1041 results about "Network conditions" patented technology

The invention discloses a real-time interactive digital human system supporting high concurrency and an implementation method thereof, and relates to the technical field of digital human interaction.The system comprises a model instance pool module used for loading the weight of a deep learning model to a shared memory area through the memory mapping technology; the multi-thread scheduling module is used for scheduling user requests by adopting a lock-free queue and a dynamic priority algorithm; the asynchronous pipeline processing module is composed of decoupled micro-services, and the modules are connected in series through asynchronous message queues; the client SDK is used for dynamically switching a rendering mode according to terminal hardware performance and network conditions; the elastic capacity expansion and contraction module is used for monitoring resource loads in real time and automatically adjusting the number of service instances; and the audio and video synchronization calibration module is used for ensuring that the synchronization error of the audio and video frames is lower than a preset threshold value through a timestamp alignment algorithm. According to the scheme, core challenges in a high-concurrency digital human interaction scene can be systematically solved, and innovative support is provided for large-scale real-time application.

The invention relates to the technical field of circuit detection, discloses a power transmission line thermochromic wire clamp heating early warning method and system, effectively solves the problem of data acquisition distortion in a strong electromagnetic environment, and improves the accuracy of state evaluation through multi-source data fusion. The dynamically adjusted early warning model reduces the risk of false alarm and missing alarm caused by equipment aging, the intelligent decision support module shortens the fault handling response time, the data closed-loop mechanism ensures the reliability of the system in the whole life cycle, and the reliability of the system in the whole life cycle is improved through multi-sensor cooperative monitoring and edge calculation processing. And the influence of environmental factors on data acquisition is reduced. The two-channel transmission architecture guarantees the data transmission integrity under different network conditions, the CRC verification mechanism effectively recognizes and corrects transmission errors, a high-quality data basis is provided for a subsequent early warning model, the abnormal data recollection mechanism avoids data missing caused by single collection failure, and continuous and stable operation of the monitoring system is ensured.

The embodiment of the invention provides a dynamic allocation method and device for AI reasoning tasks, and the method comprises the steps: monitoring the workload, memory availability, network delay, bandwidth and energy consumption of each computing node in a cloud edge cooperation system in real time, the computing node comprising a local edge device, an edge server and a cloud platform; according to the delay requirement, the calculation requirement and the data privacy requirement of the task and the real-time state of each calculation node, dynamically distributing the task to different calculation nodes; adjusting the complexity of an AI model used for executing the task according to the resource use condition of the computing node; and according to the historical performance data and the real-time network condition in the task execution process, optimizing a task allocation strategy.

The invention relates to the technical field of audio pushing optimization, and discloses an audio stream pushing crash prevention method and system, a storage medium and equipment, and the method comprises the steps: dynamically generating a format perception pre-detection thread based on a current audio stream format, and adjusting the priority and sampling frequency of the thread according to the audio bit depth and the sampling rate; multi-level intelligent buffer area management with sound quality priority is implemented, and dynamic adjustment of the size of a buffer area and adaptive switching of the buffer area are realized according to different audio formats and input source types; establishing a cross-brand equipment coordination framework, and setting a watchdog timer with preferential tone quality to perform timed monitoring so as to process response timeout; triggering an elegant degradation strategy of sound quality priority according to the resource availability score, and reducing the audio processing quality step by step to maintain the playing continuity; the key characteristics of each transmission protocol are identified and utilized, the transmission protocols are adaptively switched according to the network condition and the processing capability of brand equipment, and real high-quality and zero-interruption audio experience is brought to users.

The present invention provides systems and methods for cellular network performance monitoring and optimization, enabling SIM-based devices to dynamically adapt to changing network conditions for improved connectivity. The invention introduces a process that includes determining baseline path performance through detailed probing of network metrics, continuously assessing current path performance via real-time monitoring, and instructing the SIM to switch from its current connected mobile network carrier to an alternate carrier when predefined performance thresholds are not met. Switching instructions are securely delivered Over-The-Air (OTA) to the SIM, ensuring seamless transitions to the most efficient and reliable network path. The system leverages both active and passive application layer observations to optimize latency, throughput, and reliability while supporting diverse applications, including IoT devices, industrial systems, and consumer devices.

