563 results about "Ancillary data" patented technology

The invention discloses an Internet of Things dynamic weighing and data tracing method and system, and relates to the technical field of industrial Internet data processing, and the method comprises the steps: obtaining dynamic weighing data of a weighing event and at least one group of auxiliary data synchronized with the dynamic weighing data; based on the auxiliary data, generating a credibility weight value for the dynamic weighing data through a preset credibility model; performing weighted statistical analysis on the one or more dynamic weighing data based on the weight value to obtain a weighted statistical analysis result; and fusing the weighted statistical analysis result and the change trend of the credibility weight value, and carrying out two-dimensional fusion diagnosis to obtain a diagnosis result capable of accurately decoupling a problem root. And generating a high-credibility tracing record or issuing a control instruction according to the diagnosis result. According to the invention, through real-time quantification and management of data credibility, intelligent distinguishing of production process abnormity and measurement system abnormity is realized, and the reliability of a dynamic weighing system and the value of tracing data are improved.

The invention relates to a method for constructing a high-resolution atmosphere carbon dioxide concentration data set based on XGBoost-BO, and belongs to the technical field of environment monitoring and artificial intelligence modeling. The method comprises the following steps: preprocessing OCO-2 satellite data and multi-source auxiliary data, and fusing the preprocessed OCO-2 satellite data and multi-source auxiliary data to obtain a new data set; a Bayesian optimization method is adopted to search for an optimal hyper-parameter, a target function is optimized through second-order Taylor expansion, a regular term is introduced to control the complexity of the model, and ten-fold cross validation is used to evaluate the performance of the model; quantizing the contribution degree of each feature to model prediction through a tree SHAP method, and analyzing global feature importance ranking and feature contribution distribution of individual samples; and performing model verification by using the test set and the site actual measurement data. According to the method, the problems that an existing model-based reconstruction method is insufficient in interpretation and prone to falling into local optimum are solved, and the temporal-spatial resolution of CO2 concentration monitoring can be improved.

The invention provides a bidding total factor compliance examination method based on multi-modal model space-time sequence analysis, which relates to the technical field of data processing and comprises the steps of acquiring space-time sequence data and auxiliary data of bidding items, constructing a space-time double-flow feature network, constructing a knowledge graph based on output, and calculating an abnormal score by adopting a space-time mode encoder. And optimizing an anomaly detection result through a graph attention network, and dynamically generating a bidding compliance verification path by using deep reinforcement learning. According to the method, comprehensive analysis of bidding and tendering data can be realized, the abnormal behavior detection accuracy is improved, the manual review cost is reduced, and the verification path efficiency is optimized.

The invention provides a multi-source data fused SAR sea surface significant wave height deep learning inversion method, and belongs to the technical field of remote sensing ocean, and the method specifically comprises the steps: preprocessing buoy data, and obtaining sea wave significant wave height data observed by a buoy; acquiring dual-polarization single-view complex SAR data collected in an interference wide-width mode; preprocessing the SAR data, and taking significant wave height data obtained by buoy observation as a label of the SAR data; acquiring auxiliary data corresponding to the SAR data, wherein the auxiliary data comprises wind speed, wind direction data, rainfall data and OCN data; and constructing a multi-source data-fused SAR significant wave high-depth learning inversion model, training the multi-source data-fused SAR significant wave high-depth learning inversion model, and testing and verifying the trained model by using the test set and the verification set to obtain a final significant wave height inversion result. According to the method, the SAR data inversion capability of the model is improved.

The invention discloses a LoRa-based ship environment multi-parameter intelligent wireless monitoring system, which is characterized in that environment state data and ship operation auxiliary data are acquired through a data acquisition module, and a data processing module is used for preprocessing and standardizing the acquired data to obtain a standardized data frame; the environment safety quantification module calculates a single parameter safety score, a comprehensive safety index and a safety situation level SL, the intelligent LoRa communication module dynamically selects a communication strategy according to the SL and the current link quality and generates and sends a safety data packet, the LoRa gateway receives the safety data packet, redundancy filtering is carried out, receiving metadata is added, a final data record is formed and uploaded, and the LoRa gateway sends the final data record to the intelligent LoRa communication module. And the data center performs visual display and data storage on the final data record, and generates a network optimization instruction based on the network performance data, so that the problem that transmission reliability, real-time performance and low power consumption are difficult to consider in ship environment monitoring is solved, and the intelligent level of ship safety management is improved.

