液冷与智算中心日报｜2026-08-22
追踪液冷技术、AI 智算中心、数据中心能效、学术论文、产品发布、政策标准、投融资与供应链动态的每日中文报告。
1. 今日一句话总结
24小时内，资本继续加码智算中心，但电力、审批与能效约束已前置，液冷和算电协同正转为项目准入项。
从公开信号看，资本并未因为约束而降温，资本开支仍向AI数据中心与液冷环节集中，说明头部厂商和基础设施资本仍在前置锁定园区、容量和交付窗口；但与此同时，扩建继续推进，但电力、选址审批与能源获取仍是主约束，意味着行业竞争的关键变量已不再只是“拿到多少 GPU”，而是“能否把 GPU 放进一个可并网、可散热、可控成本、可持续运行的系统”。技术侧技术侧继续围绕高带宽互连与服务器能效优化，论文侧论文侧继续指向算电协同、液冷优化与能效度量重构，共同指向同一个趋势：单点器件优化的边际价值在下降，网络、供电、储能、液冷和调度软件的系统级协同正在上升为真正的产能约束。对产业链而言，未来更稀缺的不是单一硬件，而是把算力、热管理和能源调度耦合起来的工程交付能力。
学术与产业速览
将论文、视频、产业动态和政策项压缩为可快速扫描的标签；每个标签只保留题目、摘要和来源入口。
学术
论文、研究趋势、学术视频与方法论线索。
Shift or curtail? How much data-center flexibility is worth depends on th…
Data-center growth risks overbuilding power grid infrastructure and stranding capital. Flexible data-center operation can defer inf…
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Shift or curtail? How much data-center flexibility is worth depends on the host power grid
- 发布时间
- 2026-08-20
- 作者
- Saroj Khanal、Geon Roh、Boyu Yao、Abraham Silverman、Dennice Gayme、Charalambos Konstantinou、Jip Kim、Yury Dvorkin
- 主题
- 算电协同
摘要
Data-center growth risks overbuilding power grid infrastructure and stranding capital. Flexible data-center operation can defer infrastructure investments, but its value depends on the flexibility mechanism and the host power grid characteristics. We classify data-center load as firm, flexible or interruptible, and embed them in capacity expansion applied to market-organized, fossil-heavy PJM and carbon-capped, centrally coordinated Korea. In PJM, the flexibility value is spatial: shifting workloads between zones reduces system cost by 6% in 2028 and 19% in 2038, avoiding 4.4 GW and 8.9 GW of gas and nuclear generation. In Korea, it is temporal: shifting load into midday solar hours makes 0.5 GW of additional solar worth building in 2028 and avoids 1.2 GW of gas and 0.3 GW of batteries in 2038. In both, realistic event-shape limits diminish the value of curtailment. The results show that flexibility procurement and its value are driven by grid characteristics and policy objectives.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用文献摘要中的模型、实验或案例分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Saroj Khanal, Geon Roh, Boyu Yao, 等. Shift or curtail? How much data-center flexibility is worth depends on the host power grid[J/OL]. (2026-08-20)[2026-08-22]. http://arxiv.org/abs/2608.19622v1.
LLM-Powered Predictive Decision-Making for Sustainable Data Center Operat…
The growing demand for AI
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LLM-Powered Predictive Decision-Making for Sustainable Data Center Operations
- 发布时间
- 2026-08-18
- 作者
- Hanzhao Wang、Jingxuan Wu、Yumeng Li、Yu Pan、Guanting Chen
- 主题
- AI 运维优化
摘要
The growing demand for AI
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，AI 运维、负载预测和设施调优正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用文献摘要中的模型、实验或案例分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向冷却效率、能源利用或运维策略的改进方向。意义：对日报读者而言，它可用于判断AI 工具是否能降低运维复杂度并提升可用性。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Hanzhao Wang, Jingxuan Wu, Yumeng Li, 等. LLM-Powered Predictive Decision-Making for Sustainable Data Center Operations[J/OL]. (2026-08-18)[2026-08-22]. https://arxiv.org/abs/2608.18503.
