164 results about "False positives and false negatives" patented technology

The invention discloses a multi-source information fusion equipment health diagnosis management system, and relates to the technical field of equipment management. Comprising an information acquisition module, a feature perception module, a processing fusion module, an equipment modeling module, an evolution prediction module, a root cause diagnosis module, a cross-domain inspection module, a learning sharing module, a state evaluation module, a collaborative decision-making module, a chain account book module and a visual decision-making module. According to the method, the perspectiveness and the sensitivity of anomaly detection are remarkably improved, noise interference and sampling deviation are reduced, fused data are more stable and have higher physical consistency, differential diagnosis and individual-level prediction are supported, the interpretability of diagnosis and the decision reliability are improved, false alarm and missing alarm are avoided, the early warning reliability is improved, and the method is suitable for popularization and application. And scientific, transparent and traceable health management is realized.

The invention relates to the technical field of machine tool fault diagnosis and maintenance, and discloses a machine tool fault predictive maintenance method based on vibration analysis, which comprises the following steps: collecting vibration, temperature and acoustic emission signals and machine tool working condition parameters through a multi-modal sensor, extracting multi-domain features after preprocessing the vibration signals, and combining the working condition parameters through feature fusion and dimension reduction to obtain a machine tool fault predictive maintenance result. And establishing a fault classification model by using transfer learning, and performing hierarchical optimization. Model parameters are updated in real time based on an online learning mechanism, a fault early warning agent model is constructed to predict fault probability distribution, a dynamic threshold strategy is designed to avoid false report and missing report, and finally, related models and strategies are integrated to edge computing equipment. According to the method, multi-source data are integrated, multiple advanced algorithms are applied, machine tool faults can be accurately predicted, real-time monitoring and maintenance decision output are achieved, the machine tool operation reliability is improved, and the maintenance cost is reduced.

The invention discloses an intelligent diagnosis method for electric power secondary equipment based on digital twinning, which relates to the technical field of operation and maintenance of electric power equipment, and comprises the following steps: uniformly accessing real-time data of the electric power secondary equipment and carrying out timestamp standardization to complete cross-channel alignment; constructing an equipment-level digital twinborn body, and calculating a key business volume, a contrast deviation of an output and a field volume and a credible interval; cross-channel transition parameters, key electrical parameters and link state parameters are extracted under the unified time axis and secondary network topology, and a constraint quantity set is generated; and constructing an evidence chain on the topology based on the contrast deviation and the constraint quantity, executing root cause convergence and conflict stripping, and forming a diagnosis conclusion with a confidence level and processing steps. Through unified time reference and multi-domain twinborn contrast, triple constraint quantity weighting and evidence chain reasoning, cooperative constraint of time sequence consistency, physical consistency and safety boundary is realized, positioning precision and closed loop efficiency are improved, false alarm and missing alarm are reduced, strong isolation of simulation and production links is guaranteed, and visual tracing is realized.

The invention relates to a production abnormality automatic identification and recovery process control method, which comprises the following steps of S1, realizing second-level synchronization of multi-source heterogeneous data, constructing a real-time data flow pipeline and generating a total-factor production situation data flow through a distributed message queue by adopting a Modbus / TCP protocol analysis algorithm based on industrial Internet of Things edge calculation; through the cooperative effect of industrial protocol analysis and distributed message queues, second-level synchronization of multi-source heterogeneous data is realized, a real-time data flow pipeline is constructed, data acquisition delay is effectively eliminated, the timeliness of anomaly detection is ensured, a dynamic weight distribution mechanism of a rule engine and a long and short-term memory network prediction model is adopted, and the real-time performance of the system is improved. By combining sliding window threshold detection, the accuracy and coverage of anomaly recognition are improved, false alarm and missing alarm caused by a single detection mechanism are reduced, and multi-dimensional root cause tracing is performed by combining a fault mode knowledge base through combined application of time sequence correlation analysis and a causal diagram inference engine.

The invention discloses a fruit and vegetable juice production line equipment fault tracing method, which comprises the following steps of: acquiring parameters such as temperature, pressure, vibration, rotating speed and motor current in real time through a multi-channel sensor, and establishing a working condition characteristic database by combining filtering, normalization, statistics and frequency domain characteristic extraction; based on a support vector regression algorithm, a nonlinear mapping model of working condition features and anomaly detection thresholds is constructed, and dynamic threshold adaptive output and real-time anomaly judgment for different working conditions are realized; according to a detection result, a fault signal is automatically triggered, a model is continuously incremented and trained, the adaptability to new working conditions is improved, the accuracy, intelligence and stability of equipment anomaly detection are effectively improved, misinformation and missing information can be reduced, and the automatic operation and maintenance level of a production line is enhanced.

