84 results about "Potentially abnormal" patented technology

The invention relates to the technical field of motor testing, in particular to an online monitoring method and system for the health state of a carbon brush of a motor, and the method comprises the steps: determining a target operation load environment cluster of a target carbon brush at the current moment according to the pre-obtained load condition and operation environment condition of the target carbon brush in the current operation time period; screening out a reference operation time period; determining an abnormal loss factor and a potential abnormal wear feature vector of the target carbon brush at the current moment; according to the Lyapunov index of the vibration signal of the target carbon brush in the current operation time period and the temperature change and the current change of the target carbon brush in the current operation time period, determining a wear fault display possible feature vector; therefore, the health state of the target carbon brush at the current moment is determined. According to the invention, the potential abnormal wear condition of the target carbon brush at the current moment is considered, the health state of the carbon brush is monitored, and the timeliness of abnormal health state monitoring of the carbon brush is improved.

The invention relates to the technical field of data analysis, and discloses a trajectory prediction and abnormal behavior detection system and method, and the method comprises the steps: obtaining a target monitoring image; constructing a target track segment of the moving target based on the target monitoring image; performing prediction completion on the target trajectory segment to obtain a target trajectory of the moving target; calculating the compression degree and the expected speed deviation of the target trajectory; identifying a potential anomaly of the target trajectory based on the degree of compression and an expected velocity deviation; if the target trajectory has potential abnormality, predicting a target expected trajectory based on the target monitoring image; and calculating a matching degree between a target trajectory and the target expected trajectory, and identifying an abnormal target trajectory based on the matching degree. According to the invention, the abnormal track of the moving target can be accurately identified, and the efficiency and the foresight of abnormal track detection are improved.

The invention provides an electroencephalogram abnormal signal detection method based on step-by-step identification and multi-agent decision, and relates to the technical field of biomedical signal processing.The method comprises the steps that electroencephalogram signal data of an epilepsy patient are collected, and the data are analyzed according to a preset channel sequence and cut into a plurality of signal segments with the same length; extracting a multi-dimensional feature from each signal segment; inputting the time domain feature, the frequency domain feature and the inter-channel synchronization feature of each signal segment into an isolated forest model, and screening based on an abnormal proportion threshold to obtain at least one potential abnormal segment; based on the depth scattering feature, the wavelet transform feature, the time domain feature and the frequency domain feature of each potential abnormal segment, executing a multi-agent integration decision on each potential abnormal segment to obtain a comprehensive abnormal score corresponding to each potential abnormal segment; and determining an abnormal signal in the electroencephalogram signal data based on the comprehensive abnormal score corresponding to each potential abnormal segment. According to the method, the false alarm rate of electroencephalogram abnormal signal detection is remarkably reduced.

The invention provides an Internet of Things data acquisition and abnormity early warning method and system, and relates to the technical field of Internet of Things data processing, and the method comprises the steps: constructing a digital twin model for a physical entity at a cloud end, and issuing prediction data to an edge node; acquiring sensor data by an edge computing node at a basic frequency, and computing a residual error between real-time data and predicted data and an information entropy of a residual error sequence; dynamically adjusting the data acquisition frequency of the edge node based on a two-dimensional decision space formed by the residual error and the information entropy; and when the residual error and the information entropy both exceed the threshold values, potential abnormity is judged, an edge side abnormity analysis program is triggered, and abnormal event information is uploaded. According to the invention, through cloud edge cooperation and intelligent decision making, the acquisition frequency is reduced in a stationary period, resources are saved, the frequency is improved when an abnormal symptom appears, monitoring is enhanced, and optimal balance between resource consumption and monitoring precision is realized; double criteria are adopted to effectively filter noise interference, and the false alarm rate and the missing report rate are reduced.

The invention relates to the technical field of wastewater pipelines, in particular to a health state diagnosis method for an electrochemical energy storage prefabricated cabin wastewater collection pipeline, which comprises the following steps: collecting an operation signal of the pipeline, simulating the operation state of the pipeline based on a preset simulation model, obtaining an operation virtual signal of the pipeline, comparing the operation signal with the operation virtual signal, and determining the health state of the pipeline. Operation deviation is obtained; extracting the characteristics of the operation deviation to obtain a deviation characteristic vector; and inputting the deviation feature vector into a fault classification model obtained by training, identifying a potential abnormal type of the pipeline, and outputting a health state index. By collecting flow, sound and vibration signals, generating operation virtual signals in combination with a simulation model, comparing and analyzing deviation characteristics, inputting the deviation characteristics into a fault classification model, identifying potential anomalies and outputting health state indexes, quantitative evaluation and early warning of pipeline health are achieved, the pipeline operation state can be reflected in multiple dimensions, and the reliability of the system is improved. Diagnosis accuracy and system reliability are improved, and health grade division and operation and maintenance decision making are supported.

