6454 results about "Data center" patented technology

A system and method is described for internationalization of web pages by extracting translatable content from extensible mark-up language (XML), or similar data-centric meta-language representations of web pages or data used to build web pages. The extracted translatable content is stored in a translation task repository (TTR) accessible by the web developer and the translator. The XML representation is then modified to include selection control logic to select the appropriate translations for insertion into the final web page. The translator accesses the TTR to translate the appropriate content and saves the translations back to the TTR associated with the original translatable data. The translations are obtained from the TTR as selection cases for the selection control logic of the XML representation. As the XML is converted into the web source code, the selection logic and translations are embedded therein facilitating building the web site in multiple different languages.

The invention provides a method and system for realizing electronic channel risk control processing based on a code insertion technology, and relates to the technical field of electronic channel risk control, and the method comprises the steps: encrypting user information and channel identification information through a dual Hash algorithm to generate a dynamic session identification code, and forming a target coding sequence through employing a self-adaptive segmentation algorithm and a business feature flag bit. And constructing a dynamic risk control rule model by using a graph neural network, extracting user multi-dimensional features, carrying out weighted fusion to generate a user risk score, comparing the user risk score with an adaptive risk threshold, analyzing abnormal behaviors based on a probability graph model, and generating an intelligent risk early warning report. And calling a reinforcement learning risk control strategy engine to determine an optimal disposal strategy, and issuing a multi-factor interaction verification instruction. And after verification, selectively activating a blocking mechanism according to a verification result, synchronizing interaction data and a result to a distributed risk control data center, and continuously optimizing the risk control model based on federal learning.

The invention relates to the technical field of data center management, and discloses a data center digital twinborn simulation and decision-making system oriented to intelligent management. The system comprises a data center physical feature sensing module, a virtual space reconstruction module, an operation situation deduction engine, an abnormal behavior recognition module and a decision instruction generation module. Wherein the physical feature sensing module collects multi-dimensional operation parameters of the infrastructure in real time; the virtual space reconstruction module dynamically constructs a three-dimensional virtual model based on the collected parameters; running a situation deduction engine to simulate a resource scheduling and energy flow process; the abnormal behavior recognition module analyzes the analog data stream to detect an abnormal operation mode; and the decision instruction generation module integrates the abnormal information and generates an optimization regulation and control instruction for the physical equipment. According to the system, intelligent management of the data center is realized through real-time mapping, dynamic deduction and intelligent decision making of physical and virtual spaces, and the management precision and timeliness are improved.

The invention relates to the technical field of data center intelligent management, in particular to a data center construction and intelligent operation and maintenance management system, which comprises a dynamic environment sensing module, a heterogeneous equipment protocol adaptation module, a multi-dimensional resource dynamic scheduling module, a hidden fault prediction module and an energy efficiency optimization execution module. Physical environment data such as temperature gradient, current harmonic component and optical fiber strain rate are acquired by deploying a multi-mode sensor, and an environment characteristic matrix is constructed; standard semantic mapping of the heterogeneous protocol is realized by using a semantic slot migration algorithm; establishing a resource topological graph based on the hypergraph neural network and dynamically updating the resource topological graph; a dual-channel space-time convolutional network is adopted to realize fault prediction; and combining the fault probability matrix to generate a dynamic tuning strategy of dimensions such as cooling, electric power, network and the like, and forming closed-loop optimization control. According to the invention, integrated collaboration of multi-source information fusion, equipment intelligent control and energy efficiency adaptive optimization is realized, and the intelligence, reliability and energy efficiency level of data center operation and maintenance are improved.

The invention discloses a control method and a control system of a data center liquid cooling heat dissipation system, and relates to the technical field of liquid cooling heat dissipation, the system realizes real-time state monitoring of server operation through a data acquisition and monitoring module, integrates multiple data such as temperature, flow velocity and pressure difference, forms a detailed comprehensive heat dissipation data set, and improves the heat dissipation efficiency. And a database is constructed for storage. And meanwhile, a cooling liquid flow velocity analysis control module of the system generates a server heat dissipation coefficient CN through the server operation environment parameter C1 and the server operation parameter C2, the server heat dissipation coefficient CN is compared with a preset heat dissipation threshold C for evaluation, and the cooling liquid flow velocity is adjusted in real time. Therefore, when the load of the server is increased, the cooling intensity can be automatically increased, the chip temperature is reduced, and stable operation of the server is kept; and when the load is reduced, the cooling intensity is reduced, the energy consumption is reduced, and the cooling efficiency and the energy utilization rate of the system are remarkably improved.