The invention discloses an industrial equipment defect detection method based on multi-modal feature fusion and dynamic optimization, and belongs to the technical field of computer vision, and the method comprises the steps: 1, multi-modal industrial data collection: deploying multiple sensors in a production line, collecting data in multiple periods, constructing a defect-free and multi-type defect sample library, and carrying out multi-modal industrial data collection; a time-space aligned multi-modal label is marked; step 2, data enhancement and defect synthesis; step 3, multi-modal hybrid model training: constructing a hybrid network, and performing pre-training and fine tuning by using a dynamic loss function; step 4, edge end dynamic optimization and deployment: edge end reasoning is realized through dynamic knowledge distillation, and model fine tuning is automatically triggered when false detection and missing detection are found; and step 5, intelligent labeling and result visualization: a front-end interface displays a detection result in real time. The problem that a current target detection framework is not high in small target recognition accuracy and low in efficiency is solved, and the reliability of industrial equipment defect detection is improved.

The invention relates to a computer data processing technology, in particular to a real-time pipeline safety early warning method and system. The method comprises the steps of performing computer data processing according to pipeline topology information, dividing risk units and generating a risk unit session configuration structure; performing automatic feature extraction to generate a real-time risk feature vector; through machine learning fusion modeling, real-time features and historical baseline features are fused, and a dual-time-scale fusion feature vector is constructed; a preset intelligent model is used for reasoning and event judgment, a structured event flow account and resource adjustment parameters are generated, periodic self-optimization of sampling configuration, a model threshold value and an early warning strategy is driven, and therefore closed-loop intelligent monitoring and early warning of the state of the pipeline auxiliary equipment are achieved. According to the method, the early warning accuracy can be effectively improved, false alarm and missing alarm can be remarkably reduced, the adaptive capacity of the system is enhanced through a closed-loop learning mechanism, and an efficient solution is provided for application of the next generation of information network industry in related fields.

A computer-implemented method for computational textual search to find and display search identified information in documents. Search queries are processed over one or more documents either at a server or user device using match schemes that produce both binary (match or no match) and non-binary (i.e. multiple matching values) results and have relative fuzziness relationships. A fuzzier match scheme implies more results and fewer false negatives and a less fuzzy match scheme implies fewer results and fewer false positives. This fuzziness relationship allows, without changing a search string, for users to quickly change from a match scheme to a fuzzier or less fuzzy match scheme depending on user evaluation of results as containing too many false negatives or too many false positives—without becoming a programmer of complex search string metadata or an expert user of advanced search capabilities.

The invention relates to a water quality data abnormal drift identification and alarm method and device and a storage medium. The method comprises the following steps: acquiring water quality data within a set time length, preprocessing the water quality data, and calculating a median for data in each preset time granularity unit in the processed water quality data to obtain a corresponding median sequence; performing anomaly detection based on the local stability of the median sequence, and generating a drift judgment result when the relatively most stable part in the median sequence significantly changes; and under the condition that the drift judgment result indicates that abnormal drift data exists, determining that an occurrence reason of the abnormal drift is system disturbance based on corresponding data collected by a target sensor in the water quality monitoring equipment, and further generating alarm information aiming at the abnormal drift data. According to the embodiment of the invention, false alarm and missing alarm caused by data drift due to system disturbance can be effectively reduced, and the accuracy, the reliability and the operation maintenance efficiency of the water quality monitoring system are remarkably improved.

The invention discloses an underground pipe network construction segment supervision system based on deep learning. The method comprises the following steps: establishing a segment coordinate framework by using construction segment boundary information and mileage pile number information; constructing a super voxel graph; obtaining super voxel maps with consistent time sequences; generating a cross-modal feature vector; outputting a first abnormity identification result; updating the first dense residual neural network model to obtain a second dense residual neural network model; obtaining a third dense residual neural network model, and outputting a second uncertainty score and a third anomaly recognition result; and generating a structured anomaly report according to the third anomaly recognition result, the second uncertainty score and laws and regulations in the building information model and the geographic information model. According to the method, abnormal features of different spatial scales can be captured at the same time, meanwhile, multi-type abnormal structures are subjected to accurate grading identification, the generalization ability and the robustness adaptability to multi-scale anomalies are higher, and the probability of false alarm and missing alarm is remarkably reduced.

