71580results about "High level techniques" patented technology

An agent policy learning method with privacy protection in mobile edge computing, which belongs to the technical field of mobile communications. The method comprises: establishing an edge collaborative computing unloading model which comprises a service caching model, a task unloading model and a system cost model; on the basis of the edge collaborative computing unloading model, using the minimization of the processing cost of a task as a target to establish an optimization problem of joint task unloading, service caching, computing power resource allocation and transmission power control; abstracting an optimal problem into a partially observable Markov decision process; and on the basis of the Markov decision process, using a multi-agent deep reinforcement learning algorithm based on a federated learning method to autonomously learn a task unloading, service caching, computing power resource allocation and transmission power control policy. In the present invention, an edge collaborative computing unloading model is established, such that an agent network protects privacy security of data and sensitive information of users by means of training based on federated learning.

The invention discloses an underground facility vibration monitoring method and system based on a distributed sensor network. The method comprises the following steps: acquiring initial data of the distributed sensor network; constructing an initial network topology structure; collecting sensor node state data; dynamic topology adjustment is executed; network performance is evaluated and optimized; an optimized network topology structure is obtained; receiving original data acquired by the sensor and performing data cleaning and standardization by using a network topology structure; a filtering algorithm is adopted to remove noise, and features are analyzed and extracted; and generating a preprocessed feature data set. A multi-source data fusion algorithm module is adopted to integrate information; constructing an anomaly prediction model and optimizing parameters, and performing anomaly detection by adopting the optimized anomaly prediction model; outputting the fused data and an anomaly detection result; and judging whether to trigger early warning or not according to a preset threshold value for an abnormal detection result. Efficient and reliable real-time vibration monitoring is realized, and the anomaly detection accuracy and the system adaptability are improved.

The dynamic bandwidth allocation and resource scheduling method based on the 5G network comprises the steps that S1, a base station collects service requests, network loads and channel information of user equipment of the 5G network, and the services are divided into real-time services, big data services, key services and conventional services; s2, the real-time service calculates the bandwidth by using deep learning; the big data service is distributed according to the idle bandwidth and the data volume proportion; key service main and standby links distribute bandwidths; conventional businesses come first and then are subjected to service distribution; s3, the base station maps bandwidth resources by using a multi-carrier and dynamic subcarrier algorithm, and optimizes signal transmission in combination with a beam forming technology; s4, processing service data according to a priority scheduling queue, and verifying signal transmission according to a channel quality adjustment coding mode; and S5, updating the data and evaluating the effect according to the user feedback, and re-optimizing the scheduling strategy as required. Through intelligent dynamic bandwidth allocation and resource scheduling, efficient, flexible and reliable transmission of different types of services is realized, and the network resource utilization rate and the service quality are improved.

The invention discloses an information security management method based on data processing, and particularly relates to the field of information security management, comprising multi-source heterogeneous data acquisition, multi-dimensional feature index calculation, comprehensive threat detection calculation and adaptive threat decision. According to the method, the covert communication channel is accurately identified by constructing the encrypted traffic multi-mode detection model and combining protocol fingerprint entropy and traffic self-similarity analysis; an improved entropy weight fusion algorithm is adopted, collaborative analysis of system process abnormity, network behavior offset and hardware degradation is achieved, and a cross-dimension attack chain is reconstructed; a storage medium physical feature-cryptographic module runtime joint monitoring mechanism is created for the first time, hardware layer physical attacks and cryptographic side channel vulnerabilities are synchronously defended through temperature-delay correlation evaluation and key derivation density analysis, a complete attack surface from a physical layer to an application layer is covered, and the security is improved. And the detection precision and the response real-time performance of the complex threats are obviously improved.

The invention discloses a multi-hop communication node intelligent scheduling method and system, and relates to the technical field of communication networks. The method is used for solving the scheduling and switching problems of multi-hop relay nodes in disaster emergency communication. Residual energy, channel fading parameters and link load data of an unmanned aerial vehicle and a ground terminal are collected, a communication potential index is calculated in combination with dynamic weight fusion, and the relay capability of a node in a complex environment is accurately evaluated. A relay node selection threshold is set according to the disaster type, nodes with communication potential indexes higher than the disaster adaptation threshold are preferentially selected, an initial hybrid topology structure with preferential survivability is constructed, and the network stability and survivability are improved. An autonomous game agent model is adopted, a multi-target reward function is designed based on local channel quality, residual energy and task emergency degree, it is ensured that the network achieves collaborative optimization of energy consumption balance, transmission real-time performance and survivability under the condition of dynamic topology and node failure, and efficient and continuous optimization of the disaster emergency communication network is achieved.

