1719 results about "Data identification" patented technology

A computer-implemented method includes receiving an image of an article of interest to be evaluated relative to one or more features of interest, identifying a reference feature of a known size in the received image, identifying two or more extremities of the reference feature in the received image and a number of pixels between the two or more extremities of the reference feature, calculating a pixel size for the selected image based on the reference feature size and the number of pixels between the two or more extremities of the reference feature, annotating the received image to include one or more tolerance lines for the one or more features of interest, and determining whether the one or more features of interest in the image comply with the one or more tolerance lines. A computer program product and computer system corresponding to the method are also disclosed.

The invention relates to a multi-source sea area data intelligent supervision method, system and device and a storage medium, and the method comprises the steps: obtaining sea area geographic space data and historical sea area supervision data of a target sea area, and carrying out the monitoring construction, and obtaining an island monitoring grid; acquiring multi-source monitoring data of the target sea area according to the island monitoring grid, and performing grid data identification to obtain a global pattern spot dynamic data set; extracting a suspected specific target area coordinate of the global pattern spot dynamic data set and calculating a coordinate specific target confidence coefficient; and carrying out range comparison on the suspected specific target area coordinates, and carrying out behavior evaluation in combination with the coordinate specific target confidence to obtain a sea area specific target early warning report.

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 technical field of slope stability data identification, and particularly provides a multi-sensor fusion slope stability intelligent analysis system and an edge calculation method.The system comprises an environment sensing subsystem, a data processing subsystem and an edge calculation subsystem, the environment sensing subsystem monitors the change of the soil moisture content through a soil humidity sensor, and micro movement data of a slope are obtained through a displacement sensor; using a pressure sensor to sense the change of the ground pressure; meanwhile, pre-screening out abnormal data to form a fused data set; the data analysis subsystem is used for converting abnormal data of the fusion data set into a quantitative index of slope stability, and deducing a potential sliding risk of the slope by analyzing the relevance of the soil water content, the tiny movement data or the ground pressure; and the decision support subsystem provides specific reinforcement countermeasures according to the analysis result of the potential sliding risk. According to the method, the potential sliding risk of the side slope is comprehensively deduced by analyzing the relevance of the soil water content, the tiny movement data and the ground pressure.

The invention provides a sensitive data identification protection method and system based on intelligent matching, and relates to the technical field of data processing, and the method comprises the steps: carrying out the semantic analysis of sensitive data through a natural language processing model, and constructing a multi-level knowledge graph; performing node coding and relation intensity calculation by using a graph neural network and an attention mechanism; a graph structure importance degree evaluation algorithm is adopted to calculate node sensitivity weights and grade the node sensitivity weights; and applying a corresponding data protection scheme according to the sensitivity level, and identifying associated nodes through a graph traversal algorithm to perform hierarchical protection. According to the method, intelligent identification, accurate grading and comprehensive protection of sensitive data are realized, and the data security is improved.

The invention discloses a medical form data identification method and system based on OCR and MLLM, and the method comprises the steps: receiving original image data, and generating an optimized image sequence through adaptive contrast enhancement, regional importance weighting and multi-resolution optimization; feature extraction is carried out based on the optimized image sequence, a text-space attention map is constructed, and text recognition is realized; performing visual coding and text coding on the optimized image sequence and the text recognition result, and obtaining uniform feature representation through feature alignment; constructing an information level diagram based on unified feature representation, performing relation reasoning, and combining professional knowledge verification to obtain structured feature information; and performing data verification on the structured feature information to obtain normalized data. According to the invention, efficient identification and accurate structuring of the medical form are realized, and the digitization efficiency of the medical data is improved.

The invention belongs to the technical field of information retrieval, and discloses a multi-source cross-domain data query method and system. The method comprises the following steps: firstly, carrying out data identification, extraction and classification by utilizing a large language model, and constructing a multi-source knowledge graph; secondly, performing deep aggregation and reconstruction of multi-source cross-domain data based on a large language model to obtain a homologous knowledge line graph; thirdly, calculating the confidence degree of the query nodes and the subordinate sub-graphs according to query requirements, rejecting low-quality nodes, and extracting credible candidate sub-graphs from the knowledge graph so as to ensure that a multi-domain retrieval query result is effective; and finally, embedding the screened credible candidate sub-graphs into a cue word template, and carrying out data query by utilizing a vertical large model. According to the multi-source knowledge fusion and query method based on the large language model, cross-domain data in different sources are integrated to form domain knowledge, and accurate and efficient query of the multi-source cross-domain data is achieved through the vertical large model.

