104 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 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 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.

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 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.

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.

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.

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.

A technology is provided that can predict the environmental load of a manufacturing process based on the influencing factors of the manufacturing process at the design stage. [Solution] The environmental load prediction device of the present invention stores a prediction formula configured to calculate the environmental load using parameters that represent the characteristics of parts processed in the manufacturing process, and predicts (simulates) the environmental load of the manufacturing process by reading the parameters from process data that describes the design values ​​of the parameters and substituting them into the prediction formula.

The invention relates to the technical field of path planning, in particular to a mobile robot path planning method based on IDCAC, and the method comprises the steps: obtaining a linear velocity, an angular velocity and obstacle distribution through Agent; respectively setting a linear velocity reward value and an angular velocity reward value of the Agent, and inhibiting the deviation degree of the Agent by using the maximum value and the current value of the linear velocity; agent angular velocity deviation is inhibited by using the maximum value and the current value of the angular velocity; setting an external reward value, and establishing a mixed reward mechanism by using the external reward and an internal reward based on the linear velocity and the angular velocity; constructing a collaborative navigation architecture based on auxiliary learning and strategy learning; virtual data are generated by using a DDPM model and comparative learning, cross matching reward prediction is performed on the virtual data to simulate diversified data, and comparative learning features are embedded into strategy learning to serve as auxiliary training. According to the method, the problem of data loss caused by dimensionality reduction of a common dimensionality reduction technology is solved.

The embodiment of the invention provides a radionuclide diffusion path simulation method and device, electronic equipment and a storage medium, and relates to the technical field of nuclide diffusion analysis. The method comprises the following steps: establishing a geometric discrete model of a matrix according to the geometric shape of the matrix where the radionuclide is located and internal pore distribution, and determining the temperature and the nuclide concentration of each discrete point in the geometric discrete model according to the temperature distribution of the matrix, the initial concentration distribution of the radionuclide and the geometric discrete model; and according to the temperature of each matrix discrete point and the nuclide concentration, multiple diffusion simulation is carried out, and a diffusion path of the radionuclide in the matrix is constructed. By introducing key parameters such as matrix temperature distribution, pore structure information and initial concentration distribution, high-precision prediction simulation of a nuclide diffusion path is realized.

This invention discloses a method for assessing and analyzing the impact of key source emissions on population health, including the following steps: (1) establishing a key source emission characteristic database and clarifying the list of key sources; (2) establishing an exposed population characteristic database and generating a spatial distribution map of exposure scenarios; (3) a health risk assessment method to quantify the health risks caused by pollutants to different population groups in different exposure scenarios; and (4) assessment, analysis, prediction, simulation, and decision-making to identify key prevention and control areas of "high pollution emissions + highly sensitive populations," conduct differentiated scenario prediction and simulation, and propose targeted action recommendations. This invention establishes a full-chain correlation analysis method of "key source emissions - environmental concentration - exposed populations - health effects," identifies key prevention and control areas of "high pollution emissions + highly sensitive populations," and realizes quantitative assessment, prediction, and precise prevention and control of health risks from key source emissions, providing a scientific basis for "targeted management" and "protection of sensitive populations" decisions.

A financial planning tool for personalized optimization of retirement income, supported by artificial intelligence, comprising a data acquisition interface configured to collect user-specific and environmental financial data; a knowledge modeling unit designed to identify relationships between financial, demographic, and macroeconomic variables; a predictive simulation module trained to generate probabilistic retirement income trajectories; and a decision optimization unit based on reinforcement learning logic that calculates individual investment strategies and withdrawal policies that maximize retirement income adequacy while minimizing financial risk.

The application discloses an ethane-to-ethylene efficiency improvement method based on a refining scene, which comprises the following steps: in multiple clients, based on the local running data of each production unit, a local model is trained by creating a hybrid loss function, a local model update amount and a set of contribution metadata are generated; a central server receives the local model update amount uploaded by each client and subjected to homomorphic encryption, determines the aggregation weight of each update amount according to the contribution metadata in plaintext form, and completes weighted aggregation in the encryption domain to update the global prediction model; in the client, the global model subjected to local fine-tuning is embedded into an online optimization problem as a prediction simulator, optimal operation parameters are solved and issued, and through performance monitoring and adaptive triggering steps, the continuous iteration of the model and the closed-loop improvement of the production efficiency are realized, the data privacy and safety are ensured, the reliability and performance of the model are improved, and the continuous adaptive optimization of the ethylene production efficiency is realized.

The invention relates to the technical field of tunnel engineering maintenance, and discloses a tunnel lining self-repairing analysis method, device and equipment and a storage medium. The method comprises the following steps: acquiring real-time monitoring data containing a crack distribution image and stress-strain readings through a sensor network, and identifying a damage category and a severity level through feature extraction and a damage classification model. And searching a matched repair material formula generation scheme from a material library, and performing repair process simulation by coupling tunnel environment parameters to obtain a predictive simulation repair track. And then key control points are extracted from the track to formulate a repair scheduling plan, and the plan is dynamically adjusted according to feedback data in actual repair. According to the method, the repair operation is converted into accurate prediction from experience judgment through digital process simulation, and the execution process is dynamically optimized through a closed-loop adaptive control mechanism, so that accurate customization of the repair scheme and dynamic optimization of the repair operation are realized, and the reliability and consistency of the repair effect are improved.

This invention discloses a BIM-based transportation engineering construction progress and resource management optimization system. The system includes a three-dimensional cognitive interactive body that adjusts construction progress and resource allocation in real time, eliminating physical differences between the virtual model and the actual environment; an adaptive computing engine that uses real-time perception data and a cognitive correction mechanism to connect the virtual model with the actual construction site; a progress prediction simulation module that uses deep learning and an adaptive feedback mechanism to dynamically predict and optimize the construction progress; a virtual-reality synchronized perception grid that is constructed based on the obtained dynamic progress prediction and optimization results, and synchronizes and dynamically corrects the virtual model and the construction site; and a self-optimization module that uses self-optimization and ecological optimization mechanisms, genetic algorithms, and multi-objective optimization to achieve adaptive optimization and long-term evolution. This invention solves the problem of virtual reality distortion, allowing for precise correction of differences between virtual and real life during construction.