141 results about "Potentially abnormal" patented technology

The invention discloses a large and small model-based data anomaly detection method, system and device and a medium, belongs to the technical field of data processing and artificial intelligence, and aims to solve the technical problem of how to combine the rapid screening capability of a small model and the deep analysis capability of a large model to realize efficient and collaborative anomaly detection and cleaning. According to the technical scheme, an improved Isolation Forest small model is used for rapid screening, potential anomalies in large-scale data are rapidly detected through a dynamic threshold adjustment mechanism, adaptive feature weight distribution and multi-scale division optimization, an anomaly score is generated, and suspected abnormal data are preliminarily screened; secondly, performing fine detection on suspected abnormal data by utilizing a large model, and accurately identifying an abnormal type and generating a high-reliability abnormal result through deep semantic embedding analysis, abnormal feature clustering and domain rule verification; and finally detected abnormal data are classified and labeled, and the data characteristics are combined to process the abnormal data by adopting a deletion, correction or marking cleaning strategy.

The invention discloses an abnormal data prediction method and system based on a dynamic threshold value and pattern mining, and the method comprises the steps: carrying out the mining of an association rule and a sequence pattern of historical monitoring data, recognizing the association relationship among a plurality of monitoring indexes under different data collection sources, and extracting a frequently appearing sequence pattern; setting a dynamic threshold range based on the mean value and the standard deviation of the historical monitoring data; dynamically adjusting the threshold range according to the real-time operation state and the change trend of the historical monitoring data; and through matching the real-time data with the historical mode, predicting a potential anomaly, and giving out an early warning. According to the method and the system, through adaptive dynamic threshold and abnormal mode mining, intelligent prediction and early warning of anomalies in a data acquisition process are realized, and post-processing is converted into beforehand early warning; through real-time monitoring and model analysis, the system can detect potential anomalies in advance, optimize data quality, improve operation and maintenance efficiency and reduce manual intervention.

The invention relates to the field of anomaly detection, and discloses a PCB (Printed Circuit Board) anomaly detection method, which comprises the following steps of: acquiring a PCB template image and a to-be-detected PCB image; registering and aligning the image of the PCB to be detected with the image of the PCB template; obtaining one or more potential abnormal areas of the to-be-detected PCB image by comparing the edge energy; and comparing the potential abnormal area with a coordinate frame area corresponding to the PCB template image by using a measurement model, and confirming an abnormal state through a comparison result. According to the scheme, PCB abnormity detection can be realized accurately, and when a new PCB and a new component are introduced, only a current PCB normal model needs to be newly added.

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 an electric power internet of things operation and maintenance management method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining sensor data, carrying out the cleaning and time alignment of the sensor data, and obtaining standard operation data; based on a historical data window corresponding to the standard operation data, calculating a mean value and a standard deviation of the historical data, and further generating a dynamic detection threshold value; comparing the standard operation data of the current time node with a dynamic detection threshold value, and screening out potential abnormal data; calculating an abnormal trend factor corresponding to the potential abnormal data, and determining an abnormal development trend of the potential abnormal data according to the abnormal trend factor; and determining a corresponding causal influence weight based on a preset causal relationship network, and calculating a fault score according to the causal influence weight and the abnormal trend factor, so as to generate an early warning report according to the fault score. The method has the effect of improving the management efficiency of the power equipment.

The invention relates to the technical field of electronic information, and provides a real-time monitoring and anomaly detection method for an AI independent key in an all-in-one machine product, which comprises the following steps of: firstly, acquiring a to-be-detected all-in-one machine, identifying the AI independent key in the all-in-one machine, monitoring a key trigger signal, inquiring a signal feature code, performing spectrum analysis to obtain a feature spectrum sequence, and extracting a spectrum key value; detecting a key response track corresponding to the frequency spectrum key value, performing dynamic regularization analysis to obtain a regularized track sequence, identifying track sequence parameters, calculating track response values, collecting the change trend of the track response values, determining a fluctuation amplitude value to determine an adjustment mode, identifying a key state, comparing state data to obtain state comparison parameters, and performing dynamic regularization analysis to obtain a state comparison result; and inquiring potential abnormal factors, determining an abnormal level and an analysis index, constructing an abnormal detection signal and generating an abnormal detection scheme. The key use stability of the all-in-one machine product can be improved.