The invention discloses a bridge vibration identification method and system based on an artificial intelligence technology, and relates to the technical field of data identification, and the method comprises the steps: dividing a bridge into a plurality of bridge segments, collecting the vibration data of the bridge segments in a preset time, forming a vibration data set, and analyzing the stress bearing capability of the bridge segments based on the data. The method comprises the following steps: identifying a stress bearing limit value of each bridge section, establishing a health safety threshold value of the bridge section, providing indexes for dangerous bridge section identification, inputting real-time monitoring data into a vibration identification model, predicting a vibration influence degree, dynamically monitoring a bridge state, discovering risks in advance and taking measures. And analyzing vibration influence factors, determining influence sources, and helping to optimize coping strategies. Predicting vibration deformation trend, evaluating bridge structure change, avoiding major accidents, reducing overload stress based on a balance strategy, prolonging the service life of the bridge, regularly monitoring the health state, formulating optimized operation time and mechanism, and ensuring safe operation of the bridge.

A system to analyse impact of data breaches on sensitive data is disclosed. The system includes a hardware processor and memory with program instructions for executing various modules. The data collection module retrieves and enriches impacted data from multiple repositories. The data identification module uses data loss prevention (DLP) and named entity recognition (NER) techniques, enhanced by large language models (LLMs), to accurately identify personal information. The identity deduplication module consolidates individual references using deterministic and probabilistic techniques, while the residency inference module applies machine learning and heuristic methods to determine residency based on various data sources. The analysis module assesses impacted data to identify relevant laws and estimate fines. The automation module streamlines response actions, including generating notifications and ensuring compliance. This system enhances breach response efficiency through integrated, automated analysis and actions.

The invention discloses a multispectral satellite fire prevention method and system based on data identification, a multispectral satellite optical sensor array scans a target area for 30 minutes, the acquisition precision is guaranteed through a high-precision attitude system and a formula to obtain data, and the quality is guaranteed through adaptive filtering and multi-source data correction. A dynamic threshold value and wavelet transform are used for extracting fire features to construct vectors, the vectors are input into a fusion model to discriminate a fire in real time, early warning information including positions, fire behaviors and the like is transmitted to a ground command center within 8 minutes, data are updated every 10 minutes during the fire, the fire behaviors are evaluated according to multiple indexes in combination with a GIS and a time sequence model, strategies are adjusted, and the data are stored. The system is fast in data acquisition and processing, early warning is performed within 8 minutes, the accuracy rate exceeds 92%, the fire behavior is pre-judged 15 minutes in advance, the system is stable, data is efficiently processed, and ecological and life and property safety is protected.

The invention discloses a bridge construction abnormity monitoring data identification method and system based on big data, and relates to the technical field of bridge construction monitoring, the system comprises a collection module, an analysis module, a process matching module and an execution module, the collection module collects construction data, transmits the construction data to the analysis module, carries out linkage verification on the construction data through an analysis unit, and carries out process matching on the construction data; identifying and outputting abnormal data, transmitting the abnormal data to a process matching module, constructing a judgment standard for dynamic matching through a construction process, comparing the abnormal data with the judgment standard, outputting a preliminary judgment result, transmitting the preliminary judgment result to an execution module, executing multi-stage cross validation on the preliminary judgment result, and outputting a final abnormal judgment result. By responding to multi-source data, missed judgment is prevented, a space-time linkage verification mechanism is constructed to improve anomaly recognition accuracy, a dynamic judgment standard adapts to risks of all stages, false anomalies are filtered through three-stage verification, the efficiency is improved through full-process automation, hidden danger early warning is assisted, accidents are avoided, and bridge construction quality and safety are guaranteed.