The invention relates to the technical field of GNSS-R remote sensing, in particular to a satellite-borne GNSS-R soil humidity fusion inversion method and system based on machine learning, and the method comprises the steps: obtaining multi-source data composed of satellite-borne GNSS-R internal feature value data and SMAP external auxiliary data; the method comprises the following steps: performing preprocessing based on acquired multi-source data, taking a matched data set as input to construct a machine learning inversion model, performing track point soil humidity inversion by using an optimized inversion model, performing grid point fixed weight fusion according to a track point soil humidity inversion value, calculating the weight of each GNSS system by using an improved self-adaptive CRITIC weight method, and calculating the weight of each GNSS system according to the weight of each GNSS system. And result verification and application are carried out based on the soil humidity inversion value of the grid point, weight-fixed fusion is carried out on the characteristic value of each GNSS system track point in each grid, abnormal value interference is effectively resisted, and the output continuous grid data can be directly adapted to agricultural irrigation zoning, hydrological model input, climate research and other business scenes.

The invention relates to the technical field of plastic extrusion molding control, and particularly discloses a self-adaptive energy efficiency steady-state control method and system in a plastic extrusion molding process, and the method comprises the steps: collecting multi-dimensional data of the plastic extrusion molding process, the multi-dimensional data comprises process data representing the current production state, aging characteristic data representing the equipment health degree and auxiliary data; and performing preprocessing and feature fusion on the multi-dimensional data, calculating to obtain an equipment aging comprehensive index, constructing an aging trend prediction model based on a long-short-term memory network, and outputting an aging trend prediction value and aging rates of the screw and the machine barrel. According to the method, multi-dimensional data are collected in real time, the equipment aging comprehensive index and trend prediction model is constructed, the multivariable coupling coefficient and the multi-objective optimization function are dynamically corrected, and self-adaptive energy efficiency steady-state control under the equipment aging condition is achieved.

The invention provides a cultivated land identification method, system and device based on modal fusion and a medium, and belongs to the technical field of cultivated land identification. The method comprises the following steps: acquiring image data of to-be-identified cultivated land through an unmanned aerial vehicle, and acquiring GIS vector data and historical time sequence images of the to-be-identified cultivated land to form a basic data set; performing correction, splicing and segmentation processing on image data in the basic data set, performing unified coordinate system processing on GIS vector data in the basic data set, and calculating field piece regularity; processing the basic data set by using a single-mode visual perception model, outputting a preliminary detection result containing a probability graph and boundary information, correcting the preliminary detection result by using a multi-mode logical reasoning module in combination with auxiliary data, and generating an identification result; and carrying out visualization and vectorization processing on the identification result to generate visual data, and displaying the visual data. According to the invention, through multi-modal fusion and multi-dimensional processing, the precision and reliability of cultivated land identification are significantly improved.

A device to process data associated with a surgical event of a surgery may include a processor. The processor may be configured to receive multiple data streams during the surgical event. The processor may be configured to select a primary data stream based on a surgical data interface via which the primary data stream is received. The processor may be configured to select a secondary data stream based on a surgical data interface via which the second data stream is received. The processor may be configured to identify the surgical data interfaces. The processor may be configured to generate situational data associated with the primary data stream based on the secondary data stream. The situational data may indicate a medical decision-making factor of the surgical event. The primary data stream and the situational data may be sent during the surgical event.

The invention relates to a soil erosion amount dynamic monitoring method and system based on multi-source remote sensing data fusion. The method comprises the following steps: acquiring multi-source remote sensing data and auxiliary data, and performing geometric fine correction and atmospheric correction on an image based on high-resolution digital elevation data to obtain a data set with consistent space; performing inversion based on the data set to obtain a corrected terrain factor and a water and soil conservation measure factor; generating a time sequence rainfall erosivity factor and a time sequence vegetation coverage factor in combination with the multi-temporal satellite rainfall data; and calculating a preliminary soil erosion modulus in combination with the soil type map, and correcting by using multi-stage high-resolution digital elevation data to obtain a dynamic monitoring result of the soil erosion amount. By adopting the method, the problem of multi-source data fusion matching can be solved, and the factor inversion precision is improved, so that the accuracy of a dynamic monitoring result of the soil erosion amount is improved.

