225 results about "Cpu load" patented technology

The invention belongs to the technical field of computers, and provides a video display and storage method and system based on a wayland protocol and a storage medium in order to solve the problems that an existing wayland video system is high in rendering delay, high in CPU load, low in storage efficiency and poor in cross-platform compatibility. Video frames are directly written into an Overlay layer through DRM-KMS and mixed by bypassing a synthesizer, meanwhile, CPU memory amplitude overhead is eliminated through zero copy transmission, an ISP-GPU-VPU direct transmission assembly line is constructed, full-link DMA-BUF direct transmission is achieved, and delay is greatly reduced; iSP preprocessing, GPU shader scaling and VPU coding are all executed by special hardware, so that the load is reduced, and the multi-path processing capability is improved; vPU dynamic code rate compression is utilized, segmented storage is carried out according to events / time, metadata is synchronized to a database, redundant storage is avoided, and space is saved.

The invention discloses an industrial personal computer and multi-graphics card collaborative parallel computation acceleration system, which relates to the technical field of industrial resource allocation and parallel computation, and comprises a resource monitoring and predicting module, a resource management module and a prediction type resource preparation module, the task splitting and collaborative execution module comprises a task splitting module, a cross-node collaborative module and a collaborative operation engine; the intelligent scheduling and dynamic resource allocation module comprises an intelligent scheduler, a dynamic resource allocation module and a conflict avoidance module. According to the method, the GPU video memory utilization rate, the core utilization rate, the temperature, the video memory fragment rate, the available video memory total amount, the CPU core total utilization rate, the load condition and the idle core number index are collected in real time through a resource monitoring module, and the video memory capacity, the GPU core occupancy rate and the CPU load requirement are predicted in advance before a task is submitted in combination with a gradient boosting decision tree and a neural network prediction model; and resources are reserved, so that the scheduling delay is remarkably reduced, and the scheduling hit rate is improved.

The invention discloses an efficient data compression and transmission method based on a computing power host management operation platform, and relates to a large-scale data concurrent management technology. Comprising the steps of 1, integrating a lightweight data feature analyzer at a Kafka producer end, extracting feature indexes of data streams in real time through the data feature analyzer, and generating compression strategy parameters according to the feature indexes, and 2, performing feature recognition on structured data and unstructured data, extracting data features, and generating compression strategy parameters according to the compression strategy parameters. And step 3, based on data characteristics, CPU load of producers / consumers and network bandwidth prediction, constructing a multi-objective optimization model, dynamically selecting a compression algorithm and a compression level, selecting compression strategy parameters according to the compression algorithm, introducing a compression income ratio index, calculating a ratio of compression rate increase to compression time consumption, and performing compression strategy optimization. And preferentially selecting a compression algorithm with a high gain ratio to perform data compression and transmission.

The invention discloses a sintering process real-time optimization method based on edge calculation, and the method comprises the steps: obtaining original multi-index time series data collected at the edge side of a sintering process in real time, and recording sampling data and corresponding process labels for sensing points at different workshop section positions in a sintering production line; based on the real-time prediction task load, the edge node computing power and the network bandwidth monitoring result, whether the current node reaches an elastic migration triggering condition or not is judged, and if yes, subsequent task migration operation is executed; the calculation and storage quotas of edge nodes are dynamically adjusted based on the GPU / CPU load, the storage space and the prediction task fluctuation of each node; according to the invention, by capturing the deep relationship of time-varying coupling between process indexes, an elastic and dynamic resource scheduling and model adaptive mechanism is constructed, the edge-side dynamic graph modeling capability is enhanced, and a model self-learning optimization mechanism is introduced; the accuracy, the real-time performance and the edge system adaptability of multi-index collaborative prediction in the complex industrial process are effectively improved.

The invention discloses an AI-driven RDMA network card hardware unloading acceleration method and system based on an RISC-V architecture, the system comprises an RISC-V processing unit, the RISC-V processing unit comprises a control module and a driving module, and the driving module stores RDMA exclusive acceleration instruction set extension; the AI reasoning unit is connected with the driving module and is used for expanding and running a pre-trained network optimization model based on an RDMA exclusive acceleration instruction set to analyze a network state and an application requirement, generating a hardware unloading strategy optimization instruction and sending the hardware unloading strategy optimization instruction to the control module; the control module generates a corresponding management command according to the hardware unloading strategy optimization instruction; and the hardware unloading unit comprises a management module and an RDMA component module, and the management module receives the management command and performs parameter configuration on the RDMA component module according to the management command. According to the invention, the burden of the CPU is reduced, and the throughput is improved.

