120 results about "Detection rate" patented technology

The invention belongs to the technical field of electric power, and particularly relates to a short-time frequent jump current event clustering identification method based on a collector. The method comprises the following steps: step 1, carrying out current sampling on a monitored line, carrying out point-by-point amplitude difference comparison and adjacent waveform curvature judgment, and removing isolated burrs to obtain a jump characteristic fragment sequence; 2, traversing the jump characteristic fragment sequence in the forward direction, and generating a short-time frequent jump current event under the conditions that no less than three jumps in the same direction are satisfied and the time interval between fragments is within a preset range; 3, a look-ahead chain, a current chain and a review chain are established for the event index sequence column at the same time, and a clustering index set is output; 4, counting the occurrence frequency of each cluster index in the cluster index set; and determining that the short-time frequent jump current event corresponding to the clustering index is an effective event. According to the invention, the detection rate and the accuracy in a noise environment are improved.

The invention discloses a sailing ship safety situation awareness system, and belongs to the technical field of data analysis. The method is used for solving the technical problem that in an existing scheme, multi-dimensional data analysis and timely and reliable early warning cannot be carried out on complex sea conditions, and consequently the coping capacity of the complex sea conditions is weak. Through the whole process of biological anchoring anomaly suppression, data fusion and dynamic feature extraction, the limitation that traditional ship perception depends on a physical sensor can be broken through, high-precision perception of environmental risks and ship states under complex sea conditions is achieved, and compared with a traditional method, the abnormal sea conditions can be recognized in advance, and the method has the advantages of being high in practicability. The robustness of the data and the detection rate of small probability and high influence events can be effectively improved; cross-scale modeling is realized through deep coupling of a microscopic physical mechanism and macroscopic data features; the constructed multi-dimensional risk assessment matrix not only can output a risk value, but also can provide a risk propagation path, and can effectively improve the risk assessment precision of the ship under a complex sea condition.

The invention relates to the technical field of artificial intelligence multi-agents, in particular to a proposal review method and system based on evidence verification and multi-agent cooperation. A cross validation mechanism based on a review information pool is introduced. The cross validation mechanism is a multi-agent round table cooperation mechanism, and a preliminary review result generated by an agent is not directly output, but passes multiple rounds of mutual verification, supplementation and correction until a preset convergence condition is met. According to the evidence verification process, cognitive deviation and knowledge blind areas possibly existing in a single agent are effectively eliminated, and the omission ratio and the misjudgment rate of the review result are remarkably reduced, so that the accuracy, the integrity and the reliability of a final review report are ensured; the technical problems of low efficiency and low accuracy caused by insufficient review depth, single angle and lack of verification in the prior art are effectively solved, and the review efficiency and decision quality of enterprise project proposals are remarkably improved.

The invention discloses an eddy current testing reliability evaluation method and system based on signal space distribution characteristics, and belongs to the technical field of nondestructive testing. According to the method, eddy current signal space distribution characteristics and the detection probability (POD) and the false detection rate (PFA) in nondestructive testing reliability evaluation indexes are fused, and the method is used for comprehensively evaluating the defect detection capacity and the reliability of a detection system. By introducing the Gini coefficient to quantitatively detect the imbalance of signal space distribution, the method effectively characterizes the distinguishability of signals in a defect state and a defect-free state, and replaces traditional POD and PFA analysis methods based on single-point amplitude or phase characteristics. Compared with a traditional method, the Gini coefficient can more comprehensively represent the overall distribution characteristics of the signals, the influence of noise is small, and the method is particularly suitable for the eddy current detection environment under the condition of the low signal-to-noise ratio. Besides, a POD and PFA relation curve is constructed based on Gini coefficients, ROC analysis is performed, the defect detection rate can be effectively increased, the false detection rate can be reduced, and the reliability of the nondestructive testing system can be evaluated more efficiently and accurately.

