471 results about "Virtual sensors" patented technology

The invention provides an energy-saving temperature control optimization method and system based on a central air-conditioning simulation platform, and relates to the field of central air-conditioning simulation platforms, and the method comprises the steps: collecting the operation parameters of a central air-conditioning system in real time through an Internet of Things sensor network, constructing a multi-dimensional dynamic data set in combination with historical data and outdoor meteorological data, and carrying out the calculation of the multi-dimensional dynamic data set; a physical model and machine learning hybrid driven simulation engine is adopted, a dynamic thermodynamic model of the central air-conditioning system is established, and a multi-objective optimization function of a simulation platform model is defined according to user comfort requirements, energy consumption cost constraints and environmental policy indexes; and adopting a hybrid optimization strategy of fusion of reinforcement learning and a genetic algorithm, iteratively optimizing control parameters in the simulation platform model, deploying a digital twin system according to the energy-saving strategy set, predicting potential faults through a virtual sensor, correcting the parameters of the simulation platform model, and realizing closed-loop control and continuous optimization. The method is used for overcoming the defect that a closed-loop feedback mechanism is lacked in the prior art.

The invention relates to the technical field of wind turbine generator data analysis, and discloses an offshore wind turbine generator operation data analysis and prediction system. The system comprises a marine environment data integration module for collecting data to generate a multi-source time-space synchronization data set; the multi-modal feature fusion module is used for extracting cross-modal correlation features to generate a high-dimensional fusion feature tensor; the dynamic fault prediction module is used for constructing a two-way gating circulation network model to predict the degradation probability and the residual life of key components of the equipment; and the self-adaptive optimization control module is used for constructing a multi-target dynamic programming model to optimize a fan operation strategy. In addition, the system is further provided with a feedback correction module for correcting prediction model parameters, and a virtual sensor module based on a physical information neural network is used for monitoring tower stress and diagnosing sensor faults. According to the system, comprehensive monitoring, accurate fault prediction and optimal control of the offshore wind turbine generator are realized, the operation efficiency, reliability and safety of the wind turbine generator are effectively improved, and the operation and maintenance cost is reduced.

This document describes a virtual sensor module for generating spatial-temporal temperature measurements of a battery and a method of using the virtual sensor module to generate the above-mentioned measurements. The virtual sensor module utilizes a Proportional-Integral-Derivative (PID) controller module communicatively coupled to a trained convolutional neural network—long-short-term memory (CNN-LSTM) module to trigger the trained CNN-LSTM module to generate a refined predicted series of real temperature sequences across the battery, based on a set of optimized PID parameters (K*p, K*i, K*d), predicted error sequences, and measured temperature sequences of the battery.

The invention provides an intelligent driving simulation test method and system for an internal combustion locomotive, and relates to the technical field of intelligent driving, and the method comprises the steps: collecting the driving state information and scene complexity data of the internal combustion locomotive through constructing a virtual simulation environment; acquiring multi-modal information of the obstacle by using a virtual sensor and performing fusion processing; and constructing a deep reinforcement learning network comprising a prediction branch network and a dual-channel control network, dynamically adjusting safety, comfort and efficiency index weights based on scene complexity, and generating driving control parameters. On the premise of guaranteeing safety, dynamic balance of high efficiency and comfort of intelligent driving of the diesel locomotive can be achieved, and adaptability and reliability of an intelligent driving system are improved.

The invention relates to the technical field of water and fertilizer control, in particular to a water and fertilizer integrated control system and method based on the Internet of Things. The method comprises the following steps of obtaining environment measured data, historical environment data and regional geographic information of a farmland region, performing spatial modeling and time sequence learning on the farmland region, and constructing a farmland digital twinborn model; simulating the state of an area without sensors by using a farmland digital twinborn model to obtain virtual sensing node data; collecting multispectral image data of crops, and extracting physiological feature data of the crops; and constructing a crop physiological state model by utilizing the environment measured data, the virtual sensing node data and the crop physiological feature data, and generating crop physiological state evaluation data. Through multi-source data fusion and three-dimensional visualization integration, data-driven accurate regulation and control and whole-process intelligent management of the water and fertilizer integrated system are realized, and the intelligent level of irrigation and fertilization and the resource utilization efficiency are comprehensively improved.

