127 results about "Network adaptation" patented technology

The invention relates to the technical field of data management, in particular to an industrial control system remote monitoring data synchronization method, which comprises the following steps of: 1, generating a dynamic synchronization time window based on an equipment state sudden change frequency and a network fluctuation degree; 2, dividing transmission priorities according to the influence degree of data on equipment security control; 3, quality characteristics of available transmission paths are evaluated, data of different priorities are transmitted in a fragmented mode according to a differentiation strategy, and data streams are recombined at a receiving end based on time sequence identification; 4, after network disconnection is recovered, synchronous consistency guarantee is achieved through data segment cascade hash verification and control instruction logic conflict detection; and 5, optimizing a time window generation algorithm and a path evaluation rule according to the historical synchronization effect data. Through accurate control, priority division, network adaptability enhancement and consistency guarantee of data transmission, an efficient and safe industrial control system remote monitoring data synchronization scheme with adaptive capability is provided.

The invention discloses a wide-narrow integrated ad hoc network method, and belongs to the technical field of network communication. The method comprises the following steps: a network node generates dynamic topology information based on a narrow-band communication module periodic broadcast state beacon; according to the link quality and the node load in the topological information, coordinating the resource allocation of broadband and narrowband channels by adopting a load-aware time slot allocation algorithm; data are classified according to service types, broadband data are modulated and transmitted by adopting orthogonal frequency division multiplexing, and narrowband control instructions are modulated and transmitted by adopting differential phase shift keying; when the link quality is deteriorated, the link is switched, and the transmitting power and the frequency band parameters are dynamically adjusted; and adaptive switching and data distribution of wide and narrow band communication modes are realized. According to the method, the problems of difficulty in topology maintenance and low bandwidth and narrowband resource allocation efficiency in a high-dynamic network scene are solved, the network adaptability, the anti-interference capability and the resource utilization rate are remarkably improved through a dynamic cooperative bandwidth and narrowband communication mode, and meanwhile, the protocol switching overhead and energy consumption are reduced.

The invention provides a 5G network intelligent switching management method based on artificial intelligence, and the method comprises the following steps: collecting and processing network related data, and obtaining a network data set; constructing a switching demand prediction model used for predicting network switching time and a target and a switching effect prediction model used for predicting network performance after switching; optimizing the initial switching strategy in combination with reinforcement learning to obtain an optimal switching strategy; deploying a network switching engine model on the edge computing node, and adjusting an optimal switching strategy in real time according to the network environment change; a virtual simulation digital environment is constructed for network simulation, an optimal switching strategy is evaluated according to the simulated virtual network environment, and then a network switching engine model is optimized in real time according to an evaluation result. According to the invention, the intelligent switching decision is realized, the precision and efficiency of the switching decision can be improved, the network adaptability and diversified requirements are enhanced, and the user experience is improved.

The embodiment of the invention provides a data transmission method and device of Internet of Things equipment, electronic equipment and a storage medium, and relates to the technical field of Internet of Things, and the method comprises the steps: obtaining business data collected by the Internet of Things equipment, and determining the target business priority of the business data; acquiring network index data of communication equipment connected with the Internet of Things equipment; performing data volume prediction according to the service data to obtain a corresponding predicted data volume; performing transmission mode selection according to the predicted data volume and the network index data to obtain a first transmission strategy for the service data; and issuing the first transmission strategy to the Internet of Things equipment, so that the Internet of Things equipment transmits the service data according to the first transmission strategy, thereby optimizing the data transmission mode, realizing dynamic adjustment of the transmission strategy, improving the network adaptability, improving the success rate of data transmission, and reducing the probability of network congestion.

A system and method for a hierarchical thought supervision network with adaptive processing capabilities. The system processes data through a base graph layer of interconnected computational nodes, a telemetry layer for real-time monitoring, and one or more supervision layers composed of supervisory nodes. The base layer handles thought processing and management, while the telemetry layer continuously tracks operational metrics to evaluate processing efficiency. Supervisory nodes adapt network operations by optimizing thought encodings, generating new nodes when needed, and pruning inefficient nodes based on performance objectives. A telemetry layer continuously tracks processing efficiency using adaptive kernel functions and topology-aware distance metrics. The system maintains effective processing while dynamically adjusting to computational demands through coordinated supervision across multiple layers. This approach enables real-time network adaptation while optimizing performance and efficiency across the system.

