196 results about "Decision threshold" patented technology

The invention relates to the field of building construction safety, in particular to a risk prediction method and system for building construction. Aiming at the defects of multi-source data isolated analysis, dynamic risk response lagging, insufficient prediction precision and the like in the prior art, a unified analysis base is formed by constructing a space-time fusion data space and integrating multi-dimensional dynamic data such as structure micro-deformation monitoring, environmental parameters, three-dimensional live-action scanning, personnel positioning, a building information model and the like; based on a deep neural network architecture, designing a multi-modal feature extraction mechanism to quantify the coupling risk, and generating a partition risk probability distribution diagram; and in combination with a construction stage characteristic matching security policy library, implementing a three-level early warning mechanism and an automatic avoidance instruction. A closed-loop optimization mechanism is introduced, model parameters and decision threshold values are dynamically adjusted through actual accident feedback, and continuous evolution of a prediction system is achieved. According to the method, the active prevention and control capacity of compound accidents such as collapse and high-altitude falling is remarkably improved, and a self-adaptive intelligent protection system is constructed for a construction site.

The invention relates to the technical field of power distribution fault positioning, in particular to a power distribution single-phase earth fault positioning method and device based on multi-source information fusion. The device comprises a data acquisition module, a fault line selection module, a grading emergency module, a fault positioning module and a parameter optimization module. According to the method, the zero-sequence current is monitored in real time, the dynamic change curve is generated, the fault line is accurately identified in combination with amplitude and phase characteristics, electrical parameters, environmental factors and load importance are fused to carry out comprehensive risk assessment and start a hierarchical response mechanism, and the fault point of the fault line is accurately positioned by adopting a voltage drop method. And the monitoring frequency and the decision threshold value are adaptively adjusted according to the fault characteristics and historical data, so that response lag and insufficient precision of a traditional fault positioning method are avoided, full-flow intelligent management from fault detection, accurate positioning to parameter self-optimization is realized, the accuracy and efficiency of fault processing are improved, and safe and stable operation of a power grid is ensured.

The invention relates to the technical field of magnetic detection, in particular to a target positioning method based on geomagnetic anomaly and an axis frequency magnetic field. Comprising the following steps: S1, preparing before entering water; s2, approaching a target; s3, target detection: when no target appears, an observation signal is composed of an environment magnetic field, and the energy value of the part is very small; once the target appears, the energy of the observation signal can be continuously and obviously increased, when the energy value is greater than a preset judgment threshold, the target point is judged as a suspected target point, and if the suspected point is continuously detected for a specified time, the existence of the target is confirmed; s4, combined positioning: under a Kalman filtering framework, enabling the target to be equivalent to a magnetic dipole and an electric dipole, and correcting a position predicted by a target state equation by combining measurement equations of the two models, thereby realizing combined positioning of the target and obtaining more accurate target position information; and S5, verifying and outputting a positioning result. The method has the advantages of improving the positioning precision, enhancing the anti-interference capability and the like.

The invention provides an intelligent grading compressor control method and system based on load self-adaption, and belongs to the technical field of compressor control. The method comprises the steps that multi-dimensional data of a compressor unit is obtained through a distributed sensor network; carrying out preprocessing and feature extraction on the multi-dimensional data to obtain a feature vector; inputting the feature vector into a load prediction model to obtain a predicted load state; adjusting a decision threshold value of the grading decision tree based on the predicted load state to obtain a target grading decision tree; inputting the feature vector into a target grading decision tree to generate an execution scheme; obtaining energy efficiency feedback data for executing the execution scheme, inputting the predicted load state and the energy efficiency feedback data into the reinforcement learning model, and obtaining a control strategy by taking Pareto optimization as a target; and adjusting the control action of the compressor unit based on the control strategy. The operation energy efficiency of the compressor system is improved, and equipment mechanical loss and energy consumption are reduced.

The invention relates to the technical field of market trend prediction, and discloses a market trend prediction system driven by macroeconomic indicators. An index acquisition module of the system dynamically acquires core economic indexes such as GDP growth rate, CPI, PMI and currency supply; the data preprocessing module is used for carrying out layered noise reduction processing on the multi-source heterogeneous data; the feature engineering module constructs a market sensitive feature set through spatio-temporal feature fusion; the prediction model building module is used for building a multi-layer nonlinear prediction model based on a deep belief network; the dynamic adjustment module adopts reinforcement learning to optimize a decision threshold value and combines a Markov chain to carry out state transition planning; and the feedback iteration module analyzes and predicts deviation through Bayesian filtering and realizes strategy updating. According to the method, deep learning and reinforcement learning technologies are creatively fused, the prediction precision is remarkably improved through a dynamic calibration mechanism, and the method can be widely applied to the macroeconomic analysis fields of financial investment, industrial planning and the like.

