86results about How to "Comprehensive recognition" patented technology

The invention provides a network attack risk mapping assessment method and system, and relates to the technical field of network attack risks, and the method comprises the steps: collecting a network flow data packet to extract a multi-dimensional attack feature vector, employing a deep neural network to carry out feature learning to obtain an attack type recognition result and an attack intensity assessment value, and constructing an attack feature standard model; based on the attack type identification result and the attack strength evaluation value, combining an asset weight coefficient, a vulnerability hazard level and a vulnerability utilization difficulty coefficient to construct an asset vulnerability evaluation model; and performing association analysis on the real-time attack feature data and the asset vulnerability assessment score, constructing a risk mapping matrix, calculating a comprehensive risk score, and generating risk early warning information. According to the invention, accurate identification, dynamic assessment and timely early warning of the network attack risk are realized, and the accuracy and real-time performance of risk assessment are improved.

The invention relates to the technical field of data processing, and discloses a quality control method and system based on buoy observation data. The method comprises the following steps: performing multi-dimensional anomaly detection processing on preprocessed standard format data to obtain an anomaly detection report marked with abnormal and suspicious data points; performing multi-source data fusion and cross validation processing on the data in the anomaly detection report to obtain an initial data quality score; performing intelligent correction processing on the anomaly detection report to obtain a corrected data set and uncertainty estimation data corresponding to the corrected data set; performing consistency analysis processing on the corrected data set in a space-time dimension to obtain a space-time consistency score and abnormal region marking data; and carrying out comprehensive evaluation processing on the abnormal region mark data to obtain a target data quality grade identifier and a target quality report, and carrying out quality control strategy analysis to obtain a target quality control strategy. According to the invention, the accuracy of quality control based on buoy observation data is improved.

The invention discloses a land investigation data updating method, a land investigation data updating system, a product and a medium, and relates to the field of land investigation. Superposing vector boundary data of the preset survey range on the basic geographic information layer and importing historical land information in the preset survey range to obtain a comprehensive basic information layer; acquiring actual land types, actually measured areas and ownership condition information of various lands in a preset survey range; and comparing the actual land type, the actually measured area and the ownership condition information with the comprehensive basic information layer, identifying land utilization change data, generating a land survey data updating instruction according to the land utilization change data, and sending the land survey data updating instruction and the comprehensive basic information layer to a target client. By implementing the method, the land survey data updating efficiency can be improved.

The invention discloses a stamping production process self-adaptive control method, a stamping production line and a program product, and relates to the technical field of stamping machining. The method comprises the steps that stamping production data collected by a data collection unit are received, and the stamping production data comprise one or more of drawing inflow, die surface temperature, lubricating oil amount, closing height, blank holder force, forming force and material performance parameters; determining a quality performance prediction condition of the product based on the stamping production data; determining to-be-optimized data from the stamping production data according to the quality performance prediction condition; stamping process optimization parameters are determined according to the to-be-optimized data; and the stamping process optimization parameters are sent to a stamping controller of the stamping equipment, so that the stamping controller adjusts the stamping action of the stamping equipment according to the stamping process optimization parameters. Intelligent and automatic management of the stamping production line can be achieved, the production efficiency is improved, and the defective rate is reduced.

The invention relates to the field of electrical equipment fault diagnosis, in particular to a switch cabinet defect diagnosis method based on multi-information fusion and a related product, and the method comprises the steps: detecting a switch cabinet by adopting multiple detection methods, and extracting a feature vector from a detection signal; constructing different defect diagnosis models for different detection methods by using the feature set after dimension reduction; obtaining a to-be-detected sample of a to-be-detected switch cabinet through multiple detection methods, and inputting the to-be-detected sample to the corresponding defect diagnosis model; obtaining a basic distribution probability, fusing to obtain a final comprehensive distribution probability, and determining a final prediction result; according to the invention, through combination of multiple detection means, limitation of a single detection method is made up, multiple defect types in the switch cabinet can be identified more comprehensively and accurately, outputs of different defect detection models are fused, and robustness and accuracy of diagnosis are significantly improved.

