481 results about "Data simulation" patented technology

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 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 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 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 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 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.

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 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.

The invention relates to a software load computing resource virtualization allocation method and device, equipment and a medium. The method comprises the following steps: acquiring a task description vector of a task and real-time state data of computing resources; according to the task description vector of the task and a preset historical performance database, computing resources needed when the task is started are analyzed, and a starting feature vector is obtained; according to the starting feature vector and the real-time state data, calculating a computing power hotspot distribution diagram of the starting task; the computing power hotspot distribution map is used for representing the use condition of each computing resource at different time points when the task is started; based on the computing power hotspot distribution map, the task description vector and the real-time state data, simulating a competition condition when the task is started, and obtaining resource competition intensity quantitative data; generating a task pre-allocation scheme according to the resource competition intensity quantitative data and the real-time state data; the pre-allocation scheme is used for representing the allocation relationship between the task and each computing resource. By adopting the method, the computing power resource utilization rate can be improved.

The embodiment of the invention is suitable for the technical field of antennas, and provides a data processing method and electronic equipment, and the method comprises the steps: obtaining first channel data, obtaining an analog antenna pattern, and obtaining analog receiving data according to the first channel data and the analog antenna pattern, the first channel data is data for simulating a channel state between the first electronic equipment and the second electronic equipment, the analog antenna pattern is data for simulating a pattern of an antenna in the first electronic equipment, and the analog receiving data is data for simulating a second positioning signal; the second positioning signal is a signal obtained by receiving a first positioning signal sent by the second electronic equipment by an antenna in the first electronic equipment, and the second positioning signal is used for determining the position information of the first electronic equipment, that is, the simulation receiving data is obtained through simulation; the influence of the parameters of the antenna and the GNSS specification on the precision of the satellite positioning signal is evaluated.

The invention discloses a network load balancing distributed strategy optimization method. The method comprises the following steps: acquiring node load data; a gravity and repulsive force field is simulated to dynamically adjust load distribution; each data center node deploys a load balancing agent, and a task allocation strategy is dynamically adjusted through multi-agent reinforcement learning training; resource allocation optimization: based on a resource allocation optimization target, optimizing allocation from tasks to nodes with the target of minimizing data transmission cost and calculation delay and satisfying resource constraints at the same time; and the load index and the actual task delay of each node are monitored in real time, and the overall performance of the system is evaluated. The method can adapt to load fluctuation of the ground; the reward function ensures that the proxy not only optimizes local performance in a high dynamic scene, but also maintains the stability of a global network structure; the load attention mechanism enables the agent to dynamically pay attention to nodes with lower load and better delay.

The invention relates to the technical field of battery simulation, in particular to a temperature adjustment method and system for simulating a battery based on a battery simulator. The method comprises the following steps: collecting the material thermal conductivity, thickness and layer number information of the battery cell lamination structure, and recording the information as battery cell lamination structure data; performing thermal resistance calculation on each layer of the battery cell according to the battery cell lamination structure data to obtain thermal resistance distribution data of each layer of the battery cell; performing space-time inversion simulation on the heat accumulation condition in the battery cell in the rapid charging process to obtain battery cell heat accumulation simulation data; and determining current density distribution data in the battery cell in the rapid charging process according to the thermal resistance distribution data of each layer of the battery cell and the battery cell heat accumulation simulation data. Through data monitoring technology data and data simulation technology, thermal resistance distribution calculation of heat accumulation in the battery cell is realized, accurate simulation of a heat accumulation path is realized, cooling efficiency is improved, and battery safety is enhanced.

There is no system that calculates transportation project costs that reflect the ability of users to adjust the transportation vehicle's travel route to avoid interference between the transportation vehicle or large cargo and the current terrain, so it is not possible to quickly select the optimal transportation route. [Solution] The large cargo transportation simulation system 1 comprises a condition receiving unit 10a, an acquisition unit 10b that acquires 3D current terrain model data of the route the transport vehicle will travel, a simulation unit 10c that simulates whether the 3D model of the transport vehicle or the 3D model of the large cargo will interfere with the 3D current terrain model based on multiple conditions and the 3D current terrain model data, a display unit 11 for the simulation results, and an adjustment receiving unit 10d that accepts user adjustments to the transport vehicle's travel route, and the system calculates transportation project costs that reflect the results, allowing for the rapid selection of the optimal transportation route.