The invention discloses an energy efficiency data detection processing method and system for a data center, and the method comprises the steps: deploying a sensor and a distributed collection network, and achieving the collection of energy efficiency data through the calculation of an energy efficiency change rate and the adjustment of a self-adaptive sampling frequency; dynamic energy efficiency prediction is realized by adopting adaptive time window adjustment, short-term and long-term error feedback correction, energy efficiency trend modeling and multi-level prediction fusion; an energy efficiency perception load evaluation index is introduced to realize energy efficiency maximization; an intelligent group decision model is constructed, and global allocation of computing resources is realized through dynamic balance scheduling optimization, task allocation stability analysis and adaptive convergence adjustment; based on an energy efficiency autonomous learning framework, initial strategy training, reinforcement learning optimization scheduling strategy, adaptive tuning and dynamic adjustment are carried out by utilizing imitation learning, and adaptive optimization is realized. According to the invention, under the condition that the business of the data center changes rapidly, the allocation of the computing resources can be adjusted accurately, rapidly and dynamically, and the optimal energy efficiency is achieved.

The invention discloses a link reliability tracking and optimizing method combined with real-time monitoring, and relates to the technical field of data transmission. Comprising the following steps: constructing a topological graph of a full link based on component information and a data flow relationship of a data medium station, and deploying a data acquisition probe to acquire link data in real time; a potential fault or an abnormal link is automatically identified; based on the data map, sorting the data dependency relationship of the upstream and downstream of the link, recording the relevance and dependency path of each component, and forming an accurate link blood relationship map; link abnormal conditions are captured and backtracked, log information is summarized in a unified mode, and centralized analysis and problem positioning are carried out; and on the basis of operation historical data of the link, according to the time sequence prediction model, identifying a bottleneck and a high-risk area in the link, dynamically adjusting a transmission strategy of the link, and optimizing load balance and a link priority of a station in the data in real time. According to the invention, the influence on the service when the fault occurs is reduced, and the abnormal problem positioning efficiency is improved.

The invention provides a data center inspection robot monitoring analysis method and system based on machine vision, and relates to the technical field of inspection robots, and the method comprises the steps: obtaining a video stream and environment parameter data collected by an inspection robot, and carrying out the detection and recognition of an abnormal state through a deep learning target; and constructing a multi-modal data fusion analysis model to form a knowledge graph to generate a root cause analysis result, and planning an inspection path based on priority scores. According to the invention, intelligentization and precision of data center monitoring are realized, and inspection efficiency and fault diagnosis accuracy are improved.

The invention discloses a data center machine room AI energy-saving control method and system, a digital twin model of a machine room operation state is constructed through a holographic perception and heterogeneous data fusion technology, centimeter-level monitoring of an equipment state and environmental parameters is realized, and the system integrates a laser radar array, an acoustic sensor and a gas sensor network. The time-space alignment of multi-modal data is completed by combining edge computing nodes, holographic mapping including thermodynamic characteristics, vibration characteristics and gas leakage risks is formed, historical temperature control strategy characteristics are extracted by adopting a variational auto-encoder based on a dynamic strategy generation mechanism of generative artificial intelligence, and a load trend is predicted by combining a long-short-term memory network. Constructing a self-adaptive strategy pool; the multi-agent reinforcement learning framework enables temperature control, equipment scheduling and power grid response to form game optimization, the strategy robustness in a complex scene is improved, and the system innovatively fuses power grid real-time electricity price and carbon transaction data so as to establish a multi-target decision system.

The invention provides a computer room energy consumption and computing power balance optimization method and system based on swarm intelligence, and relates to the technical field of data center management, and the method comprises the steps: collecting server node real-time operation data, constructing a multi-objective optimization function, predicting an energy consumption and computing power change curve through a recurrent neural network, and generating an initial task distribution scheme; and a global optimal allocation scheme is searched by adopting a parallel ant colony algorithm, the running state of the server is monitored, and task migration and energy consumption adjustment are executed, so that collaborative optimization of energy consumption reduction of the machine room and balanced allocation of computing power is realized, and the service quality and the resource utilization efficiency are improved.

