101 results about "Sensing system" patented technology

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 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 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 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 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.

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 present invention discloses a method for processing grid data of terrestrial water storage change and a storage medium, belonging to the field of data processing for prediction purposes. To solve the problem of processing accuracy loss during the downscaling process of grid data of terrestrial water storage change, the present invention first obtains two types of hydrometeorological grid data of the study area and multi-source original grid data of terrestrial water storage change; uses the generalized triangular hat method and the least squares method to fuse the multi-source original data to obtain fused data; for the grid points in the study area, the elements of the first hydrometeorological grid data are removed one by one, input into the LSTM prediction model, and simulated by the residual correction method to screen out the key hydrometeorological elements whose influence on accuracy exceeds the threshold; the grid point sub-regions are divided according to the key elements, and each sub-region is trained based on the LSTM prediction model and the residual correction method to obtain the optimal model and the corresponding data subset; the data of the months containing extreme points are corrected using the optimal model to obtain the final result. The present invention is used in a remote sensing system and has the advantage of high accuracy.

The application discloses an edge device intelligent sensing method and system based on information freshness, constructs a Markov decision process model by analyzing key factors in the edge device intelligent sensing system. Secondly, the system state is updated in real time through dynamic adjustment of the sensing state, the parameters of the device are adjusted according to the new environmental changes, so that the system can quickly respond in a complex environment. In the aspect of joint optimization, the Lagrange multiplier method is used to jointly optimize the information age and energy consumption, so as to maximize the freshness of information while meeting the energy constraint. Finally, through the deep Q network, the system can continuously optimize the decision strategy through the experience replay mechanism, so that the edge device can minimize the information age under the condition of energy limitation in the changing environment. The application improves the timeliness of the sensing information.

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.

The invention relates to a method (100) for adapting resource usage for a sensing system (10), comprising the following steps: - Performing (101) a sensing procedure, wherein the sensing procedure comprises sending of at least one sensing signal by the sensing system, - Analyzing (102) a sensing result based on the performed sensing procedure and preferably further based on a sensing requirement, wherein the sensing result comprises at least one detected object, - Adapting (103) resource usage for sensing based on the analyzing (102). Further the invention relates to a sensing system and a computer program.