The invention belongs to the technical field of information technology and intelligent health service, and discloses a community old-age care service resource optimal configuration method based on edge computing, which comprises the following steps: collecting physiological data, environmental data and activity data of old people to form original heterogeneous data, uploading the original heterogeneous data to a data warehouse through an urban and rural community network adaptation mechanism, generating a space-time tagging multi-modal data set; constructing a dynamic digital twinborn model, generating a space thermodynamic diagram, identifying different level priority requirements, and generating a time period allocation scheme through a conflict priority scoring mechanism; an MILP model is constructed to dynamically optimize edge node computing power and functional area space resources, and a resource optimization report and a block chain transaction list are generated; desensitizing the original data of the old people, creating a digital identity card, generating an encrypted service log, and synchronously recording migration behaviors of the old people; and triggering network strategy switching and priority threshold self-adaption, and carrying out resource scheduling and strategy closed-loop optimization.

The invention discloses an ultrasonic image off-line acquisition and dynamic synchronous processing method and system, which realizes off-line acquisition and dynamic synchronous processing of an ultrasonic image under the condition of network interruption through six functional modules, namely a network state monitoring module, an online data synchronization module, an image acquisition processing module, an off-line mode switching module, an off-line diagnosis processing module and an increment synchronization module. The method comprises the following steps: acquiring network connection state data and judging a working mode; pulling and caching tasks and template data in an online state; obtaining multi-modal image data and performing double-writing storage; detecting network interruption and switching to an offline mode; performing AI analysis on the images acquired offline and generating a report; and detecting network recovery and executing incremental data synchronization. According to the invention, the problem of service interruption caused by dependence of a traditional PACS system on a network is solved, the network adaptability and robustness of the system are improved, the operation and maintenance cost is reduced, and the system fault recovery time is shortened.

The invention relates to the technical field of power transmission line monitoring, and discloses a power transmission line monitoring data transmission method based on Lora and 5G hybrid networking, and the method comprises the steps: determining a network access mode through a hybrid networking selection algorithm, and generating a communication link through dual-module networking configuration; original monitoring data are processed by using a data framing compression algorithm, and a path is selected and verified through dynamic routing optimization; the modulation format is adjusted through the space-time alignment data of the multi-modal data fusion model and the adaptive modulation technology; optimizing transmission by using an abnormal data filtering algorithm and a dynamic priority scheduling program; and feedback learning mechanism evaluation performance and updating algorithm parameters. According to the invention, network adaptability, data transmission efficiency and reliability are improved, data fusion processing is optimized, adaptive optimization of transmission performance is realized, operation and maintenance cost is reduced, and safe and stable operation of a power system is guaranteed.

The invention relates to the field of multimedia communication and network control, in particular to an intelligent network control method for low-delay video return and related equipment. The intelligent network control method comprises the following steps: acquiring network key indexes including bandwidth, delay, jitter and packet loss rate of a network link in real time, and providing real-time network environment data support for transmission strategy adjustment. According to the method, an intelligent control mechanism combining network state perception and video content feature recognition is constructed, so that the technical problem of low-delay video return in a complex network environment is effectively solved. Specifically, key indexes of a network link are collected in real time, and a lightweight CNN model is introduced to analyze the video content activeness, so that dual perception capabilities for a network environment and content features are formed, data support is provided for dynamic adjustment of coding parameters, and accurate balance between video quality and network adaptability is realized.

The invention discloses a court cross-network audio and video signaling two-way interaction system and method. The system comprises a multi-protocol access adaptation module, a security isolation module and the like. The multi-protocol access adaptation module is provided with a special access adaptation unit for multiple protocols; the security isolation module realizes physical isolation through a security data exchange platform and a one-way audio and video optical shutter, and a built-in signaling security detection unit detects signaling; the signaling conversion module converts the signaling into an internal uniform format and adapts to the internal uniform format; the signaling processing module processes signaling, extracts information, schedules resources and feeds back a result; the network adaptation module adjusts transmission parameters; the identity authentication and authorization module uses multi-factor authentication and fine-grained authorization. The method comprises the steps of signaling initiating and receiving, identity authentication and authorization, multi-protocol access adaptation, signaling conversion processing and the like. The system solves the problems of cross-network communication protocol compatibility and the like, and improves the convenience and safety of judicial services.

