1225 results about "Fingerprint recognition" patented technology

The invention relates to the technical field of intelligent control of test equipment, and discloses a semiconductor device test equipment control system and method based on industrial data processing, and the method comprises the steps: collecting multi-source heterogeneous data of semiconductor device test equipment, and carrying out the preprocessing; constructing a structural causal model, and performing root cause identification through anti-fact reasoning by using the structural causal model; constructing an abnormal test fingerprint and a knowledge base; generating an intervention scheme, evaluating the generated intervention scheme, and selecting an optimal intervention scheme; processing the detected anomaly, and generating and implementing a preventive control strategy based on historical anomaly data and a causal analysis result; according to the invention, by introducing innovative technologies such as causal inference, anti-factual inference, abnormal test fingerprint identification and Monte Carlo tree search, intelligent control of semiconductor test equipment is realized.

The invention discloses a network security analysis early warning system based on artificial intelligence, and the system comprises a data collection layer which captures full flow based on DPI, aggregates firewall logs, terminal behaviors and threat intelligence, and constructs a structured data pool; through TLS fingerprint identification of AI driving, the encrypted traffic is penetrated, and a sampling strategy is dynamically adjusted in combination with reinforcement learning. The intelligent analysis layer is used for carrying out cross validation on known threats and abnormal behaviors; the time sequence CNN extracts encrypted traffic features, and a novel threat detector is rapidly generated by using historical attack fragments in combination with a meta-learning framework; sHAP value driving dynamic feature selection and optimization feature vector input; the decision-making early warning layer is used for fusing multi-source features through a Bayesian network and generating 0-100 score risk scores; a self-adaptive threshold module is combined to adjust a score threshold in real time, and a high-risk event is pushed; the collaborative response layer is used for triggering a preset decision tree, deploying a GAN dynamic honeypot to trap an attacker and reversely tracing; the Neo4j visually restores the attack path, and blocking is executed after the threat is confirmed by a progressive response mechanism.

A method is provided for dynamically generating a digital watermark for a data file. The method includes receiving a request to access the data file from a user; dynamically generating an encryption key based on at least one parameter selected from the group consisting of the identity of the user, the time of access, and the mode of access; embedding a digital watermark into the data file using the dynamically generated encryption key, wherein the digital watermark is unique to the request; providing access to the data file with the embedded digital watermark to the user; and storing information related to the encryption key and the parameters used for its generation in a secure database.

The invention provides a river pollutant traceability method and a river pollutant traceability system. The method comprises the following steps: constructing a potential pollution source feature fingerprint database fused with an LSTM time sequence feature extraction model, carrying out abnormal water quality fingerprint identification on a monitored river reach, if identification is abnormal, collecting an upstream water sample, detecting to obtain a water quality fingerprint, comparing the water quality fingerprint with the abnormal water quality fingerprint, determining a target river reach according to a comparison result, and determining the target river reach according to the comparison result. The method comprises the following steps: sampling all enterprises of a target river reach in real time, comparing detection result data with detection result data of an abnormal water sample, determining potential pollution sources according to a comparison result, determining high-matching candidate sources from the potential pollution sources by utilizing an LSTM (Long Short Term Memory) time sequence feature extraction model, carrying out space transition verification on the high-matching candidate sources, and carrying out space transition verification on the high-matching candidate sources. And determining the pollution source according to the verification result. According to the method, the accuracy, efficiency and result reliability of tracing the river pollutants can be effectively improved, and the scenes of multi-source pollution, intermittent emission and the like of complex rivers can be effectively handled.

An Automated AI-Driven Lifestyle Orchestration System proactively manages a user's daily life by intelligently populating calendars with relevant, time-sensitive marketing objects. This is achieved through sophisticated AI-driven analysis of user directives, preferences, and real-time location data obtained via computing devices like smartphones. The system leverages advanced media identification modules employing fingerprinting and watermarking to accurately match user-captured media (via a Percipient Sample Pack (PSP)) to specific content and associated products. It facilitates dynamic commerce through hierarchical linked lists for purchasing authentic or similar items, supported by an affiliate program. This inventive solution significantly improves personal organization and digital commerce by transforming passive interaction into a proactive, intelligent, and monetizable user experience.

