64 results about "Sensing system" patented technology

The invention relates to the technical field of intelligent sensing systems, in particular to an algorithm and system for avoiding water surface floating objects and improving monitoring precision based on radar water level monitoring, and the method comprises the following steps: collecting radar echoes, constructing spatial-temporal characteristics based on waveform asymmetry and coherent attenuation gradient, and calculating the spatial-temporal characteristics; according to the method, the central moments of the front edge and the rear edge of the echo are calculated, the waveform asymmetry is quantified, the dynamic stability is analyzed in combination with the attenuation gradient of the multi-time-lag coherence coefficient, the floating object interference is recognized from the double dimensions of the spatial form and the time memorability, and the actual water level is inverted. An effective scattering trailing interval is positioned, nonlinear approximation operation is performed on trailing data in combination with an index physical model, a shielded water surface energy distribution curve is reconstructed, a derivative zero point is solved, distance measurement deviation caused by floating objects is corrected, the real water level height is effectively restored, and the monitoring anti-interference capability and the data accuracy in a complex hydrological environment are improved.

The invention discloses a water conservancy project full life cycle operation and maintenance management method and platform, and relates to the technical field of water conservancy project management.The weir crest water level Hw, the gate flow Qo, the gate opening and closing pressure Pg, the structure dip angle Gq and the iron concentration cFe are accurately collected through a multi-source physical sensing unit; and the sensor type, the arrangement position and the acquisition condition corresponding to each parameter highly accord with the operation characteristics of the water conservancy project, so that the data source is stable, the coverage is wide, and a high-frequency real-time data sensing system is formed. The mechanism breaks through the problem of low credibility of previous parameters, and lays a high-quality data foundation for subsequent operation state modeling and situation map construction. A structural stability correlation function constructed based on a water conservancy physical mechanism is introduced as an evaluation index, sliding window recognition and inversion repair are performed on abnormal fluctuation in original data, and different from a conventional statistical elimination method or an interpolation filling method, reasonable traceability correction can be performed by using a known physical relationship of a system.

The invention relates to the technical field of intelligent sensing systems, and provides a cross-well earthquake and VSP-based VTI anisotropic parameter inversion method, which comprises the following steps: acquiring first arrival information and speed information recorded by a zero well spacing VSP in a target exploration area, and establishing a near-well initial vertical speed model; acquiring first arrival information of excitation points at different depths of an inter-well earthquake in the area, and calculating to obtain the transverse velocity of a VTI anisotropic medium at each depth; solving the anisotropic medium value of each depth VTI; obtaining a VTI anisotropic medium delta value; and performing interpolation smoothing along a geologic horizon by using the epsilon value and the delta value of the VTI anisotropic medium to obtain an epsilon field and a delta field of the VTI anisotropic medium so as to form a VTI anisotropic body. The objective of the invention is to solve the problem of low accuracy of sedimentary rock feature analysis caused by poor matching of a velocity model and anisotropy parameters in traditional VTI anisotropy research.

In some examples, a system includes a device comprising one or more electroencephalogram (EEG) sensors. The device is configured to collect, from a subject, an EEG signal using the one or more EEG sensors. The system further includes one or more processors; and a memory storing instructions that, when executed by the processors, cause the one or more processors to receive, from the one or more EEG sensors of the device, the EEG signal collected from the subject; and apply a model to the EEG signal to determine a cognitive reserve of the subject. The model is trained using a plurality of sets of training data, each set of training data including a training EEG dataset collected from a training subject and a training cognitive reserve score corresponding to the training subject that is determined based on metrics separate from the training EEG dataset.