The invention relates to the technical field of network set top boxes, and discloses a firmware segmented loading method and device in the starting process of a network set top box. The method comprises the following steps: performing firmware division on the network set top box to obtain a firmware segment set and an inter-segment dependency relationship matrix; determining a real-time state vector of the starting state based on hardware sensor data of the network set top box; performing starting stage identification on the network set top box according to the real-time state vector to obtain a current starting stage identifier and a time sequence optimization condition parameter; carrying out loading decision and Markov state transition analysis on the firmware segment set based on the current starting stage identifier and the inter-segment dependency relationship matrix to obtain a loading action sequence; and carrying out target path loading on the firmware segment set according to the loading action sequence and the time sequence optimization condition parameters to obtain a loading execution result. According to the method, the firmware segments can be flexibly combined and dynamically scheduled according to actual requirements, and the loading efficiency and stability under different network conditions are ensured.

The invention relates to an office process automation data analysis method based on a symmetric encryption algorithm, and the technical scheme comprises the following steps: introducing an encryption strategy generator, and dynamically adjusting the parameters of the symmetric encryption algorithm including the key length and the encryption round number through the real-time analysis of the data communication content including the data sensitivity, the frequency and the data volume; constructing a self-adaptive optimization model, and adjusting an encryption strategy according to external conditions such as a network state, equipment computing power and user behaviors; fragmenting the data packet before transmission, and allocating different transmission paths according to current network conditions and security evaluation; constructing a dynamic data recombination module at a data receiving end, and verifying and recombining data when encrypted data packets of different paths arrive; a lightweight key negotiation protocol based on symmetric encryption is adopted, and a key is generated and negotiated immediately when two communication parties initiate data exchange; a secret key life cycle management mechanism is introduced, and the service life and the number of times of limitation are set for each secret key.

The invention discloses a reliable communication system based on a publishing and subscribing mode in a satellite-ground weak network environment, and aims to solve the problems of high round-trip delay, limited link bandwidth, high instantaneous packet loss rate, easiness in blocking and switching influence of links and the like in a data transmission process between a low-orbit satellite and a ground station. A publishing and subscribing model is introduced into an application layer to realize service logic decoupling, deep fusion with an underlying enhanced QUIC transmission protocol is realized, and mechanisms such as message persistence, breakpoint resume, forward error correction and connection migration are combined, so that stable, orderly, complete and safe transmission of service data can still be ensured under the conditions of link instability, quality fluctuation and communication interruption, and the service data transmission efficiency is improved. And high availability and continuity of a satellite-ground communication task under a weak network condition are realized.

The invention discloses a network optimization method based on an online conference, which relates to the technical field of real-time audio and video transmission, and comprises the following steps: in a transnational network, deploying monitoring probes at nodes participating in the online conference for monitoring network parameters such as network delay, bandwidth, packet loss rate and jitter in real time; the method comprises the following steps: processing network parameters and predicting abnormity by using a statistical analysis algorithm and a time sequence model to obtain network condition information, and then combining global network topology and using a reinforcement learning Q-Learning model and a graph neural network GNN topology prediction algorithm; according to the method, the network condition is monitored and predicted in real time, the optimal transmission path is output by using the reinforcement learning Q-Learning model and the GNN topology prediction algorithm, the bandwidth fluctuation is predicted through the LSTM, the packet loss rate is predicted based on the Bayesian network, the data transmission strategy is optimized, the network delay, the packet loss rate and the jitter are effectively reduced, and the network performance is improved. And the smoothness and the stability of the conference are improved.

The invention discloses an AI-based automatic connection and handshake protocol for a trusted data space connector. The AI-based automatic connection and handshake protocol comprises a data supply end connector, a data demand end connector and a platform operation end, the data supply end connector is used for collecting and preprocessing data; the demand end connector is used for receiving and processing data; the platform operation end is used for connection management, authority distribution and security auditing; the method has the beneficial effects that through automatic connection and an intelligent handshake protocol, manual intervention is reduced, and the data circulation cost is reduced; multi-factor authentication, dynamic encryption and privacy protection technologies are adopted to ensure the safety of the whole life cycle of the data; aI and machine learning technologies are introduced, so that the system can intelligently select an optimal strategy according to real-time network conditions and data features; interoperation and data sharing among different data spaces are realized, and circulation and value mining of data elements are promoted.

