47 results about "Computing center" patented technology

This invention discloses a rack-mounted CDU liquid cooling system, comprising a compatible heat exchanger, multiple internal circulation units, and an external circulation unit. The compatible heat exchanger has multiple non-interconnected heat exchange tubes and an inner cavity surrounding each heat exchange tube. The multiple internal circulation units are respectively connected to each heat exchange tube to form multiple independent liquid-cooled internal circulations, with at least some internal circulations containing different internal coolants. The external circulation unit is connected to the inner cavity to form a liquid-cooled external circulation, containing external coolant. Compared with existing technologies, the rack-mounted CDU liquid cooling system provided by this invention, through the design of a compatible heat exchanger, connects computing devices using the same internal coolant in series within the same internal circulation unit, and then uses a single external circulation unit to absorb the heat from each internal circulation unit. This effectively accommodates the liquid cooling needs of various computing devices, reduces the complexity of the computing center's liquid cooling system, and lowers the difficulty of expanding and maintaining the computing center.

The application relates to the field of construction scheduling control, in particular to a water conservancy engineering construction mechanical group collaborative operation intelligent scheduling system, which comprises the following steps: a geological mechanical coupling twin platform constructs a bidirectional coupling closed loop of a geological field and construction mechanical behavior according to real-time working condition data of a region to be constructed, obtains a dynamic geological attribute field quantity, and fits a geological disturbance sensitive coefficient of the region to be constructed; a cloud edge end collaborative network collects real-time working condition data through an intelligent agent node; a cloud computing center solves a global construction task scheduling scheme by adopting a multi-objective optimization algorithm; an edge computing node adopts a multi-agent deep reinforcement learning algorithm to perform real-time rolling optimization on the collaborative operation of construction machines in a local jurisdiction region according to a screened optimal local construction task distribution scheme, generates a local collaborative scheduling instruction, and schedules each construction machine. The scheduling system can realize real-time sensing of geological changes, reduce the probability of engineering accidents, and dynamically optimize the collaborative operation of the mechanical group.

The application discloses an integrated marine computing power center system for realizing hydrogen storage and energy supply by marine energy, which comprises a marine computing power center (1), a seawater heat exchange module (2), a temperature control module (3), a membrane distillation module (4), a SOEC water electrolysis module (5), a hydrogen storage module (6), a liquefaction module (7), a SOFC fuel cell module (8) and a hydrogen storage and energy regulation module (9). Normal temperature seawater exchanges heat with cooling water of the marine computing power center at the seawater heat exchange module, the heat-exchanged seawater enters the membrane distillation module after being regulated by the temperature control module, and fresh water is separated out, the fresh water is electrolyzed by the SOEC water electrolysis module to generate hydrogen, the hydrogen is stored by the hydrogen storage module and is used for power generation of the SOFC fuel cell module, the excess hydrogen is liquefied and stored by the liquefaction module, and the hydrogen storage and energy regulation module regulates the supply, storage and replenishment of the hydrogen. The application provides an integrated solution for the marine computing power center to be completely self-sufficient in energy, without needing to access external power supply, and with zero carbon emission in the whole process.

This invention discloses a low-conductivity coolant for microchannel water-cooled plates and its application. The coolant, by mass fraction, comprises 85-95% propylene glycol, 1-5% deionized water, 0.05-0.15% imidazole, 0.1-0.3% toluenetriazole, 0.10-0.4% sebacic acid, 0.1-0.3% benzoic acid, 3-4% triethanolamine, and 0-0.06% defoamer. The coolant is stirred evenly with deionized water, and the resulting mixture is placed in the micron-level fluid channels inside the microchannel water-cooled plate. The high-speed flow of the mixture absorbs the heat dissipated by the chip. The coolant prepared by this invention exhibits low corrosivity, high reserve alkalinity, low foaming tendency, and low conductivity. This coolant can be used safely and efficiently in microchannel structures, meeting the growing heat dissipation needs of AI big data computing centers.

