1131results about How to "Efficient analysis" patented technology

The invention belongs to but not limited to the technical field of fault diagnosis and maintenance of power equipment, and discloses an intelligent diagnosis and maintenance system and method for power equipment based on a knowledge graph. A data acquisition and preprocessing module acquires real-time operation data and historical data of the power equipment; the knowledge graph construction module associates various kinds of information of the power equipment to construct a knowledge graph; the intelligent diagnosis module performs association analysis on power equipment faults based on a knowledge graph and a machine learning algorithm, identifies fault modes and potential faults, and predicts possible faults of the equipment; the maintenance suggestion module automatically generates a detailed maintenance scheme according to the diagnosis result; the operation and maintenance visualization module provides visual fault diagnosis results and maintenance suggestions, supports multi-level data display, and helps operation and maintenance personnel to clearly understand the equipment state. The operation safety of the power equipment is improved, the operation and maintenance cost is remarkably reduced, and the overall efficiency of equipment management in an intelligent power grid environment is improved.

The invention provides a sewage treatment data optimization method and system based on cloud computing, and relates to the technical field of data processing.The method comprises the steps that real-time key information, including water quality indexes, equipment operation parameters and energy consumption data, collected by monitoring equipment in real time is received, the key information is preprocessed, and a processed data set is obtained; constructing a cloud database according to the processed data set, and performing data analysis including trend prediction and anomaly detection by using the cloud database to obtain an analysis result; and according to the analysis result, constructing a sewage treatment data optimization model, and inputting the water quality standard, the equipment operation efficiency and the energy consumption cost parameters into the sewage treatment data optimization model to generate an optimization scheme. By collecting and analyzing sewage treatment key information in real time and constructing and optimizing the data model, intelligent adjustment and optimization of the sewage treatment process are realized, the water quality is improved, the energy consumption is reduced, and the equipment operation efficiency and the overall treatment effect are improved.

The invention provides a power distribution network symbiotic simulation system and method based on digital twinning. The system comprises a power distribution network digital twinning simulation platform, a cloud edge hierarchical deployment collaborative architecture and a power distribution network terminal. The digital twinborn simulation platform for the power distribution network comprises a data layer, a model layer, a service layer and an application layer which are covered in sequence by taking a cloud platform as a bottom, and the cloud edge hierarchical deployment collaborative architecture performs hierarchical deployment according to different service functions of the power distribution network. According to the invention, through the constructed full-attribute isomorphic power distribution network digital twinborn simulation platform and the cloud edge hierarchical deployment collaborative architecture, the fusion demand of multi-scene multi-service flow is fully considered, through the symbiotic simulation technology, dynamic feedback and collaboration between the simulation system and a real system are realized, and the highly integrated and collaborative simulation system has the advantages of high simulation efficiency and high reliability. Various changes in the power distribution network can be quickly responded, and a solid guarantee is provided for safe and efficient operation of the power distribution network.

The invention discloses a traffic incident detection system based on multi-modal data, and belongs to the technical field of traffic incident detection. The invention aims to solve the problems of poor traffic event detection capability and incapability of effectively associating context information caused by insufficient video data in a training process of an existing traffic event detection method. Comprising the following steps: acquiring high-dimensional visual features of a sample event image and a sample event video by adopting a visual encoder; obtaining a text feature vector of the cue word description text of the sample event by adopting a text encoder; a feature mapping layer is adopted to obtain high-dimensional visual features aligned with the text feature vectors; common multi-modal features are obtained through a multi-modal joint learning module; obtaining a semantic prediction result and an answer type text sequence of the sample event through a large language module; and obtaining a traffic event classification result through a traffic event classification module. The method is used for on-line detection of traffic events.

The invention discloses a soil fertility intelligent assessment method and system based on image recognition, and relates to the technical field of data processing, and the method comprises the steps: obtaining land image information of a to-be-assessed region, the land image information comprising a multispectral image, a thermal imaging image and an RGB image of the to-be-assessed region; carrying out image preprocessing on the land image information, and carrying out vegetation coverage and health assessment according to the multispectral image: speculating soil characteristics according to the RGB image: speculating the humidity change of a soil surface layer according to the temperature distribution of a thermal imaging image, and identifying a drought region and a region with sufficient moisture; integrating the high-resolution texture features of the RGB image, the spectral information of the multispectral image and the speculation result of the temperature distribution of the thermal imaging image to generate a comprehensive analysis result; and classifying soil types and evaluating fertility grades according to a comprehensive analysis result. The problem that in the prior art, in intelligent soil fertility evaluation, workers need to carry out on-site sampling examination is solved, and the technical effect of improving the soil fertility evaluation efficiency is achieved.