The invention relates to the field of data processing, and particularly discloses a mountain environment sensing system and method based on multi-sensor fusion, which not only independently analyzes the internal data characteristics of each sensor through a real-time data quality evaluation module to evaluate the inherent quality, but also introduces cross-modal non-consistency measurement. By comparing the geometric structure understanding of different sensors on the same scene, whether conflicts and contradictions exist among the data is judged. Based on the dynamic confidence score obtained through evaluation, an adaptive feature fusion mechanism of confidence modulation is further constructed. According to the mechanism, confidence can be used as a key modulation factor, the contribution weight of each sensor feature is dynamically adjusted in the fusion process, so that when information provided by any sensor is unreliable, the influence of the information is actively inhibited, and an information source with high confidence is emphasized. Furthermore, the system can still output a stable and reliable sensing result when faced with extreme challenges of glare, dense fog and the like.

The invention provides a multi-sensor system key index prediction method for a multi-scale network based on reinforcement learning optimization, and the method comprises the steps: collecting and preprocessing multi-sensor time sequence data, and constructing a multi-scale time sequence feature extraction module through a plurality of parallel time convolution network branches; a sensor graph structure learning module is constructed based on a Query-Key double embedding strategy, a bidirectional graph convolution processing module is constructed through an attention mechanism, a regression prediction model is constructed based on a multilayer perceptron network, and a reinforcement learning prediction correction module is constructed based on a reward mechanism; a two-stage training strategy is adopted for training and optimization, and finally a multi-scale space-time deep network prediction model based on reinforcement learning optimization is obtained; and performing real-time prediction by using the multi-scale space-time deep network prediction model based on reinforcement learning optimization, and outputting a prediction result. According to the method, accurate and efficient prediction of the key indexes of the multi-sensor system can be realized, and a reliable and efficient technical solution can be provided for intelligent monitoring and prediction analysis of the multi-sensor system.

The invention belongs to the technical field of light regulation and control, and particularly relates to an intelligent dimming method based on environmental data driving, which comprises the following steps of: acquiring external and internal multi-source environmental data of a vehicle in real time through a vehicle-mounted environmental sensing system, performing data fusion on the acquired multi-source environmental data by using a multi-sensor fusion algorithm, and obtaining a dimming result; constructing an environment state feature vector based on the fused data, and constructing an input feature vector of subsequent intelligent dimming by introducing historical data features; inputting the constructed input features of intelligent dimming into an intelligent dimming decision model, and outputting an intelligent dimming decision according to the intelligent dimming decision model; according to the invention, by adopting the dynamic weight distribution and weight fusion modeling technology based on the environment change rate and the driving state, the technical effect of comprehensively reflecting the external illumination change, the weather complexity and the driver state by the environment state feature vector is realized.

The present application belongs to the field of geophysical exploration and geoscience information sensing technology, and relates to an intelligent sensing system for ground construction risk monitoring, comprising: a pulse generation module for generating a detection pulse sequence with a preset time interval; an orthogonal polarization modulation module for mapping the detection pulse sequence with an orthogonal polarization state and loading a time delay modulation; a distributed sensing module for receiving pulse combination and generating a backscattering signal; a common mode interference cancellation module for superimposing the backscattering signal in the photon domain to generate a phase push effect, in-situ canceling a static resident stress background noise, and extracting a dynamic birefringence phase shift amount; and a signal decoupling analysis module for identifying a dynamic shear signal according to a covariance tensor principal eigenvalue extraction value. The present application utilizes an optical domain physical interference cancellation mechanism to annihilate static background interference, realizes high-fidelity physical extraction of weak dynamic signals, and improves the identification ability of the system for structural medium shear instability.

The invention relates to the technical field of power systems, and discloses a hybrid multi-target coordination control system based on SVG and a synchronous phase modifier, which significantly improves the decision quality and foresight of an intelligent agent by fusing a real-time state and future prediction, and improves the decision efficiency compared with a single system state vector. The enhanced state characterization not only comprises real-time parameters such as voltage deviation and reactive power difference, but also integrates future uncertainty probability information predicted by the random forest, so that the intelligent agent can simultaneously sense the current state and the expected evolution trend of the system, and the enhanced environment sensing capability enables the Q learning algorithm to make a more predictable decision, so that the method is more suitable for popularization and application. According to the method, the coordination strategy of the SVG and the phase modifier is adjusted in advance before voltage fluctuation occurs, state characterization is enhanced, comprehensive environment information is provided, an intelligent agent can better balance the relation between quick response and steady state support, the voltage stability is guaranteed, the economical efficiency of system operation is improved, and finally multi-target collaborative optimization in the true sense is achieved.

