1078 results about "Web environment" patented technology

The present invention discloses a blockchain-based Industrial Internet of Things (IIoT) data management system and method, where the system includes a physical layer, an edge layer, a network interface layer and a cloud layer; the physical layer includes a plurality of different IIoT device monitoring systems; the edge layer is configured to build an IIoT blockchain network; the cloud layer includes cloud servers which are configured to be deployed in a real blockchain network environment to improve performance and effectiveness of the blockchain network. The present invention can effectively solve the problem of parameter optimization lag in industrial production applications.

The invention relates to a data access method and system of an intelligent Internet of Things gateway. The method comprises the following steps: performing feature matching and protocol identification on a plurality of sensors and terminal equipment connected to the intelligent Internet of Things gateway by using an equipment feature library to obtain an equipment protocol type; analyzing the original data to obtain standardized data; performing encryption processing to obtain an encrypted security data packet and an encrypted session key; performing multi-factor transmission channel selection to obtain an optimal transmission channel combination; and based on the optimal transmission channel combination, sending the encrypted security data packet to the Internet of Things platform, verifying the received data confirmation information based on a consensus algorithm, and outputting a data access state report through a data visual interface and an AI voice assistant. The processing strategy can be dynamically adjusted according to the equipment characteristics, the data characteristics and the network environment, and the flexibility and adaptability of the system are improved.

A system may include an Al convergence system of systems for a value chain network environment.

The invention belongs to the technical field of electrical digital data processing and resource allocation, and provides an agent lightweight deployment method and a computing power elastic allocation method, lightweight deployment realizes one-time development of multi-platform deployment by constructing a plug-in container packaging tool and a multi-architecture compiling engine; static analysis is used for stripping redundant dependence of the model, and the model is dynamically cut in combination with edge resources; developing an intelligent resource description language, integrating a control group and a filtering technology to realize container-level resource monitoring, constructing a 12-dimensional dynamic state space fusing a node state, task characteristics and a network environment through computing power elastic distribution, and introducing a time delay, cost and reliability three-dimensional weighted reward model to quantify distribution earnings; an edge cloud cooperative training framework and an edge execution strategy are designed, experience is collected, a cloud end trains a Q network through federal learning, an optimization decision is played back in combination with priority experience, the agent deployment efficiency and the resource utilization rate are improved, and data privacy is guaranteed.

A network environment comprises a plurality of host machines that are communicatively coupled to each other via a network fabric comprising a plurality of switches that in turn include a plurality of ports. Each host machine comprises one or more GPUs that execute customer workloads. Described herein are different approaches that provide for addressing the problem of handling network overlay encapsulation without causing adverse impact to the performance of workloads executed on the GPU clusters.

The invention relates to the technical field of earth leakage protection monitoring, and discloses an earth leakage circuit breaker remote monitoring system integrated with the Internet of Things. An electric leakage characteristic modeling module of the system obtains real-time monitoring data, extracts current waveform characteristic parameters, identifies an abnormal waveform mode and a first occurrence time sequence, and generates an original electric leakage characteristic template; the network path generation module performs topological sorting on the monitoring nodes based on the template, establishes an equipment connection path and constructs an Internet of Things topological path; a path anomaly analysis module extracts a node sequence and screens abnormal transmission paths to obtain an abnormal path set; the risk level judgment module collects terminal node risk tags and obtains a risk level tag group through matching; and the topological structure output module counts associated nodes, divides abnormal paths, establishes a mapping relation and generates a remote monitoring topological table. The system improves the accuracy and efficiency of electric leakage monitoring, and is suitable for a complex power network environment.

A system and method for configuring a client device to communicate utilizing a secure web gateway. The method includes generating a virtual network interface having a namespace with a plurality of names, wherein a first name of the namespace is assigned to a client device; assigning a second name of the namespace to a resource accessible to the client device through a zero trust network environment, wherein the resource is deployed in a secure network environment; and configuring the client device to communicate only through the virtual network interface.

