162 results about "Channel analysis" patented technology

The invention provides a power station equipment state real-time monitoring and diagnosing method and system based on cloud edge collaboration, and the method comprises the steps: adjusting a data collection period dynamically determined based on an adaptive sampling frequency adjustment algorithm, and collecting a vibration signal, a temperature signal and a current signal through a multi-source heterogeneous sensor array disposed in a power station equipment body; carrying out preprocessing by utilizing the edge computing node, generating a compressed feature vector, and uploading the compressed feature vector to a cloud end through an MQTT protocol; a multi-modal data fusion analysis module is started through a cloud, a three-dimensional evaluation matrix of the equipment health state is constructed in combination with historical operation data and environmental parameters of the equipment, and a calculation task distribution strategy between an edge calculation node and the cloud is adjusted in real time according to an evaluation result of the three-dimensional evaluation matrix. Abnormal mode recognition based on a deep residual network and fault source tracing double-channel analysis based on a physical model are executed, fault types and fault reasons are diagnosed, and the accuracy and timeliness of fault diagnosis are guaranteed.

The invention discloses an intelligent interaction system based on a large language model and a knowledge graph, and relates to the technical field of artificial intelligence and intelligent interaction. According to the system, data processed by a data acquisition and preprocessing module is stored in a local knowledge warehouse in a three-layer nested structure; the multi-modal semantic understanding module constructs a knowledge system in which a triple knowledge graph and a vector database are complementary by optimizing a BERT model and performing dual-channel analysis; the intelligent interaction module realizes natural language interaction and business task automatic triggering based on an RAG technology and a dialogue memory mechanism; the information change identification and analysis module generates a personnel change risk report and performs early warning; and the summary report automatic generation module outputs a structured report. According to the method, the problems of data splitting, complex interaction and slow response in traditional enterprise management are solved, the functions of system intelligent question answering, personnel change risk analysis, structured report automatic generation and the like are realized, and the management efficiency and the information consistency are improved.

The invention relates to the technical field of video analysis, in particular to an AI-based video analysis and management system, which comprises a channel evaluation module, a track extraction module, a cross-source fusion module, a content screening module and a scheduling generation module. In the method, key frame optimization is realized by combining frame activity index evaluation in an object identification process, track continuity breakpoint repair is realized based on a displacement sequence and a direction change trend in a track extraction process, and track matching fusion is realized through region and direction cross mapping in cross-channel analysis. Effective channels are identified based on track keeping features in content screening, and a structured identification directory is constructed in combination with label mapping in a scheduling process, so that target identification and track splicing among different channels have a full-process dynamic completion and consistency verification mechanism, the cross-video-source target identification precision and stability can be effectively improved, and the target identification efficiency is improved. And thus, more efficient content screening and structured recognition can be realized in a multi-source video environment.

The invention provides a side channel analysis method and device based on a feature coding convolutional neural network, equipment and a medium. The method comprises the following steps: acquiring side channel energy trace data of target equipment; a pre-trained first neural network model is utilized to perform feature coding and decoding on the energy trace data to obtain coding features, and the first neural network model comprises a feature head coder and a feature head decoder; inputting the encoding features into a feature head encoder of a second neural network model, performing multi-scale feature fusion through a classification sub-model of the second neural network model, dynamically weighting the encoding features by using an attention mechanism, and extracting target leakage features; calculating guess probability distribution of each candidate key based on the target leakage characteristics; and determining a target key of the target device from the candidate keys based on the guess probability distribution.

The present invention discloses a low-voltage switch cabinet fault diagnosis method and system, and relates to the technical field of power equipment fault detection. A low-voltage switch cabinet fault diagnosis system includes: a fault diagnosis module and a fault prediction module. The present invention can flexibly adapt to different working environments and fault modes through real-time monitoring and dynamic time interval adjustment, ensuring that potential fault information is captured in time at critical moments; dual-channel analysis of fault data and sensor monitoring data, combined with multi-scale convolution and bidirectional time series feature extraction, can deeply mine the spatial and temporal features in the data and improve the accuracy of diagnosis; using attention mechanism and residual enhancement technology, the expression ability of features is further improved, ensuring the efficiency and reliability of fault diagnosis results; through the continuously optimized low-voltage switch cabinet fault prediction model, efficient prediction of faults is achieved.