The invention discloses a double face recognition area early warning safety protection method, and relates to the technical field of face recognition, a face recognition camera, a database, a background server and a client are included, and the method has the advantages that under strong light, an intelligent light adjustment algorithm can automatically reduce the exposure, improve the contrast and enhance the color saturation; the face features are highlighted, the overexposure phenomenon of the face is avoided, under the backlight condition, the backlight compensation algorithm is started, the overall brightness of the face is improved by increasing the edge brightness and adjusting the hue, the accuracy of feature extraction is ensured, and in addition, based on the face detection technology of the deep neural network, through deep training of massive face image data, the accuracy of feature extraction is improved. According to the invention, the camera can learn the face features in various complex environments, and even if the face is partially shielded and is located at different angles or under poor light conditions, the system can accurately carry out detection and recognition, so that the stability and accuracy of face recognition are greatly improved.

The invention belongs to the technical field of natural language processing and intelligent knowledge retrieval, and provides an open type question implicit intention recognition method and system based on multi-agent reasoning. According to the technical scheme, multi-language background knowledge is constructed through a multi-language translation agent and an information retrieval agent; based on the constructed multi-language background knowledge, subject analysis is carried out, multi-field expert agent information is generated, and a multi-field expert agent is dynamically constructed; according to the guide problem proposed by the host agent, generating respective implicit intentions and reasoning argument text contents by using the multi-field expert agent according to the guide problem proposed by the host agent; and performing quality evaluation on the implicit intention and the reasoning argumentation text content by utilizing the host agent, and constructing an implicit intention library by taking the speech of each screened expert agent as a node. The defects in the aspects of processing complex semantic understanding and the like are effectively overcome, and the implicit intention in the open type problem is recognized more accurately and comprehensively.

The invention provides a multi-source data fusion-based reservoir floating object monitoring method, device and equipment, a storage medium and a product, relates to the technical field of environment monitoring, and aims to solve the problems of low efficiency, limited coverage range and insufficient data precision in related technologies of reservoir floating object monitoring. The method comprises the following steps: acquiring various data; the multiple kinds of data comprise multispectral satellite remote sensing data, unmanned aerial vehicle aerial photography data, ground sensor data and meteorological data; performing data fusion on the various data to obtain fused data; the data fusion comprises space fusion, time fusion and feature extraction and fusion; inputting the fusion data into a floating object identification model to identify the floating object, obtaining a monitoring result, and carrying out data analysis; the data analysis result comprises a floating object distribution diagram, statistical data and a change trend diagram; and judging whether the total number of the floating objects in the statistical data exceeds a preset threshold, and outputting early warning information if the total number of the floating objects in the statistical data exceeds the preset threshold.

The invention discloses an illegal behavior identification method and device, computer equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: building an alarm model based on a preset alarm rule, and obtaining alarm data of a business system based on the alarm model; based on a K-means clustering algorithm, dividing the alarm data into a plurality of clusters to obtain a clustering result; based on the clustering result and a historical violation sample of the business system, training a preset artificial intelligence model to generate a corresponding violation identification model; and based on the violation identification model, identifying the violation behavior of the business system. Through the method provided by the invention, the illegal behaviors can be automatically, quickly, comprehensively and accurately identified, so that the security risk of a service system can be reduced.

The invention relates to the technical field of voiceprint recognition, in particular to an anti-fraud communication method based on voice change recognition. The method comprises the following steps: acquiring multi-dimensional voice feature data of a user, wherein the multi-dimensional voice feature data comprises timbre, frequency features, language rhythm, emotion features and pause modes; performing voice change possibility prediction according to the multi-dimensional voice feature data to obtain voiceprint feature prediction data; performing deep learning model training according to the voiceprint feature prediction data, and mapping voiceprint states and features under different call conditions to obtain voiceprint feature map data; and according to the voiceprint feature map data, carrying out simulation analysis on voice change recognition behaviors in different call states so as to obtain voice change recognition simulation data. According to the method, the call environment noise data and the network quality data are obtained in real time, the call quality model is constructed, cross-scene optimization and real-time adjustment are carried out, and the influence of the environment on recognition is effectively reduced.

The invention discloses a large model reasoning method and device based on sensitive information protection and related equipment. The method comprises the steps that firstly, an original input text is obtained, sensitive entities in the original input text are detected through a named entity recognition NER model, relation extraction and attribute extraction are conducted, and a sensitive information knowledge graph is constructed; then, screening sensitive information in the original input text according to the knowledge graph, and replacing the sensitive information to generate a desensitized input text; next, calling a first large model of the server side, and executing a specified task by using the desensitized input text to obtain a result text; and finally, calling a second large model of the user side, and restoring the sensitive information in the result text by using the original input text, the desensitized input text and the result text to obtain a complete text. Large models configured at different ends are utilized, sensitive information is protected, and meanwhile practicability of result texts is taken into account.