The invention provides an intelligent operation and maintenance and fault prediction system and method for a data center, and the system comprises a self-healing decision module, a closed-loop verification module, an automatic execution module, a monitoring feedback module, and a strategy optimization module, and is characterized in that the self-healing decision module is used for determining the type and severity of a fault. The self-healing decision tree is generated according to the fault type and severity in combination with expert knowledge and historical data, the self-healing accuracy and safety are improved, the self-healing decision is executed through the automatic execution module, manual intervention is reduced, the operation and maintenance efficiency is improved, the self-healing process is monitored through the monitoring feedback module, feedback information is collected, and the operation and maintenance efficiency is improved. The self-healing effect is evaluated, the correctness and effectiveness of self-healing operation are ensured, a self-healing strategy is continuously optimized through a strategy optimization module according to feedback information and new data, the self-adaptive capacity and long-term performance of the system are improved, the system can continuously learn and adapt to a new fault mode, and therefore the self-healing capacity is continuously improved.

The invention relates to the technical field of data center heat dissipation, and particularly provides a single-phase and two-phase immersion liquid cooling method and system based on AI intelligent decision, and the method comprises the steps: injecting coupled data into a dynamic feature extraction engine, and outputting a thermodynamic state evolution tensor which comprises the characteristics of a temperature change rate, a load-heat flux density coupling coefficient and the like; the thermodynamic state evolution tensor is input into the deep neural network model, the temperature and pressure matched with the current thermodynamic state evolution tensor are calculated, and a closed-loop control instruction set capable of being executed by equipment is generated; a closed-loop control instruction set is injected into an execution mechanism set, execution mechanisms execute power reconstruction and flow channel switching according to instructions, gaseous fluorinated liquid is liquefied and flows back through an efficient condenser in a two-phase mode, and heat dissipation mode self-adaptive switching and heat cycle reconstruction are achieved. The system comprises a server, an AI algorithm controller, a cooling liquid storage device, a condenser, a circulating pump, an electric valve, a pressure release valve and a temperature sensor. The heat dissipation efficiency and the system reliability are remarkably improved.

The invention discloses an accurate flow control method and system for a two-phase cold plate cooling data center, and relates to the technical field of two-phase cold plate cooling, and the method comprises the following steps: S1, collecting multi-mode operation monitoring data in real time, and carrying out the data preprocessing; s2, constructing a multivariable short-time-sequence prediction model, predicting the cooling demand, and performing optimization regulation and control on a cooling demand prediction result; s3, the target regulation and control flow of the cooling liquid is predicted, and flow and cooling execution measures are taken according to the target regulation and control flow prediction result; the opening degree of the valve is accurately adjusted and evaluated in real time, and accurate flow control is achieved; s4, integrating multi-mode operation monitoring data, a cooling demand prediction result, a target regulation and control flow prediction result and a valve opening accurate regulation evaluation result, and constructing a parameter optimization and safety fault-tolerant mechanism; the problems of chip safety and energy consumption risks caused by cold plate temperature overshoot and cooling capacity regulation lag under high-load fluctuation of the server are solved.

A process can include receiving, by an edge compute unit, a pre-trained machine learning model from a cloud management platform, wherein the edge compute unit is deployed to an edge location and configured to obtain one or more sensor data streams at the edge location. The edge compute unit can transmit one or more batch uploads of information associated with inference performed by the edge compute unit using the pre-trained machine learning model and the one or more sensor data streams. The edge compute unit can receive one or more updated machine learning models generated by the cloud management platform responsive to the one or more batch uploads of information, wherein the one or more updated machine learning models are based on retraining or finetuning of the pre-trained machine learning model with the one or more batch uploads of information.

