670 results about "Data simulation" patented technology

The present invention provides a privacy-enhanced structured data simulation and generation method and system. The method comprises: step 1, a data conversion phase: performing normalization preprocessing on data; step 2, a probabilistic graphical model construction phase: on the basis of a Bayesian form, constructing the posterior distribution for variational inference from the data having undergone normalization preprocessing in step 1, obtaining an association relationship between the features of structured data by using a Stein variational gradient descent method, and when introducing differential privacy noise, using a Monte Carlo estimation algorithm to automatically obtain the amount of noise that needs to be added for each update step; and step 3: a data generation phase: using the association relationship obtained in step 2 as a metric set to generate more accurate simulation data than real data. The beneficial effect of the present invention is that: the method of the present invention avoids gradient clipping when applying DP-SGD, thereby avoiding the selection of clipping parameters and reducing the adverse effects of gradient clipping on an inference process.

The invention discloses a control strategy model construction method based on big data, and relates to the technical field of intelligent control, a traffic situation deduction and emergency strategy generation module uses a multi-agent traffic simulation technology, simulates a traffic situation in combination with real-time traffic data, evaluates risks and early warns potential crisis by integrating multiple factors, and provides a control strategy model for the traffic situation deduction and emergency strategy generation module. And after an early warning is received, an emergency strategy is generated by using an intelligent algorithm, the strategy is evaluated and optimized by using virtual rehearsal, a result is fed back to a management department, and a synergistic effect with other modules is achieved. Traditional traffic detection equipment data is integrated with multi-source heterogeneous data such as mobile phone signaling, shared bicycle and online hailed bicycle GPS, intelligent vehicle-mounted equipment and public traffic operation, more comprehensive and accurate information such as traffic flow, flow direction, travel behavior track and the like is provided, and rich and accurate basis is provided for traffic management decisions.

The invention discloses a power transmission line icing real-time monitoring method and system based on multi-parameter fusion, and relates to the field of data processing, and the method comprises the steps: receiving conductor physical parameters at the same moment, and simulating an icing track of a target power transmission line section in combination with current environment data; determining a windward side of the icing track based on the current wind direction, and simulating a windward icing layer of the icing track by taking the windward side as a starting surface, taking the current wind speed as a reference wind speed and adopting an icing layer simulation strategy; based on the topographic data of the target power transmission line section, determining a high-risk icing area in the icing track, and determining icing layer data of the high-risk icing area; determining an icing galloping area in the icing track based on the vibration parameter and the strain parameter; and fitting the windward icing layer, the high-risk icing area and the icing galloping area with the icing track, and optimizing the fitted icing track according to the icing layer data to generate a visual interface. According to the invention, the accuracy of icing monitoring of the power transmission line can be effectively improved.

The invention discloses a purchase demand prediction method and system based on big data analysis, and relates to the technical field of supply chain management and intelligent prediction, and the method comprises the steps: collecting multi-source heterogeneous data, and carrying out the data fusion and preprocessing; based on a causal inference technology, key influence factors of purchase demand changes are analyzed, and a purchase demand prediction model is constructed; dynamically adjusting the prediction model by using real-time data, and generating a real-time or periodic purchase demand prediction result; carrying out data simulation and model optimization aiming at an extreme scene of a purchase demand; and optimizing a supply and demand matching strategy based on a prediction result, and carrying out iterative improvement on the prediction model through a feedback mechanism. Through multi-source heterogeneous data fusion, causal inference, dynamic adjustment and feedback optimization mechanisms, high-precision purchase demand prediction is realized, a supply and demand matching strategy is optimized, the adaptability of the model to market fluctuation and extreme scenes is enhanced, the purchase cost is effectively reduced, and the supply chain management efficiency and stability are improved.

