3916results about How to "Easy to analyze" patented technology

The invention relates to the technical field of model construction, in particular to an obstetric delivery decision model construction method based on a knowledge graph. The method comprises the following steps that multi-dimensional information collection is conducted on a pregnant woman to be delivered, individualized data are obtained, and the multi-dimensional information comprises a medical record text, a laboratory examination result, an ultrasonic image and fetal heart rate monitoring data; performing standardization and structural processing on the individualized data to generate a standardized multi-modal data set; performing entity identification and relation extraction processing on the standardized multi-modal data set according to a preset obstetrical domain ontology model to obtain an initial knowledge graph; and carrying out priori knowledge-based mapping expansion on the initial mapping knowledge domain to obtain a multi-modal mapping knowledge domain. Through an attention mechanism and a decision support interface, the interpretability of the model is enhanced, and doctors can understand and trust decision suggestions.

The invention provides a private domain live broadcast user behavior analysis and drainage recommendation method based on deep learning, and relates to the technical field of deep learning, and the method comprises the steps: collecting user behavior data, constructing a multi-mode deep neural network for processing, extracting user time sequence behavior features, extracting user text intention features through employing a residual connection structure and comparative learning, and carrying out the drainage recommendation of the private domain live broadcast user. User behavior link characteristics are acquired through a time sequence diagram attention network, user layering is performed through a Gaussian hybrid clustering model, a user loss probability is predicted by using a deep cross neural network, future activeness and purchasing power of a user are predicted in combination with a recurrent neural network, and commodities are matched through a diagram attention commodity knowledge network. And performing recommendation by using the improved double-tower deep neural network in combination with difficult sample mining and a variational auto-encoder, and searching an optimized push strategy to obtain an optimal push strategy.

The invention relates to a traffic flow prediction optimization method based on an intelligent optimization algorithm, which is applied to the technical field of traffic flow prediction. The system comprises a multi-modal data acquisition and fusion module, a hybrid deep learning prediction module, an intelligent parameter optimization module, an integrated learning module, a multi-scale error correction module, a prediction result analysis and visualization module, a distributed learning and privacy protection module and the like. Wherein the hybrid deep learning prediction module adopts an attention mechanism to fuse models such as CNN, Transform and LSTM, and introduces an adaptive error correction mechanism, so that the prediction precision is remarkably improved; the intelligent parameter optimization module accelerates the model search efficiency by using technologies such as quantum computing and swarm intelligence optimization; the integrated learning module and the adversarial training strategy enhance the robustness and generalization of the model; and the multi-scale error correction module adopts a time sequence decomposition and self-adaptive online learning method to improve the accuracy of long-term and short-term prediction.

The invention relates to the technical field of surface defect detection, and discloses a metal surface defect detection method and device, equipment and a storage medium. The method comprises the following steps: performing multi-frequency eddy current scanning on the surface of a metal workpiece to obtain a four-dimensional original data set containing space coordinates, excitation frequency and impedance response; performing adaptive filtering and compensation on the four-dimensional original data set to obtain target signal data; performing adaptive imaging and frequency fusion on the target signal data to obtain a two-dimensional image; inputting the two-dimensional image into a lightweight adaptive convolutional neural network for multi-dimensional feature extraction to obtain a multi-dimensional feature map; performing texture analysis and anomaly detection on the multi-dimensional feature map to obtain a comprehensive anomaly score map; and carrying out multi-modal data fusion and decision making on the comprehensive abnormal score graph to obtain a metal surface defect detection result, and improving the detection accuracy of the metal surface defect.

The invention relates to the technical field of law enforcement intelligent alarm, in particular to a multi-mode intelligent analysis method and system based on law enforcement supervision. The method comprises the following steps: acquiring a law enforcement scene video file from a law enforcement supervision recorder platform, and performing audio and video segmentation and law enforcement behavior information identification analysis to obtain law enforcement scene audio behavior information data and law enforcement scene image behavior information data; performing timestamp and spatial position extraction processing on the law enforcement scene video file, performing multi-modal time-space synchronization association analysis, and performing law enforcement behavior sequence connection processing and event behavior intention recognition analysis at the same time to obtain law enforcement scene event behavior intention information data; and obtaining a preset law enforcement event behavior knowledge graph, and carrying out behavior normative score calculation and nonstandard behavior intelligent alarm optimization analysis to generate a law enforcement scene event nonstandard behavior optimization suggestion report. According to the invention, the accuracy of law enforcement nonstandard behavior supervision alarm can be improved.