The invention provides a network information security adaptive threat intelligence analysis and response method and system. The method comprises the following steps: acquiring an encrypted traffic load byte stream, and segmenting the encrypted traffic load byte stream into a time sequence traffic matrix according to a time window; and analyzing the communication metadata, matching the features of the threat intelligence library, and generating a dynamic threat fingerprint based on protocol compliance and behavior abnormality. And performing multi-stage wavelet packet decomposition on the matrix, extracting the energy spectrum density, the information entropy and the time-frequency variable coefficient of the high-frequency component, and performing weighted fusion to generate a frequency-domain composite abnormal index representing heartbeat signal characteristics. And identifying hidden heartbeat periodicity and protocol violation modes through a multi-mode fusion mechanism in combination with the frequency domain index and the threat fingerprint, and outputting a threat score. And if the attack exceeds the threshold value, intercepting the flow, verifying a new attack feature, and then reversely updating the threat intelligence library to form a defense self-evolution closed loop. According to the method, automatic interception and closed-loop updating of the threat intelligence library are realized, and the defense adaptability is remarkably improved.

The invention relates to the technical field of Internet of Things, and discloses a multi-factor dynamic authentication Internet of Things network security access platform, which adopts a multi-factor authentication mechanism, combines biological characteristics, behavior characteristics and environment characteristics of equipment, performs identity verification through biological recognition, equipment fingerprints and behavior analysis, and utilizes a dynamic authentication strategy. Detecting an abnormal IP behavior and triggering a security policy by adopting a dynamic IP binding technology, combining with equipment fingerprint identification and learning and analyzing an equipment network behavior mode; a dynamic key management mechanism is adopted, and keys are automatically generated, distributed and updated according to different devices, application scenes and communication requirements; machine learning and artificial intelligence technologies are utilized to comprehensively analyze historical access data, abnormal behavior modes, environment security states and the like of equipment, and the security risk of equipment access is automatically evaluated if suspicious equipment is detected. The method has the advantage of improving the security of the Internet of Things.

A non-invasive liquid integrity monitoring system using dielectric fingerprinting and machine learning to detect and identify contamination in sealed containers is described. The system may employ externally-mounted sensors that measure dielectric properties through electromagnetic interrogation, comparing measurements against baseline signatures to detect deviations indicating contamination, tampering, or degradation. Industry-specific ML models enable identification of specific contaminants with confidence, providing alerts without breaching container integrity.

The invention provides an intelligent data security exposed surface risk assessment method, system and device and a medium, and relates to the technical field of information security, and the method comprises the steps: constructing an exposed surface asset map through an asset fingerprint recognition engine, and calculating an asset exposure index; constructing a vulnerability knowledge base, and identifying potential vulnerabilities; based on the standard vulnerability score, the vulnerability utilization tool activeness and the attack event frequency in the set time period, calculating the triggering probability of the potential vulnerability as a dynamic vulnerability score; malicious IP access frequency and leakage record matching degree are obtained through firewall logs and dark web monitoring data, and threat intelligence factors are obtained through calculation; calculating an exposed surface risk value in combination with the asset exposure index, the dynamic vulnerability score and the threat intelligence factor; and based on a time decay model, according to the exposed surface risk value and the business influence factor, calculating a residual risk value, and obtaining an exposed surface risk assessment result. The accuracy of risk assessment is further improved through multi-dimensional data fusion.

The invention discloses a cross-website domain small sample website fingerprint identification method oriented to an onion network. The method comprises a pre-training strategy based on feature alignment and domain adversarial training and a fine tuning strategy based on a double attention module. In the pre-training strategy, a multi-channel matrix traffic representation mode is designed, and rich traffic features are provided; constructing a feature extractor based on a multi-scale convolution structure and a double-attention mechanism, and supporting learning and adaptive weight distribution of different granularity features; and designing a composite loss function fusing classification loss, feature distribution difference loss and domain adversarial loss, and realizing effective feature alignment between the source domain and the target domain. In a fine tuning strategy, a fine tuning strategy based on a double-attention module is adopted, and the rapid adaptive capacity of the model to a new task is improved while the stability of pre-training features is kept. According to the method, even in the face of complex website domain changes, rapid adaptation to a new target domain data set can be achieved by depending on a very small number of samples, and an effective technical basis is provided for anonymous communication network governance practice.

