388 results about "Data heterogeneity" patented technology

The invention discloses a directional detection method and system for a power supply shell based on a multi-source heterogeneous sensor, belongs to the field of material characteristic detection, and aims to solve the problems of single dimension, weak anti-interference performance, data isomerism and poor adaptability in traditional industrial detection. Through systematic innovation of multi-source sensor collaboration, dynamic mode control, cross-modal data fusion and self-learning optimization, a high-precision, high-efficiency and high-reliability solution is provided for the precision manufacturing field, and an optical, infrared and ultrasonic multi-modal collaborative detection framework is constructed; the optical unit realizes accurate capture of surface texture and morphology through polarized light imaging and 3D structured light scanning, the infrared thermal imaging unit analyzes the heat conduction characteristic of a material, and the ultrasonic array analyzes internal structure defects to form a surface-material-internal full-dimension detection capability; and nine types of defects such as scratches, pits, weld marks, bubbles, sink marks, material layering, stress cracking, thermal stress abnormity and material pollution are covered.

The invention provides an intelligent traffic vehicle behavior modeling method and system based on collaborative learning, and relates to the technical field of unmanned driving, and the method comprises the steps: obtaining traffic data in a target region; performing data calibration processing according to the traffic data to obtain a collaborative perception data set; a vehicle space-time trajectory function is generated according to the collaborative perception data set, and a conflict domain space-time distribution diagram is obtained; according to the vehicle space-time trajectory function and the conflict domain space-time distribution diagram, probability distribution characteristics of the vehicle driving intention and a multi-workshop competition cooperation relation matrix are extracted, and a multi-dimensional interaction characteristic vector is obtained; and behavior modeling is carried out according to the multi-dimensional interaction feature vectors, and a vehicle behavior model representing interaction behavior rules and decision logic between vehicles is obtained. According to the method, through multi-vehicle cooperative sensing data calibration and dynamic fusion of road geometric constraints, the problems of single-vehicle sensor shielding and cross-vehicle data heterogeneity are effectively solved, and the global consistency of vehicle position and motion state estimation is remarkably improved.

The invention discloses a high-resolution remote sensing image accurate classification system and method based on deep learning multi-modal fusion, and the method comprises the steps: S1, carrying out the preprocessing and fusion optimization of multi-modal data; S1.1, carrying out the standardization and normalization: carrying out the standardization and normalization of remote sensing data of different modals, and eliminating the influence caused by the difference between the different modals, the difference between the resolution, the spectral range and the like; for optical images, contrast may be enhanced by histogram equalization. The method aims at solving the problems of data heterogeneity, calculation efficiency, over-fitting, difficulty in labeling, real-time performance, interpretability and the like in an existing method by adopting multi-modal data optimization preprocessing, deep fusion model design, automatic labeling and semi-supervised learning, a lightweight model and hardware acceleration technology and a strategy for enhancing interpretability. Through the optimization, the system can maintain high classification precision, improve the calculation efficiency, reduce manual intervention, enhance the transparency and generalization ability of the model, and meet the actual application requirements.

The invention discloses an e-commerce personalized recommendation method based on cross-domain collaborative filtering, and relates to the technical field of e-commerce, and the method comprises the steps: collecting and preprocessing data, including user behavior data and commodity attribute data, of different fields of an e-commerce platform; materializing the collected data in different fields, and integrating the data into a structured e-commerce knowledge graph; and constructing a user-based cross-domain collaborative filtering model based on the behavior data of the user in the multiple fields, and predicting commodities in which the user is interested. By integrating data in different fields and constructing user portraits and commodity portraits, the problem of inaccurate recommendation caused by data heterogeneity in a traditional recommendation system is effectively solved, interests and preferences of users in different fields can be more accurately captured through cross-domain data association analysis, and the recommendation efficiency is improved. Therefore, personalized recommendation better meeting the requirements of the user is provided for the user, the recommendation accuracy and satisfaction degree are remarkably improved, and the user stickiness and the platform competitiveness are enhanced.

The invention discloses an intelligent garden management method and system based on big data, and belongs to the technical field of garden management. Microclimate region division is carried out on a garden through a clustering algorithm, and the region representative weight of each sensor is calculated in combination with a data heterogeneity response factor and a microtopography interference factor; weighted fusion of sensor data is realized, and a more representative environment estimation value is generated; according to the method, prediction and anomaly detection are carried out on a plant growth environment trend based on time sequence analysis, a garden regulation and control instruction is automatically generated, actual plant state feedback is introduced to realize dynamic optimization and adjustment of a weight, and the method effectively improves the accuracy of environment perception and the intelligent level of decision response, and improves the user experience. And resource misuse and plant health risks caused by information distortion are obviously reduced.