The invention provides a micro-grid main grid cooperative switching intelligent regulation and control method and system, and relates to the technical field of power grid regulation and control. According to the method, the historical operation data of the micro-grid and the main grid are collected in real time, and the future operation state parameter set is generated, so that the operation states of the micro-grid and the main grid are accurately predicted. The dynamic stability index of the micro-grid and the power grid health index of the main grid are calculated, the two indexes serve as input, a switching decision management model is applied to generate a switching tendency score, and the switching tendency score is compared with a dynamic decision threshold value in real time to judge whether switching conditions are met or not. According to the method, the collaborative switching control instruction is automatically generated according to the correlation index and the switching mode rule, and the micro-grid is finely pre-adjusted in advance, so that smooth seamless switching between the micro-grid and the main grid is realized when the physical switching action is executed, and the safety, the reliability and the stability of the switching operation between the micro-grid and the main grid are improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data processing and decision-making method, device, equipment and medium, and the method comprises the steps: obtaining multi-dimensional source data, carrying out the standardization processing, screening the standardization data based on a decision-making scene, and obtaining a core index set, the method comprises the following steps: establishing and optimizing indexes, mining association characteristics among the indexes, constructing and optimizing a dynamic statistical model, carrying out standardization processing on newly added multi-dimensional source data, obtaining a key decision index value by utilizing the dynamic statistical model, and carrying out comparison judgment on the key decision index value and a preset decision threshold value, and generating a decision scheme by combining the comparison judgment result with a preset scheme generation mechanism. According to the method, multi-scene adaptive support and real-time decision optimization are realized through unified multi-dimensional data standardization, dynamic scene-driven core index screening and association characteristic mining, real-time model updating and closed-loop decision scheme generation.

The invention discloses an intelligent operation and maintenance management system and method based on digital twinning, and relates to the technical field of intelligent operation and maintenance management. The system comprises a parameter acquisition and anomaly identification module, a digital twin mirror image module, a fault trajectory generation module, a non-dominant fault positioning module and a closed loop verification module. The parameter acquisition and anomaly recognition module acquires data and recognizes anomaly based on a composite judgment threshold value; the fault trajectory generation module drives digital twin mirror image backtracking to generate fault time sequence trajectory data; the non-dominant fault positioning module constructs a reference scene and compares, analyzes and identifies non-dominant associated fault points; and the closed-loop verification module generates a verification instruction, and the verification instruction is verified by the physical device and then fed back to the digital twin mirror image to complete closed loop and confirm the fault root cause. According to the method, fault full-time-sequence tracing and hidden fault accurate identification are realized, and the operation and maintenance reliability and accuracy are improved.

The invention relates to the technical field of image recognition, in particular to an abnormal security scene monitoring method and system for infrared thermal imaging, and the method comprises the steps: collecting a picture of a monitoring region through an infrared thermal imager, building a video frame sequence, and constructing a Gaussian mixture model for each pixel point; performing spatial analysis on the video frame sequence, establishing a foreground patch, and determining artificial environment spatial interference of each pixel point in the current frame through the foreground patch; performing time analysis on the video frame sequence in combination with the artificial environment space-time interference to obtain the artificial environment space-time interference of each pixel point in the current frame; evaluating space-time interference of an artificial environment by using a Gaussian mixture model, and judging and marking whether each pixel point in the current frame is in a candidate state or not; and presetting a decision threshold, counting the number of continuous frames with candidate state marks, comparing the number of continuous frames with the decision threshold, and distinguishing normal and abnormal environmental changes. Benign environment changes and real threats are effectively distinguished, and a large number of invalid alarms are prevented from being generated.