The invention discloses a security vulnerability detection method and device, a computer program product and a storage medium, and belongs to the field of code detection.A structured graph of a to-be-detected code can be constructed according to structured information of the to-be-detected code, and then the to-be-detected code is coded to obtain a text coding vector; and coding the structured graph of the to-be-detected code to obtain a structured coding vector, and finally inputting the comprehensive vector into a pre-trained detection model so as to obtain a security vulnerability detection result. The problem of how to reduce the omission ratio and the false alarm rate of security vulnerability detection of codes is solved, and the beneficial effects that security vulnerability detection is performed from two dimensions of semantics of code texts and semantics of structured information, and the omission ratio and the false alarm rate of security vulnerability detection of codes are reduced are achieved.

The invention discloses a double-channel confrontation and progressive self-training driven cross-working-condition open set domain adaptive fault diagnosis method. In order to suppress an interactive negative migration effect, a three-stage two-way adversarial progressive self-training network DAPN is designed, separation of unknown target samples and feature aggregation of known samples are realized through cooperation of a gradient inversion layer and a two-way adversarial discriminator, and inter-class discrimination and intra-class aggregation are obviously enhanced. When the performance of the DAPN is evaluated in two open set domain adaptive scenes, for open set proportions of different intensity domain offsets and changes, the method always keeps high classification accuracy and unknown class detection rate, and the performance is obviously superior to that of a comparison baseline. According to the method, two-way confrontation and a progressive self-training mechanism are combined, so that accurate classification of known faults and effective identification of unknown faults are realized.

The invention provides a hospital adverse event risk identification method and system based on causal reasoning, and the method comprises the steps: responding to an operation request of a user for reporting a hospital adverse event based on the optimization of medical advice data processing and a medical quality management process in an operation risk identification scene, inputting the detailed information of the event through a formatted form, and carrying out the operation of the event. Through multi-node, multi-auditor and multi-department cooperative auditing, an adverse event form reported by a user is subjected to approval, flow and summary analysis, medical advice data is subjected to structured storage by adopting visual enhancement OCR, dynamic knowledge graph verification and a time sequence causal reasoning model, a time window division algorithm is adopted, and medical advice data is obtained. The causal discovery algorithm constructs an unplanned re-operation association network, early warning of adverse events of operation complications is carried out, and compared with a traditional mode, the medical advice processing efficiency, the transcription error rate and the unplanned operation detection rate are all greatly improved.

The invention discloses a target detection method and device based on domain self-adaption, equipment and a medium. The target detection method based on domain adaptation is realized through a target detection model, the target detection model comprises a dynamic domain adaptation module, and the dynamic domain adaptation module comprises an image level domain classifier with a first dynamic confrontation gradient inversion layer and an object level domain classifier with a second dynamic confrontation gradient inversion layer. In the back propagation of the training process of the target detection model, the gradient inversion intensity of the first dynamic confrontation gradient inversion layer is dynamically adjusted according to the image level domain classification loss, and the gradient inversion intensity of the second dynamic confrontation gradient inversion layer in the back propagation is dynamically adjusted according to the object level domain classification loss; according to the method, the target detection precision under the severe weather condition can be improved, the target missing detection rate and the target false detection rate under the severe weather condition are reduced, and then the safety and the reliability of the automatic driving system are improved.

To detect apple snails from environmental DNA with a high detection rate.SOLUTION: This method amplifies and detects environmental DNA in a sample with a first primer having a specific base sequence, a second primer having another specific base sequence, and a probe having still another base sequence.SELECTED DRAWING: None