The invention discloses a hydraulic engineering operation and maintenance monitoring system based on digital twinning, and the system comprises the steps: S1, a data collection module which is used for obtaining multi-modal actual detection data from a plurality of monitoring points of a hydraulic engineering; s2, a data generation module which simulates and generates multi-dimensional virtual monitoring data and an abnormal sample library related to hydraulic engineering operation and maintenance; s3, a data fusion module which fuses the multi-dimensional virtual monitoring data and the multi-modal actual detection data; s4, a digital twinborn model construction module which dynamically constructs a multi-dimensional digital twinborn model of the water conservancy project based on the fused multi-modal data set; s5, an anomaly detection module which detects potential anomaly and sudden faults through an anomaly sample library generated by the generative adversarial network in combination with a multi-dimensional spatial-temporal trajectory clustering analysis method; and S6, an operation and maintenance monitoring module which is used for providing real-time operation and maintenance monitoring service for the multi-dimensional digital twin model. The method has the advantages of being high in dynamic adaptive capacity, high in monitoring accuracy and high in abnormal response speed.

The invention relates to the technical field of hydrological data analysis, in particular to a dynamic hydrological coupling data analysis method and system for a complex watershed. The method comprises the following steps: acquiring historical meteorological data, historical hydrological data and historical remote sensing data, and performing hydrological cycle simulation analysis to generate a rainfall-evaporation-permeation-runoff hydrological cycle simulation model; performing drainage basin gridding processing on the rainfall-evaporation-permeation-runoff hydrological cycle simulation model to generate a hydrological cycle gridding model; obtaining actually measured meteorological data and actually measured hydrological data, and carrying out space-time simulation division and hydrological space-time correlation analysis to obtain a hydrological change space-time correlation correlation relationship among the sub-partitions of each drainage basin; and performing drainage basin space-time coupling early warning processing on the hydrological cycle gridding model to generate a hydrological change space-time coupling early warning response result corresponding to the complex drainage basin. The method can accurately analyze and predict the hydrological change and the mutual coupling relation thereof in the complex watershed.

The invention relates to a knowledge-enhanced large model data analysis agent visualization method, which comprises the following steps of: performing syntactic analysis by using a PCFG algorithm, identifying syntactic components and relationships of sentences, and splitting the sentences into sub-sentences with complete logic; extracting keywords and entity words of the sentences by combining a word segmentation technology with a business word segmentation library; recommending and summarizing candidate segmented words through a recall strategy, carrying out rough arrangement, fine arrangement and fine arrangement on the candidate segmented words, and correcting and complementing a segmented word result; splitting and analyzing the natural language problem based on the fine-tuned large model, and generating SQL statements meeting business requirements; analyzing the data set generated by the SQL, extracting field information and business meanings, and identifying an association relationship between fields; and automatically recommending chart styles according to data features and user preferences, assembling an analysis report and distributing the analysis report according to a specified frequency. According to the method, knowledge enhancement and large model technologies are combined, so that the efficiency and effect of data analysis and visualization are remarkably improved.

The invention relates to an intelligent monitoring method and system for an air environment monitoring station, and belongs to the technical field of environment monitoring. The method comprises the following steps: acquiring multi-modal monitoring data, performing data association analysis on the multi-modal monitoring data by adopting a multi-source data association model, and establishing an association relationship among the multi-modal monitoring data; performing data exception monitoring on the multi-modal monitoring data, and triggering and starting an exception type diagnosis event when data exception is monitored; extracting a first data subset related to an exception type from the multi-modal monitoring data; extracting a second data subset associated with the first data subset from multi-modal monitoring data; and performing fault diagnosis based on the first data subset and the second data subset, and outputting an abnormality diagnosis result. According to the method, the multi-source data association model is utilized, the association between different types of data can be effectively analyzed, detailed fault analysis and diagnosis are carried out, the fault response time is shortened, and the operation and maintenance efficiency is improved.

The invention relates to the technical field of network security, and discloses an LLM-based network threat flow detection rule automatic generation method and system. According to the system, an automatic rule generation framework based on a large language model is constructed for network threat traffic, and the system comprises a cue word module, a large language model module, a rule engine module and a rule base management module; the four modules are executed in sequence to form a complete link from traffic information input to rule automatic generation and application verification; according to the method, domain knowledge is fused into a pre-training model through LoRA fine tuning, small sample cue word engineering and a correction mechanism based on engine feedback, verification is carried out through an actual detection engine, and the effectiveness of a rule automatically generated by the model is verified. According to the method, the network threat traffic sample can be fully and effectively analyzed, meanwhile, the requirement for training data is reduced, the rule set capable of being practically applied is generated, and the network defense capability is improved.

