Server power consumption dynamic optimization and cooperative heat dissipation control system based on AI

Abstract

The invention discloses an AI-based server power consumption dynamic optimization and cooperative heat dissipation control system, which belongs to the technical field of computer system optimization, and comprises a multi-source data acquisition module for acquiring server hardware, machine room environment and heat dissipation equipment data in real time through a distributed sensor and a software probe; the AI intelligent analysis and decision module constructs a hybrid intelligent framework, extracts multi-dimensional features to predict power consumption and temperature trends, and generates a comprehensive optimization decision; the power consumption dynamic adjustment module adjusts hardware parameters and cooperates with process scheduling; the cooperative heat dissipation control module dynamically adjusts heat dissipation equipment; and the running state monitoring and feedback module monitors data, compares the data with a threshold value, and performs early warning and feedback in case of abnormality. Through multi-module collaboration and AI enabling, intelligent dynamic collaborative management of power consumption and heat dissipation is realized, data acquisition is accurate, power consumption is balanced, operation cost and energy consumption are reduced, and requirements of data centers of different scales are met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer system optimization, and particularly relates to a server power consumption dynamic optimization and collaborative cooling control system based on AI. BACKGROUND

[0002] With the rapid development of cloud computing, big data and artificial intelligence technology, the scale of data center server clusters continues to expand, and the operation load and power consumption demand of servers are increasing synchronously. Traditional server power consumption control mostly adopts a static threshold adjustment mode, that is, a fixed power consumption upper limit and hardware running parameters are preset, and only when the power consumption exceeds the threshold during server operation, passive adjustment measures such as frequency reduction and load limiting are triggered. This method lacks dynamic adaptation capability to load changes, business demands and environmental factors, and often causes contradictions such as insufficient performance due to excessive power consumption reduction or power consumption control lag leading to hardware overheating, and cannot achieve precise balance between power consumption optimization and performance guarantee. At the same time, traditional cooling control and power consumption management are independent of each other, and cooling equipment mostly runs in a fixed mode and is only coarsely adjusted according to the overall temperature of the machine room, which is difficult to match the differentiated cooling needs of different servers and different hardware components, resulting in serious waste of cooling resources, and the coexistence of problems such as insufficient cooling in some areas and excessive cooling in some areas.

[0003] The in-depth application of AI technology in various fields provides a new solution for server management, but the existing AI-based power consumption or cooling control schemes still have obvious defects. Some schemes only focus on single-dimensional optimization, either only focusing on power consumption reduction while ignoring cooling pressure or only optimizing cooling effect without considering power consumption coordination, lacking the linkage mechanism of the two, resulting in poor overall system operation efficiency. Another part of the AI model mostly adopts an offline training mode, and the model parameters are fixed, which cannot adapt to dynamic scenarios such as server load fluctuations, hardware aging, and changes in environmental temperature and humidity in real time. With the passage of running time, the model prediction accuracy and decision rationality gradually decrease, and it is difficult to maintain the optimization effect for a long time. In addition, the monitoring and feedback mechanism of the existing system is not perfect, and the data collection dimension is single, mostly focusing on the power consumption and temperature data of the core hardware, lacking comprehensive consideration of key factors such as environmental parameters, cooling equipment status, and business priority, resulting in one-sided AI model input data and insufficient scientificity and comprehensiveness of optimization decisions.

[0004] In large-scale data center scenarios, the aforementioned problems are amplified. The heterogeneity of server clusters, the dynamic fluctuations of business loads, and the complexity of the data center environment make the limitations of traditional static management and single-dimensional optimization solutions increasingly prominent. Excessive power consumption not only increases data center operating costs but also exacerbates heat dissipation pressure and triggers hardware failure risks; while unreasonable heat dissipation strategies cannot effectively solve overheating problems and also cause energy waste, which goes against the development trend of green data centers. Therefore, there is an urgent need to build an intelligent system that can achieve dynamic power consumption optimization and coordinated heat dissipation control. Through the deep empowerment of AI technology, multi-dimensional data can be integrated to establish a dynamically adaptable optimization mechanism, achieving a global coordinated balance between power consumption, performance, and heat dissipation, and meeting the needs of efficient, stable, and energy-saving operation of data centers. Summary of the Invention

[0005] The present invention proposes an AI-based server power consumption dynamic optimization and collaborative heat dissipation control system to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an AI-based server power consumption dynamic optimization and collaborative heat dissipation control system, comprising: The multi-source data acquisition module deploys a distributed sensor network and software monitoring probes to collect real-time operating parameters, power consumption data, and core temperature data of core hardware such as server CPU, memory, hard drive, and graphics card. It also simultaneously collects ambient temperature, humidity, airflow speed, air pressure data, and operating status data of cooling equipment in the computer room. Operating parameters include operating frequency, voltage, and load ratio. The sampling frequency is dynamically adapted according to the hardware type. The sampling frequency for core hardware data is at the millisecond level, while the sampling frequency for environmental and cooling equipment data is at the second level, ensuring the real-time and comprehensiveness of data acquisition. The AI ​​intelligent analysis and decision-making module constructs a hybrid intelligent framework that integrates a time-series prediction model and a reinforcement learning model. It takes historical and real-time data acquired by a multi-source data acquisition module as input, and extracts power consumption correlation features, temperature change features, and load trend features through feature engineering. The time-series prediction model uses a long short-term memory network to predict the peak power consumption and peak temperature within a preset period in the future. The reinforcement learning model uses power consumption optimization rate, heat dissipation efficiency ratio, and performance guarantee as reward objectives to generate a comprehensive optimization decision that includes hardware operating parameter adjustment schemes, load balancing distribution strategies, and heat dissipation equipment control instructions. The power consumption dynamic adjustment module receives hardware operation parameter adjustment schemes output by the AI ​​intelligent analysis and decision module. Through the server hardware management interface, it adjusts the CPU turbo frequency range, memory bandwidth, hard disk read / write cache strategy and graphics card power limit threshold in real time. At the same time, it coordinates with the operating system's process scheduling mechanism to dynamically allocate CPU cores and memory resources, prioritize the resource supply for core businesses, limit the power consumption of non-core businesses, and achieve fine-grained dynamic control of power consumption. The collaborative heat dissipation control module, based on the heat dissipation equipment control commands in the optimization decision, combined with real-time environmental parameters and server temperature distribution data, dynamically adjusts the cooling power, air outlet temperature, and air supply angle of the precision air conditioner in the computer room, the speed levels of the server's built-in fans, and the start / stop status of local heat dissipation modules, to establish a collaborative linkage mechanism between power consumption and heat dissipation. It can predict heat dissipation needs in advance based on changes in power consumption and achieve proactive adjustment of heat dissipation capacity. The operation status monitoring and feedback module monitors the server hardware operation status, power consumption data, temperature data, and working parameters of the heat dissipation equipment in real time. It compares the monitored data with preset safety thresholds and performance thresholds in real time. When the data exceeds the threshold range or abnormal fluctuations occur, it generates multi-level early warning information and immediately feeds it back to the AI ​​intelligent analysis and decision-making module to trigger iterative adjustments for optimization decisions. At the same time, it records abnormal data and processing. The data storage and model iteration module adopts a distributed storage architecture to store the collected raw data, optimization decision records, operation status logs and AI model parameters. It supports long-term data retention and fast retrieval. It optimizes the training sample set of the AI ​​model through historical data review and analysis, regularly starts the model retraining process, and updates the model parameters in combination with new operation data and changes in business scenarios to continuously improve the model's prediction accuracy and decision rationality. The visualization management and interaction module provides a graphical user interface, allowing administrators to view the system's operating status, AI model decision results, server power consumption and temperature change curves, and the working status of heat dissipation equipment in real time. It allows administrators to modify system configuration parameters, adjust threshold ranges, and manually intervene to optimize decisions according to business needs. It also supports multi-channel push and remote response of early warning information, improving system operation and maintenance efficiency.