The invention relates to a mechanical equipment fault data identification method based on artificial intelligence, and belongs to the technical field of data processing and artificial intelligence. The problems of insensitive signal processing, insufficient feature extraction, significant noise interference, low model training efficiency and the like in fault diagnosis in the prior art are solved. According to the method, a self-adaptive normalization method based on energy density is provided, the influence of non-stationary signals is effectively inhibited, and key features are reserved; through mixed energy entropy feature extraction and inter-band energy jump penalty terms, the sensitivity to a complex fault mode is significantly enhanced; constructing a fault feature enhancement strategy based on a Gaussian potential well, and reinforcing the response of a fault feature accumulation area; a weighted loss function and a gradient directional correction mechanism are adopted, so that the robustness and accuracy of the model are improved; and in combination with an entropy weighted learning rate and a covariance scaling strategy, adaptive training is realized, and the convergence speed and adaptability are improved. According to the method, the precision and efficiency of mechanical fault diagnosis are improved, and support is provided for industrial intelligent development.

The invention discloses a satellite remote sensing target data identification method based on artificial intelligence, which belongs to the technical field of data processing, and realizes distributed model training and data mining of a multi-node joint model by performing satellite remote sensing target identification on an acquired spectral vector and adopting a model training architecture of distributed federated learning; carrying out manual annotation on the acquired spectral vector data; a generative adversarial network based on multi-scale spectral decomposition is adopted for sample generation, and then data expansion is achieved; and performing data identification by adopting a satellite remote sensing target data identification model. According to the method, the generative adversarial network based on multi-scale spectral decomposition is adopted for sample generation, and the data set is expanded. A self-encoder algorithm is used for carrying out feature dimension reduction, feature independence is enhanced through a nonlinear conversion layer, and redundant information between features is reduced. A kernel extreme learning machine algorithm based on conjugate gradient compensation is adopted, a kernel function is mapped to a high-dimensional space, and the classification accuracy and efficiency are improved.

The invention provides a risk data analysis method based on an LLM large language model, risk data analysis is carried out based on the LLM large language model, data identification, classification and risk feature extraction can be carried out by utilizing the LLM large language model in the whole process, risk feature vector training learning is carried out on the LLM large language model, and the risk data analysis efficiency is improved. The defect that a large number of training samples are needed can be reduced; meanwhile, the LLM big language model cue words on all dimensions are constructed and input on one LLM big language model, the LLM big language model can continue to further carry out risk analysis on the classification and recognition result on all dimensions, and the risk analysis result of the risk data on all dimensions is rapidly and deeply mined and output. Therefore, the risk data can be intelligently processed by the LLM in the whole process, the risk types and all dimensions can be analyzed through one model, and the risk data analysis efficiency is greatly improved.

The invention relates to the technical field of broadband spectrums, and discloses a broadband spectrum monitoring system based on dynamic channel allocation, which comprises a data acquisition unit, a data identification unit, a region division unit, a data alignment unit and a channel allocation unit.The system dynamically adjusts the coverage area through beacon nodes, accurately calculates the coverage radius in combination with the urban environment, and improves the coverage efficiency. Meanwhile, a channel allocation strategy is optimized by a game theory, the change of a frequency spectrum environment is responded, and frequency spectrum state parameters are constructed by fusing multi-dimensional characteristics, so that communication smoothness is guaranteed, blind areas of monitoring nodes are eliminated, data space-time alignment is realized, the monitoring sensitivity and the resource utilization rate are effectively improved, and stable frequency spectrum and reliable communication under large-scale activities in cities are ensured; and the dynamic and reasonable distribution of the channels is ensured.

The invention relates to a multi-factor fusion Internet of Things outlier data identification method, and belongs to the technical field of Internet of Things, and the method comprises the steps: determining an identified target factor, fusing a plurality of influence factors associated with the target factor, obtaining corresponding historical data, and carrying out the preprocessing; performing correlation analysis on the specific indexes, and selecting index data having significant influence on target factors to form a final effective index combination; constructing a prediction model combining the LSTM with a feature attention mechanism, performing prediction by using the data after correlation analysis screening, outputting a prediction value in a future period of time, and obtaining an actual prediction value through reverse normalization; a prediction model is adopted, an outlier data identification method fused with multi-factor comprehensive analysis is adopted, an isolated forest algorithm is used for modeling, prediction residual errors and influence factors serve as input features, and the abnormal score of each data point is calculated; and based on the distribution of the abnormal scores, dynamically setting a threshold value by adopting a sliding window technology, and judging the data points exceeding the threshold value as outlier data.

