209 results about "Detection rate" patented technology

The invention belongs to the technical field of target detection and recognition, and particularly relates to a thinking chain-based large and small model collaborative target detection and recognition method. According to the method, the small model is responsible for most of easy-to-detect targets, the calculation pressure of the large model is reduced, the large model is responsible for suspected samples, vision and language multi-mode reasoning is combined, the overall false detection rate and the omission ratio are both reduced, confidence evaluation is conducted through the joint probability, automatic screening and manual rechecking of uncertain results are achieved, the reliability of key results is guaranteed, and the method is suitable for large-scale popularization and application. According to the'pseudo thinking chain + pseudo label 'method, by means of reasoning and labels generated by the model, data dependence on manual labeling is reduced, only low-confidence samples are manually confirmed, the human intervention range is narrowed, the human cost is remarkably saved, and semantic information with finer granularity is provided for the model by introducing phrase-level feature descriptors. And the identification capability of complex target attributes and states is improved.

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 provides an insulator defect detection method GAC-YOLO based on YOLOv7, and relates to the field of identification and detection of insulator targets. Comprising the steps that a GSConv module is optimized, and the calculation amount and the memory requirement are reduced while the precision is guaranteed; an ACmix attention mechanism is introduced, convolution and self-attention are fused, the prediction ability of the model is enhanced, and then the detection effect is improved; the centralized feature pyramid is fused, information exchange and integration can be achieved through feature maps of different scales, more comprehensive insulator features are further obtained, and therefore the insulator detection precision is improved. And performing an ablation experiment on each module of the model, and performing a contrast experiment with a mainstream deep learning model to obtain a final insulator detection model. According to the model realized by the invention, on the basis of ensuring the original detection rate of the YOLOv7, the accuracy of the model in insulator defect detection is 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 discloses an industrial defect detection method based on reverse distillation learning, which relates to the technical field of industrial defect detection, and comprises the steps of generating and filtering a noise picture, and fusing reverse distillation learning with an attention mechanism. According to the method, the fine defect detection and positioning capability under the complex background is improved, and particularly, the defects of small defect area and low background contrast ratio under the condition of complex background texture or relatively high noise level are overcome; according to the performance, the image level AUROC is 99.0%, the pixel level AUROC is 98.23%, the AUPRO can reach 95.02%, and the AUROC and the AUPRO are far higher than other indexes based on distillation learning. In the aspect of real-time performance, the detection speed of a single picture is high and can reach 25ms, the real-time performance is in the front, and the defect detection performance and the defect positioning performance are higher; noise synthesis and filtering are used for improving the small defect detection rate and reducing the false detection rate; the MFF + attention mechanism is used for improving the logic defect detection rate.

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 a wafer defect detection method based on one-shot, and belongs to the technical field of target detection, and the method comprises the following steps: inputting data, and taking preprocessed data as network input; constructing a twin network double-branch structure; training the model by a meta-learning strategy; and detecting by using a dynamic threshold value. According to the method, only one reference sample and a small number of defect samples are needed to be matched with a data enhancement technology to quickly extract sample features, model training is completed, and the detection efficiency is remarkably improved while the dependence of an algorithm on large-scale labeled data is reduced; the similarity threshold value is dynamically adjusted through the image sharpness value, compared with a traditional manual threshold value setting method, the automation degree is higher, and the false detection rate and the missing detection rate can be effectively reduced; and through a meta-learning strategy, when a new task is encountered, the model can be quickly adapted to the new task only by performing internal circulation for one time to perform fine adjustment on the model.

The invention discloses a self-adaptive adjustment method for a time sequence anomaly detection threshold, which dynamically estimates an anomaly upper bound according to anomaly score distribution in a time sequence local window, realizes self-adaptive adjustment of the detection threshold, improves the anomaly detection accuracy, reduces the false alarm rate and the omission ratio, and improves the detection accuracy. And good robustness and practicability are shown.