The invention provides an intelligent power distribution cabinet safety early warning method, system and device and a storage medium, and relates to the field of electric power. The method comprises the steps of obtaining multi-source heterogeneous operation parameters of the intelligent power distribution cabinet, and performing feature extraction on the multi-source heterogeneous operation parameters to obtain operation feature parameters; performing parameter fusion on the operation characteristic parameters to obtain fused characteristic parameters, and inputting the fused characteristic parameters into a preset fault early warning model to obtain fault early warning data; and inputting the fault early warning data into a preset rule matching engine for rule matching to obtain a matching result, and generating an early warning signal according to the matching result. According to the method, the accuracy and timeliness of safety early warning of the intelligent power distribution cabinet are improved.

The invention provides a method and a system for identifying and cleaning abnormal data, which relate to the technical field of data processing, and are used for identifying and cleaning abnormal data by acquiring real-time data flow, determining a self-adaptive threshold value by using a trained Gaussian mixture model and detecting abnormal data points. A deep belief network and long and short term memory network composite model is adopted to carry out deep feature learning and anomaly verification, and high-level feature representation and anomaly degree quantitative evaluation results are obtained; and evaluating a conditional dependency relationship among the abnormal data points by using the probability graph model, constructing a target probability dependency graph, and generating an abnormal data point recognition result. Based on an identification result, a reward mechanism excitation algorithm is applied to optimize a cleaning strategy; according to the method, the to-be-determined abnormal region is identified, the multi-modal fusion technology is introduced to perform correlation analysis, and the data in the to-be-determined abnormal region is judged and cleaned based on the analysis result to obtain the target cleaning strategy, so that the abnormal data identification and cleaning efficiency and accuracy are improved, and the method has relatively high adaptive capability and expansibility.

The invention relates to the field of transformer parameter regulation and control, in particular to a parameter optimization method and device for a coupling type transformer. The method comprises the following steps: acquiring multi-dimensional real-time working monitoring parameters and real-time working environment state parameters of the coupling type transformer; performing nonlinear dynamic analysis and global state sensing modeling on the multi-dimensional real-time working monitoring parameters so as to construct a transformer global state sensing model; performing multi-parameter combination definition on the multi-dimensional real-time working monitoring parameters, performing dynamic operation simulation based on a transformer global state sensing model, and collecting operation simulation response data of a plurality of schemes; performing multi-target dynamic balance analysis on the operation simulation response data of the plurality of schemes to obtain an optimal transformer working parameter combination; and carrying out environment dynamic parameter change analysis on the real-time working environment state parameters, and constructing a real-time environment condition distribution field. According to the invention, efficient and self-adaptive adjustment transformer parameter optimization is realized.

The invention relates to the technical field of forest carbon sequestration, in particular to a forest carbon sequestration remote sensing monitoring method and system, and aims to solve the problems that in the prior art, a carbon sequestration prediction result can be obtained by inputting a target area image into a preset carbon sequestration model, a more comprehensive carbon reserve prediction model cannot be constructed in combination with a vegetation index, and the carbon sequestration prediction result cannot be obtained by using the preset model. The problem that the prediction result is inaccurate due to the fact that the features of specific tasks cannot be effectively handled is solved. According to the invention, ground survey data and vegetation indexes are combined through the prediction model construction module to construct a comprehensive carbon reserve prediction model, and the carbon reserve prediction model is obtained through machine training learning and can effectively cope with the characteristics of a specific task, so that the prediction accuracy and flexibility are improved; and a high-resolution carbon reserve distribution map is generated through an efficient machine learning algorithm, scientific decision and management are supported, and an important tool is provided for carbon reserve research.

The invention discloses an evaluation method based on water ecological environment monitoring, and relates to the technical field of water ecological environment evaluation. Comprising the steps of data collection: obtaining a river basic information data set, a monitoring equipment information data set and a historical monitoring information data set, and building a cloud server to store the river basic information data set, the monitoring equipment information data set and the historical monitoring information data set. According to the method, each region is accurately monitored through region division of river landforms and a targeted monitoring method, the timeliness of monitoring data is ensured, the water quality condition can be evaluated in real time and the water quality of the corresponding region under unique weather can be evaluated by acquiring a region monitoring data set and a weather information data set in real time to cooperate with an evaluation model, and the water quality evaluation accuracy is improved. In addition, weather changes can be monitored and early warned, timely support is provided for dealing with emergencies, and by summarizing the method, related departments can formulate more reasonable treatment schemes, the water quality management efficiency and effect are improved, and the water quality problem is found and solved in time.