An autonomous job scheduling system for distributed ledger technology is disclosed, leveraging photonic quantum computing and generative artificial intelligence (AI). The system collects transactional and operational data from various nodes within a distributed ledger network, focusing on transaction types, sizes, and priorities. This data is filtered for urgency and resource intensity, analyzed against real-time network conditions and business rules to ascertain job execution parameters. A photonic quantum computing system processes these parameters to optimize job scheduling strategies, including quantum annealing and simulations. The AI engine generates dynamic smart contracts deployed to distributed ledgers by a job orchestration engine. The system monitors job execution, gathering performance data and providing feedback for real-time adjustments. The job orchestration engine updates job scheduling in response to these adjustments, enhancing the efficiency and responsiveness of the blockchain network.

The invention relates to the technical field related to intelligent production control, in particular to a coal mine production equipment control method and system based on remote body interaction, and the method comprises the steps: coal mine production equipment uploads parameters, analyzes the state, configures a twin architecture, and carries out local decision abnormity early warning. A remote end is communicated with an interaction device, biological characteristics are reflected as a pose instruction, time delay compensation and track deviation prediction are embedded, a multi-mode feedback unit triggers smooth control according to the pose instruction, and tactile feedback is enhanced in combination with a vibration spectrum. The technical problems that the network condition of the underground coal mine environment is poor, obvious communication delay exists in remote control, operation instruction lagging exists, and the response speed and operation safety of equipment are affected are solved, dynamic time delay is predicted through the LSTM network, track deviation is compensated, the real-time performance and stability of remote control are improved, accurate execution of instructions is ensured, and the service life of the equipment is prolonged. And the remote operation efficiency and reliability of the coal mine equipment are improved through high-precision pose control and real-time compensation.

The invention provides a network flow regulation and control system and method and a storage medium, and relates to the technical field of network communication. The network traffic regulation and control system comprises: an information collection unit, which is used for obtaining historical traffic data of all media plane network elements connected with a control plane network element and a topological relation of the media plane network elements; the AI model updating unit is used for updating the traffic regulation and control model of the control plane network element according to the historical traffic data and the topological relation, and the strategy generation unit is used for obtaining a traffic distribution strategy of the control plane network element; and the strategy execution unit is used for dynamically adjusting the media traffic of each media plane network element according to the traffic distribution strategy. According to the method, the network data is collected and analyzed in real time, and dynamic updating of the AI model is combined, so that dynamic regulation and control of the network traffic are realized, the method can quickly adapt to changes of network conditions, and the flexibility and efficiency of traffic regulation and control are improved.

The system (100-2) and method for identification of high ranked neighbor cells in a telecommunications network provide an efficient and accurate approach to selecting neighboring cells with superior performance for handover purposes. The system (100-2) leverages performance metrics, algorithms, and dynamic adaptation techniques to determine the suitability and ranking of potential neighbor cells. By considering factors such as signal strength, signal quality, interference levels, load balancing requirements, and operator-defined policies, the system (100-2) evaluates the performance of neighbor cells and assigns them scores or rankings to identify the higher ranked neighbor cells. The system's dynamic adaptation ensures that the rankings remain up to date and responsive to changing network conditions. The present system (100-2) and method optimize handover decisions, enhance network performance, and provide users with seamless connectivity and improved quality of service.

The invention relates to a vehicle cloud collaborative perception decision-making high-speed automatic auxiliary navigation driving method. The method comprises the following steps: S1, a vehicle end sends collected vehicle end data to a cloud end; s2, the cloud generates a first decision instruction based on the vehicle end data of all vehicles and other sensor data; s3, after the vehicle receives the first decision instruction, the current moment is recorded as a second timestamp, and accumulated time delay is calculated; s4, judging whether the accumulated time delay is smaller than a pre-configured time interval threshold value or not, and if yes, executing the step S5; s5, generating a first speed constraint boundary condition according to the instruction content; and S6, the vehicle end takes the first speed constraint boundary condition as one of constraint conditions of the decision model to generate a second decision instruction. Compared with the prior art, the method has the advantages that the problem of low driving safety of high-speed automatic auxiliary navigation caused by limited single vehicle detection distance is solved under the conditions that the vehicle end computing power requirement is hardly increased, the network condition is poor, and particularly the visibility is poor.

