66results about How to "Improve anomaly detection accuracy" patented technology

The invention discloses a railway fastener abnormality detection system based on monocular vision and laser speckles. The railway fastener abnormality detection system is used for detecting whether fasteners are abnormal or not and comprises laser speckle projectors, area array cameras, a wheel encoder, an RFID detector and an industrial personal computer, wherein the laser speckle projectors andthe area array cameras are arranged above a bottom sleeper of a train body, the laser speckle projectors are positioned right above the fasteners and project laser speckles towards fastener areas perpendicularly, the wheel encoder is fixed on a rotating shaft of a wheel and connected with the area array cameras, the RFID detector is fixed below the train body, and the industrial personnel computerpositioned in a carriage is connected with the area array cameras, the wheel encoder and the RFID detector. The railway fastener abnormality detection system has the advantages that with mottled grains on the surfaces of the fasteners increased through the laser speckle projectors, image block matching precision can be improved and the three-dimensional topography of the fasteners is constructedaccurately, so that various abnormalities of different types of fasteners can be detected effectively.

The invention discloses a distributed migration network learning-based intrusion detection system and a method thereof, and mainly solves the problems that the prior method has low efficiency in detection of some attack types and is difficult to search data again. The whole system comprises a network behavior record preprocessing module, an abnormality detection module and an abnormal behavior analyzing module. The network behavior record preprocessing module completes the quantification and normalization processing of a network behavior record; the abnormality detection module uses an abnormality detection learning machine to completes the classification and identification for an input record, determines whether the record is a normal behavior, and completes the detection if the record is a normal behavior or transmits the record to the abnormal behavior analyzing module if the record is an abnormal behavior; and the abnormal behavior analyzing module uses an abnormal behavior analyzing learning machine to carry out the classification and identification of the input records and outputs the attach type of the record. The system and the method have the advantages of using other existing resources to improve the detection rate for the prior attach types with low detection rate and avoiding searching the data again and can be used for network intrusion detection.

The purpose of the present invention is to provide an outdoor unit for an air conditioner such that erroneous determination of abnormality in an outdoor unit fan is reduced and abnormality detection accuracy is improved. To achieve this, this outdoor unit for an air conditioner comprises an outdoor unit with a fan, and is provided with a vibration detection means for detecting vibration of the fanand a control device that performs at least one or more retry operation for changing rotation of the fan if the vibration detecting means detects an abnormality in the fan. Examples of the retry operation include an operation for reducing and then increasing the rotational speed of the fan, an operation for stopping and then restarting the rotation of the fan, and an operation for rotating the fan backward and then rotating the fan forward.

The invention discloses a railway track fastener anomaly detection system based on binocular vision and laser speckle, comprising laser speckle projectors, cameras, laser displacement sensors, a wheelencoder, an RFID (radio frequency identification) detector and an industrial personal computer, wherein the laser speckle projectors and the cameras form a binocular stereo vision speckle imaging system for performing three-dimensional stereo measurement on fasteners; the laser displacement sensors are used for measuring mounting supporting heights of the fasteners so as to control imaging of thecameras; the wheel encoder and the RFID detector are used for measuring mileage; the industrial personal computer is used for acquiring and processing images and detecting whether the fasteners are abnormal by means of binocular vision stereo matching. The railway track fastener anomaly detection system based on binocular vision and laser speckle has the advantages that a laser speckle source isused to manually add speckle lines to the surface of fasteners, image block matching precision can be improved, three-dimensional shapes of the fasteners are accurately reconstructed, and various anomalies of different types of fasteners can be effectively detected.

The invention relates to a detect device and relative detect method, which can detect according to different worse condition. Wherein, said detect device is used to execute for processing abnormal judgment, based on the mode of normal data from the good product, while it has the function that judge the quality based on variable judgment mode and the function that judge quality based on the non-variable judgment mode; when the sample data is not enough, or the distribution of good product in character space is instable, with not enough estimate accuracy on the shape of normal area, it executes the judgment based on the judge mode of opposite objects, and processes the final judgment according to the result; when the sample data is enough, the distribution of good product, and the shape of normal area are stable, it executes judgment only based on the parameter judge mode.