The invention discloses an IDI full-project cycle risk digital assessment method and system, which have the beneficial effects that potential risk factors are identified from project data by adopting a text mining technology, risk factor importance assessment is carried out by utilizing a mode of weighting word frequency through information entropy, risk influence factors can be comprehensively identified, and the risk assessment accuracy is improved. Exploring the internal influence of project quality and risk factors; a multi-level and multi-dimensional risk evaluation system is constructed by adopting an analytic hierarchy process, so that the quality risk state of a project can be comprehensively reflected, and a quantitative standard of an evaluation index system is given; according to the method, a project risk evaluation model is constructed, a project risk scoring model is constructed from four dimensions of a hierarchical weight matrix, a severity matrix, a conditional probability matrix and an index occurrence probability matrix of risk indexes, various risk factors are considered, and the mutual relation and occurrence possibility between risks can be known more comprehensively; a Monte Carlo simulation method is adopted to grade and divide project risks, and the risk can be quickly traced, monitored and managed according to a result.

The invention discloses a permanent magnet generator air gap eccentricity fault identification method and system, relates to the technical field of permanent magnet generators, and uses wavelet transform to extract time-frequency features and generate multi-band feature vectors, so that the system can more sensitively identify subtle features of air gap thickness deviation. And the extraction and analysis precision of characteristic signals of different frequency bands is effectively improved. And secondly, analyzing the air gap thickness deviation condition based on the feature vector, constructing an eccentricity degree index, and quantifying the eccentricity degree, so that the severity of the air gap eccentricity fault of the permanent magnet generator can be intuitively judged. And when the eccentricity degree exceeds a set threshold value, the system automatically sends a fault type checking instruction, so that the fault detection process is more intelligent. Afterwards, abnormal changes of all positions are analyzed through infrared thermal data and temperature gradients, air gap foreign matter faults possibly causing air gap eccentricity are further recognized, and the limitation of single signal analysis in traditional detection is made up.

The invention discloses an intelligent online detection method and system for the continuity of a paint film of an enameled wire, and relates to the technical field of electrical equipment, and the method comprises the steps: scanning the paint film of the enameled wire through an electromagnetic wave emitter, and generating initial evaluation data of the integrity of the paint film; based on the generated paint film integrity initial evaluation data, capacitance change of the surface of the enameled wire is detected, and a two-dimensional capacitance imaging graph of the paint film is generated; detecting the stress distribution of the enameled wire paint film according to the two-dimensional capacitance imaging graph and the paint film integrity initial evaluation data, identifying a stress concentration area, and generating a paint film stress distribution graph; comprehensively processing the stress distribution result, the capacitance imaging graph and the paint film integrity initial evaluation data, identifying a defect area and a defect type of the enameled wire paint film, and generating a paint film detection report; according to the method, the multi-source data is projected into the same coordinate system, the stress, capacitance and electromagnetic wave characteristics are comprehensively analyzed, and the defect area can be more comprehensively and finely identified.

The invention discloses a virtual currency wallet address anomaly detection method and system based on self-adaptive different matching message aggregation, and the method comprises the steps: firstly, processing initial transaction data, obtaining the characteristics of nodes, carrying out the data normalization, converting the original data into an original transaction graph, carrying out the random walk sampling of the original transaction graph, obtaining the training graph data of a model, and carrying out the random walk sampling of the training graph data; carrying out information transmission among the nodes by adopting a graph neural network operator of self-adaptive disparity aggregation to obtain embedded vectors of the nodes; taking the obtained embedded vector of the node as the input of a prediction model; constructing a node prediction model by adopting a plurality of linear layers, and carrying out stochastic gradient descent parameter optimization on prediction of a node category probability score and calculation of node disparity; and finally, obtaining a virtual currency wallet address anomaly detection model through training, and detecting an abnormal wallet address in the virtual currency transaction network by using the model. According to the invention, the wallet address with illegal transaction in the virtual currency transaction network can be accurately identified.