The invention relates to the technical field of information security, in particular to an AI video detection method based on multi-feature branch fusion, and the method comprises the following steps: extracting a frame extraction color image from a video, and obtaining a standardized frame sequence after preprocessing; calculating an inter-frame differential volume; obtaining a time sequence spectrum volume according to the standardized frame sequence; performing bilateral filtering decomposition on the standardized frame sequence to obtain an illumination consistency volume; respectively inputting the three types of volume features into a deep convolutional neural network, and after feature extraction and fusion, outputting an AI forgery probability through a classifier; and comparing the AI forgery probability with a decision threshold to generate a video category label, and calculating an index. According to the method, the multi-dimensional features of the time-space domain, the frequency domain and the physical illumination domain are fused, the potential traces of the deeply-forged video are effectively captured, the stable detection performance is kept under various video quality conditions, the accuracy is improved, and reliable technical guarantee is provided for media information security.

The invention relates to a medical equipment fault prediction method and system based on multi-index fusion, and relates to the technical field of medical equipment fault prediction, and the method comprises the steps: collecting and associating multi-source heterogeneous operation data of target medical equipment; reversely analyzing and labeling event influence intervals in the time sequence dynamic monitoring data, and constructing a labeled time sequence data set of equipment state evolution; constructing a multi-task fusion prediction model; acquiring latest time sequence dynamic monitoring data in real time, inputting the latest time sequence dynamic monitoring data into the multi-task fusion prediction model, and synchronously acquiring an output future equipment comprehensive state index attenuation gradient prediction value and a discrete event occurrence probability prediction value; a fusion prediction score is calculated and a preventive maintenance alert is generated when a dynamically adjusted decision threshold is exceeded. The problems of high false alarm rate and insufficient early warning accuracy caused by the fact that traditional medical equipment fault early warning adopts a single judgment basis and related multi-class data are not integrated are solved.

Method and system for processing encrypted data comprising evaluating a client's encrypted data vector by at least one decision tree provided by a server. This method comprises, for evaluating a client's encrypted data vector by at least one decision tree (22) provided by a server (6), in a comparison phase: by a first execution environment, implementing a homomorphic encryption scheme, calculation (52, 54) of an evaluation vector, comprising encrypted components representing a difference between a component of the encrypted data vector and a decision threshold value associated with a corresponding node of said at least one decision tree, and providing the evaluation vector to a second execution environment which is a secure execution environment, executed by said server but controlled by said client.and by the second secure execution environment: - decryption (56) of said evaluation vector by applying a private key of said client, determination (58) of the sign of each component of the decrypted evaluation vector and provision of an encrypted sign vector to the first execution environment. Figure for the abstract: Figure 4,

The invention belongs to the technical field of resource scheduling, and discloses a dynamically constrained multi-resource scheduling method and system based on a decision threshold, and the method comprises the steps: obtaining a real-time resource state and a current environment constraint state, and calculating a value function under the current resource state and the current environment constraint state; constructing a multi-source dynamic resource coupling system model, establishing a cross-state optimization equation set based on the model and the value function, and solving the cross-state optimization equation set to obtain a decision threshold; constructing a two-state environment constraint indicator, judging an executable state of an environment constraint state based on the indicator, calculating a resource proportion according to a real-time resource state, and obtaining a relative state index; and based on the decision threshold and the executable state, constructing a resource scheduling dynamic decision engine, and performing real-time decision on multi-resource scheduling. According to the method, the dynamic decision engine is constructed to process the resource state and the constraint state in real time, a scientific threshold type decision signal is output, and the decision efficiency of resource scheduling with constraints is remarkably improved.

The application relates to the technical field of electric digital data processing, in particular to a collection method based on a dynamic threshold value, equipment and a medium. The method comprises the following steps: obtaining a set of experimental crawler programs whose crawling results on a target data source are successful, which are included in an experimental collection group; obtaining a crawling success rate of each program in the set on a candidate data source in a candidate data source set; determining a crawling characteristic judgment index of the target data source according to the crawling success rate; if the index is greater than or equal to a preset judgment index threshold value, determining a collection power of the target data source according to a comparison result of a collection power index of the target data source and a preset collection power index threshold value, and collecting the target data source based on the collection power; otherwise, determining the collection power of the target data source according to a comparison result of the collection power index of the target data source and an updated collection power index threshold value. The application can dynamically adjust a decision threshold value, and balance resource efficiency and a collection success rate.