The invention relates to the technical field of image-text recognition, in particular to a big data image-text recognition system and method based on artificial intelligence, and the system comprises an element identification module, a text keyword extraction module, a mapping network construction module, an image-text perception adjustment module, a decision diagram construction module and a format adaptability adjustment module. In the invention, through deep pixel analysis and visual feature matching, accurate analysis of the relationship between the object in the image and the visual label is realized, the accuracy of image-text data identification is improved, the specific association between the image content and the related text data is accurately understood, and the constructed image network is subjected to semantic association calculation, so that the accuracy of image-text data identification is improved. According to the method, the semantic model can dynamically adapt to new data input, the decision path of the image-text content is optimized, the image-text recognition efficiency is effectively improved, the classification decision is more accurate, the image-text recognition quality and speed in the big data environment are optimized, and the processing performance and the application flexibility are remarkably improved.

The invention relates to the technical field of inventory management, in particular to an intelligent warehouse management system for main shaft parts based on digital twinning, which comprises a main shaft mapping module for acquiring a main shaft shipping space and an allocation path, comparing shipping space nodes with a moving timestamp difference to generate an identification result, the fluctuation identification module extracts channel nodes to analyze fluctuation threshold deviation and generate a report, the allocation control module generates a scheduling list based on queuing duration and shipping space data distribution weight, and the abnormity positioning module extracts unallocated parts to analyze interface difference and mark abnormal links. And the state early warning module integrates four indexes of abnormal data identification risk paths to generate a management scheme. According to the method, bin position nodes and main shaft movement timestamps are synchronously compared to identify difference nodes, a dynamic weight distribution mechanism is established in combination with channel node fluctuation analysis, an interruption link difference trend and interface delay data are integrated to construct a risk assessment model, and abnormal conduction path positioning and early warning are realized. And scheduling conflicts and resource waste caused by data deviation are reduced.

The invention discloses a memory data migration method and electronic equipment, and relates to the technical field of computers, and the method comprises the steps: determining the access heat of a storage device accepting the access of a computing node based on a memory access task executed by a switching controller and an address mapping table, so as to determine a source device and a candidate data unit, according to the access parameter of the candidate data unit and the state parameter of the storage device, calculating a performance score after the candidate data unit is stored in the storage device so as to determine the data unit to be migrated and the target device, so that cold and hot data identification from the perspective of a computer fast interconnection memory heterogeneous system is realized; the rationality of data migration is improved on the basis of quantitative performance scores; according to the method, the to-be-migrated data unit is controlled to be migrated from the source device to the target device, and the address mapping table is updated, so that the data migration task is prevented from influencing the host work of the computing node, and a cold and hot data migration scheme without being perceived by the host in the computer fast interconnection memory heterogeneous system is realized.

The invention relates to a data desensitization method and device based on a BERT model. The method comprises the following steps: preprocessing business data by utilizing a BERT model; identifying key data in the preprocessed business data by using the trained BERT model; identifying sensitive information in the key data by using the trained BERT model; according to the data type, the data sensitivity and the context information of the sensitive information, a corresponding desensitization strategy is selected from the desensitization strategy library to conduct desensitization processing on the sensitive information, through preprocessing, key data identification and desensitization processing on the service data, complex data content can be efficiently processed and understood, in addition, in the desensitization process, the data processing efficiency is improved, and the data processing efficiency is improved. According to the method, the desensitization strategy is dynamically selected and adjusted based on the data type, the data sensitivity and the context information, so that the data desensitization process can be adaptively adjusted according to the specific data use environment and the sensitivity level, and the desensitization technology can keep effectiveness and applicability in different application situations.

