331 results about "Domain analysis" patented technology

The invention discloses a low-dose CT reconstruction method and system based on global optimization iteration deep learning, and the method comprises the steps: collecting CT cross section data of a normal dose, and generating low-dose CT data; the method comprises the following steps: establishing a dual-domain analysis compression iteration model, constructing maximum posteriori estimation based on a CT projection chordal graph data composite Poisson noise generation mechanism, subdividing CT projection data into a projection domain and a chordal graph domain, sequentially updating the projection data, the chordal graph data and image data, and constructing an iteration algorithm to realize CT global optimization modeling. Expanding an iterative algorithm into a trained reconstruction network; inputting the paired data into the expanded network for training, and storing a model with the minimum output result loss; a sub-network and an attention mechanism are added for CT data features, jump connection weights are finely adjusted, middle layer neural network parameters are finely adjusted and updated through middle supervision, and a global optimization iteration deep learning model NGACI-Net is obtained. According to the method, the problems that the low-dose CT image quality is poor, the reconstruction efficiency is low, and generalization and interpretability are lacked are solved.

The invention relates to the technical field of data acquisition and processing, and provides a real-time data acquisition and processing method and system for a distributed control system, and the method comprises the steps: collecting a data flow in real time through a terminal sensor, analyzing the probability distribution of a data change rate through a sliding window, and obtaining a data change rate; and a sensitivity threshold is intelligently generated by combining dynamic factors such as equipment electric quantity and network load. When data mutation exceeds a threshold value, the system automatically extracts a key change section and generates time, period and variable quantity three-dimensional features, and meanwhile, the frequency domain analysis result is fused to enhance the anti-interference performance. And the event emergency degree is calculated based on the time-space correlation of the spectrum energy entropy and the characteristic parameters, and finally a processing queue sorted according to priorities is formed and the bandwidth is dynamically allocated. According to the method, through multi-dimensional data fusion and resource adaptive adjustment, the response speed of the system to emergencies, the data discrimination precision and the stability in a complex environment are improved.

The invention relates to the technical field of iron and steel smelting, in particular to a continuous casting quality data processing method based on multi-modal cognitive reasoning, which comprises the following steps of: synchronously acquiring multi-source sensor data, performing time alignment, format standardization, noise filtering and normalization processing, extracting image / thermal image spatial features through CNN (Convolutional Neural Network), and calculating the continuous casting quality data according to the image / thermal image spatial features. The LSTM is combined with frequency domain analysis to extract vibration / sound time sequence features, cross-modal alignment fusion is realized through feature vectorization, quality prediction, anomaly detection and real-time deployment are realized based on a deep neural network and an attention mechanism by fusing process knowledge, and then slab defect causes are accurately identified by integrating a multi-modal reasoning result. Through time synchronization, format unification, noise filtering and data filling, the problem of heterogeneity of multi-source sensor data is solved, and time dislocation and noise interference are eliminated; abnormal data are cleaned by an outlier removal technology, the reliability of input data and the model robustness are improved, and efficient cooperative processing of multi-modal data is ensured.

The invention relates to the technical field of data monitoring analysis, and discloses a centrifugal machine data remote monitoring analysis method and system based on a cloud computer, and the method comprises the following steps: collecting centrifugal machine operation index data, carrying out time stamp synchronization, carrying out the recognition and elimination of abnormal values in the data, and carrying out the data integration through data normalization processing, and generating synchronous monitoring data. According to the invention, the data frequency domain analysis of the centrifugal machine is implemented on the cloud platform, the vibration signal frequency spectrum and energy distribution are accurately identified, the efficient marking and trend analysis of abnormal data are realized, the detailed degree and response speed of abnormal detection are enhanced, and the monitoring of the running state of the centrifugal machine is more accurate; by combining real-time trend analysis and historical fault data, the accuracy of fault prediction is improved, the classification of fault modes is more scientific, the situations of false alarm and missing alarm are effectively reduced, the timeliness and pertinence of preventive maintenance measures are enhanced, and the running efficiency and safety of the centrifugal machine are remarkably improved.