The invention provides a multi-angle photographing and three-dimensional reconstruction method and system for soft tissue, and relates to the technical field of soft tissue three-dimensional reconstruction, and the method comprises the steps: analyzing soft tissue features based on deep learning, generating a bulb tube motion track and sampling interval data, and controlling a mechanical arm to drive a bulb tube to collect projection images at multiple angles; screening an optimal image sequence by using an image quality evaluation network, and performing preprocessing such as material decomposition, feature extraction, space registration and image optimization; and inputting the optimized image and the material features into a reconstruction model, performing iterative calculation in combination with an anatomical structure template to generate density distribution data, performing curved surface reconstruction and intelligent tissue segmentation based on graph convolution based on the density distribution data, and finally generating a three-dimensional model containing tissue layering information.

The invention relates to a database query method and device based on a large language model agent and electronic equipment, and the method comprises the steps: obtaining and analyzing query request information, recognizing a query request intention type, carrying out the problem splitting of the query request information according to the query request intention type, and obtaining sub-query request information; searching and obtaining an SQL statement corresponding to the sub-query request information in the target knowledge base according to the category of the query request intention, constructing and generating an SQL statement prompt word and sending the SQL statement prompt word to the large language model when the SQL statement is not searched, executing according to the returned SQL statement to obtain a sub-query result, integrating the sub-query result and obtaining a query result of the query request information, and generating a data visualization code based on the large language model, executing and outputting a query result and a visualization display result thereof. By combining natural language processing, a large language model and a multi-item optimization technology, the efficiency and usability of data query are greatly improved, and non-technical users can conveniently analyze complex data.

The invention relates to the technical field of quality traceability management, in particular to an organic fertilizer quality safety traceability management method based on multi-source data. The method comprises the following steps: collecting organic fertilizer source data in real time from links of organic fertilizer production, circulation, storage and use, performing standardization processing, and performing production batch synchronous set processing and traceability association connection file establishment to generate an organic fertilizer multi-source data traceability association connection file; and carrying out production batch tracing extraction processing on the organic fertilizer multi-source data traceability association connection file, carrying out organic fertilizer quality index evaluation analysis and quality index error limitation analysis, carrying out organic fertilizer quality safety abnormity traceability processing and abnormity decision support processing, and generating an organic fertilizer quality safety abnormity problem decision support report. Therefore, the corresponding organic fertilizer quality safety abnormity traceability problem management work is executed. The scientificity and accuracy of organic fertilizer quality safety management can be improved.

The invention relates to the technical field of laser welding, in particular to a laser welding system control method for metal accessories. The method comprises the following steps that automobile industry metal fitting data are obtained, welding demand analysis is conducted according to the automobile industry metal fitting data, and therefore welding demand data are obtained; laser welding feature extraction and resistance spot welding feature extraction are carried out according to the welding demand data, so that laser welding data and resistance spot welding data are obtained; carrying out welding simulation according to the laser welding data and the resistance spot welding data so as to obtain welding simulation data; welding form feature extraction and welding strength feature extraction are carried out according to the welding simulation data, so that welding form data and welding strength data are obtained; and carrying out welding defect detection according to the welding form data and the welding strength data so as to obtain welding defect data. Automatic adjustment and accurate control of the laser welding process are achieved based on the laser welding technology.

The invention discloses a database index data prediction method based on double attention and static fusion GCN. Multiple kinds of database index data are collected to serve as a data set, and the data set is divided into a training set, a verification set, a test set and a static index embedding vector. Scale layers with different lengths are obtained through continuous down-sampling. The time expansion double attention layer extracts time dimension information of different receptive fields; a static feature fusion dynamic GCN layer captures information of a spatial dimension, and global dependence of data is obtained in combination with an attention layer between fragments. And aggregating the information of each time slice to calculate a predicted value. And calculating a loss rate, carrying out back propagation by using an Adam optimization algorithm, continuously updating model parameters, carrying out repeated training until the training loss rate converges, and storing the weight as an optimal model. And inputting test set data into the stored optimal model, and outputting a prediction value and prediction precision. According to the method, the defect of sharing the adjacent matrix is overcome, and the prediction accuracy of the model is improved.