The invention provides an advanced memory performance monitoring and anomaly detection method, system and device, and relates to the technical field of memory monitoring, and the method comprises the steps: capturing a current memory operation executed by a monitored kernel and corresponding operation data through a memory delay monitoring module based on a kernel tracking point mechanism, the memory delay alarm module is used for judging whether memory delay alarm is performed for a monitored kernel; under the condition that the judgment result is yes, constructing a memory delay diagnosis event corresponding to the monitored kernel based on the operation data through a diagnosis event generation module; and through a diagnosis event distribution module, the memory delay diagnosis event is added to the to-be-processed diagnosis event queue, so that the memory diagnosis framework processes the memory delay diagnosis event contained in the to-be-processed diagnosis event queue, and a performance monitoring and anomaly detection result corresponding to the monitored kernel is obtained. According to the method, the monitoring fine granularity can be remarkably improved, and the false alarm and missing alarm conditions in the performance monitoring and abnormity monitoring process are reduced.

The invention discloses a cutting machine tool wear detection method and system based on image recognition, and relates to the technical field of wear detection, and the method comprises the steps: collecting multiple tool nose images in a short time window through triggering signal control, obtaining the time sequence information of a tool state, and eliminating the interference through combining with an image preprocessing and time sequence alignment technology. A U-Net model is used for precisely segmenting a single-frame image to obtain a tool abnormal area, dynamic association among multiple frames of images is deeply excavated through connected component analysis and time sequence consistency verification, a real abrasion area is screened out from a multi-frame sequence, and finally a tool abrasion detection result is obtained. Therefore, non-wear interference can be effectively distinguished and eliminated, and the defects that a traditional method is insufficient in precision and prone to false detection and missing detection are overcome, so that the accuracy and robustness of a detection result are remarkably improved, false alarm and missing alarm are avoided, and the intelligent production requirement of the modern industry is met.

The invention discloses an abnormal user behavior detection method and system based on a large-flow data stream, and the method comprises the following steps: receiving a user behavior event data stream in real time, and carrying out the analysis and structural processing of the data stream; performing feature engineering on the structured data based on a preset time window and a dynamically maintained user state, and generating a feature vector representing an instant and historical behavior mode of a user; performing anomaly scoring on the feature vectors by using a detection model; performing dynamic weight fusion on the abnormal scores of multiple dimensions output by the detection model for the same behavior event to generate a comprehensive abnormal score; and triggering early warning or automatic response operation based on the comprehensive abnormal component. According to the invention, the real-time requirement of abnormal user behavior detection can be met on the basis of reducing the false alarm rate and the missing report rate, and the method has relatively strong adaptability to service changes.

The invention relates to the technical field of image recognition, in particular to a petrochemical pipeline leakage recognition method based on an unmanned aerial vehicle inspection image, which comprises the following steps: generating a pixel-level surface normal vector field according to an unmanned aerial vehicle inspection image sequence, and calculating an angle variation between adjacent vectors in the pixel-level surface normal vector field, and generating a surface normal vector gradient map in combination with luminosity change information of the multiple frames of images. According to the method, the analysis range is focused in the determined geometric deformation candidate area, the unique gloss and color change characteristics of liquid leakage are captured, and finally, the wet abnormal coordinate set and the geometric deformation area are subjected to space alignment and overlapping judgment, so that the high credibility of an identification result is ensured, and the identification accuracy is improved. False report and missing report caused by interference of environmental factors such as illumination change and surface stains of a single feature source are avoided.

The invention relates to the technical field of medical monitoring, in particular to an off-bed activity multi-mode monitoring risk early warning method and system. According to the method, data such as bed surface pressure, non-contact posture / displacement and vital signs are collected, time alignment and modal confidence evaluation are carried out, cross-modal consistency is calculated and gated fusion is carried out, and a risk score is obtained by combining off-bed urgency degree prediction, postural intolerance physiological response and pipeline traction risk. And outputting a prompt / early warning / alarm and adaptively updating a threshold value and parameters under nursing feedback, thereby reducing false alarm and missing alarm and realizing advanced intervention.