The invention discloses a large model deployment method based on heterogeneous resource scheduling and multi-model collaborative reasoning, and relates to the field of artificial intelligence and server system deployment. Comprising the following steps of: 1, calculating a deployment score of a target model by utilizing a deployment scoring function according to a GPU video memory residual rate, a CPU load, network delay and a current task quantity of each node through a heterogeneous resource sensing scheduler, and selecting a node with the highest score of the target model to load or reuse the target model; a cache index key is generated, and whether key value KV pairs supporting multiplexing of the target model exist or not is inquired; step 3, if the cache is hit, skipping a forward propagation stage, and directly decoding based on a cache result, otherwise, executing complete forward reasoning and writing the result into the cache; 4, based on a plurality of user requests, performing fusion according to user priorities, model weights, reasoning costs and cue word lengths to generate execution sorting weights, and scheduling execution models in batches according to the weights; and 5, returning the model response to the user, and releasing or updating node state information.

The invention provides a multi-modal data active recommendation method based on cross-modal dynamic fusion and equipment resource perception. The method is applied to the technical field of data processing. The method comprises the following steps: acquiring multi-modal data through a smart home device end; performing modal feature extraction on the multi-modal data to generate a voice feature matrix, an environment probability matrix and an equipment state vector matrix; adopting a cross-modal dynamic fusion algorithm, combining time dimension and space dimension weights, and carrying out adaptive weighted fusion on the voice feature matrix, the environment probability matrix and the equipment state vector matrix; the CPU load and the memory occupancy rate of the central control equipment are detected in real time through the equipment resource monitoring module, and a data calculation strategy is dynamically adjusted; and based on the fused multi-modal feature matrix, generating an active recommendation instruction through user portrait modeling and knowledge graph association, and driving the smart home device to execute adaptive adjustment. In this way, the recommendation efficiency can be improved.

The invention provides a multi-player voice game performance optimization method and device based on dynamic resource management. The method comprises the following steps: acquiring a core source code of a game voice tool and pre-compiling the core source code to generate a byte code file; analyzing a target scene demand and pre-loading a related resource file; performing real-time acquisition and analysis on memory occupation and CPU load to obtain performance state data of the current system; based on the performance state data, when it is detected that memory occupation and / or CPU load exceed a preset threshold value, a pre-compiling mode and a resource loading strategy are automatically adjusted; when it is detected that the multi-person voice function is started, resources and byte codes needed by voice processing are placed in a high-priority queue, and cooperative scheduling is carried out on memory occupation and / or CPU loads. According to the method, the loading delay can be reduced, the frame rate can be improved, the communication delay can be reduced, and real-time voice processing and high-load requirements of games can be dynamically considered.

The invention discloses an Android video self-adaptive secondary transcoding method and system and a medium, mainly relates to the technical field of secondary transcoding, and is used for solving the problem that part of videos cannot be played on some Android devices due to a transcoding mode in an existing scheme. Or the video playing experience is difficult to comprehensively optimize due to the fact that the video playing experience cannot be comprehensively optimized due to the fact that the video playing experience is not combined with the real-time feedback in the playing process. Comprising the steps that when it is determined that a playing report error occurs and the error indicates that the coding format is incompatible, the format with the highest priority in the coding formats supported by equipment is selected from a coding list contained in software parameters to serve as the final coding format, and secondary transcoding is conducted according to the final coding format; triggering a screen resolution and code rate down-regulation program when lagging occurs, a playing error occurs, the error is not incompatible in coding format or playing stagnation occurs, and the CPU load in the hardware parameters is greater than a preset value; and carrying out secondary transcoding based on the down-regulated screen resolution and code rate.

The invention discloses a method for dynamically setting dog feeding time of a watchdog circuit, which comprises the following steps of: collecting and monitoring power consumption of a CPU (Central Processing Unit) and a corresponding load, and fitting the load and power of the CPU according to monitoring data so as to reflect the running state condition of a system; power consumption of a monitoring CPU is collected in real time, the real-time CPU load is calculated according to the fitting relation between the CPU load and power, and the watchdog feeding time of a watchdog circuit is determined according to the CPU load; a square wave period is set, square waves are collected to set timeout time, and the timeout time is kept consistent with dog feeding time, determined according to CPU loads, of a watchdog circuit. According to the method, the CPU power consumption and the load are fitted, the CPU load is monitored in real time, the square wave period is adjusted, the dog feeding time is dynamically adjusted according to the CPU load, resource waste is avoided, and the method is suitable for the fields of embedded systems, industrial control and the like.