The invention relates to the technical field of optimization methods, in particular to an action type model optimization method based on reward feedback, which comprises the following steps of: SI, constructing a multi-level reward function; s2, starting and initializing; s3, observing a current web environment state; s4, executing the action; s5, environment feedback; s6, constructing a Reward model reward signal processing module; s7, reward signal processing; s8, constructing an action evaluation network; s9, action evaluation; s10, updating the strategy; s11, judging whether the task is completed or a termination condition is reached; according to the method, a more comprehensive action feedback mechanism is established, the final reward is considered, the effect of the intermediate action is fully utilized, the action of the intelligent agent is evaluated and fed back more meticulously, the intelligent agent is guided to explore and utilize the environment more reasonably, and the user experience is improved. And an action feedback mechanism encourages the intelligent agent to try more actions which may contribute to realizing the target, and meanwhile, invalid or harmful actions are avoided, so that the exploration of the intelligent agent in the web environment is more efficient.

The invention provides a conference system, an intelligent agent and a conference processing method, and the system comprises an edge device which is used for carrying out the preliminary screening and feature extraction of a conference link file, and determining a first feature; the cloud server is used for analyzing the first feature after receiving the first feature sent by the edge device, and determining a conference link and conference starting time; the clock and network state monitoring module is used for evaluating the quality of a network environment where the intelligent agent is located within a first preset time before the conference starting time after receiving the conference link and the conference starting time sent by the cloud server, and selecting an optimal network access strategy to access the conference; the knowledge graph construction module is used for constructing a first knowledge graph according to the conference data; the conference recording and analyzing module is used for performing real-time translation according to the source language and the target language of the spokesman during the conference; and analyzing the conference content by adopting the optimized pre-trained large model and a multi-modal fusion algorithm to generate a conference summary document.

The invention discloses a TSN scheduling optimization method and device based on flow sensing autonomous learning, equipment and a medium. The method comprises the following steps: deploying a lightweight flow detection module in a switch or a router, and after a controller receives a data request, automatically identifying a newly arrived unknown service flow by using the lightweight flow detection module, and judging whether the newly arrived unknown service flow is a periodic TT flow or an unpredictable burst flow; the controller performs classification management on the identified service flow types, collects topological information and flow requirements of the whole network and issues the topological information and the flow requirements to the terminal nodes through a network management interface; the controller constructs an intelligent queue scheduling task based on the collected network topology information and traffic demand and converts the task into a Markov decision process MDP, network resources, queue states and priorities are used as a state space, a scheduling strategy is used as an action space, a reward function is designed in combination with throughput and delay indexes, and an intelligent queue scheduling task is obtained. Driving a dynamic environment through real-time data and training a DRL model; an enhanced queue scheduling mechanism Pro-CQF is adopted, different priority labels are configured according to classified flow types, and then mixed flow scheduling is carried out; the controller periodically collects time delay, packet loss rate and end-to-end transmission delay indexes and feeds back the indexes to the DRL model, and a scheduling strategy is updated online. According to the method, the traffic sensing and scheduling efficiency is greatly improved in a network environment in which multiple service flows coexist and end-side equipment functions are different, and the reliability and the expandability of the TSN in industrial Internet of Things, edge computing and other high-real-time application scenes are remarkably enhanced.

The invention discloses a data flow monitoring method and system based on Internet communication, and relates to the technical field of data flow monitoring, and the method comprises the steps: carrying out the time-space modeling of historical features, constructing a flow mode library through three-dimensional features, combining dynamic period detection with multi-scale fusion, and extracting normal period behaviors and abnormal modes. Real-time exception collaborative verification is carried out, a threshold detector and a prediction detector which run in parallel are utilized, multi-dimensional scores are adjusted through scene perception dynamic weights, and real exceptions and false alarms are accurately distinguished; dynamic parameter correction is carried out, PID control parameters are optimized based on false alarm feedback and reinforcement learning, an exponential decay update mode library is combined, fusion of threshold adaptive adjustment and a long-term and short-term flow mode is realized, and the robustness of the system is improved. According to the invention, the precision and robustness of data traffic monitoring can be effectively improved, false alarms and missing alarms are reduced, and the method adapts to a complex and changeable network environment.

The invention discloses a quadruple port hiding method and device based on a zero-trust architecture, and belongs to the field of computer network security. The quadruple port hiding method comprises a first door knocking operation and a terminal trust evaluation method based on a space-time matrix fusion multi-factor model; performing multiple identity authentication based on trust; knocking a door at a tunnel port; and knocking the service port. According to the invention, a quadruple-port hidden zero-trust access control scheme is adopted, and door knocking is carried out on a zero-trust security management platform, an identity authentication port, a VPN tunnel port in an SDP gateway and a service port, so that comprehensive protection enhancement of user access and service is realized. According to the invention, an additional security layer is added through a quadruple port hiding technology, so that an attacker is difficult to find an effective attack entry point easily; and meanwhile, more refined access control can be realized based on a trust identity authentication mechanism, and a complex network environment can be adapted, so that security vulnerabilities existing in a traditional SPA mechanism are avoided.

