603 results about "Information gain" patented technology

The invention relates to the technical field of flood early warning, and discloses an urban flood disaster early warning method and system based on artificial intelligence, and the method comprises the steps: collecting five types of information, i.e., meteorological perception, hydrological monitoring, geographic space, urban operation and social perception in real time, and obtaining multi-source data with precise space-time coordinates; through preprocessing, gridding space-time alignment and key feature screening, rainfall accumulation and confluence evolution related features are extracted; constructing a physically constrained space-time fusion deep learning model, and outputting a future ponding depth prediction result in combination with a multi-head attention mechanism; environmental changes such as urban terrains and drainage facilities are adapted through incremental updating and transfer learning; and fusing the ponding depth, the influence range and the regional vulnerability characteristics to generate multi-level early warning, and synchronously outputting a spatial distribution map, a time evolution trend and affected object evaluation information. According to the invention, urban flood control and disaster reduction decision making and public accurate risk avoiding can be effectively supported.

The invention relates to the technical field of artificial intelligence and big data analysis, in particular to a data operation system and method based on a knowledge graph, and the method comprises the steps: extracting an entity and semantic relationship from multi-source business data, and constructing a dynamic evolvable initial knowledge graph; node features are aggregated, and multi-dimensional situation state vectors are generated in combination with gating loop unit modeling behavior path dependence; through a structure-semantic coupling attribution scoring mechanism, a statistical information gain and semantic similarity are fused to identify a core driving factor, and a causal regression model of the factor and an operation target is established; dynamically adjusting the edge weight and the structure of the atlas in real time, and triggering a new path discovery mechanism to continuously optimize the atlas; and according to a quantitative business target, reversely extracting a high-confidence influence path from the atlas, and generating a personalized strategy combination through intervention simulation and multi-target Pareto optimization, thereby realizing intelligent recommendation and decision closed loop driven by an operation target. According to the invention, higher-precision operation situation awareness and strategy generation are realized.

The invention provides a sewage system traceability analysis and intelligent monitoring method, system and device based on a graph neural network, and a storage medium, and belongs to the technical field of environment monitoring and artificial intelligence. The invention aims to solve the technical problems of low efficiency, low precision, difficulty in processing multi-source data, poor monitoring network and the like of the existing sewage system pollution tracing method. The method comprises the following steps: constructing a sewage system knowledge graph fusing multi-source heterogeneous data such as water quality and water volume; adopting a multi-scale graph neural network model to learn pollution propagation characteristics based on the knowledge graph; after a pollution event occurs, pollution path backtracking is carried out in combination with physical models such as flow conservation so as to identify a pollution source; bayesian inference is introduced to carry out uncertainty quantification on a traceability result so as to assess the credibility of the traceability result; and finally, dynamically optimizing the layout of the monitoring points based on information gain and other criteria. According to the invention, rapid and accurate positioning of the pollution source can be realized, and the method is suitable for intelligent supervision of an urban sewage system.

The invention discloses a remote sensing image multi-source heterogeneous data fusion processing method and system, and the method comprises the steps: extracting global information from remote sensing image data through employing a convolutional neural network, extracting image local features through cutting operation, and capturing local feature information in the remote sensing image data; constructing a cross-time-domain attention mechanism for the local feature information through a cyclic matrix to extract mutual information among different modal variables, and screening highly-associated cross-time-domain key information; establishing a cross-time-domain sensing hierarchical aggregation module for the cross-time-domain key information, and obtaining detail information and edge information of the remote sensing image; acquiring global fusion data by adopting an asymptotic fusion strategy, and introducing a loss function to reduce a semantic gap; according to the method, missing information is repaired by adopting an interactive network model of mixed contrast learning, complete real-time remote sensing image multi-source heterogeneous fusion data is obtained, and efficient and high-precision fusion processing of the multi-source remote sensing data is realized by constructing a multi-collaborative deep fusion framework.

The invention provides a landslide risk dynamic early warning method and system based on a Bayesian network, and belongs to the field of geological disaster intelligent prediction, and the method comprises the steps: constructing a disaster-inducing factor data set, and screening key factors based on a Pearson's correlation coefficient and an information gain method; an FP-Growth algorithm is further utilized to extract association rules among high-confidence factors, a Bayesian network structure is guided to be optimized, and a Bayesian network model with a causal relationship is constructed; the model supports an incremental learning mechanism based on a newly added landslide sample, can dynamically update a conditional probability table, and realizes landslide probability prediction and risk grade division in combination with a Bayesian forward reasoning result. The method has the advantages of being high in causal reasoning ability, high in model structure expression ability, excellent in prediction precision and capable of supporting real-time updating and risk partition, and is suitable for an intelligent risk assessment and early warning system for landslide disasters.