The invention relates to the technical field of data identification, in particular to a spectroscopy-based Chinese herbal medicine component identification method and system. The method comprises the following steps: carrying out difference wavelength characteristic analysis and wavelength calibration treatment on a quantitative Chinese herbal medicine sample to be detected to obtain spectrum calibration data of the Chinese herbal medicine sample to be detected; performing spectral feature recognition analysis and feature vector conversion processing on the spectral calibration data of the to-be-detected Chinese herbal medicine sample to obtain a spectral feature vector of the to-be-detected Chinese herbal medicine sample; performing vector classification processing and feature point set sampling processing on the spectral feature vectors of the Chinese herbal medicine sample to be detected to obtain spectral feature point sets of different components to be detected in the Chinese herbal medicine to be detected; and obtaining a spectral feature point set of known components of the Chinese herbal medicine, and carrying out incidence matrix mapping analysis and component extreme value identification analysis on the spectral feature point set of different to-be-detected components in the to-be-detected Chinese herbal medicine to obtain a component identification result of the to-be-detected Chinese herbal medicine sample. According to the invention, specific components in the Chinese herbal medicine can be identified more accurately and efficiently.

The invention discloses an industrial control ring network monitoring system and a monitoring method thereof. The system comprises a data acquisition module which is used for acquiring an industrial control ring network data packet in an industrial control network; the data processing module is used for carrying out data cleaning and data arrangement on the collected data and carrying out protocol matching on a data packet so as to identify an industrial protocol in the data packet; the data identification module is used for carrying out protocol matching on data in the network data packets and identifying communication data of each network packet; through organic cooperation of a plurality of units, the system realizes omnibearing monitoring, analysis and dynamic adjustment of the network, so that the system architecture provides a real-time, intelligent and self-adaptive network security solution, and compared with a traditional intrusion detection system, the system pays more attention to real-time optimization of network performance, and the network security is improved. Under the condition that the bandwidth occupancy rate is relatively high, unnecessary interference to other servers is reduced, so that the problems of lengthy and tedious protocol identification are solved.

The invention discloses a 3D point cloud target identification method based on a bidirectional long-short-term memory network, which comprises the following steps: firstly, preprocessing original point cloud data, carrying out feature embedding on the preprocessed point cloud data, then carrying out data feature extraction based on the bidirectional long-short-term memory network, carrying out local feature aggregation and global feature aggregation on the data, and finally carrying out target identification on the 3D point cloud target. Feature extraction is enhanced, and the data expression ability is improved; and constructing a point cloud data identification model based on a full connection layer, training and evaluating the model, and performing point cloud data identification based on the model. According to the scheme, under the condition that the calculation precision is not reduced, the long-term dependency relationship between the 3D point cloud sequences is captured with few calculation resources and memory resources, aggregation is carried out based on the extracted features, and accurate recognition is achieved.

The invention relates to the technical field of financial accounting and artificial intelligence combination, in particular to an artificial intelligence-based financial data identification and accounting method, which comprises the following steps of: firstly, calculating a fractal dimension by using a multi-level grid splitting technology, and constructing a fractal financial semantic map covering transaction and exchange entities; and performing interference operation by using a quantum mixing aggregator, fusing fractal atlas attributes and subject candidate results to obtain interference scores, and performing symbol rule screening or manual auditing confirmation to generate calibrated subject information. Then, the subject information is used for automatically generating an accounting entry and performing account book archiving, and the whole decision process is recorded under the quantum hash technology; by continuously analyzing archived decision data, the system can adaptively adjust fractal dimension calculation and interference parameters, and precise recognition and efficient accounting of complex financial data are realized.

The invention provides a transformer substation operator safety monitoring method based on video analysis. A monitoring system comprises a video acquisition module, a deep learning identification detection module, a frame difference module and a subsequent logic module. The video acquisition module is used for acquiring video data of substation field operation personnel, then inputting video images into the deep learning identification detection module for data identification, and transmitting detection and identification results into the rear frame difference module for data comparison. A comparison result is transmitted to a follow-up logic module to judge whether the behavior of the substation field operation personnel is safe or not. According to the substation operation personnel safety monitoring system based on video analysis, a frame difference method and a deep learning identification detection method are combined; the transformer substation environment is monitored in real time to analyze whether field operators correctly wear safety helmets, whether safety belts are hung at low positions and used at high positions, whether construction behaviors are compliant, whether dangerous behaviors exist and the like, and the construction standard and safety of the transformer substation operators are ensured.