[0007] As a further alternative to the present invention, it also includes an adaptive learning unit of an AI hybrid intelligent framework, which optimizes model parameters in real time through an online gradient descent algorithm, and the parameter update process satisfies... ,in Let be the set of model parameters at time t. This is the initial set of model parameters. Let be the parameter learning rate coefficient at time τ. The model loss function at time τ with respect to the parameters gradient, Let be the input data matrix at time τ. Let be the regularization coefficient at time τ. As a Brownian motion increment, this unit can adapt in real time to the effects of server load fluctuations, hardware aging, and environmental changes, continuously maintaining the model's prediction and decision-making performance.

[0008] As a further alternative to the present invention, it also includes a core business priority dynamic evaluation unit, which constructs a priority evaluation system based on the business response time requirements, data processing importance, and user access frequency. The priority weight of each business is determined through quantitative evaluation. During power consumption adjustment and resource allocation, power consumption quotas and hardware resources are allocated according to priority weights. When the overall power consumption of the server is close to the preset upper limit, the performance of high-priority businesses is guaranteed to be unaffected, and power consumption is only implemented for low-priority businesses, so as to ensure a dynamic balance between power consumption optimization and business service quality.

[0009] As a further alternative to the present invention, the power consumption dynamic adjustment module also has a power consumption prediction deviation correction mechanism. By comparing the power consumption prediction value of the AI ​​model with the actual monitoring value, the prediction deviation rate is calculated. When the deviation rate exceeds the set range, the feature weights and prediction factors of the model are dynamically adjusted. At the same time, the influence coefficients of hardware operating years and ambient temperature and humidity on power consumption are introduced to correct the input parameters of the power consumption prediction model, improve the accuracy of power consumption prediction, and provide a more reliable basis for fine power consumption adjustment.

[0010] As a further alternative to the present invention: the collaborative heat dissipation control module adopts a zoned heat dissipation optimization strategy, dividing the computer room into multiple independent heat dissipation areas according to server cluster density, business type, and geographical location. Each area is configured with a dedicated heat dissipation control sub-module, which generates a personalized heat dissipation plan based on the average power consumption, maximum temperature, and equipment distribution density within the area. Through the coordinated adjustment of heat dissipation parameters between areas, local overheating or waste of heat dissipation resources is avoided, thereby improving the overall heat dissipation efficiency.

[0011] As a further alternative to the present invention: the operation status monitoring and feedback module supports the intelligent root cause analysis function. When abnormal data is detected, it constructs a fault correlation map by combining historical fault cases, hardware operation logs and environmental change data. Through correlation analysis, it locates the root cause of the abnormality, distinguishes whether it is caused by hardware failure, software abnormality, overload or environmental factors, and provides managers with accurate fault handling suggestions, shortening the fault investigation and repair time.

[0012] As a further alternative to this invention, it also includes a dynamic heat dissipation efficiency evaluation module, which quantifies the balance between heat dissipation effect and energy consumption cost by constructing a multi-dimensional heat dissipation efficiency model. The evaluation formula is as follows: ,in This is a value for evaluating heat dissipation efficiency. Let τ be the environmental adaptation coefficient at time τ. Let τ be the difference between the server core temperature and the ambient temperature at time τ. Let be the heat dissipation airflow rate at time τ. Let τ be the total power consumption of the heat dissipation equipment at time τ, and T be the evaluation time period. The evaluation result provides quantitative support for optimizing the operating parameters of the heat dissipation equipment and adjusting the heat dissipation strategy, so as to achieve reasonable control of heat dissipation energy consumption.

[0013] As a further alternative to the present invention: the data storage and model iteration module adopts a data layered storage strategy, storing high-frequency real-time data in a high-speed memory database, and storing historical data and model parameters in a large-capacity distributed database. At the same time, data compression algorithms and hot and cold data separation mechanisms are introduced to reduce storage resource consumption. In terms of data security, encrypted storage and access control are adopted to prevent data leakage and illegal tampering, and to ensure the security and integrity of system data.

[0014] As a further alternative to the present invention, it also includes an extreme scenario emergency handling module. When encountering extreme situations such as power outage warnings in the data center, heat dissipation equipment failures, or sudden overloads, it automatically activates an emergency optimization mode. By quickly shutting down non-core services, reducing the operating parameters of core hardware, and activating backup heat dissipation equipment, it controls the server power consumption and temperature within a safe range. At the same time, it calculates the sustainable runtime under extreme scenarios, providing administrators with an emergency response time window to ensure the continuous operation of core services to the greatest extent possible.

[0015] As a further alternative to this invention: the visualization management and interaction module supports multi-dimensional data statistics and analysis functions, and can generate server power consumption trend reports, heat dissipation efficiency analysis reports, business performance correlation reports, etc., intuitively displaying the system optimization effect through data visualization charts. It also provides API interfaces to achieve seamless integration with data center management platforms and server cluster management systems, supporting integrated system management and collaborative scheduling, and ensuring the quality of interface data interaction meets requirements. ,in Scoring the quality of interface data interaction. The weight coefficients for the k-th type of interactive data are... Let k be the transmission rate function of the k-th type of data. Let m be the transmission integrity function for the k-th type of data, m be the total number of interactive data types, and T be the data interaction statistical period. This score is used to continuously optimize the interface data transmission strategy.