The invention relates to the technical field of audio, in particular to an intelligent sound effect optimization system based on dynamic frequency gain adjustment, which comprises an audio signal processing module, an environmental noise suppression module, a dynamic frequency gain adjustment module, a user interaction interface module and an intelligent learning module, through cooperation of an audio signal processing module, an environmental noise suppression module, a dynamic frequency gain adjustment module, a user interaction interface module and an intelligent learning module, the audio signal processing module is used for receiving an original audio signal and performing preliminary processing and analysis; the environmental noise suppression module is used for monitoring the environmental noise level in real time and correspondingly adjusting the audio signal so as to eliminate or reduce the influence of the noise; real-time adjustment can be carried out according to different acoustic environments and audio signal characteristics through dynamic frequency gain adjustment, it is ensured that the best sound effect experience can be provided in any environment, and the sound effect optimization effect in the complex environment is improved.

The invention discloses a bridge pile foundation stress nondestructive testing method and system, the system comprises a sensor arrangement module, a data acquisition module, a data transmission module, a data processing module, a data analysis module, a result display and early warning module, a calibration module, a verification module and the like, sensors are various in type and high in precision, data acquisition multichannel synchronization is realized, and transmission is stable and reliable. Various technologies are used for data processing to construct a model, and a result is visually displayed and pre-warned. The system is further provided with an environment monitoring and structure health assessment module, comprehensive and accurate detection is guaranteed through an innovative formula and method, and efficient operation of the system is guaranteed through cooperative work of all modules. Through multi-module cooperation and innovative design, bridge pile foundation stress high-precision detection, environmental factor analysis and structure health assessment are achieved. Early warning can be carried out in time, bridge safety is guaranteed, a scientific basis is provided for maintenance management, and the reliability and safety of bridge operation are improved.

The invention provides a power grid energy disturbance suppression method for electric heating gas state information transmission collaboration, and relates to the technical field of information, and the method comprises the steps: obtaining multi-source heterogeneous state information of an electric heating gas energy network; aiming at the multi-source heterogeneous state information, dynamically converting a heterogeneous communication protocol into a unified protocol format by adopting a self-adaptive communication protocol conversion technology; and for the decoupled state information, parallel processing is carried out by adopting a distributed stream computing framework, so that the real-time performance and expandability of state information processing are improved. A distributed stream computing framework is adopted to construct an efficient processing assembly line, the block chain technology is utilized to guarantee the integrity and reliability of data transmission, a deep learning algorithm is applied to construct an intelligent analysis model, a multi-energy-flow coupling relationship and an evolution law are mined, and a multi-energy-flow analysis result is obtained. According to the method, data processing results of all links are fused to form a global state information view, decision support is provided for power grid energy disturbance suppression, and efficient processing, analysis and visualization of the state information of the electric heating gas multi-energy flow system are achieved.

The invention belongs to the technical field of computers, and particularly relates to a zero-code agent construction platform. The system comprises a module database, a semantic analysis and intention decomposition unit, a task flow tree generation unit and an agent construction unit, the semantic analysis and intention decomposition unit is used for performing image language analysis on a natural language instruction input by a user, decomposing the natural language instruction into a plurality of intention units with a sequence, and constructing an intention vector; the task flow tree generation unit is used for generating a plurality of corresponding task flow trees according to the intention vector; and the agent construction unit is used for analyzing all the task flow trees, finding out an optimal task flow tree, calling all target function modules in the task flow tree and completing the construction of the agent. According to the method, the precision of semantic analysis, the flexibility of task logic generation and the automation degree of agent construction are remarkably improved.

The invention discloses an electric power optical communication network alarm analysis method and system based on a time-space diagram neural network. The method comprises the following steps: collecting data of an electric power optical communication network, preprocessing the collected data, and modeling the preprocessed data into a graph structure; extracting and learning the features of the nodes in the graph structure by using a space-time graph neural network, and generating space-time embedded representation of the nodes in combination with space feature extraction and time feature extraction; classifying the nodes based on the spatio-temporal embedding representation of the nodes to obtain prediction labels of devices corresponding to the nodes; and according to the prediction label of the equipment corresponding to the node, carrying out analysis and influence evaluation on the alarm condition in the power optical communication network. According to the method, the electric power optical communication network is represented as the graph structure containing the space-time information, the space-time dependence of the electric power optical communication network can be effectively captured based on the space-time graph neural network, the space relationship in the graph structure and the dynamic change in the time sequence are processed at the same time, and the accuracy and timeliness of alarm analysis are improved.

