输入 界面 Layer
传感器 signals, PLC events, SCADA and HMI data, DAQ readings, equipment alarms and engineering documents enter through controlled interfaces.
面向半导体设备、工业仪器和电子控制环境的 AI 辅助监督智能。
连接传感器、PLC、DAQ 系统和工程数据,无需替换现有安全或控制基础设施。
请求 工程 试点 · 不绕过 PLC、安全继电器或急停
5 项输入 → 可投入循环的产物
一种自定义智能处理架构,在任务执行前选择正确的处理层。
传感器 signals, PLC events, SCADA and HMI data, DAQ readings, equipment alarms and engineering documents enter through controlled interfaces.
结构化机器数据被解析和标准化。可比较专门输出来识别格式错误、不完整或冲突的信息,然后才考虑是否使用AI。
任务 are classified by type, complexity, format, risk and required accuracy, then routed to rules, parsers, 分离式系统, SLM, LLM or a human engineer.
重复且可预测的工作负载无需持续调用AI即可继续运行,从而减少令牌消耗、计算需求及对外部模型的依赖。
阈值 validation, state verification, equipment logic, alarm classification, trend rules and safety policies provide repeatable processing.
高级推理 is reserved for complex troubleshooting, multi-signal interpretation, engineering document analysis, root-cause assistance and technical summarisation.
验证 remains separate from the generating model and checks rules, 历史, safety policy, parser results and engineering limits. Inconsistent outputs are rejected, rerouted or flagged.
合格 engineers retain authority for safety-critical actions. AI cannot approve its own recommendation and direct critical machine control remains blocked.
NeuralOps reduces unnecessary AI computation by processing structured and predictable workloads through parsers, rules and 分离式系统.
内部 AINNA CI/semiconductor workload case study — different scale from the 中小企业 bank-statement benchmark (7.5M → 1.0M 令牌).
仅限内部案例研究。 图表 are illustrative of AINNA operational architecture segmentation. 实际 token use depends on workload mix, model selection, routing configuration and deployment environment. Not a guaranteed production outcome.
对确定性和基于解析器的工作负载实现接近零的幻觉暴露。AI生成的输出仍须经过独立验证和人工审批。
NeuralOps 通过减少不必要的计算来改善 ESG 表现,同时保留人类问责和组织控制。
从采集到合格人工审查的受治理信号路径。
读取传感器、PLC、控制器和 DAQ 信号。
标准化协议、格式和机器事件。
根据复杂度和风险选择规则、解析器、独立系统、SLM 或 LLM。
识别异常、漂移和异常运行模式。
校验 recommendations independently from the generating model.
关键操作需要合格人工审查。
通过配置的轮询、事件、流式或历史数据接口,从兼容的PLC、控制器和仪器采集传感器和机器数据。
监控PLC和控制器信号以确保正常运行并检测信号异常。
AI 辅助异常检测在故障发生前识别异常模式供工程师审查。
面向晶圆厂设备事件的AI辅助告警关联、故障模式分析与根因假设生成,并提供合规支持与工程师主导的确认流程。
问题出现时通过多种渠道即时通知技术人员。
自动化的每日机器健康报告,包含趋势和性能洞察。
状况-based monitoring and predictive-maintenance indicators can provide early 警告 of developing equipment degradation when sufficient validated historical and sensor data are available.
通过部署特定的接口集成兼容的数据库、API、仪表盘、控制器、历史数据库和 AI 服务。
支持在现有机器旁部署独立的监督监控模块,但须经过现场评估、接口验证、网络安全控制和计划的安装程序。
探索 how NeuralOps observes signals, detects abnormalities and routes engineering decisions without bypassing existing control 系统.
演示 only NeuralOps never bypasses PLC, safety relay, interlock or emergency stop. 严重 actions stay under qualified engineer approval, and approved corrective actions must remain within validated operating limits.
模拟 关卡 only · no 实时 controller link · E-stop / interlock retained by plant 系统
审核 the simulated recommendation before choosing a workflow outcome.