A wireless device receives, from a base station, a system information block (SIB) indicating time information for when a cell, provided via non-terrestrial network, stops serving an area. The wireless device receives, from a location management function (LMF), a provide assistance data message, for a multi-round transmission time (RTT) positioning, indicating at least one downlink positioning reference signal (DL PRS) resource. The wireless device starts a measurement period for measuring user-equipment (UE) reception-transmission (Rx−Tx) time difference measurements. The wireless device initiates, based on the time information indicated by the SIB, a satellite switch, wherein the satellite switch is to switch satellites without changing a physical cell identifier (PCI). In response to the satellite switch occurring during the measurement period, the wireless device suspends, during an interruption time for the satellite switch, measuring the at least one DL PRS resource.

The invention provides a multi-modal data acquisition and intelligent analysis application method based on AI glasses. The method comprises the steps that the AI glasses collect text data through a camera and an OCR assembly, collect image video data through a binocular camera and an infrared sensor, collect audio data through an array microphone, and collect auxiliary data through an I MU and an eye movement tracking sensor; according to the AI glasses, a local lightweight AI model is used for basic analysis, and complex tasks are uploaded to a cloud large model for cooperative processing, including information summarization, deep mining and knowledge extraction; the AI glasses realize real-time pushing, scene assistance and content generation and output through an information application module according to an analysis result; according to the AI glasses, an end-cloud collaboration module and a federated learning framework are adopted, a local cloud processing strategy is dynamically adjusted, only feature parameters are uploaded, and it is ensured that original data are locally reserved.

A method includes ingesting by a set of computing nodes of a parallelized database, a dataset for storage therein. The method further includes formatting, by a lead computing node of the set, the dataset in a primary data format to produce a primary data formatted dataset, and storing the primary data formatted dataset in system state data. The method further includes receiving, by a first computing node of the set, a query request involving the dataset and a first data function, determining that the first data function triggers a first data conversion optimization, accessing the primary data formatted dataset from the system state data, converting the primary data formatted dataset from the primary data format to a secondary data format to produce a secondary data formatted dataset, and processing the secondary data formatted dataset in accordance with the first data function to produce a first function output.

The invention discloses a classification model privacy disclosure risk black box detection method and device, and relates to the technical field of machine learning security, and the method comprises the steps: constructing an auxiliary data set; generating an auxiliary model covering different privacy risk levels; training a shadow model and a black box member reasoning attack model corresponding to the shadow model; evaluating the privacy risk score of each auxiliary model by using the attack model, and labeling a high-risk / low-risk label; calculating a correction prediction entropy difference, and screening the first k samples with the maximum difference to form a query data set; extracting a prediction result of each auxiliary model on the query set, calculating and correcting a prediction entropy and a mean value, a variance, a kurtosis and a skewness thereof, and splicing into a k + 4-dimensional risk feature vector; training a risk detection classifier; similar features of the model to be detected are extracted and input into the classifier, and the classifier performs five-level privacy disclosure risk level mapping according to the final risk probability prediction value and outputs the five-level privacy disclosure risk level mapping. The method is mainly applied to risk assessment of classification models in high-privacy sensitive fields such as financial credit investigation and medical diagnosis.

The invention discloses a multi-mode perception fusion machine vision cooperative control system and method, and relates to the technical field of vision cooperative control, and the system comprises a dynamic positioning module which is used for collecting the multi-dimensional data of an unmanned plane and an unmanned vehicle, the multi-dimensional data specifically comprises bandwidth signal data, visual image data, geomagnetic intensity data and auxiliary data, relative positioning between the unmanned aerial vehicle and the unmanned vehicle is achieved according to the collected data, and real-time position data of the unmanned aerial vehicle and the unmanned vehicle are obtained and transmitted; through accurate positioning and adjustment, the unmanned aerial vehicle can reach a designated position more accurately, distribution failure caused by positioning deviation is avoided, the success rate of logistics distribution is improved, when the similarity is high, the unmanned aerial vehicle directly returns to a safe height without adjustment, continuous flight of the unmanned aerial vehicle in an unsafe environment is effectively avoided, and the safety of logistics distribution is improved. The safety risks of collision, falling and the like are reduced, and the safety of the unmanned aerial vehicle and the container is protected.

