# 冷链物流 IoT 看板 - 全程温湿度与轨迹追溯 URL: https://dc3.site/zh/demo/cold-chain

本看板演示 IoT DC3 在冷链物流场景的全温追溯：从冷库储位到车门交接，运输轨迹叠加温层信息，温控合规带与断链桑基识别风险段，全程温湿度与轨迹可追溯，守护品质与合规。

## 看板亮点 - 车队温层地图：运输轨迹叠加温度分层，途中温度异常即刻可见。 - 冷库温区分布与温控合规带：储位级温度合规状态。 - 断链桑基：识别风险段与断链环节，月台作业甘特支撑交接管理。 - 制冷剂回路、能效与车队排行、全程温度趋势。 ## 业务价值 - GSP/HACCP 全程温度合规，举证数据完整。 - 断链可定位到运输段与交接点，责任界定清晰。 - 制冷能效对比，冷库运行成本可优化。 ## 如何基于 IoT DC3 落地 车载温控终端（GPS + 温湿度）经 MQTT/HTTP 上报 IoT DC3，冷库温湿度与制冷设备经 Modbus 接入；轨迹与温度数据统一时序存储，断链规则自动生成告警与追溯记录。详见[官方文档](https://docs.dc3.site/)与 [GitHub 仓库](https://github.com/pnoker/iot-dc3)。 ## 常见问题 ### 看板数据是真实的吗？ 本页为演示数据（mock）；真实接入车载终端与冷库温控后，追溯链路与本页一致。 ### 温度数据能存多久？ 时序历史库支持按年长期存储与导出，满足 GSP 等法规对温度记录保存期限的要求。
--- # 智慧环保 IoT 看板 - 大气水质污染源监测 URL: https://dc3.site/zh/demo/eco-monitor

本看板演示 IoT DC3 在智慧环保场景的全域感知：大气网格化监测站点分布与在线率，污染玫瑰图识别特征污染源，六因子雷达评估空气质量，AQI 全程可查，从超标预警到溯源问责。

## 看板亮点 - 大气网格 GIS：全域污染一张图，热点网格即刻可见。 - AQI 趋势与污染玫瑰图：风向玫瑰辅助溯源分析。 - CEMS 排放排行、水质断面、站点在线率与超标告警日志。 ## 业务价值 - 超标事件分钟级预警，处置留痕。 - 污染玫瑰与网格热力结合，溯源方向有依据。 - 企业排放排行支撑精准监管。 ## 如何基于 IoT DC3 落地 国控/省控站点、微型站与 CEMS 设备多源异构接入 IoT DC3（协议驱动 + ODBC 数据抽取）；超标规则引擎按因子与时长分级告警，历史数据支撑污染过程复盘。详见[官方文档](https://docs.dc3.site/)与 [GitHub 仓库](https://github.com/pnoker/iot-dc3)。 ## 常见问题 ### 看板数据是真实的吗？ 本页为演示数据（mock）；真实项目接入监测站点后即可得到同款一张图。 ### 支持哪些监测因子？ 位号模型不限定因子：大气六参、VOCs、水质五参、噪声、烟气排放等均可作为位号接入与告警。
--- # 新能源充电 IoT 看板 - 充电桩负荷与光储充协同 URL: https://dc3.site/zh/demo/ev-charging

本看板演示 IoT DC3 在新能源充电场景的城域运营：充电桩 LBS 分布与实时负荷一目了然，订单流转与单枪利用率热力识别繁忙场站，光储充桑基呈现绿电消纳，支撑充电网络协同调度。

## 看板亮点 - 全城充电站地图与功率胶囊：负荷分布与繁忙场站实时可见。 - 时段订单热力与单枪利用率排行：识别高峰与闲置枪位。 - 储能 SOC、变压器仪表、光储充桑基与电能质量监测。 ## 业务价值 - 错峰引导与负荷调度，缓解电网压力。 - 变压器过载预警，场站安全运行。 - 光储充协同展示绿电消纳路径。 ## 如何基于 IoT DC3 落地 充电桩、储能与光伏设备经 MQTT、Modbus 等驱动接入 IoT DC3，运营订单可经 ODBC/HTTP 对接；负荷、SOC 与变压器容量统一建模，规则引擎对过载与电能质量异常实时告警。详见[官方文档](https://docs.dc3.site/)与 [GitHub 仓库](https://github.com/pnoker/iot-dc3)。 ## 常见问题 ### 看板数据是真实的吗？ 本页为演示数据（mock）；真实充电网络接入桩群与订单系统后即可运行。 ### 支持有序充电吗？ 支持落地：平台提供实时负荷数据底座与命令下发通道，有序充电策略可按站点与时段下发执行。
--- # 工业物联网可视化看板 URL: https://dc3.site/zh/demo/ --- # 能源微电网 IoT 看板 - 光储协同与功率平衡 URL: https://dc3.site/zh/demo/microgrid

