98results about How to "Reduce noise interference" patented technology

The invention discloses a cable fault positioning method and system based on reflected wave decomposition and kurtosis sorting, and aims to solve the problem that a cable fault is difficult to detect under a low impedance change rate. The method comprises the steps of transmitting an incident excitation signal to a cable to obtain a first reflection signal in the cable; an improved VMD algorithm is adopted for decomposition to obtain K IMF components, and the IMF with the maximum kurtosis is selected as a second reflection signal; acquiring a time-frequency distribution curve of the second reflected signal by using short-time Fourier transform and frequency spectrum redistribution technologies; and constructing a two-dimensional energy-time curve by extracting an energy value and time under the frequency of the maximum energy amplitude point, and determining a fault position. The system is composed of an incident excitation module, a decomposition module, an STFT-RSP module and a fault positioning output module, and the positioning method is achieved. According to the method, the limitation of a traditional spread spectrum time domain reflection positioning method in cable fault detection is effectively overcome, and an efficient and accurate instructive technology is provided for cable fault positioning and detection.

The invention discloses an urban green land vegetation extraction method based on an unmanned aerial vehicle multispectral remote sensing image. Comprising the following steps: 1) determining an image acquisition area, and performing unmanned aerial vehicle path planning; 2) performing data acquisition through an unmanned aerial vehicle to obtain a high-resolution multispectral image; (3) splicing the acquired images through measurement software Pix4D mapper 4.4 to generate a digital orthoimage; and 4) combining the orthoimages of the spectral bands by using ENVI 6.0 software to obtain images of five bands including blue, green, red, red edge and near-infrared. According to the method, a background area which does not meet the vegetation spectral reflectivity characteristic is effectively inhibited, and the abnormal phenomenon that the reflectivity of a red-edge wave band is lower than that of a red wave band or higher than that of a near-infrared wave band is also solved, so that noise interference is effectively reduced; meanwhile, no obvious intersection phenomenon exists between the gray statistical characteristic values of the vegetation and the non-vegetation characteristics, and the accuracy of green land vegetation collection is improved.

The invention relates to the technical field of radio orientation, in particular to a measuring method capable of tracking and recording motion trail information of a vibrating rod, which comprises the following steps: arranging at least four UWB (Ultra Wideband) base stations, fixing UWB transmitters at the joint of the vibrating end of the vibrating rod and a rubber tube, and fixing IMU (Inertial Measurement Unit) sensors at the vibrating end of the vibrating rod; according to the data output by the UWB transmitter and the IMU sensor, the position and posture of the vibrating rod in the detected target mold are calculated through a positioning algorithm, and the motion trail of the vibrating rod is further calculated; and according to the vibrating time, the vibrating area and the vibrating area overlapping degree of the vibrating rod, the vibrating state of the concrete is analyzed. The UWB technology and the IMU technology are fused, so that high-precision positioning of the vibrating rod can still be kept under the ground where wireless signals such as GPS signals cannot be transmitted, and the vibrating quality can be conveniently detected and evaluated.

The invention discloses a shield tunnel settlement prediction method based on artificial intelligence. The method comprises the steps of data acquisition, data optimization, construction of a shield tunnel settlement prediction model, model optimization and settlement prediction. The invention relates to the technical field of tunnel construction monitoring, in particular to a shield tunnel settlement prediction method based on artificial intelligence. According to the scheme, data cleaning is carried out by defining a dimension refining distance function and calculating gravitation between data, potential relations between the data are deeply mined, noise interference is reduced, and the accuracy and reliability of the data are improved; by designing a mapping function, a circulation control layer, a depth correlation layer and a loss function, the recognition and prediction precision of the model on a complex settlement mode is improved; model parameters are dynamically searched by designing an intervention factor, defining an adaptive step length, performing bidirectional iterative search and gradually adjusting search, so that the search efficiency is improved, time consumption and a loss value of model training are balanced, and the model is more efficient and stable.

