21results about How to "Solve missing detection" patented technology

The invention relates to the technical field of coal mill powder pipes, in particular to a powder leakage defect automatic identification method based on digital twinning and AI video analysis, and the method comprises the steps: obtaining operation parameters and environmental parameters of a coal mill powder pipe, calculating a powder leakage risk characterization coefficient, and judging whether there is a potential risk of powder leakage; image data of the surface and the surrounding area of the powder pipe in the initial starting period are collected through an AI video analysis module, powder leakage characteristic parameters are extracted, a characteristic change curve segment is drawn, similarity comparison is conducted on the characteristic change curve segment and the reference characteristic curve segment, and whether a suspected powder leakage area exists or not is judged; actual operation parameters and model prediction parameters of the powder pipe in a stable operation period are obtained based on a digital twin model, the fluctuation degree of parameter deviation is calculated, and whether the powder leakage defect exists or not is comprehensively judged by combining a suspected powder leakage area judgment result; the early-stage accurate identification of the powder leakage defect is realized, the detection efficiency and precision are improved, and the operation and maintenance cost and the safety risk are reduced.

The invention discloses a rape pod detection and counting method and system based on YOLOv8, and the method comprises the steps: constructing a rape inflorescence pod image data set, and carrying out the training and testing of the YOLOv8, and obtaining a detection model with stable and standard detection precision; inputting the detected rape inflorescence silique image into the detection model, and outputting a bounding box and confidence coefficient for calibrating the rape silique by the detection model; and sorting the bounding boxes, calculating an overlapping degree between two adjacent bounding boxes, screening the bounding boxes according to the overlapping degree, and counting the number of the bounding boxes reserved by screening to obtain the total number of rape inflorescence siliques. The detection model obtained by the method meets the detection requirements of small-target rape pods, the problems of missing detection and repeated counting are solved through bounding box screening, and the counting accuracy is high. According to the system, full-process automation of input-detection-counting-output is achieved, the operation threshold is lowered, and the actual use requirement of a user can be better met.

The invention discloses an educational academic literature tracing method and system based on retrieval enhancement generation. The method comprises the following steps: performing fine-grained analysis on the multi-source literature, constructing a document object containing fields such as a title, an abstract, a methodology, an experimental result and a conclusion, and establishing a field-level index and a citation network feature score; identifying an academic intention queried by a user by utilizing a large model, dynamically loading a weight mapping table according to the academic intention, and performing weighted rearrangement on multiple paths of retrieval results to obtain candidate core literatures; generating a traceability text with an explicit label based on the candidate literature; and executing reference consistency verification by using the natural language inference model, and executing illusion correction according to a verification result. According to the method, through structured field analysis and dynamic rearrangement of intention driving, the problems of semantic fragmentation and reference fictition when a general RAG is used for processing complex academic literatures are solved, and the academic traceability preciseness and accuracy are remarkably improved.

The invention relates to the technical field of artificial intelligence image recognition and data mining, and discloses an image data mining processing system and method combining a visual large model and a visual small model. The method aims at solving the problems that in the prior art, the image recognition recall rate and accuracy are difficult to consider, the calculation cost and the processing speed are unbalanced, the data management efficiency is low, and the model generalization ability is insufficient. The system comprises a data input module, a frame extraction deduplication module, a picture library construction module, a visual large model judgment module, a visual small model judgment module, a label preprocessing module, a manual inspection module, a training data generation module, a model training module and a data security module. According to the method, the generalization ability is guaranteed through preliminary screening of an LPM (Deic) large model, missing detection is complemented through secondary verification in cooperation with a YOLO small model, false alarms are eliminated in combination with manual verification, meanwhile, double models are optimized through targeted sample training, the pain point of missing detection or misjudgment of a single model is effectively solved, and accurate and comprehensive consideration of target recognition is achieved.