The invention discloses a network line intelligent operation and maintenance monitoring management system and method. The method comprises the steps that an intelligent network monitoring and optimizing system regularly collects network equipment information through a protocol, and a monitoring agent collects server and machine room environment data; processing data in real time by adopting distributed storage and a stream processing framework; predicting a network fault by adopting threshold monitoring and a machine learning model, and identifying a bottleneck and scheduling resources based on flow analysis; in combination with deep packet inspection and an AI algorithm, network traffic is monitored in real time, security threats are identified, and IPS system protection is deployed; a repair operation is automatically triggered through a self-healing strategy, and backup resources are started; the AI engine analyzes historical and real-time data, generates network topology and link state visual content, and automatically generates reports, optimization and decision support; according to the intelligent operation and maintenance monitoring management system and method for the network line, a fault tree analysis and decision support system is introduced, and a plurality of fault reasons are intelligently analyzed and repair measures are automatically executed.

The invention relates to the technical field of data processing, and discloses a road infrastructure health monitoring and evaluation method and device, equipment and a medium. The method comprises the following steps: performing multi-mode acquisition and multi-path transmission processing on data acquired by an intelligent sensor network to obtain an original monitoring data set; cleaning, standardizing and fusing the original monitoring data set to obtain preprocessed structured data; performing multi-dimensional spatial correlation analysis on the structured data through a multi-scale time sequence analysis algorithm to obtain health state evaluation data of the road infrastructure; based on the health state evaluation data, performing dynamic threshold calculation and intelligent diagnosis on the road infrastructure to obtain early warning information and a preliminary diagnosis result; and performing expert knowledge base matching and multi-criterion decision analysis on the early warning information and the preliminary diagnosis result to obtain a maintenance decision suggestion and an optimization scheme. The road infrastructure health monitoring and evaluation efficiency and accuracy are improved.

The invention relates to the technical field of network security situation generation, in particular to a network security situation generation method based on multi-view monitoring. The method comprises the following steps of: constructing a multi-view data acquisition system, acquiring real-time information from a multi-dimensional data source, and comprehensively capturing potential threats by utilizing an intelligent sensing technology; cleaning, noise filtering and feature extraction are performed on the acquired data based on a dynamic threshold self-adaption multi-factor cleaning method, so that the accuracy and consistency of the data are ensured; a dynamic analysis algorithm based on deep learning is adopted, the incidence relation between the security events is mined, and the development trend of potential threats is predicted; constructing a joint learning model, and comprehensively evaluating the security state of the network environment; the security situation is dynamically displayed by using a visualization technology, and a security policy is quickly generated in combination with an automatic early warning and response mechanism to resist threats. According to the method, accurate evaluation and prediction of the network security situation are realized, and support is provided for security decision-making in a complex network environment.

The invention relates to a cross-system data intercommunication method, platform, device and medium, and belongs to the field of data transmission, the method comprises the following steps: accessing at least two heterogeneous data source systems through a protocol adapter; analyzing a field name, a data type and a constraint condition of each source system, generating a standardized metadata file, and extracting an original data stream; performing data cleaning and format conversion on the original data stream, and outputting cleaned structured data and processing logs; performing dynamic encryption according to the sensitive level mark, generating a security data packet queue based on the access permission attribute of the target system, sending the security data packet queue to the target system, and recording a transmission log; verifying the data consistency of the target system and the source system, carrying out abnormal retransmission, updating a transmission log, and generating a blood relationship map containing a data flow path; and dynamically adjusting a data cleaning rule and a transmission parameter according to the blood relationship map and the updated transmission log. The stability and efficiency of data intercommunication can be improved.

A modular surgical system for use in a surgical procedure is disclosed. The modular surgical system includes a control module including a power supply and surgical modules including a first surgical module and a second surgical module. The first surgical module is arranged in a stack configuration with the control module and the second surgical module. The power supply is configured to output power to the first surgical module and the second surgical module. A power backplane is configured to deliver the power to the first surgical module and the second surgical module through the first surgical module. A control circuit is configured to adaptively adjust power allocations to the first surgical module and the second surgical module.

A collaborative task offloading and service caching method based on graph attention multi-agent reinforcement learning, which method belongs to the technical field of mobile communications. The method comprises: constructing an MEC system in an MEC network scenario where there are multiple base stations and diversified service requests; modeling a collaborative task offloading and service caching problem by taking as an objective the maximization of a system utility based on QoE; converting the collaborative task offloading and service caching problem into a decentralized partially observable Markov decision process; and using an algorithm based on graph attention multi-agent reinforcement learning to solve the decentralized partially observable Markov decision process obtained by means of conversion. By means of a simulation experiment, compared with a benchmark algorithm, significant improvements are made in terms of a cache hit rate, system utility and computation success rate to the algorithm proposed herein.

