274 results about "Reference vector" patented technology

The invention discloses a noise signal recognition method based on deep learning. The method comprises the steps of original signal collection, signal preprocessing, noise recognition feature extraction, noise signal recognition model establishment and noise signal intelligent recognition. The invention relates to the technical field of digital signal data processing, in particular to a noise signal identification method based on deep learning, and innovatively provides a self-adaptive signal mode decomposition method based on a combined judgment mechanism of energy threshold value judgment and energy range value judgment. Decoupling and reservation of different frequency bandwidth information in an original signal can be effectively completed, and the recognition accuracy of non-stationary communication noise is improved; a frequency band filter bank constructed based on a Gaussian function is introduced, so that the accuracy of communication signal identification is improved; a method for constructing a noise recognition model by fusing graph neural network structure modeling and an original type reference vector matching mechanism is innovatively proposed, and the recognition robustness of the model to non-stationary communication interference signals is improved.

The invention relates to the technical field of intelligent monitoring and fault diagnosis of a photovoltaic power generation system, in particular to a photovoltaic grid-connected box electric energy quality monitoring system based on data acquisition of the Internet of Things. Comprising an environment perception and time domain synchronization unit which is used for triggering sampling by using a unified clock source and generating a full-dimensional synchronous original data stream; the ideal reference physical reconstruction unit is used for calculating a theoretical output amplitude envelope and generating an ideal state reference vector; the fault mechanism parameterization evolution unit is used for converting the fault mechanism into a fault operator, selecting a high-risk fault mode according to the current operation condition, and superposing the corresponding fault operator onto the ideal state reference vector to generate a theoretical damaged state waveform; the double-rail-difference manifold extraction unit is used for obtaining a real residual vector and a theoretical fault residual vector; and the isomorphic feature coupling judgment unit is used for calculating the similarity between the real residual vector and the theoretical fault residual vector. According to the invention, the robustness of power quality monitoring under complex meteorological conditions is significantly improved.

The invention relates to the technical field of image analysis, in particular to a battery cell assembly polarity detection system based on machine vision, which comprises a reference acquisition module, a brightness analysis module, a feature extraction module, a response identification module and a classification judgment module. According to the invention, the reference containing the reference vector direction angle and the initial gray average value is established, so that the direction and brightness deviation caused by illumination change in the current detection period can be quantified, and the compensation instruction is generated according to the direction and brightness deviation to dynamically adjust the exposure time length and the light source voltage, thereby realizing the real-time correction of the acquired image; meanwhile, the local direction entropy of the tab area is calculated in the corrected image, and weighting processing is carried out in combination with the neighborhood brightness mean value difference, so that the distinction degree of surface textures and gloss characteristics of materials with different polarities is enhanced, and the accuracy of the surface texture and gloss characteristics of the materials with different polarities is improved. And finally, performing comprehensive judgment according to multi-dimensional indexes such as a brightness reflectivity mean value and a local direction entropy strength ratio.

Examples in the present disclosure relate to hand chirality estimation. Tracking data captured by one or more sensors associated with an extended reality (XR) device is processed to determine positions of a plurality of joints of a hand of a person. A reference vector is generated based on a first subset of the positions. The first subset of the positions includes positions of at least two metacarpophalangeal joints. A plurality of bending angles is determined based on at least a second subset of the positions. Each bending angle represents an angle between a respective pair of articulating bones that is measured in relation to the reference vector. An estimated chirality of the hand is identified based on the plurality of bending angles. Operation of the XR device is controlled using the estimated chirality of the hand.

The invention provides a wear coefficient correction method for a shield cutter, and relates to the technical field of wear monitoring, and the method comprises the steps: collecting a time sequence magnetic field signal obtained by a magnetic sensor array disposed on a non-rotating part of a shield tunneling machine, and synchronously obtaining a cutter angle coding signal; performing time-frequency conversion and filtering on the magnetic field signal to obtain a noise reduction time sequence signal; determining a geometric position sequence of each shield cutter in combination with the angle coding signals; based on the magnetic sensor pose, the tool position and the magnetic dipole model, theoretical magnetic field mapping between the tool and the sensor is constructed, and a system response matrix is generated; reconstructing an independent magnetic feature vector of each cutter to obtain an optimal feature set; generating a vector difference through comparison with a reference vector, inputting the vector difference into an abrasion identification model, obtaining the abrasion loss and type identification of each cutter, and correcting an abrasion coefficient; the method improves the wear recognition precision and reliability, and is suitable for tunnel construction under complex geological conditions.