The invention provides an Internet of Vehicles broadcast frame radio frequency fingerprint identification method based on LMMSE channel estimation. The method comprises the following steps: obtaining subcarrier data in a resource grid based on an obtained signal of a physical side link broadcast channel; performing root-mean-square delay expansion and channel autocorrelation matrix construction based on the subcarrier data, and combining priori signal-to-noise ratio regularization and time domain windowing operation to obtain a channel estimation value; initial radio frequency fingerprint features are obtained through the channel equalization and the channel estimation value, and the improved neural network and the initial radio frequency fingerprint features are utilized to carry out classification identification on the Internet of Vehicles equipment. Influences of noise and channels on fingerprints are effectively considered, and the purpose of extracting fingerprints of different devices in a complex environment more accurately is achieved.

A non-invasive liquid integrity monitoring system using dielectric fingerprinting and machine learning to detect and identify contamination in sealed containers is described. The system may employ externally-mounted sensors that measure dielectric properties through electromagnetic interrogation, comparing measurements against baseline signatures to detect deviations indicating contamination, tampering, or degradation. Industry-specific ML models enable identification of specific contaminants with confidence, providing alerts without breaching container integrity.

A modular system designed for privacy-preserving content recognition and supplemental content delivery across web and mobile environments. The system employs lightweight character sampling and vision-based recognition to generate unique content fingerprints without storing or replicating original data. It features a hybrid processing architecture, using local computing resources for intensive tasks while optimizing performance on resource-constrained devices. Core functionalities include multi-method content fingerprinting, real-time monitoring with adaptive sampling, and secure supplemental content association. Operating entirely on the client-side, it complies with website terms of service and privacy regulations. Advanced features include AI-driven content recognition, blockchain-based verification, and granular content targeting through resizable selection interfaces. This technology enables seamless delivery of supplemental content while preserving privacy, reducing resource usage, and ensuring scalability across browsers, mobile applications, and edge devices. It is particularly applicable in industries such as education, retail, and secure data sharing.

The invention designs a cross-Tor version domain small sample website fingerprint identification method based on comparative learning. The method comprises three parts of a pre-training strategy based on comparative learning, a fine tuning strategy based on double-branch confidence alignment and a flow classification strategy driven by a prototype. The method comprises the following steps: firstly, constructing a discriminative representation space through a trunk convolution feature extractor and a traffic attention module based on a pre-training strategy of comparative learning, and introducing supervised comparative learning to obtain feature representation with a cross-domain migration capability; secondly, a double-branch confidence alignment fine tuning strategy is adopted, the stability of a model structure is kept in combination with a partial freezing mechanism, and rapid adaptation to a target domain is achieved through joint optimization of cross entropy loss and weighted comparison loss; and finally, introducing a prototype-driven classification strategy, and on the basis of a semantic structure formed in a fine tuning stage, realizing accurate identification of Tor traffic in closed world and open world scenes by using a category prototype, so that the cross-domain generalization ability of small sample website fingerprint identification is remarkably improved.

The invention relates to the technical field of dynamic modeling and digital twinning, and discloses a digital twinning dynamic modeling method for new material research and development, which comprises the following steps: constructing a low-order linear reference model, calculating a residual signal flow, and extracting residual non-statistical topological characteristics by using a sliding window to retrieve a system matrix correction increment; constructing a parallel shadow verification model to execute virtual deduction, and screening and executing parameter hopping according to a convergence divergence ratio; according to the method, through a closed-loop mechanism of residual form fingerprint identification and shadow deduction verification, the problem of capturing discontinuous physical phase change under low computing power is solved, and real-time high-fidelity synchronization of the model on complex microscopic evolution is realized while environmental noise interference is suppressed.

The invention discloses an enhanced equipment fingerprint identification system and method based on time-frequency domain feature fusion. The system is composed of a signal processing module, a feature extraction module, a feature fusion module and an equipment identification module. The method comprises the following steps: firstly, splitting IQ into three time domain features, converting the IQ into a frequency domain and a time-frequency domain by using corresponding FFT and STFT formulas, and extracting corresponding features; respectively inputting the features of the time domain, the frequency domain and the time-frequency domain into a feature extraction module for feature extraction; the extracted features are sent to a feature fusion module, and final fusion features are generated; and finally, the fusion features are transmitted to a classification model, and fingerprint matching and identification of the equipment are completed. By adopting the enhanced equipment fingerprint identification system and method based on time-frequency domain feature fusion, the security, accuracy and robustness of the Internet of Things equipment are improved, the sensitivity of the system to noise interference is reduced, the equipment fingerprint extraction cost is reduced, and the higher identification accuracy is realized.

