140 results about "Virtual sensors" patented technology

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 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.

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 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 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.

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.

The invention discloses an automatic driving test method and system. Belongs to the technical field of automatic driving testing. According to the method, a high-fidelity virtual environment is constructed by collecting real scene data, and a virtual sensor is configured; mapping the real track to a virtual space, and generating a dynamic traffic flow in combination with a car following and lane changing model; after the real vehicle and the virtual intelligent agent are synchronously initialized, the real vehicle runs in the physical field and periodically updates the state; the virtual traffic flow is dynamically evolved according to the state of the main vehicle, and the virtual sensor senses the target in the field of view and generates a virtual sensing result; real sensor data is used as a reference, and virtual and real perception information is fused to form unified input; and circularly executing until the task is completed, wherein each virtual agent only participates in one test. According to the scheme, high-reality and high-efficiency closed-loop testing is achieved, the defects of a traditional method in the aspects of scene richness and testing safety are overcome, and the reliability and repeatability of verification of the automatic driving system are improved.

A system includes an interface configured to receive first sensor data from multiple first sensors associated with a monitored system and also includes one or more processors. The one or more processors are configured to provide first input data to a plurality of regression models of a first virtual sensor corresponding to a particular sensor of the monitored system. The first input data is based on the first sensor data. The one or more processors are configured to obtain, from the plurality of regression models, output data based on the input data. The output data from each regression model of the plurality of regression models represents an estimated sensor reading of the particular sensor. The one or more processors are also configured to determine a simulated sensor reading for the particular sensor based on the output data, determine a confidence estimate associated with the simulated sensor reading, or both.

A computer-implemented method for generating synthetic inertial measurement unit (IMU) data comprises: accessing a video dataset containing a multitude of video frames depicting at least one human performing a physical activity; using a pose estimation model, extracting three-dimensional (3D) body mesh data from the multitude of video frames, where the 3D body mesh data represents a human posture in each of the multiple video frames; generating an animation of the 3D body mesh data, where the animation comprises a sequence of 3D body meshes representing the human movement; defining a multitude of virtual sensor positions on the 3D body mesh; and generating synthetic inertial data for each virtual sensor.

The invention relates to a method for determining a failure probability, in particular at least one state of increased failure probability, of at least one cell system (10a) with at least one electrochemical cell (12a), wherein in at least one determination step (14a; 14b; 14c; 14d) at least one degradation parameter of the at least one electrochemical cell (12a), which in particular allows conclusions to be drawn about a degradation state of the at least one electrochemical cell (12a) in a defined state, is determined by means of a virtual sensor unit (16a). It is proposed that in one determination step (14a; 14b; 14c; 14d) a failure probability of the at least one electrochemical cell (12a) is determined as a function of the degradation parameter.

The application discloses a point cloud driven thoracic cavity whole organ dynamic reconstruction and respiratory monitoring method and system, and the method implementation comprises the following steps: based on the thoracic medical image data of a target object, constructing three-dimensional geometric models corresponding to each target structure in the thoracic cavity, and converting the three-dimensional geometric models into static point cloud data; based on the respiratory motion law of the target object, driving the static point cloud data to perform dynamic deformation simulation in a respiratory cycle, and obtaining dynamic point cloud data synchronized with the respiratory phase; synchronously associating the dynamic point cloud data with electrical characteristic parameters in time and space, and constructing a digital twin thoracic cavity model; deploying a virtual sensor assembly that dynamically deforms with the thoracic cavity in the digital twin thoracic cavity model, and simulating and monitoring the dynamic respiratory process of the target object, and generating a virtual physiological signal; and through signal processing on the virtual physiological signal, a dynamic reconstruction image reflecting the respiratory motion is obtained. The continuous simulation of the whole respiratory motion process is realized, and the time and space resolution and diversity of the virtual database are improved.

The invention discloses a vehicle fault-tolerant control method under the abnormal condition of a multivariable measurement sensor, and belongs to the crossing field of electric digital data processing and intelligent driving vehicle control technologies. The method comprises the following steps: firstly, based on spatial-temporal correlation and physical consistency constraints of multi-source sensor data, carrying out redundancy modeling and credibility evaluation on a key state of a vehicle; then, when it is detected that the sensor is abnormal, real-time compensation and correction of failure observation are achieved through a state reconstruction model driven by redundant data; and further combining a rapid fault identification and virtual sensor switching mechanism to construct a fault-tolerant control strategy for the minimum function demand of the vehicle so as to ensure that the vehicle can still maintain basic driving and safety control capability under the condition that the functions of part of sensors are damaged. According to the invention, the real-time performance of the system is ensured, the safety and robustness of the intelligent driving vehicle in a complex environment and an abnormal condition are obviously improved, and the method is suitable for a multi-sensor fusion intelligent vehicle control system.