The invention relates to the technical field of computers and the field of data testing, in particular to a multivariable real-time measurement and state testing method oriented to a data center. The method comprises the following steps: acquiring original data from a sensor, equipment and an application program in a data center, and filtering, cleaning and formatting the original data to generate preprocessed data; storing the preprocessed data to a distributed file system, and constructing a reverse index and B-tree index structure to realize efficient retrieval and redundant backup; intelligent analysis is carried out on the stored data by introducing a machine learning model and an automatic operation and maintenance strategy, the abnormal state of the key performance index is identified, and the resource use condition is predicted; and based on an intelligent analysis result, executing dynamic scheduling and optimal allocation operation of the resources. Through multi-variable real-time monitoring and intelligent scheduling, the resource utilization efficiency and the operation stability of the data center are improved.

The invention discloses a data center operation and maintenance fault prediction system and method based on deep learning. The system comprises a multi-source heterogeneous data acquisition module, a data preprocessing module, a deep learning prediction model module and the like. The method comprises the following steps: acquiring multi-dimensional operation data of a data center through full-quantity acquisition of multi-source data, and inputting a CNN-LSTM-Attention hybrid model to realize fault prediction after preprocessing and feature enhancement; fault grades are divided in combination with fault grading, early warning is pushed in multiple channels, a coping strategy is intelligently generated, the effect is verified in a closed loop mode, and finally the model is iteratively optimized. According to the scheme, the fault prediction precision and real-time performance are improved, the operation and maintenance response time is shortened, the service interruption risk caused by faults is reduced, and the method is suitable for efficient operation and maintenance of large-scale data centers.

The invention provides a data center energy efficiency optimization method and system. A data acquisition node is arranged at the inlet end of a cooling unit to obtain refrigerant transportation data, the data are input into a path optimization model constructed based on a physical topological structure of the cooling unit, and a dynamic adjustment coefficient set containing heat exchange efficiency, phase change delay and pressure compensation parameters is generated. Meanwhile, the temperature difference generating capacity is converted into a driving parameter set based on the heat source distribution map by obtaining the surface waste heat of the cooling unit. And in combination with the dynamic adjustment coefficient set and the driving parameter set, the temperature gradient compensation amount, the phase change trigger threshold value and the pressure balance interval are determined, joint optimization is carried out, and a refrigerant flow rate control scheme adapting to machine room environment temperature fluctuation is generated. And energy efficiency optimization of the data center machine room environment is realized based on the refrigerant circulation parameters. According to the technical scheme provided by the invention, the energy efficiency optimization effect of the data center is remarkably improved by optimizing the flow speed control of the refrigerant.

The invention relates to the technical field of line arc abnormity monitoring, in particular to a data center operation and maintenance service environment monitoring system which comprises a multi-mode sensing unit, a dynamic baseline modeling unit, a transition state abnormity extraction unit and a grading early warning unit. The dynamic baseline modeling unit builds a three-layer safety baseline model, the base layer builds harmonic references in a segmented mode according to the load rate, the environment layer generates voiceprint feature template libraries of different temperature and humidity intervals, the time layer fits a 24-hour change trend envelope line, and the transition state anomaly extraction unit separates and decouples an arc voiceprint feature frequency band through a blind source and calculates the energy ratio. The harmonic abrupt change phase deviation amplitude is analyzed in combination with window sliding correlation, the abnormal evolution index is generated through fusion, the graded early warning unit triggers response according to the index, transition state abnormity is accurately recognized, the perspectiveness and reliability of operation and maintenance early warning are improved, and the method is suitable for safe operation and maintenance of data center equipment.

The invention discloses a manufacturing enterprise data center system and method based on a large model, and the system comprises a data collection layer which is used for constructing a multi-source heterogeneous data collection system; the data storage layer is used for constructing a data warehouse system comprising multiple layers and realizing data lake storage; the data processing layer and the service layer are used for deploying a model and integrating an anomaly detection module, a multi-modal fusion engine and a prediction model; the service layer is used for an interface group, integrating a Drive / ClickHouse query engine and a Redis cache, and supporting multi-condition combined query and millisecond response; deploying a visual service module and an RBAC / ABAC authority control system to realize data grading sharing; and the application layer is used for developing a production optimization application, a supply chain management application and a CRM application. Through a uniform data acquisition interface and an efficient data acquisition tool, mass data inside and outside an enterprise can be integrated, data islands are broken, centralized management and sharing of the data are realized, and availability and value of the data are improved.