The invention discloses a credit loan fraud data identification method and system based on artificial intelligence and a graph algorithm, and relates to the technical field of credit loan risk control. User information and behavior data are collected from multiple sources, a relation network graph is constructed after cleaning, dense communities are identified, cross-community intermediary nodes are positioned, community portraits are generated, behavior patterns are quantified, and similar groups are clustered. According to the method, a complex fraud mode is accurately recognized, hidden association is mined through a graph algorithm, the recognition accuracy is improved in combination with community portraits and behavior clustering, the dynamic adaptability is high, real-time feedback and incremental learning ensure that novel fraud is rapidly coped with, and the method is suitable for popularization and application. A high-interpretability report meets compliance requirements, user trust is improved, large-scale data is efficiently processed, modular design adapts to different credit and loan scenes, cost is reduced through automatic decision, the omission ratio is reduced, fraud loss is directly reduced, and the rate of return on investment is high.

The invention relates to the technical field of carbon emission data processing, in particular to a carbon emission data processing method, device and equipment and a readable storage medium. According to the technical scheme, the method comprises the steps of data acquisition and preprocessing, abnormal data identification and correction based on a hybrid machine learning model, energy optimization configuration assistance and real-time monitoring and feedback adjustment, and data acquisition and preprocessing: constructing a multivariate data acquisition network and preprocessing the acquired data; therefore, standard carbon emission data for subsequent analysis can be obtained. Through a multi-element acquisition network and fine preprocessing, the comprehensiveness, accuracy and timeliness of data are greatly improved, a foundation is built for accurate analysis, abnormity is accurately recognized and corrected through a hybrid machine learning model, the accuracy is improved, data reliability is guaranteed, association between carbon emission and energy is mined, enterprises are assisted in optimizing energy configuration, and the energy utilization efficiency is improved. The carbon emission is reduced, and the system stability is enhanced by real-time monitoring feedback.

The invention discloses a supervised learning-based data recognition method and system, particularly relates to the technical field of data recognition, and is used for solving the defects of an existing supervised learning method in low-frequency feature capture and feature association modeling. Training data are collected from a plurality of data sources and samples which do not meet a quality threshold are filtered; performing information gain measurement on the features through a dimension reduction algorithm, and screening a feature set with high information gain; generating a sparse feature combination set by using an association rule algorithm, and extracting key low-frequency features and an interaction relationship thereof; inputting the low-frequency feature set into the gradient elevator for supervised training, and strengthening the influence of the low-frequency features through an adaptive weighting strategy; in the training process, the gradient updating trend and Shapley value distribution of the low-frequency features are monitored, a weighting strategy is adjusted for abnormal features to correct model weight distribution, the generalization ability and prediction precision of the model are improved, and the method is suitable for feature recognition and analysis of complex data scenes.

The invention belongs to the technical field of ocean temperature data identification, and discloses a thermocline identification method and system based on hyperbolic tangent function fitting. The method comprises the steps of data preparation and preprocessing; selecting a section range; fitting a curve; and determining a thermocline upper bound and a thermocline lower bound through a rolling function. According to the method, the hyperbolic tangent function is used for fitting the S-shaped characteristic curve of the temperature-depth profile, and the structure of the ocean thermocline is accurately described. Compared with a traditional Sigmoid function, the hyperbolic tangent function is better in performance when fitting a steep change area, and rapid temperature change of a thermocline central area can be caught more finely. According to the method, fitting parameters (amplitude, steep degree, central position and vertical offset) are optimized by adopting a trust region reflection algorithm, so that a fitting result is ensured to have higher precision and robustness, and the method is suitable for complex ocean profile data.

The invention provides a crane hoisting monitoring method and system based on multi-normal-form visual fusion and a medium, and relates to the technical field of computer vision. Acquiring an image of the target acquisition area through an image acquisition device, and obtaining a real-time image acquisition video stream; preprocessing the real-time image acquisition video stream to obtain real-time image frame data; performing real-time image frame data identification based on the visual detection model to generate detection information; inputting the detection information into a visual tracking module, and tracking the identification object to obtain tracking information; acquiring a fixed dangerous area or a dynamic dangerous area based on the detection information and the tracking information; and identifying a dangerous condition in the video stream according to the dynamic dangerous area or the fixed dangerous area, obtaining dangerous condition accumulated data and triggering an alarm. The technical problems that in the prior art, the monitoring means is single, and dynamic dangers are difficult to accurately recognize are solved, and the technical effect of accurately recognizing potential dangers of hoisting operation in real time is achieved.