1208results about How to "Improve recognition accuracy" patented technology

The invention relates to an underwater acoustic target recognition system and method based on multi-modal depth feature fusion, and belongs to the field of underwater acoustic target recognition. The method comprises the following steps: firstly, performing preprocessing on an obtained underwater acoustic target original audio and associated metadata, and constructing a multi-modal data set; and then the constructed multi-modal sample is input into an identification model, the model extracts deep representation of each modal through a multi-branch feature coding network, depth alignment and complementary aggregation of different modal features are realized by using a cross-modal cross attention mechanism guided by potential query, and a target identification result is output based on a decision network of mixed experts. According to the method, experimental verification is carried out on two disclosed underwater acoustic data sets, the experimental result verifies the effectiveness of the multi-modal deep fusion and hybrid expert adaptive decision strategy adopted by the method, and through mining the complementary advantages of acoustic features and semantic priori, the multi-modal deep fusion and hybrid expert adaptive decision strategy is obtained. And the robustness and generalization ability of the underwater acoustic target recognition system in the strong-noise and multi-working-condition environment are remarkably improved.

The invention relates to the technical field of rural image inspection and recognition, and discloses a multi-source image collaborative inspection and recognition analysis system and method for a digital rural area, and the method comprises the steps: collecting multi-source image data in real time; obtaining a plurality of characteristic parameters corresponding to each image data item in the image data set, and carrying out space-time registration and multi-scale fusion processing on the plurality of characteristic parameters of each image data item; performing target detection and identification analysis on the plurality of feature parameters in the fusion feature parameter set, and constructing an abnormal point identification model; setting a plurality of abnormal point change thresholds according to the inspection coordinate data set for classification processing to obtain a plurality of abnormal point categories; and setting a corresponding co-processing scheme according to the plurality of abnormal point categories, and setting early warning information corresponding to the change trends of the plurality of abnormal point categories based on the co-processing scheme. According to the invention, the intelligent degree and response efficiency of rural inspection are improved, and the safety and sustainable development of digital rural construction are effectively guaranteed.

The invention discloses a medical image segmentation method, system and device based on multi-attention and multi-scale fusion, and relates to the technical field of image segmentation, and the method comprises the steps: constructing an MAMF-Net model which comprises an encoder and a decoder which are in multi-layer jump connection; the encoder adopts a hybrid architecture of convolution and Transform, and is integrated with a self-adaptive expansion convolution method; the decoder integrates dual-channel attention gating and a multi-scale global channel feature enhancement method to enhance features transmitted by jump connection, and combines features extracted by the encoder to fuse and reconstruct a segmentation result; training the MAMF-Net model by adopting the historical medical image sample set to obtain a medical image segmentation model; and obtaining any medical image to be identified and inputting the medical image to the medical image segmentation model, and determining a corresponding segmentation result. The problem of insufficient fusion of global semantics and local details in medical image segmentation is solved.

The invention relates to the technical field of power distribution automation and intelligent power grid equipment, in particular to an FTU-based intelligent voltage regulation protection bypass cooperative control system, which comprises a state acquisition module, a state identification module, a self-adaptive disturbance judgment module, a voltage regulation cooperative module and a state reconstruction module, the system establishes an operation state vector model by synchronously sampling the voltage and current of a distribution line and the states of a circuit breaker and a bypass switch; judging line operation conditions according to vector state classification, and generating corresponding action priorities and time windows; unified scheduling and interlocking control are carried out on the voltage regulation unit, the protection unit and the bypass unit through the cooperative control execution module, and logic cooperation and state closed-loop updating of multiple control objects are achieved; according to the invention, time sequence coordination and intelligent linkage of voltage regulation, protection and bypass operation can be realized under complex working conditions, the voltage stability and the operation reliability of the distribution line are improved, the fault conflict probability is reduced, and the operation and maintenance intervention is reduced.

The embodiment of the invention discloses a malignant load identification method, device and equipment, a medium and a program product, and relates to the technical field of power load monitoring. The method comprises the following steps: performing modal decomposition on an original power utilization sequential sequence to obtain a plurality of intrinsic mode components, and reconstructing intrinsic mode components which do not belong to noise components to obtain a target power utilization sequential sequence; performing feature extraction on the target power consumption time sequence to obtain target power consumption features, and inputting the target power consumption features into a pre-trained malignant load identification model for identification to obtain an identification result; the malignant load identification model is obtained by updating model parameters of a weak learner based on a natural gradient descent method and performing training optimization. The lightweight malignant load learning model obtained through training in the scheme can be deployed and operated on the intelligent electric meter, high-quality input features are obtained through multi-mode decomposition and reconstruction, the accuracy of malignant load recognition is improved, and accurate recognition of the malignant load based on the lightweight model is achieved.