本看板演示 IoT DC3 在分布式能源场景的协同监控：光伏出力、储能荷电状态（SOC）与负荷功率平衡一目了然，电气单线拓扑追踪每一度绿电的去向并核算碳足迹，支持峰谷套利与调度策略。

## 看板亮点 - 电气单线拓扑潮流：光伏、储能、负荷与电网之间的功率流向实时流动。 - 功率平衡与绿电率：每一度绿电的来源与去向清晰可查。 - 储能 SOC 仪表与充放电趋势，峰谷套利策略效果回放。 - 碳减排柱状、支路负载胶囊、发电单元排行与事件日志。 ## 业务价值 - 峰谷价差套利与需量控制，直接改善用电成本。 - 绿电消纳最大化，碳减排可量化、可申报。 - 支路与变压器负载可视化，预防过载。 ## 如何基于 IoT DC3 落地 光伏逆变器、储能 PCS、BMS 与关口表通过 Modbus TCP、MQTT 等驱动接入 IoT DC3，秒级采集功率与 SOC；结合电价时段配置规则引擎，可实现充放电窗口提醒与越限告警，Spring AI 闭环可进一步接入预测与调度决策。详见[官方文档](https://docs.dc3.site/)与 [GitHub 仓库](https://github.com/pnoker/iot-dc3)。 ## 常见问题 ### 看板数据是真实的吗？ 本页为演示数据（mock），光伏出力按昼夜规律模拟；真实项目中接入逆变器与表计即可。 ### 能做有序控制吗？ 平台具备命令下发通道（含边缘执行），控制策略可作为命令模板下发到储能或负荷设备。
--- # 油气管网 IoT 看板 - 管线压力与管存调峰 URL: https://dc3.site/zh/demo/oil-gas

本看板演示 IoT DC3 在长输管线场景的 SCADA 监控：千里管线纵断面呈现高程与压力分布，储罐液位与管存调峰实时核算，毫帕级压力可查，保障管输安全与调度。

## 看板亮点 - 管线纵断面与 GIS 地图：长输管线压力、流量分段呈现。 - 管存与调峰趋势：管存量变化与调峰能力可查。 - 压缩机仪表、储罐液位与罐壁参数、井口参数、站场排行、气质质量趋势。 ## 业务价值 - 管存优化与输配调度有数据依据。 - 压力异常与疑似泄漏分级预警。 - 输送气质与计量数据合规留痕。 ## 如何基于 IoT DC3 落地 站控与 SCADA 系统通过 Modbus、OPC-UA 等驱动接入 IoT DC3，关键测点秒级采集；阈值与变化速率双规则捕获压力异常，历史时序支撑管存核算与调度复盘。详见[官方文档](https://docs.dc3.site/)与 [GitHub 仓库](https://github.com/pnoker/iot-dc3)。 ## 常见问题 ### 看板数据是真实的吗？ 本页为演示数据（mock）；真实管线接入 SCADA 后，压力与管存趋势与本页形态一致。 ### 长输管线的数据量扛得住吗？ 边缘侧可做聚合与降采样，云端时序存储按位号分区，配合 TimescaleDB 支撑海量测点长期留存。
--- # 精准农业 IoT 看板 - 大棚墒情与微气候监测 URL: https://dc3.site/zh/demo/precision-agri

本看板演示 IoT DC3 在设施农业场景的精准感知：云端大棚分层呈现温湿度、光照与 CO₂ 微气候，土壤体积含水率下渗曲线与灌溉甘特图指导水肥决策，毫秒级响应环境变化。

