327 results about "Server load" patented technology

The invention relates to the technical field of liquid cooling, and discloses a server cabinet liquid cooling control method and system based on intelligent liquid cooling distribution, and the method comprises the steps: collecting the temperature, cooling liquid flow, pressure and environmental parameters of each node in a server cabinet in real time through a liquid cooling distribution unit; the load change of the server is predicted based on a dynamic distribution algorithm, and the pump set rotating speed and the valve opening degree of the liquid cooling distribution unit are dynamically adjusted so as to dynamically distribute the cooling liquid flow according to needs; a multi-parameter coupling optimization algorithm is adopted, and the energy efficiency ratio of the liquid cooling system is adjusted according to the temperature, the cooling liquid flow and the pressure; cooling liquid leakage is detected through a multi-mode sensor, a liquid leakage protection mechanism is triggered in a grading mode according to the severity degree of leakage, and the liquid leakage protection mechanism comprises local pipeline isolation and whole system shutdown. The system corresponds to the method. By adopting the method, the energy efficiency improvement and the reliability enhancement of the heat dissipation system of the server cabinet are realized.

The invention discloses a railway intelligent construction site safety penetration type management messenger platform which comprises a multi-modal data fusion processing module, an edge computing node cluster module, a three-dimensional visual penetration type management interface module, an intelligent early warning and emergency response module, a self-adaptive network transmission module and the like. Real-time cleaning, alignment and correlation analysis are realized by using a dynamic data calibration algorithm, and a data island is broken; the edge computing node cluster carries out localization preprocessing and the like on data in a key area, so that the load of a central server is reduced; the three-dimensional visual interface is based on a digital twinborn construction model, supports drilling type viewing and realizes three-dimensional monitoring; the intelligent early warning system adopts a reinforcement learning model to automatically trigger multi-channel early warning; and the adaptive network transmission module dynamically switches communication modes to ensure low-delay transmission of key data. The platform realizes real-time acquisition and integration of construction site data and reduces manual intervention.

The invention discloses a resource scheduling method of a server and electronic equipment, and relates to the technical field of network optimization. Time features based on historical load data and spatial features based on load correlation of adjacent servers are introduced in a feature extraction stage; the dynamic change rule of the server load and the collaborative influence of the cluster environment are comprehensively captured, and the input information richness and context sensing ability of the prediction model are improved. The method comprises the following steps: predicting load data at a target moment through a load prediction model, carrying out weighted calculation on a server load state in combination with a task type weight, quantitatively evaluating actual occupation demands of different task types on server resources, and dynamically adjusting a task allocation strategy based on an inverse relation between the load state and an allocation priority. According to the method and the device, the problem that a load balancing strategy cannot be optimized in a short time according to fluctuation of task loads and changes of resource consumption can be solved, and refined scheduling of server cluster resources and collaborative optimization of task allocation efficiency are realized.

The invention discloses a dynamic business data updating and querying method and system, and particularly relates to the technical field of information processing, and the method comprises the following steps: S1, real-time data acquisition and preprocessing; s2, updating the dynamic data; s3, intelligent query optimization; s4, result feedback and visualization are carried out; and S5, performing anomaly detection and self-repairing. According to the method, the real-time processing capability and query efficiency of massive heterogeneous data are remarkably improved, the distributed sensor network is combined with lightweight encryption and a differential privacy mechanism to realize localized cleaning and feature extraction of multi-source data, the load of a central server is effectively reduced, and the risk of sensitive information leakage is avoided; an incremental synchronization mechanism depends on a message queue and Hash chain log technology to ensure the atomicity and traceability of data updating in a high-concurrency scene, and by combining a dynamically generated space-time multi-dimensional index and an adaptive weight distribution algorithm, the system can intelligently analyze query semantics and optimize path selection, and the response time of complex query is greatly shortened.