The invention discloses a federated learning client selection method based on reinforcement learning and a federated learning system. The method comprises the following steps: a client performs local detection training and collects loss, delay data and other related data; the central server divides the clients by using mean shift clustering based on data distribution of the clients, and regards each cluster as an independent agent; the intelligent agent dynamically selects a client based on the multi-dimensional state information, and optimization selection is carried out by adopting an exploration strategy; the client uploads model update after local training; and the central server carries out aggregation updating, and the intelligent agent optimizes client selection through a multi-target reward function according to a feedback adjustment strategy, maximizes a model convergence speed and balances communication and calculation overhead. By using the method and the system of the invention, under challenged environments of data heterogeneity, computing resource limitation, communication delay and the like, the client can be intelligently selected, the training efficiency of federated learning is optimized, and the performance and generalization ability of a global model are improved.

The invention discloses an agricultural data intelligent contract cross-domain sharing method based on a master-slave chain, and particularly relates to the technical field of agricultural data sharing. Edge computing nodes are deployed through a slave chain, multi-source heterogeneous agricultural data are collected, a dynamic trust level model is constructed, credibility evaluation and screening are performed on the data, and highly-credible data are synchronized to a main chain for sharing; the low-credibility data is reserved in slave chain verification; the main chain realizes transparency of a data sharing rule based on a smart contract, constructs a dynamic risk prediction model in combination with a space-time convolutional network, performs probability distribution prediction on a crop abnormal coefficient, optimizes a resource allocation scheme, and triggers hierarchical early warning by using the smart contract to realize efficient allocation and dynamic optimization of cross-regional resources; the problems of sharing obstacles, insufficient data credibility and inaccurate resource allocation caused by agricultural data isomerism in the prior art are effectively solved.

The invention relates to the technical field of petroleum and natural gas engineering, and discloses an oil and gas reservoir optimized mining method based on multi-modal data, which comprises the following steps: constructing an oil and gas reservoir multi-modal data acquisition system, seismic wave field data, logging interpretation data, production dynamic data, micro-seismic monitoring data, underground temperature and pressure time sequence data and shaft structure parameter data are obtained through the acquisition system; and performing space-time alignment processing on the acquired multi-modal data, establishing a unified geological coordinate system and a time reference, and eliminating data isomerism caused by different acquisition frequencies and spatial resolutions. A six-dimensional heterogeneous data acquisition system covering a seismic wave field, well logging interpretation, production dynamics, micro-seismic monitoring, an underground temperature and pressure time sequence and shaft structure parameters is constructed, so that the depiction precision of a reservoir porosity field, a permeability field, a saturation field and a pressure field is essentially improved.

The invention discloses a drug risk monitoring method based on multi-source data fusion, and relates to the technical field of data analysis. Through multi-source data fusion and processing, the problems of data isomerism and information islands are solved, a comprehensive and high-quality data basis is provided for drug risk monitoring, a weighted collaborative filtering algorithm and a Bayesian network are adopted, potential risk signals are accurately mined, real-time dynamic monitoring and early warning of drug risks are achieved, and the drug risk monitoring and early warning efficiency is improved. The timeliness and the reliability of monitoring are obviously improved; meanwhile, the real-time stream processing framework is used for carrying out incremental updating on newly-added data, dynamically adjusting a risk assessment result, and carrying out automatic early warning when a risk value exceeds a standard, so that the contradiction between a complex process and a rapid early warning demand is effectively solved, the efficiency and accuracy of drug safety supervision are improved, and powerful support is provided for guaranteeing the public drug safety.