The invention discloses an AR-GIS fusion-based underground pipeline three-dimensional visualization and real-time sensing system, and relates to the field of data analysis, and the system comprises an index definition module which is used for presetting a plurality of structure indexes and attribute indexes required by underground pipeline construction; the extraction and evaluation module is used for presetting indexes according to the index definition module, extracting associated data from the existing GIS image and the acquired data source, automatically evaluating the completion degree of data extraction, and identifying and marking missing points which are not obtained; the model construction module is used for comparing the data completion degree calculated by the extraction and evaluation module with a preset completion threshold value; three-dimensional visualization and real-time perception of the underground pipeline are achieved, data integrity and accuracy are remarkably improved by automatically evaluating data completeness and intelligently predicting missing point data, user interactive abnormal marking and dynamic region division are supported, accurate data supplementary collection is guided, and efficiency and safety of management and maintenance of the underground pipeline are improved.

In some examples, a system includes a device comprising one or more electroencephalogram (EEG) sensors. The device is configured to collect, from a subject, an EEG signal using the one or more EEG sensors. The system further includes one or more processors; and a memory storing instructions that, when executed by the processors, cause the one or more processors to receive, from the one or more EEG sensors of the device, the EEG signal collected from the subject; and apply a model to the EEG signal to determine a cognitive reserve of the subject. The model is trained using a plurality of sets of training data, each set of training data including a training EEG dataset collected from a training subject and a training cognitive reserve score corresponding to the training subject that is determined based on metrics separate from the training EEG dataset.

Environment sensing system, comprising at least one sensor (10) for providing location data (12) about objects (18), wherein the location data (12) includes velocity data, and comprising a computational means (14) for converting the location data (12) into an environment model (16) that represents kinematic object data of the objects (18) and assigns an object (18) to a respective object class (K), wherein the computational means (14) is conditioned to preferentially output environment models (16) in which, for an object (18) corresponding to at least one predefined object class (K), at least one predefined physical relationship of the object data to location data (12) assigned to the object (18) is better satisfied for those location data (12) assigned to the object (18) that correspond to a predefined association criterion than for the totality of the location data (12) assigned to the object (18).

The present application relates to the technical field of intelligent sensing system, in particular to a mortar bonding strength detection method and device. The present application firstly carries out multi-dimensional synchronous sampling on displacement, pulling force and pulling force growth rate in the pulling process, eliminates the misjudgment risk of single threshold being easily disturbed, and provides reliable data basis for subsequent analysis; secondly, through analyzing the cooperative fluctuation characteristics of displacement and pulling force, a nonlinear discrete index is determined to accurately distinguish the gap free state and the effective loading state, and the sensing accuracy under complex working conditions is significantly improved; then, the pulling force growth rate and the nonlinear discrete index are fused to quantize the impact risk caused by the coupling of high speed and free in real time, and the active accumulation and sensitive control of the risk are realized; finally, according to different pulling contact stages, the control strategy is adaptively switched, the overshoot risk is eliminated from the physical root, the detection process is stable and reliable, the accuracy of mortar bonding strength detection is effectively improved, and the possibility of low strength detection is reduced.

