18 results about "Green data center" patented technology

The invention discloses a method and system for configuring a refrigerating system switching threshold value based on a large reasoning model, and relates to the technical field of green data center artificial intelligence energy saving in the mobile communication technology. The method for configuring the switching threshold value of the refrigerating system based on the reasoning large model comprises the steps that normalization scoring is conducted on collected CPU power consumption of a threshold value adjusting module, air cooling power consumption of a precise air conditioner and liquid cooling power consumption of a cooling cold plate, and an air cooling and liquid cooling switching threshold value model evaluation score is obtained; inputting the evaluation score and the cue word of the air cooling and liquid cooling switching threshold value model into a reasoning large model to obtain an air cooling and liquid cooling reasoning switching threshold value; and dividing the air cooling and liquid cooling reasoning switching threshold into a plurality of accuracy levels according to the evaluation score of the air cooling and liquid cooling switching threshold model, and updating the corresponding switching threshold when the threshold adjustment module executes the prediction of the corresponding accuracy by adopting a preset prediction model by using the plurality of accuracy levels. According to the invention, the switching accuracy and the energy utilization efficiency of the cabinet cooling system are improved.

The invention relates to the technical field of geothermal energy development and utilization, in particular to a superconducting pipe mediated geothermal transmission ground power generation system which comprises a geothermal collection and transmission module, a heat energy receiving and separating module, a power generation module and an energy gradient utilization module. The geothermal collection and transmission module collects heat energy from a geothermal rock stratum in a phase change mode through a closed circulation superconducting pipe and transmits the heat energy to the ground. The heat energy receiving and separating module receives the heat energy and separates the heat energy to form a high-temperature heat source and a low-temperature cold source; the power generation module uses a high-temperature heat source to drive power generation; the power input end of the data computing power center is connected with the power generation module to receive power, and the cooling input end of the data computing power center is connected with the heat energy receiving and separating module to receive a low-temperature cold source to cool internal equipment. Efficient transmission and gradient utilization of geothermal energy and direct energy supply of the green data center are achieved, and the system has the advantages of being high in efficiency, good in environmental protection property and high in resource utilization rate.

The invention discloses a non-exchange data center internal network architecture based on free space optical communication, and belongs to the technical field of optical communication and data center networks. According to the framework, a server with a tunable light emitter, a light distribution beam combiner and the like are arranged in a cabinet, and non-line-of-sight communication is realized through a spatial light element; all-optical routing is completed through wavelength, spatial mode and time slot joint multiplexing, and a traditional electric / optical switch is not needed. The optical path routing in the architecture adopts passive devices, optical fiber wiring is avoided, online expansion of a cabinet and a server is supported, deployment and energy consumption cost is remarkably reduced, high-bandwidth, low-delay and low-power-consumption green data center communication is achieved, and the system can be widely applied to scenes such as cloud computing and big data.

The invention relates to the technical field of cloud computing and green data centers, in particular to a cloud computing data center energy efficiency optimization and carbon reduction method and system based on computing power collaboration. The system comprises a multi-source data acquisition module, a computing power collaborative arrangement engine, a multi-target real-time optimizer, a carbon market linkage interface, a hierarchical collaborative scheduling architecture and an interpretable decision output unit. A unit effective cost model is constructed by fusing electricity prices, carbon emission factors and energy efficiency indexes, a rolling time domain optimization and hierarchical scheduling mechanism is combined, cross-center computing power resources are dynamically coordinated on the premise of guaranteeing SLA, multi-target optimization of electric power cost, carbon emission and migration overhead is achieved, carbon market signal linkage and scheduling decision traceability are supported, and the scheduling efficiency is improved. And the energy efficiency and the low-carbon operation level of the data center are effectively improved.