This application discloses a modeling method, apparatus, and equipment for a flight early warning center, relating to the field of civil aviation early warning technology, and is used to solve the technical problems of poor early warning accuracy, consistency, and centralized control capabilities in existing technologies. The method includes: constructing a rule management center using dynamic rule management services and dynamic rule loading services; the rule management center having standardized early warning event models and early warning rule distribution protocols; constructing a dynamic early warning rule library and an early warning processing center using the Drools rule engine; constructing an early warning calculation center using the Drools rule engine and the Disruptor asynchronous processing framework; and constructing a flight early warning center based on the dynamic early warning rule library, rule management center, early warning processing center, and early warning calculation center. Therefore, this application can effectively improve early warning accuracy and consistency through "Drools rule engine, Disruptor asynchronous processing framework, standardized early warning event models, and early warning rule distribution protocols."

This invention discloses a space-based computing power network system, belonging to the field of integrated space-ground information network technology. The system includes space-based nodes deploying edge computing power resource pools, ground stations, and a ground-based cloud computing center. The ground station is equipped with a space-based computing power orchestration and management platform, including a computing power orchestration module and a network control module. The platform collects real-time information on the network's computing power, network, storage, and algorithm resources, forming a unified resource view. When a user station initiates a service computing power request including computing power scale, algorithm type, and network QoS requirements, the platform generates a computing power allocation strategy through the computing power orchestration module and a space-based network path through the network control module, supporting step-by-step or joint optimization to achieve resource collaborative scheduling and optimal configuration. Finally, the strategy and path are distributed to each node, and the system responds to the user station. This invention supports real-time on-orbit processing of services, reduces space-to-ground transmission latency and bandwidth requirements, and enhances the intelligence and autonomous operation capabilities of the space-based network.

This invention provides a method and apparatus for generating tokens using computing power acceleration on an intelligent computing cloud platform. It relates to the fields of intelligent computing centers, smart computing centers, computing infrastructure, and smart computing cloud technologies. The method for generating tokens using computing power acceleration on an intelligent computing cloud platform includes: Step S1, establishing a full-text index of a target data set; Step S2, responding to a received data query request, determining whether to enable document index acceleration; Step S3, if document index acceleration is enabled, extracting scalar filtering conditions and vector filtering conditions; Step S4, performing a full-text search based on the scalar filtering conditions to obtain a first data set; Step S5, performing a vector search on the vector index of the first data set based on the vector filtering conditions to obtain a matching second data set, and returning it to the user. This invention can improve the speed of vector database processing with scalar constraints on intelligent computing cloud platforms, reduce resource consumption, and increase token generation speed.

A comprehensive and centralized system for managing and tracking communications being delivered through both digital communications channels and physical communication channels. A centralized computing hub receives communication requests from originating systems of record or via user input. In response to receiving a communication request, content is generated or otherwise obtained and quality assurance checks are performed to assure that the content meets the requirements of the originating system of record and is applicable to the intended communication recipient. Once the quality checks have been performed and the content is deemed verified, delivery of the communications is orchestrated in accordance with the chosen communication channel. The timeliness of the communications is tracked throughout the system and alerts are generated in response to the timeliness exceeding thresholds. Thresholds are intelligently determined on a continuous basis using rolling averages of previous communications that result in predictions of future communication averages.

This invention relates to the field of power generation devices for computing centers, specifically a containerized generator set applied to computing centers. It includes a housing, with an installation platform located on the lower inner side of the housing. An arc-shaped shielding layer is connected to the upper part of the installation platform, forming four continuous heat exchange chambers between the shielding layer and the housing. Each heat exchange chamber is filled with coolant and is sequentially designated as a heat exchange chamber, an insulation chamber, a cooling chamber, and a storage chamber. A partition plate divides each heat exchange chamber into an outer heat exchange chamber and an inner heat exchange chamber. The outer heat exchange chamber is connected to the cooling system of the computing center, and the inner heat exchange chamber is located outside the shielding layer. By forming four continuous heat exchange chambers filled with coolant, a systematic stratified transfer and storage of heat is achieved.

This invention relates to the field of computing resource scheduling technology, proposing a task scheduling method and system for computing centers based on carbon-computing power coupling optimization. The method includes the following steps: acquiring real-time status data of each node in the computing center, and a queue of tasks to be processed; calculating the carbon efficiency value of each node according to the carbon efficiency dynamic equilibrium equation, and allocating any task in the task queue based on the carbon efficiency value of each node to obtain a first scheduling scheme; inputting the first scheduling scheme into a carbon-aware scheduling model based on Markov decision method and dual deep Q-network algorithm, wherein the carbon-aware scheduling model generates a second scheduling scheme based on the current status of each node; and deploying the second scheduling scheme to the physical system of the computing center. This invention integrates the spatial coupling characteristics of carbon footprint and computing tasks, effectively improving resource utilization while ensuring the reliability of the task scheduling scheme.

