1377 results about "Identification system" patented technology

The invention relates to the technical field of power equipment monitoring, and discloses a hydropower station high-altitude equipment fault intelligent identification system based on multi-sensor fusion, which collects multi-modal data in real time and evaluates data quality by deploying multi-source sensors at key parts of high-altitude equipment. Extracting multi-scale features of each modal, performing normalization processing, calculating a fusion weight based on feature saliency and data credibility, and performing weighted fusion and dimension reduction on the multi-modal features; based on the fusion feature vector, intelligent matching analysis of fault features and intelligent identification of fault types are carried out; a fault identification result is obtained; in addition, the system also comprises safety monitoring of overhead working personnel, and realizes closed-loop management from fault identification to safety maintenance. According to the invention, early weak faults can be accurately identified, and the safe operation level of equipment and the intelligent degree of operation safety management are improved.

The invention relates to a tunnel three-dimensional disease intelligent identification system and method based on a large model, the system comprises a point cloud data acquisition module and a processor, and the processor comprises a data processing module, a three-dimensional tile optimization module and a disease identification module. The data processing module carries out standardization, noise reduction and registration processing on the received point cloud data; the three-dimensional tile optimization module constructs a multi-level tile pyramid structure based on the registered point cloud data, establishes a mapping relation between a space coordinate and a tile index, compresses tile data based on a curvature point cloud simplification algorithm and adjusts texture quality to form a three-dimensional tile image; calculating a comprehensive score of the tile quality to verify the quality of the three-dimensional tile image; a disease identification module extracts multi-modal fusion features of the preprocessed three-dimensional tile image and geometric features corresponding to disease types; fusing the multi-modal fusion feature and the geometric feature to obtain a joint fusion feature; performing field fine tuning on the joint fusion features; and obtaining a disease identification result.

The invention provides an intelligent gas meter remote meter reading method and system based on the Internet of Things, and relates to the technical field of intelligent gas meters. The method comprises the following steps: collecting local metering data and Internet of Things transmission data; constructing a dual-source data multi-dimensional conflict identification system, and screening conflict data groups; performing accuracy verification on the conflict data group; an encrypted transmission channel is established, and the traceability of the data transmission process is realized; triggering a grading early warning mechanism for the abnormal usage data deviating from the baseline; and carrying out iterative updating on the meter reading data knowledge base, and carrying out remote calibration. According to the method, double-source data conflicts are identified through multiple dimensions, conflict data are corrected in combination with a hybrid model, metering accuracy is guaranteed, and disputes are reduced; through constructing a baseline model and a grading early warning mechanism, abnormal usage is accurately identified and disposed, hidden dangers are timely prevented, and service is excellent. Through dynamic on-demand remote calibration of equipment, the metering precision is guaranteed, the operation and maintenance cost is reduced, the user data transparency is improved, and intelligent gas management is assisted.

Systems and methods involving various registration and authentication workflows are disclosed herein. A user may be authenticated without the use of static usernames or passwords. In some embodiments, an authentication identifier may be generated that is associated with an authentication request for a user to access a protected resource (e.g., a web app). An authentication code may be generated based on the authentication identifier. The authentication code may be sent to a computing device to be provided to the user, who may provide the authentication code to an application on their mobile device. The mobile device may send a payload containing the authentication identifier, credentials saved on the user device from a previous registration step, and a digital signature. The digital signature may be authenticated using contents of the payload before validating the authentication identifier.

The invention discloses an autoclaved aerated concrete member surface defect intelligent identification system based on image processing, and particularly relates to the field of defect identification, comprising an image acquisition module, an image preprocessing module, a defect candidate region extraction module, a defect identification and classification module, and a result output and alarm module; according to the method, a high-definition industrial camera is used for collecting a component surface image, and adaptive median filtering and a Retinex algorithm are adopted for image denoising and enhancement, so that the influence of noise and uneven illumination is eliminated; utilizing an improved multi-threshold segmentation and Canny edge detection algorithm to accurately extract a defect candidate region; the method comprises the following steps: extracting three types of feature parameters of shape, texture and gray scale, and inputting the three types of feature parameters into a deep learning model taking ResNet50 as a basic network to realize automatic identification and classification of four types of typical defects of cracks, holes, unfilled corners and surface peeling; and finally, the system divides severity levels according to the defect size, and triggers differentiated visual alarm and linkage control.