The utility model discloses a detection circuit based on power system transistor driving, which comprises a signal input circuit and a direct current bias circuit, the signal input circuit and the direct current bias circuit are both electrically connected with a differential reduction circuit, the differential reduction circuit is electrically connected with an isolation operational amplifier module, and the isolation operational amplifier module is electrically connected with a differential proportion circuit. The differential proportion circuit is electrically connected with the comparator; the differential proportion circuit and the comparator are electrically connected with an MCU. The direct current bias circuit, the differential reduction circuit, the isolation operational amplifier module, the differential proportion circuit and the MCU are all electrically connected with the DCDC isolation system. The sampling isolation voltage operational amplifier ensures that the system is safe and reliable; single power supply sampling is adopted, the cost is reduced, expansion is facilitated, and the detection cost can be greatly reduced when a system needs to detect driving circuits of multiple transistors at the same time; reference voltage is added to the in-phase end through operational amplifier following, and noise interference is reduced.

The invention discloses a ground resistance comprehensive test system and method, and belongs to the field of ground test, and the ground resistance comprehensive test method comprises the steps: determining a test monitoring area of ground resistance, drawing a power grid distribution three-dimensional model on the test monitoring area, and marking each ground point in the test monitoring area; detecting a grounding resistance value on the grounding point, and evaluating whether the grounding resistance value of the grounding end on the grounding point is abnormal or not; and for the grounding point with the abnormal grounding resistance value, calculating the equivalent grounding resistance value of the grounding point according to the soil parameters on the grounding point, and evaluating whether the grounding system on the grounding point is damaged or not. The test system comprises a grounding resistance tester, a wireless data module, a soil parameter acquisition module and a far-end monitoring center. According to the invention, the grounding resistance of the grounding end is tested by using the multi-frequency alternating-current electric signal, so that the test requirements of different grounding systems under various working conditions can be met, and the universality and the practicability are ensured.

The invention discloses a low-noise amplifier based on an inverter and a multi-objective optimization method of the low-noise amplifier. The low-noise amplifier comprises the inverter, an input capacitor CI, a feedback capacitor CF and a single-stage operational amplifier. The phase inverter is formed by connecting a PMOS (P-channel Metal Oxide Semiconductor) tube and an NMOS (N-channel Metal Oxide Semiconductor) tube in series, the input capacitor CI and the feedback capacitor CF are respectively of a split capacitor structure, the positive input end of the single-stage operational amplifier is connected with the output end of the phase inverter, the negative input end of the single-stage operational amplifier is connected with reference voltage, the output end of the single-stage operational amplifier is connected with a grid electrode of the NMOS tube through a resistor R2, the grid electrode of the PMOS tube is connected with a resistor R1, and the resistance value of the resistor R1 is equal to that of the resistor R2; the stability and the noise performance of the amplifier circuit are improved; by using a multi-objective optimization design method, the design parameters of the low-noise amplifier are optimized so as to meet the amplification requirements of ultrasonic pulse echo signals with different frequencies, and the design efficiency is improved.

The invention discloses a method for generating a micromechanical property distribution diagram of a solid waste composite cement-based cementing material, and belongs to the technical field of performance prediction of solid waste composite cement-based cementing materials, and the method comprises the following steps: acquiring BSE, EDS and nanoindentation data of a sample; identifying indentation areas in the nanoindentation mark graph, and generating masks corresponding to the areas; performing statistics on the regional masks to obtain element content data of each indentation region; establishing a prediction model by adopting machine learning based on the regional element content and the elasticity data; performing super-pixel segmentation on the BSE image to match the scale of the indentation area; calculating element features of the super-pixel regions, and predicting the elastic modulus of each super-pixel region; and mapping the predicted elastic modulus to a corresponding region to generate an elastic modulus distribution diagram. According to the method, prediction of the two-dimensional space distribution of the internal elastic modulus of the solid waste composite cement-based cementing material can be realized, and data and visual support are provided for material heterogeneity research and multi-scale modeling.

The invention relates to a generative artificial intelligence context management technology, and discloses a knowledge tree-based structured memory system and method, a computer readable storage medium and electronic equipment. According to the system, structured memory with a father-son relationship is stored through a knowledge tree, a retrieval strategy is determined according to current input, related nodes or branches are recalled, and a memory context is generated for a model to call after deduplication, truncation, pruning, hierarchical summarization and priority retention processing are carried out under context budget constraint; and meanwhile, node addition, deletion, modification and combination, conflict marking, version management and cutting control are supported, so that the accuracy, consistency and long-term task stability of generated contents are improved. The knowledge tree construction module is used for acquiring external information and executing node generation, updating, deletion and branch maintenance, and can write back a knowledge tree after output; when conflicts exist, version management is executed, and cutting is carried out according to correlation, credibility, redundancy and the like.