The embodiment of the invention discloses a service index abnormity detection method and device based on a time sequence and an electronic device, and the method comprises the steps: determining a residual error sequence corresponding to a target service index, the residual error sequence being a sequence formed based on time sequence arrangement; based on at least one preset time sliding window,performing normalization processing on the residual value in a residual sequence segment corresponding to a preset time sliding window to obtain at least one relative residual sequence segment, the residual sequence segment being a residual sequence segment corresponding to a time period represented by the preset time sliding window in the residual sequence; and performing anomaly detection on thetarget service index based on the at least one relative residual sequence segment.

The invention is applicable to the technical field of distributed system detection, and provides an anomaly detection method and system of a distributed system, a service terminal and a memory. The anomaly detection method comprises the steps of acquiring state data of the distributed system, establishing a statistical model, introducing a time observation sequence, establishing a support probability model of the statistical model on the time observation sequence, and obtaining a corresponding anomaly detection result on the basis of the state data and the support probability model. Accordingto the method, the time observation sequence is introduced into the statistical model, whether the state of the distributed system is abnormal or not is detected through the support probability model,and the accuracy of anomaly detection is improved.

The invention relates to diagnosis operation device and method of an elevator. A reference mode corresponding to the environmental temperature, the traveling status and the like before an earthquake occurs is set before diagnosis operation is implemented, and the abnormity detection precision in diagnosis operation can be greatly improved. The diagnosis operation is carried out after control operation in the earthquake is implemented, i.e. a lift car of the elevator travels, preset elevator equipment is simultaneously measured, and a measured value and the reference mode are compared to judgewhether the equipment is abnormal or not. Before the earthquake occurs, the reference mode corresponding to the equipment is prestored in a reference mode storage part (3). In the preset period afterthe earthquake occurs, the variation is measured when the reference mode corresponding to the equipment is relatively stored in the reference mode storage part (3). Then, the reference mode corresponding to the equipment is reset before the diagnosis operation starts according to the reference mode stored in the reference mode storage part (3) before the earthquake occurs and the variation obtained in the preset period after the earthquake occurs.

The invention discloses a time sequence detection method and device, equipment and a storage medium. The method comprises the following steps: acquiring sequence points in a time sequence; obtaining afirst determination result of whether the sequence point is an abnormal point or not through a pre-constructed statistical model, and obtaining a second determination result of whether the sequence point is an abnormal point or not through a pre-constructed unsupervised learning model; if the first determination result is consistent with the second determination result, taking the sequence pointdetermined as a normal point as a normal sample, and taking the sequence point determined as an abnormal point as an abnormal sample; and obtaining a detection result of each sequence point in the time sequence through the classification model, and marking abnormal points in the time sequence according to the detection result. According to the technical scheme provided by the embodiment of the invention, the problems of missed detection and false detection when a single statistical model or an unsupervised learning model is adopted to detect the sequence points in the time sequence are avoided, and the accuracy and reliability of marking the abnormal points in the time sequence are improved.

The present invention provides an elevator door control device that improves the abnormal detection accuracy of the door opening and closing action by properly detecting the abnormality of the torque command, and reduces the false detection of the abnormal door opening and closing action. It is equipped with a speed command unit that outputs a speed command; The speed control unit of the torque command based on the deviation between the speed command and the feedback speed; store a plurality of torque command graphics formed by sampling a plurality of torque commands according to the door opening and closing action, and store the torque command graphics based on the multiple torque command graphics The reference torque instruction graph storage unit of the reference torque instruction graph obtained by each common sampled torque instruction; the abnormal detection torque graph generation unit that generates the abnormal detection torque graph from the reference torque instruction graph; when the torque instruction exceeds An abnormality avoidance unit that outputs an abnormality avoidance command to a speed command unit when an abnormality detection torque pattern is detected.