The invention discloses a network equipment abnormity monitoring and early warning method and system based on AI, and belongs to the technical field of network equipment monitoring, and the system module comprises a multi-source data acquisition module which obtains various characteristic parameters of network equipment; the data transmission and processing module is used for transmitting and preprocessing the characteristic parameters; the intelligent analysis module performs comprehensive analysis based on the characteristic parameters and judges whether the network equipment is abnormal or not; and the early warning response module performs hierarchical response based on a judgment result of the parameter analysis module. According to the invention, under the normal operation condition of the network equipment, the potential abnormal risk of the network equipment can be judged according to the influence condition of the environment where the network equipment is located and the relative deviation condition of each piece of network equipment, so that the potential abnormal risk can be handled in time, thereby reducing the occurrence of subsequent abnormal situations and improving the safety of the network equipment. And normal and stable operation of network equipment is ensured.

The invention discloses an intelligent data anomaly detection method and system based on an Internet of Things integrated management and control technology, and the method comprises the steps: S1, collecting and preprocessing intelligent data, and constructing a standardized data set; s2, performing preliminary anomaly detection on the standardized data set by adopting an isolated forest algorithm, and screening out potential abnormal data points; s3, constructing a self-supervised training sample, performing enhancement detection on the abnormal data point set, and generating a correction factor; s4, recalculating an abnormal score for each abnormal data point based on the correction factor; s5, abnormal data points output through re-correction are classified and stored, and log information is generated; and S6, dynamically optimizing the model parameters according to a detection result fed back in real time. According to the method, an efficient and scientific optimization scheme can be provided in Internet of Things intelligent data anomaly detection, and remarkable technical values and economic benefits are brought to practical application.

The invention discloses an elevator operation fault early warning method and system, relates to the technical field of intelligent elevator control, and aims to improve the operation safety and fault response capability of an elevator in a potential abnormal state. By constructing a load critical drift test working condition, a sudden non-vertical stress test working condition and a weak power supply fluctuation interference test working condition, abnormal behaviors which are difficult to appear but have potential safety hazards in the normal operation process of an elevator are induced; the method comprises the following steps: acquiring operation data under various working conditions, respectively calculating a drift overload response coefficient, a guide rail non-vertical interference coefficient and a power supply induced instability coefficient, respectively comparing the coefficients with preset thresholds, and judging whether operation faults such as brake response mismatching, asynchronous action of safety tongs and door lock control interference exist or not; and if the judgment result is abnormal, a targeted strategy is automatically generated and early warning is given out. According to the method, active recognition and classified response of elevator faults can be achieved, and the maintenance efficiency and the operation reliability are effectively improved.

The invention discloses a grotto temple cavern microenvironment monitoring data quality control and evaluation method, and particularly relates to the field of grotto temple cavern microenvironment monitoring data, and the method comprises the steps: obtaining grotto microenvironment monitoring data, carrying out the time-space reconstruction of the grotto microenvironment monitoring data, and building a data expression basis with a consistent attribute structure. An execution entry is provided for subsequent processing; potential abnormal fragments are identified from the cross relation between the time rhythm and the numerical value change, abnormal points are eliminated, and the validity of the cave microenvironment monitoring data value domain is guaranteed; the problem of disordered timestamp sequence is recognized, a correct sorting structure is recovered in combination with a trend rule, and time sequence consistency is guaranteed. By constructing a multi-stage closed-loop processing chain with rhythm change recognition, numerical mutation elimination, trend sequence recovery and structure quality fusion as the core, structure unification, anomaly recognition, time sequence correction and credibility output of monitoring data are achieved, and therefore effectiveness, consistency and application value of the monitoring data are systematically improved.

The invention relates to the technical field of ERP (Enterprise Resource Planning) data processing, in particular to an ERP data exception analysis method and system based on big data. Extracting time sequence features and frequency domain features of the ERP potential abnormal data, inputting the time sequence features and the frequency domain features into a fluctuation detection model, carrying out fluctuation recognition on the ERP potential abnormal data to obtain ERP fluctuation data, and carrying out multi-dimensional linkage analysis on the ERP fluctuation data through a covariance analysis method to obtain linkage strength among different types of ERP fluctuation data; according to the linkage strength and the time sequence characteristics and the frequency domain characteristics of the ERP potential abnormal data, a service category target threshold value is obtained through a dynamic threshold value adjustment model, abnormal judgment is conducted on the ERP fluctuation data according to the service category target threshold value, when the ERP fluctuation data exceed the target threshold value range, the ERP fluctuation data are marked as first target abnormal data, and a data abnormal early warning mechanism is triggered. And more accurate data anomaly analysis is realized.