The invention provides a prefabricated dish semi-finished product defect identification method and system with an AI algorithm, and the method comprises the steps: extracting semantic features from a refined defect candidate region set, carrying out the feature mapping of each region through a deep convolutional network according to the demands of atypical defect identification, and obtaining the defect description represented by a high-dimensional feature vector; marking original image data through a final defect identification result, and for inhibition of complex background interference, performing outward expansion from defect edge features by adopting a region growing algorithm to obtain complete defect region boundary information; after complete defect area boundary information is obtained, defect distribution changes of continuous batches of images are compared through a time sequence analysis method according to the monitoring requirement of production process fluctuation, and the quantitative basis of process adjustment is determined.

The invention relates to the technical field of power equipment fault diagnosis, and discloses a power plant equipment multistage fault diagnosis method and system based on a dynamic decision tree, and the method comprises the steps: 1, collecting equipment operation data through multiple sensors, and carrying out the preprocessing of multi-source data; comprising noise filtering, outlier elimination and missing value filling; step 2, extracting feature values according to the time domain features and the frequency domain features, constructing a state decision tree model based on a C4.5 algorithm, optimizing attribute split points by an information gain rate, and optimizing generalization ability by an REP post pruning strategy; and step 3, monitoring operation data of the power plant equipment based on the state decision tree model, diagnosing and predicting equipment faults according to the monitoring data, outputting fault levels according to monitoring results, and triggering corresponding grading responses. The fuel power plant equipment fault diagnosis method provided by the invention has the advantages of high precision, low false alarm, capability of effectively distinguishing fault levels and dynamic self-adaptive capability.

The invention belongs to the technical field of wireless positioning, and discloses a non-line-of-sight interference-resistant dynamic positioning method and a base station layout method for positioning. Comprising the following steps: calculating original distance measurement results of each initial base station in a label and a sub-region, obtaining current shielding probability and current signal intensity of the initial base station and current geometric accuracy factors of different initial base station combinations in an initial base station set, selecting candidate base stations, calculating information gain of each candidate base station pair, and obtaining the information gain of each candidate base station pair; selecting candidate base stations with information gains meeting preset requirements to construct a target base station subset, inputting an original ranging result of the target base station subset into a Kalman filter based on a motion state space model for processing, and compensating an NLOS time delay error through a sliding window mean value to obtain a positioning result of a label and an NLOS time delay error estimation value, the problem that the positioning result is not accurate enough in a complex environment is solved.

The invention provides an intelligent medical multi-round dialogue diagnosis reasoning method and system based on deep learning, and relates to the technical field of deep learning, and the method comprises the steps: constructing time sequence features through employing an attention mechanism for time sequence symptom description, and carrying out bidirectional sequence modeling to generate comprehensive symptom features; using the medical knowledge graph to detect logic contradictions and information loss to generate standardized features; calculating information gain of an inquiry direction based on a deep neural network to generate optimal inquiry content; and iteratively updating according to user feedback until the diagnosis information entropy is lower than a threshold value, and outputting a diagnosis result. According to the invention, the accuracy and efficiency of medical diagnosis are improved.

The invention relates to the technical field of communication, and discloses a three-dimensional radio environment map construction method and system, a terminal and a storage medium, and the core is to construct a'base station unmanned aerial vehicle 'bidirectional interaction closed-loop optimization framework to realize efficient construction of a high-precision map. Self-adaptively fusing sparse radio measurement data and environmental building structure features; introducing a confidence evaluation mechanism based on adversarial learning and position weighting, and generating a pixel-by-pixel confidence map; an intelligent planning method based on a trajectory diffusion model is designed, local perception constraint and long-term information gain are cooperated with a classifier-free guide mechanism, and an optimal trajectory considering both safety and sampling efficiency is generated; and a continuously self-optimized closed-loop system is formed through newly acquired data of the unmanned aerial vehicle and periodical updating of the model. According to the invention, a high-reliability technical basis is provided for applications such as urban air communication and spectrum resource management.