The invention relates to the technical field of intelligent operation and maintenance of photovoltaic power stations, in particular to an AI video recognition system for photovoltaic power station equipment inspection, which comprises a data acquisition unit, a data preprocessing unit and a defect recognition unit, and is characterized in that characteristics from one mode are used as query, evidence information serving as keys and values is searched from corresponding area characteristics of other modes, and the data acquisition unit is used for acquiring data; the method is used for synergistically diagnosing compound and early defects with weak or invisible characteristics in a single mode. According to the method, cross-modal collaborative reasoning can be realized: when a suspicious feature is found in one modal, whether evidence features capable of mutually verifying exist in the same position in other modals or not can be inquired, the diagnostic logic of field experts is simulated, different physical phenomena can be associated, and the probability of mutual verification is reduced. Therefore, early-stage or composite defects which are extremely difficult to find in any single mode can be accurately identified. Therefore, the problem that the recognition performance is reduced due to environmental interference such as illumination and shadow can be fundamentally solved.

The invention discloses a comprehensive remote sensing recognition system for hidden danger of ice lake outburst. The system comprises a data acquisition and preprocessing module, an ice lake extraction module, an ice dam stability evaluation module, a risk calculation module and a visualization and early warning module. According to the system, optical images, SAR images, LiDAR point cloud and meteorological data are utilized, firstly, a glacial lake boundary is extracted through a water body index, the area and the volume are calculated, then the water level rising rate is monitored in combination with multi-temporal data, the dam body stability is evaluated by utilizing three-dimensional terrain and deformation parameters, and the dam body stability is evaluated by combining the temperature melting corrosion rate and triggering factors such as rainfall, earthquakes and inflow. And constructing a multi-factor outburst risk index model, and finally carrying out visualization and graded early warning on a result in a GIS platform. The method can achieve the quick, comprehensive and intelligent recognition of the hidden danger of ice lake outburst, has the advantages of being high in monitoring precision, high in automation degree, high in early warning timeliness and the like compared with a traditional single data or experience judgment method, and can be widely applied to the disaster prevention and control work of dense ice lake areas such as the Qinghai-Tibet Plateau and the Himalaya mountain.

The invention relates to the technical field of intelligent traffic and road maintenance, and provides a road disease intelligent identification system and method based on artificial intelligence and Beidou positioning, and the method comprises the steps: carrying out the edge calculation preprocessing of noise reduction, image enhancement and region-of-interest segmentation of collected road image data, and extracting initial image features; inputting the initial image features into a deep learning disease recognition model based on transfer learning optimization, outputting a disease type and a disease grading result, and forming multi-source fusion data; performing spatio-temporal data association analysis on the multi-source fusion data to realize disease environmental impact assessment; the method comprises the following steps: predicting future development conditions of road diseases, sending out early warning information, dynamically evaluating road health condition grades according to road disease data, making an optimal maintenance plan according to positions, types and grades of the diseases and environmental influence evaluation results, and optimizing traffic dispersion and route recommendation according to the road disease conditions.

The invention discloses a simulation platform for autonomous tracking of a subsea pipeline by a UUV. The simulation platform comprises a mother ship command and control node system used for issuing a task instruction to a UUV motion control and situation display system; the UUV motion control and situation display system is used for sending a course adjustment instruction to the UUV underwater unmanned vehicle and carrying out real-time graphical dynamic display on the navigation information of the UUV underwater unmanned vehicle; the UUV side-scan sonar model system is used for receiving the motion attitude information of the UUV underwater unmanned vehicle and carrying out detection result data production calculation on a target area in combination with the motion attitude information; the UUV subsea pipeline detection and identification system is used for carrying out pipeline identification according to side scanning detection result data and sending pipeline information to the UUV pipeline tracking strategy system; the UUV pipeline tracking strategy system is used for calculating the track information of the UUV underwater unmanned vehicle according to the pipeline information and the navigation information of the UUV underwater unmanned vehicle, and sending the track information to the mother ship command control node system.

A ship sub-assembly weld seam identification method, comprising: creating a point cloud weld seam identification training dataset by means of point cloud discretization, virtual sampling, weld-zone semi-automatic interactive annotation, and style transfer-based augmentation of ship sub-assembly CAD data; training a point cloud weld seam identification model, and exporting a weld seam identification model data file; using a 3D point cloud camera array as a sampling camera to collect workpiece 3D point cloud data of a sub-assembly workpiece to be welded at the production site; by means of the weld seam identification model data file, obtaining weld seam annotation position data and positioning a weld seam; and issuing a welding process plan and instruction to control a welding robotic arm and a control device therefor to complete sub-assembly workpiece welding. Also provided are a ship sub-assembly weld seam identification system, a computer device, and a computer-readable storage medium. The method overcomes the shortcomings in conventional 3D weld seam identification methods such as point cloud incompleteness and identification failures caused by steel types and the imaging angles of 3D cameras, thereby greatly improving the adaptability of automatic weld seam identification methods to complex workpieces.