This application discloses a hybrid energy storage optimization configuration method for data centers that considers carbon emissions throughout the entire lifecycle, comprehensively taking into account the coupling relationship between the data center's thermodynamic environment, battery life degradation, and carbon footprint. The method first constructs a thermodynamic-based dynamic PUE model and a unified matrix model for batch processing loads. Second, it introduces an active frequency division mechanism for the hybrid energy storage system, utilizing supercapacitors to smooth high-frequency fluctuations and extend lithium battery life. Then, it establishes a dynamic implicit carbon emission model to quantify the impact of equipment degradation on the carbon footprint throughout the entire lifecycle. Based on this, a two-layer multi-objective optimization framework is proposed: the upper layer optimizes capacity configuration using the NSGA-II algorithm, and the lower layer performs rolling scheduling across multiple typical days using the CPLEX solver. This not only achieves decoupled optimization of economic costs and carbon emissions but also effectively improves the lifespan and environmental benefits of the energy storage system, making it suitable for low-carbon planning and operation of green data centers.

The application discloses a data center hybrid energy storage optimization configuration method considering whole life cycle carbon emission, which can comprehensively consider the coupling relationship among the thermodynamic environment of the data center, the battery life attenuation and the carbon footprint. The method firstly constructs a dynamic PUE model based on thermodynamics and a batch processing load uniform matrix model; secondly, an active frequency division mechanism of the hybrid energy storage system is introduced, and a super capacitor is used to smooth high-frequency fluctuations to prolong the service life of a lithium battery; then, a dynamic implicit carbon emission model is established to quantify the influence of equipment attenuation on the whole life cycle carbon footprint. On this basis, a double-layer multi-objective optimization framework is proposed, the upper layer optimizes the capacity configuration through an NSGA-II algorithm, and the lower layer solves multiple typical days rolling scheduling through a CPLEX solver. In this way, not only the decoupling optimization of economic cost and carbon emission is realized, but also the service life and environmental benefits of the energy storage system are effectively improved, and the method is suitable for low-carbon planning and operation of green data centers.

The invention provides a multi-network fusion heterogeneous complementary power supply system of a zero-carbon park / green data center, which is a highly integrated and innovative micro-grid architecture, introduces a'single-line looped network 'technology as a core, and is composed of a new energy power generation cluster single-line looped network, a park / data center direct-current power distribution single-line looped network and an alternating-current power distribution three-phase looped network. The new energy power generation cluster is connected in series with n power generation systems through a single-line looped network, and the looped network DC / DC converter, the DC / AC inverter and the DC input end of the multi-port bidirectional energy storage energy router form a closed loop. The DC power distribution single-line looped network is connected in series with the n DC / DC power distribution devices, the AC / DC rectifier and the DC / DC end of the energy storage router; the AC power distribution three-phase looped network is connected in parallel with the AC / AC power distribution equipment, the transformer and the DC / AC end of the energy storage router. According to the invention, the high-efficiency consumption of new energy is realized through a single-line looped network architecture, and the high-reliability and high-efficiency power supply requirements of a zero-carbon park / data center in a 100% new energy supply scene are met.

The invention provides a data center efficient cooling energy supply system and method based on artificial intelligence, and belongs to the technical field of efficient cooling energy supply. The data acquisition module acquires multi-dimensional data of a data center and extracts core feature data. The multi-target optimization configuration module adopts a grey wolf optimization algorithm to set a multi-target cooling energy supply strategy and decomposes the multi-target cooling energy supply strategy into sub-targets. And the intelligent regulation and control module generates a cooling equipment operation parameter control instruction based on the sub-target, dynamically adjusts a cooling system combination mode and optimizes an energy distribution scheme. And the monitoring feedback module collects operation data of the cooling energy supply system in real time, performs deviation analysis with the sub-targets, evaluates the target achievement degree and forms a feedback adjustment signal. The cooling energy supply strategy is subjected to multi-objective optimization through the artificial intelligence algorithm, the equipment operation parameters and the energy distribution scheme are dynamically adjusted, the overall energy consumption of the cooling system is effectively reduced, the energy waste is reduced, the development trend of a green data center is met, and the operation cost is reduced for users.