A system includes one or more “BotMagnet” modules that are exposed to infection by malicious code. The BotMagnets may include one or more virtual machines hosting operating systems in which malicious code may be installed and executed without exposing sensitive data or other parts of a network. In particular, outbound traffic may be transmitted to a Sinkhole module that implements a service requested by the outbound traffic and transmits responses to the malicious code executing within the BotMagnet. Credentials for services implemented by a BotSink may be planted in an active directory (AD) server. The BotSink periodically uses the credentials thereby creating log entries indicating use thereof. When an attacker accesses the services using the credentials, the BotSink engages and monitors an attacker system and may generate an alert. Decoy services may be assigned to a domain and associated with names according to a naming convention of the domain.

The invention discloses a multi-modal content auditing method and system and a computer storage medium, and relates to the technical field of content auditing, and the method comprises the steps: collecting texts, pictures, audio and video resources on the Internet in real time; pre-training texts, pictures, audio and video resources through self-supervised learning to generate a multi-modal large model base; the multi-modal large model base adapts to the auditing task through fine tuning training, and a multi-modal model is obtained; utilizing a prompt project to guide the multi-modal model to generate an understanding question and answer data set as a fine tuning data set; based on the fine tuning data set, constructing a multi-modal auditing model through iterative fine tuning process training; according to the method, the auditing rule and the situation adaptability scoring function are dynamically generated, context perception analysis is carried out on the real-time auditing request, a more accurate auditing strategy is generated, the method has remarkable technical advantages in the field of multi-modal content auditing, and powerful support is provided for achieving an efficient and safe network environment.

A process for enhancing network security includes collecting network configuration and network environment information from users, including the identification of suppliers of goods and services. The collected information is parsed to identify individual security risks and a value is assigned to each security risk identified. A cumulative security score is calculated for each user using the values assigned to each security risk, and suggestions are offered to improve security.

The invention provides a network security decision-making method and device based on a large language model. The method comprises the following steps: collecting network state data and converting the network state data into natural language description; determining a semantic feature vector corresponding to the natural language description, and retrieving target threat knowledge matched with the semantic feature vector in a knowledge base storing various basic network threat knowledge; generating an analysis prompt text in combination with the target threat knowledge and the natural language description, inputting the analysis prompt text into a large language model, sequentially executing network threat analysis steps indicated in a preset thinking chain according to the analysis prompt text, and determining a network anomaly attribution and a corresponding protection strategy; and issuing the protection strategy to each distributed execution unit deployed in the target network environment, and converting the protection strategy into an equipment configuration command and executing the equipment configuration command. A closed-loop intelligent defense process from information perception to semantic understanding to collaborative response can be realized, and the real-time performance, the accuracy and the expandability of a security decision are improved.

An attack path modeling and discovery system and process of the present combines comprehensive network data with machine learning to determine the greatest risk to a network environment by exposing the path of least resistance an attacker would likely take. This system and process considers both the likelihood of a threat agent to exploit a vulnerability, and the potential for loss when that threat occurs. The system utilizes two key models to accomplish this goal. First, Knowledge Graphs (KG) are leveraged to comprehensively model relationships across an environment, and second, Graph Neural Networks (GNNs) are used to predict the path of least resistance to the network's user-defined most valuable assets.

The invention relates to the technical field of real-time rendering, cloud edge collaboration and three-dimensional visualization, in particular to a multi-instance cloud edge collaboration rendering method based on an unreal engine. Through cloud centralized scheduling and edge node plug flow collaborative architecture, on-demand distribution, dynamic start and stop and life cycle management of rendering instances are realized, and the resource utilization efficiency is remarkably improved; the system combines a relay agent mechanism of TURN and Nginx, gets through access restrictions among a public network, a local area network and a plurality of subnetworks, and realizes unified access and high-adaptability pixel flow transmission capability. Through edge-side integrated pixel flow and signaling service, distributed instance management and load balancing scheduling, the concurrent processing capability and horizontal expansibility of the system are enhanced, and the requirements of complex three-dimensional application scenes such as multiple users, real-time interaction and high-performance rendering are met. Through a cloud edge separation architecture, a relay forwarding mechanism and instance dynamic scheduling, efficient and stable pushing of unreal engine pixel streams in a multi-network environment is realized.