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 target detection method based on multi-modal data fusion, and relates to the technical field of target detection. According to the target detection method based on the multi-modal data fusion, a dynamic contrast enhancement and edge perception filtering coupling design is adopted, and reflective noise and real defect features are effectively separated through regional gray variance adaptive stretching and directional noise suppression, so that the false detection rate is reduced; under the synergistic effect of the dilated convolution feature retention branch and the deformable feature fusion mechanism, the effective receptive field is expanded while the downsampling information loss is avoided, so that the small defect detection rate is greatly improved; on the basis of a BRDF parameter library of a CookTorrent model and illumination consistency correction, the optical authenticity of synthesis defects is guaranteed, and the problem of scarcity of industrial scene samples is relieved; quantitative preprocessing, depth separable convolution and dynamic NMS threshold adjustment form collaborative optimization.

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 discloses an adhesive tape bonding state evaluation method and system, and relates to the technical field of intelligent evaluation, and the method comprises the following steps: screening a key evaluation part, executing first operation after first judgment, carrying out first detection and second detection on a to-be-evaluated adhesive tape, obtaining to-be-analyzed data according to detection parameters after first regulation and control, and carrying out second detection according to the to-be-analyzed data. Carrying out conjoint analysis to obtain a debonding severity degree and a debonding position, and constructing a first mapping relation; high failure risk areas are screened through historical data, the detection efficiency is improved, detection parameters are fed back and adjusted in real time according to the first detection result, the omission ratio is remarkably reduced, image analysis and physical signals of second detection are deeply fused, blind areas of a single detection means are made up, an iterative failure mode library is formed, and the detection efficiency is improved. The parameter self-adaptive design can be compatible with different adhesive tape materials and bonding base materials, and recalibration for each scene is not needed.

The invention provides an industrial process small fault detection method based on slow independent feature probability difference, and belongs to the technical field of industrial process fault detection. The technical problem that traditional PCA is low in small fault detection rate is solved. According to the technical scheme, industrial data are standardized, slow-varying and fast-varying feature spaces are extracted through slow feature analysis SFA, and a probability difference detection index is constructed for the slow-varying features in combination with JS divergence and a sliding window; performing ICA (Independent Component Analysis) on the quick change features to construct a slow independent principal component, and constructing a slow independent feature probability difference index in combination with JS divergence and a sliding window; and adopting a Bayesian strategy to fuse the two feature probability difference indexes to generate a comprehensive detection index BIC. The method has the beneficial effects that noise-containing quick change features are denoised through ICA, the probability difference is enhanced by using JS divergence, noise interference is suppressed, and the tiny fault detection rate is remarkably improved.

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 instance segmentation detection method and application based on improved YOLOv8s. The method comprises the following steps: collecting a to-be-segmented data set, preprocessing the to-be-segmented data set, constructing an improved YOLOv8s model, and training the preprocessed data set; the method is applied to target detection of images and / or videos, such as segmentation detection of second-phase particles of nickel-based superalloy. According to the improved model, an SPDConv layer and a small target detection layer are added, and an SIoU loss function is adopted, so that the small target detection performance of the model is greatly improved, and the omission ratio is reduced; second-phase particle dispersion calculation is updated and embedded into the second-phase particle identification process, batch detection is achieved, and indexes such as the average area fraction, the average number, the average size and the dispersion of the second-phase particles can be directly calculated; the model segmentation performance is good, the operation speed is high, the target detection process is opened, and the method can also be applied to other processes, intelligent recognition of macroscopic objects and online recognition and calculation of videos.

The invention relates to the field of defect detection, and discloses a dumper carriage weld defect detection method and device and a storage medium, and the method comprises the steps: collecting dumper carriage weld defect images to construct an original data set; marking the original data set, and dividing the original data set according to a preset proportion; constructing an improved compartment welding seam detection model based on a YOLOv8n network structure; inputting the training set into the improved model and the original YOLOv8n model for training; and different degrees of salt and pepper noise are added in the original data set, and the detection performance of the original model and the improved model under the extreme noise condition is tested. According to the method, the problems of high missing detection rate and false detection rate of weld defect detection in the prior art are solved, so that the weld defect detection precision is improved, the calculation complexity of the YOLOv8n model is reduced, and the YOLOv8n model can be better deployed on industrial edge equipment.

