2755 results about "Feature matrix" patented technology

The invention relates to the technical field of data processing, and discloses a comprehensive evaluation method and system for the health degree of a distribution network cable. The method comprises the following steps: collecting cable joint multi-source monitoring signals, normalizing the monitoring signals to obtain a degradation degree feature vector, correcting multi-physics field coupling model parameters, obtaining a recessive degradation index through finite element calculation to obtain an enhanced feature vector, and performing time-frequency domain decomposition to extract multi-scale feature parameters to obtain a comprehensive feature matrix; a double attention mechanism calculates a feature weight and a time sequence correlation degree to obtain a deterioration trend prediction value, and fuzzy integral is fused with a multi-classifier output probability to obtain a health degree evaluation grade and an early warning result. According to the invention, the early defect identification accuracy and the degradation trend prediction precision are improved.

The invention relates to the technical field of digital economic risk control, and discloses a digital economic risk identification system and method based on artificial intelligence. A risk data acquisition engine of the system obtains transaction behavior data streams from a plurality of digital economic transaction platforms in real time, and converts the transaction behavior data streams into a structured transaction feature matrix; an abnormal mode detection engine extracts time sequence abnormal features through a deep residual network to generate an abnormal feature vector set; the risk association analysis engine constructs a risk propagation path map through a graph neural network, and outputs a risk association degree scoring matrix; the dynamic threshold adjustment engine performs adaptive threshold calibration according to the historical risk event database to generate a dynamic risk threshold vector; and the risk decision engine compares the scoring matrix with a dynamic threshold value, marks risk transaction nodes and generates a risk early warning instruction set. The system can adapt to digital economic transaction characteristics, and the comprehensiveness and accuracy of risk identification are improved.

The invention relates to the technical field of computer network security, and discloses a computer network security access control management method based on big data. The method comprises the following steps: constructing a network security situation knowledge graph, collecting a real-time access behavior sequence through a probe, and synchronizing the real-time access behavior sequence to the knowledge graph; simulating a network entity interaction state in the knowledge graph, and predicting a threat propagation path and a potential intrusion behavior; setting a dynamic access control strategy, constructing a multi-dimensional feature matrix in combination with a real-time access behavior sequence association influence degree and a strategy execution priority constraint condition, calculating a strategy conflict risk score by using a deep learning model, comparing with a preset threshold to judge whether a conflict exists or not, and if yes, reconstructing the strategy; and automatically executing access blocking, session termination and data encryption operations according to the reconstructed strategy, recording an execution log and security feedback data, and updating the knowledge graph in real time. According to the method, the dynamic property and the security of access control are improved, and security threats in a complex network environment can be effectively handled.

The invention provides a combined wind power prediction method suitable for a distributed wind power plant, and the method comprises the steps: collecting the real-time meteorological data and historical power data of a wind power plant cluster, carrying out the cross-wind-plant data collaborative cleaning, and generating a time-space aligned standardized data set. Constructing an adaptive spatio-temporal feature extractor, outputting a spatio-temporal feature matrix, and inputting the spatio-temporal feature matrix into the spatio-temporal adaptive neural network, the graph attention prediction model and the physical constraint decision tree model to generate three prediction sequences. And the sequences are fused through a space-time collaborative attention mechanism to generate a dynamic weighted combination prediction result. And performing physical constraint correction on the result by using a space-time residual error correction network to generate a final prediction sequence. And updating the neural network topological structure based on the prediction error distribution, and outputting a prediction result with uncertainty evaluation to a power grid dispatching system. According to the method, the precision and reliability of wind power prediction of the distributed wind power plant can be improved, and the stability and economy of power grid dispatching are improved.

The invention discloses a machine vision-based intelligent detection method for steel galvanized surface defects, which comprises the following steps: S1, acquiring and preprocessing a steel galvanized surface image to obtain a standardized image; s2, constructing a specular reflection probability graph according to the brightness distribution and the gradient magnitude, and calculating a reflection intensity value; s3, calculating a structure tensor matrix, determining a main direction angle and an anisotropic consistency coefficient, and generating a direction feature matrix; s4, establishing a multi-scale direction adaptive phase kernel function, and performing phase modulation in a frequency domain by adopting an improved phase stretching transformation algorithm; s5, inverse Fourier transform is executed, and a phase response matrix is extracted; s6, performing weighted fusion to obtain a comprehensive phase response diagram; and S7, setting a threshold value according to the noise variance and the statistical characteristics, executing binarization and morphological processing, and outputting a defect region and boundary coordinates. According to the invention, high-precision identification and boundary positioning of steel galvanized surface defects are realized.