The invention discloses an early warning method and system for outbreak of drug-resistant bacteria in a hospital based on dynamic aggregation degree evaluation. The method comprises the following steps: acquiring hospitalization information of a patient, microbiological inspection data and spatial layout information of an inpatient area to obtain initial data; judging whether a new case of drug-resistant bacteria is detected on the current day; if not, gradually reducing the historical aggregation degree by using an exponential decay formula until the historical aggregation degree returns to zero; if yes, calculating a newly added detection rate, a space aggregation degree and a time aggregation degree, and calculating a total aggregation degree of the day; dividing an inpatient area; dynamically generating a threshold value for the high-frequency inpatient area by adopting a quartile method to carry out abnormal value judgment, and identifying an abnormal aggregation degree for the low-frequency inpatient area by using a fixed absolute value threshold mechanism to obtain an early warning level; and early warning information of different levels is pushed according to the early warning level, and a prevention and control suggestion plan is attached. By implementing the method provided by the invention, the accuracy and efficiency of early warning can be remarkably improved, so that patients and medical personnel are better protected from the threat of drug-resistant bacterium infection.

The invention provides an isolation switch cabinet fault detection method and device, equipment and a storage medium, relates to the technical field of power system monitoring, and solves the problem of low fault detection accuracy of an isolation switch cabinet in the related technology. According to the scheme, the modal features corresponding to the modal data are identified and analyzed by using the physical information neural network, the knowledge base constructed based on the knowledge graph and the fault mapping table, so that the fault condition of the isolation switch cabinet can be cooperatively analyzed in combination with multiple pieces of modal data, the omission ratio and the false alarm rate are reduced, and the reliability of the isolation switch cabinet is improved. The accuracy of fault detection of the isolation switch cabinet is effectively improved.

The invention discloses a method for automatically screening common errors in a BIM model in engineering. The method comprises the following steps: opening the BIM model, and opening an automatic screening device for the common errors of the BIM model; selecting the BIM model to be subjected to error screening from the BIM models; selecting a function module of error type screening to be executed in the BIM model common error automatic screening device, and operating the device; the device circularly traverses the selected BIM model and sequentially compares BIM model data according to a set error screening rule, if the BIM model data accords with the error screening rule, error prompt information is generated, and if the BIM model data does not accord with the error screening rule, the step is quitted; the BIM model checking efficiency is greatly improved, and meanwhile the problems that manual checking is low in efficiency, high in omission ratio and high in misjudgment rate are solved.

The invention discloses a vehicle violation behavior detection method and device for a traffic gate, and relates to the technical field of traffic management, and the method comprises the steps: recognizing the identity of a vehicle based on a real-time vehicle passing image obtained by the traffic gate, and extracting a corresponding behavior constraint rule and a behavior index set from a behavior rule database; extracting real-time behavior characteristics in combination with the image data, identifying difference indexes and collecting additional information; and extracting additional behavior characteristics, combining the additional behavior characteristics with the real-time behavior characteristics, and carrying out illegal behavior detection based on the behavior index set. Therefore, the technical effects of improving the monitoring and detection accuracy and enhancing the adaptive capacity and the detection rate are achieved.

The invention relates to a malicious software detection method based on multi-feature fusion and interpretability analysis. According to the technical scheme, static features are extracted through'macroscopic-microscopic 'double paths, and a dynamic behavior knowledge graph is constructed in combination with sandbox monitoring, stain analysis and semantic abstraction and converted into feature vectors through graph embedding; multi-modal feature deep interaction and accurate classification are realized through a double-end cross attention-triple fusion-deep classification architecture; a hierarchical interpretable framework is constructed based on cross-modal causal alignment, case reasoning and anti-fact analysis, and a complete decision evidence chain is generated. The method has the advantages that comprehensive representation of the form, semantics and intention of malicious software is achieved, the detection precision and robustness are remarkably improved, meanwhile, the'black box 'dilemma of the model is solved, credible explanation is provided for security analysis, the method is suitable for complex network threat detection scenes, and it is verified through experiments that the method has good application prospects. According to the method, the detection rate of the Windows malicious software is remarkably increased, and the false alarm rate is effectively reduced.