The invention discloses a data desensitization display method and device. The method comprises the following steps: in response to a display request of to-be-displayed data sent by a target object, obtaining a biological feature vector of the target object at the current moment; target data is obtained, and the target data is obtained after the to-be-displayed data is encrypted; after the biological feature vector of the target object at the current moment is successfully authenticated, determining an encryption seed of the target object according to the biological feature vector of the target object at the current moment and pre-stored auxiliary data; generating a private key based on the encryption seed; and decrypting the target data by using the private key, and displaying the to-be-displayed data obtained after decryption. The method provided by the invention at least solves the technical problem that the display mode is single due to the fact that the data is displayed in a role-based access control mode in related technologies.

The present invention belongs to the technical field of federated learning. Disclosed are an efficient heterogeneous federated learning method and system based on hybrid distillation, and a device and a medium. The method comprises: a server initializing a global model parameter, and setting a preset total number of training rounds and the number of clients participating in each round of training; in each client, loading a corresponding local dataset, executing random transformation on the local dataset, and generating client distillation data; sampling several sub-networks from an original network of each client, training each sub-network on the client distillation data to obtain an updated local model parameter, and uploading the local model parameter to the server; and the server receiving the local model parameter, and then, in combination with a preset auxiliary dataset, performing server distillation, so as to obtain an updated global model parameter, and sending same to the client. The technical solution of the present invention can overcome the heterogeneity issue in federated learning, and can also improve the communication efficiency and robustness against attacks while avoiding privacy leakage.

The invention provides a cable terminal assembly and a cable terminal abnormal heating monitoring system, and relates to the technical field of power cables, and the method comprises the steps: collecting the operation data and environment auxiliary data of a conductor connection point, an insulating layer, a shielding layer and a grounding terminal in a preset monitoring period, and carrying out the time alignment processing, and obtaining the standardized multi-dimensional time series data. Time domain features are calculated, frequency domain features are extracted through fast Fourier transform, and the time domain features and the frequency domain features form single-dimensional time sequence features. Constructing a data association matrix, screening strong association data pairs according to Pearson's correlation coefficients and mutual information values, and calculating dynamic covariance by adopting a sliding window method to form cross-dimension association features. And splicing the two types of features and performing dimension reduction to obtain fusion feature vectors, splicing the fusion feature vectors of the current monitoring period and the previous monitoring period and the next monitoring period into a time sequence input sequence, and inputting the time sequence input sequence into an improved LSTM model to output an abnormal probability value, thereby realizing abnormal heating monitoring of the cable terminal.

The invention discloses a remote sensing time series data analysis method and system considering geographic object space-time correlation, and belongs to the technical field of data analysis. The method comprises the following steps: acquiring and preprocessing a multi-source multi-temporal remote sensing image and auxiliary data to generate a consistent time sequence data stack; dividing the image into voxels in a three-dimensional time-space domain, carrying out adjacent tracking, identifying a geographic process object with an evolution behavior, and recording the attribute of the geographic process object; judging a space-time topological relation between geographic process objects, and generating a unified space-time relation table through composite reasoning; constructing a geographic process object space-time diagram model by taking the geographic process object as a node and the space-time relationship as an edge, and extracting and standardizing attribute features of the node and the edge; a graph convolutional network and a topology dynamic mechanism are used for joint modeling, and the importance index of each node is calculated. According to the method, closed-loop analysis from data processing, relation modeling and importance evaluation to decision support is realized, and the interpretability of remote sensing time sequence change detection and decision effectiveness are improved.

The invention relates to a spacecraft maneuver detection method based on TLE data and kernel density estimation. The invention relates to the technical field of spacecraft maneuver detection. The method comprises the following steps: acquiring and inputting satellite historical TLE data; gaussian filtering denoising is carried out on the input data; based on the de-noised data, propagation forecasting is carried out through an SGP4 model; constructing a joint residual sequence, and carrying out error distribution modeling and sample scoring; determining a maneuvering threshold value, and judging to obtain abnormal data; and aggregating abnormal data and carrying out maneuvering identification. The method can still realize accurate and stable track maneuver automatic detection under the constraint conditions of unstable TLE data, complex error distribution, no external auxiliary data and the like. The method has good adaptability, robustness and interpretability, and is a track anomaly detection technical scheme which is complete in structure, rigorous in logic and capable of achieving engineering landing.