The invention provides a dynamic lung compliance monitoring method based on electrical impedance tomography, and the method comprises the steps: obtaining an electrical impedance signal and pressure-volume curve data, recording a time sequence signal of lung tissue in a dynamic ventilation process through a synchronous collection device, and obtaining an original multi-dimensional data set; aiming at the second feature set, eliminating redundant parameters by applying a feature screening mechanism, and evaluating the importance of each feature in different breathing stages through an information gain algorithm to obtain a third feature set; constructing a dynamic compliance monitoring model through the weighted feature set, and fitting a lung compliance change trend by using a support vector regression algorithm to obtain a real-time prediction result; and according to the updated prediction result, evaluating the improvement degree of the calculation efficiency, and judging whether the real-time performance meets clinical requirements or not by recording the model operation time and the resource occupancy rate so as to obtain final monitoring output.

The invention belongs to the technical field of artificial intelligence and medical information, and discloses a multi-round inquiry method and system based on a large language model and session state tracking. According to the method, extraction and synonym normalization are carried out for key medical elements, and high-confidence filling and conflict resolution are continuously completed in multiple rounds of conversations; and fusing the red flag symptom rule and model prediction, and carrying out hierarchical scoring and security constraint generation on individual risks. The information gain maximization serves as a target, and the next round of clarification problem is generated in a self-adaptive mode under the risk constraint; and through cooperation of a large language model and a knowledge base / knowledge graph, sorting and gate type calibration are carried out on candidate diseases and matched departments, and doctor-seeing suggestions, examination suggestions and medication precautions are generated. Finally, efficient understanding and multi-round reasoning of the unstructured symptom information are realized through joint supervision of the session state, the slot confidence and the risk hierarchy.

The invention discloses an anti-shielding three-dimensional scanning measurement field construction method for large-size measurement, and belongs to the technical field of optical three-dimensional measurement. The method comprises the following steps: firstly, performing adaptive sampling based on geometric features of a workpiece to generate a candidate mark point set; secondly, constructing a virtual measurement scene, simulating a dynamic scanning process by using a ray tracing technology, and calculating the visibility and observation quality of each point under multiple viewpoints; then measuring precision is quantified by using a Fisher information matrix, and a layout optimization model which takes maximization of information gain as a target and meets coverage rate and engineering constraint at the same time is established; and finally, solving the model to obtain an optimal mark point subset, and verifying and outputting the optimal mark point subset. According to the method, the problems of measurement interruption, low point distribution efficiency and difficulty in guaranteeing precision caused by shielding of a large-size complex component in scanning are solved, and automatic construction of a high-robustness and high-precision measurement field is realized.

The invention relates to the technical field of data classification, in particular to a hospital intelligent inquiry log analysis method and system.The method comprises the following steps that medical concept depth is counted, symptom keywords are extracted, statistical information gain and semantic consistency scores are calculated, split decision-making indexes are generated by combining the gain and the scores to construct an intention classification tree, and an intention classification result is obtained; inputting a log to generate an inquiry intention identifier, recognizing an error node based on diagnosis and treatment correction feedback and updating a punishment value, establishing a virtual error correction link, calculating a path confidence fusion node, and establishing a physical branch to generate a reconstruction classification tree when a guide weight exceeds a limit. According to the method, keyword statistics is upgraded into three-dimensional feature analysis by introducing medical concept hierarchies, a classification tree following a medical system is constructed, a reverse penalty and virtual link mechanism is constructed by using feedback, node fusion and reconstruction are executed based on confidence, classification logic self-adaptive evolution is realized, and intention recognition interpretation and robustness are improved.

The invention discloses a radar target track identification method based on multi-scale observation and time sequence analysis. The radar target track identification method comprises the following steps: determining a track sequence of a target to be identified; performing feature extraction on the track sequence by using a multi-scale window to obtain multi-scale track features; performing mask operation on the multi-scale track features to obtain mask track features; and inputting the mask track characteristics into a pre-constructed time sequence analysis model to obtain depth track characteristics through depth time sequence analysis, and realizing radar target track identification based on the depth track characteristics. According to the radar target track identification method, feature information is fully acquired, and time sequence information is comprehensively utilized, so that accurate and efficient identification of a radar target is realized.

