1789results about How to "Quick identification" patented technology

The invention provides an HBase client main-standby switching method and system based on fault perception, and relates to the technical field of database management, and the method comprises the steps: collecting system operation data through a distributed fault monitoring module, building an anomaly detection baseline model, carrying out fault diagnosis based on a system health assessment distribution matrix, and generating a fault characteristic spectrum; and switching is triggered according to a risk level evaluation result, and the optimal standby node is selected for sequential switching by using a deep strategy learning algorithm, so that rapid and accurate fault detection, diagnosis and intelligent switching can be realized, and the reliability and performance of the system are improved.

The invention discloses an industrial product quality detection method and system based on machine vision, and relates to the technical field of quality detection.During operation of the system, through an industrial camera and an optimized light source layout, high-resolution image acquisition is performed on electronic components in real time by adopting multi-angle shooting, a series of optimization processing is performed on the acquired images, and the quality of the electronic components is detected. The method comprises the following steps: extracting shape, texture and color features according to geometric characteristics of an electronic component to form key parameters for judging defects, identifying potential defects of the electronic component by using a deep learning model algorithm, calculating to obtain a quality evaluation index Q, and calculating the quality evaluation index Q according to calculation results of feature extraction and defect detection. And comprehensively evaluating the quality of each component, comparing the quality evaluation index Q with a first preset threshold value M and a second preset threshold value N to obtain a quality evaluation grade, feeding unqualified detection information back to a production line, and removing unqualified components.

The invention provides a power distribution network reliability cooperative control method and system under distributed power supply access, relates to the technical field of electric power, and designs a power distribution network reliability index system considering a distributed power supply based on a load demand uncertainty model, and adopts a Monte Carlo simulation method. Evaluating the reliability of the power distribution network in combination with the power distribution network reliability index system, and formulating a cooperative control strategy including distributed power supply output control, load demand response management and network topology reconstruction according to an evaluation result; a multi-target cooperative control optimization model with reliability improvement, economical efficiency optimization and environmental benefit maximization as targets is established, the multi-target cooperative control optimization model is solved by adopting an improved particle swarm algorithm, and an optimal cooperative control strategy is generated; and based on the optimal cooperative control strategy, issuing and executing a control instruction to each control object.

The invention relates to the technical field of business process compliance inspection systems, in particular to a compliance inspection system and method for an enterprise business process, and aims to solve the problems that in the prior art, an enterprise business process compliance inspection system generally adopts a static risk assessment model and lacks automatic and intelligent data acquisition and processing means, so that the enterprise business process compliance inspection system is inconvenient to use. And the accuracy and timeliness of risk assessment are insufficient. The system comprises a data acquisition module, a risk assessment module, a monitoring and early warning module, a map construction module, an intelligent recommendation module and a report generation module. According to the invention, through automatic data acquisition, preprocessing, risk assessment and monitoring early warning, the efficiency and accuracy of compliance inspection are significantly improved; by utilizing artificial intelligence and big data technologies, the system can quickly identify potential compliance risk points and perform quantitative evaluation, so that errors caused by manual intervention and subjective judgment are reduced, and the efficiency and accuracy of compliance inspection are improved.

The invention discloses a construction progress detection system and method based on three-dimensional model and field data fusion, and relates to the technical field of power grid construction engineering. The system comprises a data acquisition module, a data preprocessing module, a data matching module, a data fusion module, a progress detection module and a feedback module, the data matching module matches point cloud data with a panoramic image, a clonal selection algorithm is applied to optimize matching parameters, and on-site three-dimensional basic data is constructed; the data fusion module performs deep fusion on the BIM model and on-site three-dimensional basic data, and constructs a three-dimensional fusion model for construction progress detection by using a GeoBIM technology; and the progress detection module compares the real-time acquired panoramic image of the construction site with the three-dimensional fusion model frame by frame, and automatically detects the construction progress, so that efficient acquisition, accurate processing and real-time monitoring of construction site data are realized, and the efficiency and decision ability of construction management are remarkably improved through automatic progress detection and real-time feedback.