The invention discloses an abnormal access data detection method and device, and relates to the technical field of computers. A specific embodiment of the method comprises the steps of training an abnormal access data detection model according to a pre-established initial training set; after the training is finished, determining an abnormal probability of access data in a pre-established initial verification set by utilizing an abnormal access data detection model; labeling a plurality of pieces of access data of which the abnormal probability and accuracy meet a preset discrimination condition in the initial verification set as abnormal access data, and adding the abnormal access data into an initial training set; training the abnormal access data detection model according to the currenttraining set; and inputting to-be-detected access data into the trained abnormal access data detection model, and judging whether the to-be-detected access data is abnormal access data or not according to an output result. According to the embodiment, the abnormal access data detection model can be continuously optimized by utilizing the dynamically adjusted training set, so that the abnormal detection accuracy of the model is improved.

An anomaly detection method includes generating second data by adding normal noise to first data; generating third data by adding abnormal noise to the first data; inputting the first data, the second data, and the third data to a neural network; calculating a first normal score, a second normal score, and a third normal score; calculating a third difference based on a first difference and a second difference, the first difference being based on a difference between the first normal score and the second normal score, the second difference being based on a difference between the first normal score and the third normal score; changing the neural network so that the third difference becomes smallest; inputting, to the changed neural network, fourth data that is unknown in terms of whether the fourth data is normal or abnormal; and determining whether the fourth data is normal or abnormal.

The invention provides an anomaly detection method and device based on a user peer-to-peer group, and the method comprises the steps: carrying out the clustering of a preset number of times of a userset comprising a target user according to a preset clustering algorithm, obtaining a preset number of clustering clusters to which the target user belongs, wherein each clustering corresponds to one clustering cluster; according to a preset number of clustering clusters to which the target user belongs, obtaining a peer-to-peer group probability of the target user and any user in the preset numberof clustering clusters, and constructing a probability clustering cluster based on the peer-to-peer group probability; selecting a preset threshold number of behavior characteristics from a preset behavior characteristic pool, and for each selected behavior characteristic, based on the probability clustering cluster and the selected behavior characteristic, constructing an isolated forest tree containing the target user; and detecting whether the target user is an abnormal user or not according to the obtained isolated forest tree and a preset anomaly detection algorithm. The anomaly detection precision can be effectively improved.

The invention discloses an anomaly intrusion detection method for electric power communication network. And extracting the second target data from the first target data, that is, extracting the key features, so as to improve the detection accuracy; on the other hand, after the second target data is derived, the BP_Adaboost neural network model of electric power communication network is constructedaccording to the second target data, and the BP_Adaboost neural network model is trained, finally, according to the trained BP_Adaboost neural network model, the anomaly intrusion detection of electric power communication network is carried out. Compared with the traditional BP neural network model, the BP_Adaboost neural network model converges quickly and further improves the detection accuracy. In addition, the invention also discloses an electric power communication network abnormal intrusion detection device, a device and a storage medium, and the effect is as above.

The invention relates to the technical field of computer vision, in particular to a multi-view video anomaly detection method based on sparse coding. The method comprises: performing multi-view feature extraction on frame images; sparse coding being applied to the features from different perspectives to obtain sparse representations of the features from different perspectives; obtaining a consistency representation matrix under a frame image according to the sparse representation information and assigning corresponding weight values to the consistency representation matrix between two adjacentframes to obtain a dictionary A, then the reconstruction error of sparse representation coefficients being obtained by using dictionary A to test the video data of abnormal events, and the standardized multi-view video anomaly detection model being obtained. The invention extracts the multi-view angle characteristic of the video frame image, establishes the multi-view angle video anomaly detection model, integrates the characteristic information under the multi-view angle of the video to carry out the anomaly detection, and utilizes the time want-to-dry property between two adjacent frames ofthe video, reduces the loss of local information, and improves the anomaly detection accuracy.