The invention provides a fully mechanized coal mining face sensor fault diagnosis method and device and electronic equipment, and the method comprises the steps: obtaining the current working stage of a target hydraulic support, and obtaining the time period sensing data of a to-be-diagnosed sensor on the target hydraulic support at the current working stage; according to the current working stage, a target diagnosis path is determined, and the target diagnosis path is formed by combining one or more diagnosis algorithms; and obtaining a fault diagnosis result of the to-be-diagnosed sensor according to a target diagnosis path and the time period sensing data. According to the method, the adaptive target diagnosis path is dynamically selected according to different working stages of the target hydraulic support, and analysis is performed in combination with the corresponding time period sensing data, so that the problem of false alarm or missing detection caused by mismatching of working conditions in a traditional static diagnosis method is effectively solved, the accuracy and robustness of sensor fault diagnosis are remarkably improved, and the fault diagnosis efficiency is improved. The low false alarm rate, the low omission rate and the high operation and maintenance efficiency are achieved.

The application is suitable for the technical field of eye movement data processing, and provides a pilot gaze object recognition method and system based on eye movement data. The method comprises the following steps: sampling video data into images, constructing a training data set, training a YOLOV8n neural network, and obtaining a YOLO lightweight target detection model; based on the YOLO lightweight target detection model, frame-by-frame recognition is performed on the video data to be recognized; according to the relative position relationship between the gaze objects, the preliminary recognition result is checked and corrected; the pointer position of the instrument type gaze object is detected, and the gaze area of the gaze object is reconstructed according to the pointer position; and according to the gaze point coordinates in the table data and the reconstructed gaze area, the gaze object at each moment is determined. According to the table data and the video data recorded by the eye tracker, the pilot's gaze object at each moment in the flight training process can be accurately recognized.

The invention relates to the technical field of photovoltaic panel detection, in particular to a photovoltaic panel defect detection method based on airborne thermal infrared images. The method comprises the following steps: acquiring multi-view thermal infrared images collected by the unmanned aerial vehicle in at least two different flight attitudes, and generating a normalized temperature difference field for eliminating environmental and geometric interferences through registration and processing; segmenting and determining a thermal anomaly region based on the statistical characteristics; internal defects and surface covering defects are automatically distinguished by calculating angle invariance indexes of the thermal behavior characteristics under multiple view angles; respectively matching an internal defect feature library or a surface covering feature library; the detection reliability is evaluated by calculating the confidence coefficient of decision, and when the confidence coefficient is insufficient, the reason is analyzed according to the infrared image resolution and the feature variation, and the flight height, the hovering duration or the aerial photography angle of the unmanned aerial vehicle is adaptively optimized to recollect data. According to the method, the problem that real faults and pseudo defects in the photovoltaic panel cannot be automatically and reliably distinguished is solved.

This invention discloses a method and system for detecting life jacket wearing at port berths. The method includes: constructing a monitoring area and deploying computing nodes; training a YOLOv8 model for initial and secondary detection, a ResNet model for illumination classification, a Uformer model for rain and fog recovery, and a YOLOv8 model for semantic segmentation; performing illumination scene recognition on the acquired images and adaptively selecting the corresponding preprocessing method; using the initial detection results, for personnel targets with insufficient pixel size, predicting their position by tracking and calculating the magnification factor, controlling the PTZ camera to magnify and perform secondary detection for confirmation; using the semantic segmentation results to determine whether the personnel are located in the land-based work area; and combining target tracking, triggering an alarm only when the personnel are not wearing life jackets, are located on land, and their status continuously exceeds a threshold. The system includes an intelligent PTZ camera, a management platform, and a processing unit. This invention solves the problems of difficult identification of small targets at long distances, poor adaptability to complex illumination, and high false alarm rate due to rule-based methods.