The invention provides a distributed system fault positioning diagnosis method and system based on log analysis, and relates to the technical field of fault diagnosis, and the method comprises the steps: collecting call data between micro-service nodes, constructing a service dependency graph, and calculating the influence weight of each node as a fault source; and modeling the service dependency relationship graph by using a graph convolutional neural network, and constructing a fault propagation prediction model. And when an abnormal node is detected, inputting the node into the prediction model, calculating a fault propagation path and an influence range, outputting an influenced service node list and an influence degree thereof, carrying out risk grade division and alarm, and meanwhile, visually displaying the fault propagation path. According to the method, the fault source can be quickly and accurately positioned, the fault propagation influence is predicted, and the reliability and stability of the distributed system are improved.

The invention provides an intelligent runoff pollution monitoring and response method and system, and the method comprises the steps: arranging a plurality of monitoring points in a runoff region, collecting water quality, hydrological and meteorological data in real time through a sensor, transmitting the data to a data processing center through a wireless network, carrying out the preprocessing, storage and analysis, and calculating the comprehensive pollution index of each monitoring point. And comparing with a set index threshold value. When the comprehensive pollution index exceeds a threshold value, the system identifies and evaluates key water quality parameters so as to determine a reasonable parameter threshold value; once data of a monitoring point exceeds a preset pollution threshold value, an alarm mechanism is triggered, then a pipe network water flow model and a geographic information system are combined to reversely track a pollution path, accurately position a pollution source and provide a basis for pollution control, and the process comprises the steps of screening suspected pollution source nodes, calculating a propagation path probability and preferentially checking a high-probability path. The traced pollution source or path probability is lower than a preset value.

The invention provides a space competition situation threat level evaluation method and system based on credibility weighting, and relates to the technical field of information, and the method comprises the steps: constructing a threat feature vector based on historical orbit data and an operation state, and obtaining an initial target threat probability vector through a deep learning model; then, a credibility weighting coefficient is calculated based on the multi-source situation awareness data, and an initial threat probability vector is corrected in combination with a feature weighting coefficient; and finally, classifying target threat levels by adopting an integrated learning method, determining a target priority in combination with the target orbit parameters and an ant colony algorithm, and outputting a threat target sequence and a situation threat assessment report. By fusing the multi-source situation awareness data and the credibility weighting mechanism, the threat level of the space target can be evaluated more accurately, and the reliability and efficiency of space situation awareness are effectively improved.

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 a chip overheating automatic protection control method and system, and relates to the technical field of chip protection, and the method comprises the steps: arranging distributed temperature sensors on a chip, obtaining the real-time temperature data of different regions in the chip, dynamically adjusting the data collection frequency and precision, and generating an optimized temperature data flow; and inputting the optimized temperature data stream into a pre-trained temperature anomaly detection model, detecting the temperature anomaly condition of each region in the chip in real time, outputting the temperature anomaly level of the abnormal region, and triggering thermal emergency processing of different intensities according to the temperature anomaly level. The temperature and power consumption information of the abnormal area is sent to the global manager; the method comprises the following steps: generating an initial chip protection strategy, predicting a future temperature change trend of a chip, identifying a potential high-temperature abnormal region, optimizing the initial chip protection strategy by combining a prediction result, generating an optimal chip protection strategy, and realizing chip overheating automatic protection control according to the optimal chip protection strategy.

The invention discloses a big data analysis-based early warning method for abnormal emission of pollution facilities. The method comprises the following steps of S1, collecting data and constructing a data set; s2, storing and preprocessing a data set; s3, constructing a space-time propagation network of the external events by using the graph neural network model; s4, modeling and predicting the time sequence features by using a mixed depth generation model; s5, analyzing the causal relationship between the external event and the facility emission behavior through a causal inference model; s6, adopting a Bayesian optimization algorithm based on uncertainty estimation of a strategy to dynamically adjust an emission early warning threshold value; s7, generating a new abnormal emission mode label by using a self-supervised learning model and a few-sample learning technology; and S8, adopting an interpretable artificial intelligence technology to generate an abnormal source tracing report, and providing coping suggestions and action schemes. According to the method, the early warning of the abnormal emission of the pollution facilities is realized by utilizing a cross-time-space event inference and self-supervised learning method.