## 看板亮点 - 大棚气候趋势：温度、湿度、CO₂ 与光照的长时间曲线。 - 温室剖面分层热力：垂直方向的温湿梯度不再靠单点均值猜测。 - 土壤墒情下渗剖面：根系层水分动态与灌溉下渗过程。 - 作物胁迫仪表、灌溉甘特与灌溉事件日志、户外气象雷达图。 ## 业务价值 - 按墒情灌溉，节水节肥且减少胁迫损失。 - 霜冻、高温、大风等气象风险提前预警。 - 种植过程数据留痕，支撑标准化与溯源。 ## 如何基于 IoT DC3 落地 低功耗农情传感器经 LoRa/NB-IoT 网关以 MQTT 上报 IoT DC3，气象站与水肥机经 Modbus 接入；位号模型统一管理空气、土壤、灌溉三类数据，规则引擎驱动胁迫告警与灌溉提醒。详见[官方文档](https://docs.dc3.site/)与 [GitHub 仓库](https://github.com/pnoker/iot-dc3)。 ## 常见问题 ### 看板数据是真实的吗？ 本页为演示数据（mock）；真实大棚接入传感器后，墒情与气候曲线与本页形态一致。 ### 大田（非大棚）场景适用吗？ 适用。位号模型不绑定场景，大田气象站、土壤墒情与阀门控制器同样以「设备-位号」方式接入。
--- # 智慧楼宇 IoT 看板 - 能耗暖通与空间占用 URL: https://dc3.site/zh/demo/smart-building

本看板演示 IoT DC3 在智慧楼宇场景的综合管理：楼层 3D 热力呈现空间占用与人员分布，分项能耗桑基拆解暖通、照明、动力流向，焓湿图辅助空调工况优化，构建会呼吸的健康建筑。

## 看板亮点 - 楼层能耗热力：哪一层、哪个区域在耗能一眼识别。 - 分项能耗桑基：照明、暖通、动力、插座的能耗流向拆解。 - 冷机 COP 与负荷曲线、焓湿图：冷站运行工况可视化。 - 空间占用时间线、水箱液位、合规条与事件日志。 ## 业务价值 - 建立能耗基线，量化节能改造收益。 - 冷机 COP 偏离及时暴露，运行策略持续优化。 - 占用与合规数据支撑空间运营与考核。 ## 如何基于 IoT DC3 落地 楼宇自控（BA）系统、分项计量电表与水表通过 Modbus、MQTT 等驱动接入 IoT DC3，也可经 ODBC 驱动直连既有能源管理数据库；分项能耗按位号建模，规则引擎对超标楼层与低 COP 冷机自动告警。详见[官方文档](https://docs.dc3.site/)与 [GitHub 仓库](https://github.com/pnoker/iot-dc3)。 ## 常见问题 ### 看板数据是真实的吗？ 本页为演示数据（mock）；真实楼宇接入 BA 与计量表后即可得到同款视图。 ### 既有 BA 系统的数据能接进来吗？ 可以。除 Modbus/MQTT 等协议接入外，IoT DC3 提供 ODBC 驱动，可直连 BA 或能源系统的数据库抽取数据。
--- # 智慧工厂 IoT 看板 - OEE 产线实时监控 URL: https://dc3.site/zh/demo/smart-factory

本看板演示 IoT DC3 在离散制造场景的实时监控能力：以设备综合效率（OEE）为核心，汇聚产线设备状态矩阵、六大损失瀑布与工艺流向拓扑，一屏掌握设备节拍、产量与良率。所有数据经 IoT DC3 多协议驱动统一采集与标准化，支持边云协同与告警联动。