The invention discloses a safety monitoring and warning method and system for elderly people living alone based on data analysis, and relates to the technical field of intelligent monitoring, and the method comprises the steps: collecting the image data of the daily life of the elderly people living alone in an indoor environment in real time; monitoring the physiological sign data of the elderly living alone in real time; performing feature extraction on the acquired daily life image data of the elderly living alone by using a convolutional neural network to obtain human body image features; analyzing the monitored physiological sign data of the elderly living alone by using a recurrent neural network to obtain abnormal state reference data; according to the human body image features and the abnormal state reference data, identifying various abnormal states of the elderly living alone in the home environment; under the condition that various abnormal states of the elderly living alone in the home environment are identified, sending an alarm message to a remote guardian terminal; and the remote guardian terminal receives the alarm message.

The invention relates to a risk behavior detection method and system based on the DLP technology, and the method comprises the following steps: building a user risk behavior monitoring model based on the DLP technology, and the user risk behavior monitoring model is used for collecting and monitoring user behavior data and IM communication software communication data; establishing a user risk behavior identification model through a machine learning technology according to user historical behavior data collected by the user risk behavior monitoring model, and monitoring daily operation behaviors of the user in real time; according to IM communication software communication data collected by the user risk behavior monitoring model, whether risk content exists in the IM communication software communication data or not is recognized through the NLP technology; and according to an output result of the user risk behavior identification model and risk content identified by the NLP technology, constructing a comprehensive risk assessment model, when a risk value output by the risk assessment model is higher than a preset value, giving a risk alarm, and automatically recording related data to provide evidence for subsequent investigation and auditing.

The invention provides a factor identification method and device for limiting vehicle perception performance and a storage medium. According to the method, a factor analysis network model is constructed for any sensing target of a vehicle sensing system, a conditional probability table is obtained through statistics in a training sample set, observation frequencies and expected frequencies of factor nodes are obtained based on the conditional probability table, chi-square statistics is calculated according to the multiple observation frequencies and the multiple expected frequencies, and the vehicle sensing target is obtained. The factor analysis network model and the conditional probability table are updated according to chi-square statistics until the factor analysis network model is stable, factor nodes are extracted from the stable factor analysis network model to serve as factors for limiting the vehicle perception system to perceive the target, and according to conditional probabilities in the conditional probability table, the vehicle perception system is determined according to the conditional probabilities in the conditional probability table. And the false positive rate or the false negative rate of the vehicle sensing system to the sensing target in different states of each factor node is calculated, so that the factors influencing the vehicle sensing system can be comprehensively and accurately identified.

The invention is suitable for the field of advertisement auditing, and discloses an advertisement content isolation auditing method, system and device and a medium, and the method comprises the steps: receiving advertisement content; the advertisement content is classified, and text advertisements, picture advertisements and video advertisements are identified and classified; the advertisement contents of different classifications are audited respectively; wherein when the picture advertisements are classified, digital watermarks are embedded in the picture advertisements, and the digital watermarks comprise category information of the picture advertisements. In the picture advertisement classification and auditing process, the digital watermarking technology is innovatively adopted in the picture advertisement classification and auditing method. By embedding the category information of the picture advertisement into the digital watermark and adding the watermark into the picture advertisement, quick classification and efficient management of the picture advertisement are realized, the accuracy and efficiency of picture advertisement classification are improved, convenience is provided for subsequent auditing work, and the user experience is improved. And the overall quality and efficiency of advertisement auditing are further improved.

The invention belongs to the technical field of data analysis, discloses a real-time vibration data acquisition and analysis method and a related system, and adopts an LSTM (Long Short Term Memory) neural network to capture time sequence characteristics in vibration signals and reserve time domain information, so that the change of the running state of a generator set can be reflected more accurately. Through the combination of the LSTM neural network and the K-means clustering algorithm, the model can deeply learn complex features of vibration signals and distinguish different modes, thereby improving the recognition precision of abnormal data. The K-means clustering can classify the signals in the data processing process, so that the trained LSTM model is more accurate and effective. The LSTM neural network has the capability of processing long sequence data, can adaptively analyze different vibration state signals in multiple modes, is not limited to the operation state of a specific mode, and thus is suitable for a complex generator set operation environment.