The invention discloses an industrial control equipment vulnerability identification method and system based on a double-BERT model, and the method comprises the steps: an asset analysis stage: analyzing the flow to generate structured asset data; in the semantic matching stage, a multilingual Sension-BERT model is used for extracting vectors and matching a vulnerability library; in the version identification stage, the BERT is finely adjusted to judge the influence condition of the version; and in the vulnerability confirmation stage, an identification result is manually rechecked. The system comprises an asset processing subsystem and a vulnerability intelligent identification subsystem, and the asset processing subsystem comprises a flow analysis module, an equipment fingerprint identification module, an asset information management module, a scanning task scheduling module and a vulnerability display and screening module. And the vulnerability intelligent identification subsystem comprises a semantic vector matching module, a version matching judgment module and an identification result output module. According to the method, the problems of high asset isomerism, weak semantic matching capability and low version comparison accuracy in the current industrial network can be solved, and the vulnerability identification quality and efficiency are improved.

The systems and methods disclosed herein orchestrate task execution among autonomous (or semi-autonomous) AI agentic models (“agents”) responsive to a received query by using a hierarchical semantic fingerprinting framework to generate semantic-aware fingerprints for the query and agents. Queries and descriptions of agent capabilities are processed by a series of hierarchical levels using locality-sensitive hash (LSH) functions, where subsequent layers encode more complex semantic information and generate longer hash values. The hash values are aggregated into a semantic fingerprint. A bloom filter cascade uses a series of increasingly accurate hierarchical bloom filters to reject agent fingerprints that differ from the query fingerprint. The remaining agent fingerprints are compared bitwise to the query fingerprint, and those closest to the query fingerprint are selected to generate a routing path for the query. Responses from the selected agents are aggregated into an output that is responsive to the input.

The invention relates to the technical field of intelligent monitoring of power distribution systems, and discloses an intelligent digital electric box power utilization monitoring management system based on the Internet of Things. The system comprises a data acquisition module, a data processing and analysis module, an intelligent control module, an Internet of Things communication module and a user interaction and management module. The data acquisition module acquires electrical and environmental parameters in real time; the data processing and analyzing module constructs an electricity utilization fingerprint database of the equipment, calculates a real-time threshold value through a dynamic threshold value engine, and realizes equipment-level anomaly recognition in combination with an anomaly detection engine; the intelligent control module executes a control action according to a detection result; the Internet of Things communication module realizes two-way data transmission; and the user interaction and management module provides a visual operation interface. According to the invention, through a dynamic threshold mechanism, equipment fingerprint identification and multi-level early warning control, equipment-level hidden faults are accurately identified, and the power utilization safety and the intelligent management level are improved.

The invention relates to the field of network security and the deep learning field, in particular to a satellite internet-oriented Tor website fingerprint identification method, which is implemented by constructing a self-supervised contrast learning framework and is implemented by generating a multi-view sample; extracting a feature matrix of the time sequence granularity; generating a synthesized negative sample based on linear insertion, and constructing a contrast with the positive sample; and track representation is extracted, and joint optimization is carried out in combination with cross entropy loss and mask reconstruction loss. Compared with a traditional website fingerprint identification method, the robustness of the model to satellite inherent noise caused by the Doppler effect can be enhanced. According to the method, multi-scale semantic features are provided for the weak enhancement view and the strong enhancement view, the inter-class distinction degree is improved by synthesizing negative samples, and the misjudgment rate is reduced; the joint loss optimization framework enables the model to give consideration to global statistical characteristics and local burst characteristics, and under the conditions of strong dynamic topology and limited labeling of satellite internet constellations, the accuracy and adaptability of website fingerprint recognition are remarkably improved.

The invention relates to the technical field of fingerprint identification, in particular to a wireless communication system and communication method of a fingerprint machine. The method comprises the following steps: collecting a fingerprint original data matrix through a capacitive fingerprint sensor, and carrying out fingerprint data preprocessing to obtain a fingerprint optimization feature vector; performing AES-GCM encryption on the fingerprint optimization feature vector by using the server side and the equipment side to generate primary encrypted feature data; therefore, through multi-level fingerprint feature optimization and encryption processing, based on a channel quality self-adaptive modulation coding and layered compression transmission technology, and a full-flow decryption discrimination and performance evaluation mechanism, the security and reliability of the system are improved, and the security and reliability of the system are improved. The problems that in a traditional fingerprint recognition system, data security is insufficient, transmission efficiency is low, and authentication accuracy is difficult to guarantee are solved, the security, reliability and communication transmission efficiency of the fingerprint recognition system are improved, and efficient protection and stable verification of biological recognition data are achieved.