The invention discloses a multi-crack width quantification method and system based on machine vision and a local unit method, and relates to the technical field of civil engineering structure crack detection, and the method comprises the steps: processing a crack image of a civil engineering structure through a vision segmentation model, and carrying out the automatic division through a connected domain marking function, identifying a connected domain of each crack; in combination with a connected domain analysis method, generating an external rectangular frame of the crack region; dividing an external rectangular frame of each crack area into a plurality of local units; calculating center point coordinates of all crack pixels, and performing linear fitting on all crack pixels in each local unit; based on an edge detection and corner detection algorithm, extracting edge pixel coordinates and corner coordinates of the crack, and performing linear fitting to obtain an edge fitting straight line; and the crack width of each local unit is determined, and the complete crack width parameter of each crack is determined. According to the invention, the technical problem of low quantification precision of the crack width in the prior art is solved.

The invention discloses a state prediction method for a centralized flywheel energy storage system, and belongs to the technical field of power system energy storage, and the method comprises the steps: obtaining the real-time operation parameters of each flywheel unit in the centralized flywheel energy storage system at the current moment; synchronously inputting the real-time operation parameters into a short-time high-frequency processing channel and a long-time trend processing channel for domain division processing, and generating a real-time abnormal probability value and an accumulated degradation index; generating a comprehensive prediction state index based on the real-time abnormal probability value and the accumulated degradation index; and dynamically adjusting an operation control strategy of the energy storage system according to the comprehensive prediction state index. According to the method, the short-time high-frequency processing and long-time trend processing dual-channel domain analysis technology is adopted, the abnormal evaluation parameters are dynamically corrected in combination with the electromechanical coupling model, minute-level accurate prediction of the flywheel health state can be achieved, the service life of equipment is prolonged, and the frequency modulation stability of a power grid is guaranteed.

The invention discloses a data processing method and system for an industrial-grade data acquisition instrument, and relates to the technical field of electric digital data processing. The system comprises an acquisition module, an analysis module and a management module, the acquisition module acquires operation parameters and production environment data of industrial equipment and transmits the operation parameters and the production environment data to the analysis module; performing single-domain analysis and association processing on the operation parameters and the production environment data to generate a cross-domain association analysis result, generating a threshold range through dynamic threshold adjustment, re-evaluating the operation parameters and the production environment data, generating an analysis result after threshold optimization, transmitting the analysis result to a management module, and managing the operation parameters and the production environment data on the basis of the analysis result after threshold optimization. And generating an optimal scheme and automatically synchronizing the optimal scheme to a workshop system, presetting edge side primary early warning and edge side multi-level early warning mechanisms, mining parameter causality through cross-domain association analysis, dynamically adjusting a threshold value in combination with working conditions and environmental factors, generating an optimal scheme through edge cloud cooperation and multiple algorithms, and enabling industrial intelligent fine management.

The invention belongs to the technical field of image processing, particularly relates to an image effect enhancement method and system, and aims to improve the image quality through multi-domain collaborative adaptive processing. The method comprises the following steps: firstly, determining an optimal quantization level through content-aware adaptive quantization, generating a quantization image and a region complexity graph, then decomposing the image into smooth, edge and texture sub-bands through multi-scale domain analysis, constructing a characteristic spectrum, then, applying histogram equalization, anisotropic diffusion and nonlinear sharpening to different sub-bands through adaptive characteristic enhancement, and finally, carrying out adaptive characteristic enhancement on the different sub-bands. And residual error optimization is carried out. And finally, multi-domain collaborative fusion is carried out to construct an enhancement matrix, fusion masks and feature correction are applied to obtain a final enhanced image, and the method is suitable for various images, retains details, enhances different regions in a targeted manner, and has a remarkable effect.