The invention discloses a contract evaluation method, system and device based on a large model and a medium, and the method comprises the steps: obtaining a target contract, carrying out the data preprocessing, obtaining the preprocessed contract data, and inputting the preprocessed contract data into a preset contract large language model; the preset contract large language model stores a vector knowledge base including coastal bulk cargo transportation knowledge, legal clauses and industry regulations; in a preset contract big language model, performing contract classification and contract summary extraction on the current preprocessed contract data by using a first model agent to obtain contract summary information; performing contract risk assessment on the current preprocessed contract data to obtain risk assessment information and / or risk avoidance suggestion information; performing benefit measurement and calculation on the current preprocessed contract by using a third model agent to obtain benefit measurement and calculation information; according to the method, the examination and evaluation of the large model in the coastal bulk cargo transportation contract are realized, and the purposes of saving the labor cost and improving the business turnover rate are achieved.

The invention relates to the technical field of behavior recognition, in particular to a machine learning-based surgical personnel video behavior recognition method and system. The method comprises the following steps: acquiring a video frame sequence and a point cloud frame sequence from a hand washing area of an operator through an RGB camera and laser ranging equipment to obtain the video frame sequence and the point cloud frame sequence; performing hand point cloud segmentation on the point cloud frame sequence to obtain hand point cloud data; performing action original data generation according to the video frame sequence and the hand point cloud data to obtain action original data; and extracting position and attitude information of hand key points from the original motion data to obtain video key point feature data. Visual information is captured through the RGB camera, three-dimensional depth information is obtained through the laser ranging device, fine analysis and evaluation are carried out on actions in combination with the deep learning network, and finally higher recognition precision, robustness and automation level are achieved.

The invention provides an integrated circuit packaging quality evaluation method and system based on a nondestructive testing technology, and relates to the technical field of integrated circuits, and the method comprises the steps: obtaining a three-dimensional fluorescence response signal of a packaging structure through a two-photon excitation microscopic system, and building a stress distribution characteristic graph and a stress gradient grading graph through the analysis of the spatial distribution of the fluorescence signal; a quantum state nitrogen vacancy probe array is arranged in the stress abnormal area, electromagnetic field response signals of the quantum state nitrogen vacancy probe array under the stress effect are collected, and accurate mapping of a stress field and an electromagnetic field of the packaging structure is achieved; based on the obtained multi-physical field data, establishing a thermal electromagnetic coupling equation set, and calculating an interaction relationship among the physical fields; by analyzing evolution characteristics of each detection area under the multi-field coupling effect, a stability evaluation system including a mechanical failure risk and an electromagnetic failure risk is established, and accurate evaluation of the quality grade of the packaging structure is realized.

The invention discloses an automatic auditing and analyzing method for cultivated land soil data. The method comprises the following steps: S1, acquiring original soil data through multi-spectral imaging of an Internet of Things sensor and an unmanned aerial vehicle to form a data set; s2, using an extended Kalman filter to reduce noise and remove abnormal values, and using time sequence interpolation to complement missing data; s3, analyzing and auditing the preprocessed data based on the time sequence, and identifying an abnormal mode; s4, analyzing and fusing auditing data by using a causal reasoning graph neural network; s5, performing multi-dimensional analysis and prediction on the fused data by applying a big data analysis technology; s6, realizing real-time data visualization and interactive display through a visualization method driven by quantum computing; s7, using a natural language generation technology to generate a report for the soil data set and the analysis result; and S8, implementing encryption and access control in the data processing and transmission process. The invention provides a comprehensive soil data processing solution, and the data auditing efficiency, accuracy and safety are effectively improved.

The invention provides a wind power plant fault analysis method and system based on multi-dimensional information association, and relates to the technical field of fault analysis. Field domain configuration information of a target wind power plant is read, a secondary loop topology is established, topology node identification is performed based on data homology and data correlation, a network time protocol is constructed, and the wind power plant fault is analyzed based on the secondary loop topology. Constructing a fault prediction module by combining a fault knowledge graph, determining a transmission timestamp, carrying out data return constraint on the multi-dimensional source data by combining a network time protocol, transmitting the multi-dimensional source data to the fault prediction module, carrying out wind power plant fault prediction, and carrying out fault operation and maintenance management on a target wind power plant based on a fault prediction result. The technical problem that in the prior art, comprehensive analysis on multi-dimensional information of a wind power plant is lacked, and consequently fault prediction accuracy and real-time performance are insufficient is solved. The technical effects of comprehensively and accurately analyzing the multi-dimensional information of the wind power plant and improving the accuracy and real-time performance of fault analysis and prediction are achieved.