The present application belongs to the technical field of cooling improvement of electrical equipment, and discloses a two-phase liquid cooling-Carnot cell system for data center cooling and energy storage, which comprises a boiling pool, a compressor, an expander, a pump, a water tank, a stop valve, an electronic expansion valve, a condenser and an evaporator; the boiling pool is used for cooling data center servers, the compressor and the water tank are used for recycling waste heat generated in the data center cooling process, and the expander is used for generating power by utilizing the waste heat. The system can switch among the natural cooling mode, the energy storage mode and the power generation mode according to the electricity price in the operation period. In the conventional period, the natural cooling mode is operated to cool the data center by air cooling; in the valley electricity period, the energy storage mode is operated to store waste heat of the data center cooling loop; and in the peak electricity period, the power generation mode is operated to convert the stored waste heat into electric energy. The present application combines the two-phase immersion liquid cooling technology with the Carnot cell energy storage technology, and provides a solution for the coupling of green data center cooling and energy storage systems.

The invention discloses a green data center power supply system large-model intelligent adjustment terminal load equipment power supply method, medium and equipment, and the method comprises the steps: collecting the reading of an intelligent electric meter in a cabinet corresponding to different terminal load equipment, obtaining the power consumption data per minute of the cabinet, and carrying out the serialization per minute; the method comprises the following steps: acquiring average power consumption of cabinets within a minutes after a certain time point, acquiring power consumption data within b minutes before the time point as initial features, adding timestamp features, acquiring power consumption data from the initial features of each cabinet, combining the power consumption data with the timestamp features to form input feature vectors, and gathering z input feature vectors into a sample data set; inputting the input feature vector of the zth cabinet at the current time point into the prediction model, and outputting an average power consumption prediction value within a minutes in the future; and obtaining the future total predicted power consumption of all cabinets, obtaining the net demand according to the power generation predicted value of the power supply system, comparing the energy storage of the energy storage module, and judging whether to access the power grid. According to the invention, the problem of pre-judging the energy consumption condition of the terminal load equipment is solved.

The invention relates to a green data center facade facing material aided design system and method. The system is developed based on a Visual Basic programming language, adopts a Windows application program architecture, and comprises a material data management module used for collecting and sorting basic data of exterior facing materials and providing dynamic classification and keyword retrieval functions of the materials; the material combination and cost calculation module is used for obtaining comprehensive cost information by combining material market price information according to the material combination configured by the user, the proportion of each material and the selection condition of the thermal insulation material; the visual display module is used for configuring a high-definition picture and detailed attribute description for each material so that a user can click the picture to check detailed attribute information of the material, and providing three-dimensional effect display of the material; and the design auxiliary module is used for providing a rapid comparison function of design schemes, and comparing the cost and effect of different schemes based on the real-time adjustment of material combination and proportion or thermal insulation material selection of a user.

The invention relates to the technical field of energy consumption management and resource scheduling optimization, and discloses a green data center energy-saving control method and system, and the method comprises the steps: obtaining real-time equipment operation data and real-time load data, and constructing an initial energy consumption model; dynamically adjusting model parameters according to the initial energy consumption model and the real-time load data to obtain a dynamic energy consumption model; according to the dynamic energy consumption model, optimizing a resource scheduling strategy to obtain a preliminary resource allocation scheme; dynamically adjusting the preliminary resource allocation scheme according to the real-time load data to obtain a dynamic resource allocation scheme; according to the dynamic resource allocation scheme and the real-time equipment operation data, performing allocation scheme optimization to obtain an optimized resource allocation scheme; and deploying and implementing the optimized resource allocation scheme, and performing real-time dynamic fine adjustment to obtain a real-time resource allocation scheme. According to the method, self-adaptive adjustment of dynamic conditions of load surge or equipment isomerism enhancement can be realized.

The invention discloses a green data center end-to-end intelligent operation and maintenance voltage load adjusting method, system and device and a medium, and belongs to the field of data center energy saving, aiming at the problem that the CPU current frequency of a using terminal is influenced by large fluctuation of input voltage and input current caused by natural factors such as wind power and solar energy. The method comprises the following steps of: subtracting computing power required by a running service in a CPU from total computing power of the CPU to obtain current idle computing power of the CPU, when the power supply voltage suddenly changes, if the idle computing power of the CPU is lower than a set abnormal computing power threshold value, judging the sudden change of the power supply voltage as abnormal sudden change, sending a voltage regulation instruction, and regulating the voltage of the power supply voltage; evaluating the performance of all the CPUs, and dividing the CPUs into a plurality of groups according to the performance scores of the CPUs; and each group of CPUs respectively updates an abnormal computing power threshold value for the next round of voltage regulation. According to the invention, the energy utilization rate can be optimized, the false alarm rate of abnormality diagnosis is reduced, and the accuracy of voltage regulation is improved.