The invention discloses a carbon emission monitoring method and system for a data center, and the method comprises the steps: collecting the power generation proportion data of different energy types in real time, predicting the power supply change trend of renewable energy in combination with meteorological information, calculating the power supply proportion of each energy type, and constructing a dynamic weight matrix; constructing a time sequence prediction model by using historical and real-time renewable energy data, and predicting the future renewable energy power supply capability; carbon emission factors and priorities of different data centers are calculated, a task migration feasibility matrix is constructed, calculation tasks are dynamically migrated to a low-carbon emission data center according to a scheduling revenue function, a task scheduling path is optimized, cross-regional data transmission time delay and energy consumption are reduced, and cross-regional optimization scheduling of computing power is achieved; a multi-dimensional time sequence prediction model is constructed, the carbon emission trend is predicted, and carbon emission abnormity is monitored in real time. According to the invention, the problem of matching of computing power load and renewable energy of a data center can be solved.

The invention discloses an agent collaborative optimization data center management system based on knowledge graph driving, and relates to the technical field of data center management. The system specifically comprises the following modules: a modeling and entity management module, a cross-regional global scheduling optimization module, an agent game negotiation optimization module, an agent trust management module, a game negotiation conflict identification module, a reasoning evidence management module and a cross-regional consistency verification module. By establishing a complete knowledge graph model, systematic modeling of resource attributes, constraints, historical decisions and strategy preferences is realized, a unified data basis is provided for negotiation among multiple agents, the problem of a suboptimal solution caused by information asymmetry is effectively solved, a hierarchical reasoning mechanism is adopted, and the probability of resource disruption is reduced. In combination with global-region-node three-level reasoning and multi-round game negotiation, recursive optimization from global to local is realized, the reasoning complexity is effectively reduced, and the negotiation efficiency is improved.

A method for diagnosing diseases in human patients can include providing a set of global X-ray diffraction (XRD) data to a global data center (GDC) and processing the set of global XRD data and categorizing it into data clusters, where each data cluster corresponds to a diagnostic indicator for assessment of a physiological or pathological condition. The method can further include communicating local XRD data and local patient data from a local autonomous cell (LAC) to the GDC, where the local XRD data includes information about a tissue sample measured using a tissue diffractometer of the LAC, and wherein the tissue sample includes skin. The method can further include processing the local XRD data, comparing it with the data clusters to determine a local diagnostic indicator for the local patient, and communicating the local diagnostic indicator to the LAC.

The invention discloses an embedded system construction method and device, and the method comprises the steps: loading a configuration file which is a. Config file generated based on configuration parameters selected by a user through a visual configuration interface; when the CONFIGCONANAUTOUPDATE flag bit is detected to be in an activated state, the Conan warehouse data center is accessed, the latest compatible version of each Conan software package dependency item is inquired, and a semantic version constraint analysis algorithm is adopted to update a version identifier in the configuration file into the latest compatible version; generating a description file containing a dependency item list based on the updated configuration file, and generating a dependency lock file through a Hash locking mechanism; and importing the pre-compiled binary package into the construction environment, calling the corresponding pre-compiled binary package according to the version information and the Hash check value in the dependency lock file in the firmware compiling process, and executing hierarchical compiling operation.

The invention relates to the technical field of anti-electromagnetic interference, in particular to an anti-electromagnetic interference method for a flywheel energy storage system of a data center, which comprises the following steps: acquiring change rate data of the rotating speed and the angular speed of a flywheel, judging the attribution of a disturbance interval, recording change characteristics, extracting an interference identifier, comparing the interference identifier with impedance change, and generating a switching node. Frequency response and reflection coefficient characteristics of the filtering unit are detected, a coupling channel generation label is identified, and output fluctuation is evaluated to generate an anti-interference capability state label. According to the method, the surface resistivity of the shielding layer is dynamically switched according to the comparison result of the specific interference triggering identification bit and the signal link impedance change state in each disturbance stage through classification judgment and correlation marking of inertial disturbance, so that the effect of suppressing electromagnetic interference in real time is optimized; the influence degree of electromagnetic interference can be dynamically evaluated in the system operation process, and the adaptability of the energy storage system to the electromagnetic interference is judged and adjusted in real time.