The invention belongs to the technical field of data centers, discloses an energy efficiency evaluation method of an artificial intelligence data center, and solves the problem that a static evaluation method is disjointed with the real-time operation state of the data center in the prior art by constructing a dynamic feature library and an AI adaptive weight model. The dynamic feature library collects and associates service scene labels and time-space coupling data in real time, and provides a scene basis for an AI adaptive weight model, so that weight adjustment is converted from passive response index mutation in the prior art to active adaptive service scene change. Nonlinear influences of dynamic factors such as server load fluctuation and environment adjustment on energy efficiency are effectively captured, so that an energy efficiency evaluation result better fits the actual operation state of the data center, energy efficiency factor relevance changes caused by service switching of the AI data center are adapted, scene-based self-adaptive adjustment of weights is achieved through a deep learning model, and energy efficiency evaluation accuracy is improved. The limitation that the fixed weight distribution ignores the difference of different service scenes in the prior art is solved.

The invention discloses a computing power server load prediction method and system, and belongs to the field of data processing, the computing power server load prediction method comprises the following steps: collecting historical load data of a computing power server, and preprocessing the historical load data, the preprocessing comprising data cleaning, time window construction, and data set division; constructing an LSTM (Long Short Term Memory) model, and training the LSTM model; predicting a future computing power server load through an LSTM model; compared with the prior art, the method has the beneficial effects that the overall computing resource utilization rate is effectively improved by collecting the historical load data of the computing power server, training the LSTM model, finally predicting the future computing power server load through the LSTM model and allocating the computing resources according to the predicted data; meanwhile, abnormal tasks are recognized, and the abnormal tasks are processed in time when appearing, so that other tasks are prevented from being affected.

The invention discloses a distributed server load balancing system based on multi-modal data fusion. The distributed server load balancing system comprises a multi-modal sensing layer, a fusion analysis layer, an intelligent decision-making layer and an elastic execution layer, the multi-modal sensing layer collects multi-source heterogeneous data; the fusion analysis layer is used for mining an association relationship between data of different dimensions; meanwhile, converting multi-source data into a unified feature vector, and outputting a dynamic load balancing strategy; the intelligent decision-making layer generates an executable load balancing strategy according to the fusion analysis result; and the elastic execution layer converts the strategy generated by the intelligent decision-making layer into an actual action, and deals with load change through an elastic mechanism. According to the method, server performance, network topology and request semantic data are fused, a unified feature tensor is generated by using a cross-modal attention mechanism, and load association is accurately quantified; on the basis of cooperation of a global mixed integer programming model and a local lightweight DQN agent, load migration is completed in a very short time, and the response speed is increased.

The invention, which relates to the technical field of information and communication, discloses an intelligent operation and maintenance monitoring system for a machine room, comprising a state monitoring module, an abnormity early warning module, a data acquisition module, a load evaluation module and a control module. The state monitoring module acquires temperature distribution state data information and vibration state data information in real time; the abnormity early warning module performs correlation analysis on the data, judges whether the server is normal or not and sends out an early warning signal; the data acquisition module acquires memory state data information and network transmission state data information of the server after receiving the early warning signal; the load evaluation module analyzes the load condition of the current server in combination with the trained load degree evaluation model; and the control module judges whether the server is in overload operation or not according to the current server load condition, generates a corresponding control instruction and executes the control instruction, so that stable operation and operation and maintenance efficiency of the server are guaranteed.

The invention provides a component-level collaborative editing control method, which is applied to a multi-user collaborative editing control system, and the system comprises a plurality of front-end engines and a back-end server. A front-end engine obtains user editing operation data and a rear-end real-time version snapshot, component structure difference comparison is carried out, and a component operation list is generated; and performing component conflict judgment based on the operation list, merging and editing operation data according to a conflict judgment result and a preset conflict resolution strategy, generating processed version snapshots, sending the processed version snapshots to a rear end, storing the processed version snapshots by a rear-end server, and sending the processed version snapshots to each front-end engine. Compared with the prior art, the method has the advantages that the data transmission quantity and the load of the rear-end server are reduced by a lightweight architecture through the front-end component level difference comparison and the preset conflict resolution strategy, the rear-end server does not need to be frequently requested for conflict resolution and data merging, the response speed and the collaborative editing efficiency are remarkably improved, and the user experience is improved. And better user experience is brought to the user. The system has the same beneficial effects.