The invention relates to the field of power supply equipment monitoring, in particular to a distributed power supply state monitoring method and system based on optical fiber communication sensing. The method comprises the following steps: acquiring a power supply equipment distribution diagram; performing spatial layout distribution analysis on the power supply equipment distribution diagram to obtain power supply node layout distribution data; performing distributed topology reconstruction based on the power supply node layout distribution data to construct a power supply node topology network; acquiring real-time monitoring data of the power supply equipment based on the optical fiber communication sensing node; performing time window fine-grained division on the real-time monitoring data of the power supply equipment to obtain a plurality of time windows; performing multi-layer time-frequency local decomposition on the real-time monitoring data of the power supply equipment based on a plurality of time windows, and constructing a multi-period spectrum feature map; and extracting a power supply equipment time sequence voltage sequence based on the power supply equipment real-time monitoring data. According to the invention, efficient and accurate power supply equipment state monitoring is realized.

The invention provides a sewage treatment operation cloud platform based on Internet of Things and GIS, and the platform is characterized in that a data collection layer is used for obtaining user water consumption data and sewage pipe network data, and distributing all the data to each edge calculation node; the data storage layer is used for temporarily storing the data preprocessed by the edge computing node into a high-frequency storage chain, aggregating to obtain sewage pipe network comprehensive data, and permanently storing the sewage pipe network comprehensive data into a low-frequency storage chain; the data analysis layer is used for extracting sewage pipe network comprehensive data in the low-frequency storage chain, constructing a multi-dimensional dynamic GIS model by utilizing a GIS system, analyzing and predicting the multi-dimensional dynamic GIS model through a sewage treatment rectification analysis model, and generating a sewage treatment rectification plan of the sewage pipe network according to an analysis and prediction result; and the application service layer is used for acquiring all the sewage treatment and rectification plans and distributing all the sewage treatment and rectification plans to a management client held by a sewage pipe network manager. The sewage treatment device has the effect of efficiently and intelligently realizing sewage treatment.

The invention relates to a deep learning-based historical urban style integrity evaluation method and system. The method comprises the following steps of: obtaining urban street view image data of a plurality of point locations; extracting a sky ratio and a green vision rate of an urban street scene image according to the urban street scene image data, respectively taking the sky ratio and the green vision rate as a street profile scale spatial feature and a natural element spatial feature, and calculating a signage color number and a color harmony degree of the urban street scene image as building color spatial features; calculating a traditional material proportion and material consistency of the urban street scene image as material mechanism spatial features; for the urban street view image data, obtaining an expert score obtained based on an ELO scoring algorithm; and constructing and training an evaluation model based on an artificial neural network based on the various spatial features and the small-scale expert scoring data set, and realizing historical urban style and appearance integrity evaluation by using the evaluation model.

The invention provides a railway signal monitoring data generation method and system, and relates to the technical field of railways, and the method comprises the steps: carrying out the time-space correlation fusion processing of obtained carrier frequency characteristics, response coding information, the receiving time of transponder signal data, train operation speed data and train positioning data, and generating a monitoring data record under a unified coordinate system; adopting a rule-based data compression algorithm to adaptively select a compression rule and a compression rate to perform data compression on the monitoring data record; packaging the compressed monitoring data record into a monitoring data file in a standard format; and the data management server decompresses and decodes the monitoring data file, cleans, classifies and indexes the data in the monitoring data file by adopting a MapReduce parallel computing framework, stores the structured monitoring data into a distributed database, and performs fragmentation and redundant backup management on the database.

The invention belongs to the field of image processing, and particularly discloses an imaging simulation method and device based on a point spread function and electronic equipment, and the method comprises the steps: building a light source model corresponding to an image shot by an optical system, and obtaining the point spread function of a point light source of the light source model after the action of the optical system; obtaining error point spread functions under different optical system models; performing weighted averaging on each error point spread function according to a preset weight to obtain a comprehensive point spread function; forming an array diagram by the comprehensive point spread function according to light distribution, and carrying out convolution on the array diagram and a shot image to obtain a degraded image simulating a blurring effect caused by aberration; and applying the distortion model of the optical system to the shot image to obtain a distortion-simulated deformation effect image, and realizing imaging simulation of the optical system based on the deformation effect image and the degraded image. According to the invention, the imaging accuracy and efficiency can be improved.