A voice interaction method, system, device, and storage medium are disclosed. The method includes: acquiring historical voice interaction data of a target user, their viewing location, and an audio stream at that location; acquiring a knowledge graph of multiple exhibits within the area where the viewing location is located; determining the target user's voice interaction commands in the audio stream; associating the voice interaction commands with historical voice interaction data based on a preset contextual dialogue model to obtain contextual dialogue data; determining the target user's intent based on the voice interaction commands and contextual dialogue data; determining the explanation content based on the exhibit knowledge graph and the target user's intent, and generating a target display scene based on the explanation content; selecting a target virtual guide from a preset virtual guide library based on the explanation content, and displaying the target display scene; and having the target virtual guide explain the target display scene based on the explanation content. This application can improve the accuracy of voice interaction in VR exhibitions.

The embodiment of the invention provides an image detection method and computing equipment. The method comprises the following steps: segmenting a first image to be detected into a plurality of image blocks; performing frequency domain analysis on each image block to obtain each frequency domain analysis result corresponding to each image block; based on each frequency domain analysis result, determining a plurality of first image blocks meeting a preset screening condition from the plurality of image blocks, the preset screening condition at least comprising: the frequency domain analysis result indicates that the richness of the contained high-frequency features is the first N, and the high-frequency features correspond to an area in which the gray scale variation amplitude exceeds a specified threshold in the image blocks; determining image features corresponding to the first image based on the plurality of first image blocks through an extraction network; and determining a detection result at least indicating whether the first image is the generated image or not through a detection network at least based on the image features so as to realize accurate detection of the generated image.

The invention discloses an ontology recognition method and device oriented to a decision knowledge graph, and the method comprises the steps: carrying out the ontology knowledge extraction of text data of a decision-related field, and obtaining an ontology text set; the ontology text set comprises an attribute text set and a rule text set of each ontology; the attribute text set comprises various attribute texts; the rule text set comprises various rule texts; preprocessing the ontology text set to obtain a preprocessed text data set; performing ontology type identification processing on the text data set of each ontology in the preprocessed text data set to obtain a type identification result of each ontology; the type of the ontology comprises a fact type ontology, a rule type ontology and a decision type ontology.

The invention relates to a method for early warning of the development state of road internal voids, an electronic device and a storage medium, and belongs to the technical field of road internal monitoring. In order to solve the problem of timely and accurate early warning of road internal voids, the invention includes using a three-dimensional ground penetrating radar to collect road internal images, annotating the collected images, obtaining a data set for road internal void identification, constructing a road internal void identification model, constructing a loss function between a basic model, an auxiliary model, a first reinforcement model and a second reinforcement model for road internal void identification, and constructing an overall loss function; using the data set for road internal void identification to train the basic model, the auxiliary model, the first reinforcement model and the second reinforcement model for road internal void identification, and obtaining a trained basic model for road internal void identification as a road internal void intelligent identification model; constructing a digital twin model based on the road internal void intelligent identification model; and establishing a method for early warning of the development state of road internal voids.

The application relates to the technical field of system reliability analysis, in particular to a mixed Copula system reliability analysis method based on failure modes, which comprises the following steps: acquiring multi-failure mode degradation data, combining Bayesian inference to estimate parameters of a basic Copula function, rotating and transforming the basic Copula function to construct a mixed static Copula model, judging and optimizing the mixed static Copula model based on root mean square errors, extracting a time-varying correlation parameter sequence by using a sliding window, smoothing and denoising the time-varying correlation parameter sequence by using Kalman filtering, and constructing a mixed dynamic Copula model, and calculating the reliability of the system at different operation time points in combination with a failure mode limit state function. The application can represent the non-symmetrical tail correlation between the multiple failure modes and the time evolution law thereof, and improve the accuracy, stability and engineering applicability of system reliability analysis.