The invention belongs to the technical field of data processing, particularly relates to a server data processing method and system based on deep learning, and aims to solve the problems of low modal fusion efficiency, poor model adaptability and insufficient resource utilization rate in multi-modal data processing. The method comprises the following steps: firstly, preprocessing mixed to-be-processed data and dividing the mixed to-be-processed data into structured data, image data, text data and time series data subsets; a time-varying neural network topological structure is constructed, network edge weights are dynamically adjusted based on server loads and data features, and preliminary extraction and correlation capture of multi-modal features are achieved; generating a fusion feature vector by using a cross-modal feature extractor of the attention mechanism; a dynamic adaptive data processing module is used for matching the deep learning model for parallel processing; and finally, constructing a reinforcement learning reward function based on the processing efficiency, the resource occupancy rate and the accuracy rate, and carrying out joint iterative optimization on the model.

The invention discloses a server temperature monitoring and regulating method and electronic equipment, and relates to the technical field of servers. According to the scheme, the sensor group comprising a plurality of different types of temperature sensors is used for monitoring the temperature of each component in the server, so that the measurement error is obviously reduced; meanwhile, the working temperature interval of the component compared with the measured temperature value is not a fixed interval, but is dynamically adjusted according to the change of the load in the operation process of the server, so that the optimal heat dissipation effect is realized during temperature regulation and control; when it is confirmed that temperature regulation and control need to be carried out, the target cooling liquid flow is determined through a pre-constructed cooling liquid flow prediction model, the model is specifically constructed based on the average temperature of all parts of the server, the heat dissipation power and the server load, the optimal cooling liquid flow can be predicted to achieve energy consumption minimization of a pump set, heat dissipation energy consumption is effectively reduced, and the service life of the pump set is prolonged. And comprehensive optimization of the heat dissipation effect is realized.

The invention discloses an intelligent water meter recognition system based on a three-layer edge computing architecture, which has the following beneficial effects: (1) based on the three-layer edge computing architecture, the intelligent water meter recognition system gives full play to the advantages of edge computing, the load of a central server is reduced, and the service life of the central server is prolonged; and the service life of the water meter image data acquisition equipment is prolonged. (2) YOLO-METER is improved on the basis of YOLO11 and is used for water meter reading recognition, a FastC3k2 module and an MRFBlock are introduced into the YOLO-METER, and the FastC3k2 module can improve the image feature extraction performance and enhance the low-contrast feature extraction capability of a water meter image by reducing the number of parameters of a model and FLOPs; and the MRFBlock obviously expands the receptive field of the model, enhances the capability of autonomously selecting features, improves multi-scale fusion, and can optimize the feature selection of a digital roller region and improve the positioning capability of a water meter reading region. According to the invention, the calculation cost is reduced while the identification precision is improved.

The invention discloses a data center low-carbon control method and system, and relates to the technical field of data centers, and the method comprises the steps: collecting the environment parameters and equipment operation data of a data center in a hot-summer and warm-winter region in real time based on a multi-mode sensor network; constructing a climate-adaptive space-time diagram convolutional network, taking a server rack, a cooling tower and a power distribution cabinet as nodes, taking a cold and hot flow path as a dynamic weighted edge, embedding climate feature vectors, and generating a thermal load prediction map; a dynamic refrigeration strategy and a server load migration strategy are generated by taking a thermodynamic equation and a fluid dynamic model as strategy optimization boundary conditions through a physical constraint reinforcement learning framework and combining a thermal load prediction map; and based on the dynamic refrigeration strategy, the air conditioning system is controlled to start a hybrid refrigeration mode according to the real-time climate type, the water cooling main cycle and indirect evaporative cooling standby loop are synchronously operated, and the cold charging and discharging logic of the chilled water storage system is switched according to the electricity price time period.