The invention relates to the field of search engines, and discloses a search optimization method and device, equipment and a medium. Performing weighted calculation according to the CPU load, the memory occupation, the data read-write speed, the network throughput and the index query frequency, and determining the load score of each node; if it is monitored that the load fraction of any node meets preset conditions, different search optimization schemes are executed according to the preset conditions met by the current node, and the preset conditions comprise the first preset condition, the second preset condition and the third preset condition; the search optimization scheme is in one-to-one correspondence with an index migration scheme of a high-load node, an index pre-distribution scheme of a new node and a load balancing scheme of a hotspot index based on different preset conditions; and in response to the current search optimization scheme, adjusting index distribution of each node to complete search optimization. By dynamically sensing load data, adaptive weight hash mapping is performed according to a search optimization scheme, so that the query performance is improved, and the storage utilization rate is optimized.

The invention relates to the technical field of converters, in particular to a three-phase converter control method and system, a medium and equipment. The method comprises the steps that a main processor transmits sampling original data of a three-phase converter to a first storage area; the coprocessor reads the sampled original data, performs operation to obtain operation result data, and transmits the operation result data to the second storage area; the coprocessor runs at a period higher than the preset control frequency of the main processor, and immediately triggers the operation of the sampled original data after completing the collection of the sampled original data; the main processor performs control loop calculation, reads the operation result data when the newest operation result data is needed, and loads the newest operation result data for updating operation; the main processor runs the control program at the preset control frequency, and the control program triggering time is set in a minimum allowable time window before the control output quantity loading moment. Through the above arrangement, real-time sampling is effectively realized, CPU load is optimized, and the control performance of the converter is improved.

The invention discloses an Android Service plug-in implementation method, an Android Service plug-in implementation system, Android Service plug-in implementation equipment and a medium. The Android Service plug-in implementation method specifically comprises the following steps: deploying proxy service in a host application, creating a standard interface IServiceInterface, and fusing host and plug-in resources by rewriting a context loading method of Android Service; constructing a service life cycle prediction model through a federated learning framework based on multi-device service behavior data collected during proxy service operation; generating a dynamic scheduling strategy based on an output result of the service life cycle prediction model in combination with the equipment electric quantity, the network type and the CPU load; and based on a dynamic scheduling strategy, writing the plug-in metadata hash, the federated model fingerprint and the device state snapshot into a block chain smart contract, and verifying the integrity of the plug-in through a light node on the chain to obtain a block chain verification result. According to the method, the stability and the reliability of the Service plug-in are improved, and powerful support is provided for the hot update function of the Android application.

The invention provides a game performance optimization method and device based on dynamic scheduling and a storage medium. The method comprises the following steps: dynamically adjusting a byte code compiling strategy based on operation data, and reducing the execution priority of a byte code compiling task when monitoring that a multi-person voice team function is started and a CPU load reaches a preset condition; performing dynamic scheduling on allocation of the CPU cores based on the operation data, allocating high-priority tasks to the CPU cores with loads lower than a preset load threshold for execution, and allocating other tasks to the remaining CPU cores; carrying out scheduling and intelligent cache management on the resource file according to the operation data; and when monitoring that the network bandwidth or the network delay reaches a preset condition, adjusting a multi-person voice data transmission strategy based on the operation data. According to the method and the device, dynamic scheduling can be performed according to the real-time state during game running, tasks and resources are flexibly allocated, CPU occupation is reduced, and game running lagging is avoided.

The invention discloses an audio test data processing method, system and device based on zero-copy NIO and a dynamic sliding window, and the method comprises the following steps: S1, building a memory mapping file channel, and directly mapping an audio file to a process address space; s2, constructing a double-buffer processing pipeline, and realizing asynchronous decoupling of a collection thread and a calculation thread by adopting a producer-consumer mode; and S3, a dynamic sliding window mechanism is adopted to maintain a real-time audio data interval, and the window size is dynamically adjusted according to a television CPU load. According to the technical scheme of the invention, the problems of high memory occupation, high detection delay and low cross-application data reading efficiency existing in audio testing of a television system in a multi-application concurrent scene are solved.

