341 results about "Device fingerprint" patented technology

The invention discloses a power grid abnormal flow detection method based on multi-modal data fusion, and the method comprises the steps: collecting the multi-source heterogeneous data of a power grid through an edge calculation node, including the current waveform of an intelligent electric meter, the state variable of an SCADA system, network protocol metadata and an equipment log event; performing space-time alignment preprocessing on the original data, converting an unstructured log into a time sequence by adopting a sliding window mechanism, and eliminating sensor noise through an LSTM auto-encoder; constructing a multi-dimensional feature space which comprises a frequency domain feature, a spatial-temporal feature and a protocol feature, and obtaining a feature vector; the feature vectors are input into a hybrid detection model, the model is composed of an isolated forest algorithm, an improved CNN-LSTM classifier and an information entropy-based rule engine which are connected in parallel, and a dynamic weight fusion strategy is adopted to output an abnormal probability; and when the abnormal probability exceeds a dynamic threshold, triggering a multi-level response mechanism: sending a traffic shaping instruction to the edge device, generating a device fingerprint portrait on the cloud platform, and storing abnormal event features through a block chain.

A distributed intelligent authentication method based on dynamic multi-modal fusion relates to the field of network security, and adopts an alliance chain + DAG hybrid block chain architecture, combines a threshold signature to realize secret key fragment management, and switches among PBFT, Raft and probabilistic algorithms through a dynamic consensus mechanism to improve authentication efficiency. The multi-mode authentication module is based on a dynamic weight distribution algorithm, integrates biological characteristics, behavior analysis, equipment fingerprints and environmental factors, and combines an LSTM-GAN model and a quantum random number driven challenge-response mechanism to realize zero-trust verification under environmental perception. The session management module generates a session key by using a chaotic mapping algorithm. In the aspect of privacy protection, CKKS homomorphic encryption, zero-knowledge proof and attribute-based encryption are fused. According to the method, the block chain technology, the secure multi-party computing technology, the machine learning technology and the quantum cryptography technology are fused, and a high-performance, high-security and strong-privacy-protection distributed authentication solution is provided.

The invention relates to the technical field of data access security, and discloses a container mirror image security management method and system, and the method comprises the steps: constructing a container mirror image, generating a differential encryption key through a strategy center, carrying out the encryption strategy of a kernel dependence layer, a runtime environment layer, an application code layer and a sensitive configuration layer, and forming a hierarchical protection basis. If an access request is triggered, firstly collecting equipment fingerprints and geofence information and evaluating an environmental risk score, verifying access authority and an access scene matching degree through attribute-based encryption, analyzing operation track characteristics in real time, identifying an abnormal mode, and if the three-layer verification is passed, generating a temporary access token; according to the method, access request authority is verified, a temporary access token is matched, key fragments are synthesized, a master key is only temporarily generated in a memory and encrypted and stored, and through combination of a double-layer encryption channel and inner-layer and outer-layer defense, the anti-attack ability of container mirror image transmission is improved, and man-in-the-middle attack and data tampering are effectively coped with.

The embodiment of the invention provides a multi-platform e-commerce order management method and system based on cloud data analysis, and the method comprises the steps: carrying out the analysis and standardization processing of order data through employing a dynamic format conversion channel, constructing a dynamic priority evaluation matrix in combination with real-time logistics load and historical aging data, and carrying out the analysis and standardization of the order data. A geographic position weight model is constructed based on equipment fingerprint identification and address similarity calculation, an order topology aggregation scheme is generated through a spatial incidence matrix, a distributed decision tree is constructed according to inventory fluctuation prediction and supplier response rate, a picking path is optimized, and finally logistics node data is integrated to construct a visual tracking interface. According to the technical scheme, abnormal order real-time early warning and compensation path planning are achieved through a self-repairing mechanism, logistics state holographic projection is generated, the flexibility and efficiency of the multi-platform e-commerce order management system are improved, and the problems of high complexity, poor expansibility, performance bottleneck and the like existing in a traditional system are effectively solved.