The invention provides a fingerprint signal processing method and device and electronic equipment, and can be applied to the technical field of fingerprint identification. Based on the method, before specific implementation, fingerprint pixel units in a fingerprint pixel unit array circuit can be connected with a preset gain amplifier with adjustable gain, and an improved fingerprint pixel unit array circuit is obtained. In specific implementation, when a fingerprint related signal is received, a matched target convolution kernel is determined in the fingerprint pixel unit array circuit; wherein the range of the target convolution kernel comprises a plurality of fingerprint pixel units; and traversing the fingerprint pixel unit array circuit by using a target convolution kernel, and processing the fingerprint related signal to obtain a target fingerprint signal. Therefore, the fingerprint pixel units in the fingerprint pixel unit array circuit can be fully utilized, the received fingerprint related signals are processed by constructing and using the corresponding target convolution kernels, the signal-to-noise ratio of the signals is effectively improved, and target fingerprint signals with high quality are obtained.

The invention discloses a robust radio frequency fingerprint identification method based on Barlow Twins domain adaptation. Aiming at the problems of training and deployment environment distribution offset and cross-domain identification performance reduction caused by wireless channel multipath and time-varying characteristics in the prior art, the invention provides a domain adaptation framework combined with feature decoupling. The method comprises the following steps: firstly, converting a received time domain signal into a short-time Fourier transform spectrogram to reserve a time-frequency structure; then, a double-end convolutional encoder is constructed, bottom layer features are extracted through a shared backbone network, and device fingerprints and channel features are obtained in an identity branch and a channel branch respectively; in order to realize thorough decoupling of the two types of features, Barlow Twins-based independence regularization is introduced, and the identity features and the channel features are statistically orthogonal by minimizing a cross-correlation matrix of the identity features and the channel features, so that purer fingerprint features are obtained, channel interference is effectively eliminated, and generalization and recognition precision of the model under an unknown channel are remarkably improved.

The invention discloses an unmanned aerial vehicle radio frequency fingerprint identification method and device, a medium and equipment, and relates to the technical field of computers. According to a time-frequency graph corresponding to a radio frequency signal, time domain convolution is carried out by sliding along a time axis of the time-frequency graph, time domain features of the time-frequency graph are extracted, frequency domain convolution is carried out by sliding along a frequency axis of the time-frequency graph, frequency domain features of the time-frequency graph are extracted, and heterogeneous combined convolution kernels with different sizes are adopted in the time domain convolution and the frequency domain convolution, so that the time-frequency graph is more accurate. Therefore, time-frequency anisotropic convolution is realized, and short-time dynamic evolution and local energy distribution characteristics are respectively captured. According to the method, the uniform operation defect of traditional isotropic convolution is avoided, parameter redundancy is reduced and feature discrimination is enhanced through physically interpretable dimension decoupling, and the accuracy of radio frequency fingerprint identification of the unmanned aerial vehicle is improved.

Disclosed herein are embodiments of methods, devices and systems for device fingerprinting and automatic and dynamic software deployment to one or more endpoints on a computer network. The device fingerprinting systems and devices herein are configured to operate with limited data without sitting between network devices and the internet, without monitoring all network traffic, and without limited or no active scanning. The embodiments herein may passively collect information as distributed peers and may perform very limited active scans. In some embodiments, the information is used as an input to a custom hierarchical learning model to fingerprint devices on a network by identifying attributes of the devices such as the operating system family, operating system version, and device role. In some embodiments, a dynamic deployer selection process may be utilized to simply and efficiently deploy software. Some embodiments herein involve end-to-end encryption of credentials in a deployment process.

The invention relates to the technical field of intelligent control of double-drum washing machines, and discloses an intelligent control system of an energy-saving double-drum washing machine, which comprises a hardware execution unit used for providing multi-mode sensing, accurate liquid management, mechanical execution and energy recovery functions; the control processing unit is used for processing the received data, operating an algorithm and coordinating the operation of each component of the system; the cooperative control software module runs on the main control board and comprises an acoustic fingerprint identification and fault diagnosis module and an intelligent fluid precise counterweight and cooling module; an AI-driven collaborative decision and adaptive optimization module; the invention discloses a micro heat pump energy recovery and precise temperature control linkage module. By means of the high-sensitivity acoustic array sensor and the acoustic fingerprint recognition and fault diagnosis module, clothes imbalance is recognized, point-to-point liquid balance weight is achieved by means of the micro-fluidic spraying valve array, the optimal dehydration cooperative scheduling strategy is generated, water and electricity resources are greatly saved, and the dehydration efficiency is improved.