The invention discloses a resource allocation method and device based on bit map pre-calculation, and the method comprises the steps: generating a global resource state bitmap according to the CORESET configuration in a PDCCH, and carrying out the pre-calculation of each possible candidate PDCCH to form a candidate pre-allocation bit map set; extracting a corresponding pre-allocation bitmap from the candidate pre-allocation bitmap set as a candidate set bitmap; and comparing the candidate set bitmap with the global resource state bitmap, judging whether the resources are conflicted or not, and if the resources are not conflicted, determining that the resources are available and further occupying the resources. Complex function calculation at the scheduling moment is transferred to pre-calculation at the initialization stage, only simple bitmap retrieval and bit operation are needed during scheduling, the PDCCH resource allocation time consumption is greatly shortened, real-time intensive calculation is replaced with pre-stored bitmaps, the CPU load is remarkably reduced, a processor focuses on a core scheduling algorithm, resource redundancy consumption is reduced, and the scheduling efficiency is improved. And the overall capacity and the energy utilization efficiency of the communication system are effectively improved.

Systems and methods are disclosed for optimizing network routing and data processing through dynamic route optimization. The invention features a dynamic route optimization engine that uses real-time performance metrics such as latency, bandwidth availability, memory utilization, CPU load, and throughput capacity. It allows for rule-based configurations, enabling custom parameters for transaction processing, data retrieval, and user authorization. Integration with network monitoring tools provides continuous visibility into network performance. The system supports multi-cloud and hybrid cloud environments, dynamically selecting optimal paths based on real-time conditions and regulatory compliance. Predictive analytics anticipate network congestion, allowing proactive routing adjustments. Adaptive algorithms learn from past conditions to optimize performance continuously. Scalability is achieved through automated updates to routing tables and security groups. The invention reduces latency and ensuring efficient data transfer, providing a cost-effective and reliable solution for modern network management, addressing a long-standing need for adaptability and efficiency in network routing.

Systems and methods for anomaly detection are described and contemplated herein. An Isolation Forest algorithm is implemented for both training a plurality of machine learning models and accurately detecting multiple anomaly patterns in computer equipment time series data, such as CPU loads, temperatures, RAM usage, and other computer equipment metrics.

The invention discloses an HPC cluster benchmark energy efficiency adjusting and optimizing method based on software and hardware joint optimization, and belongs to the field of cluster energy efficiency adjusting and optimizing. The method is divided into two aspects of software tuning and hardware tuning, in the software aspect, after energy efficiency sensitive parameters under the operation of a reference real machine are obtained through parameter importance analysis, a sampling algorithm in the parameter optimization process is optimized in combination with an energy efficiency prediction model, the optimization and model training overhead is reduced, the model convergence speed is increased, and the optimization efficiency is improved. The off-line low-cost parameter optimization is realized; in the aspect of hardware, on the basis of optimal cluster operation parameters, feature extraction and training are carried out on the utilization rate of a CPU core group in a reference operation period to obtain a clustering model for identifying a CPU load mode, and the resource demand of a load can be judged from a global perspective, so that corresponding dynamic frequency modulation is carried out, and the energy efficiency operation performance of the cluster is improved.

The invention relates to the technical field of resource allocation, in particular to an object recognition and task processing method and system for multi-machine cooperation of competition robots, and the method comprises the following steps: pre-defining a plurality of service quality levels of a target object recognition and path planning calculation task of a cooperative robot, and obtaining CPU resource consumption and task processing performance corresponding to each grade based on the visual sensor data resolution and algorithm complexity under different grades. According to the method, a plurality of task service quality levels are defined in advance, and different visual data resolutions and algorithm complexity are mapped into CPU resource requirements and task processing performance indexes, so that quantitative association of tasks and resource requirements is realized, and the resource allocation accuracy is improved; and meanwhile, the CPU load state of the robot cluster is sensed in real time, a dynamic QoS regulation and control instruction is established by combining the load change trend and the task emergency degree, and the real-time performance and sensitivity of resource allocation are improved.