The invention particularly relates to a mobile camera management system capable of adaptively adjusting video coding quality. According to the mobile camera management system capable of adaptively adjusting the video coding quality, a network condition monitoring and predicting module is used for monitoring the current network bandwidth and packet loss rate in real time and predicting the network state; the video coding control module is used for dynamically adjusting video coding parameters according to the current network bandwidth and the ROI; and the adaptive adjustment strategy module is responsible for issuing a video coding strategy to the video coding control module according to the data fed back by the network state monitoring and prediction module so as to realize dynamic adjustment of the video coding quality. The mobile camera management system capable of adaptively adjusting the video coding quality can avoid video lagging and frame loss when the network bandwidth is relatively low, is suitable for various mobile devices such as a vehicle-mounted device, an unmanned aerial vehicle and a wearable camera, effectively improves the continuity and availability of video transmission, and realizes stable application in an environment with unstable signals.

The invention discloses a power distribution cabinet remote monitoring system based on wireless communication, and relates to the technical field of remote monitoring, and the system comprises a communication scheduling module which is used for receiving an uploaded data packet, carrying out link evaluation through an uploading link optimization method in combination with a real-time network condition, and carrying out link optimization according to an actual link condition and the priority of the uploaded data; selecting an initial uploading link and uploading data; the Bluetooth self-healing module is used for evaluating the link quality according to the link quality data in a data uploading process, dynamically switching an active link and a standby link according to a link quality evaluation result, and carrying out Bluetooth task transmission; the remote control module is used for carrying out data reduction by adopting a multilayer decryption mechanism and a mixed format analysis mechanism based on the encrypted Bluetooth task data, evaluating the operation state of the power distribution cabinet, generating a control instruction through a fuzzy logic control method according to an operation state evaluation result and executing the control instruction; through a dynamic link switching mechanism of the Bluetooth self-healing module, continuous and stable transmission of monitoring data of the power distribution cabinet is ensured.

The invention discloses a method for testing the connection strength of a bolt fastener, and relates to the technical field of fastening mechanical analysis. The bolt fastener connection strength testing method comprises the following steps: acquiring bolt data through a built-in sensor in an electric wrench, analyzing, classifying and transmitting the bolt data to a relay station to obtain corresponding response bolt data, then sequencing the response bolt data according to priorities, and uploading the response bolt data to a cloud platform according to network conditions; prompting operation is carried out through the cloud platform and is fed back to the relay station; the effect of improving the accuracy of the connection strength test of the bolt fastener is achieved, and the problem that the accuracy of the connection strength test of the bolt fastener is low due to discontinuous signals in the prior art is solved.

The invention relates to the technical field of network switching, in particular to a heterogeneous network dual-mode self-adaptive switching method and system. According to the method, the multi-dimensional environment sensing data and the initial signal intensity are acquired, the signal intensity is calibrated in a dynamic environment, the dynamic response of the network state to real-time change is realized, and the connection state characteristic and the side network consumption index of the dual-mode network are acquired by combining the calibrated signal intensity. The switching demand can be evaluated on the basis of the data, the switching demand can be judged more accurately through the multi-dimensional evaluation mechanism, the limitation that a traditional method only depends on signal strength is avoided, and the switching demand can be evaluated by generating an optimized directed time sequence graph and a network performance data set. According to the technical scheme, the equipment can make a scientific and reasonable switching decision according to the real-time network state snapshot data and the preset optimization time sequence graph, and the connection requirements of the equipment can be dynamically adapted under the variable network conditions, so that the optimal network experience is kept, and the network connection quality is improved.

