34062 results about "Terminal equipment" patented technology

The invention provides a monitoring early warning analysis method based on artificial intelligence and a server, and the method comprises the steps: firstly collecting a multi-source monitoring data stream of a target monitoring area, which comprises at least two kinds of real-time monitoring data and time-space label information, so as to determine a collection position and a timestamp, and then carrying out the spatial-temporal feature extraction, the method comprises the following steps: generating a spatial-temporal characteristic matrix with a hierarchical association relationship, analyzing a dynamic mode of the matrix through a preset anomaly recognition network, determining a potential anomaly event and an anomaly propagation path, and generating an adaptive dynamic early warning strategy including a differentiated trigger condition and a response instruction according to a path topological structure and event attribute parameters. And finally, optimizing and adjusting the strategy in real time by utilizing historical early warning feedback data, and outputting the strategy to a terminal equipment cluster, thereby comprehensively and accurately analyzing the monitoring data, and realizing efficient and intelligent monitoring and early warning.

The invention belongs to the field of intelligent edge systems and privacy enhancement computing, and particularly relates to a privacy protection-oriented robot large model cloud edge end collaborative reasoning and federated learning system, which comprises a cloud server layer used for deploying a large-scale pre-training model and executing complex reasoning and global federated learning coordination; the edge calculation layer is used for deploying an intermediate layer model and executing local data aggregation, privacy protection processing and intermediate feature calculation; the terminal equipment layer is used for deploying a lightweight model and executing data acquisition, primary processing and lightweight reasoning; the federated learning framework is used for optimizing the model; the privacy protection module is used for integrating data localization, differential privacy, homomorphic encryption, secure multi-party computing and a block chain verification mechanism; the adaptive allocation module is used for dynamically adjusting computing resources. According to the method, the problems of privacy leakage risk, computing resource limitation, network delay, insufficient data isolation and the like of the traditional AI service in a robot scene are solved, and efficient privacy protection and data security isolation are realized.

The invention provides an external wall visual detection method and system based on deep learning, and the method comprises the steps: firstly obtaining an initial visual image set of a plurality of external wall image units which are collected at different shooting angles and under different illumination conditions and are provided with pixel coordinate marks, and then carrying out the feature preprocessing of the initial visual image set, and generating a standardized feature image set; then, a pre-trained outer wall defect detection model is called to carry out defect feature recognition on the standardized feature image set, a preliminary detection result is obtained, and a final detection result is determined according to confidence distribution of a defect type candidate set in the preliminary detection result and spatial position relevance of candidate areas. And finally, generating an outer wall defect detection report containing defect type statistical information and a position distribution diagram based on a final detection result, and sending the outer wall defect detection report to the target terminal equipment to trigger subsequent maintenance operation. Therefore, the accuracy and efficiency of outer wall detection are improved.

The invention relates to the technical field of the Internet of Things, in particular to an intelligent device communication method and system based on the Internet of Things. The method comprises the following steps: acquiring spectrum occupation data, electromagnetic interference mode data and terminal equipment energy state data of a plurality of communication channels in an industrial environment through a preset distributed spectrum sensing network; performing adaptive spectrum analysis processing on the spectrum occupancy data and the electromagnetic interference mode data to obtain channel quality evaluation data including interference source characteristics and channel attenuation characteristics; and according to the channel quality evaluation data and a pre-acquired historical channel performance memory bank, performing priority ranking on the available communication channels to obtain dynamic channel priority data based on time continuity. The spectrum resource allocation condition in the industrial environment is monitored in real time through the distributed spectrum sensing network, the current interference can be identified, and the interference can be pre-judged through an advanced prediction model.

The invention discloses a data security dynamic evaluation system and a protection method, belongs to the technical field of information security, and is used for solving the problem of terminal equipment access control and behavior risk dynamic collaborative protection. The method comprises the following steps: generating fingerprints through terminal equipment features, verifying authorization, inputting access feature slices into a time sequence model by authorized terminal equipment, generating a trust score based on cosine similarity of a behavior sequence and a prediction sequence, and constructing a Markov model to dynamically select an authentication mode according to the trust score; calculating a risk index by combining network and geographic information, and distributing the risk index to a real or virtual environment; and the unauthorized terminal equipment distributes the virtual environment after registration. Differentiated monitoring is carried out, a time sequence library is established in the virtual environment, and doubt scores are generated through sequence matching; the state deviation of the real environment is calculated through a hidden Markov model, and the risk score is generated by fusing the multi-dimensional features. And based on the result management authority, the suspicion terminal equipment isolates and shrinks the authority, the compliance terminal equipment authenticates and migrates, and the access is regulated and controlled according to the minimum privilege principle and the time window mechanism.