The invention relates to the technical field of textile bio-manufacturing and industrial process automation control, in particular to a bio-based fabric production temperature and humidity intelligent regulation and control system which comprises a data acquisition unit used for acquiring temperature and humidity sensor values in a fermentation curing workshop in real time and reading current heating power of an actuator and a fan rotating speed instruction; the reference state reconstruction unit is used for receiving the input state vector and generating an interference-free reference vector; the disturbance simulation unit is used for generating a theoretical damaged state vector representing a specific fault mode; the double-track difference unit is used for generating a real difference feature vector and a theoretical difference feature vector; the coupling regulation and control unit is used for calculating a similarity value of the real difference feature vector and the theoretical difference feature vector in a number domain space; according to the method, non-ideal factor interference is effectively eliminated, and the physical interpretability and numerical traceability of the reference data are ensured.

The invention relates to a BIM model-based maintenance method and system for a split-phase vertical laminated GIS bus cylinder. The method comprises the following steps: collecting point cloud data through laser scanning to construct a three-dimensional model; key feature points are extracted, and displacement reference vectors are determined; constructing a displacement constraint matrix and optimizing a path; generating an interactive three-dimensional space model; simulating a dismounting sequence and evaluating the risk; generating an emergency instruction and a tool track; and the maintenance process is optimized by feeding back the cycle correction model. Visual simulation, precise control and dynamic optimization of the whole overhaul process are achieved, and the overhaul operation precision, efficiency and safety are remarkably improved.

The embodiment of the invention discloses a database query optimization method and device based on a large model and a medium, and relates to the technical field of databases. The method comprises the steps that under triggering of a current query request, a query SQL text of the current query request is collected in real time, and real-time environment data of a current execution engine is obtained; vectorizing the query SQL text and the real-time environment data to determine a query reference vector; the query reference vector is input into a pre-trained space-time attention model, performance bottleneck positioning data for the current query is determined, a query optimization strategy is determined based on the performance bottleneck positioning data, and the query optimization strategy comprises an index optimization strategy and a query rewriting strategy; and optimizing the current query request according to the query optimization strategy, determining optimized query information, performing rewriting security verification on the optimized query information in a preset database copy environment, and executing database query based on the optimized query information after the rewriting security verification is passed.

The invention provides a multi-source heterogeneous data unified semantic representation method based on a large language model. The method comprises the following steps: extracting semantic embedding vectors of modal data through a shared trunk model, realizing cross-modal fusion by virtue of a dynamic modal adapter, generating a uniform representation vector, performing semantic alignment by taking a text modal as a reference, and finally obtaining a text reference vector capable of comprehensively reflecting semantic information of multi-source heterogeneous data. According to the method, the strong representation capability of a large language model is fully utilized, the multi-modal data is encoded in a unified semantic space, the problems of feature space splitting and difficulty in semantic alignment caused by modal difference in a traditional method are effectively solved, the flexibility and semantic consistency of multi-modal data processing are improved, and the method is suitable for large-scale popularization and application. A high-quality semantic representation basis is provided for subsequent tasks such as cross-modal retrieval and semantic reasoning, and the generalization ability and efficiency of the model in practical application are enhanced.

A method for classifying a digital certificate as malicious or non-malicious includes receiving the digital certificate from a network source and extracting textual fields from the certificate. The extracted text is embedded into a high-dimensional vector using a pretrained transformer-based encoder. The resulting test vector is queried against a vector data structure populated with reference vectors derived from known benign and malicious certificates. A similarity search is performed to identify a set of nearest reference vectors. A classification decision is made based on the labels of the most similar / nearest neighbors, using a voting mechanism. If a given set or number of them are labeled as malicious, the certificate is classified as malicious. If not, it is classified as benign. The classification result may trigger a network security action, such as blacklisting the associated IP address or identifying a botnet command and control server. The system may use various embedding techniques, including concatenating subject and issuer fields or embedding individual certificate attributes separately.