The invention belongs to the technical field of brain-computer interfaces, and particularly relates to a post-stroke dysphagia rehabilitation recognition system, feedback stimulation equipment and a storage medium. The system comprises a signal acquisition module, an electroencephalogram signal preprocessing module, a neural network feature extraction module, a statistical feature extraction module and a swallowing intention recognition module, and can recognize whether a patient has a swallowing intention or not through a Mama deep learning model based on space-time statistical features. The invention also constructs brain-computer interface equipment for rehabilitation of dysphagia after stroke in combination with electroencephalogram signal acquisition equipment and electrical stimulation equipment. The technical scheme of the invention has the advantages of being accurate in recognition, good in rehabilitation treatment effect and capable of forming center-periphery closed loop feedback, and has a very good application prospect.

The invention relates to the technical field of semantic segmentation, in particular to a night semantic segmentation method based on low illumination enhancement and edge optimization, and the method comprises the steps: inputting an image into a low-light enhancement repair network based on the Retinex theory, local contrast enhancement and adaptive feature fusion, obtaining a denoised and enhanced intermediate image, and carrying out the edge optimization of the intermediate image; inputting the intermediate image into a semantic segmentation network to obtain a category distribution diagram of each pixel; inputting a discriminator embedded with a channel attention module according to the category distribution diagram of each pixel, and optimizing a generator composed of a low light enhancement repair network and a semantic segmentation network through a multi-task joint optimization loss function; and inputting a night image to be detected and segmented into the optimized generator, and outputting a segmentation result. By adopting the method, the low-light enhancement repair network is combined with a local contrast enhancement and channel feature fusion mechanism, so that the overall brightness of the image is improved, the details and edge structures of the image are reserved, and the perception capability and robustness of the model are improved.

The invention discloses a rice seedling leaf age intelligent identification system based on a depth camera, and belongs to the technical field of agricultural intelligent equipment and machine vision. Comprising a monitoring point gridding path planning module, a mechanical arm cooperative positioning data acquisition module, an RGB-D data preprocessing module, a dynamic foreground seedling accurate extraction module, a leaf age recognition confidence evaluation module, a leaf age data visualization presentation module and an integrated control strategy feedback module. RGB color features and connected domain analysis are combined to optimize a mask, a front single seedling is accurately segmented, background interference is eliminated, the confidence coefficient is dynamically calibrated through a deep learning model and image quality features, texture definition and edge continuity features are combined to generate calibrated confidence coefficient, confidence coefficient evaluation is directly associated with an agricultural decision, and the accuracy of the agricultural decision is improved. The leaf age result is generated only based on reliable data, the personnel rechecking demand is reduced, and the decision-making efficiency is improved.

The invention discloses a power electronic transformer working mode recognition method and device, equipment, a medium and a product, and relates to the field of artificial intelligence, and the method comprises the steps: collecting original inductive current data during the operation of a power electronic transformer; performing adaptive segmentation normalization processing on the original inductive current data to generate a normalized inductive current sequence; calculating a wavelet packet energy entropy and a time domain differential entropy of the normalized inductive current sequence, and splicing the wavelet packet energy entropy and the time domain differential entropy into a two-dimensional fusion feature vector; inputting the normalized inductive current sequence and the two-dimensional fusion feature vector into a trained deep learning model to obtain a prediction probability vector; based on the prediction probability vector, the working mode category with the maximum probability value is selected as the recognition result, and the recognition precision of the working modes of the power electronic transformer can be guaranteed under the working conditions of high noise interference or rapid mode switching.