The embodiment of the invention belongs to the field of financial science and technology, and relates to a risk analysis method and device, computer equipment and a storage medium, and the method comprises the following steps: obtaining growth time sequence multi-dimensional information, meteorological time sequence image information and growth environment parameter information, carrying out the preprocessing, and carrying out the space-time registration, feature extraction and data fusion; obtaining a crop growth monitoring data set; performing crop growth condition evaluation based on the crop growth monitoring data set to obtain a crop growth condition evaluation result; determining a key influence factor according to the crop growth condition evaluation result, and performing growth condition prediction according to the key influence factor and the crop growth condition evaluation result to obtain a growth trend prediction result; and performing crop growth risk analysis according to the crop growth condition evaluation result and the growth trend prediction result to obtain risk early warning information. According to the invention, the risk of crop growth can be accurately and effectively analyzed, so that risk early warning information can be generated in real time for early warning.

The invention discloses a quality-graded remote sensing image composite cloud removal method, system and device. The method comprises the following steps: preprocessing an acquired remote sensing image; based on the preprocessed data, thin cloud radiation correction is carried out; based on the data after thin cloud radiation correction, thick cloud detection is carried out; based on the data after thick cloud detection, thick cloud missing reconstruction is carried out; performing atmospheric correction based on the data after the thick cloud missing reconstruction; and performing data quality grading based on the data after atmospheric correction. According to the method, a series of standardized processing is realized on data acquired under different space and time conditions, the influence of atmospheric radiation attenuation is eliminated, and the data are formatted into surface reflection data with consistent observation characteristics, so that the utilization rate of remote sensing images is improved, and effective comparison and analysis of subsequent application research are promoted.

The invention discloses a craniocerebral injury degree diagnosis auxiliary system based on a medical image, and relates to the technical field of medical treatment, a primary judgment module can accurately judge whether a patient has the risk of interstitial encephaledema or not by analyzing the accumulation degree of interstitial fluid in white matter and quantitatively calculating a penetration anomaly coefficient Syxs, the ability of early risk screening is provided, and the risk of interstitial encephaledema can be accurately judged. Potential problems can be recognized in time before disease deterioration, a clinician is prompted to intervene rapidly, and therefore the prognosis effect of a patient is remarkably improved. When the risk of interstitial encephaledema exists, the type diagnosis module further distinguishes lesion types (such as brain contusion or other injuries) by extracting related distinguishing features in a multi-modal feature set, calculates a locking coefficient Sdxs, and provides a scientific quantitative basis for diagnosis of lesion types based on specific features of the lesions. According to the accurate type diagnosis method, diagnosis errors can be reduced, and reliable support is provided for clinical decision making.

The invention relates to an intelligent cervical cancer screening method based on a multi-modal large language model, which comprises the following steps: data collection and processing: collecting and processing colposcope images and cervical cancer related medical reports, and pre-training a model for extracting key image and text features and providing input for the multi-modal model to enhance the accuracy and efficiency of cervical cancer screening; setting a multi-modal fusion model; fusing the image features and the text features; model training and tuning: taking the colposcope image and the corresponding text data as input, carrying out joint training on the multi-modal model, and finding an optimal configuration; and model evaluation and verification: carrying out performance evaluation on the model, analyzing the performance of the model on different categories and carrying out clinical verification. According to the method, colposcope images can be efficiently processed and analyzed, cervical cancer screening and diagnosis can be processed, a multi-modal large language model of a medical report can be generated, prognosis prediction, disease progress prediction and risk assessment can be carried out, and doctors can be helped to make more effective diagnosis and treatment plans.

The invention discloses an oil reservoir dynamic monitoring method and device. The method comprises the steps that sound wave data and temperature data collected by distributed optical fiber sensors in real time are obtained; the distributed optical fiber sensors are deployed in a plurality of injection and production wells according to a preset deployment rule; the sound wave data and the temperature data are input into an oil reservoir dynamic monitoring model, an oil reservoir fluid three-dimensional dynamic graph is output, and the oil reservoir fluid three-dimensional dynamic graph displays oil reservoir fluid state data; the reservoir dynamic monitoring model is obtained by training a Transform model by using historical sound wave data, temperature data and reservoir multi-source information in advance; in the training process, time and space grid alignment processing is carried out on historical sound wave data, temperature data and oil reservoir multi-source information, time-space sequence features are obtained, and the model identifies oil reservoir fluid state data in the time-space sequence features based on self-attention and multi-attention mechanisms. The accuracy and reliability of oil reservoir monitoring and analysis can be improved.

