127 results about "Sensing system" patented technology

The invention discloses an equipment state sensing system based on peak shifting and danger situation sensing, which relates to the technical field of electric digital data processing and comprises a data acquisition unit, a model training and optimizing unit, a situation sensing unit, a peak shifting regulation and control unit and a system interface and user interaction unit. According to the method, the device state data feature similarity is dynamically calculated by using the Gaussian kernel function, the similarity matrix is adjusted in combination with the gradient update item, dynamic weighting is performed in feature extraction, and redundant features are reduced by adopting the adaptive discarding probability, so that the feature extraction efficiency of the model in a high-dimensional feature space is improved; the problem that the dimension reduction effect is affected by too many redundant features of the equipment state data is solved. Equipment state features are extracted layer by layer through a deep forest algorithm of a multi-layer random forest structure, a decision tree is optimized in combination with a dynamic adjustment weight mechanism and a reinforcement learning strategy, and the classification capability of the model for equipment state changes and abnormal states is enhanced.

The invention discloses a digital intelligent switch cabinet state comprehensive sensing system based on AI, and the system comprises a multi-source data collection module, a data preprocessing and synchronization module, an edge calculation feature extraction module, an AI intelligent fusion recognition module, an expert rule diagnosis module, and a cloud comprehensive evaluation and decision module. Various types of sensors are deployed to respectively acquire environmental parameters, electrical parameters and partial discharge signals generated in the operation process of the switch cabinet to form an original multi-modal data stream. The method has the advantages that the recognition precision and response speed of the complex operation state of the switch cabinet are improved, hidden faults under multi-modal data mismatch can be effectively found, and the misjudgment and missed judgment risks are reduced. Meanwhile, a closed-loop diagnosis system is constructed, intelligent evaluation and interpretable feedback of fault types, positions and trends are achieved, scientificity and reliability of operation and maintenance decisions are enhanced, and the method is suitable for intelligent upgrading of an electric power system.

The invention discloses a GIS disconnecting switch multi-state intelligent sensing system, and relates to the field of GIS disconnecting switch state monitoring. A self-calibration multi-mode sensor is deployed for multi-source data acquisition, and novel sensors including terahertz imaging and the like are included; in data preprocessing, deep learning noise reduction is applied, fuzzy entropy is used for dynamic weighted fusion, and abnormal values are processed by an improved algorithm; feature extraction is combined with a plurality of frontier algorithms to process vibration signals, and CRNN is used to analyze acoustic signals; the QPSO evidence theory is adopted for state fusion perception, the node relation is learned by means of GNN, and the weight is adjusted according to the information gain rate; state assessment and early warning are based on transfer learning, GAN and LSTM-attention mechanisms, and early warning priorities are ranked by FAHP. According to the invention, multi-mode accurate acquisition, intelligent data processing, deep feature mining, innovative fusion perception, accurate evaluation and early warning and efficient fault diagnosis and positioning are realized, the state of the GIS isolation switch can be comprehensively and accurately perceived, the system is self-learned and optimized, the equipment safety is guaranteed, the risk of a power system is reduced, and the power operation and maintenance benefits are improved.

The invention discloses a technology development situation awareness system and method, and belongs to the field of information processing, and the system comprises a data input module, a field overview module, an intelligence task question and answer module and a data output module. The method comprises the steps of 1, receiving a user question, and performing deep semantic analysis; 2, constructing a thinking chain and cue word engineering system based on metadata; 3, executing thinking chain reasoning based on large language model driving and generating a first draft of a report; step 4, executing metadata consistency checking of the first draft of the report, executing report content updating, and presenting a research report and evidence; and 5, archiving high-quality scientific data in the research report. According to the method, key feature rules in technology development can be effectively mined, comprehensive, convenient, efficient and intelligent perception of the technology development situation is realized, automatic reasoning of a technology situation perception result is provided, and efficient and credible intelligence reference is provided for decision making.

A navigation equipment real-time fault diagnosis method based on cross-modal attention fusion belongs to the technical field of power grid equipment fault diagnos.The method comprises the steps that firstly, the type of real-time diagnosis data capable of being obtained by navigation equipment is systematically analyzed, and the real-time diagnosis data is divided into four modals of sensor data, electrical data, acoustic data and text data; after data cleaning is completed, preliminary feature extraction is implemented. Then, constructing a fault diagnosis model based on a cross-modal attention mechanism, constructing a training data set by using the joint features, and carrying out neural network model training; after model training is completed, real-time fault diagnosis of the navigation equipment can be realized through a data pre-processing technology and a fault diagnosis model based on acquired data. According to the scheme, a multi-modal real-time sensing system is constructed, deep feature interaction is realized in combination with a cross-modal attention mechanism, and closed-loop diagnosis capability is established. And core guarantee is provided for real-time state sensing and accurate fault positioning of navigation equipment.