The invention relates to the field of industrial Internet of Things, in particular to an edge computing communication method and system for the industrial Internet of Things, and the method comprises the following steps: 1, a user issues a task to a service platform through a user platform, and after the service platform receives a task request, the service platform sends the task request to the user platform based on a device type and a task type associated with the task; classifying and marking the tasks, and determining real-time levels of the tasks and importance weights of associated production links; 2, the service platform transmits the classified tasks to a corresponding service sub-platform, the service sub-platform obtains historical operation data and edge node basic information of task associated equipment, generates a first calculation task carrying a task attribute tag according to a classification result, and sends the first calculation task to a management platform; and step 3, the management platform carries out unified management on the plurality of first computing tasks. By adopting the technical scheme of the invention, industrial scene characteristics can be fit, and task priority, resource dynamics and network adaptability are considered.

A system and method for adaptive neural network architecture implementing sophisticated supervision and signal transmission capabilities. The system comprises a layered neural network monitored by a hierarchical supervisory system that collects operational data and implements architectural modifications. A meta-supervisory system oversees the supervisory process, tracking adaptation patterns and extracting generalizable principles from successful modifications. The system implements novel signal transmission pathways that enable direct communication between non-adjacent network regions through adaptive transformation components and coordinated timing mechanisms. This multi-level approach enables dynamic network adaptation while maintaining operational stability through careful monitoring and controlled modification procedures. The system's innovative architecture allows neural networks to evolve their processing capabilities during operation while preserving reliable performance through sophisticated supervision and controlled signal propagation.

The invention relates to the technical field of network security, in particular to a distributed key management and encryption communication method oriented to a big data environment. The method comprises the following steps: establishing a system architecture and configuring safety parameters; generating a master key and a session key through the random number; segmenting the master key into a plurality of segments and storing the segments to different nodes; encrypting the session key by using the public key of the receiver, distributing the session key through the secure channel, and decrypting the session key by the receiver to obtain the session key; constructing a Merkle tree through an AES-GCM function, and introducing a network adaptation factor into an encryption process by considering that security requirements and network conditions of data encryption in different environments are different; and regularly updating a secret key, implementing an access control strategy, and recording a log to perform system audit so as to guarantee long-term security. According to the invention, the efficient AES-GCM encryption algorithm is used, so that the speed and security of data encryption are improved. By implementing the access control strategy, only the authorized user can access and operate the key, and the security of the system is improved.

The invention belongs to the technical field of communication, and particularly relates to an efficient dynamic network adaptation method based on a 5G RedCap module, and the method comprises the steps: 1, obtaining and defining a 5G RedCap module parameter, a modulation coding mode parameter, a transmission parameter and a queue parameter; 2, according to the 5G RedCap module parameters, the modulation and coding mode parameters, the transmission parameters and the queue parameters obtained and defined in the step 1, multi-target multi-constraint dynamic optimization is carried out on resource block allocation, transmitting power and modulation and coding mode indexes of each 5G RedCap module; and 3, constructing a Lagrange function, setting an optimality criterion by means of a KKT condition, solving the multi-target multi-constraint dynamic optimization problem in the step 2, and carrying out dynamic network adaptation according to a result obtained after solving. The method is used for realizing optimal balance among low power consumption, high throughput and low time delay.

The invention relates to a method and apparatus for generating a project-specific network architecture. The invention relates to a method for generating a project-specific network architecture, said method comprising the steps of:-providing (S1) a base model, in particular a large language model, with LoRa network adaptation; providing (S2) a model library having training data pairs, each of which consists of input data having a model application and / or model and / or hardware and / or software specification, and output data having at least one network architecture belonging to the respective input data; -selecting (S3) item-specific training pairs on the basis of said library of models; and-training (S4) the LoRa network of the base model on the basis of the item-specific training pair for generating the item-specific network architecture.