The invention relates to the technical field of image analysis and processing, in particular to a high-precision detection method based on image analysis, which comprises the following steps: acquiring an original image to be detected, performing preprocessing operation on original image data, including normalization processing, de-noising processing and brightness adjustment, performing multi-scale feature conversion on the preprocessed image to generate feature matrixes of different scales, calculating the correlation degree between the feature matrixes through a correlation self-adaptive fusion module, and generating a fusion feature map; calculating a significance score of each pixel point in the fused feature map, generating an abnormal score matrix, and determining a significant abnormal region in combination with the deviation matrix; original image information is decomposed into different scales through a multi-scale feature conversion module, local and global abnormal features of an image are captured by using comprehensive information of feature matrixes of different scales, information redundancy among the features of the scales is eliminated, and the identification degree and the detection accuracy of an abnormal region are improved.

The invention relates to the technical field of circuit board line planning, in particular to an automatic design arrangement and path planning method and system for a double-sided board and multi-layer board printed circuit. The method comprises the following steps: acquiring input parameters of a circuit board, and analyzing to generate an initial structure of a wiring grid diagram; abstracting a wiring problem into a graph model, and extracting a wiring topological structure and density distribution characteristics; a deep reinforcement learning algorithm is adopted to optimize and generate a wiring path, and factors such as a wiring direction, layer selection and path length are considered at the same time; detecting, adjusting and optimizing wiring design rule conflicts on the basis of generating the wiring path; a comprehensive evaluation system of a wiring path planning scheme is established, indexes such as wiring efficiency, density and interlayer balance are considered, preset quality requirements are met through iterative optimization, and an optimized wiring path is output. According to the method, the wiring strategy can be adaptively learned and improved, and the reliability, efficiency and quality of wiring planning are improved.

The invention discloses an energy industry intelligent algorithm optimization method based on federal learning. The method comprises the following steps: S1, collecting and preprocessing local energy data; s2, acquiring and preprocessing expert example data; s3, constructing a comprehensive data set by applying a multi-modal data fusion technology; s4, each participant performs local model training on the comprehensive data set by using a federal adaptive optimization algorithm to generate local model update; s5, the central server aggregates local model updating to generate a global model; s6, applying a soft actor-commentator algorithm in the global model, and combining an inverse reinforcement learning technology to optimize an energy distribution strategy; s7, creating a virtual simulation environment by using a digital twinning technology, and testing and optimizing the performance of the global model; and S8, providing a user-friendly interface and a natural language processing interface, and realizing personalized energy management suggestions. According to the method, federated learning, a soft actor-commentator algorithm and an inverse reinforcement learning technology are combined, and an energy distribution strategy is optimized.

The invention discloses a transformer noise detection method based on season trend decomposition and time sequence prediction, and belongs to the technical field of transformer noise anomaly detection. Comprising the following steps: S1, acquiring noise data of a transformer, and preprocessing the noise data; s2, adopting an STL decomposition model to obtain a seasonal component, a trend component and a residual component; s3, performing Transform time sequence prediction on the seasonal components and the trend components to obtain prediction results of the seasonal components and the trend components; s4, performing anomaly detection on prediction results of the seasonal components and the trend components, and outputting a detection result; and S5, carrying out weighted composition on the detection results of all the components, and outputting a diagnosis result. According to the method, the time sequence data of the noise of the transformer substation is decomposed, predicted and subjected to abnormal point detection, and reliable technical support is provided for state monitoring and fault prevention of transformer substation equipment.

The invention relates to the technical field of LED reliability testing, and provides an LED lamp residual life prediction method, device and equipment, and the method comprises the steps: carrying out the real-time collection of the operation state parameters of an LED lamp, obtaining the operation state data, environment data and work load data, inputting the operation state data into a preset degradation model for analysis, and obtaining the residual life of the LED lamp. Obtaining a real-time degradation trend curve and analyzing a degradation trend interval; and carrying out weighted fusion calculation on the degradation trend interval by utilizing the environment data and the working load data to obtain a residual life prediction value of the LED lamp, and generating a residual life prediction report according to the residual life prediction value of the LED lamp. By collecting operation state parameters, environment data and working load data of the LED lamp in real time and generating a residual life prediction report, life changes of the LED lamp in different environments and working conditions are achieved, and the problem that an existing light attenuation detection method neglects factors of environment and load changes, and life prediction is not accurate is solved.