The invention discloses an ocean multi-mode environment perception and intelligent ship navigation decision-making method based on a double-branch vision-semantic encoder. The method comprises the following steps: S1, acquiring a multi-source data image containing a ship and a surrounding environment thereof from an existing public maritime data set or platform; s2, training a double-branch vision-semantic encoder by using the multi-source data image, and inputting a to-be-processed image extracted in real time into a multi-modal feature matrix in the trained double-branch vision-semantic encoder; s3, based on the multi-modal feature matrix, obtaining positioning information of the ship and surrounding environment elements, and constructing a dynamic security domain model; and S4, in combination with the dynamic security domain model and the multi-ship relative position relationship, carrying out quantitative evaluation on the navigation risk, and generating a self-adaptive navigation strategy based on an evaluation result. According to the invention, high-precision ship positioning and environment element identification under complex weather and illumination conditions are realized by using all-weather characteristics and multi-scale visual feature coding of SAR imaging.

The invention relates to the technical field of risk assessment, in particular to an AI enterprise financial risk dynamic assessment method and system. The method comprises the following steps: obtaining enterprise multi-dimensional financial data, operation activity data, industry environment data and macroeconomic index data, carrying out cleaning, standardization and time sequence alignment processing, and integrating the data into a financial risk assessment multi-dimensional feature matrix; constructing a dynamic weight distribution model based on the financial risk assessment multi-dimensional feature matrix, endowing differentiated weights, and generating a time sequence weighted feature vector; risk level mapping and comparative analysis are carried out on the time sequence weighted feature vectors, risk points exceeding a threshold value are identified, and feature sources of the risk points are traced; and constructing a risk conduction path map based on the risk points and feature sources thereof, simulating risk evolution trends under different intervention measures, and evaluating and generating a dynamic evaluation report containing risk early warning levels, key influence factors and intervention measure suggestions. According to the invention, the enterprise financial risk assessment efficiency can be improved.

The invention discloses an intelligent analysis system for ultrasonic phased array detection data of a welding seam, and relates to the technical field of nondestructive testing, and the intelligent analysis system is characterized in that a four-dimensional wave field tensor is constructed by collecting full-waveform ultrasonic data under multi-channel, multi-path and multi-angle conditions; extracting reflected signals which keep time coherence in a discontinuous path, and generating a high-dimensional coherence feature matrix; inputting the features into a self-supervised contrast learning model to obtain a defect semantic embedding vector; recognizing a suspected weak defect area based on the distribution density and the boundary change trend, and performing reverse beam focusing in combination with original data to obtain a defect three-dimensional positioning map; the shape bifurcation index and the boundary stability are calculated through topological analysis, and intelligent discrimination of artifacts and microcrack defects is achieved; the method does not need label data, has high automation and robustness, and is suitable for weak defect identification of complex structure welding seams.

The invention discloses an HPLCamp (High Performance Liquid Chromatography) based on deep learning. The invention discloses an HRF dual-mode communication adaptive coding modulation and anti-noise method. The method comprises the following steps: acquiring an optical radio frequency signal amplitude-phase change rate and synchronously sampling and normalizing; calculating a node amplitude-phase residual error to generate a nonlinear mapping coefficient; monitoring coherent change to solve a drift trend, adjusting a modulation coding optimization scheme, compensating distortion and outputting an anti-noise result. According to the method, the instantaneous amplitude and phase of the optical radio frequency dual-mode signal are extracted, a multi-dimensional amplitude-phase characteristic matrix is formed in combination with time domain synchronization and a normalization template, differential residual modeling and nonlinear mapping coefficient calculation are carried out between impedance nodes, and dynamic compensation of amplitude-phase mismatch and envelope offset is achieved. A drift trend quantity is generated based on coherent offset parameter differentiation, feedback is provided for modulation format and coding strategy optimization, amplitude equalization and phase correction are completed, the signal synchronization degree and amplitude-phase consistency are improved, and the steady-state response and anti-disturbance performance of a transmission link are enhanced.