A numerical control system detects a state amount indicating a state of machining operation of a machine tool, creates a characteristic amount that characterizes the state of machining operation fromthe detected state amount, infers an evaluation value of the state of machining operation from the characteristic amount, and detects an abnormality in the state of machining operation on the basis ofthe inferred evaluation value. The numerical control system generates and updates a learning model by machine learning that uses the characteristic amount, and stores the learning model in correlation with a combination of conditions of the machining operation of the machine tool.

The invention discloses a mobile armrest inspection device of a passenger conveyer. An image is restricted by a detection body (5), so that three sets of joints obliquely arranged with respect to the elongation direction of the inner steel wires buried in the mobile armrest (2) are detected.When the abnormity is continuously detected by an image processor (8) and the variation amount delta P of each light amount is within a predetermined range of a time interval delta t, a joint is determined by a determination mechanism.When the variation amount delta P of each light amount is not within thepredetermined range of the time interval delta t, a joint is not determined by the determination mechanism.When the joint is determined, no abnormality on the joint is judged by an abnormality determination mechanism.When the joint is not determined, an abnormality on the joint is judged by the abnormality determination mechanism.Therefore, whether the abnormality of a tension element is existed or not can be quantitatively and fully detected by the inspection device, including the abnormality of the joint of the inner steel wires buried in the mobile armrest (2).

The invention relates to diagnosis operation device and method of an elevator. A threshold value for judging abnormity in diagnosis operation can be properly set according to the specification and the like of the elevator, and the abnormity detection precision in diagnosis operation can be greatly improved. The diagnosis operation is carried out after control operation in an earthquake is implemented, i.e. a lift car (9) of the elevator travels, the torque current of a gear-driven hoisting machine is simultaneously measured, a measured value and a preset reference mode are compared, and abnormity is judged when a difference value exceeds the threshold value, wherein preset elevator information including the gear ratio of a gear (14) of the hoisting machine and the diameter of a driving rope pulley (12) is stored in an elevator information storage part (4) before the earthquake occurs. In the preset period after the earthquake occurs, the preset status information of the elevator is detected so as to obtain the elevator information changing along with environments and the like. According to the elevator information and the status information, the threshold value of the torque current for the hoisting machine is determined before the diagnosis operation starts.

The invention discloses a distributed migration network learning-based intrusion detection system and a method thereof, and mainly solves the problems that the prior method has low efficiency in detection of some attack types and is difficult to search data again. The whole system comprises a network behavior record preprocessing module, an abnormality detection module and an abnormal behavior analyzing module. The network behavior record preprocessing module completes the quantification and normalization processing of a network behavior record; the abnormality detection module uses an abnormality detection learning machine to completes the classification and identification for an input record, determines whether the record is a normal behavior, and completes the detection if the record isa normal behavior or transmits the record to the abnormal behavior analyzing module if the record is an abnormal behavior; and the abnormal behavior analyzing module uses an abnormal behavior analyzing learning machine to carry out the classification and identification of the input records and outputs the attach type of the record. The system and the method have the advantages of using other existing resources to improve the detection rate for the prior attach types with low detection rate and avoiding searching the data again and can be used for network intrusion detection.

The invention belongs to the technical field of software engineering and cloud computing, and particularly relates to a distributed system call chain and log fusion anomaly detection method. The method comprises the following steps: constructing a call chain event relation graph according to a call chain and log data on the basis of the call chain and log data during operation of a distributed system, and learning a call chain event relation graph mode during normal operation of the system by utilizing a graph neural network and a single-classification deep learning method; detecting a newly generated call chain event relation graph in real time during online use, and identifying a call chain generating an abnormal behavior; the method specifically comprises the steps of log event analysis, call chain event analysis, event vectorization, call chain event relation graph construction, graph neural network model training and online anomaly detection. According to the invention, operation and maintenance personnel and developers can be helped to quickly discover system exceptions, corresponding alarm information is generated, the speed of fault positioning and online problem solving is accelerated, and the labor cost is reduced.