支持的集成类别和示例第三方硬件。
设备 shown represents supported integration categories and example third-party hardware. 可用性, compatibility and supported functions depend on the exact model, protocol, driver, firmware, interface and deployment scope. Third-party product and company names remain the property of their respective owners and do not imply endorsement, partnership or bundled supply.
压力 calibration standard or deadweight tester used to generate traceable reference pressure, subject to the specific installed model and calibration 状态.
仪器仪表高精度数字压力测量设备
仪器仪表工业 pressure transmitter with a 4–20 mA output and optional HART communication, depending on the selected model.
仪器仪表用于空气和气体压力校准的气动压力发生或比较泵。
仪器仪表旋片 / 隔膜真空源系统
仪器仪表精准 pressure reducing valve 系统
仪器仪表用于温度传感器的干体 / 微型浴槽校准器
仪器仪表类型 K/J/T/N/R/S thermocouple measurement device
仪器仪表白金 resistance temperature detector (Pt100)
仪器仪表科里奥利 / 磁 / 涡街流量传感器系统
仪器仪表电容式相对湿度探头模块
仪器仪表用于洁净室监测的光学颗粒计数器 (OPC)
仪器仪表静电放电监测系统
仪器仪表National 仪器 数据采集系统
DAQ & 切换NI SCXI传统信号调理和切换硬件,仅在具备兼容接口、驱动程序和部署要求的地方受支持。
DAQ & 切换NI 继电器控制器开关模块
DAQ & 切换基于 USB 的数据采集设备
DAQ & 切换模拟输入/输出信号模块
DAQ & 切换数字化 input/output signal module
DAQ & 切换信号 conditioning module for accurate readings
DAQ & 切换串行通信转换器模块
DAQ & 切换Modbus TCP/RTU/ASCII网关设备
DAQ & 切换用于网络集成的以太网 I/O 模块
DAQ & 切换Siemens / Allen-Bradley PLC 适配器模块
自动化用于定制自动化的微控制器控制盒
自动化Raspberry Pi / 工业 PC 网关系统
自动化机电继电器控制面板系统
自动化2/2 or 3/2 way solenoid valve control 系统
自动化用于流体系统的泵控制模块
自动化用于生产跟踪的传送带传感器模块
自动化射频识别读取器系统
自动化1D/2D条形码和QR码扫描站
自动化红外/激光光学传感器模块
自动化工业机器视觉相机系统
自动化步进/伺服电机控制模块
自动化机器 alarm event logging 系统
监控Cleanroom monitoring dashboard 系统
监控色温 and humidity monitoring dashboard
监控电源 current monitoring 系统
监控机器 vibration monitoring 系统
监控空气压缩机监控系统
监控水务 pump monitoring 系统
监控使用配置的压力、升压速率、流量或专用泄漏测试数据,提供真空系统状态监控和泄漏指示支持。
监控传感器-历史 monitoring and potential drift detection using calibration records, reference comparisons, redundancy checks or validated analytical rules.
监控生产 产量 and 拒绝 趋势 分析 Statistical monitoring of production yield, reject categories, equipment events and process trends.
监控状况-based monitoring and predictive-maintenance indicators that can provide early 警告 of developing equipment degradation when sufficient validated data are available.
监控No components match this search or filter. 清空 search or choose another signal category.
NeuralOps 作为监督和决策支持层运作。它不会取代确定性机器控制、经认证的安全功能、紧急停机系统或机器联锁。
能力 shown are deployment patterns, not universal guarantees. Final functionality depends on equipment compatibility, sensor coverage, protocol support, data quality, cybersecurity 设计, validation scope, operating procedures and customer approval.
AINNA NeuralOps provides monitoring, anomaly detection and decision support. 安全-critical commands and production changes require authorised human approval before execution. 常规 non-safety actions may be automatically executed only when they are explicitly pre-authorised, bounded by validated operating limits, protected by existing PLC, DCS, safety relay and interlock logic, and supported by a documented fallback procedure.
严重 alert and monitoring-loop performance is validated for each deployment.
设计用于在容量验证后从单台设备扩展到分布式系统。
Supports encryption, role-based access and 审计追踪s when configured for the deployment.