The invention discloses a charging pile cooling control method and system based on AI prediction reinforcement learning, and relates to the technical field of cooling control. The charging pile cooling control method and system based on AI prediction reinforcement learning comprises the following steps: S1, collecting charging heat dissipation data of a charging pile, and preprocessing the charging heat dissipation data; s2, constructing a time sequence characteristic matrix, inputting the time sequence characteristic matrix into a temperature prediction model, outputting a temperature prediction sequence of a controlled target, evaluating a thermal runaway risk in a prediction stage, and constructing a thermal risk identification sequence; s3, constructing a cooling strategy optimization model, inputting the real-time state vector into the cooling strategy optimization model, outputting an adjustment instruction, and issuing and executing the adjustment instruction; and S4, the execution deviation of the adjustment instruction is evaluated, the cooling strategy is adjusted based on the evaluation result, and a cooling strategy feedback sample is generated. The problems of energy consumption waste and cooling imbalance caused by lack of real-time prediction and self-adaptive regulation and control capabilities in the cooling control process of the existing charging pile are solved.

The invention provides a bridge settlement monitoring method and system, and relates to the technical field of bridge engineering monitoring, and the method comprises the steps: firstly selecting a component surface region directly associated with settlement conduction in a bridge structure as a visual clue node, and constructing a bridge structure visual clue network; setting shooting parameters of image acquisition equipment according to spatial distance parameters in the bridge structure visual clue network to form a multi-clue collaborative image set; then, carrying out settlement conduction characteristic modeling on the multi-clue collaborative image set, extracting a visual characteristic variable quantity, establishing a corresponding relation with a settlement conduction path, and generating a settlement conduction characteristic matrix; inputting the settlement conduction characteristic matrix into a preset model, converting the settlement conduction characteristic matrix into settlement displacement parameters of corresponding components, and integrating the settlement displacement parameters to form bridge settlement state information; and finally, based on the settlement state information, generating a monitoring instruction containing a settlement conduction path identifier and settlement parameters of each component, and transmitting the monitoring instruction to a monitoring terminal. The method is comprehensive, accurate, economical and efficient, and can effectively guarantee the safety of the bridge structure.

The invention relates to the technical field of power equipment state monitoring, and particularly discloses a power cable operation state real-time evaluation system based on multi-mode deep learning, and the system comprises the steps: synchronously collecting the load, partial discharge and temperature strain data of a cable through a current and voltage sensor, an ultrahigh frequency sensor and a distributed optical fiber sensor; feature extraction and cross-dimension fusion are carried out on the multi-source data, and a multi-dimensional feature matrix is constructed; a pre-trained deep learning model is utilized to analyze internal association between the features, and cable health degree scores, fault risk levels and defect type identification results are output; automatically generating and executing a load adjustment, loop switching or precise maintenance strategy according to an evaluation result; according to the method, the problems of inaccurate evaluation and early warning lag caused by data isolation analysis of a traditional monitoring method are solved, and real-time accurate evaluation and intelligent closed-loop operation and maintenance of the operation state of the cable are realized.

The invention relates to a flying dust monitoring data processing and classifying method based on multi-source sensing fusion, and the method specifically comprises the following steps: firstly, deploying multi-source flying dust monitoring sensor nodes in a target region to collect data, carrying out the marking, and generating a data set; performing continuous wavelet transform on the acquired data, extracting a wavelet energy spectrum and a Shannon entropy, and splicing to obtain an enhanced feature tensor; secondly, through a two-stage fusion and coding strategy, frequency band energy features are extracted through wavelet packet decomposition, multi-channel cross-correlation, statistical moment and ratio features are calculated to form time sequence mode coding features, and multi-source heterogeneous feature fusion is achieved in combination with a local time sequence feature matrix; then constructing a deep learning model containing a multi-scale time sequence feature extraction and dynamic fusion module, and inputting a fusion feature matrix for training; and finally, inputting the preprocessed new monitoring data into the trained model, and outputting a dust source and pollution level classification result. The dust monitoring data classification accuracy and the dust source identification precision can be effectively improved.