The invention relates to the field of real-time visualization of road conditions, in particular to a data simulation visualization rendering method and system for urban traffic road conditions. The method comprises the following steps: extracting a real-time satellite streetscape image based on urban satellite remote sensing scanning, and performing scene pixel-level segmentation to obtain scene texture rendering parameters; scene illumination visual identification is carried out according to the real-time satellite streetscape image, traffic scene background modeling is carried out based on scene texture rendering parameters, and a real-time scene background model is constructed; the method comprises the following steps: acquiring urban-level multi-source traffic monitoring data flow, performing vehicle state sensing, and constructing a multi-dimensional particle feature matrix; road network topological correlation analysis and global traffic network state perception are carried out according to the real-time satellite streetscape images, and a road network state perception model is constructed. According to the invention, a real real-time traffic environment is visualized, scene effects in different traffic states are presented, the current road condition can be rapidly evaluated, and the traffic control decision efficiency is improved.

The invention discloses a multi-unmanned aerial vehicle cooperative positioning method and application, and relates to the technical field of unmanned aerial vehicle control and navigation, and the method comprises the steps: constructing a three-dimensional visual simulation platform, including dynamic environment modeling, sensor data simulation and virtual-real combination verification; identifying and tracking a target through a multi-sensor data fusion and deep learning algorithm of the three-dimensional visual simulation platform; based on a multi-unmanned aerial vehicle cooperative positioning algorithm, realizing real-time estimation of the position and state of the target through geometric modeling and interactive multi-model unscented Kalman filtering; information fusion and collaborative decision among multiple unmanned aerial vehicles are realized through a wireless communication network; and optimizing the flight path, task allocation and target identification strategy of the unmanned aerial vehicle based on simulation feedback. The problems that a traditional unmanned aerial vehicle cluster is low in positioning precision and poor in target recognition and tracking capacity in a complex environment can be solved.

The invention belongs to the technical field of field effect transistor performance prediction, and particularly relates to a reliability prediction method, device and system for a field effect transistor, and the method comprises the steps: determining the correlation between the time sequence data of environment parameters, electrical parameters and performance parameters in historical operation records and the change of the performance parameters through a correlation analysis method when the time sequence data changes; key performance parameters are screened out, then a virtual operation model of the field effect transistor is constructed before life prediction is conducted through the screened key performance parameters, and historical data are used for conducting validity verification on the virtual operation model; finally, dynamic changes of environment parameters and electrical parameters are simulated through historical data when the successfully verified virtual operation model is utilized, the degradation trend of key performance parameters is recorded, residual life prediction is carried out based on the degradation trend, prediction efficiency is improved, life prediction can more accurately reflect actual working conditions, and the service life prediction efficiency is improved. And the accuracy of residual life prediction is improved.

The invention provides a tunnel deformation early warning method and system based on digital twinborn and data monitoring, and belongs to the technical field of computer data simulation.The method comprises the steps that firstly, entity rock stratum dynamic monitoring data, real-time construction disturbance data and initial digital twinborn model configuration parameters of a tunnel construction area are obtained; and then virtual-real dynamic coupling and feedback iteration processing is carried out on the virtual-real dynamic coupling model to generate an initial virtual-real coupling digital twinborn model, the initial virtual-real coupling digital twinborn model is iteratively optimized based on real-time construction disturbance data to obtain a final virtual-real coupling digital twinborn model, and deduction of the rock stratum deformation linkage effect is carried out according to the final virtual-real coupling digital twinborn model. The method comprises the following steps: generating a rock stratum deformation linkage path set, performing risk linkage mode identification on the rock stratum deformation linkage path set, determining a tunnel rock stratum large deformation risk linkage mode, and finally generating construction early warning information including a risk linkage diffusion area, a risk linkage evolution period and an emergency response priority according to the risk linkage mode, and transmitting the construction early warning information to a tunnel construction management system. And the tunnel construction safety is effectively guaranteed.