The invention relates to the technical field of 5G-R network detection and monitoring, discloses a 5G-R network situation awareness method based on distributed monitoring and multi-source information fusion, and aims at solving the problems that a 5G-R network is large in scale, high in dynamic performance and complex in data isomerism. Multi-source data of a core network, a wireless network, special equipment, interface monitoring, detection equipment, a GIS and the like are collected in real time; according to the method, technologies such as Pearson's correlation coefficients, FP-Growth, Bayesian analysis, a time sequence point process, a Hookes theory, a Gaussian mixture model, a decision tree, S-ARIMA, Boxplot, N-sigma, iForest, regression analysis, a neural network and KL divergence are combined to realize multi-source data fusion, intelligent analysis and visual perception, including network alarm, application quality, operation and maintenance and resource management. According to the method, the comprehensiveness, the real-time performance, the fault diagnosis accuracy and the operation and maintenance efficiency of the 5G-R network are improved, and the requirements of low delay and high reliability of a railway scene are met.

The invention discloses a personalized federated learning method based on knowledge fusion distillation and a storage medium, and belongs to the technical field of personalized federated learning, and the method comprises the steps: a server carries out the pre-training and distributes a diffusion model to each client, and generates a local synthesis data set, initializing a global model as a student model of a client to perform knowledge fusion distillation training, and guiding the student model training by the teacher model optimized in the last round; and after training is completed, the client uploads student model parameters to the server for federal aggregation to update the global model, and meanwhile, the personalized feature extraction capability and reliability of the next round of teacher model are enhanced by using synthetic data, so that a closed-loop learning framework of global cooperation and local personalized collaborative optimization is formed. According to the method, the problems of client model drifting, performance attenuation and convergence rate slowing caused by data heterogeneity can be solved.

The invention discloses a truss structure wind-induced dynamic response prediction method and system based on physical enhancement. The method comprises the following steps: carrying out feature extraction and alignment fusion on input data containing condition parameters and wind speed time sequence data by utilizing a long short-term memory network and a physical enhancement attention mechanism; extracting multi-scale features from the fusion features through expansion convolution, and performing weighted aggregation on the multi-scale features; the physical priori knowledge of structural vibration is fused into position coding and a self-attention mechanism so as to carry out response prediction; and integrating physical model information of the truss structure and a dynamic control equation into a loss function, and calculating physical information residual loss so as to improve the physical interpretability of a prediction result. According to the method, data heterogeneity can be eliminated, complementary information can be fused, the multi-scale characteristic of wind-induced response is coped with, the accuracy and efficiency of wind-induced dynamic response prediction of the truss structure are effectively improved, and the physical interpretability and generalization ability are enhanced.

The invention discloses a federal large model knowledge collaborative training method supporting multi-modal heterogeneous clients, which comprises the following steps: each client receives a model initialization parameter issued by a central server, and applies adaptive differential privacy noise to independently train a heterogeneous lightweight model based on local private data; updating the model to which the noise is applied and uploading a modal identifier of the model to a central server side; after model updating and modal identification of each client are received, based on a modal perception weighted consensus fusion mechanism, knowledge of each client is fused to update a global large model; and the central server side issues the updated presentation layer parameters of the global large model to the client side for initialization of the next round of local training. According to the method, on the premise that a public data set or specific task setting is not needed, comprehensive compatibility of data isomerism, client dynamic participation, model diversity and privacy protection requirements is achieved, and the adaptability, stability and knowledge utilization efficiency of large model federation training are remarkably improved.

The invention relates to the technical field of injection molding production, in particular to an injection molding production real-time monitoring and intelligent scheduling management system based on the Internet of Things, which comprises a data acquisition module, a data fusion module, an anomaly detection module, a self-adaptive scheduling module and a feedback and learning module, the data acquisition module is used for acquiring running states, environmental parameters and logistics information of all equipment in the injection molding production process in real time; the data fusion module is used for carrying out preprocessing, standardization and time sequence alignment on data from different devices and different communication protocols; and the abnormal detection module is used for analyzing the fused data in real time by adopting an abnormal detection method based on a statistical threshold value and machine learning. In the invention, a full-process closed-loop system comprising a data acquisition module, a data fusion module, an abnormal detection module, a self-adaptive scheduling module, a feedback module, an online learning module and the like is constructed; the problems that in an existing system, data are heterogeneous, data quality is not high, anomaly detection lags behind, and scheduling decision is not intelligent are effectively solved.

The invention belongs to the technical field of asynchronous federated learning, and discloses a data isomerism-oriented knowledge alignment asynchronous federated learning method. According to the method, a data quality perception aggregation strategy is introduced, and a knowledge distillation mechanism based on the old degree is combined, so that a global model is subjected to balanced training on heterogeneous data of different devices, and the generalization ability of the model is improved. Meanwhile, a self-adaptive learning rate adjustment mechanism based on aggregation frequency and weight is designed, and it is ensured that contribution of different devices to the global model is more fair. According to the method, the training deviation in asynchronous federated learning is effectively relieved, the accuracy and stability of a global model are improved, and the method has a considerable application value for a real federated environment.