The invention relates to the field of physiological status real-time monitoring, provides a virtual patient physiological status real-time monitoring method and system based on digital twinning, and is used for solving the problems of non-linear combination deviation and multi-source data synchronization lag of virtual patient physiological status monitoring caused by dependence on a static model and one-way feedback. The method comprises the steps that a valve three-dimensional model is built based on aorta CT data, and a simulation model containing motion conditions is built in combination with a fluid mechanics equation; the blood flow pressure difference and vortex parameters of valve opening and closing are calculated in real time, an optical fiber virtual sensor is embedded in the topological surface of the valve to simulate blood flow deformation, and a valve state mechanical signal is output; parameters and signals are input into a digital twin engine, particle trajectories and refractive indexes are adjusted to generate a visual interface, blood flow velocity-pressure gradient spatio-temporal data are extracted, model boundary conditions are reversely corrected, and valve movement is synchronized with the physiological state of a patient. According to the technical scheme, heart valve movement is simulated in real time, and the real heartbeat rhythm of the patient is automatically matched.

The invention discloses an intelligent perception driven real-time data monitoring and safety evaluation system and method in the technical field of intelligent perception, and the method carries out the monitoring and evaluation after the data fusion of a sensor, and the data fusion comprises the steps: obtaining the real-time data of the sensor; constructing a virtual sensor model in the multi-level neural network by using real-time data of the sensor; performing data prediction according to the relevance of the multi-source data through the virtual sensor model to obtain the output of the virtual sensor model; performing error correction on the output of the virtual sensor model according to an error correction mechanism; and performing data fusion based on the output of the virtual sensor model after error correction. According to the method, a virtual sensor model is generated on the basis of a multi-level neural network in combination with real-time data of different sensors, and nodes which may have data drift or errors are predicted and calibrated in real time on the basis of relevance of multi-source data, so that highly consistent and high-precision data fusion is realized.

In various examples, physical sensor data may be generated by a vehicle in a real-world environment. The physical sensor data may be used to train deep neural networks (DNNs). The DNNs may then be tested in a simulated environment—in some examples using hardware configured for installation in a vehicle to execute an autonomous driving software stack—to control a virtual vehicle in the simulated environment or to otherwise test, verify, or validate the outputs of the DNNs. Prior to use by the DNNs, virtual sensor data generated by virtual sensors within the simulated environment may be encoded to a format consistent with the format of the physical sensor data generated by the vehicle.

The invention discloses a boiler fault self-diagnosis method and related device, and the method comprises the steps: S1, collecting boiler operation parameters, generating virtual data in combination with a digital twinborn model, and constructing a multi-modal monitoring data set; s2, carrying out preprocessing and anomaly detection on the data set by utilizing an edge computing node, and obtaining a preliminary anomaly signal and a feature vector; s3, uploading the abnormal signal and the feature vector to a cloud end, and performing simulation verification through a digital twin engine; s4, inputting the feature vector and a verification result into a hybrid enhancement diagnosis model, and outputting a fault type and a probability; s5, reasoning a fault source and a propagation path in combination with the knowledge graph according to the fault type and the probability; and S6, generating a maintenance scheme based on the fault information, and optimizing the diagnosis model by using operation and maintenance feedback. According to the method, the boiler operation parameters are collected and combined with the digital twinborn model to generate the virtual sensor data, the multi-modal monitoring data set is constructed, synchronous monitoring of multiple data is achieved, and one-sidedness of single-parameter monitoring is avoided.

The invention relates to the technical field of public safety and intelligent fire protection, in particular to a real-time processing method and system based on fire alarm data, and the method comprises the steps: environment steady state reconstruction: accessing non-fire environment dynamic parameters; presetting thermotechnical static parameters of the building space; constructing a space thermal inertia differential equation; generating an ideal reference baseline; knowledge-driven simulation: receiving the ideal reference baseline; calling a synthesis operator in a preset disaster interference knowledge base; executing dynamic superposition injection; generating a virtual sensor state flow which has physical characteristics and contains environmental background characteristics; homomorphic judgment: executing double difference calculation; obtaining a real residual feature vector; geometric homomorphism verification is executed; outputting fire alarm triggering, interference filtering or fault prompting instructions; according to the method, the problem of false alarm caused by non-stable fluctuation of the environment background in the background technology is solved, and dynamic fusion and scene adaptation of the standard signal model and the real environment background are realized.