The application discloses a kind of server operation management method, system, device, server and storage medium, it is related to the field of server, which comprises: determining a plurality of preset power consumption values;Each preset power consumption value is set as the power consumption ceiling value of server, and the stress test is carried out on the server under each preset power consumption value, to obtain the maximum power consumption value and performance value of server;Based on all maximum power consumption values and all performance values, the best energy efficiency ratio is calculated;The optimal power consumption value obtained according to the best energy efficiency ratio is set as the power consumption ceiling value of server.The application can ensure that the energy efficiency conversion ratio of server is best when high load service, avoid causing resource waste, so as to promote green data center construction.

The application discloses a large model inference energy-saving method based on fine-grained DVFS, and relates to the technical field of artificial intelligence.The application proposes an inference energy-saving framework for high-energy-efficiency LLM services, performs iterative level and load-aware GPU frequency control, and guarantees delay service quality.The framework is composed of three modules: first, a frequency-delay predictor based on machine learning, which estimates the delay of each iteration at the candidate GPU frequency by using lightweight iteration features;second, an SLO-oriented frequency controller, which selects a feasible frequency within the candidate range guided by the sweet spot interval;third, a low-overhead runtime optimization layer, which reduces and hides the overhead of online control by combining adaptive decision caching and asynchronous execution.The application has clear energy-saving effect, good stability and scalability, is convenient for engineering landing, and is conducive to the construction of green data centers and computing power infrastructure.

The application discloses a kind of computing power center energy efficiency intelligent monitoring and optimization management method, it relates to computing power center energy efficiency management technical field, the method includes following steps: S1, multi-source sensor network deployment and calibration;S2, multi-source data fusion acquisition and pre-processing;S3, multi-dimensional energy efficiency evaluation index system construction;S4, energy consumption anomaly identification and root location;S5, early warning information push and intelligent emergency disposal;S6, energy efficiency optimization suggestion generation and iterative update;The computing power center energy efficiency intelligent monitoring and optimization management method is through multi-dimensional data fusion acquisition, intelligent analysis and accurate early warning, realizes the rapid identification and root location of energy consumption anomaly, simultaneously adapts national green data center evaluation requirement, provides reliable data support for subsequent energy efficiency optimization, improves computing power center green low-carbon operation level.

The application discloses a green data center cold load prediction method based on a large language model. The method first constructs a multi-source operation data set, then constructs a data center domain knowledge base, and converts time series into text description, and fuses the two to obtain a context-aware template, realizing knowledge-enhanced semantic expression. Through a knowledge fusion alignment strategy, the time series features and text features are mapped to a unified latent space, ensuring cross-modal semantic consistency. An adaptive prefix tuning and global interactive self-attention mechanism is proposed to capture complex coupling relationships between devices and multi-scale dynamic features, thereby enhancing the model's generalization ability in dynamic environments. The method is significantly better than traditional deep learning models in terms of cold load prediction accuracy, stability and computational efficiency, and can effectively support intelligent energy management and carbon emission reduction optimization of green data centers, providing a new intelligent solution for low-carbon computing infrastructure.

The application discloses a kind of green data center multi-resource collaborative planning method, computing device and storage medium, the method is executed in computing device, including: in combination with endogenous uncertainty and exogenous uncertainty, the operation benefit of green data center system is obtained, green data center system includes distribution network line, green data center and terminal user, endogenous uncertainty includes terminal user participation demand response willingness, exogenous uncertainty includes renewable energy power generation output and terminal user data demand;Based on the investment cost and operation benefit of green data center system, the objective function of green data center multi-resource collaborative planning is constructed;Constraint condition is generated, constraint condition includes planning phase constraint and running phase constraint;Through objective function, in combination with constraint condition, green data center is carried out multi-resource collaborative planning.