The invention discloses a server cluster load balancing cooperative processing system and method and electronic equipment, and relates to the technical field of servers, and the method comprises the steps: receiving node operation data sent by each edge node in a region; carrying out load prediction in the region according to the node operation data and a pre-trained neural network model to obtain a prediction result; determining a dynamic scheduling weight; solving calculation is carried out according to the multiple pre-stored node mapping schemes, preset solving parameters and a pre-established target function, and a multi-target Pareto solution set is obtained; performing preferential processing according to the Pareto solution set and the dynamic scheduling weight to obtain a target node mapping scheme; and according to the target node mapping scheme, controlling each edge node in the region to execute load balancing cooperative operation. The problem that the load balancing performance of the server cluster is poor is solved, and the server load balancing performance of the server cluster is improved by selecting a proper target node mapping scheme based on the node operation data collected by each edge node of the edge layer.

The invention discloses a state grid cloud-based cloud service deployment method and system, and relates to the field of cloud service deployment, and the method comprises the steps: collecting service types needing to be deployed in a power grid, analyzing the use demands of tenants, and evaluating the server performance demands of the service types; installing a Ceph distributed storage framework on a plurality of storage nodes of the state grid cloud platform; the method comprises the following steps of: disassembling a service category into a plurality of independent micro-services, packaging each micro-service in an independent container through Docker, and performing automatic management on the container by using a Kubernetes container arrangement tool; docker is used for achieving multi-tenant isolation, a dynamic resource scheduling mechanism is introduced, and the number of container instances is adjusted according to changes of server loads and use requirements of the tenants; and testing the multi-tenant micro-service container environment, and performing online deployment on a state grid cloud platform after the test is passed. According to the method, the Docker is adopted to realize multi-tenant isolation, and a dynamic resource scheduling mechanism is introduced, so that efficient, safe and extensible cloud service deployment is realized.

The invention relates to a load balancing algorithm based on deep learning. The load balancing algorithm comprises the following steps: step 1, system initialization and model preparation; 2, receiving and preprocessing a user request; step 3, collecting and analyzing server load data; 4, intelligent load distribution decision making; 5, processing and forwarding the request; step 6, an error processing and retry mechanism; 7, evaluating and optimizing the model and the strategy; 8, the user requests are completed, and the system is in a standby state. According to the invention, through the technical means of deep learning modeling, reinforcement learning mechanism, adaptive queue management and global load optimization, intelligent prediction, dynamic adjustment and global optimization of the server load are realized; and the positive effects of improving the load balancing efficiency, shortening the response time, improving the resource utilization rate, improving the user experience and the like are achieved.

The invention discloses a micro-service deployment and task unloading optimization method for tasks with complex dependency relationships. The method comprises the following steps: a system architecture covers a plurality of base stations, edge servers and user equipment; an application program generated by the user equipment comprises a plurality of micro-service tasks with a complex dependency relationship, and the tasks are unloaded to an edge server to be processed; in a task unloading process, optimizing a task unloading decision in each time slot, and managing a micro-service layer cache by adopting a long-time scale updating strategy; through joint optimization of a micro-service layer caching strategy and a task unloading decision, a system performance problem is constructed into an optimization model; firstly, an unloading path of a task is planned by applying a belief propagation algorithm, and then a caching strategy is trained by means of a deep reinforcement learning model; the problem of complicated task unloading and cache updating in different time scale scenes is solved by utilizing an alternative optimization algorithm, and finally, a trained model is deployed in an edge server, so that the long-term average time delay of the system is minimized, and meanwhile, the load balancing of the server is realized. According to the method, the utilization efficiency of system resources can be remarkably improved, the time delay is reduced, and various application requirements sensitive to the delay are fully met.