The invention relates to an abnormal data identification method and device, computer equipment and a storage medium. The method comprises the following steps: constructing a user social network graph according to user communication information; dividing risk groups according to the user login information and the user transaction information to obtain high-risk groups; performing association marking in the user social network graph according to the high-risk group to obtain a potential abnormal group; marking a target user to be identified for the user login information and the user account information of the potential abnormal group; and obtaining the logout times of the to-be-identified target user, and marking the to-be-identified target user as target abnormal data according to the logout times. The method and the device can be applied to financial anti-fraud application scenes, target abnormal data with fraud risks in users can be effectively and accurately identified, and the identification reliability is improved.

The invention discloses an intelligent adjusting method and system for an automobile clamp, and relates to the technical field of automobile manufacturing, through accurate three-dimensional position data acquisition and intelligent automatic adjustment, the clamp can quickly adapt to the actual position and angle change of parts, manual intervention is greatly reduced, and the assembly precision and efficiency are improved; the state monitoring module tracks the pressure, temperature and operation state of the clamp in real time and compares the pressure, temperature and operation state with standard values, the operation state and potential abnormity of the clamp can be effectively monitored, faults can be early warned in advance, and therefore the risk of sudden faults is reduced. The data analysis module comprehensively analyzes the operation state factor F1, the clamping force factor F2, the environment factor F3 and the adjustment precision factor P1 of the clamp, generates a comprehensive abnormal index X1, can accurately judge the maintenance requirement of the clamp, automatically adjusts and processes faults, greatly simplifies the maintenance process, and improves the system reliability and the maintenance efficiency.

The invention provides a data evaluation method and device based on historical local anomalies, equipment and a medium. The method comprises the following steps: performing data preprocessing on historical data in environmental monitoring, calculating a k-neighborhood distance, a reachable distance, a local reachable density and a local anomaly factor, and generating reference density distribution information; processing the reference density distribution information, merging test data points with historical data one by one to calculate an LOF value, screening out potential abnormal samples in combination with a set threshold, and generating potential abnormal sample information; for potential abnormal samples, Z-Score is calculated according to feature dimensions, and abnormal index information of each dimension is generated in combination with a set threshold value; processing the potential abnormal sample information and the abnormal index information of each dimension, marking a sample abnormal state and positioning an abnormal source index, and generating a sample abnormal state mark and abnormal source index positioning information; and processing the sample abnormal state mark and the abnormal source index positioning information to generate abnormal information of the environmental data.

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 discloses a control method and device of a fault diagnosis system and a storage medium, and belongs to the technical field of fault diagnosis systems. The method comprises the steps that fault description information and multimedia data are received, text features and multimedia features in the fault description information and the multimedia data are extracted, the text features and the multimedia features are fused based on an attention fusion module, fusion features are generated, sensor data of to-be-analyzed equipment are obtained, sensor time sequence features are generated, and the fault description information and the multimedia data are analyzed; and the fusion features and the sensor time sequence features are mapped to a hidden space, the fusion features and the sensor time sequence features are aligned in the hidden space, joint representation is generated, and a target fault type is identified through a fault classification model. According to the method and the device, the user subjective description and the joint representation of the equipment objective signal are fused, so that the joint representation can simultaneously capture the fault phenomenon details described by the user and the potential abnormal mode in the sensor time sequence signal through cross-modal feature coupling, and the comprehensiveness and the reliability of the fault diagnosis result 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 discloses a medical water purifier auditing and tracking method, and particularly relates to the technical field of auditing and tracking, and the method comprises the steps: when an abnormal parameter of a medical water purifier is identified, calling a user who modifies the abnormal parameter from a previous period to a current detection period, and carrying out the clustering analysis of the time point of modifying the water purifier by the modification user, the historical operation information of the modification users is determined by comparing the similarity of the operation characteristics of the modification users, the historical habit information of the modification users is determined according to the time sequence analysis of the modification abnormal parameters of the modification users, the historical operation information and the historical habit information of the modification users are comprehensively analyzed, and suspected users are determined; the association information and importance information of the medical water purification machine modified by the suspected user every time are extracted, the potential abnormal modification record of the suspected user is determined, the operation safety of the medical water purification machine can be improved, potential abnormal operation can be found in time, and the auditing and tracking efficiency is improved.

