291 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.

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 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 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 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 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 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.

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.

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.

The invention relates to the technical field of data processing, and discloses a motor controller research and development method and system based on an intelligent algorithm. The method comprises the steps that after parameter identification is carried out on a motor, PID parameters are optimized through an adaptive differential evolution algorithm; constructing a double-loop control structure according to the optimal PID parameters; designing a virtual sensor based on the optimized double-loop structure and obtaining a health index; fusing the multi-source signal to obtain a fusion speed signal; therefore, a backstepping sliding mode controller is realized and system verification is completed. The motor control system with high fault-tolerant capability is constructed by fusing various intelligent algorithms and virtual sensor technologies, the basic operation performance of the motor can be kept even if the sensor is abnormal or fails, and the reliability and the safety of the system are improved.

The invention provides an industrial sensor intermittent fault detection method and device and a medium. The method comprises the following steps: acquiring a process input vector of a target industrial process at a current moment and a process output measurement value measured by a sensor to be measured; determining a process output predicted value according to the process input vector and the virtual sensor model; calculating a current residual between the process output measured value and the process output predicted value; if the current residual error exceeds a target early warning threshold value, marking an early warning signal, and generating a first fault mark under the condition that the continuous triggering times of the early warning signal on a time sequence reach a first preset times; based on the residual sequence from the historical starting moment to the current moment, the posterior probability that the sensor to be detected is in a fault state at the current moment is calculated through a hidden Markov model, and a second fault mark is generated under the condition that the posterior probability is larger than or equal to a preset probability threshold value; and if at least one of the first fault mark and the second fault mark is established, determining that the to-be-detected sensor has an intermittent fault.

This invention relates to a method for visually recognising and tracking objects and events in 3D space with high accuracy, reduced computational load and fast setup, using virtual sensors that are designed to ensure high reliability and reduce costs by maximising their potential for accurate object recognition for given visual input data. The main task of a virtual sensor is to provide cost-effective and reliable data and replace the use of humans and / or hardware sensors to detect and recognise objects or events that are important to a given entity, such as an industrial enterprise and various business entities. The solution is well suited for industrial applications, especially for tracking larger objects of known shape or appearance, such as in large industrial halls, warehouses without fixed racking systems, container docks, train docks, car parks, etc. It is particularly suitable for tracking coils, metal pieces and larger building components. The method provides a reliable source of i data for obtaining a digital twin of the monitored objects, which represents real-time information about each stock keeping unit (SKU) in the warehouse (WH), bringing significant benefits to WH managers and thus saving human labour spent on searching for materials, improving management flow, reducing overall equipment effectiveness (OEE) of vehicles, improving throughput, quality, etc.

The invention provides a simulation monitoring method and device for a vehicle intelligent cabin production line, and the method comprises the steps: building a multi-level model based on a plurality of level nodes corresponding to a target vehicle intelligent cabin production line; performing logic reconstruction on operation and control activities set by the target vehicle intelligent cabin production line to obtain a multi-level logic model; a mapping transmission channel is correspondingly established between the virtual sensor and the monitoring point, and a simulation monitoring model is obtained, so that real-time data collected by the target vehicle intelligent cabin production line are synchronously associated in the multi-level logic model; and performing visual rendering on the simulation monitoring model, setting a functional unit in the simulation monitoring model, obtaining a simulation monitoring system, and performing monitoring management on the target vehicle intelligent cabin production line. By means of the method, the accuracy and the real-time performance of monitoring the vehicle intelligent cabin production line are improved, and then the efficiency and the stability of producing the vehicle cabin are improved.

The invention provides a wake flow control method and system based on multi-source heterogeneous wind field data, and the method comprises the steps: generating a three-dimensional true value wind field according to the observation data of a multi-source heterogeneous sensor in a target wind field region, and carrying out the sampling in the three-dimensional true value wind field through a virtual sensor, and obtaining three-dimensional virtual observation data; based on the three-dimensional virtual observation data, performing style migration by using a conditional generative adversarial network to obtain enhanced observation data; performing wind field prediction on the target wind field area based on the enhanced observation data, and outputting three-dimensional wind field prediction information; according to the three-dimensional wind field prediction information, utilizing a federated average algorithm to generate a wake flow control strategy of the target wind field area; according to the method, the three-dimensional true value wind field and the virtual observation data are generated through the multi-source heterogeneous data, the prediction precision can be improved through enhancement processing, the control strategy is generated in combination with the federal algorithm, the data heterogeneous and sparse problems can be solved, the wake flow control accuracy and efficiency can be improved, and the data privacy can be protected.

The invention discloses an intelligent agricultural intelligent monitoring system and method based on big data, relates to the technical field of agricultural intelligent monitoring, and aims at the core defect of a terrace monitoring scene, through curvature analysis and a microclimate attenuation model, the monitoring of a physically unreachable area is converted into a computable problem of a digital twin space, and the real-time monitoring of the terrace monitoring scene is realized. The sensor deployment requirements of abrupt slopes and ridges are obviously reduced; the terrain continuity coefficient accurately depicts the integrity of the stepped structure, and the humidity gradient quantifies the environment propagation rule of the adjacent plots, so that the virtual sensor network can dynamically generate blind area soil moisture content data, and the terrain adaptability defect of an OODA closed loop is overcome; besides, physical nodes and virtual nodes are integrated by adopting a heterogeneous topological graph, effective neighborhood information is dynamically screened by a graph attention mechanism through spatial attenuation and microclimate similarity, and strong dependence of traditional federal learning on data homogenization is avoided.