The invention discloses an FPGA (Field Programmable Gate Array)-based numerical control system, which comprises an FPGA chip and a plurality of drivers, and is characterized in that the FPGA chip comprises a DMA (Direct Memory Access) core, a descriptor management module, a first bus management module, a second bus management module and a plurality of transmission channels; the DMA core is connected with the first bus management module, the second bus management module and the descriptor management module, the first end of the transmission channel is connected with the first bus management module and the second bus management module, and the second end of the transmission channel is connected with the driver; the descriptor management module is connected with the first bus management module and is used for generating a descriptor chain table; the DMA core is used for traversing the descriptor chain table to generate a data transmission request, and the second bus management module determines a driver based on the data transmission request and gates a transmission channel corresponding to the driver. The descriptor management module is configured in the FPGA chip, so that multiple times of data interaction between the CPU and the FPGA chip is avoided, the CPU load is remarkably reduced, and the communication efficiency of the numerical control system is improved.

The invention relates to a cloud computing resource load prediction method based on TCN-PatchTST (Trusted Cryptographic Network-PatchTST). Comprising the following steps: collecting CPU load time sequence data to form a training sample set; carrying out preprocessing and feature construction on the training sample, and selecting a memory utilization rate load value which has obvious influence on a CPU load value as an input feature to predict the CPU load value; according to the method, a TCN-PatchTST neural network is constructed, a two-channel feature extraction architecture is designed, local time sequence features are extracted through causal convolution and expansion convolution of TCN, a long-term dependency relationship is captured by using partitioning processing and a channel independence mechanism of PatchTST, and a feature fusion layer is designed to integrate multi-scale information; the TCN-PatchTST neural network is trained; and inputting a test sample into the trained TCN-PatchTST neural network to obtain a CPU load value prediction result of the test sample. According to the method, load prediction is carried out through the historical load time sequence data, the prediction precision of the CPU load value of the cloud computing resources is effectively improved, and technical support is provided for intelligent scheduling of the cloud resources.

The invention belongs to the technical field of power systems, and relates to an RDMA protocol efficient analysis and forwarding method based on an RISC-V architecture, hardware acceleration of a protocol analysis and verification process is realized by introducing a hardware acceleration module and a customized RISC-V instruction set, so that the analysis speed is greatly improved, and the CPU load is reduced; in the protocol analysis process of a data packet, the flexibility and customizability of an RISC-V architecture are utilized, an instruction set is optimized according to the special requirements of an RDMA protocol, header information of the data packet can be efficiently extracted, CRC verification can be carried out, the load condition of each calculation unit can be monitored in real time through dynamic flow scheduling and load balancing and a hardware scheduling module, and the real-time performance of the data packet is improved. Flow distribution is intelligently adjusted, the overload problem of a single computing node is avoided, and efficient forwarding of data packets is ensured. Besides, based on the customized processing capability of the RISC-V architecture, the forwarding path of the data packet can be flexibly adjusted according to the real-time network topology and routing information, and the adaptability and the processing capability of the whole network are improved.

The invention relates to the technical field of network security, and discloses a DPDK-based firewall CPU load determination method, apparatus and device, and a medium. The method is applied to a firewall, the firewall comprises at least one thread, and the method comprises the following steps: in a preset time period, aiming at each thread, repeating the following polling operations: determining whether a queue directed at the polling operation of the single thread is empty or not, and when the queue directed at the polling operation of the single thread is not empty, executing the polling operation of the single thread; determining a first processing duration corresponding to the polling operation so as to determine a total processing duration corresponding to each thread; the first processing duration is the duration for the CPU to process the data packet in the queue for the polling operation; the total processing duration is the total duration for the CPU to process the data packet in the preset time period; and determining a CPU load corresponding to each thread according to the total processing duration corresponding to each thread and a preset time period. According to the method and the device, the CPU load can be accurately determined by measuring the total processing duration of each thread in the preset time period.

The invention discloses an intelligent task scheduling and collaborative execution method and a heterogeneous computing system, and the method comprises the steps: receiving and analyzing a computing task, and extracting the computing, data and constraint features of the computing task; decomposing the task into sub-tasks with a dependency relationship; acquiring load, computing power and power consumption states of the NPU, the GPU and the CPU in real time; on the basis of task characteristics and real-time states, multiple allocation schemes are evaluated through a pre-constructed cost model, and an optimal scheduling decision for allocating the sub-tasks among the heterogeneous computing units is dynamically generated; a flow line collaborative execution plan across at least two computing units is arranged according to a decision, sub-task execution is scheduled, meanwhile, cross-unit data flow and synchronization are managed, and a corresponding system comprises an NPU, a GPU, a CPU, a system memory and an intelligent heterogeneous resource scheduler for executing the method. According to the method, fine scheduling, self-adaptive load balancing and multi-objective optimization of heterogeneous computing resources are realized, and the comprehensive efficiency and energy efficiency of the system are remarkably improved.

