231 results about "Virtual sensors" patented technology

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 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 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 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 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 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 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 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).

The invention discloses a self-adaptive three-dimensional flow field reconstruction method, device and system for open channel flow and a medium, and relates to the technical field of open channel flow measurement. The method comprises the following steps: acquiring multi-modal data, and preprocessing the multi-modal data; and performing preliminary three-dimensional flow field prediction on the preprocessed data through a lightweight prediction model, and performing consistency verification and anomaly detection by an anomaly detection module. And when the abnormal event is detected, packaging the multi-modal measurement data in the occurrence time window of the abnormal event and uploading the packaged multi-modal measurement data to a cloud server. And only the metering result and the equipment state data are uploaded under the condition of no abnormal working condition. And the cloud server receives the uploaded data packet, performs three-dimensional flow field reconstruction on the data through a physical prior guide large model, and obtains virtual sensor data of the failed sensor and a three-dimensional uncertainty field. And according to the reconstructed complete three-dimensional flow field, in combination with the real-time water level and the latest riverbed topography, performing numerical integration on the flow cross section to obtain a cross section flow value.

The invention relates to the technical field of coal mining, in particular to a coal mining prediction method and a coal mining prediction device. The method comprises the steps of obtaining geological basic data, and obtaining a regional geological database based on the geological basic data; based on the regional geological database, carrying out three-dimensional geological modeling by adopting a multi-field coupling modeling method; performing dynamic iteration correction on the three-dimensional geologic model based on the data consistency constraint of the virtual sensor node; production working condition parameters of the working face in the mining process are obtained in real time, the production working condition parameters are converted into constraint conditions of the three-dimensional geologic model, and dynamic modeling in the mining process of the working face is carried out; based on the production working condition parameters and the three-dimensional geologic model, intelligent analysis and multi-field coupling simulation are carried out, and a mining prediction result is obtained; updating the mining decision in real time based on the mining prediction result; the device is applied to the method, and accurate prediction, dynamic optimization and safety guarantee of the mining process are achieved.

The application belongs to the field of vehicles, and particularly relates to an algorithm verification system and method based on digital twinning. The system comprises four subsystems, namely a digital bench system, a physical bench system, a real vehicle bench system and an interactive terminal system. The digital bench system realizes switching and fusion of multiple working conditions and multiple scenes and input of virtual sensor data through software. The real vehicle system and the physical bench system receive input of the digital bench system through a communication connection module, thereby performing testing and outputting measured data to the interactive terminal system. The system covers three types of testing, namely pure model simulation, hardware-in-the-loop sensor data testing based on digital twinning and real vehicle interactive testing based on digital twinning. The four subsystems of the application realize interactive combination based on API, can effectively test chassis domain braking and steering performance, and can effectively verify accuracy of vehicle lateral and longitudinal control algorithms.

The present invention relates to a virtual sensor performance monitoring device. The device includes a sensor, and a processor configured to monitor performance of a virtual sensor that predicts an amount of dissolved oxygen in an aquaculture farm on the basis of aquaculture farm environment data collected through the sensor.

The application relates to the technical field of PLC control, in particular to a conveying line digital twin virtual debugging system and method based on a B / S architecture. The system comprises a three-layer B / S architecture composed of a PLC simulation layer, a business logic layer and a three-dimensional visualization layer. The PLC simulation layer is used for running a PLC program to obtain feedback signals of controlled equipment, simulating the control logic of the PLC on the controlled equipment based on the feedback signals, and sending control signals to the business logic layer. The business logic layer is used for obtaining the control signals and performing business logic operation based on the control signals and the feedback signals to calculate the movement of the simulated controlled equipment and obtain real-time position data. The three-dimensional visualization layer is used for displaying the running state of the controlled equipment based on a pre-constructed virtual model of the controlled equipment and the real-time position data to show the running result of the PLC program, and generating feedback signals based on virtual sensors on the virtual model of the controlled equipment and sending the feedback signals to the business logic layer.

Systems, methods, and other embodiments described herein relate to improving the testing of electronic devices using a rolling test bench and simulated sensor data. In one embodiment, a method includes relaying sensor output from a sensor in a vehicle, through an interface device to a test device on the vehicle. The interface device bridges the sensor and the test device. The method also includes generating simulation data. The simulation data simulates the sensor output from the sensor connected to the test device through the interface device. The method also includes relaying, through the interface device, the simulation data to the test device.