The invention relates to the field of computer vision, and provides a dynamic map modal semantic segmentation method and system for vehicle-mounted mining video monitoring. A mining area video is captured through a vehicle-mounted camera, video frame features are deeply extracted by using a pre-trained first convolutional neural network, multi-layer features are fused to enhance the representation capability, then a time dimension dynamic map and a space dimension dynamic map are constructed, a dynamic map mode is formed after fusion, an attention mechanism is introduced, and the attention of the mining area is enhanced. And distributing weights according to the similarity of the feature vectors, and focusing the correlation among the key areas. And extracting context information by using a global average pooling layer, and combining with the weighted original feature map to fuse global and dynamic map information. In addition, local context information is also combined to enhance feature understanding. And outputting an accurate semantic segmentation result based on the comprehensively fused feature map, and distributing a label representing an object or a background category to which each pixel belongs for each pixel, thereby realizing deep understanding and efficient analysis of mining area video information.

The invention particularly relates to a data analysis method and system. The method comprises the steps of extracting original data corresponding to a target service from a target data source according to a pre-configured data extraction strategy of the target service, loading the original data to an operation data storage layer of a data warehouse, and storing the original data; operating a data storage layer to output the original data to a detail data layer; the detailed data layer performs data preprocessing on the original data to obtain basic data and outputs the basic data to the summarized data layer; the summarizing data layer performs aggregation processing on the basic data according to a current data processing task corresponding to the target business, obtains structured data corresponding to the target business, and outputs the structured data to the application data service layer; and the application data service layer performs data analysis on the structured data according to the service configuration information corresponding to the current data processing task, and outputs a corresponding data analysis result. According to the scheme, efficient and accurate data management and analysis can be realized.

The invention discloses a camera calibration method, system and device based on image processing, and the method comprises the steps: obtaining a calibration plate image at multiple angles, and carrying out the image preprocessing, and obtaining a calibration plate coordinate; performing distortion removal and coordinate transformation on the calibration plate coordinates to obtain first calibration plate coordinates and second calibration plate coordinates, further obtaining calibration plate attitude transformation data, and obtaining first coordinates in a coordinate system of the image acquisition device through coordinate transformation; the translation amount of the calibration plate is obtained, a fitting plane and a fitting transformation matrix are obtained through fitting, the first coordinates are projected to the horizontal plane and fitted, the circle center coordinates of the horizontal circle face are obtained, and then the fitting circle center coordinates are obtained; obtaining a normal vector of a fitting plane, and combining the fitting circle center coordinate to obtain a rotating shaft linear equation; and based on the rotating shaft linear equation and the fitting plane, obtaining the motion radius and the height of the image acquisition device. Through the image processing technology, the accuracy of camera calibration is improved, and the working efficiency and the data reliability are effectively improved.

The invention belongs to the technical field of gene sequencing, and particularly relates to an efficient and high-throughput gene sequencing data processing system. The data processing system comprises a data acquisition module, a data preprocessing module, a data storage and management module, a data comparison and alignment module, a variation detection module, a data integration and analysis module, a visualization and report module and a quality control and verification module, and the data acquisition module is used for extracting DNA or RNA and comprises phenol chloroform extraction, column purification and magnetic bead extraction; and detecting the concentration and the quality of the sample by using a spectrophotometer or a fluorescence quantifier, and carrying out pollution removal and degradation treatment on the extracted nucleic acid. According to the method, a comprehensive, accurate and efficient data processing solution can be provided for genomics research by optimizing the data processing flow, improving the data quality, enhancing the analysis capability and supporting automation.

The invention discloses a lower limb movement gait recognition method and system based on feature fusion, and belongs to the technical field of gait recognition. Comprising the following steps: acquiring a surface electromyogram signal and a motion inertia signal of a to-be-predicted object; carrying out feature extraction on the surface electromyogram signal and the motion inertia signal by utilizing wavelet transform, obtaining gait features and screening an optimal feature combination; and processing the optimal feature combination through the trained gait recognition model to obtain a gait recognition result. Complex multi-modal data can be effectively processed and analyzed, so that high-precision gait recognition and evaluation are realized; the problem that the accuracy of gait evaluation needs to be improved due to the fact that single signal processing is difficult to comprehensively reflect a complex gait dynamic process in the prior art is solved.