The invention discloses a high-precision pressure sensing and self-calibration system, and relates to the technical field of pressure sensing, the system comprises a pressure sensing module, an environment monitoring module, a data processing module, a digital twinning module and a self-calibration module, the pressure sensing module collects and converts pressure signals through a high-precision sensor, and the environment monitoring module obtains environment parameters in real time; the data processing module carries out amplification, filtering, analog-to-digital conversion and timely frequency domain analysis on the initial electric signal, and outputs preprocessed data in combination with environmental parameter dynamic compensation. The digital twin module constructs a sensor virtual model, and simulates and outputs an ideal pressure predicted value; and the self-calibration module compares the predicted value with the preprocessed data, generates a final pressure value through deviation calculation, iterative learning and data fusion, and can also calculate the health index of the sensor and perform early warning maintenance. The system realizes high-precision pressure induction and real-time self-calibration, improves the measurement accuracy and prolongs the service life of the sensor.

The present invention relates to a method and system for detecting an anomaly of an electrical submersible pump, ESP. Specifically, the proposed invention provides: physics-based anomaly assessment, automatic data imputation using virtual sensors network (203), automatic continual learning based on automatic model drift detection, automatic failure prevention based on reinforcement learning, remaining useful life (RUL) estimation, and cohort analysis.

In various examples, to support training a deep neural network (DNN) to predict a dense representation of a 3D surface structure of interest, a training dataset is generated using a simulated environment. For example, a simulation may be run to simulate a virtual world or environment, render frames of virtual sensor data (e.g., images), and generate corresponding depth maps and segmentation masks (identifying a component of the simulated environment such as a road). To generate input training data, 3D structure estimation may be performed on a rendered frame to generate a representation of a 3D surface structure of the road. To generate corresponding ground truth training data, a corresponding depth map and segmentation mask may be used to generate a dense representation of the 3D surface structure.

The invention relates to the technical field of digital twinning, in particular to an industrial equipment digital twinning system and an intelligent optimization method, and integrates digital twinning modeling, edge computing, cloud computing and visualization technologies. The method comprises the following steps: firstly, constructing an equipment digital twinborn body through three-dimensional modeling and a CAD drawing, and simulating and acquiring data by using a virtual sensor; and the edge calculation module synchronizes physical equipment and digital model data to realize collaborative perception, reasoning, state evaluation and early warning. And the cloud computing module further analyzes the data and optimizes the operation parameters of the equipment. The visualization module integrates multivariate data in a three-dimensional interface, visually displays the equipment state and optimization information, responds to a user instruction, realizes comprehensive, real-time and interactive equipment monitoring and management, and improves the intelligent level and operation and maintenance efficiency of industrial equipment, and the systematic method not only improves the overall efficiency of industrial equipment management, but also improves the operation and maintenance efficiency of the industrial equipment. And reliable technical support is provided for digital transformation of enterprises.

The invention provides a marine meteorological data quality control method based on a cross-parameter correlation network, which belongs to the technical field of marine meteorology, and comprises the following steps: constructing a cross-parameter correlation network model comprising an air temperature and air pressure correlation rule, a humidity and air temperature linear correlation rule and a wind speed and air pressure gradient extraction correlation rule; a sliding window algorithm is adopted to calculate a correlation coefficient in real time and identify correlation abnormity, a multivariate abnormity detection mechanism of four dimensions of parameter threshold overrun, spatio-temporal change rate abnormity, probability density distribution offset and correlation verification failure is established, a fuzzy comprehensive evaluation method is adopted to calculate a comprehensive abnormity index, and data abnormity is judged. And the authenticity of the abnormal data is confirmed in combination with a multi-sensor cross validation mechanism, and finally the abnormal data is restored by adopting a virtual sensor data reconstruction algorithm based on correlation network reverse calculation, so that the technical problem of poor abnormal data detection effect in the prior art is solved.