The invention relates to the field of Internet text content recognition, in particular to a text specific information recognition and judgment method, which comprises the following steps of: (S1) acquiring text data, performing preprocessing and word segmentation, calculating a hash value of each word through an improved hash algorithm, forming a dictionary, calculating short-term and long-term frequencies by utilizing a multi-level time sequence model, and calculating a short-term frequency and a long-term frequency; identifying abnormal high-frequency words as potential new word candidate words, and mapping the abnormal high-frequency words to a high-dimensional vector space to generate a comprehensive semantic vector; and (S2) constructing a context semantic graph model by using the comprehensive semantic vector, analyzing context compatibility of words, constructing a multi-task learning sentiment analysis model, respectively calculating sentiment scores and specific field correlation scores, comprehensively calculating the scores to obtain the total confidence coefficient of each candidate word, identifying potential new words, and obtaining the candidate words. According to the invention, the capability of identifying hidden illegal contents and the reliability of identifying and judging specific text information can be improved.

The invention relates to the technical field of customer and merchant credit evaluation, in particular to a dynamic performance behavior-based customer and merchant credit evaluation method and system and a medium. The method comprises the steps of obtaining historical transaction data of a customer and a merchant; identifying dynamic performance behavior information from historical transaction data of the customer and the merchant by adopting a data mining algorithm; based on the identified dynamic performance behavior information, constructing a dynamic index system of customer and merchant credit evaluation; weighting each index in the dynamic index system to obtain the weight of each index; obtaining latest dynamic performance behavior information of the customer and the merchant; the method comprises the following steps of: extracting required indexes from latest dynamic performance behavior information of a customer service provider based on a dynamic index system of customer service provider credit evaluation, and performing standardization processing on the indexes to obtain standardized values of the indexes; and carrying out weighted calculation on the standardized value of each index and the weight of the corresponding index to obtain the credit score of the customer service provider, so that the accuracy and the real-time performance of the customer service provider credit evaluation can be improved.

The invention discloses a driving style recognition system based on a natural driving database, and relates to the technical field of automobile driver driving style recognition, and the key point of the technical scheme is that the driving style recognition system comprises a feature extraction module, an offline clustering analysis module and an online recognition and verification module. According to the invention, a driving style identification system based on a natural driving database is established, and feature extraction and clustering analysis are carried out on a high-dimensional large-volume database, so that the driving style of a driver is effectively identified; according to the system, the comfort of the vehicle is considered, the vehicle state response representing the riding comfort is introduced into a driving style recognition model, the recognition precision of the driving style is effectively improved, a feasible scheme is provided for analysis and processing of vehicle high-dimensional mass sample data in China, and meanwhile, the driving style recognition precision is improved. The system can provide technical support for safe driving and riding comfort of the vehicle.

The invention provides a centrifugal compressor surge margin calculation method and device based on flow field feature recognition. The method comprises the steps that characteristic lines of flow and pressure of an original centrifugal compressor are obtained, design parameters of the original centrifugal compressor are adjusted to calculate N characteristic lines, and a flow field distribution cloud picture is extracted to form an image set; analyzing the position of the calculation point corresponding to each image in the image set on the characteristic line, and determining the label of each image; segmenting the image set with the label into a sub-feature training set and a comprehensive feature training set; inputting the images in the sub-feature training set into a centrifugal compressor flow field feature judgment model to obtain a flow field feature recognition model; performing secondary training by using the comprehensive feature training set to obtain a comprehensive flow field feature recognition model; and calculating the newly generated geometric surge margin of the centrifugal compressor. According to the centrifugal compressor surge margin calculation method and device based on flow field feature recognition, the margin in the pneumatic optimization process can be accurately and rapidly calculated.

The invention relates to a signal control scheme identification method, system and device based on vehicle passing data, and a medium. The method comprises the following steps: obtaining vehicle passing data classified according to a traffic flow direction; carrying out self-correlation analysis processing on the vehicle passing data in each flow direction, and carrying out grouping processing on the obtained signal period data to obtain a division scheme of a signal control time period; performing curve fitting on the vehicle passing data of each flow direction in each signal control time period in each intersection, and identifying a maximum value point and corresponding moment data of each fitted curve; by comparing the time data of the maximum value point of the fitting curve of each flow direction in the same time period, the traffic flows of different flow directions with deviations smaller than a set threshold value are classified into the same signal phase, and graph superposition is carried out on the fitting curves of different signal phases in the same signal period. And determining the phase sequence and the phase duration of each signal phase based on the maximum value point and the intersection point of the fitted curve after superposition. The technical problem that in the prior art, a signal control scheme is poor in recognition effect is solved.