The invention discloses a self-updating method and device of a financial risk control model, a storage medium and a terminal, relates to the technical field of data processing, can be applied to the field of financial risk control, and mainly aims at solving the problem that the model recognition accuracy is low due to the fact that an existing financial risk control model is not timely updated. The method mainly comprises the following steps: acquiring anomaly prediction scores and confidence coefficients of different newly-added samples obtained in a process of performing anomaly identification on newly-added sample data in a production environment by a financial risk control model; extracting a low-confidence sample from the newly added samples according to the abnormal prediction score and the confidence; performing clustering processing on the low-confidence samples, and constructing an updated training sample set according to target samples extracted from each cluster; and performing incremental learning update training on the financial risk control model based on the training sample set, so as to continue to execute anomaly recognition of subsequent sample data based on the financial risk control model completing update training. The method is mainly used for updating the financial risk control model so as to improve the timeliness of model updating.

The application discloses a kind of nnUNet zebra fish juvenile whole brain vascular system segmentation methods based on autonomous data set training, it is related to high-resolution imaging technology, image processing and medical image segmentation field, the method makes full use of zebra fish live transparency and fluorescent label advantage, obtains high-resolution whole brain three-dimensional vascular image data, and constructs high-quality segmentation truth value database by semi-automatic segmentation and artificial correction, training is carried out using nnU-Net deep learning model, realize the three-dimensional automatic segmentation of zebra fish brain vascular system signal.The application method significantly improves the degree of automation and precision of image segmentation, effectively solves the problems of low efficiency, high artificial dependence and poor repeatability of traditional brain vascular segmentation.The method is suitable for large-scale high-throughput data processing, can provide efficient, standardized image processing scheme for zebra fish brain vascular development mechanism and brain vascular disease model research, and has wide application prospect.

The invention discloses a traffic video multi-dimensional semantic understanding method and system based on a multi-modal large model, and relates to the field related to intelligent traffic, and the method comprises the steps: obtaining multi-source collection data in real time; preprocessing the multi-source collected data to obtain a traffic data sequence; performing multi-modal feature extraction and cross-modal feature alignment on the traffic data sequence, and establishing a traffic feature alignment vector; inputting the traffic feature alignment vector into a multi-modal large model to obtain a multi-dimensional preliminary semantic understanding result; performing multi-dimensional semantic analysis and optimization on the preliminary semantic understanding result to obtain a structured semantic report; and according to a feedback data set of the structured semantic report, carrying out periodic increment updating optimization on the multi-modal large model and the traffic rule knowledge base. The technical problems of single semantic understanding dimension and insufficient recognition precision in a complex scene in the existing traffic video semantic understanding are solved, and the technical effects of enriching the semantic coverage dimension and improving the recognition precision of the complex scene are achieved.

The invention provides an intelligent pipeline inner wall damage identification method based on voiceprint feature extraction, and relates to the technical field of sound wave detection.The method comprises the steps that pipeline acoustic time sequence data, medium flow velocity and pressure data, medium type identification and structure parameter information are collected; performing multi-scale feature extraction on the acoustic signal, constructing a voiceprint feature expression model and a damage evaluation model based on a voiceprint feature learning network, introducing a structural voiceprint stability coefficient, a damage type voiceprint separation coefficient and a damage evolution risk coefficient, and combining a multi-level threshold judgment mechanism to determine the damage type of the acoustic signal. Graded early warning, damage type identification and evolution risk discrimination of the inner wall damage of the pipeline are realized; when an uncontrollable damage evolution risk is identified, a maintenance strategy is automatically triggered, and manual nondestructive testing and repairing are guided; and adaptive optimization is carried out on model parameters to form a closed-loop correction mechanism for pipeline inner wall damage identification and risk assessment, so that the pipeline operation safety and the intelligent level of maintenance decision are improved.

The invention belongs to the technical field of image contrast, particularly relates to a water conservancy safety monitoring system based on dynamic vision, and aims to solve the problems that in an existing water conservancy safety monitoring process, the image quality and the recognition accuracy are remarkably reduced under complex and dynamically changing illumination and environment conditions, and the image quality is poor. In order to solve the problems of unstable monitoring performance and insufficient reliability caused by the lack of adaptive optimization and multi-source information deep fusion capability, the invention provides the following scheme: the system comprises a data acquisition module, the data acquisition module is connected with an illumination evaluation module, the illumination evaluation module is connected with a scene classification module, and the scene classification module is connected with a data processing module. According to the invention, through a dynamic vision technology, adaptive image enhancement and multi-source data fusion of a water conservancy project under complex illumination and environment conditions are realized, the image quality and defect identification accuracy of all-weather monitoring are significantly improved, and the environmental adaptability and reliability of the system are enhanced.