The invention discloses a secure cloud workstation and a resource scheduling method suitable for a high-altitude weak network environment, and the secure cloud workstation comprises a virtualization management platform which is used for carrying out the virtualization processing of core computing components of a plurality of servers contained in a back-end server cluster, and forms a plurality of virtual hosts; the front-end thin client only reserves display and input functions, establishes connection with the virtualization management platform, receives a virtual host human-computer interface pushed by any virtual host in the virtualization computing resource pool, and feeds back a user operation instruction to the virtualization management platform; the transmission protocol optimization module is used for integrating graph, input and audio data transmission channels, monitoring the network bandwidth and the delay state of a high-altitude area in real time by adopting a self-adaptive compression algorithm, and dynamically adjusting the transmission image quality and the frame rate of a virtual host human-computer interface; and the resource scheduling module is used for automatically allocating computing resources to the plurality of virtual hosts based on the load data of the virtualization management platform so as to realize cross-server load balancing.

The invention provides a heat management method, device and equipment for a machine room, and the method comprises the steps: obtaining the load power of a server in real time in a current operation mode of an air conditioning unit; determining a server load power change rate K according to the server load power collected at different times; if the server load power is increased and the server load power change rate K meets the set condition, the first air supply temperature PI requirement is increased, the second air supply temperature PI requirement is obtained, and the first air supply temperature PI requirement is the air supply temperature PI requirement determined according to the current air supply temperature and the target air supply temperature; and according to the second air supply temperature PI requirement, the operation mode of the refrigeration component in the current air conditioning unit operation mode is adjusted. By acquiring the load power change rate of the server, the temperature change of the machine room can be adjusted in advance, so that the problems that the temperature of the machine room suddenly rises and heat dissipation is not timely when the power of the server in an existing machine room suddenly increases, and normal work of the server is affected are solved.

The invention provides a wind-solar-storage integrated data center power dispatching method and system, a storage medium and electronic equipment, and relates to the technical field of power dispatching. The method comprises the following steps: firstly, collecting real-time parameter data of a wind-solar-storage integrated energy system and load demand parameter data of a data center server; secondly, on the basis of the collected real-time data, modeling is carried out on power generation and energy storage characteristics of all energy assemblies of the wind-solar-energy-storage integrated energy system, and modeling is carried out on a data center server load; thirdly, on the basis of a modeling result, the sum of the minimum electricity purchase cost and the wind curtailment cost serves as an optimization target, power balance, calculation task transfer and the like serve as constraints, and an electric power dispatching model is constructed; and finally, introducing a method of combining LLM reasoning and a TD3 reinforcement learning algorithm to obtain a final optimization scheduling strategy. The convergence speed and the global optimization capability of the algorithm are improved, and stable operation and efficient energy utilization of the data center under the cooperation of multiple renewable energy sources are ensured.

The invention relates to the technical field of server equipment monitoring, and particularly discloses an alarm system for server equipment, which comprises a running state monitoring module, a data analysis module, an alarm triggering module and an intelligent optimization module. According to the system, operation parameters are collected in real time through a heat-sensitive sensor and a flow sensor, the sensitivity of the heat-sensitive sensor is adjusted through an intelligent optimization module according to the alarm triggering frequency change rate, and the data sampling period is adjusted according to alarm event time interval distribution; and dynamically adjusting the alarm priority according to the network flow fluctuation amplitude and the server load instantaneous change rate. The method can reduce false alarms, improve alarm real-time performance and enhance system reliability, and is suitable for a complex server operation environment.

The invention relates to the technical field of server management, in particular to an intelligent phase change cooling liquid thermal management system and method. According to the invention, through real-time acquisition and analysis of the internal temperature data of the server, accurate calculation of the heat flux is realized, so that an area with a relatively large temperature gradient can be rapidly identified, and it is ensured that dynamic adjustment of a heat transfer path can be tightly expanded around a heat concentration area. In combination with real-time monitoring of pressure of an inlet and an outlet of a flow channel, calculation of fluid resistance is refined, and optimization of a cooling liquid flowing path can be accurately matched with the change trend of heat in a server. Based on the prediction capability of the heat diffusion path, the heat accumulation condition of the high-heat area can be pre-judged in advance, so that the cooling process is always kept synchronous with the load change of the server. The regulation and control of the cooling liquid supplement rate not only considers the heat absorption state of the evaporation area, but also combines the dynamic change of the gas-liquid phase state, so that the cooling efficiency reduction caused by local steam accumulation is avoided.

