84 results about "Predictive simulation" patented technology

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 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 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 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 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.

The invention relates to an emergency control system comprising the following phases: a detection and sensing phase in which data is collected in real time by means of a network of sensors and operatives in the field, a processing and predictive simulation phase in which future scenarios are predicted on the basis of the collected data and historical data, a visualisation phase for visualising the previous phase in a 3D model, a progressive scenario generation phase, and a phase of interaction with operatives in the field in which an automatic response to the emergency is managed in real time and on the basis of the data obtained in the previous phases.

This invention relates to a digital twin-based intelligent construction dynamic management and control system and method, belonging to the field of intelligent construction technology. It includes a data acquisition module, a digital twin model module, a state fusion and deviation analysis module, a predictive simulation and multi-objective optimization decision-making module, and a closed-loop control execution module. It can not only map the physical state of the construction site in real time, but also proactively predict potential construction deviations by integrating multi-dimensional data such as quality, safety, schedule, and cost during the construction process. When a deviation is predicted or detected in real time, the system automatically runs a multi-objective collaborative optimization algorithm that considers the balance of the four elements of QSPC (Quality, Safety, Schedule, and Process) to generate the optimal adjustment strategy. Finally, the closed-loop control execution module translates the optimization strategy into specific instructions and issues them to the construction site, achieving proactive, predictive, and closed-loop dynamic management and control of the construction process, significantly improving the intelligence level of construction management and the overall benefits of engineering projects.

The present application belongs to the technical field of building construction quality monitoring, and particularly relates to a building construction quality traceability and multi-party collaborative acceptance method and system. The method comprises: obtaining building acceptance information, grouping the acceptance information according to collaborative units to form grouped acceptance information, determining the qualified state of the grouped acceptance information based on a preset acceptance standard, executing prediction simulation to obtain a prediction result when it is determined that all the grouped acceptance information is qualified, executing acceptance deviation evaluation when it is determined that there is unqualified grouped acceptance information, obtaining an acceptance deviation value and constructing a deviation score file, then determining a to-be-corrected parameter and a corresponding responsible unit according to the comparison result of the maximum deviation score value and a score judgment threshold, and issuing a correction instruction to the responsible unit and monitoring the processing progress. The present application constructs a closed-loop collaborative process with accurate responsibility positioning, combines prediction simulation and acceptance deviation evaluation, and judges the deviation degree through a quantitative evaluation system, thereby improving collaborative efficiency and management flexibility.

The invention discloses a self-purification prediction simulation method for a petroleum hydrocarbon polluted site under soil-water interaction based on COMSOL Multiphysics, and the method comprises the steps: obtaining historical meteorological data, and constructing a rainfall flux simulation process through a function; taking the rainfall flux as an upper boundary source item for driving the three-phase fluid to move, and constructing a three-phase fluid motion model; simulating and predicting a migration path and a phase transformation process of the dissolved phase petroleum hydrocarbon in a water phase; constructing a migration path of an electron acceptor and a migration path of gas-phase oxygen, establishing a degradation kinetic model of each path, calculating a total biodegradation flux through aerobic degradation, nitrate reduction degradation and sulfate reduction degradation, and correcting the degradation kinetic models of the paths by using correction factors; biodegradation flux and a three-phase fluid motion model are coupled, rainfall flux is constructed through a piecewise function and annual rainfall data and is directly associated to core parameter soil water phase pressure, and the physical consistency of a driving mechanism is enhanced.

The invention relates to the technical field of gas storage operation scheduling, and discloses a communicated salt cavern gas storage operation scheduling method and system, and the method comprises the steps: receiving real-time pressure data and injection-production flow data from a plurality of salt caverns; according to the data, the current fluid transmission efficiency among the salt caverns is reversely calculated; tracking a time-varying track of the fluid transmission efficiency, identifying an evolution trend of the fluid transmission efficiency according to the track, and establishing an evolution function of the fluid transmission efficiency; simulating a future operation scene based on the current fluid transmission efficiency, the evolution function of the fluid transmission efficiency and external natural gas demand prediction, and pre-judging a potential operation risk according to a simulation result; and when the potential operation risk is pre-judged, early warning is triggered, and an injection-production plan is adjusted according to the potential operation risk. According to the invention, refined and intelligent scheduling of the operation of the communicated salt cavern gas storage is realized, and the safety, reliability and economic benefits of the operation of the gas storage are remarkably improved.