This application discloses a method and apparatus for constructing a multi-scale adaptive gating MambaPlus network, belonging to the field of artificial intelligence and machine learning technology. The method includes: initializing the network configuration and constructing the basic structure; preprocessing the input data to generate a standard dataset; mapping the input data to the hidden space via an input mapping layer, and extracting backbone features from the Mamba backbone; constructing at least two parallel scale branches in the hidden space to obtain a multi-scale feature set; inputting the backbone features and multi-scale features into an adaptive gating module, dynamically allocating weights and adaptively fusing them through a hierarchical gating mechanism to generate fused features; further enhancing the features through cross-scale attention and feedforward enhancement, and then superimposing the residuals to generate the final discriminative features; finally, completing category prediction and model training evaluation. This application, while retaining the advantages of Mamba's long-range dependency modeling, addresses the problems of insufficient utilization of multi-scale information, poor adaptive feature fusion, and low robustness in complex scenarios.

The invention discloses a data auditing method and device based on artificial intelligence, electronic equipment and a storage medium, relates to the technical field of artificial intelligence, can be specifically applied to the financial and medical fields, and meets high requirements of financial and medical industries on compliance and auditing tracking while improving auditing efficiency and reducing labor investment. The method comprises the steps that in response to a data auditing request, target data to be audited are preprocessed, and a hierarchical tree structure used for describing text content and layout position information of the target data is obtained; based on the large language model, generating a field extraction cue word matched with the data type of the target data, guiding the large language model to perform field extraction on the hierarchical tree structure by utilizing the field extraction cue word, and obtaining a field extraction result output by the large language model; and performing abnormal field auditing and abnormal field evidence labeling on the field extraction result by utilizing a pre-constructed knowledge graph to obtain an auditing result of the target data, and outputting the auditing result.

The invention discloses an unmanned aerial vehicle patrol and security monitoring linkage management method. According to the system, urban geographic information data and key monitoring area coordinates are acquired, hierarchical grid division is realized by using a quadtree spatial index algorithm, and a patrol task distribution basic data structure is established; solving an approximate optimal solution of the patrol path of the unmanned aerial vehicle by adopting an improved greedy strategy, and performing optimization adjustment according to technical parameters and weather conditions of the unmanned aerial vehicle; the unmanned aerial vehicle collects video data in real time through high-definition camera equipment in the patrol process, target detection is conducted through a deep learning model, and potential safety hazards such as personnel gathering and vehicle abnormity are recognized; detecting violent conflicts and illegal intrusion behaviors in combination with a time sequence analysis algorithm, and generating an abnormal event report; cooperative work of the unmanned aerial vehicle, the ground robot and the command center is realized; and a patrol scheme is dynamically adjusted according to meteorological conditions, and robot scheduling is optimized through energy consumption management.

The invention relates to a high-precision steel surface defect detection method suitable for a complex industrial environment, and aims to solve the problems of feature coupling and background interference caused by data scarcity, long tail distribution and multi-defect coexistence. According to the method, a conditional generative adversarial network (CGAN) and a convolutional neural network (CNN) are combined, an SE-Net channel attention mechanism is integrated, and the method is used for automatic detection and classification of multiple defects on the steel surface. By introducing a physical constraint CGAN data generation method, a multi-defect coexistence composite image conforming to industrial reality can be generated, so that the diversity and accuracy of a training data set are effectively enhanced. The model optimizes the expression of defect features through multi-level feature extraction and an attention mechanism, and enhances the distinguishing ability and recognition precision of multiple defects. Experiments show that the method is excellent in performance in a multi-defect identification task, the accuracy rate of 98.89% and the F1 score of 99.72% are achieved, the method is remarkably superior to a traditional detection method and other deep learning models, and the method has high robustness and practical value in a complex industrial environment.