## 看板亮点 - 设备状态矩阵：全产线设备运行、待机、故障、停机状态一屏总览，异常工位秒级定位。 - OEE 环形与六大损失瀑布：可用率、表现性、良率三因子逐步分解，效率流失点直接可见。 - 工艺流向拓扑：从原料到成品的工序连接与关键节拍实时呈现。 - 产量与工单完成度排行：班组、产线产出横向对比，完成进度条跟踪。 - 料罐液位、趋势曲线与告警事件日志联动，支撑异常根因追溯。 ## 业务价值 - OEE 从「事后统计」升级为「实时作战」，损失瀑布直接指向改善点。 - 设备告警与状态联动，压减非计划停机时间。 - 产量与良率数据自动留痕，为精益改善和质量追溯提供依据。 ## 如何基于 IoT DC3 落地 产线 PLC、传感器与 SCADA 数据通过 Modbus、OPC-UA、MQTT 等多协议驱动接入 IoT DC3，统一映射为「设备-位号」模型（数值 + 质量位 + 时间戳）；规则引擎按阈值与持续时间触发告警，时序数据库沉淀历史趋势。详见[官方文档](https://docs.dc3.site/)与 [GitHub 仓库](https://github.com/pnoker/iot-dc3)。 ## 常见问题 ### 看板数据是真实的吗？ 本页为演示数据（mock），用于展示可视化形态与指标口径；接入真实设备后，数据链路与刷新机制完全一致。 ### 我的设备协议不在驱动列表里怎么办？ IoT DC3 的驱动是可插拔的，除 28+ 内置驱动外，支持基于自定义 TCP/UDP 协议开发新驱动，文档的「驱动开发」章节有完整示例。
--- # 智慧矿山 IoT 看板 - 瓦斯通风与安全预警 URL: https://dc3.site/zh/demo/smart-mine

本看板演示 IoT DC3 在智慧矿山场景的安全感知：井上下一张图汇聚瓦斯浓度、通风网络、人员定位与综采设备工况，三道瓦斯阈值秒级预警并联动断电与撤人，保障井下安全。

## 看板亮点 - 瓦斯浓度趋势与三道阈值线：超限分级预警。 - 通风网络拓扑与主扇仪表：风量、风压在线监测。 - 巷道剖面、人员分布、设备开机率矩阵、产量排行、水仓液位与掘进速率。 ## 业务价值 - 瓦斯与通风异常秒级告警，安全风险前置处置。 - 人员位置与设备开机率透明化，管理与考核有据。 - 水文（水仓液位）与掘进进度统一监管。 ## 如何基于 IoT DC3 落地 矿井安全监控、人员定位与综采设备系统经 Modbus、MQTT 等多协议接入 IoT DC3；瓦斯浓度按三级阈值规则告警，通风与人员数据统一建模，支持与广播、断电等联动系统的对接。详见[官方文档](https://docs.dc3.site/)与 [GitHub 仓库](https://github.com/pnoker/iot-dc3)。 ## 常见问题 ### 看板数据是真实的吗？ 本页为演示数据（mock），瓦斯浓度含偶发突涌模拟；真实矿井接入安全监控系统后即可运行。 ### 井下网络不稳定怎么办？ 边缘网关部署在井下环网侧，断网期间本地缓存、恢复后自动续传，不丢关键数据。
--- # 智慧港口 IoT 看板 - 岸桥泊位与堆场调度 URL: https://dc3.site/zh/demo/smart-port

本看板演示 IoT DC3 在智慧港口场景的调度全景：岸桥起落、堆场流转、泊位靠离一屏尽览，堆场 3D 容量与泊位作业甘特呈现全港吞吐节拍，岸桥作业效率实时可查，辅助精益调度。

## 看板亮点 - 泊位-岸桥作业甘特：船舶靠离泊与桥吊作业进度一目了然。 - 潮汐窗口：抢潮水作业窗口可视化，辅助靠离泊决策。 - 堆场区块剖面、吞吐量趋势与 KPI、STS 效率排行、桥吊 moves 仪表、无人集卡调度视图。 ## 业务价值 - 泊位与岸桥效率可量化对比，瓶颈工序可定位。 - 潮汐窗口利用率提升，减少候潮时间。 - 堆场周转与设备效率统一考核。 ## 如何基于 IoT DC3 落地 码头操作系统（TOS）、岸桥 PLC、闸口与定位数据经多协议驱动及 ODBC 接入 IoT DC3；作业事件与设备状态统一时序建模，效率指标由规则引擎实时计算。详见[官方文档](https://docs.dc3.site/)与 [GitHub 仓库](https://github.com/pnoker/iot-dc3)。 ## 常见问题 ### 看板数据是真实的吗？ 本页为演示数据（mock）；真实码头接入 TOS 与设备数据后即可复现。 ### 既有 TOS 的数据能接吗？ 可以，ODBC 驱动可直连 TOS 数据库抽取作业数据，设备侧再经 PLC 协议驱动补充实时状态。
--- # 智慧交通 IoT 看板 - 路网拥堵与信号自适应 URL: https://dc3.site/zh/demo/smart-traffic