The invention discloses an automobile auxiliary driving monocular distance measurement method and device based on a lightweight occupation prediction network, and the method comprises the steps: firstly constructing a 3D occupation prediction data set, and then constructing a monocular occupation prediction teacher model based on multi-frame time sequence fusion and time sequence stereo depth estimation, and a lightweight monocular occupation prediction student model based on single-frame input. And training the occupation prediction teacher model to obtain the occupation prediction teacher model with good performance, and evaluating the performance of the teacher model. And performing distillation training of the student model by using the teacher network intermediate layer features and output to obtain a performance-improved lightweight occupation prediction student model, and evaluating the performance of the student model. And finally, performing forward reasoning on the student model, and performing connected domain analysis on an interested instance (such as a vehicle and a pedestrian) from a reasoning result to complete monocular distance measurement. The method provided by the invention overcomes the problem of inaccurate distance measurement caused by unstable detection frame in the traditional monocular distance measurement method, and improves the performance while lightening the model by using the distillation technology.

The invention relates to the field of path planning, in particular to an intelligent routing inspection path planning method and system for power equipment, and the method comprises the steps: dividing a power equipment environment into grids based on three-dimensional point cloud data, and planning an initial routing inspection path; and after the real-time environment data is matched with the grids, obstacles are identified through connected domain analysis, and grid collision risk values are calculated. And dynamically adjusting the trajectory time sequence length of the DWA algorithm by using a local peak grid, and predicting multiple trajectories. And according to the collision risk value weighted average of the track grid sequence, obtaining the risk comprehensive degree, mapping the risk difference value of adjacent moments through a negative exponential function, generating a dynamic feedback coefficient, and correcting a DWA evaluation function. Finally, the track corresponding to the maximum dynamic avoidance evaluation function is selected, the inspection route is updated, intelligent path planning is achieved, and by introducing the dynamic feedback coefficient and collision risk evaluation, the applicability and reliability of the algorithm in different scenes are improved.

The invention provides a roadbed dynamic compaction construction quality control method and system based on rammer dynamic response. The control method comprises the following steps that an acceleration signal of a rammer in the current ramming process is obtained; data processing is carried out on the acceleration signal of the rammer, a plurality of key parameters used for predicting the dynamic deformation modulus are obtained, and the data processing comprises filtering processing, time domain analysis and frequency domain analysis which are carried out in sequence; on the basis of the multiple key parameters and a predetermined dynamic deformation modulus prediction model, the dynamic deformation modulus of the tamping point completing the current time of tamping is obtained; and comparing the dynamic deformation modulus of the tamping point with a set dynamic deformation modulus design value, and sending a tamping stopping prompt or a tamping continuing prompt based on a comparison result. According to the control method provided by the invention, the dynamic compaction construction is subjected to quality control based on the acceleration signal obtained in real time and the dynamic deformation modulus prediction model, so that the uniform compaction of the roadbed is ensured, and the construction efficiency and the construction quality are improved.

The invention is suitable for the technical field of smart beam fields, and provides a design optimization method and system for a highway smart beam field based on big data, and the method comprises the steps: collecting the related data of transportation equipment through an Internet of Things and a real-time bidirectional data interaction channel, and constructing and dynamically updating a site three-dimensional digital twin model; fusing the model and adjacent node broadcast information, and outputting an optimal real-time driving path by using an improved pheromone diffusion algorithm; setting a hierarchical processing protocol for tasks with different priorities, and recalculating an optimal path sequence and adjusting task allocation when resource conflicts or path deadlocks are detected; identifying potential risks in combination with frequency domain analysis, and dynamically adjusting paths and equipment parameters; the algorithm is continuously optimized through offline training and online learning of a historical database. The system comprises a model building and updating module, a path selection module, a conflict detection module, a path adjustment module and an operation optimization module, efficient scheduling, risk early warning and continuous optimization of the intelligent beam field are achieved, and the operation efficiency and safety are improved.

The invention relates to the technical field of power electronic device diagnosis, and discloses a reverse conducting IGBT intelligent power module fault automatic diagnosis method and system. The method comprises the following steps: acquiring multi-source monitoring data including a grid voltage waveform, a collector current waveform and a shell temperature change curve when the power module operates; then establishing a dynamic feature extraction model, performing time domain and frequency domain conjoint analysis on the multi-source monitoring data, and generating a feature parameter set; then constructing a fault feature space, and mapping the feature parameter set to a high-dimensional space to form a feature vector distribution diagram; carrying out regional division on the feature vector distribution map by adopting a self-adaptive clustering algorithm, and identifying an abnormal feature aggregation region; and finally, comparing the abnormal feature gathering area with a preset fault feature library through a mode matching engine, and outputting a fault type identification result. According to the method, multi-source data can be integrated to realize dynamic feature extraction and adaptive fault identification, and the real-time performance and accuracy of fault diagnosis are improved.