The invention relates to the field of big data analysis, in particular to a prediction method and system based on industrial big data. The method comprises the following steps: extracting an equipment data stream in a factory production process, and carrying out multi-cycle division and centralized visualization to generate a visual production data stream; performing deviation evaluation according to the visual production data flow, and marking potential abnormal equipment; calculating the change rate and the change amplitude of the potential abnormal equipment; positioning fault equipment according to the change rate and the change amplitude, and marking the fault equipment; performing multi-parameter linkage analysis and deviation attribution analysis on the fault equipment, and identifying equipment fault factors; and performing equipment failure shutdown prediction and equipment maintenance priority planning based on the equipment failure factors, and generating an equipment maintenance sequence. Based on factory big data analysis, the equipment fault time point is accurately predicted, and the accuracy of fault diagnosis and the shutdown maintenance efficiency are improved.

The invention discloses a bone nail automatic identification and dosage confirmation method based on multi-feature collaborative reasoning. The method comprises the following steps: firstly, collecting a multi-view image sequence of a bone nail box in an initial state to obtain a bone nail recognition image sequence; based on the image sequence, extracting boundary information of a bone nail placing position, and generating a bone nail structure map used for representing a relative position relation of placing areas; combining the structural atlas with the image sequence, extracting image features of each placement area, and performing comparison identification based on standard shape features of the bone nail to obtain an identification result; the recognition result is compared with a historical use behavior mode, potential abnormity is recognized, and a corrected enhanced recognition result is output; respectively executing the steps before and after an operation, obtaining an enhanced recognition result and performing difference comparison to obtain bone nail use difference information; and bone nail use confirmation information is generated in combination with the operation identifier, the identification confidence and the acquisition time, and is written into a non-tampering storage medium, so that closed-loop tracing of nail use records is realized.

The invention relates to the field of deep learning, and discloses a heart failure patient home care intelligent monitoring method combined with deep learning, which comprises the following steps: continuously acquiring the heart rate, blood pressure, breathing and body movement states of a patient in a home environment; performing conjoint analysis of time and activity states on the data streams after priority sorting, and forming a dynamic health feature set in combination with historical health records of patients and environment variable information; comprehensively evaluating the dynamic health feature set by using a deep learning model, and identifying a potential abnormal mode of short-time change through the deep learning model; marking and grouping the physiological parameters and related behavior scenes corresponding to the candidate abnormal events, locally evaluating the current health state of the patient based on grouping information, and identifying a potential high-risk condition; and according to the local evaluation result and the overall health trend of the patient, generating real-time early warning and personalized nursing suggestions. The method has the advantage of improving the identification capability of short-time abnormity.

The application relates to the technical field of network security, in particular to an encryption scene detection method and system based on double-model adaptive switching, which comprises the following steps: loading an unsupervised detection model and an unsupervised data collection module; the unsupervised data collection module collects data for the unsupervised detection model to perform unsupervised preliminary screening and obtain a real-time anomaly score; an adaptive switching controller compares the real-time anomaly score with a switching threshold to determine whether the system is abnormal; if the system is normal, the unsupervised preliminary screening is continued; if the system is abnormal, supervised fine screening is performed; a supervised data collection module collects behavior data and inputs the behavior data into the supervised detection model to determine whether the attack is an encryption virus attack; if the attack is an encryption virus attack, corresponding strategies are executed; if the attack is not an encryption virus attack, the unsupervised preliminary screening is continued. The application performs coarse-grained threat preliminary screening in a normal state, and adaptively switches the model to perform fine screening when potential abnormalities are detected, thereby guaranteeing a high detection rate and reducing the influence on system performance.

