944results about How to "Reduce misjudgment" patented technology

The invention discloses a dam safety monitoring system and method based on digital twinning, and particularly relates to the technical field of digital twinning. The method comprises the following steps: acquiring stress, deformation, displacement, inclination and environmental change data of a dam structure in real time, regularly carrying out high-precision calibration on a sensor, recording a time drift error, analyzing nonlinear sensitivity of a digital twin model under an extreme working condition, simulating the influence of the sensor error and the model sensitivity on dam evaluation, predicting a deviation degree, and calculating the evaluation accuracy of the dam. Based on a prediction result, a dynamic drift compensation algorithm is adopted to correct sensor data in real time, the sensitivity of the model is dynamically adjusted, the optimized data and model are integrated, the structural state and potential risk of the dam under normal and extreme working conditions are accurately evaluated, accurate early warning information and decision scheme are generated, and the accuracy of early warning is improved. The accuracy and reliability of dam safety monitoring and risk assessment are remarkably improved, decision delay or resource waste caused by misjudgment or missed judgment is reduced, and the dam safety guarantee capacity is improved.

The invention discloses a stationary pollution source data acquisition and calibration method and system, a medium and a program product, and the method comprises the steps: carrying out the segmentation processing of a plurality of concentration change data, and obtaining a plurality of data segments; determining the concentration change trend of various pollutants, and calculating a correlation coefficient matrix; the Euclidean distance value between the correlation coefficient matrix of the current data segment and each correlation coefficient matrix before sorting is calculated; when the Euclidean distance is greater than a preset threshold value, marking the corresponding data segment as an abnormal data segment; determining the pollutant as a target pollutant, and establishing a material balance equation; based on the material balance equation, generating a theoretical concentration change curve of the target pollutant; calculating a deviation value, and when the deviation value exceeds a preset dynamic error limit value, determining a sensor correction coefficient according to the process parameters; and carrying out calibration processing on the acquired data of the target pollutant according to the sensor correction coefficient. According to the invention, the accuracy of data acquisition and calibration of the stationary pollution source is improved, so that the influence on formulation and execution of pollution prevention and control measures is reduced.

The invention discloses a thermal power plant equipment management method and system based on data fusion, and relates to the technical field of equipment management, and the method comprises the steps: collecting the real-time data of equipment through a sensor, and evaluating the health state of the equipment through a data fusion algorithm; based on the real-time operation data of the equipment, the intelligent rule engine dynamically adjusts the evaluation standard; predicting a potential fault and generating a maintenance priority through an intelligent fault prediction algorithm by using historical data and a health state evaluation result; and according to fault prediction and an equipment state, scheduling maintenance resources are optimized, and timely maintenance and repair of key equipment are ensured. According to the thermal power plant equipment management method based on data fusion provided by the invention, comprehensive monitoring of the equipment operation state is realized, and the accuracy and comprehensiveness of data are remarkably improved by combining a multi-stage data fusion technology based on dynamic weighted average and principal component analysis.

The invention relates to the technical field of electric line fault detection, and discloses a power transmission line fault early warning system and method based on big data driving, and the system comprises a state prediction module, a risk assessment module, an early warning decision module and a management execution module. The state prediction module constructs a prediction model by using historical operation data, environmental conditions and line parameters, and outputs future state indexes; the risk assessment module comprehensively considers multi-source information such as real-time operation data and historical maintenance records, and assesses a current fault risk index; the early warning decision-making module models the management process into a Markov decision-making process, and carries out early warning decision-making based on the output of the two modules; and the management execution module is responsible for managing and executing the decision result in real time. By comprehensively utilizing the big data technology, the accuracy and the real-time performance of power transmission line fault early warning are improved, the defects of a traditional fault management mode are overcome, and a powerful guarantee is provided for safe and stable operation of a power system.

The invention discloses a terrain-level real-scene three-dimensional entity and element dynamic updating method, which comprises the following steps: S1, data acquisition and preprocessing: acquiring data of a target area by an acquisition mechanism based on entity and element type priorities, and preprocessing the acquired data to obtain model data of the target area; s2, change detection: comparing newly collected model data with historical model data, and identifying a change region and type; s3, entities and elements are extracted and updated, wherein the entities and the elements of the change area are processed to obtain updated models; s4, model fusion and optimization: fusing the updated entity and element model with the original model, and realizing efficient acquisition and preprocessing of target area data by using the multi-source surveying and mapping equipment and an acquisition mechanism based on entity and element type priorities; by dividing the small regions according to the region characteristics and correcting the acquisition frequency, the resource allocation is optimized, and the pertinence and efficiency of data acquisition are improved.