The invention belongs to the technical field of oil-gas field development, and discloses a low-salinity formation water identification method, which comprises the following steps of: obtaining data corresponding to a test oil well through drilling data, and comparing step by step according to the obtained data to judge whether water is discharged from a formation; the invention further discloses a low-salinity formation water recognition device and equipment and a storage medium. The method is suitable for identifying the low-salinity formation water, whether water flows out of the formation or not is judged in real time by draining the test oil well, an important basis is provided for identifying the low-salinity formation water, and the defects of a formation water judgment method are overcome.

The invention relates to an intelligent low-altitude economic scene passenger emotion pacifying method and system based on a large model. The method comprises the following steps: constructing a multi-modal emotional state data set of low-altitude travel passengers; wherein the multi-mode emotional state data set comprises passenger face video sequence data, in-cabin voice and audio data, heart rate variability time sequence data, respiratory frequency time sequence data and external situation context data; performing emotional depth feature extraction based on multi-source space-time signals on the multi-modal emotional state data set to obtain multi-source emotional features; emotion recognition is carried out based on the multi-source emotion features; based on the emotion recognition result, constructing a pacifying strategy generation model; and utilizing a pacifying strategy generation model to intelligently pacify the emotion of the passenger, and evaluating and feeding back the pacifying effect in real time. According to the emotion and specific inducements recognized in real time, pacifying measure suggestions which conform to safety specifications, fit scenes and are natural in language can be generated, and timeliness and specialty of passenger cabin service are improved.

The invention discloses a water conveying and supplying pipeline safety risk evaluation method and system based on CFD-AHP, and relates to the technical field of engineering safety and risk management. The computational fluid dynamics (CFD) is adopted for scene simulation, pipeline burst flow and impact force data under different working conditions are obtained, and a basis is provided for determination of pipeline failure key indexes. Based on a CFD simulation result, a multi-level security risk evaluation system is constructed, the multi-level security risk evaluation system comprises a target layer, a criterion layer and an evaluation layer, and the weight of each evaluation index is calculated through an analytic hierarchy process (AHP). And comprehensively analyzing the evaluation indexes by using a fuzzy comprehensive evaluation method to finally obtain a comprehensive evaluation result of the safety risk of the water delivery and supply pipeline. The CFD technology and the AHP method are combined, the scientific and systematic water conveying and supplying pipeline safety risk evaluation method is innovatively provided, the reliability and comprehensiveness of safety evaluation are improved, and a solid scientific basis is provided for safety management and maintenance of the pipeline.

The present invention relates to the technical field of depression recognition, and in particular to a method and system for speech recognition of depression based on deep learning. The present invention clusters speech data of a training individual to obtain speech clusters, adjusts the elements in the characteristic sequence of speech attributes of the speech data based on the number of speech data in the speech cluster, and obtains the characteristic speech value of each speech attribute; constructs a characteristic word segmentation set of the training individual according to the number of occurrences of the same word segmentation in the speech cluster to which it belongs in the word cluster set of the training individual, and the number of speech clusters in which each word segmentation exists; trains a neural network in combination with the characteristic speech value and the characteristic word segmentation set of the speech attribute of the training individual, and uses the trained neural network to detect depression in the individual to be tested. The present invention trains a neural network by combining the speech features and semantic features of the language of the training individual to improve the accuracy of depression detection.

The present application relates to the technical field of data processing, and especially relates to a real-time temperature monitoring method in high-frequency heating anchor cable production process, which performs STL decomposition on the temperature data sequence of the anchor cable in the high-frequency heating stage to obtain residual data, uses the box plot method to obtain to-be-analyzed data in the temperature data sequence according to the residual data, obtains suspected abnormal data according to the persistence of the temperature monitoring data of each to-be-analyzed data in the neighborhood range of the to-be-analyzed data, obtains the abnormal confidence of each suspected abnormal data according to the persistence difference of each two adjacent suspected abnormal data and the similarity between the residual data in the local time range of the time corresponding to each suspected abnormal data and the temperature data sequence, and monitors the abnormal temperature data of the anchor cable in the high-frequency heating stage according to the abnormal confidence and the residual data of each suspected abnormal data, thereby improving the accuracy of the temperature monitoring result of the anchor cable in the high-frequency heating stage.