The invention belongs to the technical field of semiconductor packaging, and provides an online monitoring method and system for thermal cycle failure of TSV copper salient points, and the method comprises the steps: synchronously collecting resistance data and acoustic emission signals of a test structure containing the TSV copper salient points, and writing the resistance data and acoustic emission signals into a data cache region; calculating a resistance change rate relative to an initial value based on the resistance data in the data cache region, and generating a primary trigger event when the resistance change rate exceeds a real-time judgment threshold value; in response to the primary trigger event, estimating physical coordinates of the abnormal activity; scanning the region of interest taking the physical coordinates as the center to obtain local deformation data, and fusing the physical coordinates, the local deformation data and the resistance change rate to generate a failure diagnosis conclusion; and based on the failure diagnosis conclusion, carrying out backtracking analysis on historical data before the primary trigger event occurs in the data cache region, and adaptively adjusting a real-time judgment threshold, so that real-time monitoring and intelligent diagnosis of the TSV copper bump thermal cycle failure are realized.

The invention discloses an uncertainty tracing and self-adaptive decision-making system and method based on a time sequence evolution causality graph, and relates to the technical field of artificial intelligence and data science crossing, and the method comprises the steps: receiving multi-source event observation data, and carrying out the preprocessing; estimating the real time posteriori distribution of the event through a probabilistic time warping module; real time posterior distribution is used as input, a causal graph structure is learned, and the existence probability and effect posterior distribution of edges are jointly estimated; performing uncertainty decomposition on a causal learning result, constructing an uncertainty evolution tree, and tracing the contribution of multi-source uncertainty; dynamically adjusting a decision threshold based on an uncertainty tracing result, and realizing a conservative decision through quantile calculation; and planning a data acquisition action based on a value information criterion, and generating and executing a directional acquisition instruction. According to the method, the probabilistic time warping and dynamic uncertainty tracing mechanism is introduced, so that the timestamp deviation in the multi-source industrial data can be effectively corrected, and the uncertainty source can be analyzed.

The invention relates to an automatic question answering method and device, computer equipment and a computer readable storage medium. The method comprises the following steps: acquiring an input question text; determining candidate answers of the question text; determining a decision threshold value of the candidate answer according to a question type of the question text and a preset context length; and determining a target answer of the question text from the candidate answers according to the decision threshold. By adopting the method, the decision threshold can be dynamically adjusted, so that the number of recalled candidate answers is adjusted, the correlation between the candidate answers input into the large model for reasoning and the questions is higher, and the accuracy of intelligent answering of the large model is improved.

The invention discloses a neural network cognitive diagnosis system based on rule prior, and the system comprises a parameter initialization module which is used for loading and preprocessing student answer records and question related knowledge information, and constructing an embedded layer of a neural network, and the embedded layer corresponds to a student ability vector, a question difficulty vector and a question distinction degree vector; the rule constraint module is used for introducing an interpretable constraint item; comprising the following steps of: ensuring the ability sorting consistency constraint of consistent student ability sorting and actual answering performance, forcing the negative correlation discrimination-variance negative correlation constraint of question discrimination and reply ability variance, and calibrating the difficulty of questions as the difficulty threshold consistency constraint of a decision threshold of student ability; and the diagnosis network module is used for constructing an output layer of a neural network based on the two-parameter item reaction theory and calculating the answer probability. According to the method, the neural network model fusing rule constraints is constructed, so that high-precision answer prediction is realized, and meanwhile, student ability estimation and question parameters are ensured to be consistent with an education theory.

The invention discloses a polarization code BP detection method suitable for a ReRAM memory, and relates to the technical field of new-generation communication. The method comprises the following steps: acquiring a read resistance value, performing hard decision, and dividing the read resistance value into a high-resistance unit and an uncertain unit; obtaining a noise standard deviation calculation decision threshold value of the memory array, and judging the uncertain units to obtain a judged low resistance state unit set and an uncertain unit set; if all other units in the row or column where the uncertain unit is located are high-resistance units, marking the current uncertain unit as a low-resistance unit; units in the determined uncertain unit set are marked as SFN units and SPN units at the same time, and a graph structure is formed for iteration; updating the graph structure according to an iteration result; and when the maximum number of iterations or message convergence is reached, threshold judgment is carried out, and the unreliable unit is detected again. According to the method, the detection time delay is greatly reduced while certain detection efficiency is ensured.

This application discloses a search error correction optimization method, apparatus, device, and computer program product. The method includes: acquiring user interaction behavior logs, including search behavior logs and click behavior logs; preprocessing the interaction behavior logs; performing user behavior analysis on the preprocessed interaction behavior data to obtain user behavior analysis results; making decisions on the preprocessed interaction behavior data based on the user behavior analysis results using a pre-set confidence decision threshold to obtain decision results; and updating the search error correction terminology in a closed loop based on the decision results. This application constructs a search error correction closed-loop optimization scheme based on user behavior feedback, enabling the error correction system to have self-evolution capabilities; it upgrades from "algorithm-driven" to "user intent-driven" based on massive amounts of real user behavior data, more accurately reflecting user needs; and it improves decision reliability through scientific quantitative decision-making using a confidence decision model.