The invention relates to the technical field of computer network security, in particular to a security guarantee system for cross-domain communication and data sharing of network platform software, which comprises a data security transmission module, an identity authentication and access control module, an encryption module, a security audit and anomaly detection module and an anti-interference disaster recovery module. According to the invention, through linkage of equipment fingerprint end-to-end encryption, threat adaptive dynamic key updating and a timestamp-random number anti-replay mechanism and AI risk pre-judgment, reinforcement learning path planning, digital twinning pre-verification and fault automatic switching technologies, collaborative guarantee of key security and transmission continuity in cross-domain audio transmission is realized; encryption intensity is dynamically upgraded along with threats, a transmission path is optimized in advance, and it is ensured that the audio stream resists various network attacks and interferences in low-delay transmission.

The invention relates to the technical field of short video encryption, and discloses a short video active defense encryption system based on a device fingerprint and a dynamic confusion field, and the key point of the technical scheme is that the system comprises a device fingerprint generation module, a mother video encryption module, a slice encryption module, a behavior recognition and defense module and an encryption logic update regulation and control module. The system generates a unique device fingerprint hash value through a multi-modal feature, generates a dynamic confusion field in combination with a chaotic system and a quantum random number, realizes differential encryption of a mother video and slices, and is embedded with zero-knowledge consanguinity proof to support traceability verification. The behavior recognition and defense module monitors user behaviors in real time and dynamically adjusts a confusion strategy or triggers an active defense mechanism, and the encryption logic updating regulation and control module optimizes the updating frequency according to playing data and reduces the batch crawling risk. According to the invention, the security and anti-attack capability of the short video content can be effectively improved.

The embodiment of the invention provides a security authentication method, device and system for a test platform, and a storage medium, and relates to the technical field of network security. The method comprises the following steps: performing fingerprint acquisition on virtual and real equipment to generate a unique identifier, dynamically slicing based on a software defined network to form an isolated test environment, and injecting a zero-trust access strategy associated with the fingerprint; and based on the zero-trust access policy and the device fingerprint, allocating a minimum permission through an attribute-based access control model, continuously evaluating device behaviors to calculate a dynamic trust score, and adaptively updating the permission according to the trust score. By constructing a full-process security certification system and through multi-dimensional mechanism cooperation, dynamic risk perception and timely strategy adjustment, fine-grained control of virtual and real equipment access in an industrial control scene is realized, the dynamic adaptability and risk resistance of a test platform in a complex network environment are improved, and the test efficiency is improved. Equipment access safety, authority distribution minimization and behavior monitoring continuity are ensured, and the safety risk of the industrial control system is reduced.

The invention discloses a digital twinning-based thermal heat supply system defense cooperative protection method and system, particularly relates to the technical field of industrial control system safety, and is used for solving the problems of hidden attack missing detection and response delay caused by splitting of energy flow and data flow in an existing protection mechanism. Physical-network behavior collaborative verification is realized by constructing a digital twinborn model, and precise protection is realized by quantifying coupling failure characteristics of energy flow and instruction flow. The method specifically comprises the following steps: collecting production data, industrial control logs and network traffic; constructing a digital twin model to simulate expected behaviors of equipment, communication and physical states; comparing and detecting abnormal data in real time; extracting a thermodynamic potential gradient characteristic quantity and a path integral characteristic quantity from the abnormal data; calculating a covariant difference value of the two characteristic quantities, and judging an external attack when the covariant difference value exceeds a threshold value; when an attack is triggered, a micro-isolation gateway is deployed, dynamic access control is implemented based on an equipment fingerprint, a user behavior sequence and an environment fingerprint, and an attack chain is blocked.