An artificial intelligence diagnosis system includes a diagnosis model responsive to data received from a plurality of sensors. Each of the sensors is a part of an input channel further including a converter operative to process the received sensor data. A system manager is provided and is operative with an allocator to selectively distribute sensor data to the converters. The system manager operates with the converters such that a converter which is allocated with the sensor data processes the allocated sensor data and inputs the processed sensor data into the artificial intelligence diagnosis model. A converter which is not allocated with the sensor data generates virtual sensor data according to an instruction of the allocator and inputs the virtual sensor data into the artificial intelligence diagnosis model.

An exemplary virtual sensing method and system are disclosed for predictive reliability (VIPR) procedure and / or controls that employ artificial intelligence and machine learning (AI / ML), particularly deep neural networks and multi-modal deep learning, with vehicle sensor data to create virtual sensors. The virtual sensors can be used to estimate measurements and operating conditions in a hostile environment in rockets and vehicle systems.

The application provides a GIS virtual sensor point arrangement method and system based on digital twinning, which comprises the following steps: setting different fault types at different positions in a GIS digital twinning gas chamber; arranging multiple groups of virtual sensors in the GIS gas chamber, and numbering the virtual sensor groups; respectively simulating the set fault types based on the GIS digital twinning; drawing a data change curve with fault data as the independent variable and the measured value at the virtual sensor point as the dependent variable; performing linear fitting on the data change curve, and obtaining a slope which reflects the sensitivity of the virtual sensor to the fault data; and using a genetic algorithm to take the obtained slope as an adaptability index to optimize the combination of the virtual sensor points, and obtaining the optimized virtual sensor combination.

The invention provides an ultra-trace metal ion capture type electronic-grade L (+)-tartaric acid purification system, and relates to the technical field of chemical purification. An adopted system sequentially comprises a feeding pretreatment unit, a multi-tooth coordination resin capturing unit, a double-layer chelating membrane deep purification unit, a multi-stage membrane separation unit and a whole-system online monitoring and control unit in the fluid flowing direction. A metal-tartaric acid complex is controllably deconstructed through the feeding end, metal activation is induced, differential selective capture is achieved in combination with gradient filling of multi-tooth resin, and ppb and sub-ppb-level metal removal is achieved in cooperation with a double-layer chelating membrane and micro-ultrasonic mass transfer strengthening. Meanwhile, the system adopts online electrochemical sensing, an optical probe and a virtual sensor to construct a polymorphic metal detection and Kalman filtering predictive control model, so that prospective regulation, dynamic regeneration and abnormal interception are realized, and metal ions such as Ni < + > and Fe < + > are ensured to be stably as low as a sub-ppb level; and continuous output of the electronic-grade tartaric acid under the conditions of high purity, high stability and traceability is ensured.

Various embodiments relate to a method, apparatus, and machine-readable storage medium for simulating a sensor value including one or more of the following: receiving a real sensor value from a sensor device at a first location within a system; propagating the real sensor value through a digital twin simulation of the system from a first simulated location associated with the first location to a second simulated location associated with a second location within the system, wherein propagating comprises applying at least one physics-based transfer function to the real sensor value; determining that a virtual sensor value should be generated for the second location within the system; and extracting the virtual sensor value from the second simulated location of the digital twin simulation.

The invention relates to a calendering system (100') that manufactures reinforced rubber sheets comprising one or more reinforcements (200) positioned between two layers of unvulcanized rubber comprising an upper skim (SSUP) and a lower skim (SINF). The calendering system incorporates a sheet mass control system comprising: - a first virtual sensor that performs a preliminary prediction of the mass of the manufactured sheet; - a second virtual sensor that performs a dynamic estimation of the density of the rubber mixture constituting the upper and lower skims; and - a third virtual sensor that calculates the average ratio between the mass of the upper and lower skims and the sum of the spacings between pairs of cylinders (102, 104) over the last 100 meters of the manufactured sheet; so that a mass prediction is obtained prior to an actual measurement of the mass of the manufactured sheet. Figure for the abstract: Fig. 3

Sparse local fields for simulation include obtaining a scene that includes object voxel grids for objects. The object voxel grids include an object voxel grid with the object voxel grid including a voxel positioning property, a geometry field property, and a color field property of the voxel in the object voxel grid. The operations further include identifying an image plane of a first virtual sensor and a set of virtual sensor properties of a second virtual sensor capturing the scene. The operations also include performing, using the object voxel grids, rasterization of the scene on the image plane and ray casting through the scene using the set of virtual sensor properties to generate a first virtual sensor snapshot and a second virtual sensor snapshot. The operations also include outputting the first virtual sensor snapshot and the second virtual sensor snapshot.