8results about How to "Avoid analysis" patented technology

The utility model discloses a quartz tube sleeve for assembling a bonding wire annealing tube gas sensor, which comprises a tube body, the fixing pipe is used for being inserted into an annealing pipe opening to achieve sleeving connection of the pipe body and the annealing pipe, the limiting protrusion is used for abutting against the end face of the annealing pipe opening, the mounting base is used for mounting a gas sensor, and the limiting protrusion is arranged on the inner circumferential wall of the pipe body. One end of the limiting protrusion is connected between the fixing pipe and the installation base, the other end of the limiting protrusion is arranged in an inclined mode in the direction where the fixing pipe is located, a connecting table is arranged on the side wall of the installation base, and a containing cavity used for containing the sensor body is formed in the connecting table. The gas concentration monitoring device is simple in structure and convenient and reliable to assemble, facilitates monitoring of gas concentration and has a good use effect.

This invention belongs to the technical field of network security protection and relates to a hybrid attack detection and defense method based on deep learning. The method includes the following steps: extracting multi-dimensional traffic statistical features and application layer protocol semantic features; outputting a high-dimensional behavior-related feature vector; issuing an attack trigger signal; outputting an attack context description; generating a collaborative defense command stream; outputting real defense effect feedback data; and adjusting the convolutional autoencoder model and the graph neural network. This invention solves the problem that existing network attack detection and defense technologies struggle to simultaneously meet the core protection requirements of comprehensive detection dimensions, reasonable computational resource scheduling, dynamic adaptability of defense strategies, and adaptive model iteration in hybrid attack scenarios.

The application is suitable for the fields of high-end flexible sensing preparation and multi-modal sensing detection technology, and provides a patch type ultrasonic biomarker integrated sensor and a detection method.The sensor comprises a patch type ultrasonic transducer array, which is used for real-time monitoring of muscle and bone diseases, realizes multi-parameter multi-modal data information fusion detection and analysis of muscle and bone structure-function, a microneedle electrode array, which is used for in-situ detection of biomarkers and collection of physiological electrical signals, a flexible electronic circuit, which is used for bearing and connecting the patch type ultrasonic transducer array and the microneedle electrode array, and an acoustic coupling adhesive layer, which is used for skin fitting and acoustic coupling.The application can systematically evaluate the structure, mechanics, metabolic inflammation level and nerve function activity coordination of muscle and bone, provides a new means for early identification, quantitative evaluation and personalized intervention of muscle and bone diseases, and has important clinical and industrial values.

The application relates to the technical field of elevator intelligent monitoring, and discloses an elevator abnormal behavior recognition and early warning method and system based on AI vision analysis, which comprises the following steps: receiving a door machine state signal, waking up a static target analysis process when the door is opened, calculating a target physical size based on perspective transformation and outputting a static early warning, reading a high-frequency load sampling sequence to generate a physical dynamics sequence when the door is closed, triggering a hardware interrupt when a first-order difference of the physical dynamics sequence exceeds a threshold value, waking up a dynamic behavior analysis process in response to the interrupt, intercepting a video stream to extract moving foreground pixels and projecting the moving foreground pixels to a two-dimensional grid of a car bottom plane, calculating a visual kinematics acceleration sequence and a spatial distribution variance, using a dynamic time warping algorithm to obtain a time domain matching distance parameter of the physical dynamics sequence and the visual sequence, and constructing a joint confidence based on the spatial distribution variance and the time domain matching distance parameter to output an early warning. Through multi-modal data fusion and dynamic time alignment, the application reduces the consumption of computing resources and improves the recognition accuracy.

The invention belongs to the technical field of network security protection, and relates to a hybrid attack detection and defense method based on deep learning, and the method comprises the following steps: extracting multi-dimensional traffic statistical features and application layer protocol semantic features; outputting a high-dimensional behavior association feature vector; sending out an attack trigger signal; outputting attack context description; generating a cooperative defense instruction stream; outputting real defense effect return data; and adjusting the convolutional self-encoding model and the graph neural network. According to the method, the problem that the existing network attack detection and defense technology is difficult to meet core protection requirements of detection dimension comprehensiveness, computing resource scheduling reasonability, defense strategy dynamic adaptability and model iteration adaptivity in a mixed attack scene at the same time is solved.

This invention discloses an online monitoring method, system, equipment, and storage medium for the operating status of secondary equipment in substations, belonging to the field of power system monitoring technology. The method involves acquiring the status categories of each secondary device and constructing a secondary circuit model. The theoretical values ​​for each monitoring point are calculated using the model, and the residual vector is obtained by comparing the theoretical and measured values. The operating status is then determined based on the residual vector. A target fault set is selected by analyzing the similarity between the fault feature vector corresponding to a virtual fault and the residual vector. Within the target fault set, a correlation index for each virtual fault is determined based on the correlation between the disturbance vector and the residual vector, and a suspected fault set is selected based on the correlation index. Within the suspected fault set, the fault intensity of each virtual fault is calculated by constructing a fault location model. Virtual faults with a fault intensity greater than a set intensity value are used as detection results to provide early warnings for the operating status of the secondary equipment.

The present invention relates to the field of image processing techniques. Disclosed are an image recognition method and device, a terminal apparatus, and a medical system. The image recognition method disclosed in the present invention comprises: using a first recognition model to determine whether a medical image indicates a pathological change; and using a second recognition model to perform advanced recognition on the medical image showing a pathological change so as to obtain a pathological change degree recognition result indicating a degree of the pathological change indicated in the medical image. The present invention eliminates the need to perform manual analysis and formulate a feature extraction solution, thereby enhancing efficiency and precision of medical image recognition.

The embodiment of the specification discloses a method, device and equipment for detecting abnormal information in a mine, which can comprise: acquiring a living body detection result in a preset area in the mine; if the living body detection result indicates that a target living body exists in the preset area, starting an image acquisition device in the preset area; acquiring a target image containing the target living body collected by the image acquisition device; inputting the target image into an abnormality detection model to obtain an abnormality detection result output by the abnormality detection model; the abnormality detection model is used for detecting whether abnormal information exists in the target image; the abnormal information includes that the target living body in the target image has a rule violation behavior. Based on the scheme of the application, the abnormal situation in the mine can be automatically and efficiently detected.