The invention relates to the technical field of data processing, in particular to a vCPE resource allocation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining system operation data of each monitoring point, inputting the system operation data into a preset anomaly detection model, outputting an abnormal data point, determining a vCPE resource allocation strategy according to the abnormal data point, inputting the system operation data into a preset target prediction model, and obtaining a vCPE resource allocation result. And outputting predicted operation data in a future time period, adjusting a resource allocation strategy according to the data, and adjusting vCPE resource configuration according to the adjusted strategy. According to the method and the device, the limitation that traditional vCPE resource allocation is based on static configuration or simple rule scheduling is broken through, the resource allocation strategy can be dynamically adjusted according to the real-time network condition and the user service requirement, the vCPE resource utilization rate is effectively improved, the network performance is optimized, and the problems of low resource utilization rate and limited network performance caused by an existing resource allocation mode are solved.

The invention discloses a weak network environment photographing off-line uploading method and system, and relates to the technical field of file upload, and the method comprises the steps: monitoring the network bandwidth and the use conditions of a CPU and a memory in real time, building a dynamic calculation model, and achieving the adaptive adjustment of the size of a data block; dividing the file to be uploaded into blocks of which the sizes are pre-adjusted according to a dynamic blocking algorithm, and storing the blocks locally; according to a priority calculation model, establishing a dynamic priority queue, and calculating a priority for each block uploading task; a queue scheduler is set, high-priority blocks are ensured to be preposed, a priority queue is established, the queue is reordered at set time intervals, and high-priority block tasks are ensured to be preposed; the network connection state is detected in real time, the dynamic change of the network quality is continuously monitored, the time period when the network condition meets the preset condition is intelligently identified according to the monitoring result, and locally cached blocks are timely uploaded to a service system in an offline mode according to the priority. According to the invention, the file can be uploaded according to the network state.

The invention discloses a wireless data uploading method and system for an industrial internet of things, and relates to the field of wireless data processing, and the method comprises the steps: firstly, comprehensively considering the inherent service attributes of data to determine the initial service priority of the data; meanwhile, terminal side sensing data and global network state data obtained from the gateway are fused, and a more accurate and comprehensive network quality score is calculated through double-source information. Finally, the initial priority and the network quality are dynamically combined according to the two key dimensions, and a final transmission strategy is generated. According to the method, a data uploading decision is no longer static and one-dimensional, but a two-dimensional dynamic decision which can be adaptively adjusted according to a real-time network condition, so that the sending opportunity or mode of the high-priority data is intelligently adjusted when the network is congested, the blocking of key services due to blind sending is effectively avoided, and the service quality is improved. And the robustness and the high efficiency of data uploading in a complex industrial environment are ensured.

The invention relates to the technical field of AI voice, and particularly discloses an off-line and on-line mixed use method of AI voice, which comprises the following six steps of: evaluating a network connection state to obtain a network reliability index; analyzing voice data input by the user and preamble information, determining the complexity of voice intention and generating a demand priority; and then cooperatively selecting a voice processing mode according to the network reliability index, the demand priority and the decision rule. If the mode is an off-line mode, extracting a voice feature vector, classifying responses by using a local model, obtaining a processing mode confidence coefficient according to user feedback, and if the processing mode confidence coefficient is lower than a threshold value, triggering an on-line supplementary response and updating a rule; and if the mode is an online mode, feature vectors are extracted to be matched with a cloud knowledge base, a fine classification result is obtained, and accurate response is performed by means of a cloud large model. The whole process is combined with network conditions and user requirements, and a processing mode is flexibly decided, so that high-quality AI voice interaction service is provided.

The invention provides a method and a system for transmitting a photographic image of a security camera, relates to the technical field of image transmission, and effectively divides an image frame into a differential image block and a background image block by introducing a partition mechanism based on inter-frame pixel difference, thereby remarkably improving the detection efficiency of a moving target and the pertinence of data processing. Through embedding marks such as a direction vector and a motion timestamp in a differential image block set, priority coding of a dynamic target image area is realized, and further in combination with a periodic compression strategy of a background image frame, redundant data is compressed to the greatest extent, and the overall data transmission load is effectively reduced. Meanwhile, an image fragment priority model is jointly constructed through brightness saliency and texture complexity, and structure entropy and direction gradient analysis is assisted, so that redundant backup generation of key content is realized, and data integrity under a weak network condition is greatly improved.