The invention is suitable for the technical field of recruitment portraits, and provides a recruitment talent portrait generation method and system based on multi-dimensional information, and the method comprises the following steps: collecting the resume information of candidates; performing data cleaning and data conversion on the resume information; performing feature extraction, analyzing candidate online behavior data based on a natural language processing technology, and extracting behavior patterns and interest points; performing model training and evaluation based on historical resume data to obtain a talent portrait model; the resume information, the behavior modes and the interest points of the candidates are input into a talent portrait model, candidate portraits are obtained, and the candidate portraits comprise skill ability evaluation, occupational development potential and culture adaptation degree; and providing personalized vocational development suggestions and post matching recommendations according to the candidate portraits. According to the method, through feature extraction and machine learning model training, a detailed talent portrait is generated, the scientificity of recruitment decision making is improved, suitable candidates are rapidly identified, and vocational suggestions are provided for the candidates.

The invention discloses an urban blue-green space ecological benefit key influence factor identification method. Firstly, an urban blue-green space ecological benefit evaluation index set is constructed; secondly, various index data are calculated and standardized, and an urban blue-green space ecological benefit standardized data matrix is established; thirdly, determining subjective weights of the indexes by adopting an analytic hierarchy process, determining objective weights by adopting an entropy weight method, and obtaining weights of the indexes through combined weighting; and then, constructing an entropy weight-TOPSIS model to calculate a weighted urban blue-green space ecological benefit standardized data matrix, and completing urban blue-green space ecological benefit evaluation. Then, establishing an urban blue-green space ecological benefit influence factor set; and finally, based on an urban blue-green space ecological benefit evaluation result, identifying key influence factors by adopting a GWR-XGBoost model. According to the method, a multi-criterion decision analysis method, a geographical weighted regression model and a machine learning technology are combined, so that the key influence factors of the urban blue-green space ecological benefits can be quickly and accurately obtained.

The invention relates to the technical field of building engineering, in particular to a steel structure construction quality monitoring system based on an unmanned aerial vehicle and three-dimensional scanning, which comprises a data acquisition module, a data preprocessing module, a construction defect identification and risk assessment module and a quality assessment module, the data acquisition module acquires quality monitoring data of a construction site in real time; the data preprocessing module is used for preprocessing the acquired quality monitoring data; the construction defect identification and risk assessment module identifies steel structure defects in the construction process and assesses the severity of the steel structure defects; and the quality evaluation module performs comprehensive quality evaluation on the preprocessed quality monitoring data and provides corresponding repair suggestions. According to the method, key problem areas can be quickly identified, a construction resource allocation strategy is optimized, and comprehensive and accurate data support is provided for steel structure quality evaluation in a complex construction environment.

The invention discloses a network security active defense method and system for detecting abnormal network behaviors. The method comprises the following steps: S1, constructing a virtualized isolation network environment through a software defined network technology; s2, deploying intelligent dynamic camouflage nodes in the virtualized isolation environment; s3, acquiring a disguised node attack behavior, and identifying a network abnormal behavior through a deep learning technology; s4, extracting network abnormal behavior features, and identifying potential threats through a deep learning technology; s5, a defense mechanism is automatically triggered, a honeynet is started, and abnormal behaviors and potential threats are isolated; and S6, tracing an attack path, and carrying out source positioning. The network security active defense system comprises a network construction module, a deployment module, an identification module, a honeynet, an isolation module and a tracing module. The disguised nodes are deployed through the deployment module to detect abnormal network behaviors, active defense of network security is realized through the honeynet, the isolation module and the tracing module, and the network security is improved.