The invention discloses a saliency target detection method for an intelligent auxiliary driving system, and the method comprises the following steps: 1, obtaining a driving scene image based on a driver visual region in real time through a vehicle-mounted image collection device, and carrying out the preprocessing of the image; step 2, performing bottom-layer multi-scale feature extraction, interaction and global enhancement of feature information on the backbone network, and extracting rich multi-scale feature information; step 3, the neck network comprises an AESwinTransform attention module, and a cross-layer feature guide fusion module and a multi-level feature interaction fusion module in the TFG network, and the multi-level features extracted from the backbone network are fused together; step 4, detecting and classifying the feature map; and 5, inputting the adversarial sample and the normal sample into the model in a mixed manner, and performing adversarial training by using the trained model. According to the method, the detection precision of the small saliency target can be improved, the generalization ability and robustness of the model in a complex environment are improved, and the interference of irrelevant scene information is reduced.

The invention relates to the field of electric meter box monitoring, and discloses an anti-electricity-stealing method and system for an intelligent electric meter box, which is used for solving the problem of false alarm caused by harmonic interference when anti-electricity-stealing early warning is carried out, and comprises the following steps: collecting time domain signals of current and voltage in the intelligent electric meter box in a historical detection time period; converting the time domain signal into a frequency domain signal, extracting harmonic information of each harmonic, and establishing a normal harmonic feature model to obtain a target harmonic range; the method comprises the following steps: acquiring time domain signals of current and voltage in an intelligent electric meter box in real time, extracting harmonic information, performing filtering processing according to a target harmonic range, performing first abnormality judgment by using an abnormality detection algorithm, performing multi-parameter joint evaluation if abnormality is judged for the first time, and performing second abnormality judgment according to a multi-parameter joint evaluation result. And electricity larceny early warning is carried out according to the second abnormal judgment result, so that false alarm and missing alarm are effectively reduced, and the adaptability of the system to a complex electricity larceny mode is remarkably enhanced.

The invention discloses an efficient auditing method and system based on an edge computing technology, and relates to the technical field of auditing methods and systems.The efficient auditing method comprises the following steps that S1, data collection and preprocessing are conducted, specifically, business data are collected in real time through various sensors and interfaces at edge nodes, and preprocessing is conducted; s2, edge side data analysis: performing real-time analysis on the preprocessed data by utilizing edge computing resources; s3, feature extraction and model training; s4, constructing a risk assessment and early warning model based on edge analysis and a model training result; s5, edge and cloud collaborative auditing; the edge node compresses and encrypts the processing result and the key feature data and then uploads the data to the cloud; and S6, visualizing an audit result and generating a report. According to the method, data acquisition and preprocessing, analysis and model training are realized through edge calculation, the auditing efficiency and accuracy are improved, a risk assessment and early warning model is constructed, the edge and the cloud are coordinated, and comprehensive, timely and reliable auditing support is provided for enterprises.

The invention relates to the technical field of silt dam monitoring, and discloses a silt dam monitoring information sensing integration method and system, and the method comprises the steps: collecting the monitoring data of a silt dam, carrying out the short-time risk prediction at an edge node according to the monitoring data, triggering local early warning based on the short-time risk prediction, and starting a response mechanism; a self-adaptive cooperation mechanism is established between edge nodes, and sensor abnormity and local disaster chains are identified through exchange and comparison of monitoring data between the nodes; and the cloud integrates the monitoring data uploaded by the edge nodes, generates a global risk prediction model through dynamic data fitting, and performs hierarchical emergency response according to a risk prediction result. According to the method, the safety management efficiency of the silt dam is improved, and a scientific support and an intelligent decision foundation are provided for flood prevention and fighting.

The invention provides a photovoltaic power station modeling simulation analysis system, and relates to the field of power station modeling simulation. Comprising the steps that a parameter acquisition module collects key data such as voltage, current, temperature and illumination intensity in real time through a sensor and detects the integrity of the data in combination with historical data; the modeling module adopts a segmented modeling mode according to the collected data and the physical topology of the power station, and the transient processes of different stages are respectively ensured, so that the simulation precision is ensured; the simulation module inputs different disturbance parameters based on the model, simulates load and power grid fluctuation through a disturbance generator, analyzes transient response of the system and corrects the model in real time; the multi-scene generation unit is used for automatically generating a simulation scene in an extreme environment by using historical weather and power grid data, and testing the stability of a power station; and the evaluation module generates an evaluation report by analyzing the simulation result, identifies equipment faults, proposes specific suggestions for optimizing operation, and ensures the performance and stability of the power station under complex operation conditions.