The invention relates to the technical field of logistics management, in particular to an intelligent logistics terminal equipment collaborative management and control system based on AI edge computing, and the system comprises an edge data fusion and state recognition module which is deployed at an edge node, collects multi-source data of an operation state, environment perception, a communication link and the like, and generates an equipment state representation vector through fusion; the intelligent prediction and task scheduling decision module uploads the state vector to a cloud, predicts task completion capability and fault probability, and generates a task scheduling decision packet; the scheduling strategy issuing and edge execution collaboration module issues a scheduling packet through a multi-protocol gateway, and an edge node completes task distribution, communication switching and resource scheduling and caches a key strategy. According to the invention, the real-time sensing of the state of the logistics terminal equipment, the intelligent prediction and resource optimization of task scheduling, and the quick response and fault-tolerant control in a fault scene are realized, and the operation efficiency, the intelligent level and the stability of the system are remarkably improved.

The invention discloses a coastal protection dam settlement monitoring method, and belongs to the technical field of hydraulic engineering safety monitoring. The method comprises the steps that a longitudinal monitoring section is arranged on the slope surface of the back sea side of a dam, a three-measuring-line fiber grating sensor array is arranged, and vertical displacement and horizontal dip angle data are periodically collected through a synchronous triggering unit; establishing a vertical displacement-horizontal dip angle joint analysis model by using a multi-source data fusion module, and eliminating tide level interference through a Kalman filtering algorithm to generate a settlement distribution curve; and triggering third-level to first-level early warning signals based on the grading early warning rule, and transmitting the signals to the terminal equipment. The problems that a traditional monitoring method cannot effectively separate tidal interference, the real-time performance is poor, and multi-dimensional data collaborative analysis is insufficient are solved, high-precision settlement monitoring, complex environment anti-interference and rapid emergency response are achieved through multi-measuring-line sensor deployment, dynamic filtering optimization and a graded early warning mechanism, and the method is suitable for large-scale popularization and application. And the reliability and timeliness of dam safety monitoring are obviously improved.

The invention relates to the technical field of data processing, in particular to a multi-AI algorithm collaborative intelligent management system based on a large model, and the system comprises a data access module which collects original messages of terminal equipment of multiple manufacturers; the protocol adaptation module analyzes a device communication characteristic spectrum through a quantum entanglement separator, and outputs a standardized time-space event stream; a federation modeling module creates a parallel calculation instance, and a quantum tunneling mechanism is utilized to fuse the feature vectors to generate an equipment association topology model; the chaotic scheduling module generates an atomic task fractal network and assigns the atomic task fractal network to edge nodes; the interface optimization module calculates an interface field mapping relation through a quantum Boltzmann machine; and the closed-loop correction module triggers equipment communication characteristic spectrum re-calibration. Multi-source device communication differences are eliminated through quantum protocol analysis, federated quantum fusion breaks through algorithm collaboration barriers, chaotic fractal scheduling realizes resource dynamic optimization, a closed-loop collaboration system of device access to interface configuration is formed, and the problems of protocol incompatibility of multi-source heterogeneous devices and AI algorithm collaboration obstacles are solved.

The invention relates to a wireless communication test method and system based on a portable frequency spectrograph and a medium, and belongs to the technical field of radio frequency spectrum analysis, and the wireless communication test method comprises the steps: collecting electromagnetic signal original data of a target area; constructing a dynamic electromagnetic environment baseline model; performing filtering and background subtraction processing on the original data of the electromagnetic signal; carrying out modulation mode identification, protocol analysis and terminal identification extraction on the preprocessed signal matrix, generating a multi-dimensional feature vector, carrying out convolution similarity matching on the multi-dimensional feature vector and a legal signal feature template, and distributing a threat level label; dynamically reconstructing scanning parameters of the portable spectrum analyzer according to the threat level label, generating an equipment parameter adjustment record and updating the electromagnetic fingerprint database; and generating a spectrum optimization control instruction based on the threat level label and the equipment parameter adjustment record, and issuing the spectrum optimization control instruction to the target base station or the terminal equipment. According to the application, the identification precision of the threat signal and the control closed-loop linkage capability in a complex electromagnetic environment are improved.