Predictive Failure Detection in Network Hardware Using Thermal Imaging an…
Unplanned network hardware malfunctions can interrupt services and result in expensive downtime in data centers. A deep learning-ba…
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Predictive Failure Detection in Network Hardware Using Thermal Imaging and Deep Learning with Sensor Fusion
- 发布时间
- 2026-08-05
- 作者
- Ashly Joseph
- 主题
- 热管理与液冷
摘要
Unplanned network hardware malfunctions can interrupt services and result in expensive downtime in data centers. A deep learning-based predictive maintenance strategy is presented that utilizes thermal imaging and power sensor data to detect early indicators of equipment breakdown in routers, switches, and servers. A simulated dataset was generated comprising annotated thermal pictures and power readings indicative of three operating states: Normal, Warning, and Critical. Three ImageNet-pretrained convolutional neural network (CNN) models ResNet-50, InceptionV3, and VGG16 were assessed together with a multi-modal CNN-LSTM fusion model that integrates visual and sensor time-series information. Experiments were performed with and without pre-processing procedures, including region-of-interest (ROI) extraction and normalization. In the absence of pre-processing, CNNs attained moderate accuracy (e.g., ResNet-50 at 52%), but ROI-based pre-processing significantly enhanced performance (ResNet-50 accuracy reaching 91%). The CNN-LSTM model attained the greatest accuracy of 94%, with precision and recall approaching 95%, illustrating the effectiveness of multi-modal fusion. The results validate that domain-specific pre-processing and sensor fusion substantially improve early failure prediction, providing a potential foundation for proactive maintenance of network hardware through non-intrusive monitoring.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，液冷、热管理和数据中心能效正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用框架构建和频域/系统级分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向冷却效率、能源利用或运维策略的改进方向。意义：对日报读者而言，它可用于判断液冷方案、热管理路线和高密度部署节奏。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Ashly Joseph. Predictive Failure Detection in Network Hardware Using Thermal Imaging and Deep Learning with Sensor Fusion[J/OL]. (2026-08-05)[2026-08-22]. http://arxiv.org/abs/2608.07582v1.
Steady-State Equivalent Circuit Model for Data Center Loads
Planners currently represent data centers as aggregate constant-PQ or ZIP loads in steady-state interconnection and contingency stu…
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Steady-State Equivalent Circuit Model for Data Center Loads
- 发布时间
- 2026-08-18
- 作者
- Muhammad Hamza Ali、Peng Sang、Hyeon Woo、Hyein Kang、Sungyun Choi、Amritanshu Pandey
- 主题
- 算电协同
摘要
Planners currently represent data centers as aggregate constant-PQ or ZIP loads in steady-state interconnection and contingency studies. These aggregate models are computationally convenient. However, they obscure the electrical relationship between computational workloads, server utilization, and grid-side demand. They ignore the internal power-electronic conversion stages of IT loads and assume homogeneous workload distributions across the compute clusters. This hides operating-point-dependent converter losses and efficiency variations. We propose a steady-state equivalent-circuit model (ECM) for data centers, which explicitly builds circuit models for IT loads, power supply units, cooling, and auxiliary systems. For power supply units, the equivalent circuit model explicitly represents internal power-electronic conversion stages. For IT loads, we develop a utilization-dependent server power model, and we combine it with loss-aware ECMs of power supply units. This approach captures the grid-side impact of heterogeneous workload distributions while preserving compatibility with conventional power-flow analysis. We evaluate this data center ECM in large-scale transmission power flows, using Monte Carlo simulations under heterogeneous and homogeneous cluster utilization. In comparison with the fixed-efficiency constant-PQ model, the ECM predicts that the most stressed line exceeds its thermal limit in about 30% of Monte Carlo samples. The results further show that homogeneous server utilization overstates line-loading variability by 17%-46% relative to heterogeneous server utilization, depending on the intra-cluster workload correlation.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用框架构建和频域/系统级分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Muhammad Hamza Ali, Peng Sang, Hyeon Woo, 等. Steady-State Equivalent Circuit Model for Data Center Loads[J/OL]. (2026-08-18)[2026-08-22]. http://arxiv.org/abs/2608.17925v1.