The invention discloses a low-altitude equipment cooperative sensing method, system and equipment, and belongs to the technical field of low-altitude intelligent management and control. The method comprises the following steps: firstly, receiving a sensing task and analyzing to generate a structured task demand, triggering dynamic access management according to the structured task demand, and solving an optimal equipment combination from a candidate equipment resource pool through a multi-target optimization model to construct a virtual sensing cluster; the digital twinborn deduction module performs multi-strategy prospective deduction through a game model based on the mapped twinborn state and business intention to generate a global collaborative awareness strategy; during strategy execution, the airspace cognitive map module is utilized to monitor abnormal evidence and perform causal reasoning, and a reasoning result is fed back to the digital twinborn deduction module to form closed-loop control. According to the method, the problems of resource scheduling stiffness and decision passive lag of a traditional low-altitude sensing system are solved, elastic on-demand scheduling and active closed-loop management and control of the low-altitude sensing resources are realized, and the intelligence, the self-adaptability and the robustness of the system are remarkably improved.

Embodiments of the present disclosure disclose a method, device and equipment for generating remote sensing cross-modal adversarial samples for unmanned aerial vehicles. A specific embodiment of the method includes: acquiring remote sensing data and real-time hardware state data; performing type identification processing on the remote sensing data; generating a dynamic optimization strategy based on the data type determination result; in response to the type determination result representing an image, performing first perturbation processing on the remote sensing data; in response to the type determination result representing a video, performing second perturbation processing on the remote sensing data; performing superposition processing on the adversarial perturbation data or adversarial perturbation data sequence and the remote sensing data to obtain an adversarial sample; performing attack effectiveness evaluation on the adversarial sample to obtain an evaluation result; iteratively optimizing the dynamic optimization strategy to obtain a set of hardware control parameters; and adjusting an onboard computing platform according to the set of hardware control parameters. The embodiment provides a technical paradigm for the safety testing and adversarial defense research of an unmanned aerial vehicle remote sensing system.

The application provides a single-satellite or multi-satellite re-track interference orbit control method based on an effective vertical baseline, and belongs to the technical field of space-borne SAR re-track interference. The single-satellite or multi-satellite re-track interference orbit control method is based on an eccentricity vector and a relative inclination, a reference-observation virtual formation model of re-track interference is established; based on the geometric relationship between the target detection area latitude and the subsatellite point latitude and the orbit root number, the relationship between the target detection area latitude and the effective vertical baseline is obtained, and the virtual formation effective vertical baseline of the reference-observation satellite is determined; and the in-plane / out-of-plane orbit control strategy suitable for single-satellite or multi-satellite re-track interference is obtained by taking the effective vertical baseline as an evaluation index. The application solves the problem that the orbit control strategy research of single-satellite re-track interference is relatively insufficient at home and abroad, can provide a new method and a new direction for establishing a remote sensing system of China, and can be applied to space-based situation awareness to support the improvement of the space-based attack and defense system of China.

The invention discloses a large equipment hoisting risk intelligent sensing system, and belongs to the technical field of hoisting risk identification. The method is used for solving the technical problem that an overload or out-of-control risk cannot be early warned in time due to the fact that online state monitoring of key devices such as a torque limiter and an anti-falling safety device is not integrated in an existing scheme. Through fusion of a fiber bragg grating, a millimeter wave radar and a torque sensor, full-scale monitoring from material damage to overall dynamic behaviors is achieved, an improved Transform model is combined with a space-time self-attention mechanism, dynamic updating of the eccentric distance is implemented based on torque-strain combined calculation, and the real-time control requirement of hoisting operation is met; according to the method, a three-dimensional risk tensor fusing structure defect-load offset-environment interference is constructed, the three-dimensional risk tensor is converted into a Lorenz system initial condition through state mapping, a future trajectory is predicted through a pre-training model, and end-to-end modeling of dynamic risk and nonlinear evolution is realized.

This application discloses a two-stage optimization method and apparatus for end-to-end swarm intelligence sensing systems. The method includes: first, establishing time-slot models, computational models, communication models, and energy consumption models that represent the complete task execution process between sensing devices, UAVs, and execution devices in the swarm intelligence sensing system. Based on these mathematical models, the association factors between the UAV and ground equipment, communication resource allocation, computational resource allocation, and the UAV's flight trajectory are collaboratively optimized. First, the global search capability of a genetic algorithm is used to provide a high-quality initial exploration region. Second, a deep deterministic policy gradient is used to further train and explore this region to obtain the optimal solution that reduces system communication latency and saves energy consumption, thereby ensuring the high efficiency and low energy consumption of the "sensing-communication-computation-execution" task closed loop and minimizing overall energy consumption.