The invention discloses a cable partial discharge analysis method and device based on oscillatory waves. The method comprises the steps that the oscillatory waves are applied to a tested cable, and low-frequency envelopes and high-frequency pulses are synchronously collected; performing structure normalization according to cable parameters; encoding the environment and working condition data into a condition vector to implement condition normalization; carrying out domain self-adaptive alignment with an ideal template clustered according to a model, a length and a joint / terminal type, and obtaining a difference feature; constructing a PRPD joint fingerprint, and counting a pulse phase, an amplitude, an envelope attenuation rate and / or an equivalent Q; outputting defect positions, types and confidence intervals based on the difference features and the joint fingerprints; and adaptively determining multi-gear upper pressure and advanced shutdown according to the information gain, and generating maintenance priority and retest interval suggestions. Through step-by-step normalization processing, domain adaptive alignment and fusion of PRPD combined fingerprint features, diagnosis stability and reproducibility are enhanced, weak discharge and early defect detection capability are enhanced, and complex working conditions and cross-trigger adaptability are enhanced.

The invention discloses a spatial non-cooperative target form reconstruction-oriented perception planning control integration method, which comprises the following steps of: extracting local semantic features of a target component in a single-view observation image through a pre-trained semantic segmentation network, coding the local semantic features and RGB (Red, Green and Blue) information into an MLP (Markup Language Protocol) of NeRF, perceiving a target geometric form and component-level semantics, and obtaining a component-level semantic feature of the target component; the perception result is optimized along with fly-around observation, evaluation is carried out, and an uncertainty thermodynamic diagram is generated; based on the uncertainty thermodynamic diagram, a space observation value function is constructed, spacecraft dynamics and view field constraints are combined, and an initial fly-around trajectory is generated by adopting an information gain weighted three-dimensional A * algorithm and optimized in real time; and designing a trajectory tracking control law and an attitude stability control law based on a time synchronization stability theory, and controlling the spacecraft to execute the optimized fly-around trajectory. According to the invention, a perception-planning-control closed-loop execution system is realized, and a closed-loop collaborative process of perception-evaluation-planning-control-re-perception is formed.

The invention discloses an SEO content detection and release gating method and system based on information gain, and the method comprises the steps: carrying out the paragraph-level viewpoint sentence decomposition of a collected search engine result page document and a candidate document for a target query, and constructing a consensus semantic field; performing semantic alignment and screening on the viewpoint sentences of the candidate documents and the consensus semantic field, and performing multi-source evidence verification and reliability aggregation on the screened effective newly-added viewpoint sentences to obtain macroscopic semantic novelty measurement of the candidate documents; constructing a consensus knowledge graph to identify the structural hole and calculating the gain of the structural hole; and carrying out joint scoring and issuing decisions, and outputting a structured evidence packet. According to the method, evaluation of novelty and information gain is quantified by constructing a ranking-weighted consensus semantic field; the reliability of the measurement gain is ensured through evidence conditional macroscopic divergence calculation and viewpoint sentence-level multi-source verification; and a structured evidence packet organized according to the viewpoint sentences is generated, so that the decision is well documented and auditable.

The invention relates to the technical field of biological information, and discloses a messenger RNA stability prediction method, model, device, equipment and medium, the method comprises the following steps: obtaining target sequence information of a target messenger RNA; acquiring at least two of the following target feature information based on the target sequence information by using a feature extraction module in the messenger RNA stability prediction model: first sequence feature information, Kozak sequence feature information, Motif attention feature information and manual feature information; and predicting the stability of the target messenger RNA based on the target feature information by using a prediction head in the messenger RNA stability prediction model. According to the method, the mRNA stability is predicted by fusing the universal sequence feature of the mRNA, the Kozak sequence feature of the learnable position weight, the Motif attention feature based on the hash k-mer and the manual feature, and the accuracy of mRNA stability prediction is improved.

The invention provides a traffic accident scene reconstruction method and device, and relates to the technical field of three-dimensional reconstruction, and the method comprises the steps: obtaining a video frame sequence of a traffic accident scene collected by an unmanned plane, carrying out the information gain evaluation of the video frame sequence, and constructing a key frame set; according to the key frame set, macroscopic geometric reconstruction and microscopic normal recovery based on airborne light source dynamic change are carried out on the traffic accident scene, and a macroscopic depth map and a microscopic normal field are obtained; constructing a fusion optimization model with the macroscopic depth map as low-frequency constraint and the microscopic normal field as high-frequency gradient guidance, and fusing the macroscopic depth map and the microscopic normal field by using the fusion optimization model to obtain a fused depth map; and constructing an accident scene reconstruction model according to the fused depth map. By adopting the traffic accident scene reconstruction method and device, key details of the accident scene can be captured, the sensitivity to micromorphology is increased, and the accuracy of traffic accident scene reconstruction is improved.