The invention belongs to the technical field of electric appliance insulation testing, and particularly relates to a switch cabinet insulation state evaluation method based on transient overvoltage signal analysis, which comprises the following steps: acquiring a transient overvoltage signal of a switch cabinet bus, and obtaining a first-order differential sequence and a phase space trajectory through differential filtering; by calculating the vertical deviation degree of a phase space trajectory motion vector relative to a reference vector, the trajectory deviation degree is obtained, and a divergence accumulation index is calculated so as to represent the geometric distortion of the waveform; estimating the oscillation frequency and the voltage amplitude of the signal, and obtaining the frequency amplitude synchronism based on the correlation of the variation of the oscillation frequency and the voltage amplitude so as to represent the nonlinear physical characteristics of the insulating medium; and the divergence accumulation index and the frequency amplitude synchronism are input into a preset two-dimensional characteristic plane, and the insulation state of the switch cabinet is output, so that the evaluation accuracy is effectively improved.

The invention belongs to the technical field of motor driving control, and discloses a space vector modulation strategy method for a permanent magnet synchronous motor, which comprises the following steps of: searching a region division and vector arrangement table by using regions in which Vlp and V are positioned to obtain an optimal vector sequence, and arranging vectors based on a principle of eliminating a peak of a CMV and minimizing switching operation times; and finally, calculating the duty ratio of a plurality of effective voltage vectors used for synthesizing the next period according to the amplitude-second balance principle. Compared with a control strategy that only one effective vector or two adjacent effective vectors are used in each control period, the virtual vector synthesized by the method is the same as the reference vector, and common-mode voltage spikes can be eliminated by the method; compared with NSPWM and AZPWM, the method has the advantages that area division and vector sorting are reasonably arranged, so that the switching action times are minimized, the switching loss is reduced, and the inverter efficiency is improved.

The invention discloses a package recommendation method, a control device and a storage medium. The method comprises the following steps: constructing a first user vector based on a first consumption amount of a target user for each product service; based on the second historical usage information of each neighbor user, the second historical package price and all the product services, determining a second consumption amount of each product service by the neighbor user, and constructing a second user vector; updating the first user vector based on the second user vector and the communication times to obtain a target user vector; and recommending the target package to the target user based on the first similarity between the target user vector and each reference vector in the reference package set. According to the method, after the first user vector is obtained based on the quota of the target user for each product service, the first user vector is updated based on the second user vector, the similarity with each reference package is calculated to perform recommendation of the target package, the package usage and the social relation are comprehensively considered, and the accuracy of the recommendation result is improved.

The invention relates to the technical field of data processing, and discloses a multi-source data difference attribution system and method based on tensor decomposition, and the method comprises the steps: obtaining an original engineering quantity list file, and then carrying out decoupling to generate a standardized list item text sequence and an initial value set, constructing unit prices to form a tetrahedral geometric model set, mapping the tetrahedral geometric model set to a holographic feature vector space, and generating a multi-dimensional list feature vector set; constructing a standard cost reference vector field, identifying a difference vector set and generating a visual cost difference topological data model; a natural language query instruction is responded, a target difference attribution tensor is locked, a logic verification chain is constructed, and an intelligent diagnosis report is output; according to the method, a computable logic distance is established in multi-source heterogeneous data by reversely reconstructing a high-dimensional price-rate feature space, so that the problem of attribution blocking caused by dimension collapse in traditional cost auditing is effectively solved, and accurate traceability and compliance automatic diagnosis of cost differences are realized.

The invention provides a machine room temperature and humidity real-time monitoring method and system based on multi-source fusion, and relates to the technical field of multi-source fusion, and the method comprises the steps: collecting original temperature and humidity data in real time, carrying out the data cleaning and normalization processing of the original temperature and humidity data, and generating a standardized data set; based on the standardized data set, space-time dimension data is mapped to an Euclidean space to generate a measurement point set, reference position coordinates are determined according to spatial distribution characteristics of the point set, and two orthogonal direction reference vectors are constructed; and calculating an included angle of the direction reference vector to define a sector angle division domain, respectively selecting three space-time sampling positions inside and outside the division domain, connecting the sampling positions according to a timestamp sequence to form a closed parameterized curve under curvature constraint, and generating an initial environment correction factor. The problems that existing machine room temperature and humidity monitoring precision is insufficient and maintenance is inconvenient are solved.