A Theory of Probabilistic Power Provisioning for Data Centers with Distri…
The growing power demands and variability of AI
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A Theory of Probabilistic Power Provisioning for Data Centers with Distributed Energy Storage
- 发布时间
- 2026-08-13
- 作者
- Can Emre Koksal、Richard A. Barry、Artun Sel
- 主题
- 热管理与液冷
摘要
The growing power demands and variability of AI
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，液冷、热管理和数据中心能效正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用文献摘要中的模型、实验或案例分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向冷却效率、能源利用或运维策略的改进方向。意义：对日报读者而言，它可用于判断液冷方案、热管理路线和高密度部署节奏。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Can Emre Koksal, Richard A. Barry, Artun Sel. A Theory of Probabilistic Power Provisioning for Data Centers with Distributed Energy Storage[J/OL]. (2026-08-13)[2026-08-22]. https://arxiv.org/abs/2608.12993.
Environmental and Economic Implications of Artificial Intelligence Data C…
In this study, we use electricity demand growth, cooling requirements, and backup system operation to evaluate the environmental an…
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Environmental and Economic Implications of Artificial Intelligence Data Centers in the United States
- 发布时间
- 2026-08-11
- 作者
- Johanna Bolaños-Zuñiga、Alberto J. Lamadrid
- 主题
- 算电协同
摘要
In this study, we use electricity demand growth, cooling requirements, and backup system operation to evaluate the environmental and economic implications of artificial intelligence data centers in the United States. Our results indicate that impacts are not determined solely by facility design, but by the broader electricity, water, and land-use systems in which these facilities operate. Emissions are primarily driven by electricity consumption and therefore depend on marginal generation mixes, transmission constraints, and the spatial and temporal distribution of demand. Analysis further shows that local effects include pressures on water resources, increased noise exposure, and land-use changes, with outcomes varying across regions and infrastructure conditions. The assessment of technological and operational measures shows that improvements in energy efficiency, cooling configurations, and operational strategies can reduce these impacts, although their effectiveness depends on system-level conditions. Evaluation of regulatory and market structures suggests that existing frameworks may not fully account for location- and time-specific externalities. These findings support the need for integrated policy approaches that align data center deployment and operation with electricity system characteristics, water availability, and land-use planning to improve overall environmental and economic performance.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用框架构建和频域/系统级分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向能效评价口径、运营指标和优化目标的系统化梳理。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Johanna Bolaños-Zuñiga, Alberto J. Lamadrid. Environmental and Economic Implications of Artificial Intelligence Data Centers in the United States[J/OL]. (2026-08-11)[2026-08-22]. http://arxiv.org/abs/2608.09882v1.
Beyond the Grid: Cost, Carbon, and Capital Requirements of On-Site Power …
Interconnection queues, not electricity prices, now govern where data centers can be built, and the standard levelized-cost compari…
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Beyond the Grid: Cost, Carbon, and Capital Requirements of On-Site Power Technologies for AI Data Centers
- 发布时间
- 2026-08-08
- 作者
- Eliseo Curcio
- 主题
- 算电协同
摘要
Interconnection queues, not electricity prices, now govern where data centers can be built, and the standard levelized-cost comparison answers a question no developer faces: it assumes a load profile, freezes the grid price while modeling the demand that moves it, and quotes busbar costs a facility cannot buy. This paper evaluates nine on-site supply technologies against a delivered grid whose price is endogenous to projected data-center demand, on a complete-site basis that retains standby charges, with measured GPU training load, delivered fuel prices, production-pathway carbon, and statutory 45V and 48E incentive mechanics. Nothing beats the wire: gas combined cycle produces at 47 USD/MWh but costs about 114 USD per megawatt-hour of complete site energy against a 92 USD grid; four-hour storage is physically capped near 18 percent of annual energy and, charged at the margin, dirtier than the grid; hydrogen from grid-priced power fails on cost and carbon together. An investment inversion converts these findings into capital terms: conversion-hardware learning buys nothing, because free hardware still exceeds the grid for every low-carbon arm, while global electrolyser deployment on sited sub-20 USD/MWh power brings PEM hydrogen power to about 2.2 times the grid at 300 billion USD and 1.9 times at 1 trillion USD (2.7 and 2.3 for the hydrogen engine), with a carbon reduction of roughly 85 percent (6.8-fold) against grid-power production. Grid parity is not purchasable at any budget. On-site supply is an access and depth product; most current investment targets the wrong term.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用仿真建模和情景分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Eliseo Curcio. Beyond the Grid: Cost, Carbon, and Capital Requirements of On-Site Power Technologies for AI Data Centers[J/OL]. (2026-08-08)[2026-08-22]. http://arxiv.org/abs/2608.08170v1.