The application discloses a space-time convolution short-time traffic flow prediction method based on complementary ensemble empirical mode decomposition, and comprises the following steps: acquiring original traffic flow data and performing data preprocessing; and inputting the traffic flow data into a pre-constructed CEEMD-GAT-LSTM model to perform short-time traffic flow prediction. The prediction model comprises the following steps: performing signal decomposition on the traffic flow data by using CEEMD, fully mining the spatial features of the data by using GAT, and fully mining the time features of the data by using LSTM, so that the learning efficiency of the neural network is improved. The application can effectively improve the prediction accuracy of short-time traffic flow prediction and provide favorable reference value for the traffic department.

The invention relates to radar angle measurement, in particular to an anti-multipath radar angle measurement system and method based on comprehensive angle measurement, and the method comprises the steps: an angle measurement fusion module generates multi-source pitch angle candidate data through sum-difference angle measurement and sum-sum angle measurement; the track batch optimization module is used for performing batch processing on the multi-source pitch angle candidate data of the multiple trace points in a track batch stage, selecting an optimal pitch angle and updating the three-dimensional coordinates of each trace point; the track association optimization module is used for calculating the Euclidean distance between the three-dimensional coordinate corresponding to the multi-source pitch angle candidate data of each trace point and the historical track in the track tracking stage, selecting the optimal pitch angle and updating the three-dimensional coordinate of each trace point; the flight path smoothing processing module is used for smoothing the flight path height sequence and outputting the smoothed flight path height sequence so as to reduce noise interference and improve height estimation stability; the technical scheme provided by the invention can effectively overcome the defects that multipath interference is difficult to suppress effectively, the angle measurement precision is low and the track starting stability is poor.

The invention discloses a document-level relation extraction method and system based on knowledge enhancement and evidence guidance, and belongs to the technical field of natural language processing and information extraction. According to the invention, three core modules are mainly used for cooperative work: a document graph enhancement module is used for constructing a hierarchical heterogeneous graph and fusing co-reference analysis to enhance semantic representation; the knowledge enhancement module introduces an external knowledge base and adopts a confidence coefficient filtering mechanism to reduce knowledge noise; the evidence guidance reasoning module realizes multi-hop reasoning through axial attention and evidence supervision, and solves the technical problems of decentralized modeling of reasoning capability, large knowledge integration noise, insufficient evidence guidance and limited long-range dependence capture capability in the existing method. Experiments show that the method can effectively capture inter-sentence dependence, suppress knowledge noise and improve multi-hop reasoning stability, and can be widely applied to scenes such as knowledge graph construction, intelligent question and answer and information retrieval.

The invention relates to the technical field of optical communication, in particular to an optical interconnection device based on a diffraction grating. The device comprises a plurality of input ports, a diffraction grating coupler and a plurality of output ports which are arranged in sequence, a plurality of input ports for receiving a plurality of optical signals of different wavelengths; and the diffraction grating coupler is used for improving the coupling efficiency of a target optical signal in the plurality of optical signals based on temperature adjustment, and outputting the target optical signal to a target output port of the plurality of output ports. In the invention, the diffraction grating coupler is dynamically adjusted through temperature control to change the transmission direction and the coupling efficiency of the transmission signal, and optical signal routing from any input to any output is realized. The limitation of a traditional optical interconnection fixed optical path is broken through, and greater flexibility is provided for a complex communication network. And meanwhile, the diffraction grating coupler is regulated and controlled by temperature to realize optical path switching, so that the problems of high power consumption and low precision in mechanical regulation are avoided.

The application belongs to the field of artificial intelligence model compression and edge deployment in intelligent operation and maintenance and fault diagnosis of power equipment, and discloses a transformer cooperative distillation incremental learning method and system based on time sequence consistency, which comprises the following steps: a teacher model is used to screen a no-label sample set to obtain a pseudo-label sample set, the pseudo-label sample set is combined with an original label sample set to obtain a distillation training sample set; a multi-mechanism cooperative distillation training scheme containing soft and hard label joint distillation, time sequence consistency distillation and multi-task distillation is constructed, and a student model is subjected to distillation training through the multi-mechanism cooperative distillation training scheme; and a cooperative distillation incremental learning scheme in which a cloud end continuously updates a teacher model to adapt to new multi-source monitoring data and a student model after edge distillation generates a pseudo-label to expand the distillation training sample set is constructed. The application can be used for realizing high-precision and low-cost training and continuous updating of a student model in a resource-limited terminal or an online scene.