The present application relates to the technical field of digital twinning, in particular to a building energy consumption simulation and prediction method and system based on digital twinning, comprising the following steps: identifying a step excitation starting time generated by a device state jump, calculating a thermal response lag time of a physical and virtual environment, correcting a thermal inertia parameter according to a time lag deviation to establish a dynamic calibration model, and generating an energy consumption trend prediction sequence in combination with a weather forecast. In the present application, the step time is determined by monitoring the operating state jump, the thermal response lag is calculated in combination with the measured temperature data, the deviation is obtained by comparing the lag time of the virtual environment, the thermal inertia parameter of the digital twinning model is iteratively updated using the deviation, a dynamic calibration mechanism is constructed to eliminate the influence of parameter misalignment, the thermal mechanical behavior of the virtual model is ensured to be highly consistent with the entity building, the predictive simulation is carried out according to the dynamic calibration model, and the scheduling plan and the weather forecast are imported to efficiently generate the building energy consumption trend prediction sequence.

Disclosed is a computational fluid dynamics (CFD) flow analysis method for a blade, the method comprising: generating an analysis automation model that predicts CFD flow analysis results from input analysis conditions, the generating the analysis automation model comprising: inputting a blade shape and flow conditions, determining analysis settings for flow analysis on the blade, and generating an analysis automation model that predicts CFD flow analysis results from the input analysis conditions; performing CFD simulation based on the determined analysis setup parameters to perform flow analysis on the blade, evaluating the result of the performed flow analysis on the blade, and training by reinforcement learning using an artificial neural network to meet predetermined evaluation criteria, inputting any blade shape condition and flow condition, and outputting the result of the flow analysis on the blade. And determining analysis setting parameters by applying the analysis automation model to the input blade shape and flow conditions, predicting a CFD simulation, and outputting the predicted CFD simulation as a result of the flow analysis.

The present disclosure provides improved breeding methods that allow for the selection of a member or members of a population having a desired phenotype using a limited number of genetic markers. In certain aspects, the methods for selecting members of the breeding program utilize machine learning models to predict phenotypes of a simulated progeny population.

The invention discloses a technology for optimizing MJS construction method pile construction. The optimization technology comprises the steps that A, geology and environment investigation is conducted, wherein a construction scheme is formulated through geological investigation; b, monitoring point setting: monitoring the change of a soil body in the construction process; c, data analysis: determining key construction parameters based on theoretical analysis of the monitoring data, comparing the real-time monitoring data with a preset threshold value, and analyzing the stability of the soil body; d, simulation and prediction: simulating a construction process and predicting a construction result; e, process adjustment: comparing a construction simulation result with a monitoring result, and adjusting construction parameters of different construction stages; f, quality control, wherein the construction quality of the MJS pile is regularly checked and evaluated in the construction process; and G, completing evaluation: according to an evaluation result, improving the value of the construction parameter. According to the technology for optimizing MJS construction method pile construction, the soil body change caused by MJS construction method pile construction is monitored in real time, and based on the soil body change rule, the change in the soil body in the construction stage is analyzed so as to optimize the MJS construction method pile construction technology.

The invention relates to the technical field of intelligent building and automatic control, and discloses a building environment adaptive optimization method and system and electronic equipment, and the system comprises a prediction module which is used for obtaining a digital twin model and real-time sensing data constructed based on a physical building, and carrying out the predictive simulation to generate an optimal control strategy; the cooperative control module is used for determining an adjustment task according to the strategy, initiating an auction process in which a plurality of environment control devices perform bidding based on respective marginal benefits, and determining a bid-winning device with the optimal cost according to a bidding result to generate a cooperative control instruction; the instruction issuing module is used for issuing an instruction to the bid winning equipment; and constructing a user preference model, providing decision options for quantizing comfort and energy consumption when detecting demand conflicts, and fusing user negotiation consensus into a control strategy, so that the method can realize prospective and refined cooperative regulation and control of a building environment, and effectively improve comprehensive satisfaction in a multi-user scene.

The invention relates to a reservoir pore evolution simulation experiment device, in particular to a petroleum reservoir pore evolution prediction simulation experiment device which comprises a base and a supporting seat fixed on the base, and further comprises an expansion mechanism which is movably arranged on the supporting seat and forms a cavity for placing a rock sample with the supporting seat, the expansion mechanism can execute an expansion action on the supporting seat so as to enlarge the cavity; the pressure applying mechanism is installed on the supporting seat and used for pressing the rock sample in the cavity, the pressure applying mechanism is connected with a transverse movement driving mechanism installed on the base, the transverse movement driving mechanism is connected with a water supply mechanism arranged on the base, the transverse movement driving mechanism is further connected with the expansion mechanism, and the expansion mechanism is connected with the pressure applying mechanism. The formation process of reservoir pores is simulated to the maximum extent, experimental errors are reduced, the reservoir pore structure and formation are conveniently researched, a data theoretical basis is conveniently provided for oil and gas exploration, and the method is suitable for application and popularization.