The invention discloses an AI and numerical simulation combined large-span space steel structure damage identification method and system, and particularly relates to the technical field of building monitoring, and the method comprises the following steps: P1, according to a structural design drawing and material characteristics, constructing a large-span steel structure finite element simulation model, and simulating the response of the structure under various load and boundary conditions; p2, pre-training a damage identification model according to the obtained response data, and simulating multi-scale damage evolution in combination with numerical simulation data and experimental observation data; according to the method, the problem of no physical interpretation of a pure data driving model under complex working conditions is avoided, the credibility and engineering applicability of a prediction result are enhanced, the overall process characteristics of structural damage are effectively captured, early recognition and trend prediction are facilitated, the recognition performance under a small sample is improved, and the recognition efficiency is improved. Error accumulation and efficiency loss caused by multi-link splitting in a traditional method are avoided.

The invention discloses a data simulation method based on a database function and a timed task. The method comprises the following steps: S1, acquiring source data based on a multi-modal lightweight intelligent acquisition strategy; s2, performing data cleaning and standardization so as to ensure engineering processing of simulation data quality and consistency; s3, creating a user-defined function in the database to package intelligent logic of data simulation; according to a time condition and a data mode in the timed task, intelligently performing simulation trigger judgment and simulation data generation through the user-defined function; s4, performing efficient data injection based on a buffer and batch optimization mechanism of the intermediate table; and S5, performing timed task period regulation and control. The invention further discloses a corresponding system, electronic equipment and a computer readable storage medium.

The invention relates to an aluminum plate strip rolling thickness control method and system based on thickness data simulation. The method comprises the steps that real-time working condition data in the aluminum plate strip rolling process is obtained; a roller thermal expansion dynamic model is constructed, the roller thermal crown is calculated in real time based on the real-time working condition data, and the thickness compensation amount is generated according to the roller thermal crown; a thickness dynamic response model is constructed, based on real-time working condition data, the thickness deviation, the thickness deviation change rate and the cumulative sum of the thickness deviation of the aluminum plate strip are calculated, and the thickness adjusting amount is calculated through a PID control algorithm; the thickness compensation amount and the thickness adjusting amount are fused, and a hydraulic pressing position adjusting instruction is generated; the hydraulic automatic thickness control system drives a hydraulic cylinder to adjust a roller gap according to the hydraulic screw-down position adjusting instruction; the method has the advantages that the thickness compensation amount based on multi-section temperature accurate calculation and the thickness adjusting amount based on real-time deviation dynamic adjustment are efficiently fused, and the thickness control precision and the dynamic response speed are remarkably improved.

The invention relates to the technical field of data simulation, and discloses a coal mine fully-mechanized coal mining simulation system control method and device and a simulation system. The method comprises the steps of obtaining real-time working condition parameters; obtaining a dynamic compensation coefficient; determining an actual displacement stroke according to the real-time working condition parameters and the dynamic compensation coefficient; and generating a coal cutter motion instruction of the simulation system at the next moment based on the actual displacement stroke. The compensation coefficient is dynamically adjusted according to historical real-time working condition parameters, and the motion trail of the simulation system at the next moment is adjusted based on the compensation coefficient and the real-time working condition parameters at the current moment, that is, the theoretical value is corrected through dynamic compensation, so that the displacement of the virtual model approaches the actual displacement of real physical equipment instead of directly replacing the measured value. The simulation system can simulate the actual system operation condition more truly, and a more reliable simulation result is provided for actual operation.

The invention relates to the technical field of medical data analysis, in particular to a clinical team cooperation ability assessment method and system based on multi-modal data dynamic fusion, and the method comprises the steps: collecting physiological signal data based on wearable equipment, collecting audio / video data through an environment sensor, and collecting physical operation data through simulated human equipment; adding a timestamp and a scene label; carrying out sampling frequency synchronization and time axis alignment on the multi-modal data by taking the first-aid event triggering time as a reference; calling an attention mechanism based on the first-aid scene type, and automatically adjusting the weight of each modal data; and integrating the technical operation score and the non-technical cooperation feature to generate an individual and team visual evaluation report. According to the invention, the problem of blank collection of team non-technical skill data in traditional training is solved, and the effects of quantitative evaluation and real-time feedback of collaboration capability are realized.

A method comprising obtaining (611) video data comprising meta-data representative of a level of reliability of a neural network inference process implemented by an analog device, the neural network inference process being applied to decode the video data, an analog device being an electronic circuitry unable to ensure a repeatability of an output result of said electronic circuitry when said electronic circuitry receives same input data; and, decoding (612) the video data, the implementation of the neural network inference process by the analog device depending on the metadata.