AIDC Microgrid Vulnerability Assessment Under Computing-Power Coordinated…
The rapid growth of large language model (LLM) services is accelerating the expansion of AI
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AIDC Microgrid Vulnerability Assessment Under Computing-Power Coordinated Attacks
- 发布时间
- 2026-08-11
- 作者
- Ze Yu、Hongwei Zhen、Chao Shen、Mingyang Sun
- 主题
- 算电协同
摘要
The rapid growth of large language model (LLM) services is accelerating the expansion of AI
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用仿真建模和情景分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Ze Yu, Hongwei Zhen, Chao Shen, 等. AIDC Microgrid Vulnerability Assessment Under Computing-Power Coordinated Attacks[J/OL]. (2026-08-11)[2026-08-22]. https://arxiv.org/abs/2608.10645.
Data Center Power Crisis? Utility Veteran Explains What Developers Miss
Data Center Sales & Marketing Institute (DCSMI) · 检索词：AI datacenter power grid university lecture。适合作为技术背景或研究趋势补充。
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Can Urine Cool Data Centers? Science Says No
Broken-developer · 检索词：data center liquid cooling conference presentation。适合作为技术背景或研究趋势补充。
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Data Center Cooling - A thermal efficiency approach
Anixter · 检索词：data center thermal management seminar。适合作为技术背景或研究趋势补充。
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Data Center Innovations with SEGUENTE’s COLDWARE™
Data Center World · 检索词：data center liquid cooling conference presentation。适合作为技术背景或研究趋势补充。
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How Data Centers Manage Intense Heat: Cooling Systems Explained
Equinix · 检索词：data center thermal management seminar。适合作为技术背景或研究趋势补充。
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电力并网与能源约束
本期命中 16 条，热度分 51。可作为论文检索、技术路线和后续研究跟踪关键词。
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智算中心 CapEx/扩建
本期命中 14 条，热度分 45。可作为论文检索、技术路线和后续研究跟踪关键词。
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AI 芯片供给与交付
本期命中 3 条，热度分 9。可作为论文检索、技术路线和后续研究跟踪关键词。
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产业
产业新闻、技术产品、政策标准、投融资、项目和产业视频。
数据中心产业动态：Data Center Dynamics 发布相关报道，涉及 160MW（原文标题：Vapat Enters the data …
发布时间：2026-08-22；检索窗口内；可核验指标：160MW；细节以来源原文为准，本页不复述未核验扩展信息
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AI 算力基础设施动态：Data Center Dynamics 发布相关报道（原文标题：Nvidia backs data center pow…
发布时间：2026-08-22；检索窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：Edge data center for AI workloa…
发布时间：2026-08-21；检索窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：Rocket Lab has no plans to ente…
发布时间：2026-08-21；检索窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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电力与能源约束观察：Data Center Dynamics 发布相关报道（原文标题：Sponsored: The "power-first" e…
发布时间：2026-08-21；检索窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：The what, where, and why of hig…
发布时间：2026-08-21；检索窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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数据中心产业动态：Data Center Dynamics 发布相关报道（原文标题：STT GDC backs out of planned Vi…
发布时间：2026-08-21；检索窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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数据中心产业动态：Data Center Dynamics 发布相关报道，涉及 52MW（原文标题：Evolution DC secures lo…
发布时间：2026-08-21；检索窗口内；可核验指标：52MW；细节以来源原文为准，本页不复述未核验扩展信息
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电力与能源约束观察：The Register 发布相关报道（原文标题：Cloverleaf deal is latest example of N…
发布时间：2026-08-22；检索窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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电力与能源约束观察：Data Center Knowledge 发布相关报道（原文标题：Data Center Hardware Highligh…