The invention relates to the technical field of mold management, in particular to a mold abnormity management system and method based on machine vision, and the system comprises a mold abnormity detection module, a fault prediction and health management module, an intelligent repair and maintenance scheduling module, and a data analysis and visualization module. The mold anomaly detection module collects data by using a high-definition camera and a thermal infrared imager, and identifies surface defects through a convolutional neural network; the fault prediction and health management module predicts a fault risk and performs early warning by means of time sequence analysis and a machine learning technology; the intelligent repair and maintenance scheduling module optimizes a repair strategy and task allocation by using deep reinforcement learning and a genetic algorithm; and the data analysis and visualization module displays data by means of Grafana and Kibana and realizes intelligent alarm and decision support. According to the invention, accurate detection of mold abnormity, effective prediction of faults, optimization of maintenance scheduling and efficient processing and visual display of data are realized, and the intelligent level of mold management is improved.

The invention belongs to the field of fault diagnosis, and particularly relates to a main shaft bending common characteristic diagnosis system based on a stacking integration method, and the system comprises the following modules: a sensor module which is used for collecting a vibration signal of a main shaft in real time; the data acquisition and processing module is used for converting an analog signal into a digital signal and preprocessing the signal acquired by the sensor; the feature extraction module is used for extracting features from the preprocessed vibration signals; the machine learning diagnosis module analyzes and classifies the extracted features by using stack integration based on a plurality of trained machine learning models, and judges the health state of the main shaft; and the output module is used for outputting the diagnosis result and sending out an alarm signal when abnormality is detected. Real-time monitoring and automatic diagnosis of the main shaft bending fault are achieved, compared with a traditional method depending on expert experience and manual detection, the fault detection efficiency and accuracy can be greatly improved, and misjudgment and missed judgment risks caused by human factors are reduced.

The invention discloses a robot grinding real-time defect detection and self-adaptive correction system based on deep learning. The problem that in the prior art, real-time detection, defect correction and accurate path optimization are difficult to achieve in the grinding process is solved. The image, the three-dimensional point cloud and the moment data are fused through a multi-modal sensing system, and data fusion processing is performed by adopting a time synchronization mechanism, so that high-precision defect detection is realized. And the self-adaptive correction module dynamically adjusts the pressure, the speed and the angle of the grinding tool according to real-time feedback by using a reinforcement learning algorithm, so that accurate grinding for different workpiece surfaces is ensured. The system is further combined with closed-loop feedback control, the grinding path is continuously optimized, and the consistency and stability of the grinding quality are improved. Compared with the prior art, efficient and automatic defect detection and self-adaptive correction can be achieved on the complex and irregular workpiece surface, and the production efficiency and the surface treatment quality are remarkably improved.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a video analysis processing system and method based on a multi-modal large model, and the method comprises the steps: enabling the multi-modal control large model to receive a task which is input by a user and needs to be completed through natural language description, carrying out the semantic analysis of the input of the user, extracting task information, and carrying out the analysis of the task information; a task related to video understanding is decomposed into sub-tasks and the sub-tasks are sent to a video understanding module; the video understanding module receives a video or a video clip required by a task, executes a subtask to analyze the video, and generates embedding and description of video content to be input into a multi-modal control large model; when the multi-modal control large model receives a query request of a user, a query task of the user is analyzed into executable subtasks and mapped to a video understanding task, and a response result based on the query request is generated based on embedding and description of video content generated by the video understanding large model. Therefore, effective fusion and mutual enhancement of cross-modal information are realized, and the video understanding precision is improved.

The invention discloses a mine tunnel operation period deformation monitoring method and system, and relates to the technical field of mine tunnel monitoring, and the method comprises the following steps: carrying out the real-time monitoring of the deformation condition in a mine tunnel through a sensor device, and transmitting the collected monitoring data to a data processing center through a wireless transmission module. Real-time monitoring of mine tunnel deformation is achieved through the multiple sensors and the wireless transmission technology, the problem of data lag is solved, and monitoring accuracy and timeliness are improved. And in combination with a multi-objective optimization algorithm, the system automatically adjusts key parameters such as a supporting structure and a mining sequence according to real-time data, so that an optimal safety control scheme is ensured. According to the system, the operation efficiency and safety are improved, manual misjudgment is reduced, and the control strategy can be continuously optimized according to the operation stage.