The invention discloses a traditional building micro-reconstruction performance improving system based on machine learning. According to the method, the inherent contradiction between traditional building protection and performance improvement is effectively broken through an intelligent multi-dimensional data fusion and generative design technology. A holographic information model constructed by the multi-source data acquisition module completely retains historical genes and space passwords of a building, and provides an accurate space-time reference for subsequent transformation; a two-channel analysis mechanism of the performance evaluation module brings physical structure safety and cultural value continuity into a unified evaluation system, and ensures that a transformation scheme not only meets modern use requirements but also inherits and builds wisdom. The pattern generation and space reconstruction technology of the module is creatively designed, the limitation of traditional craftsman experience inheritance is broken through, contemporary expression of traditional building languages is achieved in the digital twinning environment, and historical style protection is changed from passive limitation to active innovation.

The invention discloses a risk assessment system and method for analyzing abnormal sleep breathing of children based on CBCT dual-channel, and the method comprises the steps: collecting local three-dimensional image data of a maxillofacial region through CBCT, and extracting the sagittal area, volume and multi-dimensional angle and distance parameters of an upper airway through head correction and anatomical mark positioning; and constructing a logistic regression clinical prediction model combining single-factor and multi-factor logistic regression to realize risk prediction. Standardization and equal-interval sampling are synchronously carried out on image data, a multi-channel three-dimensional image data cube is generated, and deep learning classification is realized by inputting the multi-channel three-dimensional image data cube into a 3D ResNet network based on identical fast connection. And finally, performing weighted collaborative analysis on results of the two diagnosis channels, and outputting children obstructive sleep apnea risk classification. Through the parameter driving and image learning dual-channel fusion design, the accuracy and applicability of early screening and risk assessment of children obstructive sleep apnea are improved.

The invention discloses a side channel analysis method and device, equipment, a storage medium and a program product, and the method comprises the steps: obtaining a side channel segment related to polynomial multiplication of a secret key in mask-protected Kyber algorithm side channel data, and inputting the side channel segment into a Hamming weight classification model, the Hamming weight prediction value of the polynomial multiplication operation output result is obtained, and the Hamming weight classification model is obtained by adopting public stage data training of mask-protected Kyber algorithm side channel data; generating a candidate value set of different mask shares related to the same key coefficient according to the Hamming weight predicted value; screening out a target key coefficient from the candidate value set; and under the condition that the target key coefficient is successfully matched with the real key, determining that the side channel vulnerability that the key is stolen exists in the Kyber algorithm protected by the mask.

The invention relates to the technical field of building information model optimization, in particular to a large-volume BIM model optimization system based on intelligent loading, which comprises a model analysis unit, a function identification unit, a simplified execution unit and a model recombination unit, and is characterized in that the model analysis unit reads an IFC file and separates component data; the function identification unit outputs a decoration component (first class) set, a non-bearing structure (second class) set and a bearing component (third class) set through geometric and semantic dual-channel analysis, the simplified execution unit performs edge folding on the first class, performs vertex clustering on the second class and skips simplification on the third class, and the model recombination unit binds metadata and constructs a dynamic octree index. The problems that a traditional simplification strategy is single and core information is lost are solved, the model loading efficiency and application precision are improved, and the method is suitable for complex building scenes.

The invention discloses a mobile phone USIM (Universal Subscriber Identity Module) side channel analysis method based on an outlier. The method comprises the following steps: S1, collecting a power consumption signal generated in the process of executing an AES (Advanced Encryption Standard) algorithm by a USIM card; s2, filtering and aligning the acquired power consumption signal to generate standardized side channel curve data; s3, for each key byte position participating in S box operation in the first round of AES, constructing a candidate key byte value, and calculating an S box output value; s4, recognizing recovered and uncertain key bytes by adopting a correlation energy analysis method; s5, deriving S box output byte positions influenced by the uncertain bytes in the second round; s6, calculating the correlation of the candidate value at the position, and generating a correlation feature vector; s7, inputting a local outlier factor algorithm, and calculating an outlier value; and S8, selecting the key byte with the maximum outlier as a final key byte to form a key byte set. According to the invention, the accuracy and robustness of USIM key recovery are improved in a low signal-to-noise ratio scene.