The invention relates to the technical field of tunnel engineering safety, in particular to a tunnel fire multi-stage damage collaborative prediction method and system, and the method comprises the steps: constructing a data simulation fusion model, and carrying out the processing of fire and tunnel structure parameter data, and obtaining a multi-working-condition parameter database; constructing a heat conduction equation, a fire plume and wall surface heat exchange relational expression, a boundary layer correction model and a comprehensive damage evaluation model, and analyzing the equation, the relational expression and the model by utilizing a multi-physical field coupling unified boundary condition to obtain a multi-physical boundary constraint condition; obtaining a multi-physical constraint loss function based on a network architecture and a multi-physical boundary constraint condition, and further obtaining a comprehensive constraint loss function of a temperature field-stress field-damage index; constructing a PINN-LSTM network architecture and a damage evolution analysis model based on the fire ending initial state coding vector, the fire course feature vector and the cooling process feature vector; and performing linkage analysis on the function, the frame and the model to predict and monitor the transient damage and the long-term creep damage of the tunnel.

The invention relates to a load quantification method for a floating fan in a typhoon scene, and the method comprises the steps: constructing an environment data simulation frame which comprises an external nesting and an internal nesting, the internal nesting is used for achieving wave flow coupling and generating wave flow scene data, and the external nesting is used for carrying out wave flow coupling and obtaining external environment data of the floating fan; the method comprises the following steps: constructing a multi-time-scale load prediction framework, inputting environmental data into the multi-time-scale load prediction framework, and outputting load distribution of typhoon to a floating fan under a long time scale, the multi-time-scale load prediction framework comprising a short-time-scale dynamic coupling analysis model and a long-time-scale load prediction model, and the long-time-scale dynamic coupling analysis model comprises a short-time-scale dynamic coupling analysis model and a long-time-scale dynamic coupling analysis model. The short-time-scale dynamic coupling analysis model is used for obtaining a load prediction sample, and the long-time-scale load prediction model is used for predicting the load prediction sample and outputting load distribution under a long time scale. Compared with the prior art, the method has the advantages of being more practical in environment simulation, accurate and efficient in load prediction and the like.

The invention relates to the technical field of data simulation, in particular to a bearing fault simulation method and system. The method comprises the following steps: collecting multi-modal bearing data so as to construct a bearing distributed edge data set; extracting contact power data of the bearing distributed edge data set, and calculating a bearing rigidity change curve according to the contact power data; carrying out random parameter modeling based on the bearing distributed edge data set, and carrying out physical modeling benchmark reference on the bearing rigidity change curve to obtain a bearing fault physical-data hybrid model; therefore, by integrating multi-modal data acquisition, physical-data hybrid modeling and a dynamic hyper-parameter adjustment mechanism, the defects of a traditional bearing fault diagnosis method in the aspects of accuracy and real-time performance are overcome, and the fault prediction and early warning precision and the response speed are improved.

The invention belongs to the technical field of thermal power plant safety analysis, and relates to a risk assessment method, system and device for a hydrogen storage system of a thermal power plant and a storage medium. Comprising the following steps: collecting operation data of the hydrogen storage system in real time, and preprocessing the data; based on the preprocessed data, utilizing a trained Bayesian LSTM model to predict the hydrogen leakage probability; if the hydrogen leakage probability exceeds a preset threshold value, CFD is combined with the preprocessed data to simulate a diffusion behavior after hydrogen leakage, and the severity of a leakage event is calculated based on a simulation result; and inputting the hydrogen leakage probability and the severity of the leakage event into a Bayesian network model, calculating a risk value, and triggering a grading early warning mechanism according to the risk value. According to the method, the sensitivity and accuracy of risk identification are improved, the scientificity of early warning decision making is enhanced, and intelligent guarantee is provided for safe operation of the hydrogen storage system of the thermal power plant.