The invention discloses a communication monitoring method and a communication system. The system comprises a monitoring module, a feature extraction module, a dynamic identification module, a behavior analysis module, an adaptive optimization module and a visual interface module. The monitoring module collects communication data flow through a mirror image port, the feature extraction module extracts packet length, time interval, direction sequence and encryption features by using a multi-dimensional feature fusion algorithm, and the dynamic recognition module realizes multi-protocol type recognition based on an improved self-attention convolutional neural network. The behavior analysis module constructs a communication relation graph and performs abnormal communication tracing; and the adaptive optimization module realizes model self-learning and parameter optimization through reinforcement learning. According to the method, through sliding window sampling, self-correlation analysis, entropy detection and GNN map modeling, accurate identification and behavior restoration of implicit traffic in a complex network environment are realized. According to the method, high-precision identification can be kept under the conditions of multi-protocol mixing and encrypted communication, and the characteristics of intelligence, self-adaption and expandability are achieved.

The application discloses a kind of based on multi-objective optimization algorithm's advertisement putting real-time effect tracking method, advertisement putting data analysis technical field, including step one: obtaining advertisement putting real-time behavior data;Step two: constructing advertisement putting effect window matrix;Step three: through improved MiniRocket network, execute advertisement effect conduction difference feature extraction and continuous response fragment aggregation processing;Step four: constructing multi-objective tracking objective function set and multi-objective optimization constraint condition;Step five: using improved MOEA / D algorithm, execute state traction decomposition optimization and fragment duration constraint neighborhood replacement processing;Step six: by Canberra distance, execute target deviation analysis processing;Step seven: match target advertisement plan's real-time effect tracking state.The application improves the accuracy of advertisement putting real-time effect tracking by improved MiniRocket network and improved MOEA / D algorithm.

This invention discloses a lesion localization method and system based on deep learning, comprising: acquiring medical image data; preprocessing the images to obtain standardized data; inputting the standardized data into an improved nnFormer to construct a potential lesion morphological field; fusing the morphological field and image data to generate a multi-source lesion mechanism parameter field; segmenting the lesion occurrence mechanism parameter field, establishing evolution paths, and inversely reasoning about boundary regions; calculating topological and physiological characteristics, and jointly assessing the final lesion region using energy; and outputting the lesion localization result, including spatial location and regional extent. This invention achieves stable and accurate localization of lesion regions in medical images by constructing a potential lesion morphological field from standardized medical images using an improved nnFormer, and by fusing multi-source mechanism parameter fields, morphological evolution inverse reasoning, and topological-physiological joint energy assessment.

The application relates to the technical field of data analysis, and discloses a karst fissure identification-based open-pit mine blasting parameter optimization method and system. The method comprises the following steps: obtaining karst fissure distribution characteristic data through multi-source detection, establishing a three-dimensional fissure network topology structure, analyzing static breaking agent inflation stress propagation rules, performing multi-objective optimization solving based on stress propagation direction and intensity distribution to obtain an initial blasting parameter combination, and obtaining dynamic adjustment blasting parameters through real-time tracking and regulation according to fissure dynamic evolution in the blasting process. The application solves the problem that static blasting parameters cannot be real-time regulated according to the dynamic evolution state of the fissure network in the blasting process, resulting in deviation between the blasting effect and the expected target.

The application relates to an engine assembly behavior recognition method and device, computer equipment, a readable storage medium and a computer program product. The method comprises the following steps: acquiring assembly operator key point features, engine assembly operation component features contained in each frame of video image, and acquiring interaction behavior features between the assembly operator key point features and the engine assembly operation component features from multiple frames of video images in which engine assembly behaviors are shot; fusing the assembly operator key point features, the engine assembly operation component features and the interaction behavior features to obtain fusion features corresponding to each frame of video image, and constructing a time-space feature sequence corresponding to the multiple frames of video images based on the fusion features; inputting the time-space feature sequence into a pre-trained engine assembly behavior recognition model to obtain engine assembly behavior categories corresponding to the multiple frames of video images. The method can improve the recognition accuracy of engine assembly behaviors.

This invention discloses a system for identifying and simulating key nodes in traffic networks based on deep reinforcement learning. Addressing the high computational complexity of key node search and the lack of dynamic verification in traffic networks, this invention employs the following system: a road network topology mapping module parses GIS map data and removes redundant information to generate a weighted topology map; a deep reinforcement learning identification module, based on a deep Q-network, maps node features to graph embedding information through graph representation learning, with the optimization objective of minimizing cumulative normalized connectivity, outputting the optimal key node sequence; and a traffic effect simulation verification module uses a traffic simulation platform to establish a realistic road network model, quantitatively analyzing the impact of key node failures on average travel time and waiting time. This invention features low time complexity, the ability to generalize from small synthetic networks to extremely large-scale real networks, and simulations have verified its high application value in actual traffic management and disaster prevention.