The invention relates to a password side channel analysis method based on a secondary start genetic algorithm, and belongs to the technical field of information security passwords. The method comprises the following steps: firstly, constructing a second-order leakage corresponding formula of a cryptographic algorithm intermediate value and a leakage waveform for a first-order mask protection scheme realized by nonlinear layer table look-up; then, aiming at the generated second-order leakage, extracting an intermediate value of bit multiplexing in a register to construct an offset calculation formula of the intermediate value and a typical value; and finally, constructing a genetic algorithm fitness calculation formula for algorithm intermediate value offset, performing key recovery by adopting a secondary start genetic algorithm heuristic learning method for reducing the number of individuals in a dynamic population, and performing side channel analysis on a lightweight cryptographic algorithm for realizing a nonlinear layer by parallel table look-up.

The invention relates to the technical field of remote monitoring, in particular to a multi-source data acquisition monitoring robot based on the Internet of Things, which comprises a morpheme trigger recognition module, a task compression judgment module, a resource tilt execution module, a difference channel analysis module and a path state injection module. According to the method, dynamic switching of voice interaction states is realized through morpheme response distance and silence zone recognition, priority allocation of task resources is completed in combination with a task delay labeling mechanism, an auxiliary path is released through linkage of node states and communication port fields, resource scheduling flexibility is improved, fluctuation trend and structural offset are fused in abnormal recognition, and abnormal recognition accuracy is improved. And in path control, label field double write-in and state register linkage is adopted, a closed-loop mapping relation between a task path and a control state is constructed, and the accuracy and synchronization consistency of path regulation and control are improved.

Provided is a method for securing a security device against side-channel analysis attacks while performing a sensitive operation. It includes training an attack neural network to perform a side-channel attack against the security device while performing a sensitive operation, creating a training data set for a protective neural network by applying a plurality of elementary protection combinations to the sensitive operation while performing the sensitive operation, training a protective neural network executing on a coprocessor of the security device using the training data set for the protective neural network, and programming the coprocessor of the security device with the set of parameters for the protective neural network. Other embodiments disclosed.

The invention discloses a side channel analysis method and system for adaptive optimization of a Halpern type fixed point, and belongs to the field of side channel analysis, and the method comprises the following steps: carrying out the grouping and data processing of a plurality of side channel trace data, and obtaining processed data; constructing a side channel analysis model based on a convolutional neural network, and classifying and identifying the processed data based on the side channel analysis model to obtain a predicted label value probability corresponding to each piece of side channel trace data; optimizing the side channel analysis model by adopting a Halpern type fixed point adaptive optimization algorithm based on the predicted label value probabilities corresponding to the plurality of side channel trace data to obtain a final model; and obtaining a side channel analysis result based on the final model. According to the method, the model training stability is improved through the Halpern type fixed point adaptive optimization algorithm, the model generalization ability is enhanced, and an efficient solution is provided for a complex optimization task in a side channel analysis problem.

Side-channel analysis systems and related methods are disclosed. A side-channel analysis system is configured to generate a synthetic side-channel signal for a code for a software program using a side-channel model. The code includes human-readable instructions of a human-readable computing language. The synthetic side-channel signal includes an estimate of an actual side-channel signal that would be generated by a computer executing the code for the software program. The synthetic side-channel signal is generated based, at least in part, on token values for the human-readable instructions of the code and the training data. The side-channel analysis system is also configured to analyze the synthetic side-channel signal to detect one or more of anomalies or vulnerabilities of the software program. A method includes training the side-channel model.