The invention discloses a Qos regulation and control method, device and equipment for a log input size threshold value, and the method comprises the steps: obtaining related parameters of different time points influencing the log input size threshold value, and inputting the related parameters as input vectors into a Transform; performing global time sensing and coding on the input vector by using an encoder to generate a context-related global feature vector, and performing dimensionality reduction and compression on the global feature vector by using a decoder to obtain an optimized feature vector; utilizing a first decision-making machine to decide and output the adjustment direction of the threshold value according to the optimized feature vector, and utilizing a second decision-making machine to decide and output the adjustment amplitude of the threshold value according to the optimized feature vector; and adjusting the current log input size threshold according to the output adjustment direction and the adjustment amplitude. Therefore, the problems that an existing dynamic regulation and control scheme of the log input size threshold is limited by the complexity of a manual writing algorithm, and personal use habits of a user cannot be considered are solved.

The application discloses a kind of three-dimensional radar low altitude multipath detection and elevation dynamic measurement method of double channel, and the adaptive decision threshold of multipath decision model is constructed in combination with real-time signal-to-noise ratio, so that the judgment of target echo complex signal can be accurately identified the occurrence and end of multipath interference like " switch ", when target echo is interfered by multipath, start the elevation measurement method for multipath interference, significantly improve the environmental adaptability and robustness of radar;The imaginary part characteristics of complex single pulse ratio are used in combination with the adaptive threshold of signal-to-noise ratio, which is more accurate than the simple distance threshold in identifying the multipath state, with higher detection accuracy.The application uses the characteristics that phase information is distorted but still maintains segmented monotonicity under multipath environment, first locks the subdivided monotonic interval where the target is located, and then uses high-sensitivity amplitude information to perform table lookup inversion in the interval, without changing the 1T2R hardware architecture, the problem of angle measurement error caused by multipath interference is solved.

The invention discloses an industrial edge rolling technology unknown fault early warning method and system based on unsupervised learning. The method comprises the steps that multi-dimensional time sequence track data in the operation process of an edge rolling robot is acquired; dividing the data into independent track segments; extracting a group of statistical features covering a centralized trend, a dispersion degree and a distribution pattern from each track segment to construct a high-dimensional feature vector; a sorting method based on feature importance is adopted, a key feature subset with the most discriminative power is screened out from all the features, and therefore optimal feature representation is constructed; training a deep auto-encoder model only by using sample features in a normal state, so that the deep auto-encoder model learns the accurate reconstruction capability of normal feature vectors; during state judgment, a reconstruction error between a new sample feature vector and a model reconstruction result is calculated to serve as an abnormal score; and a decision threshold is scientifically determined through receiver operation characteristic curve analysis and a Youden index maximization criterion, and the abnormal score is compared with the threshold, so that real-time and automatic judgment of the process state is realized.

The application belongs to the technical field of signal processing, and particularly relates to a non-coherent scattering ionospheric parameter non-ambiguous estimation method based on Monte Carlo, aiming to solve the problems of inaccurate parameter estimation and poor robustness in ionospheric parameter inversion in the prior art. The method comprises the following steps: receiving measured echo data, estimating the signal noise level and determining the prior information combination mode; obtaining an expected index based on a signal noise threshold mapping library, and judging whether a preset decision threshold is met; if the preset decision threshold is met, starting an optimization selection strategy based on Monte Carlo, and obtaining a candidate solution set; performing cluster analysis on the candidate solution set, selecting a solution with the optimal fitting degree from the solution cluster containing the largest number of solutions, and taking the solution as the final non-ambiguous estimation value. Through systematic Monte Carlo simulation, the application can quantitatively evaluate the inversion reliability under the current data condition, effectively avoid invalid calculation under adverse conditions such as excessive noise or insufficient prior information, and improve the robustness and success rate of estimation.