The utility model discloses a sampling device for artificial intelligence industrial quality inspection, which belongs to the technical field of industrial quality inspection and comprises a horizontal base, a conveying device and a bottom detection device. The conveying device is mounted on the horizontal base through a bracket; the bottom detection device is mounted at the upper part of the conveying device; the bottom detection device comprises a lifting seat, a clamping plate positioned at the lower part of the lifting seat, and a camera mounting seat which is mounted on the lifting seat through a hinge seat and can rotate in a vertical plane, and a first camera is mounted on the camera mounting seat; the bottom detection device is fixed to the upper portion of the conveying device support through a portal frame. The lifting base is fixed to the bottom of the portal frame beam through a hydraulic cylinder and telescopic guide rods vertically arranged on the two sides. According to the utility model, the technical problems that the judgment standards of different quality inspectors are possibly different, fatigue is easily caused by long-time repeated work, and the missing detection rate and the false detection rate are increased can be solved, and the detection precision and the detection efficiency are improved.

The invention relates to the technical field of automation equipment, and discloses an automatic welding product strength detection device, system and method, and the method comprises the steps: 1, placing a product on a platform, obtaining the type and appearance data of the product, and calculating the optimal clamping point coordinate and the normal vector angle; obtaining process parameters of the product; 2, a chuck is controlled to clamp the product according to the clamping points, and the contact force of the chuck is judged; when the contact force is met, the chuck is controlled to be pulled up, pulling-up data are collected, and a force-displacement curve is formed; 3, in the pulling-up process, the standard reaching condition is judged according to double conditions; if not, comprehensively judging the quality of the welded product in combination with a failure mode, and outputting a judgment result; and 4, performing feature extraction on the generated curve, analyzing the process flow according to the features, and providing a process adjustment suggestion. According to the method, the detection precision is improved, the data use value is improved, the false detection rate and the omission ratio are effectively reduced, and the overall detection efficiency is improved.

The invention discloses a high-voltage cable buffer layer ablation degree evaluation method and device, and belongs to the technical field of high-voltage cable detection. Aiming at the problems of high false detection rate and low positioning precision of the existing detection technology, the method provides three steps of detection processes: firstly, drilling holes along preset intervals of a cable to take gas, analyzing gas components by using a gas chromatograph, and screening sections containing ablative characteristic gas; secondly, an infrared thermal imager is adopted to accurately position temperature abnormal points, and gas is remeasured; and finally, determining a gas weight in combination with an optimal and worst method, constructing a quantitative model through a multi-criterion compromise solution sorting method, calculating an abnormal point comprehensive score and dividing risk grades. According to the method, through a cooperative detection mechanism of gas primary screening, infrared positioning and retest evaluation, the detection efficiency and accuracy are remarkably improved, early ablation symptoms can be recognized, and the omission ratio is reduced.

The presently described techniques utilize detection p-value metrics as part of determining sample quality in the context of a methylation assay. In these approaches, the detection p-value may be understood to be the probability of observing a signal at least as extreme as the measured or detected signal. Such a detection p-value is used to assess quality of individual probes. Based on the detection p-values for a given set of probes, the quality of a given sample may be assessed as a detection rate, which may be understood to be the proportion of probes passing a p-value detection threshold.

Systems and methods for learning from mistakes to improve detection rates of Machine Learning (ML) models. The systems and methods including receiving data with labels; running the data through a trained ML model for predictions; identifying errors in the predictions based on the labels received with the data; adjusting weights associated with samples in the data based on the identified errors; and retraining the ML model with the adjusted weights.

The application provides a kind of adaptive tracking type nondestructive testing method and system for special equipment inspection, belongs to detection technical field, method includes gathering equipment historical operation and detection data to construct multidimensional time series dataset, trains intelligent model combined with deep reinforcement learning and convolutional neural network;The model is deployed to edge computing system, and the initial reference baseline of multidimension is established on site, and the current state is compared with baseline in continuous detection, through the two-way decision mechanism of intelligent model and preset logic threshold, dynamically drive mechanical arm to adjust probe pose and synchronously optimize ultrasonic excitation parameters, to actively compensate the change of coupling state, maintain the optimal detection condition, and accurately identify and evaluate defects in stable state;The system includes defect accurate identification and evaluation module, etc.;The application realizes the dynamic self-optimization of detection condition under non-stop complex working condition, improves the defect detection rate, quantitative accuracy and repeatability of detection results.