The invention belongs to the field of Internet of Things anomaly detection, and discloses an edge-cloud collaborative anomaly detection method and system for medical Internet of Things equipment, and the method comprises the steps: S1, obtaining the operation state data of the equipment and the physiological state data of a patient according to a set time length, and generating a state vector; s2, generating an equipment event sequence based on the running state data of the equipment, and encoding the physiological state data of the patient into a physiological abstract containing semantic differences; s3, calculating a process deviation score according to the equipment event sequence and the physiological abstract; s4, calculating a final score according to the physiological abstract and the flow deviation score, and determining an abnormal type label based on the final score; and S5, executing a preset response based on the abnormal type label. According to the multi-device linkage anomaly detection method, through double-channel analysis of device response timeliness and operation process consistency, unsupervised modeling and edge-cloud hierarchical deployment are combined, the problems of device collaboration missing, difficult process anomaly recognition and data scarcity are solved, and the multi-device linkage anomaly detection capability is improved.

A gearbox vibration and noise locating method, device, equipment, and readable storage medium. The gearbox vibration and noise locating method includes: acquiring a vibration signal of the gearbox under test conditions, performing a mixed channel analysis based on the acquired vibration signal to obtain a mixed order spectrum, and performing a synchronous channel analysis based on the input shaft, intermediate shaft, and output shaft as reference axes to obtain the synchronous order spectra of the input shaft, intermediate shaft, and output shaft; determining the characteristic order of the problem based on the mixed order spectrum. If the characteristic order of the problem is inconsistent with the typical order table of the shaft gear, the axis where the faulty component is located is determined based on the synchronous order spectra of the input shaft, intermediate shaft, and output shaft. When an atypical fault exists, by performing a synchronous channel analysis based on the input shaft, intermediate shaft, and output shaft as reference axes, the vibration signal can be further refined to obtain the synchronous order spectrum of each axis. By comparing the synchronous order spectra of each axis, the axis where the faulty component is located can be accurately determined, thereby achieving precise positioning.

The application discloses a computing system configured to measure a step response of a channel in an electronic device, determine a feedback voltage added to the channel by a decision feedback equalizer in response to detecting inter-symbol interference in a received symbol, and predict signal integrity of the channel based at least in part on the step response of the channel and the feedback voltage. The computing system may utilize the predicted signal integrity to determine a probability that a feedback voltage added to the channel by the decision feedback equalizer is wrong, and determine a signal integrity of the channel based at least in part on the step response of the channel, the feedback voltage, and a probability that the feedback voltage added to the channel by the decision feedback equalizer is wrong.

The invention relates to the field of information security passwords, and discloses a password security measurement method based on a large language model and a multi-side channel differentiator, which comprises the following steps: analyzing the characteristics of SPA according to simple energy, and designing various side channel differentiators such as segmentation, preprocessing, data statistics, dimension reduction, clustering and continuous rule limitation based on common analysis means; when the password equipment runs a target algorithm, acquiring side information data such as illumination, energy or electromagnetism in the running process of the password equipment; based on the collected side information data, different side channel distinguishers are selected, expert version cues with side channel analysis knowledge are formed through fusion, and analysis targets and tasks are described. According to the method, security automatic measurement can be carried out on password equipment with multiple protection means such as random delay and mask by utilizing a general large language model without expert participation or professional analysis tools, and compared with a common SPA security measurement method, the false alarm rate gt is reduced under the condition of the same sample size; the sample size is reduced by gt under the same accuracy rate; and therefore, the accuracy of the side channel security measurement is greatly improved, and the cost of resources such as labor and the like is reduced.

A computerized method comprising: tracking user behavior data of users each using a respective mobile device within a channel; detecting a demand surge in the channel based on channel analysis data for the channel, wherein the channel analysis data for the channel are based channel events for the channel; and generating one or more first incentives for one or more first users of the users based on the demand surge and incentive metrics, wherein the one or more first incentives are configured to change user behavior of the one or more first users. Other embodiments are disclosed herein.