[0016] Compared with existing technologies, the beneficial effects of this invention are: This invention constructs an intelligent collaborative system integrating multiple modules, achieving deep linkage and dynamic optimization of server power consumption management and heat dissipation control. The multi-source data acquisition module covers multiple dimensions of data such as hardware operation, environmental status, and heat dissipation equipment. The sampling frequency is dynamically adapted according to different data types, ensuring the real-time, comprehensive, and accurate nature of data acquisition. This provides reliable data support for AI model analysis and decision-making, ensuring the scientific nature of the optimization strategy from the source.

[0017] The AI ​​intelligent analysis and decision-making module adopts a hybrid intelligent framework, combining time-series prediction and reinforcement learning models. This not only accurately predicts future power consumption and temperature trends but also generates comprehensive optimized decisions guided by multi-objective balance. The adaptive learning unit updates model parameters in real time using an online gradient descent algorithm, enabling the AI ​​model to continuously adapt to load fluctuations, hardware aging, and environmental changes, maintaining high prediction accuracy and excellent decision-making quality over the long term. This solves the problem of insufficient adaptability in traditional offline training models.

[0018] A linkage mechanism is established between the power consumption dynamic adjustment module and the collaborative heat dissipation control module, enabling proactive adaptation and refined execution of power consumption adjustment and heat dissipation control. Power consumption adjustment optimizes power consumption while ensuring the performance of core business processes through the coordination of hardware parameter adjustment and system process scheduling. Heat dissipation control adopts a zoning strategy and dynamic adjustment mode to accurately match differentiated heat dissipation needs, avoiding the problems of wasted heat dissipation resources and insufficient local heat dissipation.

[0019] The multi-level early warning and root cause analysis functions of the operation status monitoring and feedback module can promptly detect and locate abnormal issues, quickly trigger decision-making iterations and adjustments, and significantly improve the system's fault response speed and processing accuracy. The data storage and model iteration module, through layered storage and regular retraining, ensures data security and efficient utilization while driving continuous optimization of AI models. The visualization management and interaction module enhances the convenience and integration of system operation and maintenance, supports multi-dimensional data analysis and external system integration, and provides strong support for integrated data center management.

[0020] In summary, this invention, through multi-module collaboration and AI technology, achieves intelligent, dynamic, and collaborative management of server power consumption and heat dissipation, significantly improving power optimization and heat dissipation efficiency, effectively ensuring server operational stability and business service quality, while reducing data center operating costs and energy consumption. Attached Figure Description

[0021] Figure 1 This is a schematic block diagram of the AI-based server power consumption dynamic optimization and collaborative heat dissipation control system proposed in this invention. Figure 2 To optimize the comparison of core indicators before and after; Figure 3 A time-series line graph showing server power consumption versus data center temperature over 24 hours; Figure 4 A comparison chart of heat dissipation efficiency for different heat dissipation areas. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0024] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0025] Reference Figures 1 to 4 A server power consumption dynamic optimization and collaborative heat dissipation control system based on AI, comprising: The multi-source data acquisition module deploys a distributed sensor network and software monitoring probes to collect real-time operating parameters, power consumption data, and core temperature data of core hardware such as server CPU, memory, hard drive, and graphics card. It also simultaneously collects ambient temperature, humidity, airflow speed, air pressure data, and operating status data of cooling equipment in the computer room. Operating parameters include operating frequency, voltage, and load ratio. The sampling frequency is dynamically adapted according to the hardware type. The sampling frequency for core hardware data is at the millisecond level, while the sampling frequency for environmental and cooling equipment data is at the second level, ensuring the real-time and comprehensiveness of data acquisition. The AI ​​intelligent analysis and decision-making module constructs a hybrid intelligent framework that integrates a time-series prediction model and a reinforcement learning model. It takes historical and real-time data acquired by a multi-source data acquisition module as input, and extracts power consumption correlation features, temperature change features, and load trend features through feature engineering. The time-series prediction model uses a long short-term memory network to predict the peak power consumption and peak temperature within a preset period in the future. The reinforcement learning model uses power consumption optimization rate, heat dissipation efficiency ratio, and performance guarantee as reward objectives to generate a comprehensive optimization decision that includes hardware operating parameter adjustment schemes, load balancing distribution strategies, and heat dissipation equipment control instructions. The power consumption dynamic adjustment module receives hardware operation parameter adjustment schemes output by the AI ​​intelligent analysis and decision module. Through the server hardware management interface, it adjusts the CPU turbo frequency range, memory bandwidth, hard disk read / write cache strategy and graphics card power limit threshold in real time. At the same time, it coordinates with the operating system's process scheduling mechanism to dynamically allocate CPU cores and memory resources, prioritize the resource supply for core businesses, limit the power consumption of non-core businesses, and achieve fine-grained dynamic control of power consumption. The collaborative heat dissipation control module, based on the heat dissipation equipment control commands in the optimization decision, combined with real-time environmental parameters and server temperature distribution data, dynamically adjusts the cooling power, air outlet temperature, and air supply angle of the precision air conditioner in the computer room, the speed levels of the server's built-in fans, and the start / stop status of local heat dissipation modules, to establish a collaborative linkage mechanism between power consumption and heat dissipation. It can predict heat dissipation needs in advance based on changes in power consumption and achieve proactive adjustment of heat dissipation capacity. The operation status monitoring and feedback module monitors the server hardware operation status, power consumption data, temperature data, and working parameters of the heat dissipation equipment in real time. It compares the monitored data with preset safety thresholds and performance thresholds in real time. When the data exceeds the threshold range or abnormal fluctuations occur, it generates multi-level early warning information and immediately feeds it back to the AI ​​intelligent analysis and decision-making module to trigger iterative adjustments for optimization decisions. At the same time, it records abnormal data and processing. The data storage and model iteration module adopts a distributed storage architecture to store the collected raw data, optimization decision records, operation status logs and AI model parameters. It supports long-term data retention and fast retrieval. It optimizes the training sample set of the AI ​​model through historical data review and analysis, regularly starts the model retraining process, and updates the model parameters in combination with new operation data and changes in business scenarios to continuously improve the model's prediction accuracy and decision rationality. The visualization management and interaction module provides a graphical user interface, allowing administrators to view the system's operating status, AI model decision results, server power consumption and temperature change curves, and the working status of heat dissipation equipment in real time. It allows administrators to modify system configuration parameters, adjust threshold ranges, and manually intervene to optimize decisions according to business needs. It also supports multi-channel push and remote response of early warning information, improving system operation and maintenance efficiency.