The invention discloses an electrical engineering drawing wiring relation intelligent identification system based on multi-modal fusion, and the system comprises a file management module which is used for supporting the opening, browsing, exporting and temporary caching of a PDF file, creating an engineering file to store an identification progress, manually correcting a record and parameter configuration, and achieving breakpoint continuous processing; and cross-drawing wiring relation linkage analysis is supported. The drawing preprocessing module comprises an image optimization sub-module and a layer separation sub-module, the image optimization sub-module is used for extracting an original vector path and filtering redundant graphic elements for the vector version PDF and carrying out denoising and binarization processing for the scanning version PDF, and the layer separation sub-module is used for separating layers based on graphic attributes.

The invention discloses a robot automatic obstacle avoidance target recognition system and method, and relates to the technical field of autonomous navigation, and the method comprises the steps: obtaining and fusing an obstacle boundary map, a passable region mask and a dynamic target hot area map, and outputting an environment feature map; inputting the environment feature map into a pre-trained deep reinforcement learning model, and generating an obstacle avoidance instruction set according to the real-time state of the robot and a global path planning result; and acquiring actual path data of the robot, comparing the actual path data with an expected path point by point, and if the continuous deviation feature exceeds a preset continuous deviation feature threshold, inputting the environment feature map, the obstacle avoidance instruction set and the continuous deviation feature into the deep reinforcement learning model for updating. According to the invention, the obstacle avoidance capability of the autonomous robot in a dynamic complex environment is improved through multi-modal sensing fusion and a closed-loop dynamic optimization mechanism.

The invention discloses a control valve drawing part intelligent identification system, and belongs to the technical field of intelligent manufacturing and drawing identification, the system obtains a direction control valve drawing sample, combines a QATM algorithm, a YOLO model, EAST and TesseractOCR, and U-Net and a graph theory algorithm to carry out multi-modal feature extraction and joint identification, constructs a three-level classification matching system, and outputs an initial identification result; through label data conversion, manual sampling auditing, initial recognition standard construction, extended label data generation and comprehensive precision label data fusion, multiple times of iterative optimization of an initial model is achieved, and finally a high-precision detection model is output. Automatic and intelligent recognition of part structures, parameters and topological relations in control valve drawings is achieved; the drawing recognition efficiency and accuracy are remarkably improved, and the method is suitable for industrial drawing digitization and intelligent assembly application.

The invention discloses an unmanned aerial vehicle group temporary communication network construction method based on visual self-organization. The method comprises the following steps: establishing an unmanned aerial vehicle node system and a security audit management system to realize flight control and message encryption exchange; airborne perception is realized through a visual candidate site identification system, and multi-modal data fusion and site scoring are carried out; domain autonomy and weighted election are realized by using a distributed voting and election system, and relay nodes are determined; scheduling, monitoring and self-adaptive reconstruction of a multi-hop path are completed based on a multi-hop relay and route maintenance system; and target detection, positioning and emergency document reporting are executed through the search and rescue positioning and situation return system. According to the method, voice and data links can be quickly recovered when ground communication facilities are damaged or unavailable, and under the condition of limited communication and energy, an unmanned aerial vehicle group can autonomously identify a safe position in a distributed manner and negotiate to establish a reliable multi-hop communication network, so that emergency rescue and positioning services are supported.

The invention discloses a low-altitude aircraft intelligent identification system based on an edge network, and belongs to the technical field of intelligent identification. Comprising a data acquisition module used for acquiring time sequence sensor data; the knowledge base construction module is used for constructing a physical rule knowledge base; the penalty value generation module is used for generating a physical violation penalty value; the model generation module is used for obtaining a physical rule embedded model; and the result output module is used for outputting an aircraft intelligent identification result conforming to the physical motion law. According to the method, the physical rule knowledge base is introduced into the edge network, and the lightweight model is trained by using the dual-target collaborative loss function, so that the effect of still outputting a physically consistent aircraft recognition result under the condition of limited computing power is achieved; the problem that in the prior art, a marginal small model generates jumping, over-limit or abnormal tracks due to lack of motion law constraints is solved.