The invention provides a working face sensor dynamic activation management method and system based on digital twinning, and provides a life and death sensor mechanism for solving the problems of model updating lag, monitoring blind areas and insufficient geological adaptability caused by frequent manual movement of a physical sensor in the prior art. A virtual sensor array covering the full-length range of a working face is preset in a digital twin model, a virtual sensor activation / deactivation update rule is triggered in real time in combination with a propulsion distance, the sensor coverage range of the digital twin model and a physical scene is dynamically synchronized, and manual intervention delay is eliminated. The system automatically reduces the distance between the virtual sensors according to geological changes, predicts missing data through the GAN, and improves the monitoring density of a high-risk area. Kalman filtering calibration errors, isolation forest algorithm detection abnormity and reinforcement learning optimization threshold values are integrated, and monitoring continuity is guaranteed. According to the method, the model maintenance complexity can be remarkably reduced, the monitoring precision is improved, and key technical support is provided for intelligent mine construction.

The invention provides an FSO multi-sensor fusion method based on unscented Kalman filtering and an RBF neural network, and the method comprises the steps: carrying out the unscented Kalman filtering time updating, and carrying out the measurement of a noise covariance adjustment mechanism and an event triggering mechanism based on light intensity self-adaption; the unscented Kalman filter measures and adjusts confidence weights of the infrared camera and the four-quadrant detector according to the channel state provided by the avalanche photodiode, and processes data output by the infrared camera and the four-quadrant detector according to the confidence weights; then, the posterior state estimation of the unscented Kalman filter at the current moment is output, and a position component is extracted and transmitted. Normalized light intensity reflecting channel quality is used as a key state to carry out synchronous estimation, an RBF neural network is used to carry out dynamic modeling and compensation on nonlinear errors of a four-quadrant detector, and a self-adaptive unscented Kalman filtering framework with a line learning capability is constructed. And finally, a virtual sensor output with high update rate, high precision and high sensitivity is generated.

The invention discloses an intelligent driving redundancy perception enhancement method and system based on a virtual sensor, and the system comprises a data collection module which is used for obtaining the detection data of a real sensor; the virtual sensor construction module is used for processing the collected multi-source data based on a deep learning algorithm so as to construct a virtual sensor; the virtual sensor is used for outputting identification data based on input real-time multi-source data; the multi-source data are original data collected by various real sensors; the sensor state monitoring module is used for detecting the working state of a real sensor; the data fusion module is used for fusing the identification data with detection data of a real sensor to obtain sensing data; and the decision execution module is used for generating a decision instruction according to the identification data or the sensing data, and the decision instruction responds to an execution mechanism of the intelligent driving system.

The invention relates to a method, in particular a computer-implemented method, for providing a data-based sensor model for use as a virtual sensor in a technical system, having the following steps: - providing training data sets that result from a test bench measurement, wherein each training data set assigns one or more state variables, in particular state variables recorded using sensors, and / or one or more predefined operating variables to a plurality of labels at a particular time, wherein the labels comprise one or more measured sensor variables to be modelled and one or more additional variables; - training the data-based sensor model with the aid of the training data sets; - implementing the sensor model in a control unit of the technical system, such that only the one or more sensor variables to be modelled are used as virtual sensors during model evaluation.

There is provided a method of determining a virtual sensor measurement for a reconfigurable thermal management system of an electric vehicle. The method comprises obtaining a plurality of virtual sensor models, each virtual sensor model operable to determine a virtual sensor measurement based on at least one sensor measurement value, wherein each virtual sensor model is associated with a configuration of the thermal management system, receiving an indication of a configuration of the thermal management system, receiving a sensor signal indicating a sensor measurement value, selecting a virtual sensor model of the plurality of virtual sensor models based on the indication of the configuration of the thermal management system, determining a virtual sensor measurement value based on the selected virtual sensor model and the received sensor signal, and outputting a signal representative of the virtual sensor measurement value.

The invention relates to a computer-implemented method for numerically simulating a system (SYS) under predefined system (SYS) boundary conditions (BCD), wherein said system (SYS) is comprising at least one physically moving element (MVE), in particular method for simulating a fluid-dynamic system (SYS), the method comprising: (a) generating of an element mesh (LTC) for the purpose of performing the simulation (SIM), wherein the element mesh (LTC) extends across the system (SYS), wherein said at least one physically moving element (MVE) moves relative to elements of the element mesh (LTC) during a period of the simulation (SIM); (b) running said simulation (SIM) to simulate the physical system (SYS) to determine at least one parameter value (PRV) of said system (SYS); (c) using a neural network (NNW), predicting at least one parameter correction value (PCV) representative of an error associated with a solution of the simulation (SIM) for the at least one parameter value (PRV) of said system (SYS); and (d) correcting the solution of the simulation (SIM) for the at least one parameter value (PRV) using the parameter correction value (PCV) to produce a corrected solution of said simulation (SIM) of the physical system (SYS). It is proposed, that said at least one parameter correction value (PCV) is assigned to the at least one physically moving element (MVE) and said at least one parameter correction value (PCV) being transported along said element mesh (LTC) elements together with said at least one physically moving element (MVE).