The invention relates to the technical field of power supply voltage quality dynamic management. The invention relates to an AI-driven distributed power supply voltage quality dynamic management system. The system comprises a data acquisition unit, an initial prediction unit, a prediction updating unit, an interference updating unit and a reactive power regulation unit. The data acquisition unit is used for acquiring position data of the wind power generation device; through multi-source data fusion and full-process closed-loop control, the problems of passive response, modeling simplification, extensive interference processing and the like in the prior art are effectively solved, the accuracy and foresight of voltage quality treatment are remarkably improved, and the system integrates position data of a wind power generation device, real-time wind power data and a meteorological network wind field streamline diagram, so that the accuracy and the foresight of voltage quality treatment are improved. According to the method, a device-weather-terrain three-in-one data sensing system is constructed by combining satellite remote sensing terrain data, the limitation of a single data source in the traditional technology is broken through, comprehensive and accurate data support is provided for subsequent prediction and adjustment, and treatment deviation caused by one-sided data is reduced from the source.

This invention proposes an adaptive cooperative control method based on time-domain decoupling and its application. Its core idea is to creatively transform a complex, strongly coupled physical problem in the spatial domain into a more manageable time-domain logical scheduling problem. This method uses an independent external sensing system to evaluate the stability of the mobile platform in real time and dynamically adjusts the time progress of tasks executed by the operating device based on this stability index: when the platform is stable, authorized tasks are executed normally; when the platform becomes unstable due to disturbances, tasks are actively paused to provide a pure stability recovery window for the platform, and tasks resume after stabilization. As a model-independent top-level controller, this invention can be compatible with any "black box" hardware combination by sending high-level abstract instructions, exhibiting excellent robustness, versatility, and engineering practicality, significantly reducing the development cost and technical threshold of mobile operating robots.

The invention provides an intelligent model construction method for evaluating the ecological value of lake water and application thereof, and relates to the technical field of digital evaluation of ecological resources. The intelligent model construction method based on lake water ecological value evaluation comprises the steps that multi-source ecological data are collected through a multi-platform cooperative sensing system, and the multi-platform cooperative sensing system comprises the steps that a satellite remote sensing system obtains spectral data not less than 10 wave bands, and the spatial resolution is not greater than 10 meters; the unmanned aerial vehicle platform carries a multispectral imager to collect image data of a 200-2500 nanometer wave band; the buoy Internet of Things network distributed on the lake surface collects time sequence data of total phosphorus, total nitrogen, permanganate index and pH value. Through multi-dimensional cooperation of the space-based satellite, the aerial unmanned aerial vehicle and the internet-of-things buoy, wide-coverage, high-precision and real-time collection of water area ecological elements is achieved, the temporal-spatial resolution reaches the industry breakthrough level, the timeliness of treatment measures and the resource release precision are greatly improved, and a standardized implementation normal form is provided for long-acting ecological protection.

The invention provides an anchorage open caisson construction autonomous regulation and control system and method based on multi-modal space-time monitoring, and relates to the technical field of bridge anchorage open caisson intelligent construction, and the system comprises a multi-source sensing unit, a multi-modal data fusion unit, a decision-making unit and an execution control unit; the multi-source sensing unit is used for collecting multi-dimensional heterogeneous monitoring data in the open caisson construction process in real time, and a holographic sensing network is constructed. The multi-modal data fusion unit adopts a spatial-temporal feature hierarchical extraction architecture to perform preprocessing, feature extraction and cross-modal fusion on multi-dimensional heterogeneous monitoring data; according to the method, multi-dimensional monitoring data such as the well hole water level, the mud surface form, the soil body reaction force and the earth surface displacement are integrated, the dynamic evolution law and the spatial distribution characteristics of the data are mined through the space-time neural network, cross-modal accurate fusion is achieved in combination with a risk perception attention mechanism, the problems that in the prior art, the data dimension is single, and characteristic mining is insufficient are solved, and the accuracy of data mining is improved. And a comprehensive and accurate construction state sensing system is constructed.