The invention discloses a water conservancy spraying area identification system based on a laser radar, and the system comprises a data acquisition and modeling module which completes the distortion removal of a point cloud, generates a signed distance field and a reflection intensity body, and builds tangential, normal and auxiliary normal coordinates; the section slicing module obtains a section point set; the feasibility feature and anisotropy Morse function module constructs a scalar field and generates a gradient field; the complex construction module is used for establishing a two-dimensional Morse-Smal complex to obtain an initial spraying area mask and a section boundary candidate; the boundary completion module is used for realizing a homotopy-preserving closed boundary according to the distance field gradient; the persistent coherence screening module is used for obtaining a stable section boundary; and the section calculation and coverage initialization module is used for completing section type fitting and information criterion selection and outputting an operation section size vector and a strip coverage initialization parameter. According to the method, the sprayable area and the section size are integrally identified, the boundary is continuous and consistent, and the method adapts to complex working conditions.

The invention discloses a regional power grid-oriented new energy power prediction deviation factor identification method and platform, and belongs to the technical field of data processing. The method comprises the steps of collecting new energy historical power station data of a target regional power grid; constructing a deviation index double-layer identification system; performing power deviation data identification and influence factor extraction on the new energy historical power station data according to the prediction deviation amplitude and the prediction deviation reason to obtain a new energy power deviation data set and a power deviation influence factor set; based on the new energy power deviation data set, deviation weight distribution is carried out on the power deviation influence factor set, and a deviation influence factor weight factor set is determined; and performing key factor identification on the power deviation influence factor set based on the deviation influence factor weight factor set to obtain a new energy power prediction deviation factor set. The technical problem of incomplete and inaccurate new energy power prediction deviation factor identification in the prior art is solved, and the technical effect of accurately and comprehensively identifying the prediction deviation factor is achieved.

The invention discloses a human and house data comprehensive treatment method, system and equipment based on a multi-level address model, and a medium, belongs to the technical field of data treatment, and aims to solve the technical problem of how to effectively integrate human and house data sources of multiple parties and improve the accuracy, consistency and availability of human and house data. The defects that in the prior art, people and house data are scattered, address description is not standard, the incidence relation is inaccurate, data quality is uneven and a unified identification system is lacked are overcome. According to the technical scheme, the method comprises the steps that a five-level core data table comprising a community table, a building table, a house table, a people and house relation table and a personnel information table is constructed; the method comprises the following steps: accessing original human and house data from a plurality of data sources, performing intelligent analysis and standardization processing on address information, and obtaining data after standardization processing; establishing a house unique identification system and a human-house multi-dimensional relation model based on the standardized data; evaluating and repairing the data quality; and standardized human and house data service is provided for the outside through the data service interface.

The invention relates to the technical field of mine safety engineering and hydrogeological information processing, and discloses a multi-scale coal seam water spatial variation analysis and main control factor identification system for group-mine combined mining, which comprises a multi-source spatio-temporal data benchmark unification and preprocessing module for generating a standardized data set; the macro-micro orthogonal collaborative dynamic permeability tensor field construction module is used for establishing a permeability tensor field; a non-Euclidean hydraulic distance calculation module based on Riemannian manifold calculates a manifold hydraulic distance; a variable-lag embedded convergence cross-mapping causal identification module calculates causal coupling strength; a causal topology feedback driving model correction module iteratively corrects the model to convergence according to the difference; and the spatial variation characteristic analysis and main control factor output module outputs a main control factor list. According to the method, a causal topology feedback-driven model self-correction closed loop is constructed, a causal intensity reverse constraint physical model obtained through convergence cross mapping is used, and the problems that a traditional physical model cannot respond to mining-induced fracture evolution in real time, and pure data mining lacks physical interpretability are solved.

The invention relates to the technical field of nondestructive testing and intelligent manufacturing, and provides a weld defect intelligent identification system based on multi-source data fusion. The system synchronously obtains a visual image, a three-dimensional point cloud and ultrasonic array data of a welding seam through a multi-source data acquisition module; a hidden danger density field construction module generates surface and internal hidden danger density fields; the cross-space association and defect topology extraction module associates and binds high-density areas meeting spatial continuity and gradient consistency conditions in the two density fields into a cross-space association cluster through density gradient consistency analysis and a preset topology mode template, and generates a defect sign vector; and finally, the defect decision module outputs the defect type, position, size and confidence coefficient based on the sign vector. According to the method, multi-source heterogeneous data is innovatively unified to hidden danger density field characterization, collaborative, accurate and explainable recognition of internal and external defects of the weld joint is achieved through topology analysis of a cross-space correlation cluster, and the limitation of a traditional single detection means is overcome.