The invention discloses a video monitoring abnormal behavior real-time detection method based on a graph neural network, and the method comprises the following steps: collecting a video frame sequence, extracting a detection frame, a key point and an optical flow feature, generating a node feature matrix, and constructing a dynamic graph structure; establishing a dynamic graph neural network model based on EvolveGCN, and updating a convolution weight by using a gating circulation unit; calculating event intensity and change rate according to the motion abrupt change signal, generating a time delay parameter and adjusting a weight modeling step length; performing low-rank decomposition and spectral radius projection on the convolution weight matrix, and adjusting a spectral constraint threshold according to an abnormal score; inputting a weight matrix to generate graph branch and hypergraph branch embedded representation; exchanging topology correction information based on a mutual generation mechanism and updating model parameters; and inputting the dynamic graph structure and the node feature matrix in real-time reasoning, calculating an abnormal score and outputting a detection result. According to the invention, adaptive evolution and high-precision anomaly detection of dynamic graph modeling are realized.

The invention provides a spatio-temporal data fusion-based power distribution network load prediction method and system, and relates to the technical field of power distribution network load prediction, and the method comprises the steps: collecting related data of a power distribution network, carrying out the wavelet transform decomposition of historical load data, obtaining a load feature matrix, constructing a hierarchical graph convolution network based on topological structure data, and extracting topological correlation features; and generating a spatial-temporal feature tensor through tensor decomposition fusion, training a depth probability prediction model adopting a variational auto-encoder structure, and adjusting prediction probability distribution in combination with environmental data. According to the method, the prediction precision is improved, a complex space-time dependency relationship can be captured, and reliable uncertainty quantization is provided.

The invention relates to the technical field of fault diagnosis, in particular to a pole-mounted circuit breaker fault diagnosis method based on multi-information fusion, and the method comprises the steps: firstly obtaining electric quantity modal data such as three-phase current and coil current and mechanical quantity modal data such as mechanism vibration and voiceprint, and constructing a time-frequency domain alignment feature tensor; then, through a graph space-time attention fusion model, deeply mining physical structure association and time sequence evolution laws among heterogeneous data, and generating a dynamic space-time feature matrix; then, respectively constructing a mechanical evidence body for representing the state of the transmission chain of the operating mechanism and an electrical evidence body for representing the working condition of the vacuum arc-extinguishing chamber by adopting a feature level-decision level mixed framework; an improved D-S evidence theory is applied for fusion, when high-conflict evidences are detected, a self-adaptive arbitration mechanism is automatically triggered, conflict weights are dynamically attenuated or distributed to uncertain items, misjudgment is effectively avoided, and high-robustness collaborative diagnosis of the electromechanical state is achieved.

The invention relates to the technical field of roadway surrounding rock danger identification, and discloses a roadway surrounding rock danger identification model construction method, which comprises the steps of collecting multi-modal data, and generating preprocessed data through synchronous calibration and denoising; extracting a seismic wave frequency domain and image texture features, and generating a multi-modal feature matrix; in combination with a geological prior clustering mining abnormal mode, generating a labeled sample data set; generating a danger identification model based on a transfer learning and feature fusion training network; and the edge deployment model performs real-time reasoning, and generates an early warning result through an adaptive algorithm. According to the method, the frequency domain features of the seismic fluctuation signals and the depth texture features of the surrounding rock images are fused, the multi-modal feature matrix is constructed, abnormal mode mining is carried out in combination with geological prior knowledge, and early weak abnormal signals such as hidden fault slippage or asymmetric microfracture extension which are difficult to find by a single monitoring means can be effectively recognized.