The invention discloses a self-adaptive dynamic control method and system for the machining process of a numerical control machine tool, and relates to the field of intelligent control, and the method comprises the steps: collecting machining data in real time through physical and virtual sensors, and constructing a standardized data set after layering preprocessing; a CNN-LSTM hybrid model is utilized to extract spatial-temporal characteristics to realize working condition classification, and an NSGA-II algorithm is combined to solve a multi-objective optimization problem to generate an optimal control parameter solution set; parameters are dynamically adjusted through fuzzy PID, and a GRU model is adopted to predict machining errors for feed-forward compensation, so that closed-loop control of perception-decision-execution-feedback is formed. The system continuously monitors the actual machining deviation, parameters are optimized again when the actual machining deviation exceeds a threshold value, and cooperative improvement of machining precision and efficiency is achieved. The method has the advantages that NSGA-II multi-target optimization, fuzzy PID correction and GRU error prediction compensation are recognized through CNN-LSTM working conditions, closed-loop feedback iteration is combined, the machining precision and efficiency are improved in a balanced mode, the service life of a tool is prolonged, and the method is suitable for complex working conditions.

The invention discloses an elevator digital twinning virtual and physical sensor data calibration method. The method comprises the steps that real-time data of multiple types of physical sensors in an elevator system and real-time data of virtual sensors in an elevator digital twinning model in a reference environment at the same moment are obtained; preprocessing the data, and comparing the acquired data of the physical sensor and the virtual sensor of the same category according to a distance function to obtain an error between the physical sensor and the virtual sensor at the moment t; comparing whether the error exceeds a set error threshold value or not; and if the error exceeds the threshold value, correcting the physical sensor data through linear regression calibration and an offset correction function, and updating the virtual sensor data at the next moment in the digital twin. The actual measurement data of the physical sensor and the measurement data of the virtual sensor of the digital twin are combined, the sensor deviation is calculated and calibrated in real time, and the problem of single sensor error can be effectively solved.

The invention provides an industrial production Internet of Things data anomaly detection method, medium and system, and belongs to the technical field of industrial production Internet of Things. Industrial equipment sensor data is collected and preprocessed to establish a multi-dimensional data set, and principal component analysis and a mutual information algorithm are used to construct a dimension reduction feature data set; a virtual sensor algorithm is utilized to make up for data missing to form an extended data set, a simulation statistical mechanical anomaly analysis model is established based on a statistical mechanical law to convert the microscopic state of massive high-dimensional sensor data into a macro thermodynamic parameter, and a statistical mechanical feature vector is calculated through a virtual particle ensemble simulation equipment operation state. A state evaluation model and a dynamic threshold function are adopted to identify an abnormal mode and perform grading marking, a feedback optimization mechanism is constructed to continuously improve the system performance, and the technical problem that a traditional algorithm has a curse of dimensionality and cannot perform effective anomaly detection due to extremely high data dimensionality of an industrial equipment sensor is solved.

The invention relates to the technical field of simulation scenes, and discloses a simulation scene trueness evaluation method, device and equipment and a storage medium, and the method comprises the steps: constructing a vehicle simulation scene, and carrying out the data collection of the vehicle simulation scene through a virtual sensor, and obtaining a simulation data set; acquiring operation data of the vehicle in a real scene through a real sensor to obtain a real data set; performing alignment processing on the simulation data set and the real data set to obtain an aligned simulation data set and an aligned simulation data set; determining scene similarity based on the aligned simulation data set and the aligned real data set; and carrying out simulation scene authenticity evaluation according to the scene similarity to obtain an evaluation result. According to the invention, the evaluation accuracy of the trueness of the simulation scene can be effectively improved.

The invention discloses a one-way valve air tightness detection method and system, and relates to the technical field of air tightness detection.According to the one-way valve air tightness detection method and system, through dynamic interaction of AI virtual sensor prediction and physical detection values and combination of time-frequency domain signal fusion, the extraction capacity of micro-leakage signals is remarkably enhanced, and misjudgment caused by background noise or residual gas in a traditional method is avoided; a multi-mode interference source correlation analysis and dynamic weight distribution mechanism is utilized to actively identify and compensate composite interference such as temperature drift and electromagnetic pulse, so that the reliability of a detection result in severe scenes such as workshop vibration and airflow disturbance is improved; and meanwhile, a model adaptation module based on transfer learning and non-contact vibration spectrum monitoring are adopted, manual parameter adjustment dependence is reduced, valve bodies of different specifications are rapidly adapted, early damage of the diaphragm is warned in advance, and the equipment maintenance period is prolonged.