The application discloses a digital thread driven intelligent management system for a clinical laboratory, which comprises a system body, and the system body is operated through the following method, and the specific method comprises the following steps: obtaining patient historical records and same batch sample data from a pre-established database by collecting current test result data, and obtaining multimodal information containing original images, detection curves and trend deviations; determining potential abnormal patterns by processing the original images and the detection curves through an abnormality detection algorithm according to the multimodal information; if the abnormal patterns are inconsistent with historical comparison results, comparing previous results of the patient through a trend analysis algorithm to judge the trend deviation degree; obtaining critical project specific threshold values according to the trend deviation degree to obtain key clue recognition results; and the application aims to solve the problem that in the prior art, when facing critical value projects, comprehensive verification and rapid release cannot be balanced, resulting in poor quality control and efficiency of the clinical laboratory.

The invention relates to the technical field of medical data processing, in particular to a urinary post-operation sign monitoring data processing method and system, and the method comprises the steps: obtaining original urinary post-operation sign time sequence flows of a plurality of monitoring devices, and carrying out the verification of data integrity and consistency. And creating a dynamic sign map for describing physiological state evolution, and injecting sign nodes and state transition edges into the dynamic sign map based on the verified time sequence flow. And performing staged partitioning on the dynamic sign map according to a postoperative rehabilitation stage division standard. And deploying independent abnormal probes matched with the stage characteristics in each partition, and performing parallel operation to scan and collect potential abnormal signals. And constructing an abnormal signal aggregation network, and carrying out fusion and priority ranking on the collected abnormal signals. And according to the network output, generating a differentiated graded early warning instruction, and calling a corresponding early warning response protocol for execution. According to the method, modeling of continuous evolution of the postoperative physiological status and staged adaptive anomaly monitoring are realized.

The invention provides a piezoelectric monitoring system and method for internal damage of a concrete pile foundation, and the method comprises the steps: calibrating a potential abnormal region of the concrete pile foundation according to first detection data generated by all piezoelectric intelligent aggregates in the concrete pile foundation in a first time period, thereby screening key piezoelectric intelligent aggregates; the monitoring range of the concrete pile foundation is effectively reduced; according to second detection data generated by all the key piezoelectric intelligent aggregates in the second time period, stress distribution change characteristics of the potential abnormal area are obtained, so that the damage change trend of the potential abnormal area is estimated, and accurate structural damage identification of the concrete pile foundation is achieved; and according to the damage change trend, expected deformation characteristics of the potential abnormal area are determined, so that the overall construction state of the concrete pile foundation is predicted, a damage early warning message is generated, the potential structural damage condition of the concrete pile foundation is effectively predicted, and the structural monitoring efficiency and reliability of the concrete pile foundation are improved.

This invention provides a method for detecting abnormal signals in electroencephalograms (EEGs) based on step-by-step identification and multi-agent decision-making, belonging to the field of biomedical signal processing technology. The method includes: acquiring EEG signal data from epilepsy patients; parsing the data according to a preset channel order and segmenting it into multiple signal segments of equal length; extracting multi-dimensional features from each signal segment; inputting the temporal, frequency, and inter-channel synchronization features of each signal segment into an isolated forest model, and filtering for at least one potential abnormal segment based on an abnormality ratio threshold; performing multi-agent integrated decision-making on each potential abnormal segment based on its depth scattering, wavelet transform, temporal, and frequency features to obtain a comprehensive abnormality score corresponding to each potential abnormal segment; and determining the abnormal signal in the EEG signal data based on the comprehensive abnormality score corresponding to each potential abnormal segment. This invention significantly reduces the false alarm rate of abnormal signal detection in EEGs.

Techniques for remotely monitoring a patient and a corresponding medical device are described. The remote monitoring includes identifying a first set of images representing a particular location of a patient's body in which at least one component of an implantable medical device (IMD) coincides, determining a projection of a change characteristic of the particular location of the body, identifying a second set of images, determining a second set of change characteristics, comparing the second set of change characteristics to the projection, and identifying a potential abnormality at the particular location of the body.

This invention discloses a data analysis-based method and system for early warning of anomalies in basic nickel carbonate production. The method includes: acquiring historical monitoring data within a preset time period; selecting key target data points by calculating data deviation; and extracting multiple historical monitoring data sequences with adjacent target data points as start and end points. During real-time monitoring, when the current monitoring data does not exceed a preset threshold, it is matched with the most similar historical sequence. By analyzing the subsequent data change rate trend of the sequence, it is determined whether there are potential anomalies in the current data. Combining static threshold early warning with dynamic trend early warning can identify risks that are still within the safe range but have abnormal development trends, realizing the transformation from post-event alarm to pre-event early warning. This significantly improves the timeliness and accuracy of early warning in the basic nickel carbonate production process, effectively ensuring production safety and stability.