The invention provides a plate edge sealing quality detection method and system based on machine vision, and the method comprises the steps: obtaining image data streams continuously collected in a plate edge sealing processing process, carrying out the light intensity change feature extraction of the image data streams, and obtaining a time sequence light variable feature matrix and a space light variable gradient map of an edge sealing region in an edge sealing image frame sequence; carrying out relevance enhancement on the time sequence optical variation characteristic matrix and the spatial optical variation gradient map through a preset characteristic enhancement model, and generating an edge sealing quality characteristic map with a space-time constraint relation; performing defect mode identification based on the edge sealing quality characteristic spectrum to obtain defect types existing in the edge sealing area of the plate and position distribution characteristics of the defects in the edge sealing image frame sequence; and generating a quality optimization instruction containing a parameter adjustment instruction according to the defect type and the position distribution characteristic, and sending the quality optimization instruction to the plate edge sealing control equipment. According to the invention, the overall precision and stability of plate edge sealing quality detection can be improved.

The invention discloses a complete switch equipment online detection method and system based on multi-sensor fusion, and relates to the technical field of equipment state detection.The method comprises the steps that temperature distribution, partial discharge signals, mechanical vibration waveforms and operation current data of switch equipment are collected, key fault features are extracted based on preprocessed multi-source data, and the key fault features are extracted; generating a high-dimensional feature matrix, constructing a graph convolution-long and short-term memory hybrid network model as a fault diagnosis model, and obtaining a defect detection result according to the spatial-temporal features; and constructing an equipment degradation index based on defect characteristics to calculate an equipment health index, performing probabilistic prediction of the remaining service life in combination with a Wiener degradation model, and triggering an early warning signal when defects are detected or the service life is lower than a threshold value. According to the method, the problems of large data limitation and insufficient fault diagnosis precision of a single sensor in the operation state monitoring of the whole set of switch equipment are solved, and fault evaluation and residual life prediction are further realized by combining a hidden defect evolution rule.

The invention provides a power system transient recording signal abnormal mode intelligent detection method and system, and relates to the technical field of power detection, and the method comprises the steps: obtaining multi-monitoring node recording signals, constructing a space-time coupling sequence set, and extracting a multi-dimensional feature matrix through dynamic scale wavelet transform; and mapping the feature matrix into a space-time topological structure, integrating the space-time topological structure into a graph convolution operator to construct an enhanced representation space, forming a discrimination criterion in the representation space to identify an abnormal mode and a diffusion link, and finally tracing and positioning an abnormal source and generating a fault diagnosis conclusion. According to the invention, the abnormal mode of the transient recording signal can be accurately detected and accurate fault positioning can be realized.

The embodiment of the invention discloses a safety monitoring method and system for building construction, and the method comprises the steps: collecting the original video data of a construction site, carrying out the denoising, illumination correction and frame rate adjustment, and outputting a standardized video data stream; extracting attitude features of the constructors and representing the attitude features as a feature matrix to form an attitude feature matrix set containing spatio-temporal information; the sensitivity and correlation of the feature matrix are analyzed, calibration noise is added after dimension reduction, and feature data conforming to differential privacy are generated; and extracting data advanced representation by utilizing a pre-training model, and completing behavior classification, dangerous area judgment and safety violation detection. And the violation risk is evaluated in combination with the risk level of the construction area, graded early warning is generated, and meanwhile violation information is recorded to form a traceable management mechanism. According to the embodiment of the invention, accurate and efficient safety violation behavior detection is realized, and intelligent technical support is provided for safety management of building construction.

The invention discloses a metering box abnormal power consumption behavior diagnosis method and system based on edge calculation, and belongs to the technical field of power monitoring, and the method comprises the steps: collecting the voltage, current and time mark data of a metering box in real time through an edge end, synchronously obtaining the physical state information of a box door state and the like, generating a space-time feature matrix through an electric parameter space-time coupling analysis method, and carrying out the calculation of the time-space feature matrix; inputting an electric parameter feature mask pruning algorithm to obtain an optimized feature set, triggering an edge-sensing sleep wake-up linkage device when current abnormity is monitored, and collecting magnetic field data; the edge end calculates a total-branch electric energy difference value, combines the magnetic field data and a box door state, and constructs an electricity consumption abnormity classification determination tree through a metering error-electricity stealing behavior coupling diagnosis method to distinguish electricity stealing behaviors; an intermittent abnormal trajectory splicing algorithm is adopted to process fragmented abnormal data to generate an electricity stealing trajectory, and the electricity stealing trajectory is fed back to an electric parameter space-time coupling analysis method to dynamically update the space-time feature weight; according to the invention, the abnormity diagnosis precision and real-time performance are improved, and the energy consumption is reduced.