发布时间：2026-08-18；近 7 天补充观察，非 24 小时窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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电力与能源约束观察：Data Center Knowledge 发布相关报道（原文标题：PJM Strategy Targets Data Cen…
发布时间：2026-08-21；检索窗口内；细节以来源原文为准，本页不复述未核验扩展信息
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财报与资本开支观察：Data Center Knowledge 发布相关报道，涉及 $3（原文标题：AI Infrastructure Pushe…
发布时间：2026-08-21；检索窗口内；可核验指标：$3；细节以来源原文为准，本页不复述未核验扩展信息
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2026 Update: Meta Is Building a Computer Three Times the Size of Central …
fragility MX · 检索词：high performance computing data center cooling workshop。用于补充产业、产品或工程部署观察。
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Aii Expert Panel | Challenges and Opportunities in the Data-Energy Triang…
Alliance for Innovation and Infrastructure · 检索词：AI infrastructure datacenter panel discussion。用于补充产业、产品或工程部署观察。
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Liquid Cooling Filtration System for Artificial Intelligence Data Centers…
种花家的航海兔 · 检索词：high performance computing data center cooling workshop。用于补充产业、产品或工程部署观察。
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产业热度指数 10/10
产业热度指数为 10/10：本期自动化检索记录到 20 条候选条目，指数按候选条目数量、来源可信度和栏目覆盖度保守计算。
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4. 最新视频观察
Data Center Power Crisis? Utility Veteran Explains What Developers Miss
专家讲座 · Data Center Sales & Marketing Institute (DCSMI) · 检索词：AI datacenter power grid university lecture
在 YouTube 打开2026 Update: Meta Is Building a Computer Three Times the Size of Central Park #meta #datacenter
技术研讨会 · fragility MX · 检索词：high performance computing data center cooling workshop
在 YouTube 打开Aii Expert Panel | Challenges and Opportunities in the Data-Energy Triangle
专家圆桌 · Alliance for Innovation and Infrastructure · 检索词：AI infrastructure datacenter panel discussion
在 YouTube 打开Can Urine Cool Data Centers? Science Says No
学术会议报告 · Broken-developer · 检索词：data center liquid cooling conference presentation
在 YouTube 打开Data Center Cooling - A thermal efficiency approach
专家讲座 · Anixter · 检索词：data center thermal management seminar
在 YouTube 打开Data Center Innovations with SEGUENTE’s COLDWARE™
学术会议报告 · Data Center World · 检索词：data center liquid cooling conference presentation
在 YouTube 打开How Data Centers Manage Intense Heat: Cooling Systems Explained
专家讲座 · Equinix · 检索词：data center thermal management seminar
在 YouTube 打开Liquid Cooling Filtration System for Artificial Intelligence Data Centers #coolingsolutions
技术研讨会 · 种花家的航海兔 · 检索词：high performance computing data center cooling workshop
在 YouTube 打开来源链接区
本次检索说明
- 当前自动化环境未配置 Tavily、Bing News 或 SerpAPI 检索密钥；脚本将使用公开 RSS/Atom、公共 arXiv 接口与固定监测源，不会编造产业新闻。
- 公开 RSS/Atom：ServeTheHome：未检索到符合条件的高相关条目。
- 公开 RSS/Atom：NVIDIA Blog：未检索到符合条件的高相关条目。
- 论文池：已从本地论文池读取 26 条候选；池更新时间 2026-08-22 08:06。
- 论文推荐：已启用 latest 模式，优先输出本期候选池中发布时间最新的论文。
- x.ai 论文解读：文本生成失败，已回退到规则化论文摘要；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 1 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 2 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 3 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 4 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 5 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 6 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 7 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- x.ai 论文配图：论文 8 生成失败，已使用内置主题图；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit. To continue making…
- AI 分析：x.ai 调用失败，已回退到规则化模板；原因：HTTP 403：{"code":"permission-denied","error":"Your team 472c8744-ad4f-4879-a588-fa7645e04979 has either used all available credits or reached its monthly spending limit…