The invention provides a network intrusion detection method and system based on artificial intelligence, and relates to the technical field of network intrusion detection, and the method comprises the steps: obtaining multi-dimensional behavior data of a terminal domain, a network link domain and an application layer domain, and employing a high-density or hierarchical collection strategy according to a network type; preprocessing the data through a standardization priority or coding priority scheme according to feature types; constructing a CNN-LSTM fusion model adaptive to network dynamics, and extracting space and time sequence correlation features; based on the sample condition, adopting full or incremental training to obtain a convergence model; a result is output through a high-precision or high-speed detection strategy in combination with scene requirements; and continuously iteratively optimizing the model based on the environmental change. The system correspondingly comprises a multi-domain data acquisition module, a data preprocessing module and the like. The method breaks through the limitation of single-domain detection, accurately recognizes cross-domain cooperative attacks, adapts to different network scenes, reduces the false alarm and missing alarm rate, improves the detection real-time performance and stability, and is suitable for various network environments such as enterprise intranets and hybrid clouds.

The invention discloses a computer cluster security protection method and system. The method comprises the following steps: collecting multi-dimensional operation data in real time through an agent program deployed at each node; feature parameters are extracted from the data, and a safety evaluation model is adopted to calculate a safety score and map the safety score into a discrete safety level; gradient protection strategies including basic monitoring, enhanced protection and strict isolation strategies are automatically matched and loaded according to the security level; and monitoring a strategy execution effect, and performing closed-loop optimization on the model parameters based on feedback data. The system correspondingly comprises a data acquisition module, a safety evaluation module, a strategy management module, a strategy execution module and a feedback optimization module. According to the method, the problems of static strategy stiffness, inaccurate risk quantification, single response means and lack of self-learning are solved, dynamic self-adaptive protection is realized, the evaluation accuracy is improved, false alarms and missing alarms are reduced, and the cluster environment is ensured to be safe and reliable.

The invention relates to an electric meter box temperature rise abnormity detection method and detection system based on multi-terminal aggregation in the technical field of power distribution safety monitoring. The electric meter box temperature rise anomaly detection method comprises the following steps: establishing a single-end baseline model of an ith terminal about temperature-current; acquiring electric power parameters of I electricity meters in the electricity meter box and terminal temperatures Tt, i corresponding to I terminals, and obtaining a steady-state temperature rise baseline temperature at the moment t in the kth detection period based on a single-end baseline model; according to the method, through aggregation analysis and correlation modeling of multi-terminal data in the electric meter box, the local anomaly and the coupling effect between groups can be identified, the accuracy and robustness of temperature rise detection are remarkably improved, meanwhile, a three-layer constraint judgment system is constructed, and the accuracy and robustness of temperature rise detection are improved. Through a hierarchical judgment mechanism of statistical constraint, physical constraint and group constraint, data statistical characteristics and physical correlation characteristics are comprehensively considered, false alarm and missing alarm caused by single-factor fluctuation can be effectively reduced, and the false alarm rate is reduced.

The invention relates to the technical field of data analysis, and particularly provides a keyword-based alarm analysis method and device, a medium and equipment, and the method can comprise the steps: obtaining the occurrence frequency of a keyword in a to-be-processed data stream in a preset time period; obtaining an abnormal parameter of the keyword based on the average frequency of the keyword in the corresponding historical preset time period and a pre-constructed target model; wherein the target model is a distribution class model based on probability statistics; and determining whether alarm information is generated or not according to the abnormal parameters and the occurrence frequency. According to some embodiments of the invention, accurate and automatic monitoring of keyword abnormity can be realized, false alarm and missing alarm of alarm are effectively reduced, and alarm precision is improved.

According to the intelligent diagnosis method and device for the operation state of the air preheater, by constructing a multi-mode state recognition and trend pre-judgment system of the air preheater, three operation states of'normal ', 'slight anomaly' and'serious anomaly 'can be quantitatively distinguished, historical trend visual comparison is supported, and scientific making of a maintenance plan is assisted; a modeling and multi-parameter combined judgment mechanism is adopted, manual experience is effectively replaced, low-probability and high-risk fault symptoms are accurately captured, false alarm and missing alarm are remarkably reduced, and dependence on manual inspection is reduced; each module of the system supports independent modeling and dynamic updating, has good adaptability and expandability, and is suitable for different units, capacities and manufacturer equipment; through early warning of hidden dangers such as blockage, leakage and aging in advance, non-planned furnace shutdown is avoided, meanwhile, the operation and maintenance rhythm is optimized, the operation resistance and energy consumption are reduced, the heat exchange efficiency is improved, and therefore the reliability and economical efficiency of equipment operation are enhanced.