The invention provides a geological reservoir fracture segmentation method fusing a 3DU-Net graph attention mechanism and graph theory connectivity detection and a system thereof, and aims at solving the technical problems of multi-modal data registration, 3D U-Net graph attention feature extraction, graph theory connectivity detection and the like. The problem of insufficient geological reservoir fracture segmentation precision caused by data heterogeneity and connectivity abnormity is effectively solved, high-precision segmentation of a complex geological reservoir fracture network is realized through accurate alignment of multi-modal data and a graph attention feature extraction and graph theory connectivity detection technology based on 3D U-Net, and the method has the advantages of high precision and high efficiency. The problems of local misjudgment and abnormal connectivity caused by the fact that a traditional method only depends on amplitude or texture features are effectively solved, and therefore segmentation precision and robustness are remarkably improved.

The invention discloses a geotechnical engineering investigation and analysis method and system based on multi-source data fusion, and belongs to the technical field of geological prospecting, and the method comprises the steps: collecting multi-source heterogeneous data of drilling holes in a karst area, and generating a comprehensive feature vector of the drilling holes; according to the drilling comprehensive feature vector, establishing a graph attention network, and generating an update feature; according to the updated features, generating an exploration prediction result of geotechnical engineering; establishing a dynamic adjustment model, and generating a model weight set value of the graph attention network; adjusting the model weight of the graph attention network according to the model weight set value of the graph attention network; according to the method, the multi-source heterogeneous data of the drill holes are converted into the comprehensive feature vectors of the drill holes, so that the problems of data isomerism and information islands are solved; in combination with a dynamic graph attention network, the model weight is optimized in real time, and the accuracy and adaptivity of karst cave distribution prediction under the complex karst geological condition are remarkably improved; and meanwhile, continuous iterative optimization can be carried out on the model, and the geotechnical engineering investigation accuracy of the karst area is improved.

The invention provides a photovoltaic prediction method, which comprises the following steps: data preprocessing: aligning time sequences of historical power generation data and meteorological data through a time sequence warping strategy, constructing an equipment health index, and inputting the equipment health index into an input layer; designing a space-time embedding layer, and performing space-time coding on data transmitted by the input layer and all meteorological data branches; defining a neural network module, designing a local-global attention layer, designing a network layer, and injecting meteorological condition constraints through a physical regularization item; a joint attention fusion layer is designed, and key features are effectively integrated; an output layer is designed, and feature integration, linear transformation, activation function setting, physical regularization and final photovoltaic power prediction generation and output are completed; and offline prediction is realized through edge optimization. The invention provides a photovoltaic intelligent prediction method fusing time-space sparse attention and a time sequence neural network, and solves the problems of insufficient data isomerism, calculation efficiency, physical interpretability, dynamic environment adaptability and the like in the prior art.

The invention discloses a personalized federated learning method based on a kernel distance between clients, a framework and an application, and belongs to federated learning and an application technology thereof. The method aims at double challenges of data isomerism and privacy protection, and is based on a federated learning framework of a conditional policy network and differential privacy. The CPN adaptively balances the conflict between the global consistency and the local personalized characteristics by dynamically generating the weights of the personalized characteristics and the global characteristics, so that the influence caused by data isomerism is effectively relieved. Meanwhile, the differential privacy technology protects the sensitive data characteristics of the client and reduces the risk of privacy disclosure by injecting Gaussian noise in gradient updating. The personalized federal learning method provided by the invention not only is superior to the existing mainstream method in global model performance and fairness between clients, but also realizes good balance between privacy protection and model performance.

The invention discloses a federated learning method based on partial label mask weighted distillation, which comprises the following steps of: constructing a federated learning system comprising a central server and a plurality of clients, and broadcasting a global model to each client by the central server; the client performs calculation by using the received global model to obtain a teacher model, regards a local model as a student model, performs distillation training on the local model by using local data, and uploads trained model parameters to the central server; and the central server performs federal aggregation on the updated local model parameters, and issues the aggregated and updated local model to each client for next round of local training. According to the method, the client side learns knowledge of the global data set in the teacher model through partial label mask distillation, learning of minority class samples in the local data set is enhanced, learning of the local model on the knowledge of the global data set is enhanced by constructing the teacher model with more complete knowledge, and the learning efficiency of the local model is improved. And the risk of disastrous forgetting caused by data isomerism is reduced.