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

The embodiment of the invention discloses a dynamic metadata sensing and self-adaptive mapping method and system, and the method comprises the following steps: deploying a lightweight probe to be agented to a plurality of heterogeneous source database systems, and building a trusted communication channel between the probe and a coordination center through equipment fingerprint generation and security authentication; a database log event is captured in real time, log analysis is carried out through an FPGA module, the change of a field structure or a semantic level is identified, and structured event data is generated; performing context embedding representation on the captured event based on a semantic understanding model, performing strategy reasoning in combination with a historical strategy and a knowledge graph, and generating a matched mapping strategy; loading and executing the mapping strategy in a trusted execution environment (TEE) to realize data conversion, cleaning and complementation, and performing desensitization processing on sensitive data through a differential privacy mechanism; and collecting performance indexes and abnormal logs of a strategy execution result, continuously training the model through a closed-loop optimization mechanism, and correcting an unreasonable strategy or updating a rule base.

The invention discloses a computer remote login identification system based on artificial intelligence, and the system comprises a remote login request receiving and processing module which is responsible for receiving a remote login request of a user, and carrying out the preliminary processing and verification; the multi-mode identity authentication module adopts a three-factor verification matrix of biological characteristics, behavior characteristics and equipment fingerprints, and adjusts weights of different authentication dimensions according to security requirements and user requirements; the lock screen state sensing module is responsible for sensing a lock screen state; the dynamic behavior analysis engine is used for monitoring behavior characteristics of the user in real time and comparing the behavior characteristics with a preset abnormal behavior mode so as to find abnormal login behaviors in time; the intelligent unlocking decision tree is used for making an intelligent unlocking decision according to the authentication score, the equipment environment and the behavior abnormality; and the security execution module is responsible for automatically filling a password and executing an unlocking operation after the user identity authentication is passed, and monitoring an abnormal condition in the unlocking process in real time in combination with equipment environment monitoring and an abnormal detection model.

The invention discloses a stable transmission method for server data encryption and rapid authentication, and belongs to the technical field of data security. The method comprises the following steps: dividing data into three levels of core sensitive data, important data and common data by utilizing a semantic analysis model; performing encryption protection on different levels of data by adopting a hierarchical encryption strategy; a master key is generated based on a chaotic system, a session key is generated in combination with a timestamp and a device fingerprint and is regularly alternated, and secure distribution is performed through a trusted execution environment and a block chain; multi-modal features are fused, the identity is verified through zero-knowledge proof, and the authentication strength is adjusted through dynamic risk assessment. The method is suitable for cloud computing, the Internet of Things and a distributed storage system, through deep fusion of a multi-level hybrid encryption algorithm, a dynamic key management mechanism and a self-adaptive authentication strategy, data security encryption, rapid authentication and stable transmission are achieved, and security, efficiency and adaptability are improved.

The invention discloses an industrial internet encryption method and system based on block chain evidence storage, and relates to the technical field of information security and block chains, and the method comprises the following steps: when an industrial internet system generates to-be-encrypted industrial data, synchronously generating identification information strongly bound with content for the industrial data; according to the method, the dynamic device fingerprints and the multi-dimensional feature information are embedded into the encrypted data, and a strong binding relationship between the encrypted data and the block chain evidence is established in combination with double encryption and a joint hash mechanism, so that the tamper-proof and anti-counterfeiting capabilities of the data are remarkably improved. According to the method, a block weighting and bidirectional reconstruction verification strategy is adopted, so that the sensitivity and the detection capability of the system to tiny tampering and local counterfeiting are enhanced, the integrity, the authenticity and the high credibility of data in the industrial internet can be effectively guaranteed in a complex industrial environment, and the potential safety hazard of evidence storage and data unhooking in the prior art is solved.

The invention provides a store distribution system and a customer portrait generation method. The method comprises the following steps: generating basic attribute representation of a customer through equipment fingerprint data of the customer in the store distribution system; extracting interactive behavior characteristics of the clients in the full-link behavior log of the store distribution system, and performing preference mapping on historical behaviors of the clients through the interactive behavior characteristics and the basic attribute representation to obtain behavior preference labels of the clients in the store distribution system; extracting behavior semantic features of the customer from the current search behavior of the customer, and performing feature splicing on the interaction behavior of the customer through the current operation path of the customer and the behavior semantic features to obtain an intention representation vector of the current interaction intention of the customer; and determining an interaction conflict relationship between the behavior preference tag and the intention representation vector, and performing conflict resolution on the basic attribute representation through the interaction conflict relationship so as to obtain a tag portrait of the customer. Based on the scheme, conflict resolution between customer attributes and customer behaviors in a store distribution system can be realized.