The invention provides an electric field operation safety management and control method and system, and relates to the technical field of power plant operation data processing, an electric field operation site is divided into a plurality of sub-regions, laser point cloud data are collected, attribute parameters of the laser point cloud data are analyzed, optimization is carried out if problems of shielding, insufficient density and the like exist, and finally a refined three-dimensional point cloud model is constructed. The modeling mode can effectively improve the scene space reduction degree and the risk area identification precision, provide high-quality basic data for subsequent operation, receive the position signal of the target entity and map the position signal to the three-dimensional point cloud model in real time, and dynamically adjust the signal mapping precision by monitoring the mapping process parameters of the position signal and dynamically adjust the signal mapping precision. Therefore, the spatial position of the entity in the operation site is accurately obtained, the position information of the entity is compared with the risk area, whether the entity enters the preset safety control threshold value or not is judged, graded early warning management and control are achieved, the site safety response efficiency is practically improved, misinformation and missing report are reduced, and it is guaranteed that the operation process is safe and efficient.

The invention discloses a bulb tube ignition intelligent early warning algorithm based on multi-factor analysis, and the algorithm comprises the following steps: collecting data through different sensors on a bulb tube, and carrying out the feature extraction of the collected multi-factor data; constructing an early warning model based on a deep learning algorithm; performing rolling updating on real-time data through a sliding window algorithm, and dynamically adjusting model parameters; and when the model predicts that the ignition risk value exceeds a preset risk threshold, an early warning signal is triggered. According to the method, through comprehensive analysis of multi-factor data, the model can more comprehensively identify various factors related to the ignition risk, so that the early warning accuracy is remarkably improved, and the probability of false alarm and missing alarm is reduced; through dynamic self-adaptive adjustment and a sliding window algorithm, data can be processed and updated in real time, the response speed is improved, and an operator can take necessary prevention measures before problems are expanded, so that the occurrence rate of ignition faults is effectively reduced.

The present application belongs to the technical field of household photovoltaic, and particularly relates to a household photovoltaic fault diagnosis method and system, which comprises: collecting the volt-ampere characteristic curve data of a photovoltaic system and synchronous environmental irradiance; extracting at least one volt-ampere characteristic curve parameter, including short-circuit current, open-circuit voltage, maximum power or fill factor; according to the current irradiance, based on a preset dynamic health baseline, judging whether the system has a fault through a first preset rule. The first preset rule realizes fault diagnosis by obtaining a parameter expected health value and comparing it with an actual extracted value. The present application adopts a dynamic health baseline to adapt to environmental changes, combines an efficient parameter extraction algorithm with an intelligent fault judgment mechanism, significantly improves the diagnosis accuracy, reduces false positives and false negatives, and is suitable for low-cost, automated operation and maintenance of household photovoltaic systems.

The application relates to the field of anomaly detection, in particular to a process execution anomaly detection method based on data driving, which comprises the following steps: obtaining a historical reference parameter set by statistically modeling and analyzing historical normal process cycle characteristic data; obtaining a drift compensation vector by robustly estimating and stably evaluating the current batch process characteristic data; obtaining a structure relaxation amplification coefficient by jointly analyzing the local sparsity and the neighborhood eccentricity of the compensated process characteristic data; obtaining a process anomaly clustering result by dynamically and adaptively constructing an equivalent distance of the compensated process characteristic data; and obtaining a device anomaly detection result by comprehensively judging and analyzing the process anomaly clustering result and the drift compensation vector, so as to solve the problem that the existing anomaly detection algorithm based on static distance measurement is prone to false positives and false negatives under the coexistence of global reference drift and internal structure relaxation of the device.