The invention discloses a multi-label website fingerprint identification method and system based on attribution analysis, and the method comprises the steps: firstly carrying out the model attribution analysis of a training sample, and constructing an average discrimination template of each website category; high contribution areas of all categories are extracted according to the average discrimination template, and a structured mask template used for guiding identification is formed; in the identification stage, sliding window matching is carried out on input hybrid network traffic, the position of a potential pseudo sub-stream is judged according to the similarity between a mask template and a window sub-sequence, and single-label identification is carried out on the extracted pseudo sub-stream, so that effective analysis of multi-label traffic is realized. The method has the advantages of being high in structure perception, high in template generalization and good in mixed flow adaptability, the website recognition capacity in a complex network environment can be remarkably improved, and the method is suitable for scenes such as encrypted communication monitoring and anonymous channel flow analysis.

The invention belongs to the technical field of radio frequency identification, and particularly relates to an unmanned aerial vehicle radio frequency fingerprint identification method for different signal-to-noise ratios based on a time-frequency graph. The invention provides a radio frequency fingerprint identification method for different signal-to-noise ratios based on a time-frequency graph and a YOLOV8 image identification technology on the basis of a traditional unmanned aerial vehicle radio frequency signal fingerprint identification technology, and the method mainly comprises the steps: constructing a new training data structure, specifically, converting an IQ signal into the time-frequency graph, and carrying out the recognition of the time-frequency graph and the YOLOV8 image identification technology. And then performing arrangement according to the unmanned aerial vehicle type (J), the signal bandwidth (B), the signal frequency band (F) and the signal-to-noise ratio (R) to obtain a structured data set, and performing identification by using an improved YOLOV8 model. Compared with the prior art, the method further improves the accuracy and speed of unmanned aerial vehicle identification, and is a scheme for identifying the model of the unmanned aerial vehicle on the hardware level without information matching. According to the method, high sensitivity of the model to a low-signal-to-noise-ratio signal is kept, and meanwhile, a large amount of misrecognition caused by low confidence is effectively inhibited.

The invention discloses a fuzzy theory fused tongue diagnosis characteristic quantitative analysis method and system, and relates to the technical field of traditional Chinese medicine tongue diagnos.The method comprises the steps that a micro piezoelectric sensor array and a hyperspectral imaging module are integrated, tongue surface Young modulus distribution data and hyperspectral reflectivity data are synchronously collected, and a multi-mode tongue picture fusion data set is generated; further performing crack region segmentation and super-resolution reconstruction through a neural network model, outputting a crack network diagram and a tongue papilla form entropy, generating topological fingerprints in combination with continuous homology analysis, identifying a mechanical gradient mutation region and extracting a metabolic index, constructing a mechanical-metabolic associated feature set, and obtaining a mechanical-metabolic associated feature set; topological fingerprints and mechanical-metabolic characteristics are fused in a three-dimensional space-time coordinate system, a dynamic intuitionistic fuzzy decision field is constructed, finally, tongue picture pathology evolution risk indexes and a visual early warning report are output through a pathology prediction model, and the system provides quantifiable decision support for'prevention before illness' in traditional Chinese medicine.

The invention discloses a honeypot automatic coping strategy generation method based on a large model. The method comprises the steps of S1, performing semantic analysis on dynamic attack behaviors; s2, performing context-aware threat reasoning; s3, adaptive strategy generation and semantic verification are carried out; s4, strategy executable compiling is carried out; s5, enhancing the efficiency of the closed-loop strategy; according to the method, the authority / service logic contradiction is thoroughly eliminated through a semantic consistency verification mechanism, so that the false alarm rate of the honeypot in the APT attack is reduced; an anti-recognition perturbation code injected by the low-entropy strategy compiling technology breaks through a traditional honeypot periodic response mode, and the fingerprint recognition success rate of an attacker is reduced; a resource penalty function of the Pareto optimal strategy sequence enables a trapping intensity mean value under limited resources to be improved; a double-channel updating mechanism promotes coevolution of a knowledge base and a constraint set, and the response generation speed for an unknown attack mode is shortened.