This invention relates to the field of big data storage technology, specifically to a big data storage service method and system. The method analyzes the temporal growth trend of ad views within the current period, combining historical comparative analysis and the stability of view growth to screen for potentially abnormal periods. Through correlation analysis of page jump sequences within these suspected abnormal periods and their differences from sequences in non-abnormal periods, it obtains abnormal jump indicators. Combined with abnormal user behavior within the current period, it determines normal access behavior. Based on normal access behavior, it integrates ad access and effective interaction data from each user to obtain storage priority for each ad and optimize resource allocation. This invention identifies normal access based on temporal behavior and jump logic differences, and dynamically adjusts ad storage strategies based on user interaction depth, reducing resource waste caused by malicious behavior and improving the effectiveness of storage resource utilization.

The invention relates to the technical field of coal mining, and discloses a coal mining-based driving working face abnormal information detection method and system, and the method comprises the steps: obtaining an acoustic emission signal generated when a heading machine works on a driving working face, separating a cutting pick-coal rock impact signal and a coal rock mass fracture signal based on a propagation path and waveform characteristics of the acoustic emission signal; identifying the wear state of the cutting pick based on the frequency attenuation characteristic of the cutting pick-coal rock impact signal, and extracting a coal rock fracture dominant frequency band based on the spectrum mutation characteristic of the coal rock mass fracture signal; compensating the energy of the coal rock fracture dominant frequency band according to the wear state of the cutting pick, and establishing a co-evolution relationship between the compensated energy and the spectrum mutation characteristic in a time sequence; according to the method, the energy precursor abnormal fluctuation is sensitively captured, so that early-stage, accurate and targeted detection of potential abnormal information of the driving working face is realized, and the accuracy and timeliness of early warning are remarkably improved.

This invention discloses a method and system for measuring the ratio of long bones in human limbs to height based on images. The method includes steps such as acquiring image sequences, selecting target frames, estimating two-dimensional human pose, predicting the ratio of long bones in limbs to height, labeling potential anomalies, and iterative updates. By acquiring human experience data, the method simultaneously verifies the model detection results and the potential anomaly labeling results based on this data. If there is a significant difference between the model detection results and the human experience data, it indicates that the model accuracy is insufficient and needs updating. If the number of abnormal individuals in the potential anomaly labeling results exceeds a preset proportion threshold, it indicates that the weighting of the linear relationship between long bones in limbs and height is incorrect. This invention offers convenient measurement, high measurement accuracy, and is unaffected by factors such as age, gender, or race; it is suitable for obese individuals and has good versatility.

The invention discloses an anomaly detection method, device and equipment of an analyte monitoring system and a medium, and relates to the technical field of medical care information.The anomaly detection method comprises the steps that the concentration of a target analyte at the current sampling moment is monitored through the analyte monitoring system, so that whether the concentration change direction of the current sampling moment is the same as that of the previous sampling moment or not is determined; if the concentration change directions are opposite, constructing a current extreme point pair comprising extreme point time and an extreme value; comparing the current extreme point pair with the previous extreme point pair, and determining a current concentration fluctuation characteristic; if the current concentration fluctuation characteristic meets a preset fluctuation abnormal condition, updating the number of potential abnormal signals, and updating the number of confirmed abnormal signals based on the updated number of potential abnormal signals to obtain an updated number of confirmed abnormal signals; and generating an anomaly detection result corresponding to the moment when the analyte monitoring system stops the current sampling according to the updated quantity of the confirmed anomaly signals. And the anomaly detection of the analyte monitoring system is accurate and reasonable.

The application provides a kind of unknown threat intelligent determination method and system based on multi-modal knowledge graph, it is related to computer security detection technical field.Therein, the application first obtains system performance time series data and security log data;Second, construct network security knowledge graph;Then, system performance time series data is converted into the state attribute of asset node in topological structure layer, and simultaneously, security log data is converted into security event node in event sequence layer, to generate dynamic interaction subgraph;Next, based on dynamic interaction subgraph, graph reasoning is carried out, and the correlation confidence is calculated, when the correlation confidence is lower than the pre-set confidence threshold, it is determined that there is abnormal behavior;Finally, according to the difference between the abnormal behavior and the known threat mode, unknown threat is determined, the technical scheme of the application not only improves the depth and accuracy of potential abnormal behavior discovery, but also improves the cognition and defense ability of unknown threat.