This invention provides a method, equipment, and storage medium for detecting microcracks in coal mine shafts. The method includes: S1: constructing a dataset of microcracks in the shaft and dividing it into training, validation, and test sets; S2: constructing a microcrack detection model; S21: constructing a backbone network; S22: constructing a neck feature fusion network; S23: constructing a head network; S3: training the detection model using the training set and updating the model's parameters. This addresses the problems of existing detection methods, such as difficulty in effectively extracting feature information, susceptibility to noise interference, and the tendency for false positives and false negatives.

Systems, methods, and computer program products are provided for structured information extraction from documents that may include an artificial intelligence (AI)-driven system for extracting structured information from documents that have information requested but do not follow a pre-defined template. The system may reduce a number of false positive and false negative errors by combining outputs of an image segmentation model and an optical character recognition (OCR) model. The system may include an approach for reducing large language model (LLM) hallucinations by using a fact-checking approach and / or auto-correcting OCR errors by using context of data fields.

The invention discloses a fault positioning method and system for a power grid, and relates to the technical field of power grid fault positioning. The method comprises the following steps: obtaining electrical quantity data and a topological structure in a target power grid, and carrying out graph structure modeling to obtain a node feature matrix and an adjacent matrix; taking the node feature matrix and the adjacent matrix as input of a pre-trained section positioning model to obtain a candidate section set; and determining a final fault section according to the candidate section set and a preset search optimization algorithm. By introducing graph structure modeling, electrical quantity data and a power grid topological structure are fused, a suspicious fault section is given by utilizing a pre-training model, and then the fault section is accurately positioned in combination with a search optimization algorithm. Compared with a traditional method, the two-stage strategy can effectively improve the accuracy and robustness of fault positioning, reduce the risk of misjudgment and missed judgment, and improve the power supply reliability and maintenance efficiency.

The present application belongs to the technical field of industrial process monitoring and fault diagnosis, and particularly relates to a kind of multidimensional data monitoring and linkage early warning method for VIGA powder production process, comprising: obtaining multidimensional time series data and dividing it into multiple data sample matrices;Sparse autoencoder model is constructed, data sample matrix is input into the trained sparse autoencoder model, historical reconstruction error sequence is obtained, its covariance inverse matrix is calculated, and abnormality judgment threshold is determined;Real-time reconstruction error of real-time data sample is calculated, real-time Mahalanobis distance reconstruction error is calculated, and when it is greater than the abnormality judgment threshold, it is determined that the VIGA powder production process is abnormal;Partial derivative is calculated, and the absolute value of partial derivative is used as abnormal contribution degree;The variable set whose cumulative contribution degree meets the preset condition is identified, and early warning information is sent out.The present application solves the problems of insufficient early abnormality capture, difficult balance of false positives and false negatives, and low abnormality disposal efficiency of existing methods.

The application discloses a refueling machine pipeline fault alarm system based on multi-source data and relates to the technical field of fault monitoring.The application solves the technical problem that there is lack of system integration and deep analysis of multi-source monitoring data, monitoring standards cannot be formulated in combination with pipeline parameter fluctuation rules in different time periods, monitoring threshold values are fixed, and false positives or false negatives are prone to occur.The multi-source data acquisition module integrates multi-type sensor data, and all-around monitoring of the refueling machine pipeline state is realized.Meanwhile, the abnormal state recognition module uses time series analysis to mine historical data rules, formulates dynamically adaptive state recognition threshold values, effectively reduces false positives and false negatives caused by one-sidedness of data or threshold value fixation, analyzes relevance through Pearson correlation coefficient analysis, divides parameter subsets through DBSCAN clustering, accurately judges whether faults are of the same root cause or parallel faults, improves fault cause positioning efficiency and accuracy, and reduces dependence on manual experience.

The application discloses a security audit and high-risk event mining method and system based on TDS protocol log and flow control, and relates to the technical field of information security.The application comprises the following steps: performing protocol analysis, service shunting, executing data preprocessing, statistical analysis, constructing a feature baseline, and extracting behavior features to construct a behavior feature operation chain to establish an operation chain library; online matching the real-time received TDS protocol ticket service data with the operation chain library to determine the legality of the flow; based on the online matching result and the feature baseline, auditing the compliance of the ticket service, mining high-risk events, and outputting the audit result.The application effectively reduces false positives and false negatives by combining behavior sequence matching and statistical feature analysis of different dimensions of detection technology, and realizes the security protection of ticket services including but not limited to gate control, ticket sales, and automatic sales through the whole-process closed loop of "flow control shunting-offline training-online matching-security audit".