The invention relates to a mobile monitoring device for a machining machine tool. The mobile monitoring device comprises a mobile box body, and a monitoring assembly, a calculation and analysis assembly, a communication assembly and an interactive feedback assembly which are arranged in the mobile box body, the monitoring assembly comprises a first current sensor and a first vibration sensor; the communication control assembly comprises a controller, a vibration decomposition unit and a noise reduction module, the controller is connected with the noise reduction module, the noise reduction module is connected with the first current sensor and the vibration decomposition unit, and the vibration decomposition unit is connected with the first vibration sensor; the communication assembly comprises an access switch, and the access switch is connected with the controller; the interactive feedback assembly comprises an IO module, a current abnormity alarm lamp and a vibration abnormity alarm lamp, wherein the current abnormity alarm lamp and the vibration abnormity alarm lamp are connected with the IO module. The IO module is connected with the controller and the vibration decomposition unit. The device can operate independently of the machine tool, can synchronously collect current and vibration data, improves the machining judgment accuracy of the machine tool, and is high in compatibility with machine tools of different models.

This invention provides a method for predicting the health trend of hydropower units based on the TCN-BiLSTM-Attention model, relating to the field of hydropower generator sets. The method involves: S1: acquiring the original vibration signal of the hydropower generator set and denoising the original vibration signal using a TTAO-VMD-EWT joint denoising model; S2: obtaining multiple operating state parameters of the hydropower generator set and calculating the Pearson correlation coefficient, Spearman correlation coefficient, maximum information coefficient, and distance correlation coefficient between these parameters and the vibration signal; S3: calculating the fusion evaluation index of each operating state parameter and the vibration signal, selecting key features, and constructing a multi-dimensional feature vector together with the reconstructed vibration signal; S4: constructing a TCN-BiLSTM-Attention prediction model to predict and output the predicted trend of the hydropower generator set's vibration signal. This method improves prediction accuracy, increases prediction efficiency, and enhances the accuracy, stability, and timeliness of hydropower generator set vibration trend prediction.

The application relates to a laser radar odometer method and system based on a state space model and window attention, comprising the following steps: projecting original point cloud data and constructing an invalid point mask to filter invalid points; performing down-sampling and region aggregation to reduce the calculation amount; designing a CNN-SSM module to extract multi-scale features considering global and local information; gradually establishing cross-frame association based on a state space window attention fusion encoder; and finally converting the associated features into a pose relationship between adjacent point cloud frames. The application solves the problems of the traditional radar odometer based on deep learning, such as loss of details in global information modeling, decline of cross-frame association confidence of point clouds and high calculation cost caused by attention calculation dependence, and realizes the laser radar odometer with efficient, high real-time and high-precision pose estimation.

This invention discloses a drug target affinity prediction method that integrates PPI quality and uncertainty. The method constructs a drug molecule map and a multimodal protein structure representation, and extracts multi-source features by combining the local PPI sub-map of the target protein. By calculating the protein's low-frequency level, prediction uncertainty, and PPI quality, a PPI quality-aware gating factor is generated to adaptively adjust the PPI information injection intensity, and a residual enhancement strategy is used to preserve the original protein features. Subsequently, the drug representation and the enhanced protein representation are fused using adaptive gating, and the result is input into a prediction network to output the drug-target affinity. This method effectively integrates protein function and interaction information, improves the prediction stability of low-frequency proteins and the model's generalization ability, and provides an accurate and reliable computational tool for drug screening and candidate molecule selection.