According to the EGANet polyp image segmentation method provided by the invention, the omission ratio and the error detection ratio can be reduced, and the colonoscopy accuracy is improved. The method is designed for three challenges: a ResNet50-SPD encoder is combined with space-to-depth layer downsampling, and a deconvolution decoder is matched, so that the problem of missing detection due to too small polyp size is solved; designing an edge attention module, supervising an advanced feature layer by using edge information, and coping with missing detection and under-segmentation caused by morphological differences; through data set reflection removal and color shift preprocessing, a global attention module is combined to capture long-distance dependence and channel context, and error detection caused by polyp and background similarity is processed. And finally fusing edge and global attention features to relieve over-segmentation. Experiments show that the segmentation precision of the scheme is superior to that of an existing model.

The application discloses a synthetic aperture radar image target detection method, equipment and medium, and belongs to the technical field of SAR image target detection. The synthetic aperture radar image target detection method comprises the following steps: S1: inputting a SAR image; S2: constructing a feature separation module; S3: performing feature extraction; S4: constructing an adaptive feature aggregation network; S5: obtaining a target detection result; and S6: optimizing model parameters. The application solves the problem that the prior art is prone to causing high false detection rate and missed detection rate of multi-scale target detection. The application separates noise features and adaptively aggregates multi-scale features, reduces the interference of a complex background, improves the influence of a noise environment and multi-scale targets on detection, directly restricts the position and aspect ratio of a prediction box by using a regression loss, optimizes the multi-scale target detection effect of a model, reduces the false detection rate and missed detection rate of SAR image target detection, and improves the robustness of the model.

The present application relates to the technical field of defect detection, in particular to a kind of generator rotor forge internal defect ultrasonic and electromagnetic composite detection method, first to the surface of forge is adaptively path electromagnetic scanning, utilize multi-frequency eddy current technology and intelligent signal analysis identification and parameterization mark near-surface suspicious area;Subsequently, based on suspicious area information, through dynamic trigger mechanism executes global scanning and local directional precision scanning, generates the defect point cloud dataset containing multidimensional attribute;Finally, through three-level fusion architecture, electromagnetic and ultrasonic data are spatio-temporal alignment and feature-level correlation, and the type of defect is accurately determined and visualized output;The present application realizes the composite detection of the internal defect of generator rotor forge, can simultaneously improve the detection rate, positioning accuracy and type identification ability of defect.

The invention provides an urban building deformation monitoring leak detection point identification method and system, and belongs to the technical field of urban building health monitoring. According to the method, the isotropic hypothesis of a traditional two-dimensional plane buffer area is abandoned, a parameterized three-dimensional confidence space conforming to an SAR side-looking imaging physical mechanism is additionally constructed on the basis of contour strong judgment, multiple factors such as the radar sight line direction, the incident angle, the building height and the shielding effect are fully considered, misjudgment of non-target scattering points is effectively avoided, and the detection accuracy is improved. The spatial affiliation relationship between the PS point and the building is more accurate, and the reliability of omission ratio evaluation is remarkably improved.

The application discloses a human posture detection method and system based on target identification. The application uses key parts as object targets, and establishes a connection relationship based on a limb heat map to express human posture features, thereby reducing the missed detection rate under the shielding condition. Moreover, the posture features used by the application already contain a human boundary box, so when human posture features, human target features and part target features are needed at the same time subsequently, an accurate and non-regression fitting prediction boundary box can be obtained without additionally loading a target detection algorithm, and the detection speed is improved.