The invention relates to the technical field of data transmission, and discloses an intelligent flight data transmission method and system based on satellite communication and a storage medium. The method comprises the following steps: carrying out real-time data acquisition and calculation on signal strength, bandwidth, delay and packet loss probability of an available satellite communication link to obtain a real-time link parameter table and a transmission channel allocation sequence; based on the real-time link parameter table, according to the priority information of the encrypted data packet queue and the transmission channel distribution sequence, performing multi-channel distribution and parallel transmission processing on the data packet to be transmitted to obtain a data packet transmission record; and carrying out statistical analysis and parameter calculation on the transmission success rate, delay time and retransmission times in the data packet transmission record to obtain transmission quality data and a system optimization parameter set. Under the condition of limited satellite communication resources, intelligent hierarchical transmission of flight data is realized, timely and reliable transmission of important data is ensured, and meanwhile, a real-time monitoring and optimization mechanism for data transmission quality is provided.

A base station may enable a power saving operation for a wireless device. The power saving operation may comprise a base station periodically operating a discontinuous transmission (DTX) mode, wherein at least one cell is in a power-on state for a first duration and in a power-off state for a second duration. Instead of following the same on / off durations of the at least one cell, the wireless device may operate a wireless device-specific discontinuous reception (DRX) mode, wherein the DRX on / off states may be implemented based on one or more messages indicating cell DTX configurations.

A non-access point multi-link device (non-AP MLD) receives from an access point multi-link device (AP MLD), while in a first power save mode, a beacon frame comprising a traffic indication map (TIM) element indicating downlink buffered units (BUs) for the non-AP MLD, where during the first power save mode the non-AP MLD wakes to listen for beacon frames on one of enabled links at least once within a listen interval. After receiving the downlink BUs from the AP MLD, the non-AP MLD switches to a second power save mode, where during the second power save mode the non-AP MLD wakes to listen for beacon frames at each target beacon transmission time (TBTT) on the enabled links until a timer expires.

The invention relates to the technical field of network security, in particular to a network security dynamic early warning method and system based on a knowledge graph, and the method comprises the following steps: collecting real-time traffic data of a network, analyzing a data sequence by recognizing the characteristics of a state sequence and using a long and short-term memory network, and recognizing a behavior mode; and constructing a state time sequence model. According to the method, structured analysis of network behaviors is realized through time dependence analysis, refined mining of incidence relations between events, determination of a trigger chain and time relation of potential threats and reasoning process of a knowledge graph, abnormal behaviors and attack behaviors can be identified more accurately, global analysis capability of network threats is enhanced, and the network threats can be analyzed more accurately. The network environment change can be adapted in real time by dynamically adjusting the early warning threshold value and the early warning parameter, a more flexible and efficient threat response mechanism is realized, the combination of the early warning signal and the security event response is updated in real time, and the response speed and the processing efficiency of the security problem are optimized.

Various solutions for using on-demand reference signal (RS) or system information block (SIB) for network energy saving with respect to user equipment and network apparatus in mobile communications are described. An apparatus may determine whether to trigger an on-demand RS / SIB or SIB request according to a trigger condition. The apparatus may transmit the on-demand RS / SIB or SIB request to a network node in an event that the trigger condition is satisfied. The apparatus may receive a response of the on-demand RS / SIB or SIB request from the network node. The apparatus may perform an on-demand RS / SIB or SIB measurement according to the response.

A base station can receive, while a primary cell (PCell) and a secondary cell (SCell) are in a second power state, an uplink wake-up signal from a wireless device. In response to receiving the uplink wake-up signal, the base station can transition the PCell to a first power state and maintain the SCell in the second power state.

Disclosed in the present invention is an SDN-architecture-based routing method for guaranteeing network QoS. The method comprises: on the basis of network topology information, performing abstraction to obtain a directed graph; then, on the basis of an improved K-means algorithm, dividing data into mouse flows, elephant flows and mammoth flows; defining a traffic transmission constraint condition; constructing a DQN neural network; training the DQN neural network by using the maximum Q value, which corresponds to an action a having the maximum reward function R, as an objective, so as to obtain a QoS guarantee routing model; obtaining an optimal path weight value; determining a data packet sending path on the basis a Dijkstra algorithm; and finally issuing path information to a switch. The present invention avoids the situations in which data processing performed by using a conventional K-means algorithm is not stable enough and a classification result is not accurate enough. During the training of a DQN neural network, data is extracted from an experience pool according to the priority of data, such that the neural network pays more attention to important data, thereby improving the learning efficiency of the neural network and the performance of the algorithm.