The application discloses a kind of image recognition method and system under complex illumination environment, the method includes: obtaining the image to be identified needing to carry out image recognition, the illumination influence area is obtained by analyzing the image to be identified, and the illumination influence area is divided into a plurality of grid units according to pre-set rule by grid division;Respectively, randomly select pixel points from illumination influence area and non-illumination influence area, and obtain corresponding pixel block based on pre-set window size;The similarity weight between the pixel block of illumination influence area and non-illumination influence area is calculated respectively, to denoise the image to be identified, and the denoising identification image is input into pre-set image recognition model to obtain corresponding image recognition result.The application solves the problem of low image recognition efficiency in the prior art under complex illumination environment.

The present application relates to the technical field of smart home appliances, and discloses a method for controlling a smart device, including: obtaining command information of a first user; determining a second user with whom the first user intends to communicate based on the command information; determining a smart device with operating noise in the space where the first user and the second user are located; adjusting the operating mode of the smart device to reduce the noise interference of the smart device in the communication environment between the first user and the second user, so as to promote smoother communication, without requiring any user to put aside what they are doing, and providing users with a more convenient communication method that effectively guarantees the quality of communication. The present application also discloses a device for controlling a smart device and a smart device.

The application discloses a kind of knowledge embedding and retrieval method and system of multi-modal PDF document, specifically relates to knowledge embedding and retrieval field, the present application uses multi-modal big model identification picture, table and other non-text elements in PDF, generates its position coordinate, converts picture into structured literal description and stores source URL;Second, the original text, picture description and URL are integrated into Markdown format, then, adopt the strategy of gradual blocking: each picture information is individually used as text block, and the remaining pure text is blocked according to character length;Subsequently, all text blocks are vector embedded, and the type is stored as metadata into vector database;Finally, in the retrieval stage, relevant text blocks are returned based on vector similarity;In the generation stage, single mode or multi-modal model generates accurate answer using text content and picture URL.

This invention discloses an entity relationship recognition method and apparatus for decision knowledge graphs. The method includes: retrieving a set of decision text information from a decision knowledge text database; preprocessing the set of decision text information to obtain a preprocessed dataset; and performing entity relationship recognition processing on the preprocessed dataset to obtain an entity relationship information set.

This invention discloses an anisotropic sparse deconvolution enhancement method and system for ultrasound tumor image segmentation. The method includes: establishing a local convolution model for ultrasound RF images based on the anisotropic characteristics of the ultrasound xz imaging plane; constructing an axis-aligned elliptical symmetric anisotropic point spread function (PSF), whose lateral and axial spread scales are obtained through physical parameter conversion or data-driven methods; constructing an ultrasound sparse reconstruction objective function incorporating the anisotropic PSF, adding non-negativity constraints, introducing a weighted Hessian continuity term and an L1 sparse prior term in the xz plane direction, and optimizing the solution to obtain the sparse reconstruction result; using this result as input, employing an RL iterative deconvolution method based on the anisotropic PSF, and achieving image boundary sharpening and noise suppression through PSF flip kernel ratio iteration; inputting the enhanced image into a tumor segmentation network to complete training and inference, thereby achieving ultrasound tumor image segmentation.

This invention provides a magnetic detection device based on diamond NV centers and the Kerr effect. It achieves the sharing of light sources and optical paths between magnetic detection technologies based on the magneto-optical Kerr effect and those based on NV centers, combining the two technologies and greatly simplifying the device structure. This increases the intensity of the optical signal received by the detection module, reduces noise interference in the signal, and thus improves the accuracy of magnetic detection. It can simultaneously meet the requirements of rapid and high-resolution detection, and improve the overall detection speed while ensuring detection accuracy.

This invention provides a lithium carbonate price prediction method based on multimodal data collaborative driving, comprising the following steps: multimodal data source identification and data acquisition; multimodal data classification and standardization; personalized noise filtering and outlier correction of multimodal data; extraction of trend and correlation features from structured data; extraction of semantic and visual / speech features from unstructured data; extraction of key information and feature quantification from semi-structured data; weighted collaborative fusion and feature optimization of multimodal features; construction and training of a two-branch collaborative prediction model; model prediction and preliminary verification of prediction results; optimization and dynamic correction of prediction results; interpretability analysis and visualization of prediction results; and method effectiveness verification and continuous improvement. This method, based on multimodal data collaborative driving, addresses the pain points of existing lithium carbonate price prediction technologies, forming a comprehensive, high-precision, and highly practical prediction solution.