The invention discloses a threat intelligence automatic generation method and device. The method comprises the following steps: acquiring safety behavior data in real time; performing feature extraction on the security behavior data to obtain target behavior features; comparing the target behavior characteristics with a boundary threshold range of a pre-trained normal baseline model; the normal baseline model is obtained based on the boundary threshold range of the behavior feature data obtained by feature extraction; inputting the target behavior characteristics into a pre-trained anomaly detection model to obtain a current anomaly score; the anomaly detection model is obtained by constructing a random isolation tree based on a historical feature matrix obtained based on historical behavior features and training based on the random isolation tree; if the current exception score is greater than a preset exception sample threshold value, judging the security behavior data as first exception data; and the first abnormal data is processed to obtain threat intelligence, so that the potential abnormal behaviors are accurately identified, and the threat detection efficiency and response capability in a complex environment are improved.

The invention relates to the field of vulnerability mining, in particular to a vulnerability mining method based on a large model, and the method comprises the steps: constructing a label combination library for a working condition terminal, and subsequently constructing a correlation model for a label combination based on a credible log fragment corresponding to each label in the label combination; based on the occurrence probability of each label combination and combined with the constraint discrete features of the association model corresponding to the label combination, effective sub-labels are set for the label combination, a current label combination of a current log record is analyzed subsequently for a new log record, and adaptability analysis is performed on the log record according to the effective sub-labels corresponding to the current label combination, so that the log record is obtained. According to the method, potential laws of the operation process of the industrial control terminal are considered, the association model of the log records under the specific label combination is constructed, the log records are analyzed based on the effectiveness adaptability of the label combination, log fragments possibly representing that potential abnormal vulnerabilities exist in a system are rapidly marked when massive log records are faced, and the system reliability is improved. And the accuracy and efficiency of vulnerability mining are improved.

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 an epilepsy prediction method. The epilepsy prediction method comprises the following steps: acquiring various physiological signal data; training a corresponding auto-encoder by using each kind of physiological signal data; acquiring physiological signal sequences in the same sequence form according to various physiological signal data and the trained auto-encoders; and training an epilepsy prediction model by using the physiological signal sequence and the epilepsy behavior event tag. According to the epilepsy prediction method, a multi-modal data fusion framework is provided, unified feature coding can be carried out on data of different modals from multiple sources, and the epilepsy prediction model is trained by using a coded sequence, so that the anomaly detection capability of the epilepsy prediction model is stronger, and the epilepsy prediction accuracy is improved. Some unobvious potential abnormal attacks are further detected, and different behavior phenotypes of epilepsy objects can be classified.

The invention relates to the field of project management, in particular to a data analysis method for project management, and the method comprises the steps: obtaining the high-dimensional data of a scaffold after preprocessing, the high-dimensional data comprising the bearing capacity, the inclination angle and the space coordinates; classifying the high-dimensional data of the scaffold into a plurality of clusters by utilizing an iterative self-organizing clustering algorithm; wherein in the clustering process, the inter-cluster separation degree and the intra-cluster compactness of each clustering cluster are calculated, and if the mean value of the inter-cluster separation degree and the intra-cluster compactness of the current iteration is larger than that of the last iteration, the number of iterations is increased; otherwise, reducing the number of iterations; and performing anomaly detection on the high-dimensional data in each cluster, and when the high-dimensional data of the scaffold at the same position is continuously marked as abnormal data points for multiple times, judging that the position is abnormal. According to the method, the clustering result is dynamically optimized, so that the potential abnormal area in the scaffold structure can be effectively identified, and the safety and the reliability of the scaffold are improved.

The invention relates to the technical field of abnormal event prevention and control, and discloses an abnormal event prevention and control method based on image recognition, and the method comprises the following steps: data collection; image preprocessing: processing the collected image to improve the accuracy of subsequent identification; an abnormal event recognition model is constructed, a multi-layer convolutional neural network model is constructed based on the deep learning technology, and training data comprises a labeling data set of normal behaviors and abnormal behaviors; real-time monitoring and analysis: carrying out image real-time analysis by utilizing the trained model, and setting a threshold value to judge whether an abnormal event exists or not; an early warning and response mechanism; and a feedback and learning mechanism: after the security personnel deal with the event, the result is fed back to the system, and self-learning and optimization of the model are promoted. Through intelligent image recognition and real-time analysis, automatic recognition and quick response to potential abnormal events are realized, the monitoring efficiency of the abnormal events is greatly improved, and various abnormal conditions can be effectively handled.

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.