[0026] This invention also includes an adaptive learning unit of an AI hybrid intelligent framework, which optimizes model parameters in real time through an online gradient descent algorithm, and the parameter update process satisfies... ,in Let be the set of model parameters at time t. This is the initial set of model parameters. Let be the parameter learning rate coefficient at time τ. The model loss function at time τ with respect to the parameters gradient, Let be the input data matrix at time τ. Let be the regularization coefficient at time τ. As a Brownian motion increment, this unit can adapt in real time to the effects of server load fluctuations, hardware aging, and environmental changes, continuously maintaining the model's prediction and decision-making performance.

[0027] This invention also includes a core business priority dynamic evaluation unit, which constructs a priority evaluation system based on the business response time requirements, data processing importance, and user access frequency. The priority weight of each business is determined through quantitative evaluation. During power consumption adjustment and resource allocation, power consumption quotas and hardware resources are allocated according to priority weights. When the overall power consumption of the server is close to the preset upper limit, the performance of high-priority businesses is guaranteed to be unaffected, and power consumption is only imposed on low-priority businesses, ensuring a dynamic balance between power consumption optimization and business service quality.

[0028] In this invention, the power consumption dynamic adjustment module also has a power consumption prediction deviation correction mechanism. By comparing the power consumption prediction value of the AI ​​model with the actual monitoring value, the prediction deviation rate is calculated. When the deviation rate exceeds the set range, the feature weights and prediction factors of the model are dynamically adjusted. At the same time, the influence coefficients of hardware operating years and ambient temperature and humidity on power consumption are introduced to correct the input parameters of the power consumption prediction model, improve the accuracy of power consumption prediction, and provide a more reliable basis for fine power consumption adjustment.

[0029] In this invention, the collaborative heat dissipation control module adopts a zoned heat dissipation optimization strategy, dividing the computer room into multiple independent heat dissipation zones according to server cluster density, business type, and geographical location. Each zone is equipped with a dedicated heat dissipation control submodule, which generates a personalized heat dissipation scheme based on the average power consumption, maximum temperature, and equipment distribution density within the zone. By coordinating and adjusting the heat dissipation parameters between zones, it avoids excessively high local temperatures or waste of heat dissipation resources, thereby improving the overall heat dissipation efficiency.

[0030] In this invention, the operation status monitoring and feedback module supports intelligent root cause analysis of anomalies. When abnormal data is detected, it constructs a fault correlation map by combining historical fault cases, hardware operation logs, and environmental change data. Through correlation analysis, it locates the root cause of the anomaly and distinguishes whether it is caused by hardware failure, software anomaly, overload, or environmental factors. This provides managers with accurate fault handling suggestions and shortens the fault investigation and repair time.

[0031] This invention also includes a dynamic heat dissipation efficiency evaluation module, which quantifies the balance between heat dissipation effect and energy consumption cost by constructing a multi-dimensional heat dissipation efficiency model. The evaluation formula is as follows: ,in This is a value for evaluating heat dissipation efficiency. Let τ be the environmental adaptation coefficient at time τ. Let τ be the difference between the server core temperature and the ambient temperature at time τ. Let be the heat dissipation airflow rate at time τ. Let τ be the total power consumption of the heat dissipation equipment at time τ, and T be the evaluation time period. The evaluation result provides quantitative support for optimizing the operating parameters of the heat dissipation equipment and adjusting the heat dissipation strategy, so as to achieve reasonable control of heat dissipation energy consumption.

[0032] In this invention, the data storage and model iteration module adopts a hierarchical data storage strategy, storing high-frequency real-time data in a high-speed memory database and historical data and model parameters in a large-capacity distributed database. At the same time, data compression algorithms and hot and cold data separation mechanisms are introduced to reduce storage resource consumption. In terms of data security, encrypted storage and access control are adopted to prevent data leakage and illegal tampering, and to ensure the security and integrity of system data.

[0033] This invention also includes an emergency response module for extreme scenarios. When encountering extreme situations such as power outage warnings in the data center, heat dissipation equipment failures, or sudden overloads, the module automatically activates an emergency optimization mode. By quickly shutting down non-core services, reducing the operating parameters of core hardware, and activating backup heat dissipation equipment, the module controls the server's power consumption and temperature within a safe range. At the same time, it calculates the sustainable runtime under extreme scenarios, providing administrators with an emergency response time window to ensure the continuous operation of core services to the greatest extent possible.

[0034] In this invention, the visualization management and interaction module supports multi-dimensional data statistics and analysis functions, capable of generating server power consumption trend reports, heat dissipation efficiency analysis reports, and business performance correlation reports, etc. It intuitively displays the system optimization effect through data visualization charts, and provides API interfaces for seamless integration with data center management platforms and server cluster management systems, supporting integrated system management and collaborative scheduling. The interface data interaction quality meets [standards / requirements]. ,in Scoring the quality of interface data interaction. The weight coefficients for the k-th type of interactive data are... Let k be the transmission rate function of the k-th type of data. Let m be the transmission integrity function for the k-th type of data, m be the total number of interactive data types, and T be the data interaction statistical period. This score is used to continuously optimize the interface data transmission strategy.

[0035] The following two examples further illustrate specific embodiments of the present invention: Example 1: Implementation of Server Cluster Application in Small and Medium-Sized Data Centers This embodiment describes the implementation process of an AI-based server power consumption dynamic optimization and collaborative heat dissipation control system for a small to medium-sized data center cluster containing 50 servers, achieving a dynamic balance between power consumption optimization, collaborative heat dissipation, and performance assurance. In the system deployment phase, the hardware installation and software configuration of the multi-source data acquisition module are completed first. Miniature power consumption sensors and temperature sensors are deployed on the core hardware of each server, such as the CPU, memory, hard drive, and graphics card. The sensors have an accuracy of 0.1℃ and 0.1W, respectively, ensuring the accuracy of data acquisition.

[0036] Ten environmental monitoring units are deployed evenly throughout the server room. Each unit integrates sensors for temperature, humidity, airflow speed, and air pressure, covering all areas of the server room. Simultaneously, software monitoring probes are deployed on each server to collect operating parameters such as operating frequency, voltage, and load percentage via the server hardware management interface. Operating status acquisition modules are installed on the cooling equipment, including three precision air conditioners and the server's internal fans, to collect data such as cooling power, outlet air temperature, airflow angle, and fan speed.

[0037] The sampling frequency is set to dynamic adaptation mode. The sampling frequency for power consumption and temperature data of core hardware is at the 50-millisecond level, the sampling frequency for operating parameters is at the 100-millisecond level, and the sampling frequency for environmental and heat dissipation equipment data is at the 2-second level. All collected data is aggregated to the data processing gateway through a high-speed data transmission link.