The application relates to the technical field of industrial automation and intelligent manufacturing, and discloses a kind of light mixed optimization systems driven by edge computing, including edge computing node, cloud computing center and data interface;The edge computing node is used to execute real-time data acquisition, preliminary scheduling scheme generation and event response operation, to realize the rapid processing of production site dynamic event;The cloud computing center is used to execute task priority prediction and global optimization solving operation, to solve the complex global problem in production planning scheduling, and the data interface is used to realize the data interaction of edge computing node, cloud computing center and external production management system. Through the mixed architecture of edge computing node and cloud computing center cooperation, combined with the light heuristic algorithm, the preliminary scheduling scheme is quickly generated, the cloud global optimization engine solves the complex problem, the response speed of production planning scheduling system is significantly improved, and the real-time demand for dynamic adjustment in production process is effectively met.

This invention provides a method and apparatus for measuring computing power on an intelligent computing cloud platform based on GPU computing power consumption and inference token values. It relates to the fields of intelligent computing centers, smart computing centers, computing infrastructure, and smart computing cloud technologies, and includes the following steps: Step S1, collecting task data, GPU runtime data, and model and engine parameters during inference on the intelligent computing cloud platform; Step S2, extracting first feature information, second feature information, and third feature information from the task data, GPU runtime data, and model and engine parameters; Step S3, performing token fitting computing power calculation based on the first feature information, GPU model, and third feature information to obtain a first computing power consumption value; Step S4, performing GPU computing power consumption benchmark calculation based on the first, second, and third feature information to obtain a second computing power consumption value; Step S5, determining a reliable computing power consumption value based on the first and second computing power consumption values. This invention can significantly improve the accuracy of computing power measurement on intelligent computing center cloud platforms.

This application discloses a fluorinated coolant modified with thermally conductive material, its preparation method, and its application, belonging to the field of thermal management and heat dissipation. The fluorinated coolant modified with thermally conductive material comprises a fluorinated coolant and a sheet-like thermally conductive material; the sheet-like thermally conductive material is selected from at least one of sheet-like alumina, sheet-like boron nitride, sheet-like aluminum nitride, graphene sheets, and flake graphite. This invention solves the problems of limited heat dissipation efficiency and poor compatibility of fluorinated coolants, possessing high dielectric insulation, excellent thermal conductivity, good stability, and long service life, and can be widely used in high-heat-generating scenarios such as artificial intelligence, new energy storage, computing centers, and electronic power devices.

A classification method for adjudication models in adversarial simulation training based on equipment characteristics includes the following steps: Step 1: The attacking force's command post sends attack command data to the attack equipment model; Step 2: The adjudication calculation center selects the necessary data from the attack command data sent by the attacking force's command post to the attack equipment model and the data of the target's own equipment; Step 3: Receive the equipment type data of the target being adjudicated and distinguish whether it is a cooperative adjudication; if it is a cooperative adjudication, proceed to Step 4; if it is a non-cooperative adjudication, proceed to Step 5; Step 4: In cooperative adjudication, receive the support characteristic information and maintenance characteristic information of the attacking and attacked forces and distinguish between information support adjudication and maintenance support adjudication; Step 5: In non-cooperative adjudication, receive the perception characteristics, soft damage characteristics, and hard damage characteristics of both parties and distinguish between perception adjudication, soft damage adjudication, and hard damage adjudication; Step 6: According to the adjudication type, distinguish whether to perform precise adjudication calculation on the target equipment or the target's own equipment.

This invention discloses a method and system for intelligent construction and dynamic optimization of supply chain data. It constructs an ontology based on OWL and defines a semantic space. It collects text and image data of business scope, performs layout detection and OCR to extract text from images, and encodes the text into visual embeddings. It then fuses TF-IDF vectors, semantic vectors, and visual embeddings to generate a feature matrix. A rule-based expression tree with AND / OR nodes is constructed to evaluate the feature matrix, outputting candidate companies and matching degrees. The expression tree is made differentiable and gated; within a sliding window, a loss function is constructed based on target number deviation and feedback from missed detections, and gradient descent is used to update keyword weights and thresholds. A relationship graph is constructed based on candidate companies to calculate PageRank centrality, and this is fused with matching degree and time decay to output a weighted ranking. Double buffering updates are used, and the keyword system is expanded with term co-occurrence networks, Louvain, and semantic drift detection to output a traceable evidence chain.