The invention provides a TTT-based switching method and device, a communication system and a storage medium. The method can be applied to the technical field of wireless communication. In the scheme, the terminal equipment can obtain the moving speed measured by the L1 layer and the adjacent region load and the business service quality requirement measured by the L3 layer, and reports the data to the source base station, so that the source base station can dynamically adjust the TTT according to the data, and the network adaptability is improved. For example, in a high-speed moving scene, the switching delay is reduced by shortening the TTT; when the load is low, the TTT is prolonged, so that the signaling overhead can be reduced, and resource waste caused by static configuration is avoided. Besides, the terminal equipment can predict future resource requirements based on historical data and trigger the target base station to perform dynamic resource pre-allocation on the terminal equipment in advance, so that the reserved resource access cell is directly used, the traditional random access process is skipped, the negotiation time during switching is reduced, and seamless switching is realized.

The invention relates to the technical field of video coding, in particular to a self-adaptive video coding method and device, equipment and a medium. Obtaining a video frame sequence, and carrying out segmentation operation on the video frame sequence; predicting the coding unit set according to a preset prediction mode; performing binary coding calculation according to the prediction error, a preset quantization parameter and a preset probability interval; evaluating according to the link state information and the historical network flow data to obtain a network broadband state; calculating according to the network bandwidth state and a preset gradient information evaluation formula to obtain a frame rate and a resolution; performing adaptive adjustment on the coding unit set according to the frame rate, entropy coding and resolution to obtain video coding; in a segmentation stage, coding units are divided according to video contents, an intra-frame mode is optimized in a prediction link, parameters are reasonably set during coding, a frame rate and a resolution ratio are dynamically adjusted through network adaptation, finally, codes are adaptively generated, bandwidth is efficiently utilized, and network changes are flexibly adapted.

The invention provides a low-power-consumption wireless ad hoc network dynamic configuration method and system based on adaptive routing, and relates to the technical field of network protocols, and the method comprises the steps: determining a data transmission path through obtaining node working state parameters and calculating an energy level value, and setting a multi-level data cache occupation threshold value; calculating a network load distribution state, and carrying out differentiated scheduling on nodes; selecting a low-power-consumption forwarding channel based on node routing adaptability; and monitoring the network state in real time and updating parameters. According to the invention, the network adaptability is improved, the communication power consumption is reduced, the network life cycle is prolonged, and the data transmission efficiency is improved.

A 3D development engine-oriented adaptive flow loading method relates to the technical field of 3D development engines, and comprises the following steps: collecting environment state data through an environment sensing module; continuously acquiring a network state index through a network monitoring module; establishing a dynamic priority calculation model, taking the environment state data and the network state indexes as input parameters, and generating a quantitative priority score for each resource project; performing dynamic reconstruction and sorting processing on the resource loading task queue based on a priority scoring result, adjusting the execution number of concurrent loading tasks according to a real-time network state index, and selecting a corresponding resource compression strategy; and establishing a resource life cycle management system, and executing allocation optimization and recovery cleaning operation of the memory resources. According to the invention, adaptive flow loading of 3D resources can be realized, and the fluency of a large-scale scene, the network adaptability and the system resource utilization efficiency are improved.

A computer system for adaptive neural network architecture implementing sophisticated supervision, pruning, and signal transmission capabilities. The system operates a layered neural network monitored by a hierarchical supervisory system that collects activation data, identifies operation patterns, implements architectural changes, detects network sparsity, coordinates pruning decisions, and manages resource redistribution. A meta-supervisory system tracks supervisory behavior patterns, stores successful modification and pruning patterns, and extracts generalizable principles from these patterns. The system manages signal transmission pathways that enable direct communication between non-adjacent network regions through signal modification and temporal coordination. This multi-level approach enables dynamic network adaptation and efficient resource utilization through pruning while maintaining operational stability. The system's innovative architecture allows neural networks to evolve their processing capabilities during operation while preserving reliable performance through sophisticated supervision and controlled modification.