The invention discloses a law shield AI intelligent case handling all-in-one machine system based on a large language model, and relates to the technical field of law artificial intelligence and judicial informatization, the system comprises an integrated terminal device, a case semantic modeling module, a class case knowledge engine, a risk prediction module, a large language model service interface and an intelligent document generation module; the system constructs a case semantic graph through multi-modal information fusion and a graph neural network, performs legal rule path matching and similarity reasoning based on a class case database, outputs structured legal suggestions and standard legal instruments in combination with user context recognition and large language model multi-round generation capability, and realizes dynamic updating of the semantic graph. The method improves the automation, structuring and interpretability of case processing, and is suitable for intelligent case handling scenes such as legal assistance, litigation assistance and judicial mediation.

The invention provides an energy storage cabin terminal remote upgrading method and system based on communication protocol optimization, and relates to the technical field of digital information transmission, and the method comprises the steps: deploying a bandwidth detection mechanism, adjusting a self-adaptive block transmission strategy, and dividing upgrading firmware into M dynamic data packets; constructing a dual-path transmission layer, and configuring a dual-path redundancy check mechanism; a missing block query rule is adopted, and a local flash memory is used for caching Hash check values of the received N dynamic data packets; analyzing a mapping relation between the block index and a multi-path redundancy check mechanism, and establishing a check mapping table; and performing real-time verification and state marking, and configuring a remote upgrading process to execute the upgrading operation of the energy storage cabin terminal equipment after incremental recombination is performed by comparing and verifying abnormal data packets. According to the invention, the technical problem of insufficient reliability of data transmission caused by lack of adaptability to a dynamic network environment due to the fact that most of energy storage cabin remote upgrading technologies in the prior art depend on a single communication path to carry out data transmission is solved.

A method for synchronizing audio reproduction in collocated end devices is presented. Each of the devices auto-correlates using noise to determine a threshold prior to the antenna receiving an audio signal. When the devices receive a common audio signal, they provide audio outputs. Each device cross-correlates its audio output with the audio outputs of the other devices. The timing of the audio output of each device is then adjusted such that the audio outputs of all of the devices align temporally with the lagging or leading device.

The invention relates to a secure communication method between an edge server and terminal equipment. The secure communication method comprises the following steps: establishing a physical layer channel state information database, generating a multi-dimensional feature vector and encoding the multi-dimensional feature vector into a terminal equipment identifier, generating an identity authentication request by the terminal equipment, and performing hash operation to obtain a first hash value; the identity authentication request and the first hash value are sent to the edge server, the edge server verifies the identity authentication request and queries a corresponding device key, performs hash operation to obtain a second hash value, generates an authentication response message after successfully comparing the second hash value with the first hash value, encrypts the authentication response message by using the device key, and sends the encrypted authentication response message to the edge server; and sending the encrypted authentication response message to the terminal equipment. And the terminal equipment decrypts by using the equipment key to obtain the session key, and communicates with the edge server based on the session key. By using the physical layer channel information and the equipment hardware characteristics, the security communication method fusing dynamic identity authentication and efficient key management improves the security, reliability and anti-attack ability of communication between the edge server and the terminal equipment.

The invention relates to the technical field of cloud side end computing, and discloses a cloud side end cooperative computing resource dynamic allocation method based on task-aware migration. The method comprises the following steps: acquiring real-time load characteristics and resource demand characteristics of calculation tasks in a cloud side end system, and dividing task priority queues in combination with task type identifiers; extracting historical execution records of tasks at cloud, edges and terminal nodes, constructing a task execution feature library, and generating a resource demand prediction model in combination with real-time load features; analyzing network transmission time delay characteristics of a cloud end and edge nodes, measuring real-time calculation capability fluctuation data of terminal equipment, and establishing an inter-node resource collaboration degree evaluation matrix; generating an initial migration strategy according to the prediction model and the evaluation matrix, monitoring actual resource occupancy deviation of the task, forming a final decision in combination with a node resource state correction strategy, triggering cross-node migration, and synchronously updating the priority queue and the evaluation matrix.