The application relates to the field of flue gas purification technology of coal-fired power plants, in particular to a flow field optimization method and device of a selective catalytic reduction denitration system and a medium, wherein the method comprises the following steps: identifying average flow velocities of a first region and a second region; calculating a region flow ratio according to the average flow velocities of the first region and the second region, and determining an adjustment strategy according to the region flow ratio and a current decision threshold value, wherein the current decision threshold value comprises a first decision threshold value and a second decision threshold value, and the first decision threshold value is greater than the second decision threshold value; obtaining a current unit load, determining reference guide plate angle values corresponding to the first region and the second region according to the current unit load, and adjusting a current actual guide plate angle to the reference guide plate angle in the adjustment strategy. Therefore, the problems that control parameters of flow field optimization of the selective catalytic reduction denitration system are fixed in the related art, and the flow field optimization effect is attenuated after long-term operation are solved.

The invention discloses a real-time sensing and dynamic regulation and control method and system for the state of an interface between a shield tunnel and a surrounding soil body. The method comprises the steps that S1, contact pressure and slurry filling state data of the interface between the tunnel and the surrounding soil body are collected in real time; s2, the edge computing node performs real-time diagnosis and trend prediction on the interface contact state based on the collected data; when the diagnosis result is that the interface pressure is abnormal, a regulation and control instruction of the distributed bionic actuator array is triggered; s3, the bionic actuator in the interface pressure abnormal area receives the regulation and control instruction to operate, and active remodeling and redistribution of an interface stress field are achieved; s4, performing effect evaluation based on the real-time sensing data to form a control closed loop; and S5-S6, operating the high-fidelity multi-physics field coupling model based on the regulation and control case data, and performing self-learning optimization and updating on the interface mechanical model parameters, the regulation and control strategy and the decision threshold. According to the invention, global real-time sensing and dynamic active regulation and control of the state of the interface between the shield tunnel and the surrounding soil body can be realized.

A method for correcting an orbit error of common-view time comparison of a space station, comprising five parts, i.e., determination of moments when two stations can simultaneously view the space station, calculation of projection coefficients of the orbit error, calculation of a three-dimensional orbit error vector set, calculation of a set of projection differences of the orbit error in line-of-sight directions of the two stations, and threshold decision. By means of calculation of the five parts, the maximum influence quantity of the orbit error on a common-view time comparison result is obtained, observation moments with the maximum influence quantity exceeding a decision threshold are eliminated, and observation points with the influence of the orbit error on the common-view time comparison less than the decision threshold are reserved.

The invention provides a WIFI system DFS channel radar signal typing identification device and method, and relates to the technical field of wireless local area networks, the device comprises a hardware layer, a firmware layer and a software layer, the hardware layer comprises a radio frequency front end, a baseband processing unit and a detection unit; the firmware layer comprises an interrupt processing module, a data acquisition module and a pulse descriptor array construction module; the software layer comprises a feature extraction module, a feature analysis module and a decision control module; wherein the hardware layer screens and caches pulses, the firmware layer processes interrupts and constructs a pulse descriptor array, the software layer extracts multi-dimensional features and adopts a density clustering algorithm for clustering, PRI histogram statistics and multi-dimensional quality scoring are combined, and then decision thresholds are adaptively selected according to three types of scenes of high sensitivity, high interference and conventional. The effective radar signal accurate identification is realized, the data integrity and real-time performance are ensured, and the interference influence is reduced.

The application provides an image storage method and device, wherein the application sets a weight for different levels of warning information, and calculates a comprehensive decision threshold in units of images, so that intelligent screening and storage of key fault images are realized, data storage pressure is effectively reduced, display pictures at key fault moments are completely retained, and reliable data support is provided for post-flight maintenance and fault analysis.

The application discloses a kind of interference identification methods based on dynamic sparse Vit, comprising: the interference signal to be identified is converted into high-dimensional feature map and feature sequence in turn;Real-time jam-to-noise ratio estimation operation is carried out on high-dimensional feature map, and jam-to-noise ratio estimation value is obtained;Energy feature, gradient feature and continuity feature are extracted based on high-dimensional feature map;And significant score map is obtained by using adjustment factor to weight fusion energy feature, gradient feature and continuity feature;Significant score map and decision threshold are compared, and binary significant mask is generated;Adjustment factor is calculated based on jam-to-noise ratio estimation value;Based on adjustment factor and binary significant mask, dynamic sparse attention calculation is carried out on feature sequence, and fusion feature sequence is output;Interference classification identification is carried out using fusion feature sequence, so that the interference identification method with low computational complexity and high interference identification precision in low jam-to-noise ratio environment is realized.