本看板演示 IoT DC3 在城市交通场景的实时脉动感知：路网拥堵热力与路口渠化呈现通行状态，绿波时距图与自适应信号优化路口配时，拥堵指数 TPI 全程可追溯。

## 看板亮点 - 路网拥堵热力图：全城干线 TPI 实时脉动。 - 路口渠化图与进口道排队长度：路口级运行状态。 - 绿波时距图：干线协调控制效果可视化评估。 - 饱和度排行、信号机在线率、溢流预警与事件时间线。 ## 业务价值 - 拥堵排行 + 热力双视角，治理有抓手。 - 绿波协调方案上线前后效果可量化对比。 - 信号机离线与故障秒级感知。 ## 如何基于 IoT DC3 落地 信号机、地磁、卡口与雷达数据通过自定义 TCP/UDP 驱动或 MQTT 接入 IoT DC3，统一为「路口-位号」模型；排队、饱和度等指标由规则引擎实时计算，异常经告警通道推送。详见[官方文档](https://docs.dc3.site/)与 [GitHub 仓库](https://github.com/pnoker/iot-dc3)。 ## 常见问题 ### 看板数据是真实的吗？ 本页为演示数据（mock），早晚双峰规律模拟；真实路网接入检测器后即可复现。 ### 能联动信号控制吗？ 平台提供命令下发通道，配时方案可作为命令下发到信号机；控制闭环由业务系统决策，IoT DC3 负责数据与执行通道。
--- # 智慧水务 IoT 看板 - 供水管网数字孪生 URL: https://dc3.site/zh/demo/water-network

本看板演示 IoT DC3 在城市供水场景的数字孪生能力：从源头水厂到末端龙头，管网 GIS 全景呈现，DMA 分区四象限漏损分析与流量桑基图定位管损热点，供水压力合格率实时可查。

## 看板亮点 - 管网 GIS 数字孪生：取水、制水、输配全链路一张图。 - DMA 分区四象限：夜间最小流量与压力交叉定位，快速锁定漏损分区。 - 供水流向桑基：从取水到售水的水量平衡与产销差一目了然。 - 泵房仪表、水库液位、水质热力与合格率、压力流量趋势全面覆盖。 ## 业务价值 - 以 DMA 夜流量法治理漏损，压降产销差率。 - 压力骤降实时预警，辅助爆管定位与应急处置。 - 水质指标持续合规留痕，满足监管报送要求。 ## 如何基于 IoT DC3 落地 水厂与泵站 PLC、RTU 及远传压力表通过 Modbus、MQTT 等驱动接入 IoT DC3；弱网环境下边缘网关本地缓存、恢复续传，规则引擎对压力/流量异常分级告警。详见[官方文档](https://docs.dc3.site/)与 [GitHub 仓库](https://github.com/pnoker/iot-dc3)。 ## 常见问题 ### 看板数据是真实的吗？ 本页为演示数据（mock）；真实项目中水源-水厂-管网的数据链路与本页展示一致。 ### 如何估算漏损？ 常见做法是 DMA 分区夜间最小流量分析：平台提供分区计量位号与历史数据，结合四象限规则即可持续评估漏损水平。
--- # IoT DC3 · 连接物理世界与 AI URL: https://dc3.site/zh/ --- # 愿景 · IoT DC3 URL: https://dc3.site/zh/vision [前往 IoT DC3 首页](/zh/) --- # Cold Chain IoT Dashboard - End-to-end Temperature Traceability URL: https://dc3.site/en/demo/cold-chain

This dashboard demonstrates IoT DC3 for cold-chain logistics: from cold-storage slots to trailer-door handover, route traces overlaid with temperature zones, compliance bands and a chain-break Sankey flagging risk segments — full temperature and route traceability.