[0038] The AI ​​intelligent analysis and decision-making module is deployed on a local server in the data center using an edge computing architecture, constructing a hybrid intelligent framework that integrates a time-series prediction model and a reinforcement learning model. The time-series prediction model is built on a long short-term memory network, and its input dimensions include historical power consumption data, load data, and environmental parameter data from the past 7 days. A total of 28 features are extracted, including power consumption change rate, load fluctuation coefficient, and environmental temperature and humidity trends.

[0039] During model training, the mean square error between the predicted and actual values ​​is used as the loss function, and an adaptive momentum optimization algorithm is used for iterative training. The number of training iterations is set to 1000, and training stops when the loss function value is lower than 0.001.

[0040] The reinforcement learning model uses power optimization rate, heat dissipation efficiency ratio and performance guarantee as reward objectives, and constructs a three-dimensional reward function, in which power optimization rate accounts for 40% of the weight, heat dissipation efficiency ratio accounts for 30% of the weight, and performance guarantee accounts for 30% of the weight. The agent adjusts and optimizes the strategy through continuous interaction with the server operating environment.

[0041] The power consumption dynamic adjustment module achieves bidirectional communication with the operating system kernel layer interface through the server hardware management interface, supporting real-time adjustment of CPU turbo frequency range, memory bandwidth, hard disk read / write caching strategy, and graphics card power limit threshold. The CPU turbo frequency range can be continuously adjusted between the base frequency and the maximum turbo frequency, memory bandwidth can be adjusted in 10% increments, the hard disk read / write caching strategy can be switched to three modes: performance priority, balanced, and power priority, and the graphics card power limit threshold can be dynamically set between 50% and 100% of the base power consumption.

[0042] Simultaneously, it coordinates with the operating system's process scheduling mechanism to allocate CPU cores and memory resources based on service priority. High-priority services are allocated independent CPU cores and dedicated memory areas, while low-priority services utilize resources through time-sharing. The collaborative heat dissipation control module divides the data center into 5 independent heat dissipation zones, each corresponding to 1 precision air conditioner and 10 servers, with each zone configured with a dedicated heat dissipation control submodule.

[0043] The precision air conditioner supports stepless adjustment of cooling power from 30% to 100%, and the outlet air temperature can be adjusted from 16℃ to 24℃. The airflow angle supports electric adjustment in four directions: up, down, left, and right. The server's built-in fan supports 10 levels of speed adjustment, with a speed range of 800 rpm to 4000 rpm. The local heat dissipation module uses semiconductor heat sinks and supports automatic start and stop based on temperature data.

[0044] The operation status monitoring and feedback module has preset three levels of safety thresholds and two levels of performance thresholds. The safety thresholds include the maximum hardware temperature threshold, the maximum power consumption threshold, and the upper limit threshold for the operation of the heat dissipation equipment. The performance thresholds include the minimum service response time threshold and the minimum hardware operating frequency threshold. When the monitored data exceeds the safety threshold by less than 10%, a level one warning is triggered; when it exceeds by 10% to 20%, a level two warning is triggered; and when it exceeds by more than 20%, a level three warning is triggered.

[0045] The root cause analysis function is based on association rule mining algorithm to construct a fault association map containing 20 common anomalies in four categories: hardware failure, software anomaly, overload, and environmental factors. The root cause is located by calculating the similarity between monitoring data and the characteristics of various anomalies.

[0046] The data storage and model iteration module adopts a distributed storage architecture, consisting of a storage cluster composed of three storage servers. One server serves as a high-speed in-memory database storing nearly 24 hours of high-frequency real-time data, while the other two serve as large-capacity distributed databases storing historical data and model parameters. Data compression uses the LZ77 algorithm with a compression ratio of 10:1. A hot / cold data separation mechanism uses a 30-day limit, migrating historical data older than 30 days to the large-capacity storage nodes. The model iteration cycle is set to 7 days, with an automatic model retraining process initiated every 7 days to update model parameters using new running data.

[0047] The visualization management and interaction module is deployed on the data center management workstation, providing a graphical user interface that allows real-time viewing of each server's power consumption curve, temperature changes, hardware operating parameters, heat dissipation equipment status, and environmental parameter distribution.

[0048] Administrators can modify system configuration parameters, adjust threshold ranges, and manually issue optimization commands through the interface. Warning information can be pushed via SMS, email, and system pop-ups. A standard RESTful API interface is also provided, supporting seamless integration with data center management platforms. Data transmission through the interface uses an encrypted protocol to ensure security.

[0049] During system operation, the adaptive learning unit of the AI ​​hybrid intelligent framework optimizes model parameters in real time, and the parameter update process satisfies the formula: in This is the set of model parameters at time t, including the weight matrix and bias vector of the long short-term memory network, and the value function parameters of the reinforcement learning model. This is the initial set of model parameters, obtained through offline training. The parameter learning rate coefficient at time τ, with a value ranging from 0.001 to 0.01, is dynamically adjusted according to the gradient of the loss function. The model loss function at time τ with respect to the parameters The gradient is calculated using the backpropagation algorithm; The input data matrix at time τ contains real-time data acquired by the multi-source data acquisition module; τ is the regularization coefficient, with a value of 0.0001, used to prevent the model from overfitting; The Brownian motion increment is used to simulate the effect of random perturbations on the model parameters.

[0050] The dynamic heat dissipation efficiency evaluation module calculates the heat dissipation efficiency evaluation value every hour. The evaluation formula is as follows: in This is a heat dissipation efficiency evaluation value, ranging from 0 to 10. The higher the value, the better the heat dissipation efficiency. The environmental adaptation coefficient at time τ is calculated based on the ambient temperature and humidity, and its value ranges from 0.8 to 1.2. Let τ be the difference between the server core temperature and the ambient temperature at time τ. Let τ be the heat dissipation airflow rate at time τ, in cubic meters per minute; The total power consumption of the heat dissipation equipment at time τ is expressed in kilowatts; T is the evaluation time period, taken as 3600 seconds. Interface data interaction quality is statistically analyzed daily, satisfying the formula: in The interface data interaction quality score is given, with a value range from 0 to 100. Here are the weighting coefficients for the k-th type of interactive data: business data has a weighting coefficient of 0.6, status data has a weighting coefficient of 0.3, and control instructions have a weighting coefficient of 0.1. is a function of the transmission rate of the k-th type of data, in megabytes per second; Let be the transmission integrity function for the k-th data type, with a value between 0 and 1, where 1 indicates complete data transmission and 0 indicates data loss or error; m is the total number of interactive data types, with a value of 3; and T is the data interaction statistical period, with a value of 86400 seconds. After the system has been running for one month, the optimization effect was statistically analyzed, and the results are shown in Table 1 below.