The utility model relates to the technical field of cloth detection, in particular to a cloth spreading mechanism for a cloth inspecting machine, which comprises a rack, a flattening mechanism is mounted at the top of the rack, a base plate mounted on the inner side of the rack is arranged below the flattening mechanism, and a tensioning component is movably connected in the rack; the flattening mechanism comprises a support fixedly connected to the top of the rack, a motor is installed at the top of the support, a driving shaft is fixedly connected to an output shaft of the motor, and a flattening assembly and a flattening assembly are connected to the driving shaft; according to the cloth spreading mechanism for the cloth inspecting machine, the motor is used for driving and enabling the smoothing assembly and the flattening assembly to operate, the smoothing assembly reciprocates on the surface of cloth through a smoothing plate, so that the cloth is flattened, then after the cloth passes through the flattening assembly, due to the fact that two sets of auger blades are symmetrically arranged on a flattening roller, the auger blades provide pulling force towards the two sides for the cloth, and the cloth is flattened; therefore, the cloth can be further flattened, and the problem of leak detection caused by wrinkles or partial overlapping on the cloth when the cloth inspecting machine works can be solved.

The invention provides a tunnel harmful gas scanning type monitoring method and equipment, and relates to the technical field of tunnel construction safety monitoring. Comprising the following steps: S1, synchronously scanning by at least two monitoring devices, recording direction angle data and a gas concentration measurement value of each ray, and acquiring tunnel boundary point cloud data; s2, calculating potential intersection points of rays from different devices in the tunnel space, and screening effective intersection points; s3, based on the tunnel boundary point cloud data, constructing a three-dimensional network model of the tunnel surface through a Delaunay triangulation algorithm; s4, in the three-dimensional grid, performing grading treatment according to concentration values from low to high, assigning values to voxels through which effective laser rays penetrate, and reconstructing a three-dimensional concentration field; and S5, generating a tunnel harmful gas concentration distribution three-dimensional model. According to the method, the path integral concentration data is converted into accurate three-dimensional space distribution information, the three-dimensional coordinates of the gas escape source can be accurately identified, accurate positioning of the harmful gas escape source is realized, and the problems of missing detection and misjudgment caused by fuzzy positioning are effectively solved.

The invention relates to the technical field of remote sensing image road extraction, in particular to a remote sensing image road extraction method based on frequency domain feature extraction and fusion, which comprises a feature extractor, a bottleneck layer and a segmentation branch. The feature extractor adopts FCB to replace traditional convolution operation, and performs frequency domain filtering operation on a frequency domain to perform feature extraction; the bottleneck layer adopts a KAN Layer to learn complex road features; according to the segmentation branch, the feature map is recovered to the original size through up-sampling, multi-scale feature learning and fusion are achieved through a multi-scale pyramid, and finally the feature map is sent to an output layer to output a prediction result. According to the method, the frequency domain convolution with the global receptive field, the bottleneck layer capable of learning the activation function and the multi-scale image reasoning fusion mechanism are introduced, the extraction precision is improved, the model parameter quantity and the energy consumption are reduced, and the high-efficiency and high-precision remote sensing image road real-time extraction requirement can be met.

The utility model relates to the technical field of textile, and particularly discloses a hairiness detector which comprises a back plate, a shell, an emitter, a receiver, an aviation plug, two first bolts, an installation assembly, a yarn guide assembly and a guide assembly, the shell is arranged on one side of the back plate, the back plate is provided with two installation grooves, one ends of the two first bolts penetrate through the corresponding installation grooves respectively, and the other ends of the two first bolts penetrate through the yarn guide assembly. The installation assembly and the yarn guide assembly are respectively in threaded connection with the shell, the emitter is fixedly connected with the shell and located in the shell, the receiver is fixedly connected with the shell and located above the emitter, the back plate is provided with a groove, one end of the aviation plug is fixedly connected with the shell, the other end of the aviation plug penetrates through the groove, and the installation assembly and the yarn guide assembly are both detachably connected with the back plate. According to the weft yarn hairiness detection device, the weft yarn hairiness can be accurately detected, the false alarm rate and the omission ratio are greatly reduced, and therefore the cloth quality is improved.