The invention provides a method for constructing a container cloud agent based on a MiniMax-Text-01 large model, a medium and equipment, belongs to the technical field of automatic operation and maintenance, and can perform fault detection, flow prediction and resource elastic expansion and contraction according to real monitoring data. The intelligent agent constructed by the invention can automatically pre-judge and give an optimal scheme for capacity expansion or migration when finding that the load of the server is abnormal, so that the frequency and the duration of manual intervention are reduced. In a multi-cloud and hybrid cloud architecture, an intelligent agent can cooperatively manage calculation, storage and network resources on a heterogeneous platform, and cross-cloud load balancing and cost optimization are realized. In addition, the intelligent agent can help a data analysis and machine learning team to better utilize a cloud GPU / TPU cluster and automatically select a proper computing power and storage scheme, so that the training and prediction efficiency of mass data is remarkably improved. In the scene of Internet of Things and edge computing, the intelligent agent can link the cloud and the edge nodes, intelligently dispatch the computing power and bandwidth of each node, and find and correct abnormal data streams in time.

The invention discloses a server energy consumption control method, system and device, a storage medium and a program product, and relates to the technical field of server management.The method comprises the steps that system index data of a server is input into a prediction model, and a dynamic first threshold value and a dynamic second threshold value are predicted through the prediction model; if the total load data of the server is smaller than the first threshold value, the first target device is controlled to enter an energy-saving state; and if the total load data is greater than the second threshold, controlling the second target equipment to enter a working state. The problems that the power supply is frequently switched, the fault risk is increased and the energy-saving efficiency is low due to server energy consumption management can be solved. According to the method, the load state of the server is judged according to the two threshold values, whether the energy-saving operation is carried out or not is determined, intelligent optimization of the energy consumption of the server is achieved, and the energy-saving efficiency is improved. In addition, the weight coefficient of the prediction model enables the two threshold values to be automatically adjusted according to the actual operation condition of the server, and the method is suitable for different service load modes and system states.

The invention provides a server load balancing method and device, a medium and a program product, and a specific implementation mode of the application comprises the steps that after a load balancer receives a task request, attributes such as basic types (authentication, authorization and charging), priorities and protocol versions of the request are analyzed firstly; meanwhile, the resource utilization rate, response time, health state and other information of the server cluster are monitored in real time, a dynamic strategy decision tree is constructed based on the data, in addition, the load balancer sends a health check request to the server regularly, whether the server breaks down or not is judged according to the state information returned by the server, and the service life of the server is prolonged. And if a fault exists, removing the fault from the request allocation list to ensure that the request is always allocated to the available server. According to the method, the dynamic adjustment of the load balancing strategy and the update of the request allocation list are realized, and the resource utilization rate of the server is improved so as to cope with the rapidly changed request load and flow mode.

The invention relates to a dual-time-domain slice resource management and DT deployment joint optimization method based on digital twinning, and belongs to the field of mobile communication. Aiming at the problems of service delay fluctuation and service quality reduction caused by large user demand difference and high occupation of digital twinning synchronization resources in an Internet of Vehicles scene, a double-time-domain layered optimization framework is provided: for a long time domain, a deep migration reinforcement learning algorithm QDTRL based on a Q value is adopted to optimize a digital twinning deployment position, and the synchronous delay and migration cost is reduced; and dynamically allocating bandwidth and computing resources through a multi-agent near-end strategy optimization algorithm MAPPO in a short-time domain, and adjusting the QoS weight of the high-fluctuation user in combination with a demand enhancement factor. According to the technical scheme, a server load dynamic pricing mechanism is introduced, and digital twin utility is quantified from integrity, load contribution and resource credibility. According to the method, the resource utilization rate is remarkably improved, the service delay demand of a user is guaranteed, and collaborative optimization of digital twin synchronization and slice resource allocation is realized.