The invention discloses an RIS-assisted unmanned aerial vehicle network-based communication and sensing integrated system and method. The system comprises a UAV, an MBS module, a user communication association decoupling module and an RIS module. The RIS intelligent reflecting surface and the unmanned aerial vehicle network are applied to the communication and sensing integrated system. In consideration of different service requirements in the system, the process is divided into a communication part and a sensing part. And a deep reinforcement learning algorithm is designed according to the requirements of the system to control the UAV flight path, base station beam forming, user association allocation and RIS phase shift in the system, so that the communication and sensing performance in the communication and sensing integrated system is optimal. According to the invention, while all functions of an original communication and inductance integrated system are reserved, RIS and unmanned aerial vehicle networks are introduced to assist the system, and an alternate iterative optimization method and a deep reinforcement learning algorithm are used to realize scheduling and decision-making of each resource. Compared with the prior art, additional channel gain is provided, and the distribution efficiency of communication sensing resources is improved.

A method (2300) performed by a first network node (901) for coordinating with a second network node (902) with respect to a user equipment, UE, energy state assistance information, UE-ESAI, configuration for a UE (900) or a group of UEs currently being served by the first network node. The method includes generating (s2302) a first coordination message. The method also includes transmitting (s2304) the first coordination message to the second network node. The first coordination message comprises information related to configuring the UE or the group of UEs to store and / or report UE-ESAI, and the UE-ESAI comprises a measured or estimated first energy metric.

The invention discloses a self-adaptive communication method and system for ubiquitous electric power Internet of Things intelligent metering equipment, relates to the technical field of Internet of Things communication, and solves the problems of inaccurate quantitative communication, insufficient communication efficiency and poor flexibility in existing intelligent metering equipment communication. According to the invention, the accuracy and reliability of data transmission are improved through a dynamic network self-organizing algorithm and a multi-hop communication mechanism, the communication efficiency is optimized through multi-path transmission and dynamic spectrum management, and the flexibility and adaptability of the network are enhanced through an adaptive modulation coding model and a real-time optimization link selection strategy. And secure transmission of the data is further guaranteed through a multi-layer security mechanism, and the communication performance and flexibility of the intelligent metering equipment are remarkably improved.

The invention provides a tunnel safety-oriented safety step pitch monitoring method and system, and the method comprises the steps: deploying an edge calculation module at a sensor node, carrying out the preprocessing and compression of collected monitoring data, reducing the data transmission amount through data dimension reduction and feature extraction, relieving the data transmission pressure, and improving the data transmission efficiency and real-time performance. An incremental learning algorithm is adopted to carry out online analysis on monitoring data, an anomaly detection model is dynamically updated according to a newly collected data sample, an early warning threshold value is adaptively adjusted, the accuracy and the real-time performance of anomaly recognition are improved, and misinformation and missing report are reduced; a multi-sensor data fusion technology is comprehensively applied, correlation analysis is carried out on sensor data of different types and different positions, the reliability and accuracy of monitoring data are improved through data redundancy and complementation, and comprehensive evaluation of the tunnel construction safety state is achieved.

Various embodiments herein provide techniques related to a main receiver of a user equipment (UE) and a wake-up receiver (WUR) of the UE. In the embodiments, the WUR receives a low-power wake-up signal (LP-WUS) from a base station. Based on the LP-WUS, the WUR may be configured to wake-up the main receiver of the UE, wherein the UE identifies received configuration information including duty cycle parameters and detects the LP-WUS based on the configuration information, and wherein the UE sets a state of the wake-up receiver based on an RRC state of the main receiver.

Systems, methods, and instrumentalities are described herein for supporting low power high accuracy positioning (LPHAP). A wireless transmit / receive unit (WTRU) may receive configuration information (e.g., in a radio resource control (RRC) reconfiguration message) from a first cell. The configuration information may indicate a first sounding reference for positioning (SRSp) configuration and a second SRSp configuration. The first SRSp configuration may be associated with a first positioning area and the second SRSp configuration may be associated with a second positioning area. The first positioning area may include a first cell ID set and the second positioning area may include a second cell ID set. The first SRSp configuration may be activated and the second SRSp configuration may be deactivated.