The invention relates to the technical field of digital twinborn and drainage basin environment monitoring simulation, in particular to a drainage basin environment data simulation deduction system based on a digital twinborn technology, which comprises a digital twinborn management center used for calling multi-source heterogeneous monitoring data of a drainage basin environment to obtain an aligned environment data sequence; the causal coupling analysis unit is used for obtaining a steady-state logic signal or a dynamic decoupling signal; when a dynamic decoupling signal is generated, the topology adaptive reconstruction unit is used for performing structural mutation recognition feedback analysis on a calculation map of the digital twin model to obtain a reconstructed topology map; when the steady-state logic signal is generated, the simulation deduction execution unit is used for performing environment evolution deduction analysis on the aligned environment data sequence based on the current calculation map to obtain a simulation deduction result; according to the method, model calculation divergence or misleading output caused by sensor faults is effectively avoided, and the calculation problem caused by multi-source data heterogeneity is solved.

The invention discloses a method and device for dynamically adjusting the clamping force of a thin-wall part, equipment and a storage medium, and relates to the field of data simulation. The method comprises the following steps: determining the clamping form and size of a pneumatic clamp tool according to the size of a thin-wall part, simulating a clamping stress process through finite element simulation, and establishing a mapping model between clamping force and deformation; initial machining process parameters are set according to the size and precision requirements of the thin-wall part, and the maximum allowable clamping force under different material allowances is obtained based on the mapping model; stability qualitative analysis is conducted on the clamping stability, and milling parameters are optimized according to the analysis result; and the milling process and the milling parameters are obtained in real time, and the clamping force in the milling process is dynamically adjusted at fixed time by controlling the pneumatic clamp tool through the PLC. Dynamic adjustment of the clamping force is achieved through the established mapping model of the clamping force and deformation, and the problem of deformation caused by rigidity change of the thin-wall part in the machining process is solved by combining layered milling optimization and PLC timing control.

The invention discloses a surrounding rock tunnel blasting dynamic simulation method, and belongs to the technical field of data simulation. The method comprises the following steps: acquiring three-dimensional geological data; constructing an initial mechanical parameter field of the surrounding rock based on the three-dimensional geological data; based on the initial mechanical parameter field, discretizing the surrounding rock area by adopting a finite element method, dividing grids and initializing mechanical attributes of each unit; based on the mechanical properties, blasting simulation is conducted, in the simulation process, the stress distribution state of the surrounding rock is monitored in real time, and whether the stress concentration phenomenon exists or not is judged; calculating a propagation path and an attenuation rate of a stress wave generated by explosive explosion in the surrounding rock through numerical simulation based on a judgment result; based on the propagation path and attenuation rate of the stress wave, the fracture mode and fragment distribution characteristics of the surrounding rock are analyzed; and obtaining a final blasting simulation result based on the stress distribution state, the fracture mode and the fragment distribution characteristics of the surrounding rock. The method can reflect the dynamic response of the surrounding rock and predict the blasting effect.

The invention relates to the technical field of data simulation processing, in particular to a thermal simulation method and system for a circuit board and a medium. The method comprises the following steps: acquiring circuit board layer data; extracting board layer material thermal resistance parameters of the circuit board layer data, wherein the board layer material thermal resistance parameters comprise copper foil thermal resistance parameters and insulating medium thermal resistance parameters; identifying the layered structure of the circuit board layer data, and recording as layered structure data; component layout characteristics are determined according to the layered structure data, interlayer thermal resistance distribution calculation is carried out on the board layer material thermal resistance parameters based on the component layout characteristics, and layered structure thermal resistance distribution data are generated. Through a data processing technology and an analogue simulation technology, heat transfer interaction analysis is carried out on the copper foil layer and the insulating medium layer of the circuit board, and the heat transfer direction and the heat accumulation effect of the circuit board are evaluated so as to construct a heat simulation model of the circuit board.

The invention discloses a low-brightness image security enhancement method and device based on deep learning. The method comprises the following steps: firstly, collecting a large number of low-brightness images covering different scenes, illumination conditions and object contents, and preprocessing the low-brightness images; then a deep learning model is constructed, the model comprises an image light simulation module and an image enhancement module, and the image light simulation module is used for simulating the influence of a light source on an image based on low-brightness image data, calculating scattering, reflection and shadow effects after interaction of light and the image, generating light simulation data and enhancing the light simulation data; the image enhancement module is used for adjusting related parameters for enhancement processing according to the light simulation data; then training the constructed deep learning model by using the preprocessed low-light image data; and finally, enhancing a to-be-processed low-light image by using the trained deep learning model. According to the method, the identification accuracy and the application efficiency of the model can be remarkably improved, the image enhancement quality is improved, and the enhanced image shows loyalty physical rationality and detail authenticity.