The invention relates to the technical field of fault detection for a wireless communication network, and discloses a fault detection method for the wireless communication network. By collecting data of base station signal strength, network delay, data packet loss rate and user equipment signal quality, the problem of insufficient fault detection caused by a single index is solved. Through multi-dimensional data acquisition, the system can comprehensively evaluate network performance, the fault detection accuracy is improved, and different fault types can be effectively identified especially in a complex fault mode. By storing the data according to the time sequence, the problem of time sequence chaos is avoided, and accurate detection of time-varying faults is ensured. The comprehensive data vector generated by each time of sampling comprises a plurality of network performance indexes, so that the fault detection and positioning capability is improved. Abnormal values are eliminated through data preprocessing, the data quality is ensured, an accurate basis is provided for subsequent analysis, and the reliability of a detection result is ensured.

The invention discloses an intelligent driving environment sensing method and system based on multi-modal data fusion, and relates to the technical field of automatic driving, and the method comprises the steps: firstly obtaining the position information of a vehicle through a GPS, and starting an auxiliary positioning mechanism if the GPS signal is weak; multi-modal data acquisition and preprocessing: after an auxiliary positioning mechanism is started, a vehicle acquires surrounding environment information through an RGB camera, a laser radar and an IMU sensor; performing feature extraction on the RGB image by using an improved deep convolutional neural network, and generating a high-precision 3D bounding box through a region suggestion network; and multi-modal information is fused through a constructed efficient image reasoning framework. Based on multi-modal data fusion and an environment perception result, the method not only can carry out accurate target identification on the surrounding environment in the automatic driving process, but also can carry out global and local path planning.

The invention discloses a tree lodging detection method and system combined with reference object image analysis. The method comprises the steps that based on an initial image shot by a low-altitude camera, key point positions are determined through a tree and reference object key point detection model, two straight lines are formed through fitting, and the current weather condition is judged. And if the weather is not the extreme weather, evaluating the state of the tree through the two fitting straight lines, and outputting the image and position information of tree inclination or lodging. If the weather is extreme weather, further judging whether the tree is submerged by accumulated water or not. If not, monitoring the change of the included angle between the fitting straight lines to detect whether the tree locates or not; if so, outputting the lodging position and direction. And if the tree is submerged by the accumulated water, calculating the height of the submerged part by adopting a reference object and accumulated water relation model, updating the key points of the tree, finally determining the state of the tree by comparing the fitting straight line, and outputting a corresponding evaluation result. By implementing the method provided by the invention, the precision and reliability of tree lodging monitoring can be improved.

The invention discloses a safety early warning and alarming system based on a subway underground excavation construction environment, and relates to the technical field of early warning and alarming, the safety early warning and alarming system comprises a safety early warning platform, the safety early warning platform is in communication connection with a data acquisition module, a data transmission module, a data analysis module, an early warning analysis module, an alarm notification module and a visual report module, all the modules are connected through electric signals. By monitoring state information of key parts, performing data processing and analysis in time and capturing subtle changes which easily generate potential safety hazards in time, potential potential safety hazards can be quickly found, and once monitoring data exceed a preset alarm threshold value, the system can immediately trigger an alarm mechanism to give an alarm. Alarm information is sent to related personnel in various modes such as short messages, mails or system notifications, so that risk response time is greatly shortened, decision time is provided for managers, and occurrence risks of safety accidents are effectively reduced.

The invention relates to the technical field of data storage, and discloses a lossless compression algorithm-based data storage method, which comprises the following steps of: receiving to-be-stored data through a data interface, automatically identifying a data format by using a data analysis tool, and performing feature analysis to obtain the size, the type and the complexity of the data; through deep analysis of data features, the method can flexibly select a proper Huffman coding processing mode, and optimizes a compression process according to different data types and complexity. In addition, a context modeling optimization mechanism is introduced, the coding mode is dynamically adjusted, and the compression ratio and the storage efficiency are further improved; efficient storage and reliability verification of data are ensured through a block storage strategy in combination with metadata management; the problem that an existing compression algorithm is low in efficiency is solved, the integrity and reliability of data storage are improved, and the method is suitable for storage requirements of large-scale data and diversified data.