The invention relates to the technical field of data processing, in particular to a multi-modal data fusion method and system for an energy-saving building data sensing system, and the method comprises the steps: obtaining environment state data, a current scene time period and a historical scene time period of each dimension at each moment; according to the difference of the environment state data at adjacent moments under the same dimension in the current scene time period, combining the feature difference between the current scene time period and the historical scene time period to obtain an environment unique index; according to the similarity of the environment state data of the historical scene time period and the current scene time period in the same dimension, obtaining a reference scene time period in combination with the unique indexes of the environment; and according to the environment state data of the reference scene time period and the current scene time period in each dimension, combining with the environment unique index to construct a fusion weight, and fusing the multi-modal data of the current scene time period. According to the invention, the fusion weight is highly matched with the current scene feature, and accurate fusion of multi-modal data can be realized.

The invention belongs to the technical field of thermal power auxiliary equipment maintenance, and particularly relates to an intelligent sensing system and method based on multi-dimensional state monitoring, and the system comprises a state monitoring module, an intelligent maintenance decision module, a maintenance operation standardization module, and a real-time collection equipment physical signal. When an abnormal condition or an early fault symptom occurs, the edge end can carry out abnormal judgment and prompt and alarm; and performing modeling analysis on the data at the cloud, acquiring real-time operation data of the equipment on line to construct a knowledge graph model, performing reasoning and decision analysis on the operation state of the equipment, and identifying a fault risk influencing the reliability of the equipment by combining reasoning and decision analysis results. According to the method, equipment signals are grabbed to analyze the overall reliability, risk and fault of the equipment, standardized and normalized operation guidance is given, and a maintenance strategy is perfected and reconstructed so as to continuously update and iterate.

The invention discloses an interpretable sequence recommendation method fusing time perception and path reasoning. The method comprises the following steps: 1) constructing a time sequence collaborative knowledge graph; 2) node representation learning is carried out on the time sequence collaborative knowledge graph through TransE; 3) designing a time sensing system, updating an article vector at the current moment through a path and an article attention mechanism, and generating an article prediction vector in combination with a user vector; 4) designing a path reasoning system, performing correlation path reasoning by using a reinforcement learning framework, and obtaining a multi-path vector through path representation learning; and 5) model training: training the time sensing system and the path reasoning system to obtain an article prediction result. According to the method, double systems are introduced to simulate the human reasoning process, and the accuracy and interpretability of the recommendation result are improved by learning the time dependence characteristics of the user behaviors in the time sequence collaborative knowledge graph.

The invention provides a target detection credibility estimation method fusing data and model uncertainty, and belongs to the field of data processing, and the method comprises the steps: obtaining an image quality score through an image quality evaluation model; outputting a target frame by using a two-dimensional detection model, and performing Bayesian correction of confidence in combination with an image quality score and a weather perception model result; meanwhile, outputting a three-dimensional target frame by using a laser radar point cloud detection model, and correcting three-dimensional detection confidence in combination with a target distance and weather conditions; according to the statistical relationship between the number of point clouds in the detection frame and the detection accuracy, constructing the point number confidence coefficient based on Bayesian reasoning; and performing structural consistency analysis according to the spatial size of the detection frame and the prior size distribution of the target category, and outputting structural matching confidence. And finally, the five indexes are fused, and the overall credibility of the sensing result is calculated. According to the method, the credible judgment capability of a sensing system in a complex environment can be improved, and the method has good practicability and engineering value.

The invention discloses a marginal device intelligent sensing method and system based on information freshness, and a Markov decision process model is constructed by analyzing key factors in the marginal device intelligent sensing system. And secondly, the system state is updated in real time through dynamic adjustment of the sensing state, and parameters of equipment are adjusted according to new environment changes, so that the system can quickly respond in a complex environment. In the aspect of joint optimization, joint optimization is carried out on information age and energy consumption by utilizing a Lagrange multiplier method, and the freshness of information is maximized while energy constraints are met. And finally, through the deep Q network, the system can continuously optimize a decision strategy through an experience playback mechanism, so that the edge equipment can minimize the information age under the condition of limited energy in a continuously changing environment. According to the invention, the timeliness of sensing information is improved.