The invention relates to the field of memory testing, and discloses a memory read-write test acceleration system and method and a storage medium. The system comprises a CPU and an SOC chip. The SOC chip comprises a read-write accelerator, a DDR (Double Data Rate) memory controller and a comparator; the CPU is used for continuously outputting a write instruction carrying a target address and target data when a read-write test in the SLT mode is executed; the read-write accelerator is used for sending target data to the comparator and sending a target address to the DDR memory controller when determining that the write instruction is a read instruction, so that the DDR memory controller initiates memory access to the to-be-tested memory to obtain read-back data; and the comparator is used for comparing the read-back data with the target data to obtain a comparison result. When the read-write test of the memory is executed in the SLT test mode, the CPU only issues the write instruction, and the read-write accelerator converts and issues the write instruction, so that the load of the CPU is reduced, and the test efficiency of the memory is improved.

The invention provides a software operation synchronization method and system based on a dual-chip architecture, and abandons a traditional mode depending on a UART / SPI / CAN communication protocol or an additional synchronization circuit. Firstly, a unique PWM duty ratio mapping relation with any difference not smaller than 5% is preset for the initial state, the system self-checking state, the OTA upgrading state, the running state, the fault state, the dormancy preparation state and other running states of the double-chip system, and then a chip A and a chip B continuously output PWM signals matched with the current states of the chip A and the chip B through common GPIO pins in a push-pull output mode. Meanwhile, PWM signals of GPIO pins of the opposite side are collected in real time in a bidirectional mode through respective hardware capture units, the duty ratio is analyzed to obtain the state of the opposite side, response operation is executed according to preset logic, synchronization failures caused by communication delay and data frame error codes of a traditional software protocol are effectively avoided, the system stability is guaranteed, meanwhile, a complex protocol stack does not need to be designed, and the cost is reduced. The CPU load rate is reduced, an additional synchronous circuit is omitted, the chip area and power consumption are reduced, and the hardware cost is reduced.

A system and method are disclosed for transforming at least one foundation artificial intelligence (AI) model into one or more embedded micro-models configured for deployment in constrained or hybrid computing environments. Each micro-model is packaged within a structured container that includes inference logic, metadata, telemetry thresholds, and fallback logic. A runtime engine monitors execution conditions and adaptively switches among inference logics or activates fallback behavior based on telemetry signals such as CPU load, memory usage, latency, or confidence score. The system supports operation on OS-less or minimal-runtime platforms and enables distributed deployment across mesh networks using multiple communication protocols. Micro-models may coordinate autonomously or under supervisory guidance and are executed within multi-dimensional or non-topological configurations. The invention enables scalable, resilient AI inference in embedded systems with limited resources.

The embodiment of the invention provides an intelligent equipment temperature control method and device, equipment, a medium and a program product, and relates to the technical field of intelligent temperature control. Performing feature engineering processing on the training data to obtain feature engineering data; wherein the characteristic engineering data comprises a temperature change rate, a CPU load and a historical temperature control mode; combining weighted splicing and attention mechanism fusion feature engineering data to obtain fusion features; based on the fusion features, training an LSTM long short-term memory network model; using an LSTM model to predict a temperature rise trend in a first set time period; adjusting the rotating speed of the cooling fan based on the predicted temperature rise trend; the intelligent dynamic control of the fan is realized, the response and control precision is improved, and the energy consumption is reduced.

A method for quality of service (QoS) flow control dynamically optimizes network resource allocation based on terminal power consumption status. An access network function receives terminal status information from an application function (AF) and determines a QoS profile for associated QoS flows from alternative profiles. The selected QoS profile is transmitted to a core network function or terminal for QoS updates. The terminal status information, which may include battery level, CPU load, or overheating status, informs the selection process to balance performance and efficiency. Core network functions manage QoS updates by interacting with the AF, access network, and terminals. The method, apparatus and computer readable medium ensure adaptive and energy-efficient QoS management for improved user experience and resource utilization in diverse network environments.