[0051] Table 1. Statistics on the System Operation Optimization Effect of Example 1

[0052] Table 1 shows that after the system was put into operation, the average power consumption of the servers decreased significantly, the average temperature of the data center was controlled within a more reasonable range, the total power consumption of the cooling equipment decreased substantially, the average response time of services was shortened, and the hardware failure rate was significantly reduced. The multi-source data acquisition module provided comprehensive and accurate data support, enabling the AI ​​model to generate scientific and reasonable optimization decisions; the power dynamic adjustment module reduced ineffective power consumption through fine-tuning parameters while ensuring service performance; the zoning strategy of the collaborative heat dissipation control module achieved efficient utilization of heat dissipation resources and reduced heat dissipation energy consumption; and the operation status monitoring and feedback module promptly handled abnormal issues, reducing the risk of hardware failure. Overall, the system achieved a dynamic balance between power optimization, collaborative heat dissipation, and performance assurance, effectively improving the operating efficiency and stability of small and medium-sized data centers and reducing operating costs.

[0053] Example 2: Implementation of Large-Scale Cloud Data Center Server Cluster Application This embodiment focuses on a large cloud data center cluster containing 500 servers, and elaborates on the large-scale application process of an AI-based server power consumption dynamic optimization and collaborative heat dissipation control system to meet the heterogeneity, dynamism and high reliability requirements of large-scale clusters.

[0054] In the system deployment phase, the multi-source data acquisition module adopts a distributed architecture and deploys 10 data acquisition gateways, with each gateway responsible for data acquisition from 50 servers. High-precision sensors are deployed on the core hardware of each server, and dual-sensor redundancy acquisition is configured for components with high heat generation such as CPUs and graphics cards. The sensor sampling frequency is increased to the 20-millisecond level to ensure the real-time and reliability of data acquisition.

[0055] 50 environmental monitoring units are deployed in the computer room according to a grid layout, with each unit covering an area of 100 square meters and supporting real-time acquisition of environmental temperature, humidity, air flow velocity, and air pressure data, with a sampling frequency of the 1-second level. The software monitoring probe supports adaptation to multiple operating systems and acquires hardware operation parameters and business load data through standardized interfaces. The cooling equipment includes 20 precision air conditioners, 500 built-in fans for servers, and 100 local cooling modules. The operation status data is aggregated to the data acquisition gateway through an industrial bus.

[0056] The AI intelligent analysis and decision-making module adopts a cloud computing architecture and is deployed on a computing cluster composed of 10 high-performance servers, supporting distributed training and parallel inference. The time series prediction model is based on an improved long short-term memory network, introducing an attention mechanism to enhance the time series feature extraction ability. The input dimension includes historical data for the past 14 days, and 56-dimensional features are extracted, including hardware aging coefficients, business type association features, environmental mutation features, etc.

[0057] The model training adopts the federated learning mode to avoid data privacy leakage. The number of training iterations is set to 2000, and the loss function convergence threshold is set to 0.0005. The reinforcement learning model constructs a five-dimensional reward function. In addition to the power consumption optimization rate, heat dissipation energy efficiency ratio, and performance guarantee degree, two new indicators, resource utilization rate and business satisfaction, are added. The weight ratios of each indicator are 30%, 20%, 20%, 15%, and 15% respectively. The intelligent agent realizes the optimized control of the large-scale cluster through a hierarchical decision-making mechanism.

[0058] The power consumption dynamic regulation module supports differential regulation of heterogeneous server clusters, formulating exclusive regulation strategies for CPUs, memories, and graphics cards with different architectures. The CPU turbo frequency range supports individual adjustment by core, the memory bandwidth supports dynamic allocation by channel, the hard disk read / write cache policy supports automatic switching according to data types, and the graphics card power consumption wall threshold supports dynamic adaptation based on application scenarios. It is deeply coordinated with the cloud platform scheduling system to achieve the linkage of virtual machine migration and power consumption regulation. When the power consumption of a certain server is too high, some low-priority virtual machines are automatically migrated to a server with lower load, and at the same time, the hardware operation parameters of the target server are adjusted to ensure overall power consumption balance.

[0059] The collaborative heat dissipation control module divides the server room into 20 independent heat dissipation zones. Each zone is equipped with one precision air conditioner, 25 servers, and 5 local heat dissipation modules, with independent temperature control targets set for each zone. The precision air conditioners support stepless adjustment of cooling power from 20% to 100%, and the outlet air temperature can be adjusted between 14℃ and 22℃. The air supply angle supports 360-degree rotation adjustment, and the air supply path is optimized through airflow simulation algorithms.

[0060] The server's built-in fan supports 20 levels of speed adjustment, ranging from 600 RPM to 5000 RPM. The local cooling module employs a hybrid liquid and air cooling system, automatically switching between modes based on temperature data. The operational status monitoring and feedback module utilizes a distributed monitoring architecture, deploying 10 monitoring nodes, each responsible for monitoring the status of 50 servers. It presets four security thresholds and three performance thresholds. The security thresholds are dynamically adjusted based on the server hardware model and its age, while the performance thresholds are set differently according to the business level.

[0061] The anomaly root cause analysis function is based on deep learning algorithms, constructing a fault correlation map containing 40 common anomalies. It supports real-time mining of potential correlations between anomaly data and fault causes, with a location accuracy rate of over 95%. Early warning information supports tiered push notifications: Level 1 warnings only notify operations and maintenance personnel; Level 2 warnings trigger automatic fine-tuning; Level 3 warnings initiate load migration; and Level 4 warnings trigger emergency power reduction and hardware protection.

[0062] The data storage and model iteration module adopts a hybrid storage architecture, consisting of a storage cluster of 20 storage servers. Five of these servers serve as high-speed in-memory databases, storing nearly 48 hours of high-frequency real-time data, while the remaining 15 serve as large-capacity distributed databases, storing historical data and model parameters. Data compression employs the LZMA algorithm with a compression ratio of 20:1. A hot / cold data separation mechanism uses a 90-day limit, with historical data older than 90 days being archived.

[0063] The model iteration cycle is set to 3 days, using an incremental training mode that updates model parameters only based on new data to shorten training time. A full retraining is performed monthly to ensure stable model performance. The visualization management and interaction module is deployed on a cloud management platform, supporting multi-terminal access and providing switching between global and local views. The global view displays the power consumption distribution, temperature distribution, and operating status of cooling equipment throughout the data center, while the local view displays detailed operating data for a single server.