The invention provides a training method and device of a body simulation model, electronic equipment and a storage medium, and relates to the technical field of model training. The method comprises the steps that first training data and second training data are obtained, the first training data are multi-modal data collected in a real environment and subjected to time-space synchronization processing, and the second training data are simulation data generated through a digital twin model; the digital twin model is a data simulation environment constructed based on the first training data; and training an initial body simulation model according to the first training data and the second training data to obtain a trained body simulation model. According to the method, the high-fidelity digital twin model is constructed, and the vision and depth of simulation data are ensured to be consistent with physical attributes of physical data in a simulation environment, so that the training precision of model training and the robustness of a control strategy are improved.

The invention discloses a multi-axis linkage numerical control machine tool control system, which comprises a cutter adjusting module, which is used for carrying out parameter adjustment according to first state data, second state data and third state data to obtain fourth state data of a cutter; the trajectory planning module is used for simulating motion trajectories of a plurality of shafts according to the fourth state data by utilizing simulation software, and performing precision compensation on the motion trajectories of the shafts; and the risk prevention module is used for predicting the first risk and the second risk and implementing prevention measures. The angle and the cutting speed of the cutter are adjusted in a self-adaptive mode, the cutter is matched with the curvature of the surface of the workpiece, so that the flatness of the machining face is improved, the situation that the extrusion force of the cutter on the workpiece is too large is avoided, deformation, except for cutting, of the workpiece is prevented, and the machining precision is improved. A plurality of main shafts and slave shafts are subjected to analog simulation, the cutting angle and the cutting speed of the cutter are further adjusted according to the simulation result, and safe operation of equipment is guaranteed on the premise that the machining precision is guaranteed.

The invention relates to the field of robot automation control, in particular to a target board pose tracking device and method based on deep learning, the device comprises a sensor, a target board, an image processor and a displayer, the target board is arranged at the tail end of a mechanical arm, and the sensor is used for collecting image data of the target board and the mechanical arm; the sensor is connected with an image processor through a data line, a software system is integrated in the image processor, the image processor is connected with a displayer, and the displayer displays the posture of the mechanical arm. A software system of the device is based on a deep learning tracking method, an imaging quality deterioration mechanism is considered, imaging quality deterioration data is constructed in a data simulation mode, a simulation data set is adopted to train a pre-training model, then part of data is collected in an artificial simulation environment to serve as real data to be used for training a target detection model, and a target detection result is obtained. And on the basis of continuous frame depth detection features and motion continuity, the accuracy and robustness of target real-time tracking are improved, and finally, an accurate pose result is obtained in combination with standard template data.

The invention provides a coal-fired boiler digital twin modeling and intelligent control method, system, equipment and medium, and belongs to the technical field of thermal equipment intelligent control. The method comprises the steps that coal element analysis data, boiler operation parameters and environment parameters of a coal-fired boiler are collected in real time and preprocessed; calculating an external heat source sum and a heat loss sum, and outputting boiler heat efficiency; a twinborn model is constructed, model parameters are calibrated through data collected in real time and calculated boiler thermal efficiency, and boiler indexes are dynamically simulated and output; taking the collected data and the analog output boiler indexes as input characteristics, predicting boiler operation parameters through an LSTM model, generating an optimization control instruction by adopting a multi-objective optimization algorithm, inputting twin model simulation, outputting prediction parameters, and executing the control instruction when the prediction parameters meet preset constraints; and updating LSTM model training data according to the operation result. Intelligent monitoring optimization of the boiler is achieved, and efficiency, safety and environmental protection performance are improved.