The invention belongs to the technical field of intelligent disaster prevention, and particularly relates to a digital twinborn intelligent operation, maintenance and evaluation system for energy dissipation and shock absorption parts. The system comprises an intelligent sensing module, a fidelity reduced-order model and a virtual-real interaction intelligent decision-making module, and a solution is provided for intelligent operation and maintenance of energy dissipation and shock absorption parts by integrating and adopting a digital twin technology, an internet of things technology, an edge computing technology and other technologies. According to the method, a digital twinborn technology is adopted to establish a fidelity virtual twinborn body of the energy dissipation and shock absorption part, the overall performance state of the part is perspective through a virtual sensor means, and a new technology is provided for whole-life-cycle operation and maintenance of the part. By means of the technology, the performance state of the core component of the engineering structure can be mastered in real time, the core component can be kept in a full-time online transparent state in the whole life cycle, and the anti-disaster toughness capacity of the engineering structure is improved on the whole.

The invention relates to the technical field of PLC control, in particular to a B / S architecture-based digital twin virtual debugging system and method for a transmission line. The system comprises a three-layer B / S (Browser / Server) architecture consisting of a PLC (Programmable Logic Controller) simulation layer, a business logic layer and a three-dimensional visualization layer, the PLC simulation layer is used for operating a PLC program to obtain a feedback signal of the controlled equipment, simulating the control logic of the PLC to the controlled equipment based on the feedback signal, and sending a control signal to the service logic layer; the business logic layer is used for acquiring the control signal and carrying out business logic operation based on the control signal and the feedback signal so as to calculate and simulate the movement of the controlled equipment and obtain real-time position data; and the three-dimensional visualization layer is used for displaying the operation state of the controlled equipment based on a pre-constructed virtual model of the controlled equipment and the real-time position data so as to display the operation result of the PLC program, generating a feedback signal based on a virtual sensor on the virtual model of the controlled equipment, and sending the feedback signal to the business logic layer.

The invention discloses a tension monitoring method, device and system for a multi-component coupled chain transmission system. The method comprises the following steps: carrying out dynamic modeling and simulation analysis on the multi-component coupled chain transmission system; on the basis of analyzing the basic structure and the working principle of the multi-component coupled chain transmission system, the chain tension of the multi-component coupled chain transmission system is analyzed and calculated; finite element analysis is carried out on the multi-component coupled chain transmission system, and a virtual chain tension sensor is designed; and carrying out experimental research on the virtual sensor design method. Real-time monitoring of the chain tension is achieved through the virtual sensor, the hardware requirement and the system cost are reduced, and the monitoring precision and the real-time performance are improved.

The present invention relates to an electronic device and corresponding method, where the electronic device includes at least two sensors. The at least two sensors are in communication with a virtual sensor including a processing unit adapted to monitor the use of the device based on input from said sensors. The at least two sensors include activity sensors sensing user activities related to the device, and the processing unit is configured to receive and analyze the input from said sensors during use and to provide and store a model representing said typical use of the device. The device further being configured to compare new measurements received from said sensors with said model, providing a signal if the new measurements from said sensors deviates by a predetermined limit from the measurements predicted by the model.

In various examples, sensor data used to train an MLM and / or used by the MLM during deployment, may be captured by sensors having different perspectives (e.g., fields of view). The sensor data may be transformed—to generate transformed sensor data—such as by altering or removing lens distortions, shifting, and / or rotating images corresponding to the sensor data to a field of view of a different physical or virtual sensor. As such, the MLM may be trained and / or deployed using sensor data captured from a same or similar field of view. As a result, the MLM may be trained and / or deployed—across any number of different vehicles with cameras and / or other sensors having different perspectives—using sensor data that is of the same perspective as the reference or ideal sensor.

