9results about How to "Improve Anomaly Detection Efficiency" patented technology

The application discloses a vehicle-mounted camera anomaly detection method and device, equipment and a storage medium, relates to the field of vehicle engineering, and is applied to a target cloud and comprises the following steps: sending an anomaly detection instruction to a target vehicle terminal of a target vehicle based on a preset trigger condition to obtain a target media file; analyzing a target statistical characteristic of the target media file to obtain a first analysis result, determining a target detection model based on a target camera corresponding to the target media file, and performing reference area checking on the target media file by using the target detection model to obtain a corresponding second analysis result; determining a first confidence degree corresponding to the first analysis result and a second confidence degree corresponding to the second analysis result by using a target confidence degree algorithm, and fusing the first analysis result and the second analysis result based on the first confidence degree and the second confidence degree to obtain an anomaly detection result corresponding to the target camera. The application improves the accuracy and efficiency of vehicle-mounted camera anomaly detection.

The embodiment of the application provides a kind of neutron flux abnormality detection method and device, electronic equipment and storage medium, belong to nuclear power plant safety technical field.The method comprises: screening reference nuclear power reaction unit.Get the first reference counting rate detection module in multiple continuous preset time steps under the core neutron counting rate.Acquire concentration influence factor, original neutron source source intensity and reference neutron source source intensity.According to the first detection sensitivity, the first reference sensitivity, the core neutron counting rate, the concentration influence factor, the original neutron source source intensity and the reference neutron source source intensity, the predicted neutron flux of the core basket is obtained by calculation.The first loading of pressurized water reactor nuclear power reaction unit is carried out, and the target neutron counting rate is obtained.According to the target neutron counting rate and the first detection sensitivity, the target neutron flux is obtained by calculation, and the abnormality is detected based on the target neutron flux and the predicted neutron flux.The embodiment of the application can improve the efficiency of neutron flux abnormality detection.

The invention provides a detection circuit, a dry node communication system and a detection method, the detection circuit is applied to the dry node communication system, the dry node communication system comprises a sender, a receiver and a transmission link, the sender is provided with a dry node and a detection circuit, the detection circuit comprises a sending detection circuit, a transmission detection circuit and a controller, the sending detection circuit and the transmission detection circuit are both connected with the dry node; the sending detection circuit is used for controlling the dry node to be opened or closed according to an instruction of the controller and outputting a corresponding sending detection signal; the transmission detection circuit is used for outputting a transmission detection signal of a transmission link when the dry node is disconnected; the controller is used for determining whether the sender has a fault according to the sending detection signal; and determining whether the transmission link has a fault according to the transmission detection signal. According to the invention, the fault position in the dry node communication system can be accurately determined through the sending detection circuit and the transmission detection circuit.

The application relates to an exception detection method and device in performance testing, a computer device, a storage medium and a computer program product. By sending a test message to a target execution device in a business system, the target execution device is instructed to obtain a test script based on a test identifier and start target performance testing. In the target performance testing, the running state information at the current time point is compared with the running state prediction information at the corresponding time point predicted based on a target long short-term memory model. If the comparison result represents that the running state information at the current time point is abnormal running state information, the corresponding running snapshot of the target execution device is collected and saved. Compared with the traditional method of determining whether retesting is needed for the exception after one stress test, the present application trains the target long short-term memory model to timely detect the abnormality of the test state at the corresponding time point when the target execution device executes the test, timely capture the corresponding running snapshot, and improve the efficiency of the exception detection.

The application relates to the technical field of network security, in particular to an abnormal network intrusion detection system based on FPGA and artificial intelligence. The system obtains and analyzes network message information of a network link entering the FPGA through a network perception unit, generates a data packet for AI intrusion detection model reasoning based on the obtained data information; an intrusion detection unit is constructed based on an AI model, outputs an intrusion detection result based on the input data packet, so that an intrusion response unit can more accurately determine a response processing mechanism based on the intrusion detection result and adjust the response processing mechanism; the analysis unit determines the intrusion detection state based on the detection rate in the preset period after the adjustment of the response processing mechanism, adjusts the corresponding parameters based on the intrusion detection state, generates AI detection model fine-tuning parameters, so that the intrusion detection state can be more effectively adjusted to be qualified. The application improves the abnormal detection efficiency of high-speed network traffic.

The invention provides an anomaly detection method and device for graph data. The detection method comprises the steps that graph data of a target graph are obtained, the graph data comprise n nodes and the connection relation between the nodes, and each node has first feature data composed of d attributes; and partial nodes in the n nodes have labels representing the abnormal degrees thereof. And then determining second feature data, related to the graph structure, of each node, including processing the first feature data of each node by using a group of band-pass filters determined according to the connection relationship to obtain high-frequency abnormal features. The table data is constructed, and the single row data records the comprehensive feature data of the single node and comprises the first feature data and the second feature data. Then, the table data and the labels of the partial nodes are input into the table basic model, and the abnormal degree of the other nodes without the labels is predicted.

The application discloses an image anomaly detection method and device, computer equipment and a storage medium, and belongs to the technical field of computers. The method comprises the following steps: determining a plurality of superpixel regions of an image, wherein the superpixel regions comprise a plurality of pixel points in the image; extracting k first superpixel features of different scales from each superpixel region, wherein k is an integer greater than 1; fusing the k first superpixel features of different scales of each superpixel region to obtain a superpixel fusion feature of each superpixel region; and determining an anomaly detection result of each superpixel region based on the superpixel fusion feature of each superpixel region, wherein the anomaly detection result represents a probability that the superpixel region in the image is abnormal. The application extracts and fuses local features in units of superpixel regions to identify the probability that each superpixel region is abnormal, can reduce unnecessary global operation, reduces the operation complexity, and improves the efficiency of anomaly detection.

This application relates to the field of device testing, specifically to a method, controller, and testing camera for detecting abnormal insertion of components. The method includes acquiring a first image of the board before insertion and a second image of the board after insertion; performing position correction on the first or second image based on a reference image in the first and second images; comparing the position-corrected first / second image with the uncorrected second / first image to obtain pin images; and determining whether the insertion is abnormal based on the geometric or structural features of the pins in the pin images. The anomaly detection process requires no manual intervention, improving the accuracy and timeliness of the detection results and increasing the efficiency of anomaly detection.