The invention discloses a Web end video monitoring distributed decoding method and system, and belongs to the technical field of network video playing. The method comprises the following steps of: actively initiating WebSocket connection by a browser end through a local loopback address port (127.0. 0.1: 19022), and detecting whether a distributed decoding program is installed in a user terminal or not; calling a local FFmpeg library by a distributed decoding program, decoding a video stream in real time by utilizing a terminal CPU (Central Processing Unit), and converting decoded video key frames into an image format originally supported by a browser frame by frame through OpenCV; the distributed decoding program packages binary data of each frame of image and an address identifier of the binary data into a WebSocket message, the WebSocket message is actively pushed to the browser, and the browser refreshes and displays the WebSocket message in a specified area of a page in real time according to the address identifier; according to the method, zero-plug-in, low-delay, cross-platform, safe and controllable Web end video decoding is realized, the server load and the network delay are remarkably reduced, and the user experience and the system compatibility are improved.

The invention belongs to the technical field of digital exhibition hall intelligent management, and particularly relates to a remote visual digital exhibition hall intelligent management platform and system. According to the invention, by dynamically monitoring and adjusting the resource allocation of each exhibition area, the system can reasonably utilize the server and network resources, avoid the resource waste, improve the overall performance, and combine the browsing weight and access traffic of the exhibition area, the system preferentially renders and loads the high-quality three-dimensional data of the hot exhibition area, thereby ensuring the smooth experience of visitors in a high-traffic period, and improving the experience of the visitors. Meanwhile, a simplified loading strategy is adopted in the unpopular exhibition area, unnecessary system burden is reduced, the rendering level and the loading mechanism of the exhibition area can be intelligently adjusted according to real-time data, self-adaptive management is achieved, manual intervention is reduced, the management efficiency is improved, and the method is based on server load information and access flow data.

The invention relates to the technical field of edge computing resource management, in particular to an edge server load evaluation method based on analytic hierarchy process and performance loss. According to the method, firstly, edge servers are divided into a common type, a computing type, a memory type and an I / O type according to heterogeneous types; aiming at each type of edge server, constructing a judgment matrix by adopting an analytic hierarchy process and solving a feature vector so as to calculate load weight coefficients of CPU (Central Processing Unit), memory and disk I / O (Input / Output) resources; calculating a real-time load based on the weight coefficient, wherein the real-time load is the weighted sum of the resource utilization rates; the performance loss is further calculated according to the nonlinear relation between the real-time load and the total load ratio; and finally, restraining the real-time load through the load threshold value, and evaluating the performance state of the server in combination with the performance loss. According to the method, the accuracy and reliability of load evaluation of the heterogeneous edge server are effectively improved, the resource utilization rate is optimized, overload of the server is avoided, and the system stability is enhanced.

The embodiment of the invention discloses a terminal equipment circulation control method and device for a server, equipment and a medium. A specific embodiment of the method comprises the following steps: acquiring initial circulation data of a target article; preprocessing the initial circulation data to obtain circulation data; generating first inertia circulation information and second inertia circulation information according to the circulation data; generating polymorphic circulation information corresponding to the end time according to the circulation data; generating predicted circulation information according to the first inertia circulation information, the second inertia circulation information, the multi-form circulation information and the real-time circulation information; in response to determining that the predicted circulation information meets the preset threshold condition, reconfiguring the circulation quantity to obtain an updated circulation quantity; and performing circulation interception control on terminal equipment corresponding to the circulation user in response to the fact that the detected circulation quantity is equal to the updated circulation quantity. According to the embodiment, the effect of reducing the load of the server is improved, and the safety of the server system is improved.