The present application relates to the technical field of intelligent identification system of crop pests, in particular to a plant field pest fine-grained identification method, system, device and storage medium based on deep learning. The identification method provided by the present application is specialized in high-precision identification of real field scenes, and can provide technical support for important work such as future development of field inspection robot, automatic identification and monitoring system of field pests and the like. In addition to pest monitoring, the field biological safety test of genetically modified plants is gradually carried out at present, and by using the identification method provided by the present application, the dynamic change of farmland insect community can be quickly and accurately identified and predicted, so that the efficiency and accuracy of ecological investigation are greatly improved.

The invention discloses a puncture tissue recognition and transmembrane event control method and system based on force sense and image fusion, and the method comprises the steps: collecting a force signal at the tail end of a puncture needle, a real-time ultrasonic image sequence and the pose data of a mechanical arm, and carrying out the timestamp alignment to construct a synchronous data set; on the basis, tissue stiffness, a force change rate, extreme value features, texture features, envelope boundary features and optical flow displacement features are extracted, the tissue level where the puncture needle is located is recognized through multi-modal fusion, and a membrane penetrating event is detected in combination with the force sense extreme value features and envelope boundary fracture; according to the tissue level and the transmembrane event, event driving control strategies such as constant-speed propulsion, deceleration early warning, rotation assistance, transmembrane braking and path compensation are automatically triggered, so that the puncture process is accurately regulated and controlled. The accuracy of puncture tissue recognition and the reliability of transmembrane event judgment can be remarkably improved, and the safety and stability of puncture operation are improved.

The present application relates to a kind of based on multimodal feature fusion distribution network disturbance source identification method, belong to the fine disturbance monitoring and diagnosis technical field of smart grid.The method includes: the disturbance class current traveling wave data extracted is carried out time-frequency conversion, generates traveling wave panorama waveform chart and two-dimensional time-frequency chart and is fused into three-channel time-frequency image, then image feature vector is extracted;The disturbance class current traveling wave data is carried out Prony modal parameter fitting, extracts modal parameter feature vector and is projected to high-dimensional feature space;The disturbance class current traveling wave data is carried out Clark transformation, calculates zero mode energy proportion feature and is projected to high-dimensional feature space;Image feature vector, projected physical feature vector and projected modulus energy feature vector are fused;Fusion feature vector is input into classifier, and the class identification result of distribution network disturbance source is output.The present application aims to solve the technical problems that the prior art has single feature representation, weak physical interpretability and difficulty in forming stable power fingerprint.

The application discloses a kind of based on behavior control's bionic robot action control method, it is related to technical bionic robot control field, including the following steps: establishing global terrain coordinate system, completing multi-sensor calibration, robot starts autonomous navigation task, vision sensor starts to collect surrounding environment image;Image is handled, and the color, texture feature in image is extracted;Interference object identification module identifies the interference object therein, and extracts its feature information;Interference index analysis module calculates interference index according to the feature information of interference object;Terrain environment complexity evaluation module real-time collection terrain environment information, evaluates the complexity of terrain environment;Action control decision module comprehensively interference index and terrain environment complexity evaluation result, makes action control decision, and sends control instruction to motion execution mechanism, the application solves the problem of bionic robot in complex environment motion intelligence and safety.

The present application belongs to the technical field of industrial internet security, and relates to an industrial internet vulnerability library establishment method, which solves the problems of low construction quality, weak traceability, insufficient continuous evolution ability and difficulty in supporting deep security application of the existing vulnerability library. The present application obtains industrial internet multi-source vulnerability data and external feedback data, obtains extraction results and initial confidence based on multi-source vulnerability data by adopting rule extraction and remote supervision deep learning joint extraction, divides the certainty knowledge base and the to-be-reviewed queue according to the double threshold after the confidence is optimized by the graph convolution network, analyzes the external feedback data of the samples in the to-be-reviewed queue as the instant reward signal, optimizes the sampling strategy and updates the model through deep reinforcement learning, extracts triples from the certainty knowledge base to build a knowledge graph and generate a version hash chain regularly, and obtains a structured vulnerability knowledge base containing a hash chain and confidence evaluation. The present application realizes high-precision construction and dynamic optimization of the vulnerability library.