The invention relates to a password side channel analysis method for bit slice structure mask multiplexing, and belongs to the technical field of information security passwords. Firstly, a second-order leakage corresponding formula of a cryptographic algorithm intermediate value and a leakage waveform is constructed for a first-order mask protection scheme realized by logical operation; then, aiming at the generated second-order leakage, extracting byte multiplexing bits to construct an offset calculation formula of the byte multiplexing bits and a typical value; and finally, constructing a genetic algorithm fitness calculation formula for algorithm intermediate value offset, and performing key recovery by adopting a genetic algorithm heuristic learning method to realize side channel analysis on parallel logic operation with first-order mask protection by adopting a lightweight cryptographic algorithm of a bit slicing technology.

This application is applicable to the field of software testing technology, and particularly relates to a side-channel analysis method based on firmware instrumentation. The method includes: obtaining the execution flow in the firmware image of the target device; determining the instrumentation parameters for monitoring the side-channel signals during the execution of the target device based on the execution flow in the firmware image of the target device, and quickly analyzing and locating the instrumentation parameters; performing side-channel signal monitoring through firmware instrumentation according to the instrumentation parameters, to obtain the side-channel data corresponding to each monitoring point and the current operation state of the target device corresponding to each monitoring point, realizing instrumentation overloading and critical operation monitoring; determining the target side-channel analysis data of the target device according to the side-channel data corresponding to each monitoring point and the current operation state of the target device corresponding to each monitoring point; and finally determining the side-channel analysis result of the target device based on the target side-channel analysis data of the target device, improving the quality of evaluation data, and realizing accurate in-depth monitoring and result analysis.

The invention relates to the field of multi-round keys, in particular to a lightweight cipher-oriented multi-round key genetic algorithm side channel analysis method, which comprises the following steps of: respectively constructing triples of round keys K1 and KT-1, and searching triples corresponding to a round key K0 and the round key K1 and triples corresponding to KT-2 and KT-1 in parallel by using a genetic algorithm; wherein the round key is a sub-key which is generated by the main key through a key expansion algorithm in the block cipher algorithm and is used for each round of operation; t is the encryption round number of the block cipher algorithm; k0 refers to a first round secret key; k1 refers to a second round of round key; kT-2 refers to a round key of the last but one round; kT-1 refers to the last round of round key; the triple is a set which is used for representing a specific round key and contains three elements, and the bit number of each element is the same as that of the round key. And when the lightweight block cipher algorithm which is realized in parallel is analyzed, the calculation complexity is lower and the recovery success rate is higher.

The invention discloses an optical signal channel structure analysis system based on a new generation of mobile communication networking, which belongs to the field of optical communication data, and comprises the following steps: a data acquisition module receives an OTN (Optical Transport Network) signal and an SDH (Synchronous Digital Hierarchy) signal; the OTN channel analysis module carries out channel structure analysis on the accessed OTN signal and carries out OTN demultiplexing and de-encapsulation mapping processing to obtain OTN channel structure information; the SDH channel analysis module is used for carrying out channel structure analysis on the accessed SDH signal and normalizing the SDH signal into SDH channel structure information; the channel structure issuing configuration module carries out regular conversion on the OTN channel structure information and the SDH channel structure information obtained through analysis, and issues the information to the ZYNQ control chip; the channel structure reporting module reports and pushes the channel structure information of the whole port to the rear-end signal processing module; and the automatic polling module performs an automatic real-time polling mechanism on the accessed OTN signal and SDH signal. The system provided by the invention can effectively realize the characteristic of integrated input and output of channelized equipment.

The application discloses a spiral rod bundle multi-channel experimental device and an experimental method based on additive manufacturing, and relates to the technical field of experimental devices.The spiral rod in the experimental device is processed by using additive manufacturing technology, and the clamping piece structure on the outer surface of the spiral rod can ensure that the thermocouple accurately measures the wall surface temperature.The sub-channel temperature measuring assembly is arranged near the inlet and outlet of the experimental device, the fluid temperature of the typical sub-channel is measured, the sub-channel analysis program is combined, the mixing coefficient of the spiral rod bundle multi-channel can be obtained, the experimental data of different experimental conditions can be obtained by adjusting the flow of the experimental loop and the fluid temperature in the lower chamber, and the Darcy friction resistance coefficient of the spiral rod bundle multi-channel can be obtained by recording the indication of the differential pressure transmitter.The experimental data of the application can provide support for the development of the design program of the spiral type rod bundle nuclear fuel.