The invention discloses an intelligent acquisition risk identification system based on deep learning, and the system comprises a time sequence sample construction module which is used for building a time sequence sample sequence; the hierarchical attention structural feature coding module is used for carrying out structural feature coding on the sequential sample sequence by utilizing a hierarchical attention network; the time sequence feature extraction module is used for inputting the structural feature vector into an ETSform model to perform time sequence feature extraction; the hierarchical time sequence collaborative attention adaptive fusion module is used for performing bidirectional attention interaction and dynamically generating a hierarchical weight and a time sequence weight through a meta-learning controller; the improved CatBoost risk identification module is used for outputting a risk score and a risk type label; and the system fusion module is used for summarizing and merging the risk scores and the risk type labels. The method and the device are suitable for merchant transaction risk identification in an acquiring business scene.

The invention discloses a railway freight car loading state detection method based on image recognition, and particularly relates to the field of railway freight car loading state detection.The method comprises the steps that in the process that a railway freight car passes through an image collection area, carriage image frames are collected at a constant frame rate through linear array imaging equipment; an image sequence is constructed based on the wheel axle displacement, the acquisition timestamp and the frame number, the image sequence comprises a main image frame, a relay interpolation frame and an interpolation pseudo frame, and the main image frame, the relay interpolation frame and the interpolation pseudo frame are arranged to support time domain modeling of subsequent state response changes. The dynamic capture and accurate identification of the instantaneous abnormal state of the loading structure are realized by constructing an anchor point tracking graph and combining a time sequence rhythm modeling and similarity discrimination mechanism of a disturbance behavior. The problems that in the prior art, an identification system lacks the space-time continuous modeling capacity for the short-time change state, and an abnormal judgment blind area and misjudgment distortion exist are solved.

The invention discloses a vehicle emission identification system and method based on artificial intelligence, and relates to the technical field of vehicle emission detection. The snapshot recognition module is provided with a multispectral camera array and a synchronous controller, dynamically adjusts imaging parameters in combination with a laser radar and a millimeter wave radar, and supports multi-type vehicle recognition; the AI algorithm processing module adopts a heterogeneous computing power architecture to allocate resources; the vehicle anomaly judgment module constructs a multi-dimensional anomaly feature chain, calls OBD data to study and judge anomaly and identifies blacklist vehicles; the data storage and calling module adopts block chain evidence storage and hierarchical storage; the mobile law enforcement adaptation module supports end-side cloud collaboration; the multi-source data fusion module realizes data space-time alignment; the dynamic model self-updating module optimizes the model based on federal learning. The method improves the comprehensiveness of vehicle identification and abnormity determination, guarantees the law enforcement data to be legal and credible, relieves the computing force pressure of a mobile terminal, supports model continuous adaptation technology iteration, assists efficient mobile law enforcement, and provides powerful support for environmental protection law enforcement.

The invention relates to the field of computer vision and image processing, and discloses an image target recognition system based on a convolutional neural network and a feature fusion technology. Comprising a quality evaluation and alignment unit, a reversible decoupling and gating recharge unit, a multi-branch feature extraction unit, an evidence fusion unit, a marginal contribution gating unit, a topology consistency and boundary refinement unit, a detection and positioning unit and a linkage control unit. Reversible decoupling of contents and degradation components is realized in a feature domain, and bitwise gating recharge is implemented in a candidate region according to a quality map, so that small target and weak texture features are enhanced. The system fuses multi-branch output based on an evidence theory, determines weight distribution by combining marginal contribution calculation, triggers gating enhancement and local refinement when a conflict or deviation exceeds a threshold value, and realizes cooperative control of feature suppression and structure correction. According to the method, the recognition stability can be kept in complex scenes such as weak light, blurring and shielding, and the target recognition precision and the system interpretability are improved.