The invention provides a dynamic encryption communication method, system and device for an Internet of Things device, and a storage medium, and the method comprises the steps: obtaining the current geographic position information of the Internet of Things device, and determining the security demand level of the Internet of Things device according to the current geographic position information and a preset geographic position list of different security levels; acquiring a network performance index of a mobile network shared channel at the position of the Internet of Things equipment, and determining a congestion degree parameter of a surrounding network environment according to the network performance index; classifying according to the service type of the Internet of Things equipment to obtain a service volume level; and integrating the obtained security demand level, the congestion degree parameter and the service volume level, dynamically selecting a corresponding encryption algorithm to encrypt sensitive data of the Internet of Things equipment, and transmitting the encrypted data to a cloud server. By dynamically selecting the encryption algorithm and adjusting according to the location information, the security and the energy efficiency can be balanced in different scenes.

The invention discloses a network information security defense detection method and system, and relates to the technical field of network information security, and the system comprises a website security check module, a downloading security check module, an access security check module and a display execution module. In daily Internet surfing, net friends often encounter network security problems, for example, users may receive phishing mails disguised as banks or download malicious software in untrusted websites, resulting in data stealing. Identity embezzlement and data leakage increase privacy and financial risks, and man-in-the-middle attack may intercept sensitive information through an unencrypted Wi-Fi network. According to the method, security detection including domain name check, certificate verification, content scanning, integrity verification and the like is carried out on websites, downloading programs and external access equipment, so that security risks are found and processed in time; and meanwhile, by adopting a virtual machine isolation technology and behavior monitoring, the protection on malicious programs is enhanced, the security risk of external equipment is evaluated, and the security of a network environment and data is ensured.

The invention belongs to the technical field of authority management, and particularly relates to a dynamic authority management system based on an integrated system, which is characterized in that on one hand, dynamic authority portraits of employees are described periodically by integrating multi-system data, and dynamic risk values of real-time operation behaviors of the employees are analyzed based on the latest dynamic authority portraits; on the other hand, through monitoring an organization event queue in real time and analyzing metadata of an event message, an organization event type is identified, a change rule corresponding to the organization event type is matched, an authority synchronization instruction is generated, and transactional operation is started; a behavior risk real-time assessment and event-driven cross-system collaboration mechanism is adopted, automatic and precise dynamic adjustment of the authority is realized, the access control security and management efficiency in an integrated network environment are improved, and in addition, a self-optimization capability based on machine learning is introduced, so that the authority management has the characteristics of continuous learning and evolution.

The invention belongs to the technical field of power information security, and discloses a power network context scene association access control method and system, and the method comprises the steps: obtaining an access request and multi-dimensional context information associated with the access request, the multi-dimensional context information comprises at least one of time information, position information, equipment state information, user behavior information and network environment information, analyzing the multi-dimensional context information, identifying a power business scene to which the current access request belongs, and dynamically generating or adjusting an access control strategy for the current access request; and performing an access control operation on the access request based on the dynamically generated or adjusted access control policy. According to the method, the access control strategy can be dynamically adjusted according to the real-time context and the specific service scene of the power network, the accuracy, the flexibility and the safety of access control are improved, and the method effectively adapts to a complex and changeable power network operation environment.

The invention discloses a traffic scheduling method and an electronic device, and relates to the technical field of traffic scheduling, and the method comprises the steps: determining the priority weight of a micro-service, and predicting a target traffic according to the historical traffic information of a network device; constructing a graph model according to the topological information of the network equipment and the dependency relationship of the micro-service, and performing embedded learning on nodes in the graph model to generate a state vector representing a network state; the priority weight, the state vector and the target traffic of the micro-service serve as input of a reinforcement learning model, and a traffic scheduling strategy of the network equipment is obtained; performing iterative search according to iterative particles formed by encoding the strategy network parameters of the reinforcement learning model and the feature learning network parameters of the graph model to determine reinforcement learning model parameters; and issuing the traffic scheduling strategy to the network equipment and executing the traffic scheduling strategy so as to solve the technical problem that a traffic scheduling method in related technologies is difficult to adapt to a dynamic and complex network environment and service requirements under a micro-service architecture, and the reliability of traffic scheduling is improved.