The invention relates to a high-security hidden communication system, and belongs to the technical field of information security. According to the system, the mark feature and the structural feature of encrypted data are eliminated through a strong encryption technology, the key feature of the encrypted data is eliminated, the key security is ensured, the ciphertext security is improved, and the risk problem brought by side channel analysis to the data encryption security is solved; a double-layer covert channel is realized through a multimedia steganography technology and a flow simulation technology, the concealment of communication behaviors is greatly enhanced, and the problem of insufficient concealment of a single-layer covert channel is solved; the hidden communication technology with both concealment and anonymity is realized by combining the hidden channel with the anonymous network, and the problems that the anonymous network has obvious traffic characteristics and network boundary nodes are easy to expose are solved.

The present application discloses a side channel attack detection method and system based on deep learning, the method comprising: deploying a side channel analysis detection system to capture the side channel timing data during encryption operation in real time; inputting the side channel timing data into an optimized deep learning model to identify abnormal side channel activities; and dynamically adjusting the execution of the encryption algorithm based on the abnormal side channel activities. The disclosed embodiment can identify slight deformations in the presence of random delay protection measures, thereby accurately predicting the key execution sequence of the encryption algorithm, with stronger adaptability and higher detection accuracy.

The application relates to the technical field of wireless communication and vehicle-mounted multimedia interaction, in particular to a vehicle-mounted video backhaul and multi-mode audio interaction system, which comprises the following modules: a service shunting module, which is used for determining the priority label of audio stream data according to an identification result; a channel analysis module, which is used for evaluating the channel capacity and interference level of current time-frequency resources; a power domain allocation module, which is used for dynamically calculating a power allocation coefficient and dividing the total transmission power of a vehicle-mounted communication terminal into a first power level and a second power level; a superposition coding module, which is used for generating a multi-layer superposition signal; and a transmission control module, which is used for directly transmitting the multi-layer superposition signal to a base station side through a radio frequency unit in a scheduling-free transmission mode without sending a scheduling request signaling, so that the competition and conflict of high-definition video backhaul and key voice interaction for limited time-frequency resources in vehicle-mounted wireless communication are effectively solved, and efficient concurrency of heterogeneous services is realized.

The present application relates to a kind of password algorithm artificial intelligence side channel analysis method based on dimension reduction and clustering, belong to information security cryptography field.First, the energy trace obtained is analyzed, to determine the area of the energy trace in which the execution of the decryption process is related to secret information operation.Analyze the energy trace of the key operation part, use the method based on sliding window gradient value to divide each operation, obtain energy trace segment.Combined with the implementation of cryptographic algorithm, the energy trace segment is analyzed, and the operation type is identified.Use principal component analysis on energy trace segment for dimension reduction, take the component corresponding to the expected dimension as the principal component of each operation.Combined with the characteristics of energy trace segment, the cluster number obtained by clustering is estimated.Use clustering algorithm to cluster the energy trace segment after dimension reduction, analyze the clustering result to determine the operation type represented by each cluster, obtain the operation type corresponding to the energy trace segment, finally obtain the secret index sequence, realize the efficient analysis of the side channel of cryptographic algorithm.

The present application relates to pathological analysis technical field, specifically to a kind of pathological section intelligent analysis method and system based on deep learning, including the following steps: based on tissue section image, utilize multi-channel analysis tissue structure and dye distribution, adjust collection parameter and optimize image definition and cell structure performance, combine spatial distribution and gray layering relationship, automatically distinguish mutation area and structure boundary, output demarcation structure positioning mark.The present application is through collection sequence optimization, parameter dynamic adjustment and spatial distribution automatic discrimination, microstructure change, dye distribution and cell arrangement relationship are mapped as structure response index, realizes the efficient linkage of collection parameter and tissue characteristics, supports the synchronous identification of spatial mutation, dye layering and demarcation relationship in complex section, greatly improves the objectivity and coherence of area identification, structure annotation and demarcation positioning, avoids feature omission, enhances the integrity of data processing link and the practical depth of medical image analysis.