The invention provides a multi-order segmentation-based structural member linetype rapid identification method and system, and relates to the technical field of engineering structure intelligent detection, and the method comprises the steps: obtaining an original image of a structural member; performing coarse segmentation on the original image to generate an initial binary mask of the structural member; automatically screening out a target mask region containing a foreground prompt point through a connected domain analysis mode; calculating bounding box coordinate information of a bounding rectangle of the target mask region through a contour detection mode; extracting a corresponding target area sub-image from the original image; adopting a super-resolution reconstruction model to enhance the sub-image of the target area; performing sub-image division on the enhanced target area sub-image; fine segmentation is carried out on the sub-images, and a high-precision mask is determined; mapping the high-precision mask to an original image through coordinate mapping; and combining an edge detection algorithm to extract an outer contour line of the structural member to complete linetype identification of the structural member.

The invention discloses a VSG virtual inertia and damping coefficient feasible region analysis, design and application method and device, and relates to the field of power system analysis processing, and the method comprises the steps: employing a VSG frequency stability analysis model to determine a frequency stability index, a virtual synchronous generator power constraint and an energy index constraint based on physical parameters, depicting a basic feasible region, and determining a frequency dynamic damping ratio and a time constraint; depicting a feasible region based on the basic feasible region in combination with a frequency dynamic damping ratio and a time constraint; and based on the customized dynamic performance requirement, determining whether the feasible region meets the feasible region existence condition, if so, taking the feasible region as a finally designed feasible region, and if not, adjusting the physical parameters, and carrying out feasible region analysis and design again. According to the invention, the problem that the fixed inverter parameters cannot meet the dynamic requirements of the system can be solved, and the problem that the system stability is poor after the static power generation unit is connected to the power grid can be effectively solved.

The invention provides an automatic equipment anomaly monitoring method and system based on image processing, and relates to the technical field of automatic equipment anomaly monitoring, and the method comprises the steps: converting an equipment operation video into a multi-dimensional physical field feature: in a motion field dimension, based on optical flow field analysis, extracting a full-period displacement statistical histogram and a space thermodynamic diagram, the motion instability characteristic of the mechanical transmission system is accurately quantified; in a vibration field dimension, an energy spectrum and a vibration thermodynamic diagram are generated innovatively through time-frequency transformation of a displacement signal of a frequency spectrum monitoring point, and frequency domain feature visualization of a hidden vibration fault is achieved; in the dimension of a structure field, edge gradient analysis and texture feature extraction technologies are fused, a time sequence structure thermodynamic diagram sequence is constructed to capture a progressive damage evolution rule, a three-field abnormal index dynamic weighting fusion mechanism overcomes the limitation of traditional single-point monitoring, connected domain analysis of a fused thermodynamic diagram is combined with an LBP texture and morphological feature decision tree, and the defect of the prior art is overcome. Automatic equipment abnormity monitoring based on image processing is realized.

The invention provides a mainboard testing method and system, and relates to the technical field of mainboard testing methods.The mainboard testing method comprises the steps that firstly, an impedance compensation matrix is generated through pre-calibration sweep frequency signals, full-band dynamic calibration is conducted on probe impedance characteristics, and signal attenuation and phase deviation caused by impedance mismatch between a testing probe and a mainboard interface are eliminated; secondly, based on multi-channel frequency domain filtering and linear combination, separating frequency domain sensitive frequency bands and time domain fluctuation characteristics of test parameters, calculating parameter weights by fusing frequency domain energy and time domain variance, avoiding the limitation of single domain analysis, and enhancing the detection sensitivity of the hidden defects of the mainboard; according to the test parameter weight vector and the probe weight mapping matrix, the test parameter weight vector is converted into a test probe weight vector, finally, the parameter importance is quantified as a basis for probe excitation intensity and sampling rate adjustment, and the beneficial effect that test resources are distributed according to needs is achieved.