The invention discloses a bridge crack intelligent identification system and method based on deep learning, relates to the technical field of bridge intelligent operation and maintenance, and solves the technical problems that the loss of crack details cannot be avoided and the attention on the crack details is insufficient in the prior art. The system comprises an image acquisition module and a semantic segmentation module, wherein the image acquisition module is used for capturing a bridge crack image; the semantic segmentation module is used for carrying out semantic segmentation on the bridge crack image by using a BSCS semantic segmentation algorithm to identify a bridge crack; the semantic segmentation module comprises a preprocessing module, a mixed feature concentration module, a multi-scale high-resolution reconstruction module and a parallel attention module; according to the invention, through global feature extraction, local detail capture and multi-scale information comprehensive extraction, the performance of the model in crack identification is improved.

The invention relates to a hemispherical resonator and hemispherical gyroscope parameter and defect identification system and method, and belongs to the technical field of hemispherical resonator gyroscopes. According to the invention, automatic identification of damping non-uniformity, rigidity non-uniformity and 1-3-order quality defect parameters is respectively carried out on the fused quartz hemispherical resonator before and after film coating and the gyroscope header after base assembly, so that the full-flow coverage test of the hemispherical resonator gyroscope is realized; various parameters are identified in the whole process from harmonic oscillator machining forming to the gauge outfit state, the testing precision, the system reusability, the operation convenience and consistency are greatly improved, and the production efficiency is improved.

A crop species identification system based on satellite telemetry data is disclosed, which includes: a receiver module, receiving plural telemetric vegetation indexes of a target area; a data cleaning module, cleaning anomalous data in the telemetric vegetation indices, to correspondingly generate cleaned index data; a feature extraction module, including at least two different convolution kernels for mapping the cleaned index data into at least two feature scale mapping data which respectively correspond to the convolution kernels, performing a pooling operation of the feature scale mapping data to generate a pooled data, and concatenating the at least two feature scale mapping data and the pooled data into concatenated data; and a classification module, including a fully-connected layer, for extracting features of the concatenated data, to generate a species classification result for the target area.

The invention provides a photovoltaic string affiliation identification system and method based on a transformer area fusion terminal. The photovoltaic string affiliation identification system comprises a background management unit, the fusion terminal, a string control unit and a voltage detection module. The fusion terminal issues an identification instruction carrying the feature coding sequence to a photovoltaic direct current loop through an HPLC network; the string control unit analyzes the instruction and controls a bypass switch to act, so that a corresponding photovoltaic module is switched between a normal state and a bypass state, and a voltage step characteristic signal conforming to a coding rule is generated in a loop; the detection module adopts an independent unit or a multiplexing inverter function to collect voltage data; and the background management unit compares the instruction time sequence with the voltage waveform characteristics to judge affiliation. The technical problems that in the prior art, hardware cost is high, signals are prone to noise interference, and continuous insertion loss is large can be solved.

The invention relates to the technical field of image recognition, in particular to an intelligent solid waste recognition system based on multi-modal fusion, which comprises a data acquisition module, a visual guidance module, a recognition module and a cooperative processing module, and is characterized in that the system quickly analyzes surface information such as visible light images, three-dimensional point clouds and infrared spectrums of materials through the visual guidance module; and coding to generate a dynamic vector for guiding a subsequent refined analysis task. The identification module constructs a two-dimensional pixel map on the internal density map according to the guidance of the dynamic vectors, and utilizes a map neural network to deeply analyze local density textures so as to identify high-value components such as an internal circuit board; and the co-processing module performs template matching of the internal components and external surface anomaly detection according to the instruction so as to assess the security risk. According to the method, the external form and internal structure information of the material are deeply fused through the strategy of coarse first, fine second and dynamic guidance, and intelligent recognition of the complex electronic waste is achieved.

The invention relates to a commercial place illegal power utilization identification system and method based on a non-intermediary technology, and relates to the technical field of electric power supervision, and the system comprises a metering module which collects the voltage and current data of various electric appliances; the main control CPU is used for generating an electric appliance operation waveform file based on the data, converting and outputting the electric appliance operation waveform file to the non-intrusive load identification module, analyzing whether illegal electricity utilization exists in an electricity utilization place, including a living risk probability and a charging risk probability, and generating and uploading alarm data when the illegal electricity utilization is detected; the non-intrusive load identification module is used for identifying and analyzing the waveform file and outputting the use condition of the electric appliance to the main control CPU; and the dual-mode communication module receives the alarm data uploaded by the master control CPU and uploads the alarm data to an acquisition master station through dual-mode carrier communication, and all the modules are in telecommunication connection. According to the invention, accurate identification and traceability positioning of hidden illegal power consumption behaviors can be realized.