## Dashboard highlights - Fleet temperature-layer map: routes overlaid with temperature zones, in-transit deviations visible instantly. - Cold-storage zone distribution and compliance bands at slot level. - Chain-break Sankey pinpointing risk segments, with dock-operation Gantt for handover. - Refrigerant loop, efficiency and fleet ranking, and end-to-end temperature trends. ## Business value - GSP/HACCP-compliant temperature records with complete evidence. - Chain breaks traceable to a transport leg or handover point. - Refrigeration efficiency benchmarking cuts cold-store operating cost. ## How to build this on IoT DC3 Vehicle terminals (GPS + temperature/humidity) report over MQTT/HTTP, while cold-store sensors and refrigeration equipment connect over Modbus. Route and temperature series share one time-series store, and chain-break rules automatically raise alarms and traceability records. See the [documentation](https://docs.dc3.site/en/) and the [GitHub repository](https://github.com/pnoker/iot-dc3). ## FAQ ### Is the data real? This page runs on mock data; with real vehicle terminals and cold-store sensors, the traceability chain looks the same. ### How long is temperature data kept? The time-series store supports multi-year retention and export, meeting GSP-style record-keeping requirements.
--- # Environmental Monitoring IoT Dashboard - Air and Water Pollution Grid URL: https://dc3.site/en/demo/eco-monitor

This dashboard demonstrates IoT DC3 for environmental monitoring: a gridded air-quality network with station availability, a pollution rose identifying characteristic sources, a six-factor radar assessing air quality, with AQI traceable from alert to source.

## Dashboard highlights - Air-quality grid GIS: region-wide pollution on one map with hot grids standing out. - AQI trends and a pollution rose for wind-direction source analysis. - CEMS emission ranking, water sections, station online rate and exceedance alarm log. ## Business value - Minute-level exceedance alerts with an auditable trail. - Pollution rose plus grid heatmaps give source tracing a direction. - Enterprise emission rankings enable targeted supervision. ## How to build this on IoT DC3 National/provincial stations, micro-sensors and CEMS devices onboard through protocol drivers or ODBC extraction. Exceedance rules alarm by factor and duration, and history supports episode replay. See the [documentation](https://docs.dc3.site/en/) and the [GitHub repository](https://github.com/pnoker/iot-dc3). ## FAQ ### Is the data real? This page runs on mock data; connect monitoring stations to get the same single-map view. ### Which factors are supported? The point model is factor-agnostic: air pollutants, VOCs, water parameters, noise and stack emissions all onboard as points with alarms.
--- # EV Charging IoT Dashboard - Charger Load and PV-Storage-Charging URL: https://dc3.site/en/demo/ev-charging

This dashboard demonstrates IoT DC3 for citywide EV charging: charger LBS distribution and live load at a glance, order flow and per-gun utilization heatmaps identifying busy stations, and a PV-storage-charging Sankey showing green-energy uptake.

## Dashboard highlights - Citywide station map with power capsules: load distribution and busy sites in real time. - Session heatmaps by time-of-day and per-gun utilization rankings. - Storage SOC, transformer gauges, PV-storage-charging Sankey and power-quality monitoring. ## Business value - Peak-shaving guidance and load dispatch relieve grid stress. - Transformer overload warnings keep sites safe. - The PV-storage-charging Sankey shows how green energy is consumed. ## How to build this on IoT DC3 Chargers, storage and PV connect via MQTT, Modbus and other drivers, with operations orders joined through ODBC/HTTP. Load, SOC and transformer capacity share one model, and rules alarm on overloads and power-quality anomalies in real time. See the [documentation](https://docs.dc3.site/en/) and the [GitHub repository](https://github.com/pnoker/iot-dc3). ## FAQ ### Is the data real? This page runs on mock data; connect your charger network and order system to go live. ### Does it support smart (ordered) charging? Yes — the platform provides the real-time load data foundation plus a command channel, so smart-charging strategies can be dispatched per station and time window.
--- # Industrial IoT Visualization Dashboards URL: https://dc3.site/en/demo/ --- # Microgrid IoT Dashboard - PV-Storage Power Balance URL: https://dc3.site/en/demo/microgrid

This dashboard demonstrates IoT DC3 for distributed energy: PV output, battery state of charge and load power balance on one screen, with a one-line topology that traces where every green electron goes and accounts for its carbon footprint, supporting peak-valley arbitrage and dispatch.