The invention discloses a handwritten element automatic segmentation and extraction method for a complex layout, relates to the technical field of handwritten element automatic segmentation and extraction, and aims to solve the technical problem that the recognition and separation precision of handwritten contents in a mixed image-text layout is insufficient. S201, a dynamic threshold segmentation algorithm is carried out; s202, context sensing connected domain analysis is carried out; s203, judging whether the elements are handwritten elements or not; s204, if the judgment result is yes, the handwriting region candidate is reserved; and S205, if not, filtering and eliminating. According to the method, the dynamic threshold segmentation algorithm and the context sensing connected domain analysis technology are cooperated, the segmentation threshold can be adaptively adjusted according to the pixel mean value and the standard deviation of the image local window through dynamic threshold segmentation, and the context sensing connected domain analysis is combined with the context information of the document to perform semantic analysis on the connected domain. The problem that the recognition and separation precision of the handwritten content in the mixed image-text layout is insufficient is solved.

The invention discloses a photovoltaic power prediction method and system based on artificial intelligence. The method comprises the steps of photovoltaic data acquisition, preliminary data processing, power prediction model construction, model hyper-parameter optimization and photovoltaic power prediction. The invention relates to the technical field of photovoltaic data processing, in particular to a photovoltaic power prediction method and system based on artificial intelligence, and the method comprises the steps: obtaining original data through photovoltaic data; a primary processing method of missing value processing, spatio-temporal data alignment, data standardization and data set segmentation is adopted; an improved graph convolutional network model is adopted as a power prediction model, nonlinear correlation is effectively captured through adaptive feature extraction and an uncertainty perception mechanism, and meanwhile, the interpretability and the anti-interference capability of the model are enhanced by combining physical rule constraint output; a resonance evolutionary optimization algorithm is adopted to carry out model hyper-parameter optimization, and a dynamic adjustment strategy is combined with frequency domain analysis and hierarchical optimization, so that the stability of the model in a variable environment is improved.

The invention discloses a data quality improvement method driven by service cognition in data, particularly relates to the technical field of data quality management, and is used for solving the problems of poor data traceability ambiguity consistency and evaluation distortion caused by existing service evolution. According to the method, a unified business knowledge base is constructed by integrating business cognition rules of multiple business fields, the difference between new business logic and existing business logic is analyzed based on the knowledge base, and business indexes in historical data are subjected to mapping configuration and version management; a local neighborhood analysis method is utilized to calculate structure entropy in a local data distribution area, a data consistency evaluation result is generated, finally, data meeting a business cognition rule is written into a data table, and meanwhile data traceability information and business logic version information are stored in an associated mode. Therefore, the problems of data traceability ambiguity and quality evaluation distortion in the dynamic evolution of the service logic of the existing data medium station are effectively solved.

The invention relates to the technical field of three-dimensional model processing, in particular to a solar wing battery selecting and matching method and system based on three-dimensional layout. The method comprises the following steps: acquiring three-dimensional data of a solar wing, and constructing a three-dimensional substrate model according to the three-dimensional data of the solar wing to obtain a three-dimensional model of the solar wing; performing structure-driven spreading strategy simulation on the three-dimensional model of the solar wing to obtain fixed simulation data, and performing fixed strategy simulation on the three-dimensional model of the solar wing to generate a disturbance type layout scheme to obtain random simulation data; performing regional structure domain analysis on the fixed simulation data and the random simulation data to obtain regional structure domain data, and performing optimal layout sorting on the regional structure domain data to obtain layout sorting data; and performing two-dimensional drawing export on the layout sorting data to obtain solar wing battery matching data. According to the method, the diversity of the scheme can be effectively expanded, the local adaptability is improved, and the high coverage rate and the optimal sorting output are realized under the condition of ensuring the structural constraint.