The invention relates to the technical field of fault diagnosis, in particular to a power system fault tracing and early warning method and system based on an AI large model, and the method comprises the steps: collecting multi-source heterogeneous data from a power system, carrying out the preprocessing, deep feature extraction and enhancement, mapping a multi-modal feature tensor in a feature subset to a unified feature space, and carrying out the recognition of the multi-modal feature tensor. Identifying the mapped multi-modal feature tensor by using a pre-trained AI large model to obtain a state feature representation and a risk score; performing similarity calculation on the state feature representation and a current state reference vector, and when an abnormal state is determined, constructing a fault propagation graph for a fault feature vector by using a graph neural network, and marking a fault source and an influence path at the same time; and the state corresponding to the next sliding time window is subjected to rolling prediction based on the set time step length, and when the risk score exceeds the set dynamic threshold value, multi-level early warning is triggered, so that the fault tracing accuracy and the early warning efficiency are improved.

The application relates to the technical field of time synchronization, and discloses a mixed time service method based on Beidou satellite navigation and TSN, which comprises the following steps: acquiring network topology structure information and historical time service error data of each synchronization node in a target distributed system, and constructing a dynamic time delay state set for jitter identification; based on the dynamic time delay state set, a current global time reference is extracted through a time calibration algorithm, a time difference reference vector sequence is generated, and initial synchronization adjustment is carried out on a TSN master node; according to the response stability of a slave node, a clock convergence function curve between nodes is constructed, whether the synchronization network is in a low jitter interval is judged based on time offset rate and time drift trend change; in combination with the node time state after directional offset suppression, a multi-channel redundant time adjustment mechanism in a time window is applied, a weight adjustment coefficient is extracted, and a non-uniform calibration factor is introduced; and according to a regression correction result, a time service stability index is evaluated in real time. The application has the advantages of improving time service stability.

The invention discloses a coal mine high-voltage switch harmonic interference elimination method and a filtering monitoring protection device, and relates to the field of electrical variable measurement, in the method, an analog signal of a high-voltage switch is collected through a mutual inductor and converted into a digital signal, a zero-sequence current abrupt change slope is calculated, an auxiliary contact state signal is read, and three-phase voltage and zero-sequence voltage fundamental frequency are obtained. And constructing a basic reference vector based on the fundamental wave frequency, and judging an operation scene according to a switching value state and a zero-sequence current abrupt change slope. Superposing aperiodic direct current components in an operation transient scene to obtain a final interference reference vector, and adopting a relatively large step length coefficient; a basic reference vector is directly used in a non-operation transient scene, and a small step length coefficient is adopted. And finally, a purified electrical parameter signal is obtained through processing of an adaptive filtering algorithm. The method is used for reducing the influence of a transient distortion component on signal purification, so that the operation reliability of the high-voltage switch comprehensive protection device under a complex working condition is enhanced.

A method of decoding and / or encoding video data is provided. The method includes: determining a block unit from the video data; determining a guided reference block indicated by a guiding reference vector of the block unit; determine a relocated intra derivation mode based on a relocated position of the guided reference block, where: the relocated intra derivation mode is used to predict a relocated reference unit, that covers the relocated position, and relocated reference parameters are derived by using the relocated intra derivation mode based on sample values of relocated reference samples, neighboring the relocated reference unit; determine the relocated reference parameters as one of intra merge derivation candidates to add to an intra merge list of the block unit; and reconstruct the block unit based on the intra merge list. An electronic device and a non-transitory machine-readable medium of an electronic device using the aforementioned method are also provided.

The invention relates to the technical field of image sensing, general image recognition and feature positioning under computer vision, and discloses an acupuncture point positioning method and system based on artificial intelligence, and the method comprises the steps: obtaining a non-rigid target section image through an image collection module; the key point identification module determines topological anchor points at two ends of the section; the reference construction module establishes a structure reference vector; a deflection calculation module extracts surface texture feature points, obtains a high-frequency component and a low-frequency component through multi-scale frequency domain decomposition, and determines a spatial deflection parameter based on the gradient of the energy distribution ratio of the high-frequency component and the low-frequency component; the proportion compensation module corrects a target distribution proportion coefficient; according to the method, the pose is inversed by using the scale invariance between the frequency domain components, the decoupling of the anatomical positioning logic and the pixel space is realized, and the non-linear interference of the non-uniformity of the biological surface and the dynamic environment illumination on the positioning precision is eliminated.