Methods, systems, and apparatuses are described herein for protecting sensitive data even when Transport Layer Security (TLS) communication sessions are compromised. A computing device may send, via a web browser plugin of a web browser application executing on the computing device and to one or more remote servers, a request for a server secret that comprises a device fingerprint, an identification of the web browser application, and an identification of the web browser plugin. The computing device may receive the server secret and a public certificate associated with the remote server. The computing device may establish, via the web browser application, a TLS session with the one or more remote servers. The computing device may generate a session key. The computing device may receive data comprising unencrypted data and encrypted data. The computing device may decrypt the encrypted data based on the session key.

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

The invention discloses an intelligent detection method and system for security vulnerabilities of a terminal layer of an electric power internet of things, and relates to the technical field of security protection of the electric power internet of things, and the method comprises the steps: collecting multi-dimensional information and network traffic characteristics of terminal equipment of the electric power internet of things, constructing an equipment fingerprint database, carrying out the comparison verification of the equipment, and obtaining a security vulnerability of the terminal layer of the electric power internet of things according to a verification result; the method comprises the following steps: classifying network traffic, identifying an abnormal traffic sequence, extracting behavior parameters of network traffic abnormity and network access abnormity, carrying out association analysis on the abnormal parameters, labeling equipment, and generating a security vulnerability detection report. The technical problem that potential security vulnerabilities are difficult to find and repair in time due to the fact that complex abnormal behaviors in a device layer and network traffic cannot be effectively recognized and handled in the prior art is solved, real-time and accurate security vulnerability detection is achieved by constructing a device fingerprint database and carrying out traffic classification and abnormal behavior analysis, and the security vulnerability detection efficiency is improved. Therefore, the technical effect of improving the security of the terminal layer of the power Internet of Things is achieved.

The invention discloses a cloud mobile phone equipment fingerprint disguising method and related equipment, and relates to the technical field of cloud mobile phone security, and the method comprises the steps: obtaining hardware entropy source data which comprises current timestamp data, a hardware unique identifier hash value and an environment noise sampling value; non-linear transformation is carried out on the hardware entropy source data based on a preset chaotic mapping algorithm, a virtual parameter set is generated, and the virtual parameter set meets a preset equipment parameter rule; covering the virtual parameter set to a multi-level system interface of the real equipment fingerprint through kernel-level injection operation; monitoring the use state of the virtual parameter set based on a preset life cycle model, and determining a parameter updating trigger condition; and when a parameter updating triggering condition is met, the nonlinear transformation is executed again to generate an updated virtual parameter set, and the updated virtual parameter set covers the multi-level system interface.

The invention discloses a Teensy virus identification and protection method for a mobile hard disk, and relates to the technical field of virus identification and protection.The method effectively identifies high-simulation attack equipment through composite equipment fingerprint construction and combination of hardware ripple features and dynamic protocol response, and breaks through the limitation of traditional single feature detection; meanwhile, the protocol state transition probability model adopts hidden Markov chain real-time analysis, microsecond-level protocol switching abnormity is accurately captured, and the problem of missing detection caused by rough time granularity of an existing scheme is solved; in addition, a window context instruction association mechanism binds a system focus state with an HID operation, and blocks a hidden attack chain formed by legal instruction combination; the hierarchical fusing strategy fuses protocol endpoint control and physical layer isolation, so that the availability of a storage function is ensured, meanwhile, attack blocking is realized, and the situation that normal use is influenced due to full-port forbidding in a traditional scheme is avoided.

A network monitoring device is connected to a communications network and monitors traffic transmitted to and from a server. The system stores a device fingerprint of devices identified as involved in attacks across the communications network, generates attack patterns for attacks across the communications network based on data packets transmitted or received by the devices during an attack based on the data packets corresponding to the device fingerprint, monitors data packet exchanges between the server and network devices, determines a set of transmission parameters for each of the data packet exchanges, compares the set of transmission parameters for the plurality of data packet exchanges to the attack patterns, and, responsive to determining a match between a first set of transmission parameters and an attack pattern, applies a tag to a network device communicating with the server via the data packet exchange indicating the network device is involved in an attack.