Provided are methods that include receiving an initial training dataset, where each data instance of the initial training dataset has a label and a first percentage of the plurality of data instances are labeled correctly, providing the initial training dataset as an input to a reinforcement learning agent (RLA) machine learning model to generate a second training dataset, where a second percentage of data instances of the second training dataset are labeled correctly and the second percentage is greater than the first percentage, training a deep learning model using the second training dataset to provide a trained deep learning model, testing the trained deep learning model using a testing dataset to generate a resultant dataset that has a detection rate, which is an indication of a number of data instances that were predicted correctly, and generating a reward parameter. Systems and computer program products are also disclosed.

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 fault detection modeling and detection rate calculation method based on SysML, and belongs to the technical field of testability. The method comprises the following steps: firstly, determining a failure mode needing to be tested of a system to be researched, product functions and performance parameters associated with the failure mode, a test point, a built-in test item and external test item information, and then establishing an initial function parameter cross-linking model consisting of product function composition and performance parameter composition of a structural unit by using a SysML module definition diagram; on the basis of the model, supplementing a fault mode and a cross-linking relation of a product, supplementing a relation between the fault mode and a test category and supplementing a test item to obtain a designed fault detection model, then calculating a fault detection rate of the fault detection model, judging whether the fault detection rate reaches the standard or not, outputting the fault detection model if the fault detection rate reaches the standard, and otherwise, continuing to adjust. According to the invention, the normalization and consistency of fault detection modeling are ensured, the obtained detection scheme can improve the efficiency and accuracy of fault detection, and the requirements of a high-complexity system are met.

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 the technical field of metal foreign matter detection, in particular to a metal foreign matter detection method, metal foreign matter detection equipment and a storage medium, and the method comprises the steps: obtaining the detection statistics of a to-be-detected product; according to the detection statistical magnitude, on the basis of a preset point taking rule, calculating to obtain average noise power; according to the average noise power, a detection threshold value is obtained through calculation; and judging whether the detection statistical magnitude is greater than the detection threshold value, if so, determining that the detection result contains the metal foreign matters, and otherwise, determining that the detection result does not contain the metal foreign matters. The method has the advantages that the precision and accuracy of metal foreign matter detection are improved, and meanwhile the detection rate and the false alarm rate can be taken into account under the condition of the low signal-to-noise ratio.

A method for detecting abnormal fishing vessel trajectories based on deep learning. It mainly solves the problems of poor applicability and low detection rate in detecting abnormal fishing vessel trajectories. The present invention integrates the features of fishing vessel trajectory data, fishing vessel operation mode and marine environment data, and proposes a real-time abnormality detection scheme for fishing vessel trajectories based on deep learning. The scheme first improves the Seq2Seq structure by using a dual attention mechanism, designs a fishing vessel trajectory prediction model (DA‑Seq2Seq), and then analyzes the predicted value and the true value and compares them according to the set threshold to detect abnormal trajectories. Experimental analysis shows that the algorithm's detection rate of abnormal fishing vessel trajectories can reach 86.6%, and the false detection rate is only 2.5%, indicating that the algorithm can detect fishing vessels with abnormal trajectories more timely and effectively.

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 invention discloses a KMT2A fusion gene detection primer probe group, a kit and a detection method, the primer probe group is specifically designed based on KMT2A gene fusion and 13 common partner genes MLLT3, AFF1, MLLT10, MLLT1, MLLT4, ELL, EPS15, SEPT6, MLLT11, MLLT6, SEPT9, AFF3 and CBL fusion sites, the mutual interference is small, the primer probe group can be used for targeted amplification of a fusion gene specific region in a single-tube mixed solution, the detection sensitivity is high, the detection time is short, the detection time is short, and the detection efficiency is high. According to the present invention, the rapid detection of any one fusion gene type in 67 types can be achieved, and compared with the high-throughput sequencing, the sensitivity is high, the detection rate is high, the single sample detection cost is low, the method is suitable for the clinical auxiliary diagnosis of the large batch of samples, and the guidance is provided for the diagnosis, the treatment and the prognosis of the blood disease patients.

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.