The invention provides a resource loading method and system based on user behavior prediction, and relates to the technical field of data processing, and the method comprises the steps: collecting user behavior data; preprocessing the user behavior data; extracting data features of the preprocessed user behavior data; according to the data features, predicting resource demand weight distribution of the user through a double-layer LSTM based on fusion of an attention mechanism and interest drift perception; calculating the priority of each resource according to the prediction result of the resource demand weight distribution of the user in combination with the importance coefficient, the resource value density and the network adaptation coefficient of each resource; constructing a resource loading priority queue according to the priority of each resource from high to low; and dynamically adjusting a loading strategy of each resource in the resource loading priority queue in combination with a network state, and loading the resources as required. According to the method, the resources can be loaded as required, the resources possibly required by the user are preferentially loaded, and the resource utilization efficiency is improved.

The invention discloses a spacecraft Hall propulsion device test working medium flow stability control system, which comprises a sensing module for detecting working condition state parameters of working medium supply equipment, and an adaptation module for exploring current working condition control parameters by combining detection data of the sensing module and similar working condition control parameter empirical data fusion, the adjusting module is used for receiving the control parameters and dynamically adjusting the adjusting weight of the adjusting device according to different phase change periods, an experience library unit and a self-adaption unit are arranged in the adaption module, the experience library unit provides experience data of similar working conditions and control parameters, and the self-adaption unit provides self-adaption data of the control parameters. And the autonomous adaptation unit fuses and explores control parameters by constructing a neural network adaptation model, so that working condition automatic identification and control parameter autonomous adaptation under the environment that the working medium type or the working medium state rapidly changes are realized; the problems of low control precision and slow response speed caused by manual presetting of control parameters of different working media in a traditional control technology are solved, and stable control of the working medium flow is realized.

The invention provides a multi-scene user controllable personalized recommendation method and system based on super network adaptation. The method comprises the following steps: acquiring a natural language query input by a user; mapping the natural language query into a control condition text based on a large language model; encoding the control condition text into a condition embedding vector by using a pre-trained text encoder; inputting the condition embedding vector into a super network, and generating an adapter parameter matrix for at least one dense layer in the basic recommendation model by the super network; adding the adapter parameter matrix and an original weight matrix of a corresponding dense layer in the basic recommendation model to obtain an adapted recommendation model; and using the adapted recommendation model to predict preference scores of the user for the articles, and generating a recommendation list based on the preference scores. According to the technical scheme, the super network is introduced to dynamically generate the lightweight adapter, so that the limitation of a traditional recommendation system in the aspects of user controllability, scene adaptability and semantic comprehension is effectively solved.

The invention discloses a network anomaly monitoring system and method based on data analysis, and belongs to the technical field of network anomaly monitoring, and the method comprises the steps: obtaining access records of a monitoring object, screening all access records of the monitoring object, and obtaining a first screening set; screening the first screening set to obtain a second screening set; analyzing a reference flow characteristic and a reference behavior characteristic of the monitoring object according to the second screening set; analyzing the abnormal flow characteristics and behavior abnormal characteristics of the monitoring object, and dividing the abnormal level of the monitoring object; according to the real-time access record of the monitored object, the real-time abnormal level of the monitored object is judged, and different protection schemes are executed; the method effectively reduces the false alarm rate and the missing report rate, improves the accuracy of network security protection, accurately recognizes potential threats, shortens the response time, solves the limitation of a traditional rule matching method, and improves the capability of the network to adapt to a complex attack mode.

The invention relates to a personalized network guarantee service pushing method and device, equipment and a storage medium. The method comprises the steps that a specific scene where a user is located is recognized through a terminal side intention perception SDK, credibility verification is carried out, and the specific scene comprises at least one of access to a preset place network, poor network quality or starting of a high-network-demand application; based on a specific scene in which the user is located, obtaining signing state data of a current service of the user; comprehensively analyzing the user service demand and the network adaptation capability based on the signing state data of the current service of the user; formulating or updating a personalized QoS (Quality of Service) network guarantee strategy matched with the specific scene, or pushing a personalized network guarantee service ordering request to a user, and performing state feedback; and executing a network service guarantee operation according to the network guarantee strategy. By adopting the method, precise scene perception and network side multi-network element cooperation can be realized, and the reach rate and the order conversion rate of the network guarantee service are improved.