The invention belongs to the technical field of pipe network monitoring control, and particularly relates to an urban pipe network leakage detection method and system.The urban pipe network leakage detection method includes the steps that firstly, data are collected at key nodes and pipe sections of a pipe network through a data collection module, and a four-dimensional original data set with spatial positioning attributes is formed; the preprocessing module adopts an improved Kalman filtering algorithm containing a pipe network material attenuation coefficient to reduce noise, and outputs high-quality data; the space-time fusion module is combined with GIS pipe network topological data, the topological weight is calculated through a node degree centrality algorithm, and a time attenuation mechanism and adjacent node features are fused to generate a comprehensive feature matrix; the dynamic judgment module judges suspected leakage based on a multi-parameter weighting model and a sliding window dynamic threshold value; the positioning engine module positions a leakage point through a two-factor algorithm of signal time difference and improved hydraulic model residual error; and finally, the linkage control module starts secondary acoustic verification, valve control, early warning and maintenance scheduling instructions are automatically generated after confirmation, and closed-loop management from detection to disposal is achieved.

The invention relates to the technical field of robot control, and discloses an industrial robot adaptive control method and system based on multi-modal sensor fusion, and the method comprises the steps: collecting multi-modal original data, and carrying out the time-space alignment; capturing space-time semantic association of visual textures, tactile pressure distribution and force sense fluctuation in the multi-modal data through a multi-head attention mechanism guided by a physical model, and performing space-time registration; a CNN-LSTM hybrid model is adopted to extract visual texture features and time sequence tactile features in the physical information enhanced multi-modal feature matrix; and carrying out dynamic weight distribution on the fusion feature vectors with physical consistency by utilizing a weight distribution model driven by element reinforcement learning to generate dynamic weighted fusion features. According to the method, the spatial positioning precision of the industrial robot in a precise assembly scene is greatly improved, the contact force control stability is greatly improved, and the control robustness in a complex operation scene is remarkably enhanced.

The invention relates to the technical field of power optical cable monitoring, and discloses a power optical cable operation situation sensing method, which comprises the steps of collecting temperature, strain, vibration and scattering signal data along an optical cable in real time through a distributed optical fiber sensor, and constructing a multi-source heterogeneous data set in combination with power grid operation data and environment monitoring data; carrying out denoising, abnormal value elimination and missing value filling processing on the data by adopting a time sequence synchronization algorithm, and extracting a temperature rise rate and a strain fluctuation frequency to generate a multi-dimensional feature matrix; by dynamically fusing multi-source data, situation awareness indexes such as a thermal stability score and an overload risk index are generated; an LSTM algorithm and a dynamic threshold model are combined to predict the operation risk level of the optical cable in the next 24 hours, a power grid dispatching system is linked through visual means such as a situation map and three-dimensional simulation, and intelligent alarm and operation and maintenance suggestions are automatically issued. The fault identification precision can be improved, the operation and maintenance cost can be reduced, and preventive maintenance can be realized.

The invention relates to the technical field of disaster prevention and reduction of a power system, and discloses an icing risk early warning method based on multi-model fusion and residual time sequence characteristic analysis, which comprises the following steps: collecting meteorological data of a line area in real time, removing abnormal values through secondary judgment of a Pauta criterion and a trend, and standardizing; adopting a TEROL algorithm to screen high-weight key features; running SWD-BP, MUL-GRNN and ELM models in parallel, constructing a dynamic weight by combining DSI, an independence weight method and an entropy weight method, and calculating a final meteorological predicted value; generating a prediction residual signal, extracting time domain features such as a mean value and a peak value, and constructing a residual feature matrix through a sliding window; and inputting an LSTM model to process a time sequence dependency relationship, and judging an icing risk level. According to the method, meteorological prediction is optimized through multi-model dynamic fusion, and deviation is analyzed and corrected in combination with residual time sequence characteristics, so that the problem of weak generalization ability of a single model is effectively solved, and the accuracy of icing risk early warning is obviously improved.