The invention provides a voice dialogue interaction method and system for industrial equipment, and relates to the technical field of man-machine interaction, and the method comprises the following steps: obtaining a voice signal of a user, and carrying out the voice enhancement processing of the voice signal of the user through an incremental adaptive filtering algorithm, and obtaining an enhanced voice; according to the enhanced voice, using an information gain transfer learning method to identify an interaction intention of the user, and generating a to-be-interacted voice based on the interaction intention of the user; based on the enhanced voice, utilizing a time delay estimation method to identify an interaction position of the user, and performing position prediction on the position of the user; according to the position prediction result, an industrial equipment horn output strategy is constructed; and outputting the to-be-interacted voice based on the industrial equipment loudspeaker output strategy so as to realize voice dialogue interaction between the user and the industrial equipment. According to the invention, convenience, high efficiency and accuracy of interaction between the user and the equipment in an industrial scene are greatly improved, and intelligent development of industrial production is facilitated.

The invention belongs to the field of natural language processing in computer intelligent information processing, and discloses a document level relation extraction method and system based on information gain and prototype comparative learning. The invention provides a document-level relation extraction model based on a graph structure, which considers two aspects of extracting more accurate node features and relieving data imbalance. The problem that an existing document-level relation extraction model generally adopts a graph-based model and faces inherent data imbalance is solved. At present, the problems that noise interference is caused by irrelevant nodes and edges in the node feature updating process, the learning ability of a model to a real relation is insufficient due to too many negative samples in a document, and all different relation types cannot be accurately predicted through multi-label classification exist in research.

The invention discloses an uncertainty guided few-sample harmful speech detection method (U-GIFT). According to the method, a pre-training language model is finely adjusted based on a small number of labeled samples, and a semi-supervised self-training and uncertainty guiding strategy is combined. Monte Carlo Dropout is started in the reasoning stage, multiple times of random forward propagation are carried out to obtain sample posterior distribution, prediction entropy and information gain are calculated, pseudo-label samples are sorted and screened, and only high-confidence samples are selected to be added into a training set. And in order to reduce the influence of a pseudo labeling error, designing a stability weighting mechanism, giving a sample weight according to a prediction variance, and constructing a joint loss function, so that the model preferentially learns a stable sample to improve the detection performance. According to the method, the semantic and attention mechanism of the pre-training model is utilized, the detection effect is remarkably improved under the conditions of few samples, imbalance, multiple languages and cross domains, models such as BERT, RoBERTa, XLM-R, LLaMA2 and DeepSeek-R1 are compatible, and the method is suitable for content auditing and risk prevention and control.

The invention relates to a drug interaction prediction method and system based on multi-view comparative learning, and belongs to the technical field of natural language processing. According to the method, two channels of a drug molecular map and a biomedical knowledge map are constructed in parallel, structural and semantic features are extracted by using a pre-trained heterogeneous map neural network, and multi-view comparative learning guided by information gain is introduced for joint optimization, so that the generalization ability and robustness of the model to unknown drug pairs are enhanced. According to the method, system evaluation is carried out on the performance of the system in two types of prediction tasks (multi-type and multi-label) and three prediction scenes. Experimental results show that the method has excellent performance in all tasks and scenes. Further case analysis also verifies the effectiveness of the system in predicting the interaction type of the unseen drug pair.

A mobile robot system is described in having a mobile robot and cloud system. The mobile robot leverages cloud computing to offload Neural Radiance Fields (NeRF) based 3D scene reconstruction. The mobile robot advantageously adopts techniques for view filtering and next-best view selection that optimize the image collection process necessary for training an NeRF model with the cloud system. These techniques enable the mobile robot to discard redundant images that do not provide significant new information about the environment. Additionally, these techniques enable the mobile robot to strategically select next-best views that maximize the information gain, while minimizing a total number of images required and the time required to capture the images. These techniques provide a significant reduction in the overall bandwidth required for providing image data to the cloud system and can result in a more accurate and higher quality 3D reconstruction of the environment.