A computer-implemented method and system for managing an authentication between user devices and authentication servers, is disclosed. The computer-implemented method includes: obtaining user credentials associated with users; retrieving information associated with device fingerprints corresponding to the user devices; generating cipher messages by at least one of: creating random numbers and encrypting the random numbers with keys derived from at least one of: the first and second index; transmitting the one or more user identities, the first index, the second index, and the cipher messages, to authentication servers; dynamically generating the private keys from private key variables; generating authentication responses by decoding authentication based questions obtained from the authentication servers, using cipher messages and the private keys; and transmitting the authentication responses to the authentication servers for adapting the authentication servers to authenticate the user devices.

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

A network monitoring device is connected to a communications network and monitors traffic transmitted to and from a server. The system stores a device fingerprint of devices identified as involved in attacks across the communications network, generates attack patterns for attacks across the communications network based on data packets transmitted or received by the devices during an attack based on the data packets corresponding to the device fingerprint, monitors data packet exchanges between the server and network devices, determines a set of transmission parameters for each of the data packet exchanges, compares the set of transmission parameters for the plurality of data packet exchanges to the attack patterns, and, responsive to determining a match between a first set of transmission parameters and an attack pattern, applies a tag to a network device communicating with the server via the data packet exchange indicating the network device is involved in an attack.

The invention discloses a USB access safety control system, and particularly relates to the field of USB access electric digital data processing, which comprises an identification module, an isolation module, an encryption continuous transmission module, an auditing module and a kernel interrupt signal triggered by USB physical insertion, and the identification module is used for receiving the kernel interrupt signal and writing the kernel interrupt signal into the interrupt event buffer area, generating a to-be-analyzed interrupt data group, analyzing and generating identification notification information and pushing the identification notification information as a USB access snapshot data frame. Strong binding of USB device access identities, interval control of permission states and traceable closed management of behavior tracks are achieved by constructing a security context vector with device fingerprints, authorization group IDs and insertion timestamps as cores and forming a dynamic association verification path among isolation state judgment, behavior integrity measurement and chain audit account books. The key problems of equipment loopback cheating, permission mismatching, audit failure and the like are solved.

The invention provides an equipment fingerprint generation method and device, electronic equipment and a storage medium, and relates to the technical field of network equipment security. The method comprises the following steps: acquiring initial equipment feature data of target equipment acquired from at least two data sources; performing stability consistency analysis on the initial equipment feature data, and determining effective equipment feature data; performing weighted integration on the effective equipment feature data through a predetermined distribution weight to obtain a multi-dimensional equipment fingerprint feature vector; and according to the multi-dimensional device fingerprint feature vector, determining a dimension-reduced device fingerprint feature vector, and performing hash operation on the dimension-reduced device fingerprint feature vector to generate a device fingerprint identifier corresponding to the target device. According to the technical scheme, the accuracy, the stability and the anti-interference capability of the device fingerprint can be effectively improved, the calculation complexity of the device fingerprint is reduced, the generation efficiency of the device fingerprint is improved, and the practicability of the device fingerprint in various complex application scenes is ensured.

The invention relates to the technical field of communication, and discloses a mobile phone terminal detection gating method and system in a communication management and control area, and the method comprises the steps: dividing the management and control area, and setting a security level, determining whether the terminal is a legal terminal or an illegal terminal according to the security level, the international mobile subscriber identity, the equipment fingerprint value, the behavior mode score and the biological characteristic data; the method comprises the following steps: monitoring electromagnetic signals in a region through distributed sensing nodes, determining equipment types of illegal equipment signals in the electromagnetic signals, and generating an interference strategy according to the equipment types, illegal equipment signal characteristics and positions; according to the invention, accurate identity recognition is carried out on the terminal through multi-dimensional feature analysis, the recognition accuracy is improved, and counterfeiting and counterfeit behaviors are effectively prevented; the adaptive interference confrontation technology is adopted, the optimal interference strategy can be automatically selected according to the illegal equipment type, accurate interference is achieved, and legal communication is protected from being affected.