The invention provides a traction substation outdoor insulator abnormity detection method. The invention relates to the technical field of computer vision, pattern recognition and intelligent systems.The method comprises the steps of: respectively constructing data sets of an insulator positioning network and an insulator image generation network; constructing an insulator positioning network, and enabling the network to obtain the capability of positioning the insulator in the image through training; constructing an insulator image generation network, and obtaining the insulator image reconstruction capability through training; inputting the traction substation image into a network model; positioning the insulator through an insulator positioning network, and extracting an insulator image; and carrying out anomaly detection on the insulator, and giving an anomaly score to each picture by the insulator image generation network; setting an abnormality judgment threshold value, if the abnormality score exceeds the set threshold value, judging that the sample is an abnormal sample, and if the abnormality score is lower than the threshold value, judging that the sample is a normal sample; and finally, performing feature extraction on the judged abnormal image and the generated image thereof, and comparing the difference to locate an abnormal area.

The invention relates to diagnosis operation device and method of an elevator. A reference mode corresponding to the environmental temperature, the traveling status and the like before an earthquake occurs is set before diagnosis operation is implemented, and the abnormity detection precision in diagnosis operation can be greatly improved. The diagnosis operation is carried out after control operation in the earthquake is implemented, i.e. a lift car of the elevator travels, preset elevator equipment is simultaneously measured, and a measured value and the reference mode are compared to judgewhether the equipment is abnormal or not. Before the earthquake occurs, the reference mode corresponding to the equipment is prestored in a reference mode storage part (3). In the preset period afterthe earthquake occurs, the variation is measured when the reference mode corresponding to the equipment is relatively stored in the reference mode storage part (3). Then, the reference mode corresponding to the equipment is reset before the diagnosis operation starts according to the reference mode stored in the reference mode storage part (3) before the earthquake occurs and the variation obtained in the preset period after the earthquake occurs.

Provided is a control method for a secondary battery in which a malfunction is less likely to occur and which can perform abnormality detection with high accuracy. This charge state estimation apparatus for a secondary battery includes: a device for generating an electromagnetic noise; a first detection means for measuring a voltage value of the secondary battery that is electrically connected with the device; a second detection means for detecting a current value of the secondary battery that is electrically connected with the device; a correction means for extracting a causal relationship between the electromagnetic noise and a driving pattern from data that is obtained by using the first detection means or the second detection means and that includes a plurality of electromagnetic noises, and performing data correction on the basis of the causal relationship; and a calculation means for calculating a charging rate by using a regression model on the basis of data after the data correction.

The invention discloses an effluent BOD sensor anomaly detection method based on a modular neural network, relates to the field of artificial intelligence, and is directly applied to the field of sewage treatment. In order to solve the problems that in the current sewage treatment process, an effluent BOD sensor drifts and abrupt abnormity cannot be detected in real time under complex working conditions, samples are automatically classified and input according to the working conditions by adopting a density-based clustering algorithm; extracting an effluent BOD auxiliary variable as an input variable of each sub-network in the modular network by using a mutual information-based method; designing a self-organizing RBF neural network based on error correction as a sub-network, and training the network through an improved Levenberg-Marquardt (LM) algorithm to improve the training speed. The result shows that the abnormity detection method is compact in structure, abnormity of the effluent BOD sensor in the sewage treatment process can be rapidly and accurately detected, and technical guarantee is provided for safe and stable operation of sewage treatment.