The utility model provides a kind of optical detection device for mobile phone middle frame appearance flaw, including outer frame, the inside fixed mounting of outer frame has arc hanging plate, the surface of arc hanging plate is installed with multiple first industrial camera and first detection light source, the middle part of one side of arc hanging plate is installed with extension plate, the surface of extension plate is installed with second industrial camera, the inside wall of the front side of outer frame is installed with multiple second detection light source and third detection light source, the bottom of the front side of outer frame is installed with third industrial camera, first detection light source, second detection light source and third detection light source constitute multi-angle light source illumination, imitate human eye, adapt to mobile phone middle frame appearance flaw detection, solve the problem of single light source angle in traditional appearance detection, to solve the quality anomaly and missed detection caused by part of defect not being imaged due to single angle.

This application relates to the field of converter station safety monitoring technology, and discloses a system for full-site area safety monitoring and personnel behavior analysis. The system includes modules for data acquisition, analysis, alarm, monitoring, control, and feedback. It integrates infrared temperature measurement, video recording, vibration, and sound sensors. Through a linkage unit, it dynamically adjusts the acquisition frequency and combines an LSTM model to predict equipment temperature trends and personnel behavior. The monitoring module assesses the overall risk of the station in real time based on a multi-parameter risk assessment formula. The control module controls cooling equipment according to closed-loop logic and optimizes time-slot scheduling strategies. The storage unit uses AES-256 encryption and TLS 1.3 transmission protocol to ensure data security. The feedback module supports remote interaction via mobile devices. This system achieves high-precision intelligent control of the converter station environment, improves anomaly detection accuracy to over 98%, reduces management costs by 35%, and enhances operational safety and efficiency.

This invention discloses an infrared small target detection method based on query set optimization and contextual prior constraints, belonging to the field of infrared image processing and target detection technology. The method first constructs a contextual prior knowledge base including scene semantics, spatial layout, and neighborhood statistical characteristics. Then, it adaptively enhances the infrared image query set, designs filters based on the frequency domain characteristics of the support set, and performs multi-scale decomposition according to the target size prior. Next, it performs cross-attention calculation between the optimized query features and the contextual prior to achieve query-context joint reasoning. Finally, it outputs the detection result through dual constraints of support set prototype matching and contextual consistency verification. This invention solves the problems of missed detections and false alarms caused by passive query set input and single saliency prior dependence in traditional methods, providing a highly reliable technical solution for infrared small target detection.

The invention provides a topology implicit deduction and delay risk prediction method and device for a construction site state, and relates to the technical field of construction intelligent prediction and risk assessment management. According to the method, a field image and a constraint prompt word set are analyzed by utilizing a visual language model, and a construction field semantic graph is constructed through logic verification and correction; constructing a BIM model of a construction site by utilizing a visual programming platform, analyzing the BIM model, and constructing a BIM semantic graph with consistent vision fields; then virtual-real two-way graph matching is executed, and a global optimal node matching set is solved; and fusing the two types of semantic maps according to the global optimal node matching set, constructing a dynamic time-varying semantic map, executing state implicit reverse deduction and delay risk prediction, and finally mapping a deduction prediction result to a BIM model to realize visual management of the construction progress. According to the method, the problems of incomplete component identification, low virtual and real information matching degree and limited progress monitoring stability can be effectively solved, and intelligent prediction and risk assessment management of a building construction site are realized.

This invention belongs to the field of range testing technology, specifically relating to static explosion damage testing of ammunition at a range. Addressing the data analysis problem of static explosion fragments, it proposes a static explosion fragment detection method based on a YOLO-FD network. The method includes the following steps: Step 1: Constructing a static explosion fragment image dataset, obtaining training, validation, and test sets for subsequent training; Step 2: Constructing and improving a YOLO-FD neural network model for training; Step 3: Inputting the acquired fragment images into the trained YOLO-FD network model, outputting fragment images containing fragment detection boxes. This invention accelerates the processing speed of target features during detection, enhances the network's detection performance for small targets, maintains lightweight design, and strengthens the global information extraction capability of the convolutional network; the network exhibits good stability and robustness during training; it boasts high accuracy and speed, effectively improving measurement efficiency and reducing the risk factor, specifically solving the problem of missed and false detections of static explosion fragments from the perspective of unmanned aerial vehicles (UAVs).