The invention relates to the technical field of fault diagnosis, and discloses a photovoltaic power station intelligent analysis and fault intelligent diagnosis method and system. The method comprises the steps of collecting electrical and environmental data of a photovoltaic power station through a sensor network, transmitting the electrical and environmental data to a data processing unit for preprocessing and standardization, and extracting multi-dimensional features to construct a feature matrix; and inputting the feature matrix into a multi-scale pulse neural network for fault diagnosis to obtain fault type and position information. And evaluating the severity of the fault according to the fault result, generating a maintenance scheme, and finally sending the maintenance scheme to the terminal equipment through the management system. The intelligent fault diagnosis system based on the pulse neural network is introduced, and the fault propagation analysis, the maintenance priority distribution, the path planning and the resource matching optimization are combined, so that the problems of low fault diagnosis precision and low maintenance resource scheduling efficiency in the prior art are solved, and the operation and maintenance efficiency and the equipment reliability of the photovoltaic power station are improved.

The invention relates to an advanced gastric cancer survival prediction system based on Lasso regression, Cox regression and an interpretable machine learning technology, and belongs to the technical field of medical artificial intelligence and intelligent decision support. According to the system, by collecting multi-modal clinical data (including demographic information, TNM staging, treatment modes, tumor grading and the like) of a patient, survival-related variables are screened by adopting Lasso regression and a Cox proportional risk model, and an optimized feature set is constructed. Based on the feature set, the system integrates various mainstream machine learning algorithms (such as XGBoost, Random Forest, SVM, Logistic regression and the like) to construct a prediction model, compares the performance of each model, and selects a model with an optimal effect as a main model. And hyper-parameter tuning is performed on the model through grid search and cross validation, so that the precision and generalization ability of the model are improved. An SHAP interpretability analysis method is introduced into the system, transparent interpretation is carried out on a model output result from the global level and the individual level, and the importance and directional effect of all variables in survival prediction are determined. Finally, the model is deployed on a terminal device, a doctor is supported to automatically output the survival probability and an explanation result after inputting patient information, and a reference basis is provided for clinical treatment decision and personalized management. The system has the advantages of high prediction precision, high interpretability, convenience in use, sustainable optimization and the like, is suitable for clinical aid decision-making scenes, and has good application prospects and popularization values.

The invention discloses an edge cloud hierarchical collaborative task unloading optimization method fused with federal learning. According to the method, a three-level collaborative architecture of a terminal equipment layer, an edge node layer and a cloud center layer is constructed, and a task is split into a sub-task set containing a dependency relationship through a task analysis module. The edge node adopts a mixed model of a graph attention network and a double-delay depth deterministic strategy gradient, generates an unloading proportion in combination with an edge cloud topology and a real-time resource state, and realizes differential privacy protection through Laplace noise. In the federal learning process, the edge node only uploads Parel encryption parameters after local training, and the cloud generates a global meta-model through secret sharing aggregation and issues and updates the global meta-model. The multi-objective optimization model takes time delay, energy consumption and communication traffic as objectives, dynamically adjusts weight coefficients, and forms a'unloading-execution-learning-optimization 'closed loop. The method gives consideration to privacy protection and real-time performance, and is suitable for scenes with high requirements for privacy and timeliness.

The invention relates to the technical field of Internet of Things, in particular to a multi-protocol conversion Internet of Things terminal communication method. Comprising the following steps: monitoring a communication data packet sent by Internet of Things terminal equipment in real time through a protocol sniffing module, and extracting a protocol feature field; matching the protocol feature field based on a dynamic protocol library, and identifying a native protocol type of the equipment; generating a protocol conversion strategy according to a target platform protocol demand, and calling a corresponding protocol analysis engine to perform semantic analysis on the data packet; the analyzed data content is reconstructed according to a target protocol format, and a unified communication interface packaging layer is added; and an optimal communication link is selected through an adaptive routing algorithm, and cross-protocol data transparent transmission is completed. The invention provides an integrated communication method capable of realizing efficient protocol dynamic identification, high-reliability semantic conversion, intelligent link optimization and safe packaging, so as to break through the technical barrier of multi-protocol Internet of Things terminal interconnection.