The invention relates to the technical field of OTA upgrading, in particular to an OTA upgrading result prediction method and system and a vehicle, and the method comprises the steps: periodically obtaining the multi-dimensional data of the vehicle, the multi-dimensional data including the current vehicle electric quantity, the OTA upgrading package size, the storage battery voltage, the environment signal strength, the network condition, the vehicle state and the current time; inputting the obtained multi-dimensional data into a trained random forest regression prediction model, and predicting the OTA upgrading success rate; and outputting a corresponding OTA upgrading suggestion according to the predicted OTA upgrading success rate. According to the method, whether upgrading can be smoothly carried out or not can be accurately predicted.

The invention belongs to the technical field of computing power distribution, and particularly relates to a dynamic computing power distribution method and system based on reinforcement learning, and the method comprises the following specific steps: S1, covering cloud, edge and end full-node scenes, and collecting computing power resource states, task demand features and cross-domain network condition data in real time; s2, on the basis of standardized data output by a cross-domain computing power sensing module, by constructing a state space fusing computing power, tasks and a network, defining an action space of computing power scheduling direction and proportion, and designing a multi-target reward function for balancing the resource utilization rate, the task satisfaction rate and long-term conflict avoidance; and realizing self-learning and self-iteration scheduling strategy generation based on a reinforcement learning algorithm. According to the invention, the reinforcement learning agent autonomously learns the computing power demand of the emergency scene and the new type of task, the rule does not need to be manually preset and modified, and the method has the advantage of realizing dynamic adaptation of computing power distribution to complex and changeable scenes.

The invention relates to the technical field of micro-service architecture and intelligent operation and maintenance, and discloses a micro-service dynamic heterogeneous routing system and method based on a large model, and the system comprises a semantic control layer, a protocol adaptation module, a path optimization module, a path scheduling module, and an execution layer. The method comprises the following steps: analyzing a micro-service API document through a large language model, extracting a semantic dependency relationship between services, and generating a service dependency graph; analyzing a business semantic chain; selecting a path with the minimum cost by applying a path optimization algorithm; optimizing path scheduling by using reinforcement learning according to real-time network conditions and service requirements; and finally executing path optimization and performing protocol adaptation conversion. According to the method, the API document is analyzed through the large language model to automatically construct the service dependency graph, the path is dynamically adjusted in combination with the path optimization and reinforcement learning algorithm, and the optimization problem of the service calling path is solved. Through the protocol adaptation execution unit, automatic adaptation of the cross-protocol service is realized, and the efficiency and maintainability of the system are improved.

The invention relates to the technical field of privacy protection of networked automobiles, and discloses a privacy protection distributed federated learning collaborative strategy for networked automobiles, which comprises a privacy protection system of networked automobiles, and the privacy protection system of networked automobiles comprises an application scene layer, a core technology layer, an edge computing layer and a data and equipment layer. According to the privacy protection distributed federal learning cooperation strategy for the networked automobile, real-time and multi-dimensional perception of a traffic environment is realized through integration of V2X communication, a road condition space-time encoder, a stream data processing engine and other technologies, accurate data support is provided for intelligent driving decision making, and the privacy protection distributed federal learning cooperation strategy for the networked automobile is provided for the intelligent driving decision making. Through a state division domain strategy module and a time-space domain adaptive division technology, resource allocation and task scheduling can be dynamically adjusted according to real-time road conditions and network conditions, the response speed and efficiency of the system are improved, and through technologies of a privacy calculation pool module, dynamic privacy budget allocation, a PKI certificate service center and the like, real-time dynamic privacy budget allocation can be realized. And strict privacy protection is realized during data sharing and federal learning.

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.

A distributed system and method for compressing and restoring data across edge computing devices and cloud infrastructure is disclosed. The system dynamically adjusts compression based on available computing resources, network conditions, and now energy constraints. Edge devices monitor power consumption and battery levels, optimizing compression parameters to extend battery life while maintaining data quality. A workload scheduler prioritizes tasks based on energy availability, offloading intensive processing to cloud infrastructure when necessary. The system utilizes an energy-aware coordination layer to balance workloads across multiple devices, ensuring efficient data flow and long-term operational stability. Homomorphic operations allow secure distributed processing on compressed data, while an adaptive neural upsampler enhances reconstructed outputs. By integrating energy optimization, the system improves performance and longevity of edge devices in power-limited environments.