The current disclosure provides methods and systems that can manage the dynamic-uncertainty induced by resources of decentralized data networks, in order to ensure the stability and sustainability of task-oriented automated operations, such as operations conducted through intelligent agents. In contrast to the state-of-the-art object-based processing, the methods and systems enable object-aware processing, to ensure the establishment of stable and sustainable associations with physical and digital web-objects, while enabling those objects to be processed dynamically with full adaptability in response to contextual and structural alterations in real-time. Thus, the disclosure provides—both human and machine—users with the ability to develop and deploy modular systems capable of engaging any conceivable physical or digital interaction with the resources of a data network, such as dynamically linking and manipulating clusters of complex web-objects to execute complex tasks in complex and dynamic web environments—most importantly—stably and sustainably.

The invention discloses a fault prediction and self-healing method and system for Internet of Things equipment, and the method comprises the steps: obtaining multi-modal sensing data of the Internet of Things equipment, and carrying out the preprocessing of the multi-modal sensing data, and obtaining multi-modal fusion data; inputting the multi-modal fusion data into a trained first network model for fault prediction to obtain a fault prediction result; inputting the fault prediction result into a trained second network model to generate a self-healing strategy to obtain a target self-healing strategy; and driving the Internet of Things equipment to execute the target self-healing strategy so as to enable the Internet of Things equipment to perform fault self-healing. According to the method, the fault prediction accuracy of the Internet of Things equipment in a complex and dynamically changing network environment can be improved, and the real-time performance of fault self-recovery of the Internet of Things equipment is improved. The invention relates to the technical field of Internet of Things.

The invention provides an efficient multi-dimensional network penetration testing method based on RAG, which comprises the following steps: firstly, identifying sub-domain names possibly existing in a target website, and sequentially expanding attack surfaces of penetration testing to obtain vulnerability information pairs; secondly, a design model generated based on retrieval enhancement is adopted, vulnerability information pairs are extracted from the queue to be utilized, knowledge items related to local knowledge base retrieval and network intelligent search retrieval are utilized, and finally, a large model generates vulnerability utilization information according to the knowledge items; acquiring target machine permission for the previously acquired vulnerability information pair construction command injector, and further scanning other hosts of the intranet accessed by the target skipping machine; finally, combining vulnerability information obtained by penetration testing, utilizing a large model to sort vulnerabilities existing in different assets, and outputting penetration testing reports for different assets. According to the method and the system, comprehensive penetration testing of cross-network and cross-system is realized, security experts are helped to quickly identify, verify and repair vulnerabilities in a complex and changeable network environment, and high-efficiency and low-cost network security maintenance is realized.

The invention provides a vision-based cross-network interaction method and system, and relates to the technical field of intelligent interaction, and the method comprises the steps: obtaining a visual interaction sequence, constructing multi-modal feature representation, achieving cross-domain semantic alignment, deconstructing visual information into a hierarchical control instruction set, and transmitting the hierarchical control instruction set to a target network environment for execution after security classification. And a bidirectional mapping relation graph is constructed to realize incremental optimization. According to the method, semantic bridging between heterogeneous networks can be established, the cross-domain control precision is improved, and meanwhile safe interaction in a network isolation environment is guaranteed.

The invention discloses a website fingerprint attack-oriented few-sample data enhancement method and system, a medium and a program product, and relates to the field of network security. The system comprises a website fingerprint acquisition device used for capturing network flow data; the website fingerprint cleaning device is used for eliminating noise data; the data enhancement device is used for carrying out data enhancement on the website fingerprint sequence and carrying out diversity evaluation and optimization; and the website prediction device is used for carrying out website classification prediction by utilizing the enhanced data enhancement sequence. According to the method disclosed by the invention, the accuracy and robustness of the website fingerprint attack model under the condition of few samples are remarkably improved by simulating the spatial and temporal change of the network environment, implementing various data enhancement strategies and combining a diversity evaluation and optimization algorithm. According to the method, the problem that the fingerprint attack performance of the website is reduced due to insufficient samples is effectively solved, and an efficient and reliable data enhancement technical scheme is provided for the field of network security.

A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user / entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies / threats were previously known. The security platform can include both real-time and batch paths / modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.