The invention discloses an intelligent computing power integration service management method and platform based on cloud side-end cooperation, and relates to the technical field of computing power integration management, and the method comprises the steps: sensing the computing power resource state of a side-end cooperation port and terminal equipment in real time at a cloud controller, and building a resource topological graph; a dynamic task demand is introduced, and a computing power scheduling vector is generated; carrying out lightweight segmentation on the cloud training model, and determining adjacent edge nodes under hierarchical limitation to form a regional elastic cluster; and deploying a digital twin simulation engine rehearsal computing power distribution scene, establishing a heterogeneous resource pooling mechanism, and carrying out computing power integration service management. The technical problems of low management efficiency and insufficient utilization rate of heterogeneous computing power resources in the prior art are solved, and the technical effects of realizing efficient management of intelligent computing power integration services and improving the utilization rate of the heterogeneous computing power resources are achieved.

The invention discloses a method for improving transformer area crosstalk of HPLC (High Performance Liquid Chromatography) and micropower wireless dual-mode communication, which comprises the following steps of: S1, electrifying an acquisition terminal equipment node, and aligning signal acquisition time windows of two communication modes; s2, combining the obtained dual-mode signal parameters with a preset transformer area topology database to generate feature vectors of multi-dimensional features; s3, training to generate a random forest model, inputting feature vectors of multi-dimensional features, dynamically calculating the affiliation weight value of the transformer area, and carrying out nonlinear weighted calculation on the feature vectors; and S4, on the basis of weight comparison and transformer area boundary node processing, correcting measurement errors by combining the transformer area attribution weight value with a preset impedance shielding compensation algorithm, generating a unique transformer area identifier, locking a networking relationship, and executing transformer area attribution judgment. According to the method, the wide coverage advantage of HPLC is reserved, meanwhile, the space attenuation characteristic of wireless communication is used for achieving accurate recognition of the transformer area boundary, and the installation and acquisition stability of the use and acquisition terminal equipment can be improved.

The invention relates to a secure communication method for an edge node and terminal equipment based on dynamic key negotiation. The method comprises the following steps: the terminal equipment sends an initial signal when initiating a communication request; the edge node generates a scenarized first key negotiation parameter and feeds back the scenarized first key negotiation parameter after passing dual identity and state verification; after the terminal device decrypts the parameter, a dynamic entropy value is collected by combining a network adaptive sensor, and a second key negotiation parameter associated with the first parameter feature is generated; the two parties calculate session keys based on an ECDH algorithm, and the consistency of the keys is ensured through dynamic entropy verification and Hash comparison; and after the key negotiation succeeds, entering a hierarchical encrypted data transmission stage, and dynamically updating a session key based on a multi-dimensional trigger mechanism. According to the method, through hardware credibility verification, a scenarized key strategy, dynamic entropy enhancement and national secret algorithm adaptation, dynamic management of keys and adaptive matching of terminal resources are achieved, attacks such as equipment counterfeiting and parameter tampering are effectively resisted, and the high-security and compliance requirements in the fields of industrial control and the like are met.

The invention discloses a lithium battery residual life prediction method and system and terminal equipment, and relates to the technical field of lithium battery health management. The method comprises the following steps: receiving a battery capacity attenuation sequence as an original input sequence, detecting and filtering abnormal data by adopting a 3 sigma criterion, and carrying out noise suppression processing on the battery capacity attenuation sequence through a Dropout mask; and carrying out normalization processing on the preprocessed battery capacity attenuation sequence, dividing the battery capacity attenuation sequence into a training set, a verification set and a test set through a sliding window algorithm, and generating a time sequence characteristic matrix and a corresponding residual service life label. According to the method, a neural network structure fusing trend prior perception and dynamic attention regulation is constructed, a multi-scale capacity modeling strategy is introduced to separate a degradation trend, fluctuation disturbance and high-frequency noise, and compared with a traditional time sequence neural network or a single attention model, pseudo fluctuation characteristics caused by capacity regeneration can be more effectively recognized, and the method is more efficient and more reliable. And the judgment accuracy of the model in a complex degradation scene is improved.