The invention discloses a radio high-frequency band malicious signal identification and self-adaptive filtering method, which comprises the steps of extracting multidimensional features, synchronously performing time-frequency domain, modulation domain and statistical domain analysis on baseband signals, extracting feature indexes including short-time Fourier transform energy concentration degree, cyclic spectrum symmetry and the like, performing feature alignment driven by environmental perception, and performing adaptive filtering. A prior interference environment model is constructed by using a frequency spectrum image, a historical interference label and geographic position information, and distributed alignment and dimension reduction are performed on feature vectors through a semantic embedding algorithm based on a graph structure. Therefore, digital signature verification, fingerprint comparison and path consistency analysis are finally completed on the FPGA platform, and safe traceability is carried out on the signal source. The problems that the false alarm rate of fixed threshold detection is high, a filtering mechanism is difficult to suppress nonlinear interference, a model lacks adaptability, a source verification means is weak and the like are effectively solved, and accurate recognition, deep suppression and security defense of malicious signals in a complex high-frequency electromagnetic environment are achieved.

The invention provides a low-voltage flexible DC system fault detection method based on complex domain analysis, and the method comprises the steps: obtaining and sampling a transient current signal in a low-voltage flexible DC system, fitting the transient current signal into a linear combination of a group of exponential functions, and obtaining a fitting exponential function; solving the fitting exponential function in a Z domain based on a Pade approximation method to obtain a complex index of the fitting exponential function; determining a fault threshold frequency according to the operation parameters of the low-voltage flexible DC system; the complex index is used as an analysis object, and the natural oscillation frequency of the low-voltage flexible direct-current system is obtained through formula calculation; and taking the fault threshold frequency as a state circle radius, constructing a fault discrimination model based on a complex plane, comparing the natural oscillation frequency with the fault threshold frequency, and outputting a fault detection result of the low-voltage flexible DC system in combination with a real part of a complex index. According to the method, the sensitivity to noise can be reduced, and meanwhile, the requirement of fault detection for speed is met.

The invention discloses a ship navigation trajectory prediction method and system based on frequency domain analysis and deep learning, and relates to the technical field of ship navigation and maritime traffic management, and the method comprises the steps: collecting ship navigation AIS data in real time, and carrying out the preprocessing operation; segmenting the preprocessed time domain data by adopting a dynamic sliding window and converting the preprocessed time domain data into frequency domain representation, and updating the frequency component of the current window by adopting a sliding updating strategy so as to construct a frequency domain feature vector and realize real-time processing and noise suppression of high-frequency data; introducing a multi-modal gating attention fusion mechanism, fusing the frequency domain feature vector and the time domain transient feature vector, and dynamically balancing the weight of the frequency domain periodic feature and the weight of the time domain burst feature; and inputting the fusion feature vector into a deep learning prediction model, converting a frequency domain prediction result output by the model back to a time domain, and generating future position, navigational speed and course information of the ship. According to the method, the prediction precision and efficiency are greatly improved by combining the frequency domain analysis and the deep learning technology.

The invention discloses an energy internet-oriented harmonic prediction data quality enhancement method, relates to the technical field of harmonic prediction, and is used for solving the problem of cross-order consistency misleading prediction of single harmonic order short-time peak misinformation damage. The method comprises the following steps: collecting photovoltaic grid-connected data to construct a power distribution network topology, identifying a neighborhood analysis subgraph and disassembling a node-order harmonic sequential sequence; extracting abnormal features, triggering cleaning through a multi-index weighted fusion triggering model, and generating a damaged order candidate set and a mask; calculating peak suspiciousness and a cross-order consistency sudden drop index to determine a damaged order; constructing a cross-order distance matrix cluster to obtain an initial anchor order candidate set, and screening and allocating weights; and on the basis of double-constraint optimization cleaning, the quality is evaluated, and the weight is optimized, so that the harmonic data quality is improved, and accurate prediction is supported.