The invention provides a renewable energy power system supply-transmission-demand-storage collaborative optimization configuration method, and relates to the technical field of smart energy, and the method comprises the steps: obtaining basic data of a plurality of provinces and cities, including renewable energy resource data and auxiliary data; establishing a collaboration degree calculation model, respectively calculating the collaboration degree of each subsystem, calculating the overall collaboration degree based on the collaboration degree of each subsystem, and constructing a subsystem collaboration strength matrix to quantify the coupling relationship among the subsystems; establishing a multi-sub-population collaborative optimization architecture, and respectively generating an initial solution set of each sub-population by adopting a collaborative degree-oriented initialization strategy; adopting an improved sparrow optimization algorithm to carry out parallel optimization on each sub-population, and obtaining an optimal configuration result when a convergence condition is met; and outputting a supply-transmission-demand-storage collaborative optimization configuration scheme of the renewable energy power system according to the optimal configuration result. The overall operation efficiency and economical efficiency of the system can be improved.

To provide a program production system that measures a delay time of a video image with respect to audio and auxiliary data by a simple method so as to realize synchronization among the video image, the audio and the auxiliary data generated at the same time.SOLUTION: The video time difference calculating unit 11 - 1 of the video delay measuring apparatus 2 extracts the time stamp V1 from the video RTP packet VTS1 and generates the video RTP packet V2 including the timing information. The video RTP packet V2 is transmitted to the video delay time difference measuring path. A video time difference calculation section 11-1 extracts a time stamp V3 from a video RTP packet VTS2 to obtain a video time difference VT. The audio time difference calculator 11-2 obtains the audio time difference ST, and the ancillary data time difference calculator 11 - 3 obtains the ancillary data time difference AT. The delay time calculating unit 13 subtracts the audio time difference ST from the video time difference VT to obtain the audio delay time SD, and subtracts the ancillary data time difference AT from the video time difference VT to obtain the ancillary data delay time AD.SELECTED DRAWING: Figure 5

According to some embodiments, a method is performed by a wireless device. The method comprises receiving a configuration from a network node. The configuration comprises an indication of assistance data the wireless device is to collect to be used with a radio link failure machine learning model. The method further comprises collecting the assistance information according to the received configuration and transmitting a report including the collected assistance information to the network node.

The invention relates to the technical field of product oil management, and discloses a product oil purchase-sale-stock multi-modal data analysis method and system. The method comprises the following steps: acquiring real-time inventory data, transportation state data and sales terminal data in a product oil supply chain to form a multi-modal data set; performing data cleaning on the data, and extracting key inventory characteristics to generate a cleaned data subset; obtaining associated historical purchase data, regional demand prediction data and market price fluctuation data based on the gas station identification information to form a multi-dimensional auxiliary data set; inputting the features into an inventory feature extraction model, generating a multi-feature vector set, and fusing the multi-feature vector set; inputting the fused feature vectors into an inventory state evaluation model set, and outputting inventory early warning level and abnormal type information; and finally, a dynamic allocation scheme is generated by combining real-time inventory data and the like. According to the method, multi-modal data are integrated, so that the comprehensiveness and accuracy of data analysis are improved, and the purchase-sales-stock management of the product oil is assisted to be more suitable for market dynamics.

The invention discloses a learnable radiology report generation method based on visual text alignment and fusion of retrieval enhancement. The method comprises the steps of collecting and respectively constructing a model training data set and an auxiliary data set, then constructing a visual text alignment and fusion model based on retrieval enhancement, and inputting the model training data set and the auxiliary data set into the visual text alignment and fusion model based on retrieval enhancement together for training. Constructing an inference model according to the trained visual text alignment and fusion model based on retrieval enhancement; and inputting the to-be-detected medical image and the auxiliary data set into the reasoning model for processing to obtain a radiology report corresponding to the to-be-detected medical image. According to the method, retrieval correlation is enhanced, meanwhile, a fine-grained vision-text alignment and fusion method is adopted to align and fuse features, and the problem that fine-grained region-sentence alignment is difficult due to weak supervision of an image report level in the medical report generation process is solved.

The invention discloses a differential measurement-based satellite-borne synthetic aperture microwave radiometer imaging correction method, which comprises the following steps of: respectively selecting a reference area and a correction area in a global range, and respectively intercepting scientific data and auxiliary data in a specific time period in the reference area and the correction area based on load normal earth observation data; averaging the visibility functions intercepted in the reference area to obtain a reference visibility function; generating a reference brightness temperature and a correction brightness temperature through a simulation model by using the auxiliary data intercepted in the reference area and the correction area respectively; performing difference and inversion processing on the normal earth observation visibility function to be subjected to imaging processing by using the reference visibility function to obtain a difference inversion brightness temperature; calculating a correction coefficient by using the reference visibility function, the reference brightness temperature, the visibility function intercepted in the correction area and the correction brightness temperature; and correcting and compensating the differential inversion brightness temperature by using the correction coefficient and the reference brightness temperature to obtain the final inversion brightness temperature.