## Dashboard highlights - Single-line topology with live power flow among PV, storage, load and the grid. - Power balance and green-rate: where every green electron comes from and goes. - Storage SOC gauge and charge/discharge trends, with peak-valley arbitrage replay. - Carbon bars, branch-load capsules, generation ranking and event log. ## Business value - Peak-valley arbitrage and demand control directly cut energy cost. - Maximized green-energy uptake with quantifiable carbon reduction. - Branch and transformer loading made visible to prevent overloads. ## How to build this on IoT DC3 PV inverters, storage PCS, BMS and gateway meters connect via Modbus TCP, MQTT and other drivers at second-level sampling. Rule engines evaluate tariff windows and raise over-limit alarms, and the Spring AI loop can take predictions into dispatch decisions. See the [documentation](https://docs.dc3.site/en/) and the [GitHub repository](https://github.com/pnoker/iot-dc3). ## FAQ ### Is the data real? This page runs on mock data with PV output following a day/night profile; connect real inverters and meters to go live. ### Can it do control, not just monitoring? Yes — IoT DC3 has a command channel (including edge execution), so dispatch strategies can be issued to storage or load devices as command templates.
--- # Oil and Gas IoT Dashboard - Pipeline Pressure and Line Pack URL: https://dc3.site/en/demo/oil-gas

This dashboard demonstrates IoT DC3 for long-distance pipelines: a SCADA view with a longitudinal profile of elevation and pressure, real-time tank levels and line-pack accounting, megapascal-level pressure tracking for safe pipeline dispatch.

## Dashboard highlights - Pipeline profile and GIS map: pressure and flow presented segment by segment. - Linepack and peak-shaving trends. - Compressor gauges, tank levels and shell parameters, wellhead data, station ranking and gas-quality trends. ## Business value - Linepack optimization and dispatch decisions grounded in data. - Tiered early warning for pressure anomalies and suspected leaks. - Quality and metering records retained for compliance. ## How to build this on IoT DC3 Station control and SCADA systems connect via Modbus, OPC-UA and other drivers with second-level sampling on key points. Threshold plus rate-of-change rules catch pressure anomalies, and time-series history supports linepack accounting and dispatch reviews. See the [documentation](https://docs.dc3.site/en/) and the [GitHub repository](https://github.com/pnoker/iot-dc3). ## FAQ ### Is the data real? This page runs on mock data; connect your SCADA to get the same pressure and linepack trends. ### Can it handle long-distance pipeline data volumes? Edge aggregation and downsampling plus cloud time-series storage partitioned by point — backed by TimescaleDB — sustain large point counts over long horizons.
--- # Precision Agriculture IoT Dashboard - Greenhouse Soil and Climate URL: https://dc3.site/en/demo/precision-agri

This dashboard demonstrates IoT DC3 in controlled environment agriculture: layered greenhouse profiles of temperature, humidity, light and CO2, soil-moisture infiltration curves and an irrigation Gantt guiding water and nutrient decisions with millisecond responsiveness.

## Dashboard highlights - Greenhouse climate trends: long-horizon temperature, humidity, CO₂ and light curves. - Layered greenhouse profile heatmap: vertical gradients instead of single-point averages. - Soil moisture infiltration profile across the root zone. - Crop stress gauge, irrigation Gantt and event log, outdoor weather radar. ## Business value - Irrigation by actual soil moisture saves water and fertilizer while avoiding stress. - Early warnings for frost, heat and wind risks. - Growing-process records support standardization and traceability. ## How to build this on IoT DC3 Low-power field sensors report over LoRa/NB-IoT gateways via MQTT, while weather stations and fertigation controllers connect over Modbus. Air, soil and irrigation data share one device-point model, and the rule engine drives stress alarms and irrigation reminders. See the [documentation](https://docs.dc3.site/en/) and the [GitHub repository](https://github.com/pnoker/iot-dc3). ## FAQ ### Is the data real? This page runs on mock data; with real sensors connected, the moisture and climate curves take the same shape. ### Does it work for open fields, not just greenhouses? Yes — the point model is scenario-agnostic: field weather stations, soil probes and valve controllers onboard the same way.
--- # Smart Building IoT Dashboard - HVAC Energy and Occupancy URL: https://dc3.site/en/demo/smart-building

This dashboard demonstrates IoT DC3 for smart buildings: floor 3D heatmaps of occupancy, a sub-metered energy Sankey breaking down HVAC, lighting and power, and a psychrometric chart for AC optimization — toward a building that breathes.