The invention relates to the technical field of industrial heat treatment equipment control, in particular to a heat treatment furnace safety control system based on a cloud platform. The multi-dimensional state sensing module is used for collecting real-time physical field data of an industrial controlled object; the signal orthogonal decomposition module is used for decoupling the original state data set into a low-frequency control signal flow and a high-frequency characteristic signal flow, and transmitting the high-frequency characteristic signal flow to a remote computing platform; the state reconstruction module is used for resolving a theoretical state value at the current moment and constructing a virtual sensor observation state; the dynamic envelope generation module is used for calculating a residual error between a theoretical state value and real-time physical field data and generating a dynamic security envelope threshold value and a confidence coefficient attenuation coefficient; and the self-adaptive safety judgment module is used for generating a final safety execution threshold value and executing safety interlocking control on the industrial controlled object. According to the method, the sensitivity of capturing the abnormal working condition in the heat treatment process is improved, and the problem that the static threshold value easily generates false alarm or missing alarm under the complex variable working condition is solved.

The invention discloses a virtual sensor modeling and deployment method and system based on spatio-temporal feature fusion and a gradient boosting strategy, and relates to the technical field of industrial process monitoring and control, embedded intelligent sensing and data-driven modeling. The method comprises the following steps: collecting multi-source sensor data under a multi-environment condition and preprocessing the multi-source sensor data; constructing a spatial-temporal characteristic system which simultaneously represents historical memory, dynamic change and a multivariable coupling relationship, and performing characteristic screening and weight reduction; under a gradient lifting framework, adopting automatic hyper-parameter optimization to obtain a virtual sensor model with compromise between performance and complexity; the trained model is converted into a unified reasoning format irrelevant to a platform, and real-time reasoning is achieved on a resource-limited embedded control unit. Compared with the prior art, the method has higher prediction precision and generalization ability under the complex dynamic working condition, the model size and the single reasoning delay are controllable, and the method is suitable for being deployed and applied in a vehicle-mounted ECU, an industrial controller and an edge node.

Apparatus and computer-implemented method for testing a technical system, in particular a system for determining a virtual sensor signal, for example a virtual sensor, comprising a model modeling a physical process, a physical input variable of which results in a physical output variable, the model is provided with a proxy model which is designed to describe a real physical process, input values are detected, simplex shapes which are expanded by the input values are determined, each simplex shape is determined in particular during a human-computer interaction with a user or the input values are randomly extracted on the simplex shape according to a predefined probability distribution, and the input values are detected by the proxy model. Determining an output value for the input value using a proxy model, mapping the input value to a prediction for the output value using the model, in particular for determining a distance between the virtual sensor signal and the prediction and the output value, the result includes a probability that the model maps the input value to a prediction of an output value for the physical input variable, in particular deviating from the physical process by more than a predetermined deviation.

The invention discloses a digital optical simulation test method and device, a storage medium and a product, and relates to the technical field of simulation tests.The digital optical simulation test method comprises the steps that modeling parameters and modeling objects are obtained, a corresponding target scene model is established, the modeling objects comprise a target vehicle, and the modeling parameters comprise the modeling parameters and the modeling objects; the modeling parameters comprise environmental parameters, physical optical parameters and motion parameters, establishing a virtual camera and a virtual sensor in the target scene model, and generating a simulation image based on the environmental parameters, the physical optical parameters, the motion parameters, the virtual camera and the virtual sensor, and performing a simulation test on the auxiliary driving system of the target vehicle in a pure digital environment. According to the invention, the simulation test is carried out by establishing the model, the real vehicle does not need to run for a long distance on the road, and the test efficiency is improved. And simulation testing can be performed on different environments, object optical materials and vehicle driving scenes by setting modeling parameters, so that the comprehensiveness of the test scene is improved.

The present invention describes apparatus / methods for enhanced data protection by determining the required level of data protection for a data source, determining at least a first data-generating device, and assigning protection scheme settings and parameters to the data generated by at least the first device. In one example, a data stream from a data-generating device that is designated as confidential / protected (e.g., a device that could potentially leak confidential information) is protected from reconstruction from unprotected data streams from one or more other data-generating devices that could be used to provide useful input to a virtual sensor of that confidential / protected sensor.