The application provides a method for identifying risk points of construction quality of a multi-ribbed beam floor based on artificial intelligence; the method organizes three-stage inspection information of formwork installation, support erection and pre-pouring review into point records according to the axis network coordinates, extracts and splices inspection items, measured and photo video semantic features, inputs the point risk score and risk source process prompts in combination with the stage conditions; and performs risk joint checking expansion based on a support chain, a formwork row and a boundary reinforcing belt correlation diagram, generates a review list according to the joint checking score and the on-duty capacity, and forms a closed loop by writing back the review conclusion; the scheme realizes phased and traceable point risk identification and review priority sequencing, improves the risk positioning accuracy and the review efficiency, and reduces the missed detection rate.

The invention relates to an AGC hydraulic system fault detection method and computer equipment thereof. Data processing modes of a feature extraction method are different (model selection of a preprocessing step and a data processing step). Firstly, candidate measurements needing to be further researched are selected by using typical fault characteristic parameter analysis; second, the selected measurements are analyzed using a feature extraction method. The step aims to extract hidden features so as to detect internal faults to the greatest extent; and finally, the classification efficiency of the fault detection algorithm is evaluated by studying the detection rate of the proposed architecture. According to the method, the detection rate of the accuracy of the AGC hydraulic system is improved, and quick diagnosis and positioning of common faults of the AGC hydraulic system of the automatic rolling mill are realized to a certain extent.

The application discloses a multi-dimensional tensor-based test literacy cognitive level intelligent labeling method and system, in order to solve the problem of multi-dimensional literacy rating performance collapse caused by expert labeling scarcity, loss of mathematical symbol semantics and long tail distribution in the prior art, a three-dimensional tensor space is constructed by fusing course standards; the first data set is used as a few-sample example, and a diagnostic thinking chain is combined to drive a large language model to generate structured pseudo-labels for unlabeled test questions in the second data set; through the incremental pre-training of the mask language model by injecting mathematical symbol prior and combining the cost-sensitive weighting mechanism based on real slot statistics, the lightweight training of the high-dimensional multi-task joint rating network is realized; finally, the active backflow closed loop of low confidence samples is executed through confidence evaluation. The application greatly improves the detection rate and rating accuracy of high-order long tail literacy under very small samples, significantly reduces the labeling cost, and realizes the engineering intelligent evaluation of cross-version mass question banks.

This invention relates to the field of surface defect detection technology, specifically to a defect detection evaluation method based on image segmentation, comprising the following steps: calculating the cumulative overlap score of the segmentation prediction map and the label map; calculating the cumulative over-detection rate of the segmentation prediction map and the label map; and obtaining image segmentation indicators based on the cumulative overlap score and the cumulative over-detection rate. This invention effectively evaluates the segmentation of small defects without affecting the structure of the image segmentation method. Furthermore, this method can additionally calculate the over-detection rate. The cumulative overlap score can equalize the importance of small and large defects, and the calculation of the cumulative over-detection rate can evaluate the over-detection rate of the model, thereby solving the problem of poor segmentation performance in existing defect detection methods.

The invention discloses an ADAS perception algorithm target detection rate evaluation method and device based on an image large model, and a storage medium, and belongs to the technical field of intelligent driving. The method comprises the following steps: acquiring a verification data packet containing an original video; reasoning the video data by using a pre-trained image large model, and automatically generating a truth value file containing a target detection frame, a category and a tracking ID; inputting the same video data into a to-be-evaluated ADAS perception algorithm for recharging to obtain an output result file of the to-be-evaluated ADAS perception algorithm; through an automatic comparison tool, according to an intersection-to-union ratio (IoU) matching rule and a continuous frame statistical rule based on a target tracking ID, an output result is compared with a truth value, and key performance indexes such as a positive detection rate, a false detection rate and a missing detection rate are obtained through calculation. According to the method, a large model is utilized to automatically generate a high-quality truth value, a traditional manual annotation or expensive truth value system is replaced, high-efficiency, low-cost and standard-unified ADAS sensing algorithm performance evaluation and comparison are realized, and the algorithm development iteration efficiency is remarkably improved.