The invention discloses a true value generation method and device of a decision planning algorithm, a storage medium and equipment, and relates to the field of intelligent driving. The method comprises the steps that at least one empty map scene is obtained, and the empty map scene comprises a road topological structure of a road section extracted from a static map; simulating the traffic flow in the empty map scene according to the traffic data containing the driving behavior of the real driver, and obtaining a simulation scene corresponding to the empty map scene; based on a tree search algorithm and the state space and decision space at the current moment, the state space at the future moment is simulated in the simulation scene, multiple decision planning paths of the vehicle are obtained, the state space comprises the driving state of the vehicle and the states of other traffic objects, and the decision space comprises a decision set of the vehicle; and taking the decision planning path with the highest evaluation in the plurality of decision planning paths as a true value of the decision planning algorithm. According to the invention, the truth value required by the decision planning algorithm can be obtained through an analogue simulation technology.

The invention discloses a ventilation cooling optimization method for an all-hydrogen-cooled gas turbine generator, and relates to the technical field of all-hydrogen-cooled generator cooling, and the method comprises the steps: analyzing cooling demands, calculating loss in combination with a high-frequency start-stop peak regulation working condition, and setting a stator RTD temperature, an iron core temperature and a temperature rise target; an end ventilation structure is designed, the diameter of the fan is optimized, and bar gaps, wind shield holes and inner supporting ring ventilation grooves are formed; optimizing variable air ducts of the iron core, and adjusting the width of the air ducts according to thermal load partitions; cFD coupling simulation verification is carried out, and a loss data simulation flow field and a temperature field are imported; carrying out physical test iterative optimization, manufacturing a prototype model, testing, and adjusting parameters until errors are qualified; operation monitoring and dynamic adjustment are carried out, and a sensor is installed to collect data in real time and adapt to working condition requirements. Cooling uniformity and efficiency are improved, peak regulation start and stop working conditions are adapted, hydrogen parameter change influences are balanced, vibration and cooling are cooperatively controlled, operation and maintenance convenience is improved through modular design, and long-term stable operation of the generator is guaranteed.

The invention provides an air filter type selection system and method based on carbon footprint management, and the air filter type selection system based on carbon footprint management comprises a product database module and a data simulation module. The data simulation module is connected with and calls the product database module; the invention discloses an air filter type selection method based on carbon footprint management. The method comprises the following steps: S1, establishing a product database; s2, inputting demand side information; s3, newly establishing a filter scheme; s4, selecting an energy-saving scheme; the method is suitable for a single-stage or multi-stage air filter scheme, the actual use condition of the air filter is simulated by inputting demand side information, calculation is carried out by adopting a step-by-step calculation principle, the use scheme is calculated step by step, it is ensured that final simulation data is calculated according to parameters of each stage of filter, and the calculation efficiency is improved. Total carbon footprint accounting is completed in the air filter type selection stage through data simulation, an energy-saving scheme can be derived, the application range is wide, and adaptability is high.

A system and method include acquiring data relating to an operation of an assembly line and generating a virtual environment of the assembly line using the acquired data. The virtual environment is defined using an assembly process model synchronized with real-time assembly line data. A task of one or more line workers is simulated using the real-time assembly line data, historical data, and sensor data. The assembly line is rebalanced in real-time based on the simulation to reduce a walk-time of the one or more line workers to perform the task.

The invention belongs to the technical field of unmanned sailboat environment perception and path planning, and discloses an unmanned sailboat reinforcement learning path planning method based on dynamic wind field reconstruction. Comprising the following steps: step 1, setting a map and an initial setting; 2, wind field preparation and LES data simulation are carried out; step 3, performing reinforcement learning path planning; step 4, meteorological station wind field sampling; step 5, dynamic wind field reconstruction based on a Navier-Stokes equation is carried out; and step 6, path extraction and result calculation. According to the method, through combination of real-time wind field reconstruction and reinforcement learning, the accuracy and adaptability of path planning are remarkably improved, the sailing time and energy consumption are effectively reduced, and the sailing efficiency and safety of the unmanned sailboat are improved.