The invention relates to an elevator door operation and maintenance method and system, an intelligent terminal and a storage medium, and relates to the technical field of elevator door operation and maintaining.The method comprises the steps that a landing door is controlled to execute the closing step in response to a closing instruction of the landing door; after the closing step is executed once, a driving frequency record of the portal crane is obtained; when the driving frequency which does not conform to the preset driving frequency interval exists in the driving frequency record, marking the driving frequency which does not conform to the preset driving frequency interval in the driving frequency record as a target driving frequency; acquiring a time period corresponding to the target driving frequency to obtain a target time period; when the sudden stop signal is not received in the target time period, the target time period is associated with the door body operation data, and an abnormal occurrence section is determined; and generating abnormal information corresponding to the abnormal occurrence section, and uploading the abnormal information to a maintenance platform. The method has the effect that the potential abnormity existing in the operation process of the elevator door is recognized, so that the potential safety hazard of the elevator door is reduced.

The embodiment of the invention provides an exception handling method, device and equipment for a transformer substation. The method comprises the following steps: acquiring time sequence data corresponding to a plurality of substation parameters of a substation; preprocessing the time sequence data corresponding to each substation parameter to obtain processed time sequence data; performing feature extraction on each piece of processed time series data to obtain features of each piece of processed time series data; for each transformer substation parameter, based on the characteristics of the processed time series data corresponding to the transformer substation parameter, performing potential anomaly identification by adopting a pre-trained anomaly identification model, and outputting the anomaly degree of the time series data; and determining the time sequence data of which the abnormal degree is greater than a preset threshold value as potential abnormal data, and giving an alarm for the data. The method is used for accurately identifying potential abnormal data in the transformer substation and realizing early warning of faults of the transformer substation, so that the operation and maintenance efficiency and the operation stability of the transformer substation are improved.

Embodiments of the invention provide an abnormal log detection method and device, a storage medium and a program product. The method comprises the steps of obtaining a first log template set of a first log set; determining potential abnormal log templates in the first log template set according to the first log template set and a second log template set, wherein the second log template set comprises a second log template constructed according to a stable second log set; determining a first log matched with the potential abnormal log template from the first log set, and screening a second log related to the semantics of the first log from the second log set; and performing semantic comparison on the first log and the second log through the first model, and determining whether the first log is an abnormal log or not according to a comparison result. By means of the method, efficient and real-time abnormal log detection can be achieved, calculation and storage expenses are remarkably reduced, the accuracy rate and recall rate of abnormal detection are improved, the method can adapt to different log formats and styles between different software systems and assemblies, and the operation and maintenance cost is greatly reduced.

The invention discloses a water conservancy facility inspection fault diagnosis method based on AI analysis, and relates to the technical field of pre-diagnosis, and the method comprises the steps: recognizing a dominant abnormal mode in a preliminary diagnosis report, calculating the attention of the dominant abnormal mode, and generating a dynamic inspection scheduling instruction; positioning a high-risk area according to the dynamic inspection scheduling instruction, collecting enhanced inspection data of the high-risk area, removing interference factors of the enhanced inspection data, extracting structural response, and obtaining a working condition data set; and performing anti-fact reasoning on the working condition data set, outputting an anti-fact diagnosis report, comparing the difference between the anti-fact diagnosis report and the preliminary diagnosis report, reasoning causes, and generating a fault diagnosis report. According to the method, the potential abnormal feature set is constructed and anti-fact reasoning is carried out, so that the causal-level disassembling capability of abnormal causes is realized, the misdiagnosis and missed diagnosis risks are reduced, and the accurate identification capability of the operation safety state of the water conservancy facility is enhanced.

The application discloses a kind of medical pure water machine audit tracking methods, more specifically related to audit tracking technical field, including when identifying that medical pure water machine exists abnormal parameter, call the user of last period to current detection period to abnormal parameter is modified, by the time point clustering analysis of modification user to pure water machine modification, and by comparing the similarity of operation characteristics between modification user, determine the historical operation information of modification user, according to the time series analysis of modification user modification abnormal parameter, determine the historical habit information of modification user, the historical operation information and historical habit information of modification user are comprehensively analyzed, determine suspect user, extract the correlation information and importance information of suspect user each time to medical pure water machine modification, determine the potential abnormal modification record of suspect user, the application helps to improve the operation safety of medical pure water machine, potential abnormal operation is promptly discovered, improve the efficiency of audit tracking.