The invention relates to the technical field of workflow management, in particular to a factory dangerous chemical supervision system, which comprises an environmental data acquisition module for acquiring temperature, pressure and chemical substance concentration environmental parameters and machine operation state and employee behavior data based on a chemical production workshop, monitoring each parameter in real time by adopting a sensor network, and sending the monitored parameters to a database; data of each sensor are transmitted to a data center in a wired or wireless mode, and real-time environment data are obtained. According to the method, abnormal values and missing values are eliminated through data processing, and the reliability of an analysis result is ensured. And standardization processing and timestamp alignment are implemented, so that data of different sources are effectively fused, and accurate risk assessment is supported. The current parameters are compared with the historical data, so that the potential risk can be quickly identified, and accurate risk level assessment is provided. The real-time performance of monitoring is improved, the emergency response is optimized, and the probability of safety accidents is remarkably reduced.

The invention relates to an unmanned aerial vehicle public safety data management platform. The method comprises the following steps: performing foreground high-frequency component extraction on a regional monitoring image acquired by an unmanned aerial vehicle based on an image processing technology of deep learning, and performing multilevel feature extraction and feature gradient mask enhancement processing on the extracted regional foreground high-frequency object image; the method comprises the following steps: acquiring shallow-layer significant feature representation and deep-layer significant feature representation of a regional foreground high-frequency object, matching fusion features based on principal components of the shallow-layer significant feature representation and the deep-layer significant feature representation, intelligently identifying whether the high-frequency object is a human body object or not, and generating a corresponding personnel activity prompt signal. In this way, the human body target in the unmanned aerial vehicle monitoring image can be quickly and accurately recognized, and therefore the efficiency of public safety management and the timeliness of emergency response are improved.

The invention, which relates to the technical field of artificial intelligence and skill training examination, discloses a multi-crowd adaptive training evaluation system comprising the following components: a user data management module, an RAG module, an answer processing module, a score analysis module, an intensive training module and a question bank updating module. According to the method, comprehensive and accurate assessment of the user ability is realized by constructing a multi-crowd adaptive training assessment system, the system constructs the user ability portrait by collecting historical data and real-time performance of the user in multiple dimensions and utilizing advanced data processing and analysis technologies, and dynamically adjusts the training content and difficulty, so that the user ability assessment efficiency is improved. According to the method and the system, the ability level and the learning requirement of different users are adapted, the personalized training mode not only improves the participation degree and the learning efficiency of the users, but also helps the users to quickly and accurately identify the ability weakness of the users, and powerful support is provided for subsequent improvement.

The invention relates to the technical field of financial data processing, in particular to a data processing method and system based on financial information bills. The data processing method comprises the following steps: acquiring multi-modal financial information bill data; and performing bill modal decomposition on the multi-modal financial information bill data to generate financial information bill modal decomposition data. Bond information price income prediction needs a large amount of financial data, such as market interest rate, bond expiration date, face interest rate and the like, the data are similar to the data form of bill modal decomposition, and a more accurate model can be obtained through standardization and feature extraction of multi-modal data. Therefore, valuable input is provided for bond price and income prediction. According to the invention, through multi-modal data fusion, counterfeited feature comparison modeling, abnormal bill clustering analysis and dynamic risk assessment, the security and accuracy of financial bill identification are improved.

The invention discloses a PE pipe quality detection method and system based on a target image, and relates to the technical field of pipe quality detection. In order to solve the problems that multi-angle image data cannot be obtained, under the condition of complex surface textures or slight defects, false detection or missing detection possibly occurs, so that the detection accuracy is influenced, and the method cannot adapt to defect detection of various types of pipes. Through multi-angle image acquisition and preprocessing, the comprehensiveness and definition of image data are improved, false detection or missing detection caused by complex surface textures or slight defects is reduced, the adaptability of a detection system and extraction and matching of target features are enhanced, the key detection area of the PE pipe can be recognized more accurately, preset standards and key features can be compared, and the detection accuracy is improved. According to the method, the quality of the PE pipe is accurately evaluated, the detection accuracy and efficiency are improved, and an operator can quickly recognize problems through visual output and a detailed detection report.