A system and method for an adaptive network architecture utilizing dynamically-encoded agents. The system processes data through a base graph layer of interconnected computational nodes, a telemetry layer for real-time monitoring, and one or more agent layers composed of dynamically-encoded agents. These agents optimize encoding strategies, generate new agents, and prune inefficient agents based on network performance objectives. A telemetry layer continuously tracks network operations using adaptive kernel functions and topology-aware distance metrics. The system may dynamically adjust network structure and resource allocation, maintaining efficient operations through encoding optimization. By leveraging short-term and long-term memory systems, the system adapts over time, improving learning retention and responsiveness. Error detection and recovery mechanisms ensure network stability during agent generation and pruning. This approach enables real-time network adaptation, optimizing performance and efficiency across multiple layers while maintaining system resilience and stability.

The invention provides a DTU, a data transmission method, a data transmission device and a medium. Wherein the DTU is in communication connection with the serial port equipment and the cloud platform; the DTU comprises a protocol analysis layer and a network adaptation layer, and the protocol analysis layer is in communication connection with the low-code development platform; the protocol analysis layer receives serial port data sent by serial port equipment; analyzing the serial port data according to the target protocol analysis rule to generate structured data; sending the structured data to a network adaptation layer; the target protocol analysis rule is generated and issued by the low-code development platform according to the target protocol configuration parameters; and the network adaptation layer encapsulates the structured data based on the target network protocol, generates network protocol data and sends the network protocol data to the cloud platform. According to the embodiment of the invention, efficient conversion from the serial port data to the network protocol data can be realized, and the data transmission efficiency and accuracy of the DTU are improved.

The invention relates to a virtual reality and network interaction technology, and discloses a virtual space multi-person interaction synchronous control method oriented to an end-cloud collaborative architecture. Through combination of end-side prediction and cloud arbitration, low-delay transmission and global consistency of a multi-user interaction state are realized. The method specifically comprises the steps that an end side carries out prediction and feature extraction on action and voice data, and redundant transmission is reduced; the key state parameters are uploaded to a cloud end and are judged by a global consistency model and a conflict arbitration mechanism in a unified manner, and a synchronization instruction is generated; and the end side performs rendering and feedback according to a result. According to the method, end-cloud hierarchical prediction and arbitration, a priority-driven protocol and bandwidth adaptive control are combined, delay and conflicts are effectively reduced, and cooperation efficiency, immersion and network adaptability are improved. The method can be widely applied to the fields of universe, online education, remote collaboration, virtual simulation and the like.

The invention discloses a PCDN-based live streaming media UDP transmission control system and method, the system comprises a client, an edge PCDN node, a core CDN node and a central server, the client is started to request an edge PCDN node list and standby CDN node information from the central server, the most suitable PCDN node is selected according to the priority, and connection is established to request content. And the edge PCDN node determines a content transmission path according to the network condition and the priority, and obtains the content from the CDN node for standby application. And the central server monitors node states in real time, adjusts priorities or replaces node information. According to the hybrid architecture collaborative optimization, dynamic network adaptation can be realized, and congestion can be accurately controlled.

The invention discloses an unmanned aerial vehicle remote identification method and system, and relates to the technical field of unmanned aerial vehicle identification, and the method comprises the steps: receiving remote identification data broadcasted by a target unmanned aerial vehicle through a wireless communication protocol; analyzing the remote identification data, and extracting unmanned aerial vehicle position information, control station position information and unmanned aerial vehicle identification information according to a message type field; in a network connection state, calling a map interface to display the track of the unmanned aerial vehicle and the position of the control station in real time; and under the state of no network connection, converting the position information into a plane-coordinate system, and generating a position mark and a historical track in an offline map. Through multi-protocol compatibility, dynamic network adaptation, high-precision data analysis and resource optimization design, the technical problems of strong network dependence, insufficient standard coverage, large decoding error, low offline map precision and the like existing in unmanned aerial vehicle remote identification in the prior art are solved; the real-time performance, the reliability and the user experience of unmanned aerial vehicle supervision are remarkably improved, and wide application value is achieved.