[0064] Administrators can configure optimization strategy templates, adjust threshold parameters, and manually intervene in the optimization process through the interface. Early warning information can be pushed via SMS, email, WeChat Work, and system pop-ups. Multiple standard API interfaces are provided, supporting seamless integration with cloud management platforms, operation and maintenance management systems, and energy management systems to achieve data sharing and collaborative scheduling.

[0065] During system operation, the adaptive learning unit of the AI ​​hybrid intelligent framework optimizes model parameters in real time, and the parameter update process satisfies the formula: , in This is the set of model parameters at time t, including the weight matrix, bias vector, and attention mechanism parameters of the improved long short-term memory network, as well as the value function and policy function parameters of the reinforcement learning model. The initial set of model parameters is obtained through large-scale offline training and fine-tuning. The parameter learning rate coefficient at time τ ranges from 0.0005 to 0.005 and is dynamically adjusted based on model performance. , The model loss function at time τ with respect to the parameters The gradient is calculated using a distributed backpropagation algorithm; The input data matrix at time τ contains real-time data and historical data fragments acquired by the multi-source data acquisition module; τ is the regularization coefficient, with a value of 0.00005, used to improve the model's generalization ability; The Brownian motion increment is used to simulate the influence of random factors on the model parameters. The dynamic evaluation module for heat dissipation efficiency calculates the heat dissipation efficiency evaluation value every 30 minutes, using the following formula: ,in This is a heat dissipation efficiency evaluation value, ranging from 0 to 10. The higher the value, the better the heat dissipation efficiency. The environmental adaptation coefficient at time τ is calculated based on the ambient temperature, humidity, and air pressure, and its value ranges from 0.7 to 1.3. Let τ be the difference between the server core temperature and the ambient temperature at time τ. Let τ be the heat dissipation airflow rate at time τ, in cubic meters per minute; τ represents the total power consumption of the heat dissipation equipment at time τ, in kilowatts; T is the evaluation time period, which is 1800 seconds.

[0066] The quality of interface data interaction is statistically analyzed every 12 hours and meets the following formula: ,in The interface data interaction quality score is given, with a value range from 0 to 100. Here are the weighting coefficients for the k-th type of interactive data: business data has a weighting coefficient of 0.5, status data has a weighting coefficient of 0.3, control instructions have a weighting coefficient of 0.15, and log data has a weighting coefficient of 0.05. is a function of the transmission rate of the k-th type of data, in megabytes per second; is the transmission integrity function for the k-th data type, with a value between 0 and 1. It is 1 when the data is transmitted completely without errors; otherwise, it decreases according to the error ratio. m is the total number of interactive data types, with a value of 4. T is the data interaction statistical period, with a value of 43200 seconds. After the system ran for one month, the optimization effect was statistically analyzed, and the results are shown in Table 2 below.

[0067] Table 2. Statistics on the System Operation Optimization Effect in Example 2

[0068] Table 2 shows that the system performs remarkably well in large-scale cloud data center scenarios. Average server power consumption is significantly reduced, average room temperature is controlled within an ideal range, total power consumption of cooling equipment decreases significantly, average service response time is shortened, hardware failure rate is reduced, and resource utilization is greatly improved. This performance is attributed to the system's scalability and multi-module collaboration mechanism: the distributed architecture of the multi-source data acquisition modules meets the data acquisition needs of large-scale clusters, ensuring data comprehensiveness and real-time performance. The cloud computing architecture and improved model of the AI ​​intelligent analysis and decision-making module enable accurate prediction and optimized decision-making for large-scale heterogeneous clusters; the deep collaboration between the power dynamic adjustment module and the cloud platform scheduling system improves the overall resource utilization and power balance; the refined partitioning strategy and hybrid heat dissipation mode of the collaborative heat dissipation control module effectively solve the problem of uneven heat dissipation in large-scale clusters; and the distributed architecture and hierarchical early warning mechanism of the operation status monitoring and feedback module ensure the stable operation of large-scale clusters. The hybrid storage architecture and incremental training mode of the data storage and model iteration modules meet the storage and model optimization needs of large-scale data. Overall, the system is fully adapted to the large-scale, heterogeneous, and dynamic requirements of large cloud data centers, achieving a multi-objective balance of power consumption optimization, heat dissipation coordination, performance assurance, and efficient resource utilization, significantly improving the operating efficiency, stability, and economy of large cloud data centers.

[0069] Reference Figure 2This invention visually demonstrates the core optimization effects of the system. Before optimization, the server consumed high power, the data center temperature was too high, and the cooling equipment was wasting significant energy, also affecting business response speed. This invention uses an AI hybrid intelligent framework to accurately predict load and temperature trends, dynamically adjusting hardware parameters and cooling strategies to achieve a balance between multiple objectives. After optimization, the server's average power consumption decreased to 305W, the data center temperature was controlled within the ideal range of 22℃, cooling energy consumption decreased by 29.2%, and business response time was shortened by 16.7%, verifying the synergistic value of power optimization and performance assurance.

[0070] Reference Figure 3 This demonstrates the dynamic optimization and timing adaptation capabilities. Before optimization, power consumption and temperature fluctuated drastically with the load, reaching a peak power consumption of 420W and a temperature of 28℃ at 12:00, posing a risk of overheating. This invention uses an LSTM timing prediction model to anticipate load changes and adjust parameters such as CPU turbo frequency and fan speed in advance. After optimization, the curve is smoother, the peak power consumption is reduced to 310W, and the temperature is stabilized at 20-22℃. This proactive adjustment avoids the performance loss caused by passive frequency reduction and reduces the waste of heat dissipation resources, confirming the dynamic adaptation advantages of the AI ​​hybrid intelligent framework.

[0071] Reference Figure 4 This highlights the optimization value of a zoned heat dissipation strategy. Before optimization, the heat dissipation efficiency varied greatly among different areas (3.6-4.5), resulting in localized insufficient heat dissipation and resource waste. This invention divides the data center into independent areas and generates personalized solutions based on the power consumption and temperature distribution within each area, dynamically adjusting the air conditioning supply and fan speed. After optimization, the energy efficiency of each area improved to above 7.0, the differences were reduced, and the average energy efficiency improved by 78%. This effect stems from the linkage between the collaborative heat dissipation control module and AI decision-making, achieving precise matching of heat dissipation resources and solving the energy efficiency imbalance problem of traditional extensive heat dissipation.