This invention relates to the field of power system energy storage technology, specifically to a method and device for site selection and capacity determination of computing-powered energy storage based on grid-connected green electricity direct connection. The method includes: acquiring 8760 hours of time-series operational data and line data from a computing center and new energy power plants, setting the connected grid as an infinite power source; using K-means clustering to obtain typical daily scenarios; constructing a parallel computing framework for three energy storage deployment scenarios, with the minimum life-cycle cost as the objective function, combined with constraints such as power balance; and solving the problem using particle swarm optimization and determining the optimal solution through economic comparison. The corresponding system includes modules for annual historical data acquisition, typical daily scenario clustering, parallel optimization framework, optimization problem solving, and solution acquisition, with each module collaboratively executing the above method. This invention fills a technological gap, improves computational efficiency, achieves optimal economic energy storage configuration, ensures power supply reliability, and facilitates the full absorption of green electricity.

The application discloses a kind of based on PBO fiber's heat-conducting layer, copper-clad plate and its preparation method and application, belong to high-performance electrical technical field.PBO fiber is used as base material, it is successively made by weaving, modification, impregnation, drying, at least meet the following conditions: ordinary PBO fiber or special-shaped PBO fiber, the cross section of ordinary PBO fiber is circular, the cross section of special-shaped PBO fiber is 3-10-blade multilobal, special-shaped degree>22%;Thermal conductivity: 20-50W / (m·K);Coefficient of thermal expansion:‑20-0ppm / ℃;Tensile modulus: 160GPa-280GPa.The application mainly aims at the problems, such as insufficient heat dissipation capacity of existing computing center PCB, limited temperature resistance and flame retardance, etc., by selecting PBO fiber (circular or multilobal special-shaped structure) with specific index system as base material, to further optimize and improve the thermal conductivity of the material, realize the synchronous upgrading of heat-conducting performance, mechanical strength, structural stability and safety performance, so as to meet the use requirements of super-high power density computing equipment.

The application belongs to the technical field of computing center building design, and specifically discloses a scalable modular computing center building design method, which comprises the following steps: determining the overall structure of the computing center building, introducing a building space adaptability coefficient, and evaluating the adaptability of the space to the computing equipment and the liquid cooling system; performing module division and assembly deployment based on a module expansion potential coefficient, optimizing building arrangement and infrastructure by using a network transmission loss coefficient; constructing a composite heat dissipation system, adopting a multi-stage waste heat recovery structure to improve energy utilization efficiency; deploying dynamic environment safety monitoring nodes, and realizing safety hazard detection and dynamic evaluation by combining a risk quantification algorithm; building a centralized control platform to adaptively adjust the operating state of each module; realizing rapid expansion through the assembled module, and evaluating the expansion scheme by using an expansion cost and cycle model. The application realizes the quantitative optimization of the computing center in multiple dimensions such as time, cost and efficiency, and is suitable for the construction and upgrading of various artificial intelligence computing centers.

This invention provides a method and apparatus for constructing a short-scale transient simulation model of a computing center, relating to the field of computing center modeling technology. The method includes: modeling the power grid side using a three-phase AC voltage source; modeling the uninterruptible power supply using a series-connected three-phase full-bridge rectifier circuit, a boost chopper circuit, and a three-phase bridge PWM inverter circuit, wherein the boost chopper circuit has energy storage elements for powering the computing center during grid-side faults; modeling the cooling module using a constant torque load; modeling the server using CPU power consumption, GPU power consumption, total server power consumption, and server variable impedance; and modeling the ZIP load using a ZIP model. This invention can more effectively describe the interaction between the computing center and the power supply system, providing a new modeling approach for the energy efficiency optimization and stable operation of computing centers.