The invention provides a data processing method and device, a storage medium and a computing device cluster, which are used for reducing the first word delay of a large model. After a target sample set is obtained, feature extraction is performed on samples in the target sample set to obtain a target vector set, then the target vector set is input into a converter block to obtain a reference vector output by an Nth layer of the converter block, and the reference vector is input into a first prediction module to obtain a reference score set; the reference score set comprises an importance score of a sample corresponding to each vector in the reference vector set, inputting the reference score set and the reference vector set into a first cutting module to obtain an updated reference vector set, and analyzing the updated reference vector set to obtain a target prediction result. The samples in the target sample set are cut at any layer of the converter block, so that the calculation amount of analyzing and processing the target sample set by the converter block can be effectively reduced, and the first word time delay of the target prediction result is effectively reduced.

The invention relates to the technical field of industrial process control, and discloses a plastic product large-scale production multi-parameter cooperative control system and method. A deviation calculation module; an adaptive compensation module; and a set value updating module. The method comprises the following steps: injecting a probe signal in a preset non-sensitive stage, synchronously acquiring a response signal, and calculating a process response feature vector; calculating the deviation between the measurement vector and a preset reference vector to generate a measurement deviation vector; determining a target compensation vector through deviation attribution in combination with prediction drift, updating a process response Jacobian matrix online, and calculating a multi-parameter cooperative adjustment amount; and updating the machine set value of the next period according to the adjustment amount. According to the method, the problems of control lag and parameter coupling are solved through prospective compensation and self-adaptive updating, and the control precision and robustness of multiple parameters in large-scale production of plastic products are improved.

The invention provides a multi-state-quantity integrated high-voltage cable distributed online monitoring method and system, and belongs to the field of high-voltage cable monitoring, and the method comprises the steps: S1, deploying monitoring nodes, and constructing a distributed monitoring network; s2, acquiring multi-dimensional state data of the high-voltage cable, and performing preprocessing and feature extraction to form a state vector; s3, calculating linear correlation among the state vectors, calculating nonlinear correlation intensity by adopting a kernel correlation analysis method, and performing weighted cooperative calculation; s4, calculating a connection weight between nodes according to the monitored network topology, and executing a distributed consistency protocol; and S5, establishing a normal state reference vector and a fault state reference vector based on historical data, and calculating a comprehensive health degree evaluation value by adopting a relative distance method. According to the method, collaborative decision-making of multi-dimensional state vectors is realized by using a distributed consistency protocol, the comprehensive assessment capability of the overall health state of the high-voltage cable is formed, and the accuracy and reliability of fault identification are effectively improved.

The present application relates to the technical field of digital management, in particular to a digital generation method and system for quantity shape planning and coordination in college campus planning and design, comprising the following steps: obtaining the pressure value by calculating the flow and speed through the pressure sensor, collecting path parameters, constructing the displacement reference vector by generating the pressure gradient difference sequence, adopting the thiesen polygon segmentation to migrate the topological node, establishing the path attribution relationship to calculate the stress distribution data, calling the distribution data to calculate the dispersion coefficient, identifying the high-risk node and generating the overall layout scheme, in the present application, the path pressure is quantified by real-time acquisition of the flow and speed combined with the passing section parameter, the displacement vector is supported by the gradient difference and length ratio, the boundary node coordinates are migrated, the space form is adjusted with the flow, the topological boundary is more actual, the stress distribution is shown by the interaction point induction path pressure, the function area is connected evenly, the flow efficiency is improved, and the campus space has higher adaptability.

The application relates to the technical field of Internet of Things device maintenance, and in particular to a power concentrator operation early warning method and device, equipment and a medium. The method collects original data of each to-be-evaluated parameter in real time and extracts a parameter feature vector, then compares the parameter feature vector with an adaptive reference vector, calculates a residual error vector to detect an abnormality, once the abnormality is found, the system determines the type according to the abnormality feature, and dynamically and smoothly updates the reference vector to improve the adaptability of subsequent detection, finally, the weighted residual error deduction value of all parameters is comprehensively scored to obtain an overall state score, and a corresponding early warning is triggered according to a preset strategy. Thus, the operation parameters of the device are comprehensively analyzed, and the abnormality early warning is automatically performed.