A fabric control protocol (FCP) and packet forwarding mechanisms are described that maximize utilization of bandwidth within massive, large-scale data centers having multi-stage data center switch fabric topologies, such as topologies that include a third switching layer formed by super spine switches. Automatic generation of data plane forwarding information referred to as FCP path information enumerates, for each data processing unit (DPU), the available FCP paths. The FCP path information may be based on unique combinations of peak points of the switch fabric for a given DPU with FCP colors assigned to network links that are used to multi-home the DPU to the switch fabric.

The invention discloses an energy consumption prediction and scheduling control method based on machine learning. According to the method, operation parameters, energy consumption curves and environment disturbance data of multiple devices are collected through a distributed sensing terminal, the data are input into a pre-trained machine learning model, and a probability prediction result of future energy consumption distribution is generated. On the basis of prediction, an intervention signal is applied in an equipment safety boundary, equipment response characteristics are obtained according to the difference before and after intervention, and an energy consumption risk map is constructed by combining the equipment response characteristics with a probability prediction result. And based on the energy consumption risk map, generating an extreme disturbance scene by using digital twinning, performing consistency check on a probability prediction result and a scheduling scheme in a data domain and a physical domain, and performing multi-stage scheduling in combination with task delays to generate a scheduling result. And finally, issuing the scheduling result to the equipment. The method can improve the accuracy of energy consumption prediction and the reliability of scheduling decision making, and is suitable for intelligent management of data centers, industrial production and high-energy-consumption scenes.

Systems, devices and methods for providing cooling to hardware components, and more specifically to manifold systems, devices and methods for thermal management of hardware in computer server racks and related equipment in computer data centers.

The invention relates to the technical field of artificial intelligence data centers, and discloses a computing power resource scheduling method and system for an AI multi-service data center, and the method comprises the following steps: S1, obtaining multi-service task request data, processing the task request data based on a time sequence analysis model, and generating resource demand prediction data; s2, according to the resource demand prediction data, heterogeneous computing resource data is converted into virtual resource pool data, and priority queue data is generated based on task priority data; s3, processing the virtual resource pool data and the task priority data based on a reinforcement learning algorithm to generate resource allocation strategy data, and processing high-frequency access data through a prefetching strategy; and S4, generating edge-cloud hierarchical scheduling data according to the task delay demand data and the network topology data. According to the method, heterogeneous resources are dynamically allocated through time sequence analysis and a reinforcement learning algorithm, the resource utilization rate is increased to 85% from 60%, and the vacancy rate is reduced to 5%.

Various approaches to efficiently allocating and utilizing hardware resources in data centers while maintaining compliance with a service level objective (SLO) specified for a computational workload is translated into a hardware-level SLO to facilitate direct enforcement by the hardware processor, e.g., using a feedback control loop or model-based mapping of the hardware-level SLO to allocations of microarchitecture resources of the processor. In some embodiments, a computational model of the hardware behavior under resource contention is used to predict the application performance (e.g., as measured in terms of the hardware-level SLO) to be expected under certain contention scenarios. Scheduling of workloads among the compute nodes within the data center may be based on such predictions. In further embodiments, configurations of microservices are optimized to minimize hardware resources while meeting a specified performance goal.

The invention relates to the technical field of data center energy management, and discloses a flywheel energy storage system control system for data center computing power load energy recovery, which comprises an energy storage unit, a load monitoring unit, a flywheel control unit and an energy distribution unit. The energy storage unit is provided with a plurality of groups of flywheel energy storage devices capable of charging / discharging; the load monitoring unit monitors load data by covering key nodes of computing power equipment through a power sensor; the flywheel control unit collects flywheel rotating speed and power grid frequency fluctuation data; and the energy distribution unit decides charging and discharging based on load data, establishes a load and frequency coordinated alternating control strategy, and dynamically adjusts and predicts the running state of the flywheel. The system adopts time sequence synchronous control, the energy distribution unit comprises multiple modules, the alternate controller supports dual-mode switching and parameter optimization, and the flywheel unit adopts an annular redundancy or star-shaped concentrated structure. The system realizes efficient recovery of computing power load energy and flywheel energy storage optimization scheduling, and improves the energy utilization rate of a data center and the stability of a power grid.