This invention provides a method and apparatus for dynamically quantifying hybrid heterogeneous computing power loss in intelligent computing cloud platforms, relating to the fields of intelligent computing centers, smart computing centers, computing infrastructure, and smart computing cloud technologies. The method includes: Step S1, running standard test tasks on each homogeneous computing power cluster to obtain load parameters and the standard computing power of each chip type; Step S2, determining the chip adaptation loss coefficient for each chip type in the hybrid heterogeneous computing power cluster of the intelligent computing cloud platform; Step S3, obtaining the load parameters and real-time total computing power of the hybrid heterogeneous computing power cluster running real-time test tasks; Step S4, determining the load fluctuation coefficient based on the load parameters of the standard test tasks and real-time test tasks; Step S5, determining the dynamic computing power loss ratio of the hybrid heterogeneous computing power cluster based on the load fluctuation coefficient, the real-time total computing power, and the standard computing power and chip adaptation loss coefficient for each chip type. This invention can significantly improve the accuracy of quantifying hybrid heterogeneous computing power loss in intelligent computing center cloud platforms.

This application relates to the technical field of power system monitoring, and in particular to a distribution network monitoring system, method, device, and medium. The system includes a microprocessor module and a cloud computing center. The microprocessor module is used to analyze the operational status of target nodes based on power parameters. When the operational status analysis result indicates an operational anomaly, an operational anomaly warning is generated. The cloud computing center is used to acquire the power parameters and operational status analysis results transmitted by the microprocessor module in real time and store them in a distribution network monitoring database. Then, multi-dimensional data analysis is performed based on the distribution network monitoring database to determine the multi-dimensional analysis results. The microprocessor module is responsible for real-time data processing and simple operational status analysis, while the cloud computing center is responsible for data analysis of complex scenarios and deep data mining, which can meet the distribution network monitoring needs in complex scenarios and improve the response speed and real-time performance of the distribution network monitoring system.

The application provides a dynamic multi-objective evolutionary method, device and equipment in an aluminum electrolysis process and a storage medium, and relates to the technical field of aluminum electrolysis. The method takes multiple candidate operation parameter combinations of an aluminum electrolysis cell as a population, takes current efficiency and single-cell aluminum direct current power consumption per ton as optimization targets, and includes the following steps: adopting non-dominated sorting and crowded distance for regular evolution during an environment stable period; when a working condition change is detected, constructing an elite set and calculating center drift and direction vector, then adopting a confidence phase response strategy to adaptively adjust the response intensity of directional translation and random restart to generate a response population; at the same time, applying a perturbation to historical good operation parameters through a lightweight memory playback mechanism to generate immigrant individuals; fusing multiple source candidate solutions for environment selection, and online updating a confidence coefficient according to performance feedback to obtain a Pareto optimal solution set under a set condition. The method provided by the application has significant advantages in the balance between environment tracking stability, convergence and diversity.

This invention relates to the field of copper alloy strip preparation technology, and discloses a high-strength, high-temperature resistant copper alloy strip and its application in AI computing centers. The copper alloy strip comprises, by mass percentage: Ni 0.6%–3.8%, Co 0.9%–1.6%, Si 0.35%–0.85%, Zn 0.03%–0.45%, Sn 0.02%–0.18%, Re 0.001%–0.02%, with the balance being Cu and unavoidable impurities; preferably, it also includes B. The preparation method includes melting and casting according to the proportions, heating in a walking beam furnace, hot rolling, milling, rough rolling, solution treatment, cold rolling, aging treatment, finished product rolling, and finished product annealing. By combining the Ni, Co, and Si precipitation strengthening system with the microstructure regulation effects of Zn, Sn, Re, and B, coarse second-phase particles in the metallographic structure are discretely distributed, and the continuous network of second-phase aggregation regions at grain boundaries are reduced. This results in a copper alloy strip with high electrical conductivity, high tensile strength, high thermal conductivity, and good high-temperature stress retention capability, which is suitable for AI computing center connection, conductive, and heat dissipation components.

The application discloses a kind of integrated energy system intelligent decision-making methods of fusion data expansion and multi-agent, comprising: the physical simulation engine of energy system is constructed, expansion data is generated, and training data set is formed with actual operation data;In AI computing center, the set of energy agents is trained using training data set;In the training process, a multi-agent coordination mechanism based on cooperative game is constructed simultaneously, the coordination mechanism takes operation scheduling agent and fault diagnosis agent as game participants, so that both learn to make joint decision-making policy when target conflict negotiates;The trained energy agent and its embedded game coordination mechanism are deployed in the energy brain of energy system, and the energy agent downloaded by the energy brain is used to predict the load of energy system, operation scheduling and fault diagnosis, and the conflict between scheduling strategy and fault diagnosis is solved according to the preset game coordination rule during operation, and the optimal operation decision is output.