The invention discloses an employment psychological analysis model construction method and an employment psychological analysis method, and belongs to the field of artificial intelligence, and the method comprises the steps: distributing a corresponding dynamic distribution weight for each psychological traits dimension according to a center vector of each psychological traits dimension and an original embedded vector of each word, combining the projection matrix and the original embedded vector of each word to generate a characteristic projection embedded vector of each word; calculating an intensity score vector of each psychological traits dimension according to the traits projection embedding vector of each word in combination with high and low expression intensity reference vectors, and adjusting a universal language model in combination with a position coding vector, the traits projection embedding vector and an attention head output vector; and training the adjusted model by using the employment psychological assessment question and answer data set to obtain an employment psychological analysis model. By implementing the application, the problem that a general language model in the prior art cannot decouple the employment psychological dimensions of the college students and cannot capture the employment psychological traits of the college students can be solved.

The invention belongs to the field of geophysical exploration, and particularly relates to a very-low-frequency three-component coil calibration method based on frequency sweep excitation, which comprises the following steps: acquiring an alternating-current excitation current and a time domain signal of a three-component coil, and performing frequency domain transformation on the time domain signal to obtain a plurality of frequency domain response vectors covering a frequency sweep range; constructing a linear equation set for describing a coil system error based on a plurality of frequency domain response vectors measured under each attitude and the calculated excitation magnetic field reference vector, and solving the linear equation set by using a least square method to obtain a calibration matrix for correcting measurement data; and the calibration matrix is adopted to correct the measurement data of the three-component coil in the use state, so that systematic errors of the very-low-frequency three-component coil can be accurately calibrated and calibrated, and the measurement precision and data reliability of a very-low-frequency electromagnetic method are effectively improved.

Embodiments of the present disclosure provide a solution for video processing. A method for video processing is presented. In one method, for a conversion between a current video block of a video and a bitstream of the video, a first steering vector associated with the current video block is determined. The first guide vector includes a first guide block vector (BV) or a first guide motion vector (MV). A first reference vector of a first reference block positioned based on the first steering vector is determined. The first reference vector includes a first reference block vector or a first reference motion vector. A vector prediction for the current video block is determined based on at least a first steering vector and a first reference vector. Vector prediction includes block vector prediction or motion vector prediction. The conversion is performed based on vector prediction.

The application discloses a kind of tower deformation monitoring method and system based on laser gyro, belong to laser gyro application technical field.The application is arranged two groups of antennas to constitute baseline in tower top, displacement sequence and first attitude sequence are generated based on the fixed vector and reference vector of baseline, simultaneously, the attitude change of tower is tracked through the pan-tilt head and image acquisition device of observation station, then the attitude data of laser gyro of observation station is collected, second attitude sequence and observation error sequence are generated in combination with zero offset vector;Further, real-time state vector is generated based on historical state vector and observation error sequence, fusion attitude sequence is generated in combination with second attitude sequence, vertical deformation degree and horizontal deformation degree of tower are calculated based on fusion attitude sequence and displacement sequence.The application can improve the accuracy of tower attitude measurement by combining antenna observation data and laser gyro attitude data for tower deformation monitoring, to ensure the reliability of tower deformation monitoring.

The invention relates to the technical field of electrical performance testing of a hydrogen production system, and discloses a key parameter monitoring and early warning method in a PEM water electrolysis hydrogen production process. The method comprises the following steps: injecting a composite excitation signal into an electrolytic cell, wherein the amplitude is related to real-time working current density and average temperature; acquiring multi-dimensional operation parameters, and performing time-frequency domain combined processing on the voltage response signal to obtain a time-frequency spectrogram; extracting a real-time harmonic distortion feature vector and an impedance variable quantity based on the time-frequency spectrogram, calculating a frequency spectrum offset quantity with a reference vector, and judging a fault tendency type and a fault tendency region by combining a frequency band energy ratio; and the spliced feature vectors are reasoned through a lightweight artificial intelligence model to output fault probability distribution, and graded early warning is executed. Through multi-signal excitation and multi-dimensional parameter fusion analysis, the timeliness and accuracy of fault recognition are improved, accurate monitoring and graded early warning of the running state of the electrolytic cell are achieved, and it is guaranteed that the hydrogen production process is stable and reliable.