The invention relates to a medical health big data exploration system, and the system comprises a demand understanding module which is used for converting a data exploration demand into a structured exploration task; the compliance review module is used for performing compliance review on the probing task; the code generation module is used for generating a query code according to the probing task and the target database characteristics; the query execution module is used for querying query result data corresponding to the query code from a target database according to the query code; the query result analysis module is used for performing multi-dimensional analysis on the query result data to obtain a multi-dimensional analysis result; a report generation module; and the result presentation module is used for presenting the exploration report in an interactive mode in a user interface and supporting iterative improvement of the report by providing a feedback mechanism, so that the problems of data isomerism, difficulty in quality evaluation, low exploration efficiency, high professional threshold, insufficient privacy protection and the like in the existing medical data exploration process are solved.

A federated byte latent transformer platform utilizing homomorphically-compressed and encrypted byte-level data. The system integrates dynamic entropy-based patching into federated learning to enable efficient, robust, privacy-preserving collaborative learning across distributed nodes. Client devices convert local data into dynamically sized patches based on entropy thresholds, encrypt these patches, and send them to a central server that processes them without decryption. The system offers improved robustness to input noise, enhanced character-level understanding, and better adaptation to low-resource languages compared to token-based approaches. It enables simultaneous scaling of both patch size and model size while maintaining fixed inference budgets, allowing efficient deployment on resource-constrained devices. These innovations address critical challenges in federated learning: efficiency, robustness to data heterogeneity, and privacy preservation.

The invention belongs to the technical field of electric power automation, and particularly relates to an automatic modeling method of power grid dispatching knowledge based on a large model and a related system.The electric power knowledge is decomposed into an explainable intermediate reasoning path through the thinking chain technology, and adaptive small models are selected according to the path to form a modeling link; a modeling link is represented as a triple form, edges and nodes are complemented, a complete knowledge graph is obtained, problems in the knowledge graph are further decomposed to construct a reasoning link, meanwhile, input data are monitored in real time, newly added entities are associated with the reasoning link, and unified representation and processing of heterogeneous power system data are achieved. Due to the fact that data from different sources are different in format, precision and semantics, according to the method, through thinking chain decomposition and small model link processing, sub-module optimization and dynamic adaptation aiming at different data types are achieved, and precision loss and adaptation difficulty caused by data heterogeneity are effectively solved.

The invention discloses a federated learning method for realizing client selection based on data feature clustering, which comprises the following steps of: firstly, performing singular value decomposition on local data by a client, and combining left singular vectors corresponding to first k singular values as local data features; secondly, performing hierarchical clustering on clients by taking included angles between local data features as similarity measurement standards; and then, constructing a client contribution quantification model, comprehensively considering data quality, sample capacity and selected times, refining contributions of the clients, and selecting h clients with the highest current contribution degree to participate in global training. And finally, during each global training, the server selects h clients from each group to carry out current global training, and the server carries out weighted aggregation to obtain a new global model. According to the method, the data isomerism is effectively relieved, the global model prediction precision is improved, and the problem of low global model precision caused by the data isomerism in a federated learning scene is solved.

The invention provides a big data standardization method and system based on data integration, and the method comprises the steps: converting original data of different data sources into intermediate format data through a dynamic format adapter based on a data conversion mapping rule in combination with a reinforcement learning Schema algorithm; generating a quantum superposition state of each field in the intermediate format data and a node in the knowledge graph by using quantum state coding; solving an optimal mapping node of the field in the knowledge graph based on a quantum superposition state by utilizing a quantum annealing algorithm; the multi-source data is integrated through the dynamic format adapter and analyzed into the intermediate format data, the problem of data heterogeneity is solved, multiple mapping possibilities are reserved at the same time on the basis of the quantum superposition state, and the multi-source data are integrated through the dynamic format adapter and analyzed into the intermediate format data. And a global optimal solution is obtained through rapid parallel search of a quantum annealing algorithm, and the accuracy and efficiency of node standardized mapping are improved.

