic information of the predetermined region under a prediction condition.SOLUTION: An information processing system 1000 performs predictive analysis on demographic information, based on predetermined demographic information including information on the number of people located in each of a plurality of predetermined region meshes included in a region to be analyzed, in a chronological order. A demographic estimation unit 2111 estimates data on people (including the number of people and attributes, such as gender or ages) for each second region mesh unit (estimation region mesh) which is smaller than a first region mesh unit, on the basis of the predetermined demographic information including information on the number of people located in a plurality of first region mesh units included in the region to be analyzed in "predetermined time (length of time or time zone)" or "location information" set as a prediction condition.SELECTED DRAWING: Figure 12

The invention relates to the technical field of parameter control, in particular to a flange machining self-adaptive cutting parameter control system based on edge computing, in the flange machining self-adaptive cutting parameter control system, a modeling module deploys a digital twin model at an edge node; the information acquisition module synchronously acquires theoretical cutting allowance as feed-forward information and real-time machining state as feedback information; a feedforward optimization unit in the adaptive optimization control module performs predictive simulation to generate a first cutting parameter instruction, and a feedback optimization unit performs real-time state simulation to generate a second cutting parameter instruction; finally, the control instruction execution module dynamically fuses the two types of instructions; all the module units cooperate to achieve organic combination of feedforward global optimization and feedback real-time compensation, the problem that machining parameters are mismatched with dynamic working conditions is effectively solved, and the precision stability, efficiency and reliability of flange machining are improved.

The invention discloses a method and system for optimizing an artificial habitat cold water supply control strategy, and relates to the technical field of coral cultivation. According to the scheme, through environment data, a temperature prediction model, a preset optimization algorithm and iterative optimization of a cold water supply control strategy, advanced prediction, simulation and closed-loop optimization control of the seawater temperature of an artificial habitat coral culture area in an open sea area are achieved. While the prevention accuracy and intervention efficiency of the coral whitening heat stress are improved, maximum energy conservation and consumption reduction are realized, and the optimal growth temperature of the coral is intelligently maintained.

The invention discloses a BIM-based hoisting construction risk data intelligent processing system, and the system comprises an Internet of Things sensing layer which carries out the sensing through the deployment of a multi-mode sensor network; the BIM-IoT data fusion layer is used for directly embedding sensor time sequence data into a BIM component attribute set by using an IFC + + spatio-temporal extension framework, and dynamically updating the sensor time sequence data through a native data pipeline; the spatio-temporal feature extraction layer is used for fusing and enhancing BIM topological features and IoT time sequence features by adopting an ST-Encoder network architecture through double-flow feature extraction and a cross-modal attention fusion mechanism in combination with prior information; the hybrid reasoning decision-making layer is used for fusing a PINN-Transformer coupling model and an FEM-Neural joint calculation mechanism, and predicting stress through physical constraint enhancement, nonlinear local modeling and an alternate execution strategy; and the digital twinning execution layer is used for real-time rendering, historical playback, prediction simulation, component-level information superposition and virtual security fencing of the construction process.

The invention relates to the technical field of finite element analysis, in particular to a Hall thruster discharge chamber structure simulation optimization method based on finite element analysis, and the method comprises the steps: setting a plurality of working parameters for a Hall thruster, and carrying out the finite element analysis to obtain a simulation result of each grid; in the simulation results obtained by the grids at the same position under different working parameters, taking the simulation results with the same simulation error distribution as a simulation result sub-sequence; respectively predicting all the simulation result subsequences by utilizing a prediction algorithm to obtain candidate simulation results, and fusing all the candidate simulation results to obtain a predicted simulation result; and dividing an abnormal region according to the prediction simulation result of the grids where all the positions are located, encrypting the grids in the abnormal region, and restoring the grids outside the abnormal region to a preset density. By dynamically updating the density of the grids, the accuracy of a finite element analysis result is ensured while the calculated amount is saved.