The invention discloses a full-link monitoring method and system for state application in an edge distribution scene, and the method comprises the steps: S1, deploying a monitoring agent at an edge calculation node, and collecting the state data and performance indexes of edge equipment; s2, sending the collected data to a central monitoring system in real time through an efficient data transmission protocol; s3, storing and managing synchronous data between the edge nodes and the center node by using a distributed database; s4, preprocessing the collected data by applying a real-time data stream processing technology; s5, performing anomaly detection and mode recognition on the preprocessed data, and generating intelligent early warning; s6, adopting a full-link tracking tool to monitor and record a full-link path requested in the system, and identifying and analyzing a performance bottleneck and a fault point; and S7, optimizing a data transmission process by using a message queue system, and implementing a data encryption and access control strategy. The method has the advantages of low delay, high data security and high system response speed.

The invention is suitable for the technical field of public rental housing management, and provides a public rental housing inspection management system, which comprises a hardware equipment layer used for collecting environment data, equipment state data and inspection data of a public rental housing, and the hardware equipment layer comprises a plurality of sensor equipment, a wireless communication module and mobile inspection equipment; the data processing layer is used for receiving, storing and processing data collected by the hardware equipment layer, and the data processing layer comprises a data receiving module, a data processing module, a data storage module, a data analysis module and an early warning module; and the application service layer is used for providing user interaction and system management services according to the data acquired by the hardware equipment layer and the data processed by the data processing layer, and the application service layer comprises a housing resource management module, an inspection task distribution module, an inspection management module, an early warning management module, a contract management module and a housing maintenance management module. The problem of low inspection efficiency in the prior art is solved.

The invention discloses an operation and maintenance control system for automatic intelligent equipment, and the system comprises an equipment monitoring and data collection module which is used for carrying out the real-time monitoring, data transmission and data storage of the automatic intelligent equipment; the data analysis and anomaly detection module is used for carrying out data preprocessing, data analysis and anomaly detection on the monitored data; the fault diagnosis and positioning module is used for performing fault diagnosis and fault positioning; the maintenance decision and execution module is used for making a maintenance decision and executing the maintenance decision according to the fault diagnosis and positioning result; and the feedback and optimization module is used for recording the maintenance process and optimizing the operation and maintenance control flow according to the maintenance feedback and the data analysis result. According to the invention, intelligent and comprehensive automatic intelligent equipment operation and maintenance control can be carried out.

The invention belongs to the technical field of seismic signal monitoring, and discloses a micro-seismic signal spectrum analysis system and method based on DVEC, and the system comprises a data collection device, a data preprocessing module, and a depth variation code-classifier. Acquiring a plurality of original vibration signals through a data acquisition device; performing fast Fourier transform (FFT) processing on the plurality of original vibration signals by using a data preprocessing module to obtain corresponding frequency domain sequence signals; splicing the original micro-seismic signals on a plurality of time domains and the corresponding frequency domain sequence signals in a dual-channel mode to form dual-channel micro-seismic signal input data; and inputting the dual-channel micro-seismic signal input data into the depth variational coding-classifier to obtain the classification category of each original micro-seismic signal. According to the method, different types of seismic signal features can be clearly distinguished, unknown types of seismic signals can be identified, and the method is expected to be an important technical means in the field of seismic signal identification.

The invention provides a control rod driving mechanism operation state evaluation method fusing multi-source characteristic information, which comprises the following steps: collecting current data generated in an operation process of a control rod driving mechanism, preprocessing, intercepting characteristic parameters of each action time sequence point in a current signal, screening the characteristic parameters to obtain key characteristic parameters, and evaluating the operation state of the control rod driving mechanism according to the key characteristic parameters. Determining uncertainty parameters of the control rod driving mechanism, performing random variable analysis, defining action reliability, constructing a multi-field dynamics simulation model of the control rod driving mechanism, and inputting random variables to perform reliability evaluation on key characteristic parameters of the control rod driving mechanism based on the multi-field dynamics simulation model; and through a hierarchical structure from top to bottom, an operation state evaluation model fusing multi-source feature information is constructed, and the operation state of the control rod driving mechanism is evaluated. The control rod driving mechanism operation state management efficiency is greatly improved, and effective support is provided for comprehensive and accurate evaluation of the control rod driving mechanism operation state.