Various aspects pertaining to apparatuses, methods, and computer-readable medium are described herein. Some aspects pertain to wireless communication between various apparatuses, such as an access point (AP) and a station (STA). The AP may have a radio, and the STA may have a first radio and a second radio. The radios may have various power states. Some aspects pertain to negotiation of various communication parameters. Some aspects pertain to communication of a packet and execution of various related operations. Some aspects pertain to certain uplink (UL) and / or downlink (DL) communications. Some aspects pertain to termination or teardown of the negotiated parameters. The written description and appended drawings provide detailed descriptions regarding these and many other aspects.

The invention provides an encryption transmission method and system of a wireless information transmission communication base station, relates to the technical field, discloses the encryption transmission method of the wireless information transmission communication base station, and belongs to the technical field of wireless communication. The method comprises the following steps: receiving a communication signal and extracting identity authentication information and a signal characteristic parameter; performing identity authentication scoring based on a deep neural network, performing signal feature scoring through spectral analysis, and constructing a communication security assessment model; a key pair is generated by adopting an elliptic curve cryptographic algorithm, and data is encrypted after being subjected to confusion transformation and diffusion transformation; transmitting a key by using a quantum key distribution technology; and selecting an optimal transmission channel based on the operation state data of the communication base station, and performing data transmission by adopting interleaving coding and adaptive power control technologies. According to the invention, the security and transmission reliability of wireless communication are improved, and the anti-interference capability is enhanced.

Disclosed is a suppression method for a multipath signal of an image mode based on correlation peaks of a satellite baseband signal, including the steps of: constructing a real-world direct-multipath two-dimensional color image mode data set; and building a multipath suppression model of a deep learning network based on a long-short term memory (LSTM) and a self-attention mechanism module, and training the model. In the present disclosure, a satellite signal can be quickly captured without losing the sensitivity of the captured signal, and a Beidou satellite baseband signal can be captured from a signal recorded in the real world, which enables the model to learn complex signal patterns, improving the accuracy and robustness of multipath signal suppression in urban complex scenes.

The present invention relates to the field of network security technology, specifically to a method and system based on network security threat information collection, comprising the following steps: based on network security threat information, extracting network attackers and attack methods, identifying and classifying attacker identifiers and target types, and standardizing data to obtain a standardized threat behavior data set. In the present invention, by extracting attack behavior information from security event logs and intrusion detection alarms, parsing subject-predicate-object structures, achieving data standardization, improving the accuracy of identifying attacker identities and attack methods, merging attack chains, enhancing intelligence coherence and path identification accuracy, using target co-occurrence probability and consistency weight calculation, optimizing event merging, reducing information redundancy, analyzing behavior node time dependency, summarizing similar attack paths, improving attack pattern recognition capabilities, sorting path priorities according to node weight values, screening high-risk chains, focusing on emergency response, and improving security defense efficiency.

The invention relates to the technical field of electromagnetic pulses, and discloses an electromagnetic pulse data transmission method. The method comprises the following steps: collecting natural electromagnetic pulse signal data of a detection target area through a six-dimensional complementary electrode array structure, and executing Kalman filtering peak extraction and characteristic frequency locking to obtain frequency-optimized signal data; executing first-order and second-order gradient difference matrix calculation and space vector synthesis to obtain signal source position coordinate data and signal intensity data; performing spectral domain reconstruction and multilayer dynamic threshold analysis to obtain a spatial clustering abnormal data packet; and selecting an optimal transmission channel to transmit the spatial clustering abnormal data packet to a cloud server. According to the invention, the influence of an external interference source is effectively eliminated, accurate extraction and characteristic frequency locking of effective frequency band signals are realized, power frequency and harmonic interference thereof are effectively suppressed, the signal-to-noise ratio of the effective frequency band of the signals is improved, and the reliability and timeliness of data transmission in a severe field environment are ensured.

The invention discloses an Internet of Things data transmission optimization system based on intelligent routing, and relates to the technical field of data transmission optimization. The heterogeneous network access module supports adaptive switching of multiple protocols such as 5G, NB-IoT and LoRa, and monitors link quality; intelligent routing decision selects an optimal path by means of deep reinforcement learning, and end-to-end delay is reduced; data distribution scheduling split data streams are transmitted in a multi-path parallel mode, and forward error correction is combined to guarantee reliability. The network state prediction uses an LSTM to predict bandwidth, and a route is adjusted in advance; the secure transmission integrates iris authentication and dynamic encryption. According to the invention, efficient, reliable and secure transmission is cooperatively realized, energy collection and dormancy strategies are combined, the equipment endurance is greatly prolonged, the network energy consumption is reduced, and the method is suitable for industrial control, telemedicine and other scenes with high requirements on real-time performance and reliability.