The invention relates to the technical field of intelligent parking management, and discloses a parking reservation management method and system. The system obtains a parking space state data set containing a parking space number, an occupation state time sequence and a reservation request frequency through a parking management platform; performing spatio-temporal feature extraction on the set to generate a multi-dimensional feature matrix, and determining a parking space association network in combination with topology analysis; screening a candidate parking space set satisfying reservation stability and space reachability from the matrix; inputting the candidate parking space set into a pre-trained priority ranking model, and generating a parking space priority score by the model through a dynamic weight adjustment algorithm; fusing the priority score and the real-time parking space occupation data to construct a dynamic distribution matrix, and simulating a parking space distribution spatio-temporal evolution process based on a queuing theory model; and separating an optimal distribution scheme and a candidate parking space sequence from the matrix, and generating final parking space reservation confirmation information. According to the reservation parking management method and system provided by the invention, the reliability and user satisfaction of the reservation system can be improved.

The invention relates to the technical field of task scheduling management, in particular to an intelligent scheduling management method for water conservancy and hydropower engineering detection tasks, which comprises the following steps of: acquiring active power data of a unit, smoothing, differentially generating a rate and an acceleration sequence to construct a load characteristic matrix, comparing the load characteristic matrix with a steady-state threshold to generate a quasi-steady-state interval, and calculating a quasi-steady-state interval; calculating a load instruction deviation to generate a steady-state confirmation identifier, calculating a predicted steady-state window based on the identifier, if the window is longer than the minimum sampling duration, generating a trigger acquisition signal, starting vibration acquisition to generate an original waveform, and uploading the original waveform. According to the method, the load characteristic matrix is constructed through power data differential operation, load instruction deviation dual verification is combined to recognize the steady-state time period, the steady-state window length is pre-judged, and collection is triggered when the requirement is met, so that noise interference introduced by working condition fluctuation is effectively avoided, and it is ensured that original vibration waveform data originates from a stable working condition; and the data sample purity and the fault analysis value are obviously improved.

The invention relates to the technical field of power system fault diagnosis, and discloses a flexible DC power distribution network fault diagnosis method and system. The method comprises the steps of collecting a fault current time sequence signal of a direct current side of the flexible direct current power distribution network, inputting the fault current time sequence signal into a pre-trained fault diagnosis model, and performing adaptive time frequency feature extraction on the fault current time sequence signal by using an interpretable complex time frequency convolution layer to obtain a time-frequency-amplitude tensor, a time-amplitude trajectory, a time-frequency trajectory and a frequency-amplitude trajectory are constructed, the trajectories are subjected to Gramer difference angular field coding, a three-channel time-frequency feature matrix is generated, the three-channel time-frequency feature matrix is input to a parallel double-branch feature extraction module, and global features and local features are extracted; and carrying out adaptive weighted fusion on the extracted global features and local features, classifying the fused features, and outputting a diagnosis result of a fault category. According to the invention, the accuracy of fault diagnosis of the flexible DC power distribution network is improved.

The invention discloses a VTE real-time monitoring and intelligent prevention and treatment system, and belongs to the technical field of intelligent prevention and treatment, and the system comprises a baseline construction module which is used for collecting multi-dimensional VTE parameters, calculating the normal fluctuation interval of each parameter to form an initial individualized baseline, and constructing a self-adaptive individualized baseline through threshold calibration; the trend identification module is used for dynamically setting a sliding window duration, calculating a VTE trend slope and a VTE product deviation, constructing a trend constraint and a product deviation constraint, and when the two constraints are not met at the same time and the continuous deviation duration is exceeded, judging that the deviation is continuous abnormal deviation; the time sequence risk prediction module is used for calculating deviation values of various parameters of the target patient, constructing a space-time fusion feature matrix, inputting a time sequence risk prediction model and outputting a VTE risk probability; and the early warning and intervention module is used for performing double judgment and intervention, setting three-level early warning and intervention measures, calculating an improvement rate to verify a prevention and control effect in real time, and realizing VTE early recognition and intelligent prevention and control.