The invention discloses a category-unlimited track obstacle boundary invasion detection method. The method comprises the following steps: S1) constructing an early warning boundary generation method and a category-unlimited obstacle detection method; s2) acquiring an image of a track area to be detected, and generating an early warning boundary on the image through an early warning boundary generation method; s3) intercepting a small image on the image of the track area to be detected based on the generated early warning boundary; s4) obtaining a normal graph from the small screenshot through a category-unlimited obstacle detection method; and S5) carrying out image processing and segmentation based on the normal graph, extracting the contour of an obstacle higher than the ground, comparing the extracted contour of the obstacle with the early warning boundary, and if there is an intersection, determining that foreign matter invasion exists and giving an early warning, thereby solving the problem of missing detection of foreign matters of unknown categories in a traditional method, and realizing unlimited category detection.

The invention provides a student behavior safety monitoring and early warning method and system based on artificial intelligence and big data, and the method comprises the steps: comparing the single opening and closing duration of a gate with a preset standard opening and closing time limit, recognizing whether the trunk width exceeds a conventional passing width upper limit or not through combining with the trunk width, and determining the blocking duration and closing delay of the gate; analyzing whether the closing delay exceeds a preset delay upper limit, evaluating a trailing gap expansion proportion according to the adjacent card swiping interval and the trunk width, and identifying whether the trailing gap forms an unauthorized passable window; integrating the expansion proportion of the trailing gap and the clamping stagnation duration of the gate, calculating a dynamic correction amount, and adding the reference judgment time limit and the dynamic correction amount to obtain an optimized trailing judgment time limit; and carrying out classification processing on subsequent gate opening and closing events according to the trailing detection accuracy and the optimized trailing judgment time limit comprehensive evaluation, and obtaining that the overall trailing identification of the gate machine is optimized to a stable operation level.

The utility model relates to a kind of thermal imaging camera interlocking control system for photovoltaic array monitoring, including thermal imaging camera, thermal imaging camera is provided with support, thermal imaging camera is set on photovoltaic panel top by support, the detection range of thermal imaging camera covers photovoltaic array;It further includes control module, control module includes first communication module and embedded processor, embedded processor is connected with thermal imaging camera communication by first communication module, embedded processor is also connected with central controller by first communication module, the input end of embedded processor is connected with light intensity sensor;Central controller is connected with second communication module, and central controller connects multiple circuit breakers by second communication module.The utility model sets thermal imaging camera to detect photovoltaic array, data processing is carried out by control module and is transmitted to central controller, and central controller controls each circuit breaker, provides hardware basis for fire detection and processing.

This invention relates to the field of agricultural intelligent seeding machinery technology, and discloses a precision seed metering device and its automatic seeding compensation system. The system includes a seed metering component, a rotary automatic seed metering device, a seeding monitoring device, and an automatic compensation turnover component. The seed metering disc has shaped holes, and an intermittent drive unit drives the seed metering disc to rotate rapidly and then stop for a long period, with the shaped holes sequentially aligning with the seed metering tubes to complete seed filling and metering. The seeding monitoring device uses a combination of photosensitive sensors and contact elements with logic detection to monitor falling seeds in real time and determine missed seeding. The automatic compensation turnover component includes a feeding metering chamber, an airflow propulsion device, and an airflow-driven rotor. The outer edge of the rotor has a single-seed cavity, releasing only one compensation seed at a time under airflow drive, achieving precise compensation. Through mechanical and pneumatic combined single-seed quantitative compensation and dual-modal mutual calibration of missed seeding detection, real-time detection and precise hole-to-hole compensation of missed seeding are achieved, significantly improving seeding accuracy and reliability.