每个场景显示观测到的信号、处理路径、输出以及所需的人工决策点。
工程 problem压力 reference and device-under-test comparison
Signals observed压力 values, stability and calibration events
处理中 route确定性 parser + trend rules
输出 generated校准 verification report
人类 decision工程师接受校准结果
预期收益已改进 可追溯性 and earlier drift visibility
工程 problem不稳定的气动压力影响测试一致性
Signals observed压力, cycle timing and regulator state
处理中 route时间序列趋势分析
输出 generated稳定性警报和检查建议
人类 decision技术人员验证调节器和泵
预期收益更一致的监控和计划性干预
工程 problem手动 channel switching creates slow, fragmented tests
Signals observed渠道 state, measurement and sequence events
处理中 route解析器 + detached sequence workflow
输出 generated结构化测试记录和异常警报
人类 decision工程师审批序列变更
预期收益可重复的测量工作流程
工程 problem晶圆厂警报序列难以关联
Signals observed警报代码、腔室状态和事件时序
处理中 route规则 first; AI only for complex context
输出 generated优先事件摘要和可能原因
人类 decision设备工程师审查建议
预期收益更快的工程分诊
工程 problem停机时间 events lack consistent classification
Signals observed产线状态、停机原因和事件持续时间
处理中 route事件解析器 + 确定性分类
输出 generated停机时间 report, loss categorisation and OEE analysis support
人类 decision生产 engineer validates category
预期收益更清晰的运营报告
工程 problem环境 excursions require timely review
Signals observed湿度, temperature and particle count
处理中 route阈值 rules + trend validation
输出 generated警报, event log and escalation record
人类 decision合格 staff assess compliance response
预期收益改善环境可见性
工程 problem两次检查之间可能遗漏退化模式
Signals observed振动, current, temperature and runtime
处理中 route状况-based monitoring and predictive-maintenance indicators
输出 generated维护 recommendation
人类 decision维护 engineer schedules action
预期收益更早 evidence for maintenance planning when validated data are available
工程 problem测量漂移可能影响工艺解读
Signals observed传感器 value, reference trend and calibration 历史
处理中 route传感器-历史 monitoring and potential drift detection
输出 generated校准 verification recommendation
人类 decision仪表工程师批准调整
预期收益参考确认后测量置信度更高
工程 problem质量 patterns are fragmented across process data
Signals observed产量, reject reason and equipment event context
处理中 route统计监控 + 情境审查
输出 generated趋势 summary and investigation queue
人类 decision工艺 engineer determines corrective action
预期收益更结构化的工程审查
工艺情境、异常路由和人工审查的模拟控制室视图。
模拟工程数据 仅供演示现有工业控制器和安全系统仍具权威性。AINNA NeuralOps作为监督和决策支持层运行。它不替代确定性机器控制、经认证的安全功能、紧急停机系统或机器联锁。
All critical machine movement, safety interlock, calibration approval and production adjustment operations require human engineer approval. 常规 non-safety actions may be automatically executed only when they are explicitly pre-authorised, bounded by validated operating limits, protected by existing PLC, DCS, safety relay and interlock logic, and supported by a documented fallback procedure. 人工审批 does not replace engineering controls. 已批准 actions must still remain within validated operating limits and 通过 applicable controller, interlock and safety checks.
部署-specific engineering controls without absolute performance or security claims. 能力 shown are deployment patterns, not universal guarantees. 安全 depends on correct configuration, credential management, patching, network segmentation and operational monitoring.
支持本地、私有云、受控VPS和混合部署模式。
推理 traffic is designed to be protected through deployment-specific VPN, encryption, access control, network allowlisting and audit logging.
生产 inference endpoints can be deployed behind customer-controlled VPN and network allowlists. No inference endpoint is exposed publicly when the private deployment configuration is correctly implemented.
工程 roles, review permissions and escalation paths can be configured per deployment.
信号 events, recommendations, validation outcomes and approvals can be recorded.
建议可以与生成它们的模型或路径分开检查。
监控-loop latency and 系统 capacity are tested against each deployment requirement.
容量可从单一设备工作流程验证到分布式监控系统。
分享 the initial engineering context, then continue through AINNA's existing secure lead workflow.
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