[0072] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An AI-based server power consumption dynamic optimization and collaborative heat dissipation control system, characterized in that, include: Deploy a distributed sensor network and software monitoring probes to collect server operating parameters, power consumption data, core temperature data in real time, and simultaneously collect data on ambient temperature, humidity, airflow speed, air pressure, and operating status of heat dissipation equipment in the computer room. A hybrid intelligent framework integrating time-series prediction model and reinforcement learning model is constructed. Historical and real-time data acquired by multi-source data acquisition module are input, and power consumption correlation features, temperature change features and load trend features are extracted through feature engineering to generate a comprehensive optimization decision that includes hardware operation parameter adjustment scheme, load balancing distribution strategy and heat dissipation equipment control instructions. It receives hardware operation parameter adjustment schemes output by the AI ​​intelligent analysis and decision-making module, and adjusts the CPU turbo frequency range, memory bandwidth, hard disk read and write cache strategy and graphics card power limit threshold in real time through the server hardware management interface. At the same time, it coordinates with the process scheduling mechanism of the operating system to dynamically allocate CPU cores and memory resources. Based on the heat dissipation equipment control commands in the optimization decision, combined with real-time environmental parameters and server temperature distribution data, the cooling power, air outlet temperature, and air supply angle of the computer room are dynamically adjusted, as well as the speed levels of the server's built-in fans and the start / stop status of local heat dissipation modules. The heat dissipation demand is predicted in advance based on changes in power consumption. The system monitors the server hardware's operating parameters in real time and compares the monitored data with preset safety and performance thresholds. When the data exceeds the threshold range or exhibits abnormal fluctuations, it generates multi-level early warning information and immediately feeds it back to the AI ​​intelligent analysis and decision-making module, triggering iterative adjustments to optimize decisions. At the same time, it records abnormal data and the processing process. The system employs a distributed storage architecture to store the collected raw data, optimization decision records, runtime logs, and AI model parameters, supporting long-term data retention and rapid retrieval. It optimizes the training sample set of the AI ​​model through historical data review and analysis, and periodically initiates the model retraining process.

2. The AI-based server power consumption dynamic optimization and collaborative heat dissipation control system according to claim 1, characterized in that, It also includes an adaptive learning unit within the AI ​​hybrid intelligent framework, which optimizes model parameters in real time using an online gradient descent algorithm. The parameter update formula is: ,in Let be the set of model parameters at time t. This is the initial set of model parameters. Let be the parameter learning rate coefficient at time τ. The model loss function at time τ with respect to the parameters gradient, Let be the input data matrix at time τ. Let be the regularization coefficient at time τ. This is the increment for Brownian motion.

3. The AI-based server power consumption dynamic optimization and collaborative heat dissipation control system according to claim 1, characterized in that, It also includes a core business priority dynamic evaluation unit, which builds a priority evaluation system based on the business response time requirements, data processing importance, and user access frequency. The priority weight of each business is determined through quantitative evaluation, and power consumption quotas and hardware resources are allocated according to priority weights during power consumption adjustment and resource allocation.

4. The AI-based server power consumption dynamic optimization and collaborative heat dissipation control system according to claim 1, characterized in that, The power consumption dynamic adjustment module also has a power consumption prediction deviation correction mechanism. By comparing the power consumption prediction value of the AI ​​model with the actual monitoring value, the prediction deviation rate is calculated. When the deviation rate exceeds the set range, the feature weights and prediction factors of the model are dynamically adjusted. At the same time, the influence coefficients of hardware operating years and ambient temperature and humidity on power consumption are introduced to correct the input parameters of the power consumption prediction model.

5. The AI-based server power consumption dynamic optimization and collaborative heat dissipation control system according to claim 1, characterized in that, The collaborative heat dissipation control module adopts a zoned heat dissipation optimization strategy, dividing the data center into multiple independent heat dissipation zones according to server cluster density, business type, and geographical location. Each zone is configured with a dedicated heat dissipation control submodule, which generates personalized heat dissipation solutions based on the average power consumption, maximum temperature, and equipment distribution density within the zone, and coordinates the adjustment of heat dissipation parameters between zones.

6. The AI-based server power consumption dynamic optimization and collaborative heat dissipation control system according to claim 1, characterized in that, The operation status monitoring and feedback module supports intelligent root cause analysis of anomalies. When abnormal data is detected, it constructs a fault correlation map by combining historical fault cases, hardware operation logs, and environmental change data. Through correlation analysis, it locates the root cause of the anomaly and distinguishes whether it is caused by hardware failure, software anomaly, overload, or environmental factors.

7. The AI-based server power consumption dynamic optimization and collaborative heat dissipation control system according to claim 1, characterized in that, It also includes a dynamic heat dissipation efficiency evaluation module, which quantifies the balance between heat dissipation effect and energy consumption cost by constructing a multi-dimensional heat dissipation efficiency model. The evaluation formula is as follows: ,in This is a value for evaluating heat dissipation efficiency. Let τ be the environmental adaptation coefficient at time τ. Let τ be the difference between the server core temperature and the ambient temperature at time τ. Let be the heat dissipation airflow rate at time τ. Let τ be the total power consumption of the heat dissipation equipment at time τ, and T be the evaluation time period.

8. The AI-based server power consumption dynamic optimization and collaborative heat dissipation control system according to claim 1, characterized in that, The data storage and model iteration module adopts a hierarchical data storage strategy, storing high-frequency real-time data in a high-speed in-memory database, and storing historical data and model parameters in a large-capacity distributed database. Data security is achieved through encrypted storage and access control.

9. The AI-based server power consumption dynamic optimization and collaborative heat dissipation control system according to claim 1, characterized in that, It also includes an emergency response module for extreme scenarios. When encountering a power outage warning, heat dissipation equipment failure, or sudden overload, it automatically activates an emergency optimization mode. By shutting down non-core services, reducing the operating parameters of core hardware, and activating backup heat dissipation equipment, it keeps the server power consumption and temperature within a safe range.

10. The AI-based server power consumption dynamic optimization and collaborative heat dissipation control system according to claim 1, characterized in that, It also includes a visualization management and interaction module, supporting multi-dimensional data statistics and analysis functions, generating server power consumption trend reports, heat dissipation efficiency analysis reports, and business performance correlation reports. It provides API interfaces for integration with data center management platforms and server cluster management systems, supporting integrated system management and collaborative scheduling. The quality of interface data interaction is determined by the following formula: ,in Scoring the quality of interface data interaction. The weight coefficients for the k-th type of interactive data are... Let k be the transmission rate function of the k-th type of data. Let m be the transmission integrity function for the k-th type of data, m be the total number of interactive data types, and T be the data interaction statistical period, used to continuously optimize the interface data transmission strategy.

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