The invention discloses a UUV trajectory data enhancement method, system and device based on time sequence fusion, and the method comprises the steps: obtaining the position data, time index data and navigation data of an underwater unmanned vehicle, and analyzing the position data to obtain three-dimensional distance data; performing normalization processing on the time index data and the three-dimensional distance data to obtain an initial time sequence data set, performing distance analysis on the initial time sequence data set to obtain initial distance data, and performing preprocessing to obtain a time sequence data set; establishing a time series data clustering model to analyze the time series data set to obtain a clustering time series data set; performing iterative updating through a particle swarm optimization algorithm and a fitness function to obtain an updated time sequence data set; and performing fitting correction on the updated time sequence data set to obtain a corrected time sequence data set. According to the method, the time index data is combined, updating is carried out through the particle swarm optimization algorithm, and then the accuracy of trajectory data simulation of the unmanned underwater vehicle is improved through fitting correction.

The invention relates to the technical field of image processing, in particular to a multi-modal image processing method and system based on deep learning, and the system comprises a self-adaptive perception and generation module which is used for exploring the new characteristics of image data, enhancing the perception capability through a generation and simulation technology, and discovering that details are difficult to capture in an existing processing mode. According to the invention, through reverse modal generation, cross-modal characteristic unification, dynamic modal reconstruction and data simulation experiments, data diversity expansion and accurate characteristic analysis are realized; through causal reasoning, knowledge migration and adversarial learning, inter-modal characteristic consistency and analysis performance are optimized; according to the multi-modal image analysis method and system, the multi-modal image analysis efficiency and accuracy are remarkably improved by combining the task-driven model switching and federated learning technology and supporting distributed model training of dynamic adaptation and privacy protection, and meanwhile, the application range of medical image analysis in a resource-constrained environment is widened.

The invention discloses a multi-view driven three-dimensional generation method, and belongs to the field of computer vision and three-dimensional reconstruction. According to the method, a multi-view image of an object is coded to generate corresponding feature representation, and support is provided for subsequent correction flow guide three-dimensional model optimization; a correction flow model is adopted to gradually convert a three-dimensional sample under prior distribution into implicit three-dimensional representation of an object under the condition of multi-view images of the object, a multi-view Transform network is adopted as a speed prediction model of the correction flow model, and the correction flow model starts from a Gaussian distribution sample and is guided according to a current state and a speed field, so that the speed of the object is predicted. The method comprises the following steps: continuously correcting a sample path, enabling a sample to be stably close to target distribution along a predetermined track, achieving efficient remodeling of an implicit expression of an object, and finally decoding the implicit expression into a three-dimensional Gaussian point cloud through a decoder, thereby realizing three-dimensional reconstruction of the object. The method has the advantages of high speed and high reconstruction quality, and is suitable for the fields of three-dimensional modeling, data simulation and the like.

The invention discloses a fire-fighting intelligent early warning system and method based on the Internet of Things, and belongs to the technical field of fire-fighting intelligent early warning. Temperature, smoke concentration and CO concentration data are obtained through an Internet of Things detector, and site position, regional function division and object flammability degree distribution data are synchronously collected; after preprocessing, generating a composite attribute label containing space coordinates, function partitions and flammability grades; constructing an equipment operation state spatial-temporal feature network based on equipment historical data, extracting multi-dimensional time sequence features, calculating a regional risk conduction coefficient in combination with a composite attribute tag, and fusing multiple parameters to generate a differentiated early warning threshold matrix; virtual monitoring grids are divided according to the correlation degree of regional functions and flammability grades, and early warning is triggered after a suspected risk area is identified and judged as an effective risk through space-time cross validation; when early warning is triggered, fire spreading is simulated in combination with site topological data, a dynamic risk map is generated, early warning information is pushed, and an emergency scheme is generated.