The invention discloses a steel plate butt weld pseudo defect ultrasonic detection method, system and device and a medium, and relates to the technical field of weld detection, the method comprises the following steps: setting a plurality of detection areas on a steel plate butt weld based on acquired structure parameters and surface characteristic data of a to-be-detected steel plate; performing multi-frequency and multi-angle ultrasonic scanning according to a preset first detection parameter by taking each detection area as a basic unit to obtain corresponding echo signal data; analyzing and identifying potential pseudo-defect signals, and calculating corresponding pseudo-defect characteristic values according to the pseudo-defect signals; taking each detection area as a basic unit, performing pseudo defect feature analysis according to the second detection parameters, and determining reliability evaluation data of pseudo defects; based on the pseudo-defect reliability evaluation data and the pseudo-defect characteristic value corresponding to each detection area, the pseudo-defect condition of the steel plate butt weld is analyzed, and a final weld quality comprehensive evaluation result is generated. According to the invention, the detection accuracy of steel plate butt weld detection is improved.

The invention relates to the field of chronic disease early warning, and discloses a chronic disease early warning intervention system based on artificial intelligence, and the system comprises a data collection module which is used for collecting multi-modal data, and the multi-modal data comprises gene detection data, proteomics data, microbiomics data, environment data and social economic data; the pre-processing module is used for pre-processing the multi-modal data to obtain basic data, and the pre-processing comprises noise removal, abnormal value and missing value processing and targeted data cleaning and feature extraction operation on the data of different modals; and a conversion fusion module. Through cooperative operation of all the modules, a complete and automatic chronic disease early warning intervention process is formed, an organic whole is formed from data acquisition, risk assessment and intervention measure formulation and implementation, and the chronic disease risk whole process management and control are achieved, so that the chronic disease prevention and treatment efficiency and effect are improved.

The present invention discloses a laser positioning method and a robot based on image information. The execution subject of the laser positioning method is a robot equipped with a structured light module. The structured light module includes a line laser transmitter and a camera without an infrared filter, so that the imaging information of infrared light and the imaging information of visible light are retained in the image captured by the camera. The laser positioning method includes the following steps: the robot controls the camera to capture an image of light reflected back from the surface of an object to be measured by the line laser emitted by the line laser transmitter, and detects the brightness and darkness type of the image captured by the camera. When the robot detects that the current frame image captured by the camera is a bright frame image, the robot searches for the position of the line laser from the current frame image by executing an inter-frame tracking algorithm. When the robot detects that the current frame image captured by the camera is a dark frame image, the robot extracts the position of the line laser from the current frame image by executing a brightness centroid algorithm.

The invention relates to the technical field of security door locks, in particular to an intelligent door lock management system which comprises a data acquisition module, an extraction module, a first determination module, a second determination module, a judgment module, an identification module, an adjustment module, a correction module and an alarm module. Through multi-level identity verification and abnormal behavior detection, the safety of the door lock is improved, the key pressure, the sliding track and the face image are acquired in real time through the data acquisition module, the face features and the shielding rate are extracted in combination with the extraction module, preliminary recognition of the target identity is achieved, and the safety of the door lock is improved. The key pressure and track consistency degree is calculated through a second determination module, whether an abnormal door opening mode exists or not is judged, a judgment module integrates the abnormal mode and the shielding rate and confirms the target identity and permission, and a recognition module recognizes the permission value according to the permission library and conducts dynamic adjustment. The problem that the prevention effect on complex security threats is low due to the fact that recognition depends on single password input is effectively solved.

The invention relates to the technical field of data analysis, and discloses a laboratory data analysis method and system based on a machine learning technology. The method comprises the following steps: firstly, introducing an adaptive threshold, and identifying and deleting abnormal data in original laboratory data by using an improved isolated forest algorithm to obtain a processed laboratory data set; secondly, improving the generative adversarial network to obtain an improved generative adversarial network filling model, and performing data filling on the processed laboratory data set for multiple times to obtain a processed laboratory data set; according to errors of experimental result data, an improved black-wing optimization algorithm is used for searching for the minimum error, and optimization of laboratory data is achieved; and finally, establishing a laboratory database, performing data storage on the processed laboratory data set, and completing laboratory data analysis. By analyzing and processing the laboratory data, the purpose of analyzing the laboratory data is achieved, and the method is accurate and objective.