An elevator door control device is provided in which the precision of abnormality detection in door opening and closing operations is increased by appropriately detecting abnormalities in torque commands, so that there is little erroneous detection of abnormalities in door opening and closing operations. The elevator door control device has: a speed command portion that outputs a speed command; a speed control portion that outputs a torque command corresponding to a deviation between the speed command and a feedback speed; a reference torque command pattern storage portion that stores a plurality of torque command patterns that are formed by a plurality of the torque commands sampled according to door opening and closing operations, and stores a reference torque command pattern that is obtained based on the torque commands at each common sampling of the plurality of torque commands; an abnormality detection torque pattern generator portion that generates an abnormality detection torque pattern from the reference torque command pattern; and an abnormality avoiding unit that outputs an abnormality avoiding command to the speed command portion when the torque command exceeds the abnormality detection torque pattern.

The invention relates to an equipment state anomaly detection method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a feature set of initial fault-free operation data of equipment as a historical initial feature set; searching the historical initial feature set according to a distance correlation coefficient between features in the historical initial feature set to obtain a historical correlation feature subset, and screening redundant features in the correlation feature subset according to a feature representative index of the historical correlation feature subset to obtain a historical feature set; obtaining a historical data matrix according to the historical feature set, and taking the historical data matrix as a training sample to obtain a trained convolutional noise reduction network; and inputting the real-time data matrix into the trained convolutional noise reduction network to obtain an abnormal level value, and judging whether the equipment is abnormal or not according to a size relationship between the abnormal level value and an abnormal threshold value. By adopting the method, higher anomaly detection precision can be obtained under a lower false alarm rate.

The invention discloses an injection molding machine energy consumption abnormity detection method and system based on a Gaussian mixture model, and the method comprises the steps: carrying out the real-time collection of the energy consumption data of a first injection molding machine, and obtaining the first real-time energy consumption data; performing data preprocessing on the first real-time energy consumption data to obtain second real-time energy consumption data; inputting the second real-time energy consumption data into a Gaussian mixture model for clustering feature learning to obtain a first clustering data set and generate a first mark training data set; performing model training according to the first mark training data set to obtain a first anomaly detection model; and inputting a first test training data set of the first injection molding machine into the first anomaly detection model to obtain first output information. The technical problem that in the prior art, when the energy consumption abnormity of the industrial injection molding machine is detected, due to the fact that data features are not comprehensive and perfect enough, multi-dimensional data classification is not accurate enough, and the false alarm rate is high, the detection precision is not high is solved.

The present disclosure relates to a device for detecting a liquid crystal module and a method for detecting the quantity of liquid crystal. The method includes a cover for fixedly accommodating the liquid crystal module. An accommodation space for accommodating collision units is formed between the liquid crystal module and the cover, the collision units being connected to the top wall of the cover through elastic members. The collision units are spaced from the liquid crystal module in a certain distance in a static status, and collide with a panel of the liquid crystal module through vibration of the cover in the vertical direction during detection. The device for detecting a liquid crystal module improves the detection efficiency.

The invention provides a series load control apparatus and a marker light apparatus capable of detecting abnormal states of loads including a lighting control unit, a solid light-emitting element etc., and continuously supplying power for the loads in the condition of malfunctions of an abnormality detecting unit of the loads. The abnormality detecting unit opens an open-closed unit (62) in a condition of malfunction due to a certain reason, and opens an output section of a second saturable unit (30). In the condition, the second saturable unit (30) repeats non-saturated-saturated states in each half cycle of an alternating current voltage, but since a first saturable unit (20) is unsaturated and supplies power for the loads (10) as usual,the lighting control unit (42) performs lighting control of light sources (43, 43) according to output current signals of a constant current power supply device (10).

An anomaly detection method includes generating second data by adding normal noise to first data; generating third data by adding abnormal noise to the first data; inputting the first data, the second data, and the third data to a neural network; calculating a first normal score, a second normal score, and a third normal score; calculating a third difference based on a first difference and a second difference, the first difference being based on a difference between the first normal score and the second normal score, the second difference being based on a difference between the first normal score and the third normal score; changing the neural network so that the third difference becomes smallest; inputting, to the changed neural network, fourth data that is unknown in terms of whether the fourth data is normal or abnormal; and determining whether the fourth data is normal or abnormal.