The invention discloses a self-adaptive cooperative control method for a distributed power supply of a power grid, which relates to the technical field of power supply cooperative control and comprises the following steps of: acquiring real-time operation data and auxiliary data, constructing an operation scene recognition model, generating a control strategy mapping relation and outputting a power regulation suggestion by utilizing a fusion optimization mechanism. The method comprises the steps of collecting and normalizing multi-source data, dividing an adjustment instruction into edge sub-tasks, executing the edge sub-tasks at edge nodes, recording and feeding back the edge sub-tasks, collecting controlled operation data, constructing an error evaluation vector and carrying out strategy fine adjustment, and comprises the steps of collecting and normalizing multi-source data to construct an operation scene recognition model, generating a structured control strategy mapping relation based on scene labels, and carrying out strategy fine adjustment on the operation scene recognition model. And outputting a power regulation instruction by using a fusion optimization mechanism, and completing task execution and data feedback at an edge node. Local strategy fine tuning is realized through an error evaluation vector, and finally, whole-process control data is structured and filed, so that a unified control process of scene identification driving, distributed collaborative optimization and edge autonomous execution is realized.

The invention discloses a contact network inspection data consistency verification method applied to 2C inspection, and relates to the technical field of railway automatic inspection. The method comprises the following steps: acquiring a 2C inspection image, and analyzing the 2C inspection image to obtain defect information; matching multi-source auxiliary data according to the defect information, and performing multi-source cross validation on the defect information based on the multi-source auxiliary data; firstly, defect information is obtained by analyzing a 2C inspection image, then multi-source auxiliary data with consistent time and space are matched for independent defect detection, confidence coefficients of consistent detection results are subjected to weighted fusion to obtain auxiliary confidence coefficients, and finally the auxiliary confidence coefficients and original confidence coefficients are subjected to weighted fusion to update the defect information; according to the method, through multi-source data collaborative verification, the problem of insufficient reliability caused by influence of a collection environment and an equipment state on a single 2C inspection image is solved, the probability of missing judgment and misjudgment of defects is effectively reduced, the accuracy of defect type recognition and the reasonability of confidence evaluation are improved, and more accurate data support is provided for overhead line system maintenance work.

The invention relates to a carbon sink quantification method and system based on multi-source data fusion and carbon flux large model inversion, and belongs to the technical field of meteorological monitoring, and the method comprises the steps: obtaining multi-source data of a target area and historical inversion data of a carbon flux large model; based on the ground carbon flux detection data and the historical inversion data of the carbon flux large model, performing dynamic calibration and parameter optimization on the pre-trained carbon flux inversion large model in the target region to obtain a region optimization model; inputting carbon-related data and meteorological and soil auxiliary data inverted by satellite remote sensing into the regional optimization model, and performing inversion to generate space-time continuous lattice point carbon flux data of the target region; based on the space-time continuous lattice point carbon flux data of the target region and the lattice point area of the target region, determining the actual carbon sink amount of the target region; and determining the net carbon sink increment of the target area based on the actual carbon sink amount of the target area, the carbon sink amount of the target area under the baseline scene and the carbon leakage evaluation value, thereby improving the carbon sink monitoring effect.

The invention relates to an IPv6 lightweight network equipment surveying and mapping method based on behavior fingerprints and hop count verification. Comprising the following steps: collecting metadata of original traffic in an IPv6 network; selecting a multi-dimensional feature vector from the metadata, processing the multi-dimensional feature vector, and generating a unique and stable fingerprint identifier for each device; sending a detection packet to the target device, calculating a forward path hop count, estimating a return path hop count of a response packet to obtain a reverse path hop count, and detecting the path by comparing the forward path hop count with the reverse path hop count; taking the fingerprint identifier as a topological node, taking a bidirectional path hop count detection value as a topological edge weight between devices, constructing an initial topological skeleton through a minimum spanning tree algorithm, and performing clustering optimization in combination with auxiliary data to obtain a network topological structure; and automatically deducing a hierarchical relationship according to the fingerprint identifier, the node hop count, the connection closeness and the behavior consistency, and generating an interactive logic topological graph for displaying abnormal nodes and associated paths.