The invention discloses an emergency scene driving behavior data processing method based on deep learning, and relates to the technical field of driving behavior data processing methods.The method comprises the steps that at least two data simulation modes are set, the two data simulation modes are a single working condition simulation scene and a comprehensive working condition simulation scene respectively, different driving data generated by different drivers in two working condition simulation scenes are collected, and abnormal samples are intelligently eliminated based on data relevance in the driving data and neural network anomaly detection. Obtaining first target driving data and second target driving data of a database for establishing a driver behavior model; according to the data processing method, the response speed and the safety of the automatic driving system can be effectively improved, and decision optimization and safety performance improvement for the vehicle AI are realized.

The invention provides an enterprise manufacturing resource configuration optimization method based on intelligent interaction and template mapping, and belongs to the technical field of intelligent manufacturing. Comprising the following steps: receiving a resource configuration request input by a user, and performing natural language analysis on the resource configuration request to generate structured resource configuration demand information; according to the structured resource configuration demand information, matching and calling a preset resource configuration template; based on the called resource configuration template, in combination with real-time resource state data in the ERP system, performing analog computation of resource configuration, and generating at least one resource configuration scheme; and feeding back the at least one resource configuration scheme to the user, and receiving feedback information of the user on the resource configuration scheme. User requirements are analyzed through intelligent interaction, an optimization strategy is matched through template mapping, real-time data simulation calculation is combined, feedback is received, and intelligentization, high efficiency and continuous optimization of enterprise manufacturing resource configuration are achieved.

The invention provides a numerical simulation method for large tumor treatment of single-needle multi-point microwave ablation, and belongs to the technical field of large liver tumor data simulation. The method comprises the following steps: establishing a finite element simulation model, and solving the finite element simulation model according to the interaction among an electromagnetic field, heat conduction and biological tissues to obtain a simulation result; constructing a single-needle multi-point microwave ablation numerical model; carrying out ablation simulation by utilizing the single-needle multi-point microwave ablation numerical model, outputting multi-point ablation result data, quantifying the size and form of a solidification area, and carrying out space overlapping analysis on a contour surface to obtain the characteristics of the solidification area; ablation simulation is carried out by setting parameter combinations of pull-back distances and ablation time of a plurality of antennas, and an optimal parameter combination is obtained by combining feature analysis of a solidification area, so that numerical simulation of large tumor treatment is completed; according to the invention, the problems of low ablation temperature, long simulation time and difficulty in accurate positioning of the antenna in the existing single-needle multi-point microwave ablation simulation method are solved.

The invention relates to a foundation pit deformation control method based on a TabPFN model and a supporting axial force servo system, and the method comprises the steps: S1, constructing a servo supporting-wall deformation data set through a servo supporting data simulation module according to the servo supporting force of the supporting axial force servo system and wall deformation response data; s2, a deformation prediction agent model module constructs and optimizes a TabPFN model, the deformation prediction agent model rapidly predicts wall deformation caused by foundation pit excavation according to the input servo supporting force, and a wall deformation prediction result is obtained; and S3, a cross-stage multi-objective optimization module constructs a multi-objective optimization framework fused with a differential evolution algorithm, sets a plurality of objectives, inputs the wall deformation prediction result obtained in the step S2 into the multi-objective optimization framework, and collaboratively optimizes wall maximum deformation control and servo force unloading risks to obtain a global optimal combination. The method is a multi-stage and multi-target servo support optimization control method oriented to foundation pit engineering.

In various examples, sensor data recorded in the real-world may be leveraged to generate transformed, additional, sensor data to test one or more functions of a vehicle—such as a function of an AEB, CMW, LDW, ALC, or ACC system. Sensor data recorded by the sensors may be augmented, transformed, or otherwise updated to represent sensor data corresponding to state information defined by a simulation test profile for testing the vehicle function(s). Once a set of test data has been generated, the test data may be processed by a system of the vehicle to determine the efficacy of the system with respect to any number of test criteria. As a result, a test set including additional or alternative instances of sensor data may be generated from real-world recorded sensor data to test a vehicle in a variety of test scenarios.

The invention relates to the technical field of printing control, and discloses an intelligent printing job scheduling method and device and a storage medium, and the method comprises the steps: obtaining the production process, performance and layout information of each job unit, carrying out the digital simulation based on the information, building a basic and deep digital model, collecting task target data, and simulating a job scheduling scheme. The method comprises the steps of obtaining equipment load information, analyzing logistics evaluation information of different scheduling schemes, evaluating each scheduling plan, selecting an optimal scheme, issuing a job scheduling strategy, tracking the equipment load in real time and adjusting a job scheduling plan, and the method optimizes job scheduling through digital simulation, prediction and evaluation, effectively improves the production efficiency, reduces the fluctuation of the equipment load, and improves the working efficiency. The flexibility and stability of a production line are enhanced, and the problem that in the prior art, the efficiency is poor due to the fact that printing process plans are controlled through artificial experience is solved.