The invention discloses an intelligent security monitoring and early warning method and system for an industrial control network, relates to the technical field of network security monitoring, and can collect multi-dimensional data such as network traffic, user behaviors and equipment states in real time through a data acquisition module and a system to ensure the timeliness and comprehensiveness of monitoring. Therefore, potential security threats can be quickly identified, and the overall security protection capability is improved. And the data preprocessing module cleans and standardizes the collected data, so that the data quality and consistency are improved, a reliable basis is provided for subsequent risk assessment, and misjudgment caused by data inconsistency is reduced. And the risk assessment module calculates a comprehensive risk index through a multi-dimensional assessment formula, different types of security threats can be identified more accurately, and a scientific basis is provided for a subsequent early warning mechanism. The multi-level early warning mechanism module divides the risk into a low level, a middle level and a high level according to the comprehensive risk index, and a targeted response strategy can be realized.

The invention relates to the technical field of industrial intelligent manufacturing, in particular to a flexible production line appearance defect optical detection method. Comprising the steps of performing image acquisition on semi-finished products and finished products on a production line; carrying out preprocessing and spatial transformation on the collected image; performing feature extraction and clustering analysis on the positive sample image of the to-be-detected product by using unsupervised learning to form a feature library; comparing the feature vector of the to-be-detected image with a feature library, and outputting the defect position and type of the sample; an improved RTDETR model with supervised learning is adopted, a sample judged as a defect is used for training, and the improved RTDETR model adopts a ResNet and Visual Transform network structure; and performing real-time detection and result feedback on the deployment model. According to the technical scheme, the appearance defect detection efficiency and the product quality of products manufactured on the flexible production line can be improved.

The invention discloses an ocean internal wave monitoring method based on deep learning, which belongs to the technical field of ocean internal wave monitoring, is used for ocean internal wave monitoring, and comprises the following steps: acquiring a flow velocity data set of an observed sea area, taking the flow velocity data set as an original data set, performing quality control on the original data set, and performing normal sequence and abnormal sequence distinguishing on the data after quality control. Dividing a training set, a verification set and a test set; constructing an ocean internal wave anomaly monitoring model, optimizing anomaly scores and reconstructing error parameters, monitoring flow velocity observation data of the sea area by using the trained ocean internal wave anomaly monitoring model, and identifying an ocean internal wave sequence; and performing internal wave mechanical characteristic analysis on the ocean internal wave sequence extracted by the ocean internal wave anomaly monitoring model, and outputting characteristics. According to the method, the behavior pattern of the normal flow velocity can be accurately captured by adopting the deep learning model, the anomaly detection of the internal wave flow is realized through the reconstruction error, the normal flow velocity and the internal wave flow velocity are effectively distinguished, and misjudgment and missed judgment caused by a traditional method are reduced.

The invention relates to the technical field of behavior recognition, in particular to a construction site safety behavior recognition method based on deep learning, which comprises the steps of collecting environmental data of a construction site in real time, preprocessing the environmental data to improve data quality and diversity, extracting feature vectors, recognizing specific behaviors and identities of construction site personnel, and predicting personnel behaviors. Potential dangerous behaviors are identified, multi-level risk assessment is carried out according to risk levels, after high-risk behaviors are identified, the system not only triggers an alarm, but also automatically takes intervention measures, and through linkage with construction site mechanical equipment, operation of dangerous equipment is automatically stopped so as to prevent accident expansion. According to the invention, through multi-modal data acquisition and fusion processing, the accuracy of behavior identification is improved, refined multi-level risk management is realized, the system can rapidly trigger alarm and automatically take intervention measures, and the emergency processing efficiency and the overall safety level of a construction site are significantly improved.

The invention relates to the technical field of breach image processing, in particular to a breach rapid situation awareness method and system based on multi-source data. The method comprises the following steps: acquiring dam monitoring station data; performing hydrological sensor network setting on the dam monitoring station data to obtain dam hydrological sensor network data; performing ultrasonic water level change trend analysis on the dam hydrological sensor network data to generate an ultrasonic water level change trend chart; performing breach node sensing on the dam hydrological sensor network data according to the ultrasonic water level change trend chart to generate breach node data and breach prediction node data; and according to the breach node data, macroscopic disaster risk analysis is carried out on the breach area, and a breach area macroscopic disaster risk schematic diagram is generated. According to the invention, through real-time data integration, comprehensive risk assessment, unmanned aerial vehicle monitoring, dynamic prediction model and multi-party cooperative response, the accuracy and response speed of breach rapid situation awareness are improved.