The embodiment of the invention provides a control unit and a control method for comfortable braking of a vehicle. The control unit comprises: a detection module configured to detect a driving behavior of a vehicle to obtain a first detection result, detect a road condition of a vehicle driving road to obtain a second detection result, and detect a driving state of the vehicle to obtain a third detection result; the processing module is configured to judge whether a triggering condition of a virtual sensor for calculating the vehicle load is met or not based on the first detection result, the second detection result and the third detection result; if it is judged that the triggering condition is met, a virtual sensor is adopted, and the vehicle load state is obtained based on multiple sets of parameters representing the current state of the vehicle; and a comfort braking module configured to determine an adjustment to a comfort braking parameter of the vehicle based on the vehicle load condition.

The invention discloses a point cloud-driven thoracic cavity whole organ dynamic reconstruction and respiration monitoring method and system, and the method comprises the steps: constructing a three-dimensional geometric model corresponding to each target structure in a thoracic cavity based on the thoracic medical image data of a target object, and converting the three-dimensional geometric model into static point cloud data; driving the static point cloud data to perform dynamic deformation simulation in a respiratory cycle based on a respiratory movement rule of the target object to obtain dynamic point cloud data synchronized with a respiratory time phase; performing time-space synchronization association on the dynamic point cloud data and the electrical characteristic parameters, and constructing a digital twin thoracic cavity model; deploying a virtual sensing assembly which dynamically deforms along with the thoracic cavity in the digital twin thoracic cavity model, simulating and monitoring the dynamic breathing process of the target object, and generating a virtual physiological signal; and performing signal processing on the virtual physiological signal to obtain a dynamic reconstruction image reflecting respiratory movement. Continuous simulation of the full-breathing movement process is achieved, and the temporal-spatial resolution and diversity of a virtual database are improved.

A radar system. The radar system includes at least three radar sensors which are connected to one another in a phase-coherent manner. A first radar sensor and a second radar sensor are disposed spaced apart from one another. A virtual sensor is created by means of bistatic measurement of at least the first radar sensor and the second radar sensor using the MIMO method. At least one third radar sensor is disposed offset to the virtual sensor. The radar system is configured to acquire an elevation angle of a target using the virtual sensor and the at least one third sensor.

The invention provides a simulation test method and device for an automatic driving algorithm, computer equipment and a medium. The simulation test method comprises the following steps: constructing a simulation test scene; inputting the virtual sensor data into an automatic driving algorithm to obtain a virtual control instruction; when a driving traffic participant corresponding to the driving vehicle is associated with a preset behavior model, simulating a first dynamic response of the driving vehicle to the virtual control instruction in the simulation test scene; determining first simulation test data based on the first dynamic response and a corresponding preset reference response; when the driving traffic participant corresponding to the driving vehicle is associated with a non-preset behavior model, simulating a second dynamic response of the driving vehicle to the virtual control instruction in the simulation test scene; determining second simulation test data based on the second dynamic response and a corresponding preset reference response; and determining target simulation test data based on the first simulation test data and the second simulation test data. Therefore, the simulation test efficiency of the automatic driving algorithm is effectively improved.

The invention discloses a vehicle-road cooperation virtual-real fusion simulation test method, device, equipment and medium, and the method comprises the steps: setting a test event in a virtual simulation environment, generating virtual sensor detection data, sending the data to a cloud control platform, carrying out the fusion processing, generating a standard vehicle-road cooperation message, and issuing the message to a real road side unit, and the real road side unit forwards the message to the real vehicle-mounted unit, the real vehicle-mounted unit analyzes the message, generates a vehicle control instruction and returns the vehicle control instruction to the virtual simulation environment, and the state of the virtual vehicle model is updated according to the vehicle control instruction in the virtual simulation environment and response verification is completed. According to the method, a virtual-real combined vehicle-road cooperation test link is constructed under a unified framework, and a complete closed loop from virtual sensor sensing, cloud fusion processing, roadside forwarding, vehicle-mounted analysis to virtual vehicle response is realized, so that the test coverage rate and controllability of a vehicle-road cooperation function in a complex traffic scene are effectively improved; and the test efficiency and the verification precision are improved.