The invention discloses an intelligent drilling and control method and system for pipe-jacking drilling, and relates to the technical field of intelligent control of pipe-jacking drilling, and the method comprises the steps: obtaining an initial geological map and drilling path planning information of a target area, building an initial geological model, initializing a multi-source sensing system of the front end and a body of a drilling machine, and obtaining a multi-source sensing system of the front end and the body of the drilling machine; the sensing system comprises an azimuth gamma probe, a resistivity imager, a seismic wave forepoling device and a thrust, torque, rotating speed, vibration and strain monitoring unit. In the drilling process, physical detection data of a stratum in front of a drill bit and dynamic response parameters in the current drilling state are periodically collected based on a sensing system, and a comprehensive observation data set is formed; performing data fusion and iterative analysis by using the initial geologic model as a priori basis and combining with the comprehensive observation data set to generate a three-dimensional geologic model dynamically updated along with the drilling process; and inputting the three-dimensional geologic model into a digital twin system deployed at an edge end.

The invention relates to the technical field of power distribution cable fault early warning, in particular to a power distribution cable intelligent fault early warning method and system based on multi-modal data fusion, and the method comprises the steps: S1, multi-modal data synchronous collection: collecting multi-type data of a power distribution cable through a sensing system comprising a specific sensor; s2, edge side preprocessing: cleaning, normalizing, compressing and transmitting the acquired data; s3, multi-modal data fusion: constructing a parameter feature matrix at a cloud end and realizing feature level and decision level dual fusion; s4, performing fault analysis and early warning: extracting spatial-temporal characteristics by using a neural network model, and outputting an abnormal vector in combination with a dynamic threshold value; the method can also comprise the steps of S5, precise positioning and decision support and S6, early warning information pushing and responding. The cable state can be comprehensively monitored, the fault recognition accuracy and the early warning timeliness are improved, and seamless integration with an existing system is achieved.

The invention belongs to the technical field of industrial equipment monitoring and corrosion prevention and control, and particularly relates to a low-temperature economizer water collection leakage monitoring system based on an intelligent sensing system. Performing preprocessing, noise reduction and standardization on the original data, judging a leakage state through temperature and humidity dual logic, and performing corrosion risk assessment in combination with acidity data; then, judging and evaluating results are reused, historical case data are fused, the leakage evolution and corrosion development trend is predicted, and prevention and control parameters are reversely calibrated; pushing accurate early warning information to each level through a hierarchical early warning mechanism, and synchronizing the analysis data and the optimized dosing scheme; on the basis, comprehensive optimization is formed for a core pain point of a traditional monitoring mode, accurate recognition and rapid early warning of leakage are achieved through cooperative linkage of matrix type temperature sensing and smoke humidity sensing, and the problem of leakage early warning lag is effectively solved.

The invention discloses a multi-sensing-node position deployment method and system based on zero-sum game, a strategy pool of multiple sensing nodes and jammers is constructed, game characteristics of both parties are analyzed based on the strategy pool, a double-layer game modeling framework is constructed, an inner layer adopts a cooperative game form, cooperative anti-interference among multiple nodes is realized, and multi-sensing-node position deployment is realized. The optimal response of a sensing node and a jammer to a strategy of an opposite side is calculated respectively to maximize the overall joint detection efficiency and the game behavior between an outer layer and a simulated sensing system and the jammer, a meta-strategy is updated and solved to obtain a mixed strategy Nash equilibrium, the robust decision-making ability of the system facing an uncertain interference strategy is enhanced, and the robustness of the system is improved. Effective detection of the sensing system in a complex environment is ensured, and the anti-interference stability is better.