The invention discloses a multi-service scene-oriented data query analysis method and system, belongs to the technical field of big data analysis and intelligent decision making, and aims to solve the technical problem of how to realize automatic query, intelligent analysis and visual presentation of data under multi-service scenes. Comprising the steps of performing intention recognition and service classification on a user request based on an agent-driven natural language analysis and classification model, and generating a query instruction, a data analysis strategy and a visual suggestion; executing the query instruction, and analyzing a query result in real time; and rendering an interactive data chart based on the returned analysis result and the visual suggestion, and supporting the user to carry out drilling, screening and prediction simulation operations based on the data chart.

A system for AI-powered employee engagement with real-time integration into enterprise applications; the system includes: a signal acquisition module configured to capture structured and unstructured employee-related data from enterprise platforms, including human resources information systems, communication platforms, and project management tools; a preprocessing unit configured to anonymize, normalize and embed the ingested data by applying field-level encryption, irreversible hashing of identifiers and transformer-based vectorization of text inputs into high-dimensional embeddings combined with standardized tabular metrics; an inference engine comprising a transformer-based sentiment model, a graph neural network model, and an attention-based temporal predictor, wherein the inference engine is configured to compute a dynamic engagement score and provide explainable attributions for engagement fluctuations; an action recommendation engine configured to map the engagement score and attribution vectors to a library of intervention policies, the action recommendation engine further comprising a reinforcement learning module configured to refine intervention strategies based on closed-loop feedback; a data protection controller configured with federated learning aggregation, differential privacy noise injection, and consent management services to ensure compliance with legal frameworks while enabling distributed model updates; and a visualization dashboard that offers role-aware organizational analyses, drill-downs to root causes, and predictive simulations for managers.

The purpose of the present invention is to provide a yield prediction simulation system and method that can simulate yield prediction by predicting a yield during a second cycle and reflecting a label variation amount on the basis of process operation data during a first cycle. Moreover, the invention also provides a yield prediction simulation system and a yield prediction simulation method, which can divide the first period into a plurality of sections based on the service life of the catalyst and the corresponding yield change, and perform key factor analysis, yield prediction and label variation analysis according to the different sections, thereby improving the yield prediction simulation efficiency. Yield prediction simulation is carried out in the second period, and the prediction accuracy is further improved in the whole period.

In a method for intelligently executing predictive simulator, a processor may input a previous input vector of conditions for a predictive simulator collected at a first time into a machine-learning (ML) model. A processor may input a current input vector of conditions for the predictive simulator collected at a second time into the ML model. A processor may determine using the ML model, a binary similarity index. The binary similarity index represents a prediction of similarity between a first output from the predictive simulator based on the previous input and a second output from the predictive simulator based on the current input.

The invention relates to the technical field of park asset management, in particular to a park asset operation management system, which comprises a unified asset sensing module for acquiring a real-time operation state, a real-time economic factor and a real-time environment factor of assets; the multi-dimensional digital twin engine is used for calculating the real-time economic dimension and the real-time ESG dimension of the assets in real time based on the data; the predictive simulation module deduces the comprehensive quantitative influence of an operation strategy on economy, ESG and service quality; and the autonomous arrangement engine autonomously selects an optimal strategy and generates a control instruction according to the deduction result and a preset decision rule. The multi-dimensional twin body capable of calculating economic and ESG cost is constructed, and simulation and autonomous arrangement are combined, so that the change of asset operation from passive response to active predictive optimization is realized; and automatic cooperative balance can be achieved among conflicting targets of cost, ESG and service quality.

This invention discloses a method for simulating extreme precipitation using a CNN-LSTM model based on multi-source data fusion. First, multi-source data of the study area are acquired, and the regional extreme precipitation is calculated. An extreme gradient boosting model is used to select the key atmospheric circulation indices that have the most significant impact on extreme precipitation in the target area. The multi-source data undergoes unified preprocessing. Then, a spatiotemporal prediction model fusing convolutional neural networks and long short-term memory networks is constructed, and a deep learning prediction model is generated through joint training of multiple features. Finally, the preprocessed multi-source data and the deep learning model are used to perform spatiotemporal prediction of extreme precipitation. This invention integrates extreme precipitation data with atmospheric circulation indices and topographic DEM data into a unified input framework, enabling the model to simultaneously utilize spatiotemporal precipitation distribution characteristics, atmospheric circulation patterns, and topographic information to jointly predict precipitation, resulting in high prediction and simulation accuracy.