The invention discloses a power grid anomaly detection and diagnosis method based on artificial intelligence and topological data analysis. The method comprises the steps of S1, forming power operation data; s2, constructing a topological network model of the power grid based on the physical structure of the power grid; s3, using a first generator to generate abnormal data in the simulation power grid based on the power operation data; s4, optimization abnormal data generation is carried out; s5, embedding the power operation data and topological structure features of the power grid into the anomaly detection model through a graph convolutional network; s6, power grid anomaly detection parameters are dynamically adjusted through the adaptive learning model, so that the anomaly detection model is continuously optimized along with the change of the power grid operation condition; and S7, based on the topological information and abnormal data generated by the generative adversarial network, combining topological data analysis, and positioning a fault node or an abnormal line in the power grid. According to the invention, the accuracy of fault prediction and positioning is improved.

The invention relates to the technical field of intelligent agriculture, and provides a negative pressure type fruit picking mechanical arm and a control system thereof.The negative pressure type fruit picking mechanical arm comprises a connecting chassis, a plurality of wheels are rotationally connected to the outer side wall of the connecting chassis, a storage groove is fixedly connected to the outer side wall of the top end of the connecting chassis, and a picking mechanism is arranged at the top end of the connecting chassis; the picking mechanism comprises a connecting frame which is mounted at the top end of the connecting chassis; by integrating an advanced mechanical arm and a visual recognition technology, the robot can accurately recognize and quickly pick crops, the picking efficiency is greatly improved, the robot is particularly suitable for large-scale agricultural production, the crop quality is improved, crop maturity judgment is conducted through deep learning models such as YOLOv5, immature crops are prevented from being picked by mistake, and the crop quality is improved. The quality of picked crops is guaranteed, the market value of agricultural products is improved, the labor cost is reduced, full-process automatic operation is achieved, the labor demand is reduced, the labor cost of agricultural production is reduced, and meanwhile the labor intensity of farmers is relieved.

The invention discloses an intelligent operation and maintenance network fault monitoring method, system and device and a medium, a communication network architecture is visualized through full-link topology reduction and real-time topology drawing, monitoring and maintenance are facilitated, and problems can be found and solved in time. Through combination of GIS and OTDR technologies, a communication optical cable fault point can be accurately positioned. The communication network architecture is visualized through full-link topology reduction and real-time topology drawing, monitoring and maintenance are facilitated, and problems can be found and solved in time; fault data in communication data can be quickly identified through real-time difference monitoring of communication data flow characteristics; by tracing the identified fault data and predicting the propagation path of the fault, multi-dimensional fault reason analysis and prediction can be carried out, the fault detection, positioning, tracing and prediction capabilities of the communication network are improved, and the intelligent operation and maintenance capability of the network is enhanced, so that the reliability of the network and the continuity of services are improved.

The invention provides a fumigation control method based on pest and disease damage trend prediction, and the method comprises the steps: carrying out the spectrum analysis of audio data information through employing a fast Fourier transform algorithm, extracting the frequency domain feature vector of the chirp sound of each pest species, and enabling the frequency domain feature vector to comprise a fundamental frequency, harmonic energy distribution, chirp duration and chirp rhythm; according to the extracted chirp frequency domain feature vector, chirps of local main agricultural pests are identified and classified by adopting a support vector machine algorithm, and the types and life cycle information of the pests are judged by matching with a preset pest species chirp feature template library; and performing spatial mapping on the predicted population density distribution data by adopting a Kriging interpolation algorithm to generate a spatial distribution diagram, determining the aggregation degree and diffusion trend of pests in each region of the field, and dividing a plurality of prevention and control regions and buffer regions.

The invention discloses a tunnel crack detection method and a tunnel crack detection system, and belongs to the field of tunnel safety detection.The tunnel crack detection method comprises the steps that a tunnel inspection robot is utilized to conduct inspection in a tunnel to obtain complete three-dimensional data in the tunnel so as to construct a three-dimensional map in the tunnel, and the position of each crack area in the three-dimensional map is determined; determining an inspection tour area corresponding to each crack area and executing path planning, and generating an optimal inspection tour path of the unmanned aerial vehicle; the optimal inspection path enables the unmanned aerial vehicle to sequentially pass through each inspection area and then leave the tunnel after entering the tunnel under the condition that obstacle avoidance conditions are met, and the total time is shortest; enabling the unmanned aerial vehicle to move according to the optimal inspection path, and scanning the crack region by using the depth camera when the unmanned aerial vehicle moves to one inspection region so as to obtain fine three-dimensional data of the crack region; and performing analysis processing on the fine three-dimensional data of the crack area to complete tunnel crack detection. According to the invention, the efficiency and precision of long tunnel crack detection can be improved.