The invention discloses a message multi-level analysis method and device, electronic equipment and a storage medium. The invention discloses a message multi-level analysis method, which comprises the following steps of: acquiring a financial message, identifying a message header and a message body in the financial message, and identifying a message type and a version number in the message header; in multiple pre-configured domain analysis modes, determining a target domain analysis mode corresponding to the financial message based on the message type and the version number in the message header, and performing domain analysis on the message body through the target domain analysis mode to obtain multiple pieces of domain information of the financial message; performing consistency processing on multiple pieces of domain information of the financial message, and performing semantic analysis on the domain information after consistency processing to obtain an entity in the financial message; according to the method and the device, the domain information in the financial message is subjected to data verification, the label of each domain is set based on the data verification result, and the entity and the label corresponding to each domain are stored, so that the multi-level analysis of the financial message is realized, and the analysis efficiency and the analysis accuracy of the financial message are improved.

The invention discloses a computer information screening system and method based on artificial intelligence, and particularly relates to the technical field of information screening. Analyzing the multi-modal feature vector of the to-be-screened information, and evaluating the distribution density in the high-dimensional feature space through the feature projection network; dimensionality reduction mapping is carried out on the multi-modal feature vectors, a distance matrix between feature clusters after dimensionality reduction is calculated, and the local folding degree of the feature space is evaluated; then, based on the feature distribution density and the local folding degree, whether the distortion degree of the feature space exceeds a preset threshold value or not is judged; when the distortion degree exceeds a threshold value, the feature loss degree is evaluated by calculating mutual information between the original features and the reconstructed features; according to the method, the distribution density, the local folding degree and the feature loss degree of the multi-modal features are integrated, the overall reliability of the information screening result is evaluated, whether the screening strategy needs to be adjusted or not is judged, the accuracy and stability of complex data screening are effectively improved, and the method is suitable for large-scale data analysis and anomaly detection scenes.

The invention relates to the technical field of mill load detection, in particular to a mill load analysis system based on a multi-parameter model, which realizes comprehensive and accurate mill load analysis through cooperative work of multiple modules. The data acquisition module adopts various sensors to comprehensively collect vibration and abrasive disc rotating speed data, and provides rich and accurate basic data for subsequent analysis; vibration data are collected by an acceleration sensor and a speed sensor; abrasive disc rotating speed data are obtained in real time through a rotating speed sensor at a specific position and are stored in combination with a historical memory module; the data analysis module performs time domain and frequency domain analysis on the vibration data, establishes a relation model with a load, analyzes the rotating speed of an abrasive disc by using a time sequence, and dynamically updates a threshold value to judge a load state; the multi-parameter metadata fusion and analysis mode can more comprehensively and accurately reflect the actual load condition of the mill, is helpful for timely finding potential fault hidden dangers, avoids equipment damage caused by abnormal load of the mill, and guarantees production continuity and stability.

The invention discloses a power transmission and transformation project evaluation method and system based on an intelligent management and control platform, and relates to the technical field of power transmission and transformation projects, and the method comprises the following steps: obtaining meteorological data of a to-be-detected region, and predicting an icing rate; obtaining line current data to carry out thermal correction on the icing rate, and generating an icing thickness dynamic distribution heat map; according to the icing thickness distribution heat map, structural safety domain analysis is carried out through a finite element analysis method; according to the method, the problems of line safety and stability under complex meteorological conditions are solved through accurate prediction of the icing rate and thickness of the power transmission and transformation line, structural safety evaluation and load optimization regulation and control.

The invention provides an image fuzzy detection method based on frequency domain analysis and deep learning fusion. The method comprises the following steps: S1, frequency domain feature extraction and quantification; s2, spatial domain feature extraction and modeling; s3, carrying out multi-modal feature fusion; s4, joint optimization and post-treatment are carried out; s5, outputting and verifying; through complementarity design of frequency domain and deep learning, complex fuzzy detection requirements of static images and video streams are covered, high efficiency and reliability are verified in industrial quality inspection, video conferences and other scenes, energy attenuation characteristics caused by global blur are accurately captured through frequency domain analysis, motion blur and out-of-focus blur are effectively distinguished, and the method is suitable for large-scale popularization and application. According to the method, local texture degradation of deep learning network modeling, dynamic track abnormity analysis of an optical flow network and complex scenes covering static images and video streams are realized through a bidirectional feature fusion mechanism, the mAP of mixed fuzzy detection is effectively improved compared with a single-mode method, and dynamic fuzzy and static out-of-focus fuzzy are effectively distinguished.