The invention discloses a vehicle trunk automatic opening method and device and a computer program product, and the method comprises the steps: firstly, after the device fingerprint verification of a target vehicle is passed, obtaining N paths of surrounding video streams through a whole vehicle panoramic camera composed of N vehicle-mounted cameras; and then pedestrian detection and cross-shot pedestrian tracking processing are carried out on each frame of image in the N paths of video streams to obtain a pedestrian detection result and a pedestrian tracking result. Generating a BEV pedestrian trajectory according to a pedestrian tracking result; a target pedestrian BEV track is screened out from the target pedestrian BEV track; and after the identity verification of the target pedestrian is passed, obtaining a state diagram of the target pedestrian according to a pedestrian detection result, inputting the BEV track of the target pedestrian, the state diagram of the target pedestrian and the scene text information of the landmark building within the preset range around the target vehicle into a VLM in combination with prompt, and when the VLM judges that the target pedestrian has the intention of opening the trunk, opening the trunk. And the trunk is automatically opened, so that the safety of the target vehicle and the vehicle use experience of the target pedestrian are improved.

The invention discloses a decentralized virtual identity key collaborative management system based on a block chain, and belongs to the field of block chain technology and cryptography. The system comprises a block chain network layer used for distributed storage of key fragments and execution of an automation strategy; the virtual identity management module is used for generating a unique identity identifier (DID) through a threshold signature algorithm after fusing the biological characteristics and the device fingerprints; the key collaborative management module is used for storing the main key in a fragmented manner through a Shamir secret sharing algorithm, and dynamically selecting an encryption algorithm to generate a sub-key according to the security score of the target platform; the verification and auditing module is used for verifying the holding legality of the secret key through a zk-SNARKs technology and injecting random noise to resist side channel attacks; and the block chain network layer, the virtual identity management module, the key collaborative management module and the verification and auditing module are connected with an encrypted communication protocol through an intelligent contract interface.

The invention discloses an aliasing power load identification and power decomposition method, system, device and medium, and the method comprises the steps: collecting electrical data containing aliasing signals, carrying out the multi-modal feature extraction, and obtaining a device fingerprint comprising a steady-state power feature, a frequency domain harmonic feature and a statistical behavior feature; constructing an equipment behavior graph based on the fingerprint features, wherein nodes in the graph represent equipment, and edges represent power behavior correlation between the equipment; performing feature aggregation and joint reasoning on the equipment graph by using a graph attention neural network, and outputting an identification result of the target equipment and a single-equipment power estimation value; the classification precision and the power fitting capability are optimized at the same time through a joint loss function guide model; and finally, high-precision identification and power decomposition in a multi-device aliasing state are realized. The method has good anti-interference capability and edge deployment performance, and is suitable for the scenes of distributed power utilization monitoring, non-intrusive load identification, intelligent energy-saving control and the like.

The invention discloses a drug inspection information sharing method and system based on a block chain, and relates to the technical field of drug inspection data management, and the method comprises the steps: sending an authentication request to a block chain network when a drug inspection device is started, verifying the state of the device according to a device digital identity certificate after an intelligent contract receives the authentication request, and sending the verification result to a server; generating a temporary operation token, encrypting the temporary operation token by using the equipment public key and returning the temporary operation token to the medicine inspection equipment; the medicine inspection equipment performs medicine inspection through the temporary operation token, synchronously acquires inspection data, a GPS position, an inspection timestamp and an equipment fingerprint, calculates a composite hash value, and encrypts the inspection data; and submitting the composite hash value to a block chain network, performing space-time compliance verification through an intelligent contract, obtaining a drug batch DID, and generating an on-chain block associated with the drug batch DID. According to the invention, the identity credibility of the medicine inspection equipment is ensured, and the authenticity, integrity and time-space consistency of inspection data are realized.