The invention discloses a multi-mode attack protection method and device for a measurement switch and a medium. The method comprises the steps that S1, the opening and closing state and magnetic field change parameters of a lock are monitored and collected in real time through a magnetic induction sensor; s2, electrical parameters of the metering switch are monitored in real time, and when sudden abnormal step change of power occurs, a time sequence monitoring mechanism is adopted to identify potential abnormity; s3, integrating the physical lock state data and the electrical parameter data, and balancing the importance of different modes through a weighting mechanism; and S4, constructing a decision tree model, performing layer-by-layer splitting from a root node according to a feature threshold, identifying an abnormal type based on a power step amplitude, and classifying abnormal behaviors. And S5, generating an encryption alarm after the decision tree confirms the attack, and uploading the encryption alarm to a remote server. According to the invention, real-time identification and response to physical invasion and electricity stealing behaviors can be realized through multi-source data fusion and intelligent decision.

This application provides a method, apparatus, electronic device, computer-readable storage medium, and computer program product for predicting the health of tightening equipment. The method includes: obtaining a batch of tightening samples of a target tightening equipment from a preset sample trend library; obtaining target sample drift characteristics of the target tightening equipment based on the batch tightening samples and a preset standard template library; obtaining target equipment drift characteristics of the target tightening equipment based on a first normal sample of the batch tightening samples and the preset standard template library; when the number of abnormal samples in the batch tightening samples exceeds a preset threshold, performing a health prediction based on the target sample drift characteristics and the target equipment drift characteristics to obtain a health prediction result. Abnormal samples refer to workpiece samples with abnormal process inspection results. The health prediction result includes at least one of the following: abnormal component type, abnormal confidence level, and abnormal level. This application can predict potential abnormalities in tightening equipment in advance, improving the accuracy of equipment health prediction results.

Systems and methods for automatically detecting instrument anomalies in a sampling device during a medical procedure may include obtaining, via a transducer of a medical device, a sequence of ultrasound images of a target site of the medical procedure. The system or method may also include detecting, in the sequence of ultrasound images, an instrument protruding from the medical device and within the field of view of the transducer. A characteristic of an instrument extending from a medical device is determined for each ultrasound image in a sequence of ultrasound images including the instrument extending from the medical device. The system or method may also include generating an alert indicative of a potential anomaly of the instrument extending from the medical device based on the characteristic of the instrument extending from the medical device exceeding a threshold.

The invention discloses a hybrid shared bicycle system environment impact assessment method and system based on digital twinning, relates to the technical field of shared bicycle system assessment, and realizes denoising and stable modeling of station occupation data by introducing a long-term baseline and a standardized data set, so that abnormal oscillation stations can be accurately identified. Through an oscillation intensity index (i.e., an accumulated variation value normalization result), normal periodic fluctuation and abnormal large fluctuation can be effectively distinguished. And a hotspot cluster set is formed by further combining density clustering, and the future short-term fluctuation direction is predicted based on a trend quantity, so that a potential abnormal region is locked in advance in double dimensions of space and time. The mechanism ensures that scheduling triggering does not depend on single-point data any more, but improves timeliness and accuracy of vehicle supplementing operation based on double judgment of stability and tendency.

The application provides a river channel regulation engineering monitoring method and system, which belongs to the technical field of engineering monitoring. The method comprises the following steps: based on channel scene information, a matching monitoring parameter set, an abnormal diagnosis rule library and an abnormal threshold set are called from a pre-constructed knowledge base, a monitoring network device is cooperatively dispatched to collect multi-source monitoring data for abnormal identification, and potential abnormal problems and abnormal confidence are obtained; if the abnormal confidence is greater than the abnormal threshold, a drone is controlled to verify the potential abnormal problems on site, and a reliability evaluation result is generated; if the reliability evaluation result is greater than a reliability threshold, the multi-source monitoring data are input into a time series prediction model to obtain a trend prediction result, which is compared with a maintenance critical threshold to obtain a spatiotemporal position of future maintenance needs, a channel comprehensive monitoring index is obtained through a mechanism-data hybrid model, and if the comprehensive monitoring index is greater than a warning threshold, warning information is generated; finally, a cooperative scheduling strategy is generated through an optimization algorithm and is executed.