This invention belongs to the field of data element market allocation technology, and discloses a demand-driven dynamic valuation method and system for data assets. The system includes a user-end module and a dynamic valuation calculation center; a buyer sends a valuation request; a demand parsing module parses natural language demands into task vectors; a data-task matching module calculates the multi-dimensional matching degree between the dataset and the task vectors and filters candidate sets; a dynamic valuation engine automatically selects a valuation model based on the matching degree and accuracy to calculate the expected value; a value fusion module integrates quality, scarcity, and risk factors to obtain the final valuation; a price generation module generates dynamic prices based on market supply and demand; and a feedback update module updates the model online based on actual results. This invention achieves accurate dynamic matching of data value and buyer demand, and has advantages such as high automation, strong privacy, and traceability. It can be widely applied to data transactions and enterprise data assetization.

The application provides a land-air collaborative agricultural patrol system and method based on vision and path planning, which comprises a path planning module and a data analysis module running in a cloud computing center, a collaborative control module and a target identification module running on an edge computing node, and an image acquisition module running on a terminal device; the terminal device comprises a robot dog, a drone and a camera, which are used for collecting farmland images from the ground at close range and local monitoring, collecting farmland images from the air in a wide range and large-scale patrol, and continuously collecting farmland images from fixed points, and converting light signals of farmland and its surrounding environment images into first image information; the edge computing node is used for receiving and processing the first image information from the terminal device and data from the cloud computing center; and the cloud computing center is used for performing global scheduling and data analysis, solving the problems of low bird and beast invasion identification precision and limited coverage in agricultural monitoring, and improving the intelligent level and efficiency of agricultural monitoring.

The application belongs to the technical field of power system operation control, and relates to a three-phase distribution network voltage optimization control method based on vertical and horizontal flexible resource cooperation and large language model enhanced federal reinforcement learning. The method sets up a three-phase distribution network power flow model and defines a voltage optimization problem, sets a state space, an action space and an initial reward function of multi-agent reinforcement learning; each regional agent obtains a low-dimensional state vector by using a large language model and a data distillation algorithm, constructs a phase correlation matrix by using phase characteristics, and generates weighted features; each regional agent independently performs local reinforcement learning training, performs constraint solving based on the three-phase distribution network power flow model, generates a semantic abstract uploaded to a computing center, generates global strategy guidance information, and issues the global strategy guidance information to each agent; the trained agent is deployed to a distribution network control system, action instructions are generated according to real-time collected system states, and vertical and horizontal flexible resources are cooperatively controlled.

The application discloses a kind of water resource investigation data real-time assimilation methods based on Internet of Things and kilometer grid, comprising: pre-processing to multi-source monitoring data, form kilometer grid data;Quality control is carried out in edge computing node, and hydrological response unit is divided, and state variable is quickly predicted using lightweight machine learning agent model;With DHRU as unit, real-time data assimilation is carried out on edge side;Based on event discrimination, significant change information is uploaded to cloud computing center;Cloud gathers global data and carries out periodic deep assimilation and model parameter global calibration, generates high-precision kilometer grid water resource state product, and the optimized model and parameter are fed back to edge node;Finally, multi-level water resource investigation service product is generated.The application solves the problem that fixed model unit does not match dynamic hydrological process and traditional assimilation calculation efficiency is low, realizes high-precision, high-efficiency real-time sensing and prediction of water resource state.

The task allocation method, apparatus, storage medium, and computer equipment provided in this application, when there are tasks to be allocated, can first determine the priority of the tasks to be allocated, and statistically analyze the current working status of each working node in each computing center and the current queuing status of the task scheduling queues in each computing center. Then, based on the current working status of each working node in each computing center and the current queuing status of the task scheduling queues in each computing center, the estimated completion time of allocating the tasks to be allocated to each computing center and the task balance of each computing center can be determined. In this way, when allocating the tasks to be allocated, the target computing center for processing the tasks to be allocated can be determined based on the estimated completion time, task balance, and priority of each computing center, and the tasks to be allocated can be allocated to the target computing center. This can improve data processing efficiency and optimize resource utilization.