The invention relates to an intelligent manufacturing production line real-time monitoring method and device, equipment and a medium. The method comprises the following steps: acquiring observation data and simulation output data in real time, performing time synchronization alignment processing to obtain a physical-virtual sensor synchronization data stream, and aggregating all comprehensive mismatch degrees according to the comprehensive mismatch degrees of each physical sensor-virtual sensor pair to obtain a global mismatch index; when the global mismatch index exceeds a preset threshold value, identifying a mismatch salient region based on each comprehensive mismatch degree, and performing sensitivity analysis on key physical parameters of the mismatch salient region to obtain a suspicious parameter subset; constructing a loss function according to the observation data and the simulation output data, and minimizing the loss function to solve the suspicious parameter subset to obtain an optimal parameter correction value; and updating the model parameters based on the optimal parameter correction value to obtain a corrected digital twinborn model. By adopting the method, the digital twin model can keep high-fidelity virtual mapping capability in the full life cycle of the production line.

The application discloses a real-time leakage positioning monitoring system and method based on electrical resistance tomography, relates to the technical field of process tomography and industrial safety monitoring, and solves the technical problem that it is difficult to intelligently identify, three-dimensionally position and quantitatively evaluate leakage events; the system comprises a sensor array, a multi-channel data acquisition and excitation module, a real-time data processing and image reconstruction module, a leakage intelligent identification and positioning module, a man-machine interaction and data management module; the core lies in that a multi-layer nested annular array and a sparse measurement strategy are adopted to improve data acquisition efficiency, a physical information neural network is introduced to reconstruct an image, a measurement topological coding matrix and a physical residual correction module are used to reconstruct the image, and finally, a time-space dual-domain leakage probability field model is constructed, a virtual sensor network and an improved D-S evidence reasoning framework are combined to intelligently identify and position a leakage diffusion mode.

The invention relates to a method for positioning at least one real sensor, in particular a safety sensor or a camera for industrial safety applications, in a real environment of an industrial plant by means of an augmented reality view based on the real environment of the industrial plant, wherein at least one simulated parameter of a virtual sensor is visualized for a user in the augmented reality view by means of a display device, wherein the virtual sensor is based on the real sensor to be positioned, wherein the simulated parameter comprises at least one position parameter, wherein the at least one position parameter specifies a position and / or an orientation of the virtual sensor in the augmented reality view, and wherein a change in the position parameter is obtained.wherein the updated position parameter based on the received change is visualized for the user in the augmented reality view by means of the display device, and wherein at least one output parameter is output which is based on the updated position parameter of the virtual sensor and which enables the positioning of the real sensor in the real environment.

A method of operating a cooling system of a vehicle includes measuring a refrigerant pressure and a refrigerant temperature of a flow of refrigerant in a refrigerant circuit of the cooling system, estimating a relative humidity of a supply airflow across an evaporator of the refrigerant circuit utilizing the measured refrigerant pressure and the measured refrigerant temperature, and changing operation of one or more components of the refrigerant circuit as a result of the estimated relative humidity.

The application relates to the technical field of wind turbine data analysis, and discloses a system for analyzing and predicting offshore wind turbine operation data. The system comprises a marine environment data integration module, which collects data to generate a multi-source spatiotemporal synchronous data set; a multi-modal feature fusion module, which extracts cross-modal correlation features to generate a high-dimensional fusion feature tensor; a dynamic fault prediction module, which constructs a bidirectional gated recurrent network model to predict the degradation probability and residual life of key components of equipment; and a self-adaptive optimization control module, which constructs a multi-objective dynamic programming model to optimize the operation strategy of the wind turbine. In addition, the system is also provided with a feedback correction module to correct the parameters of the prediction model, and a virtual sensor module based on a physical information neural network to monitor the tower stress and diagnose sensor faults. The system realizes comprehensive monitoring, accurate fault prediction and optimized control of offshore wind turbines, effectively improves the operation efficiency, reliability and safety of the wind turbines, and reduces the operation and maintenance cost.