A wireless communication method includes the following. A user equipment (UE) receives a network discontinuous communication configuration sent by a base station, where the network discontinuous communication configuration includes a network discontinuous communication configuration for a cell and / or a network discontinuous communication configuration for the base station.

Apparatus and methods of channel access priority determination for sidelink transmission are disclosed. The method includes: providing a mapping relationship between PC5 Quality of service (QoS) Indicators (PQIs) and Channel Access Priority Classes (CAPCs), based on delay requirements of the PQIs; and in response to presence of only STCH SDUs from one or multiple SL-DRBs in a SL-MAC PDU for SL transmission to another terminal device, to determine, from the mapping relationship, a corresponding CAPC for the SL-DRB from one or more PQIs from the SL-DRB; and to determine a CAPC for the SL-MAC PDU for SL transmission to the other terminal device, based on the corresponding CAPC of the SL-DRB; in response to presence of SCCH SDUs from one or multiple SL-SRB in the SL-MAC PDU for SL transmission to the other terminal device, to determine a corresponding CPAC of the SL-SRB; and to determine the CAPC for the SL-MAC PDU for SL transmission to the other terminal device, based on at least the corresponding CAPC of the SL-SRB as determined; and in response to presence of only a SL MAC CE for SL transmission to the other terminal device, to determine a corresponding CPAC of the MAC CE; and to determine the CAPC for the SL-MAC PDU for SL transmission to the other terminal device, based on at least the corresponding CAPC of the MAC CE.

The invention discloses an image encryption system and method in an intelligent power grid environment, and aims to solve the problem that an encryption strategy is static and lacks self-adaptive capability in the prior art. The image encryption system comprises terminal equipment, edge equipment and central equipment, and a dynamic key generation unit on the terminal equipment generates a dynamic key by cooperatively utilizing a physical unclonable feature (PUF) of the equipment and a real-time load of a power grid; the hierarchical encryption unit performs encryption of different intensities based on image content complexity. A topology sensing transmission module of the system analyzes power grid topology by using a graph neural network (GNN) and dynamically optimizes transmission. In addition, the image encryption system further integrates low-density parity check code encoding and decoding and a self-adaptive decryption strategy so as to enhance the anti-interference capability. According to the invention, by constructing a sensing, decision-making and execution integrated adaptive security system, the security, high efficiency and robustness of smart grid image data transmission are significantly improved.

The invention discloses a lithium ion battery immersed liquid cooling regulation and control method and device, terminal equipment and a storage medium, and relates to the field of lithium ion batteries, and the method comprises the steps: obtaining the current temperature data, historical temperature data and cooling liquid fluid parameters of a distributed array in a lithium ion battery, and determining the current temperature field distribution and predicted temperature field distribution; constructing a state space, and inputting the state space into the reference decision model, so that the model outputs corresponding actions according to the current state space by taking the minimum temperature difference of the battery module and the minimum cooling energy consumption as awards; according to the corresponding action, the micro electromagnetic valve and the cooling liquid pump are subjected to benchmark regulation and control; acquiring temperature data after reference regulation and control, and determining temperature deviation in combination with a preset temperature requirement; and regulating and controlling according to the temperature deviation. By implementing the method and the device, the stability of global temperature control is improved, so that the problem of temperature interference on other areas due to cooling liquid flow coupling caused by directly adjusting the opening degree of the valve according to local temperature difference in the prior art is solved.

The embodiment of the invention provides a cloud side-end cooperative control method and system based on remote communication. The method comprises the following steps: generating a task scheduling input data set according to real-time operation state data generated by terminal equipment in a remote communication process, current computing resource information of an edge computing node and global optimization target parameters issued by a cloud management system; calling a preset task scheduling decision model to carry out structured splitting on the cloud side-end cooperative control task to obtain original task structure information; task structure labeling information is generated for sub-task labeling priorities in combination with multi-aspect information, and cyclic matching deduction processing is performed on the task structure labeling information to obtain a hierarchical resource allocation result; and distributing the sub-tasks to corresponding cloud edge end nodes (including a first node corresponding to the terminal device, a second node corresponding to the edge computing node and a third node corresponding to the cloud management system) for execution, and configuring a task execution process adjustment mechanism based on real-time resource change to adapt to resource fluctuation.