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 relates to the technical field of artificial intelligence infrastructure, in particular to a large-model high-concurrency reasoning storage hierarchical acceleration system, which comprises an access popularity acquisition module, a pressure analysis module, a migration execution module and a heterogeneous storage pool, the access popularity acquisition module is used for acquiring the access frequency A and the access delay D of model parameters in real time. The effects of sensing system pressure in real time and accurately triggering migration are achieved by arranging the access popularity acquisition module and the pressure analysis module, the access popularity acquisition module continuously monitors the access frequency and delay of model parameters, and the pressure analysis module calculates and stores a pressure index based on a historical peak value and a dynamic threshold value. When the index exceeds the preset threshold value, migration operation is triggered immediately, the problem that delay exceeds the standard due to storage I / O bottleneck in the financial transaction peak period is solved, the system can still keep millisecond-level response under tens of thousands of concurrent requests per second, and risk misinformation and missing report caused by delay jitter are avoided.

The invention aims to provide a cooperative sensing network construction method based on Beidou and low-altitude remote sensing data. The method comprises the following steps: shooting the same place by adopting a plurality of unmanned aerial vehicles to obtain a plurality of low-altitude remote sensing data; taking the low-altitude remote sensing data of which the key frame similarity between every two low-altitude remote sensing data exceeds a threshold value as overlapped low-altitude remote sensing data pairs; fusing each pair of overlapped low-altitude remote sensing data to obtain three-dimensional low-altitude remote sensing data; acquiring Beidou positioning data; and carrying out cooperative processing on the three-dimensional low-altitude remote sensing data and the Beidou positioning data to obtain accurate positioning data. According to the invention, cooperative sensing is carried out through the Beidou positioning system and the low-altitude remote sensing system, so that the three-dimensional model map of the to-be-measured destination can be accurately obtained, and land resource investigation, agricultural resource investigation and urban daily remote monitoring and daily patrol can be assisted.

The invention relates to a method for determining parameters of a dynamic compensation filter based on an improved cuckoo search algorithm, belongs to the field of intelligent sensors, and solves the problems that a compensation filter of an existing sensor is low in precision and difficult in parameter design. Comprising establishing a dynamic compensation filter model; acquiring original input data and output data of the sensor as a sample data set; based on the sample data set, using an improved cuckoo search algorithm to optimize parameters of the dynamic filtering compensator, and using a dynamic compensation filter model to calculate a fitness value of each nest in the optimization process; obtaining a global optimal solution, transmitting the global optimal solution to a dynamic compensation filter model, performing parameter optimization on the dynamic compensation filter model, and obtaining the optimized dynamic compensation filter model as a sensor dynamic compensation model; and inputting the input data into a sensing system consisting of the sensor and the sensor dynamic compensation model in real time to obtain a compensated sensor output value. And real-time dynamic compensation of the sensor is realized.

Methods and systems are discussed. In some cases, the methods may include deploying a modular untethered downhole tool in a wellbore. The modular untethered downhole tool includes a positive buoyant system and a sensing system with a sensor. The sensing system is threadedly attached to the positive buoyant system. The positive buoyant system may be replaced by threading a replacement positive buoyant system to the sensing system.

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 provides a safety and reliability evaluation system for an intelligent sensing system, which can be applied to the technical field of automatic driving evaluation and comprises a data acquisition module for acquiring initial fault-free sensor data and transmitting the initial fault-free sensor data to the intelligent sensing system; the fault injection module is used for performing fault data injection on the initial fault-free sensor data to obtain sensor fault data and transmitting the sensor fault data to the intelligent sensing system; the result analysis module is used for receiving and analyzing the test data of the intelligent sensing system to obtain a test result; and the interactive module is used for controlling the work of the data acquisition module, the fault injection module and the result analysis module. Fault information of different formats is generated according to data formats of different types of sensors, and real-time sensor data reading, fault signal injection and data recording and analysis can be achieved. The invention further provides a device for evaluating the safety and reliability of the intelligent sensing 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.