The invention provides a federated incremental learning method and system based on prompts, and relates to the field of federated incremental learning, and for each incremental learning task in a flow task sequence, the specific steps of performing federated incremental learning are as follows: a global server distributes a latest global model to a plurality of selected clients; based on a local private training sample and a prompt pool, the client optimizes a prompt vector in a local training process by adopting a dynamic prompt selection mechanism based on a key value pair, and guides a model to output a feature with higher discrimination degree; the client uploads the optimized prompt pool and model parameters to a global server; the global server globally fuses the prompt pool and the model parameters, and the fused prompt pool and model parameters form a new global model; according to the method, the problem of data isomerism caused by continuous arrival of new category data and dynamic addition of a new client in the federal increment problem is effectively solved, and the problem of disastrous forgetting of old knowledge is solved.

The invention discloses a multi-type context-aware dialogue recommendation method based on a hybrid expert model. The aim of the invention is fulfilled by a hybrid expert framework comprising a plurality of expert module coordination systems. The knowledge graph expert model is responsible for extracting and coding structured entity relationship information, the dialogue expert model is used for deeply mining key contents and user intentions in dialogues, and the comment experts are used for analyzing user emotion and preference information in commodity comments. In this way, the system can make full use of semantic information of the structured data and the unstructured data, and the problem of semantic gaps caused by data heterogeneity in the prior art is solved. On the basis, a coordination system is introduced to serve as a core module for coordination and integration. The coordination system can dynamically allocate the output of the expert module according to the weights and contributions of different context information to generate a final recommendation result, so that efficient fusion of multiple types of context information is realized, the problems of semantic alignment and inconsistency between different types of data are solved, and the performance of the dialogue recommendation method is improved.

The invention discloses a federated learning training method and system supporting heterogeneous data, belongs to the technical field of distributed machine learning, and is used for solving the technical problem that an existing federated learning technology cannot solve three major problems of data privacy, data heterogeneity and hierarchical communication bottleneck at the same time and lacks an integrated federated learning training framework. The method comprises the following steps that: a working node acquires a local model update quantity and performs quantitative compression based on local data, a historical update record and a latest global model to obtain a compressed update package and uploads the compressed update package to an affiliated edge node; the edge node aggregates the compressed update packet sent by each working node to obtain a regional aggregation update packet; carrying out second quantization compression on the regional aggregation update packet to obtain a final uploading packet, and uploading the final uploading packet to a central server to which the final uploading packet belongs; the central server globally aggregates the final upload packet sent by each edge node to obtain a global model update packet; and optimizing the global model according to the global model updating package to obtain an optimized model.

The invention relates to the technical field of power system frequency modulation control, in particular to a thermal power generating unit collaborative frequency modulation method and system based on block chain federated learning. According to the method, a decentralized P2P network is constructed through a block chain technology, and model parameters are initialized based on a secret sharing mechanism; carrying out local training by adopting an adaptive federal near-end optimization algorithm, measuring data isomerism through KL divergence and dynamically adjusting a regular coefficient; carrying out noise addition on a sample by adopting differential privacy, and carrying out homomorphic encryption to realize parameter encrypted transmission; selecting representative nodes based on a block chain consensus mechanism to execute parameter aggregation in the encryption domain; and multi-unit collaborative frequency modulation is realized through model prediction control. According to the method, the problems of data privacy protection, heterogeneous data adaptation and decentralized cooperative training are solved, and the stability of power grid frequency regulation and control is improved.

The invention discloses a temperature self-adaptive adjustment control method and system for an automobile sensor, and relates to the technical field of automobile sensors, and the temperature self-adaptive adjustment control method for the automobile sensor comprises the following steps: S1, obtaining data; s2, performing classification and label addition on the data to form a data set; s3, extracting core temperature characteristic parameters, and constructing a four-dimensional temperature mapping matrix; s4, introducing an ant colony algorithm to iteratively optimize a temperature dynamic weighting rule, and obtaining an adjustment target and a change trend; s5, generating a regulation control strategy, and constructing a temperature fusion regulation feedback model at the same time; s6, performing iterative verification on the model, and generating a sensor three-dimensional verification report; and S7, performing security judgment on the three-dimensional verification report of the sensor. According to the method, through multi-source sensing data acquisition and segmented labeling preprocessing, temperature local mutation features and long-time time sequence dependence features are captured, and temperature data heterogeneity of traditional single-model feature extraction is broken.