The invention relates to the technical field of wind turbine generator fault early warning, and discloses a wind turbine generator fault early warning method and system based on multi-modal data fusion, and the method comprises the steps: collecting the data of a multi-modal sensor, and carrying out the time-space alignment preprocessing; multi-modal features are extracted through variational mode decomposition, STL decomposition and other methods, and cross-modal fusion is achieved through dimension adaptive projection and a multi-head attention mechanism; calculating a dynamic weight based on three factors of data quality, fault type correlation and information gain, and carrying out weighted fusion; constructing a dynamic unit topological graph, and capturing cross-unit association features by using a space-time diagram convolutional network; long-time early warning with confidence is realized through double-branch gating fusion in combination with a Bayesian neural network; a multi-label classification identification multi-fault mode is adopted, and an operation and maintenance decision is optimized through an adaptive large neighborhood search algorithm. According to the method, the long early warning window of the offshore wind turbine generator can be realized, and uncertainty quantification and intelligent operation and maintenance decision support are provided.

The invention provides a dynamic knowledge graph construction and diagnosis reasoning method oriented to intelligent inquiry, and relates to the technical field of knowledge graphs, comprising the following steps: acquiring multi-source medical data, performing entity recognition and semantic annotation, establishing a causal probability graph based on a structured entity set, and establishing a dynamic knowledge graph; and selecting an optimal questioning problem according to the information gain in the inquiry process, dynamically updating the causal probability by using the Bayesian rule, and finally propagating the conditional probability along the causal path to generate a diagnosis conclusion. According to the invention, personalized inquiry decision and accurate diagnosis reasoning are realized, and the diagnosis accuracy and efficiency of the intelligent inquiry system are improved.

A method and system are disclosed for optimizing downlink multi-user multiple-input multiple-output (MU-MIMO) transmission in time division duplex (TDD) wireless communication systems. Sounding reference signals (SRS) from user equipment (UEs) are processed using a machine-learned model to extract physical-layer channel characteristics, including Doppler spread, delay profile, and signal-to-noise ratio (SNR). Based on these features, a channel state information (CSI) acquisition operation is adaptively selected for each UE—either channel estimation (CE) alone or both CE and channel prediction (CP). When CP is used, prior channel estimates are analyzed to generate predicted CSI over a future interval. The obtained CSI is then used to configure MU-MIMO transmission parameters such as beamforming weights, modulation and coding schemes (MCS), and UE grouping. This adaptive framework improves performance in time-varying and high-mobility environments by ensuring timely and reliable CSI is used for downstream transmission decisions.

The embodiment of the invention provides a vehicle control method and device, equipment, a medium and a program product, and relates to the technical field of intelligent driving. The method comprises the steps that driver identity information is obtained based on information collected by a biological feature recognition module, a driving file corresponding to the driver identity information is obtained, the driving file is used for indicating driver driving preference, a source model is adjusted based on the driving file, a target model is obtained, and the target model is made to adapt to the driver driving preference. Multi-modal sensor data are obtained, the multi-modal sensor data comprise steering wheel holding state information and vehicle dynamic state information, and the intention recognition result is recognized based on the multi-modal sensor data and the target model; according to the method, the intention recognition model based on big data training is adjusted in a personalized manner by fusing the biological characteristics of the driver and the multi-modal sensing data, so that the individual driving preference is accurately adapted, and the accuracy and adaptability of driving intention recognition are improved.

The present invention provides an Internet of Things (IoT) intrusion detection method and system based on a hybrid model, belonging to the field of network security technology. The method and system include: preprocessing IoT input data to obtain IoT preprocessed data; using an information gain algorithm and a fast filtering algorithm to perform feature selection on the IoT preprocessed data to obtain target feature data; training and learning the target feature data using a random forest algorithm based on a feature model, and training and learning the target feature data using a convolutional neural network and a long short-term memory network based on an anomaly model, using a preset public data set for training and testing to obtain a hybrid IoT intrusion detection model; inputting the IoT input data to be detected into the hybrid IoT intrusion detection model, and outputting IoT intrusion detection results. By applying a hybrid IoT intrusion detection model, the present invention achieves both high detection efficiency and the ability to detect unknown attacks, compared to a single detection model.