The invention discloses a drainage pipe network pollution traceability and alarm system based on invariant analysis, which belongs to the field of data communication of a data center and comprises a data collection module, a cloud platform processing module, an invariant analysis module, an abnormal emission identification and alarm module and a decision support system. The supervision efficiency of the drainage pipe network is improved, environmental pollution is reduced, and sustainable utilization of water resources is guaranteed; according to the system, various sensors are installed at key nodes of a drainage pipe network, data such as water quality, water level, water volume and flow velocity are collected in real time, and the data are transmitted to a cloud platform through a wireless communication technology; by utilizing the data processing capability of the cloud computing platform and combining with an invariant analysis algorithm, the system can quickly position a pollution source, track a pollution path and give an alarm to related personnel in time; the monitoring, management and pollution prevention and control capabilities of the drainage pipe network are effectively improved, and powerful technical support is provided for urban water resource management and environmental protection.

The invention discloses a multi-mode SLAM method, system and device, a medium and a program product, and the method comprises the steps: segmenting a current image frame through employing a lightweight semantic segmentation network, obtaining a semantic mask of the current image frame, carrying out the correction, and projecting the corrected semantic mask to a three-dimensional point cloud, and obtaining a point cloud image of the current image frame under a radar coordinate system; performing point cloud segmentation to obtain a plurality of point cloud sets with different semantic classification tags; predicting a saliency mask of the current image frame, extracting visual feature points of the current image frame, and obtaining depth values of the visual feature points; and finally, tracking the visual feature points and constructing a local map. And meanwhile, a Scan-Context descriptor and a similar distance are utilized to carry out loopback detection and global BA on the point cloud set, and a global map is constructed. According to the invention, various sensor data are comprehensively utilized, the perception capability of environment characteristics is improved, and the problems of insufficient perception and reduced positioning precision in a complex environment are effectively solved.

The invention discloses a fault processing method and device for a Bluetooth module of an electric energy meter and the electric energy meter. The fault processing method comprises the following steps: S1, acquiring working parameters of the Bluetooth module; s2, analyzing the acquired working parameters of the Bluetooth module through a preset fault detection algorithm; s3, when a fault of the Bluetooth module is detected, generating a fault diagnosis report; s4, based on the fault type, automatically taking corresponding processing measures; s5, carrying out self-inspection on the Bluetooth module, and judging whether the fault is repaired or not; s6, if the fault is not repaired, continuing to execute corresponding processing measures until the fault is repaired or preset processing times are reached; s7, synchronizing data and results in the fault processing process to a data management platform; and S8, displaying the operation state and the fault information of the Bluetooth module on a user terminal interface. According to the invention, the central processing unit receives and analyzes the sensor data, so that the fault type of the Bluetooth module can be quickly and accurately identified, and the real-time performance and the accuracy of fault detection are remarkably improved.

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 discloses a structure surface layer loss state prediction method and system based on dam engineering, belongs to the technical field of dam leakage prediction, and aims to solve the problems that a hidden danger area on the surface of a structural surface of a long-shoreline dam in an existing flowing water area is not accurately identified; and the anti-fatigue damage degree of the structure is difficult to predict for the potential safety hazards. The method comprises the following steps: performing core node deployment scanning under multiple points on a risk area in a dam to obtain a comprehensive structure surface image; performing structural surface hidden danger feature recognition and extraction processing of a related pixel point matrix on the initial risk hidden danger image, and determining a structural surface hidden danger image; space-time fusion processing of multi-source external hidden danger factors is carried out on the corresponding dam sections, and external environment hidden danger features are determined; determining a hidden danger growth correlation degree between the structure surface hidden danger feature and the external environment hidden danger feature; and performing damage growth prediction on the hidden danger area of the structural surface layer of the dam, and generating a maintenance inspection scheme of the dam.