The invention belongs to the technical field of prediction analysis, and particularly relates to a video twinning park people and vehicle flow security analysis method and system based on live-action three-dimensional, and the system comprises a video twinning engine module, a video space correction and fusion module, an LI position intelligent analysis module, a real-time simulation calculation module and an intelligent analysis decision module. According to the invention, the video twin engine module synchronously integrates three types of heterogeneous data sources, a video monitoring system, an original video stream, an artificial intelligence analysis system, a target detection result, an Internet of Things sensing system and sensor data, so that dynamic mapping is realized in a unified three-dimensional scene model, a traditional system data island is broken through, and the real-time performance of the system is improved. A global space-time association view is constructed, Internet of Things data such as environment temperature and humidity and access control states can be overlaid on personnel trajectories, and the behavior analysis accuracy is improved; the positioning of the video shielding area can be complemented through Internet of Things equipment, such as a positioning label, and the influence of a monitoring blind area is eliminated.

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 demand structuring method and device for a sea area low, small and slow sensing system. The method provided by the invention comprises the following steps: constructing a structured template of a sea area low, small and slow sensing system; carrying out instantiation on the structured template; obtaining a first type of missing instances and a second type of missing instances based on the plurality of structured instances; constructing a first class of training samples by taking the first class of missing instances as samples and taking the corresponding structured instances as labels, and constructing a second class of training samples by taking the second class of missing instances as samples and taking the corresponding structured instances as labels; training the initial model by using the first type of training samples and the second type of training samples to obtain a basic model; freezing partial parameters of the basic model, and finely adjusting the parameters by using the first type of training samples and the second type of training samples to obtain a first special model and a second special model; and selecting the target model according to the currently input text, so that the target model outputs a complete structured instance based on the currently input text to improve the modeling precision.

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 relates to an intelligent driving perception evaluation method and device and a medium, and relates to the field of intelligent driving, and the method comprises the steps: collecting truth value data and to-be-evaluated data in a time space; based on a preset scene extraction rule, constructing the truth value data into a truth value scene library comprising a plurality of sub-scenes; performing spatial association on the truth value data and the to-be-evaluated data to obtain corresponding truth value output and test output in the sub-scene; and based on a preset evaluation index, comparing the truth value output with the test output to obtain an evaluation result corresponding to the evaluation index. The method is used for solving the problems of high test cost, long test period, low pertinence of evaluation indexes and the like when the driving sensing system is evaluated by using a traditional manual labeling mode.

Methods and systems are discussed. In some cases, the methods may include deploying a modular untethered downhole tool in a wellbore. The modular untethered downhole tool includes a positive buoyant system and a sensing system with a sensor. The sensing system is threadedly attached to the positive buoyant system. The positive buoyant system may be replaced by threading a replacement positive buoyant system to the sensing system.

A system and method using remote sensing instrument with hyper spectrum quantitatively measure metal dust elements in lubricating oil, which includes (not limited): Al, Cd, Cr, Cu, Fe, Pb, Mg, Mn, Mo, Ni, Ag, Sn, Ti, V, Zn, B (Boron, for Coolant), Ca (Calcium for water contaminant), and particle size, cone penetration, dropping point, steel mesh oil separation, moisture, PQ concentration, in few seconds. The instrument integrates near-field communication (NFC), Internet of Thing (IoT), Cloud computing, spectral matching and other data processing, and application software forming a system to easily operated and build a model enable self-learning to improve precision through collection accumulation. With the system, the instrument as FIG. 1 can provide comprehensive on-site analysis enable preventive maintenance of mission critical engine and rotating equipment. The characteristics of the system are easy to operate, get result quickly, and self-learning to improve precision.

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 discloses an environment intelligent sensing method, system and device based on assembly line coding and multi-branch decoding, and particularly relates to the technical field of environment intelligent sensing, and the method comprises the steps: obtaining the monitoring image data of a plurality of sensors; processing the monitoring image data of each sensor, and performing identification based on a risk cognition model according to the processed monitoring image data of the sensors to obtain a multi-aspect risk visual identification result in the working environment of the power grid; and according to a multi-aspect risk visual identification result in the power grid working environment, correspondingly generating early warning information of various risk scenes, and transmitting the early warning information through a communication channel for feedback. According to the method, the data is divided into different stages through a pipeline coding technology, so that the data processing efficiency and throughput are improved. Meanwhile, parallel processing and comprehensive analysis of multi-source data are achieved through the multi-branch decoding technology, and therefore the environment intelligent sensing system can sense and understand environment information more comprehensively and more timely.