## Dashboard highlights - Floor energy heatmap: spot the floor and zone consuming energy at a glance. - Sub-metered energy Sankey: lighting, HVAC, drives and outlets decomposed. - Chiller COP, load curves and a psychrometric chart for plant-room visibility. - Occupancy timelines, tank levels, compliance bars and event log. ## Business value - Energy baselines quantify the payback of retrofits. - Chiller COP deviations surface early so operating strategies keep improving. - Occupancy and compliance data back space operations and accountability. ## How to build this on IoT DC3 Building-automation systems, sub-metering power and water meters connect via Modbus, MQTT and other drivers, and existing energy-management databases can be reached through the ODBC driver. Sub-metered consumption is modeled as points, with rules alarming on offending floors and low-COP chillers. See the [documentation](https://docs.dc3.site/en/) and the [GitHub repository](https://github.com/pnoker/iot-dc3). ## FAQ ### Is the data real? This page runs on mock data; connect your BA system and meters to get the same views. ### Can data from an existing BAS be ingested? Yes — besides protocol drivers like Modbus/MQTT, the ODBC driver can pull directly from BAS or energy-management databases.
--- # Smart Factory IoT Dashboard - Real-time OEE Monitoring URL: https://dc3.site/en/demo/smart-factory

This dashboard demonstrates IoT DC3 in discrete manufacturing: a real-time OEE war room combining a machine status matrix, a six-big-loss waterfall and process flow topology to track throughput, yield and equipment pace. All data is collected and normalized by IoT DC3's multi-protocol drivers, with edge-to-cloud delivery and alarm linkage.

## Dashboard highlights - Equipment status matrix: running, idle, fault and down states for the whole line on one screen. - OEE ring and six-big-loss waterfall: availability, performance and quality decomposed step by step. - Process flow topology: live routing from raw material to finished goods with key cycle times. - Output and work-order completion rankings across shifts and lines. - Tank levels, trends and an alarm/event log linked for root-cause tracing. ## Business value - OEE moves from after-the-fact reporting to a real-time war room with losses pointing at improvement actions. - Alarm-to-status linkage cuts unplanned downtime. - Output and yield data is retained automatically for lean improvement and quality traceability. ## How to build this on IoT DC3 Line PLCs, sensors and SCADA connect through Modbus, OPC-UA, MQTT and other pluggable drivers, then map onto the unified device-point model (value + quality + timestamp). The rule engine raises alarms on thresholds and durations, while time-series storage keeps history for trend analysis. See the [documentation](https://docs.dc3.site/en/) and the [GitHub repository](https://github.com/pnoker/iot-dc3). ## FAQ ### Is the data real? This page runs on mock data to demonstrate the visualization and metric definitions; once real devices are connected, the data pipeline is identical. ### What if my protocol is not in the driver list? Drivers are pluggable: beyond the 28+ built-ins, IoT DC3 supports custom TCP/UDP driver development — see the driver-development guide in the docs.
--- # Smart Mine IoT Dashboard - Gas Ventilation and Safety Alerts URL: https://dc3.site/en/demo/smart-mine

This dashboard demonstrates IoT DC3 for smart mining: one map above and below ground consolidating methane, ventilation, personnel and shearer status, with three-tier methane thresholds triggering second-level alerts, power cut-off and evacuation.

## Dashboard highlights - Gas concentration trends against three threshold lines with tiered alarms. - Ventilation network topology and main-fan gauges. - Tunnel profiles, personnel distribution, equipment runtime matrix, output ranking, sump levels and tunneling rates. ## Business value - Gas and ventilation anomalies alarmed in seconds for early response. - Personnel location and equipment runtime made transparent for management. - Water (sump level) and tunneling progress supervised on the same screen. ## How to build this on IoT DC3 Mine safety monitoring, personnel positioning and longwall equipment connect through Modbus, MQTT and other drivers. Gas follows three-tier threshold rules, and ventilation and personnel data share one model, ready to interwork with broadcast and power-interlock systems. See the [documentation](https://docs.dc3.site/en/) and the [GitHub repository](https://github.com/pnoker/iot-dc3). ## FAQ ### Is the data real? This page runs on mock data including occasional gas surges; connect the mine safety system to go live. ### What about unstable underground networks? Edge gateways sit on the underground ring network, buffering during outages and resuming automatically — critical data is not lost.
--- # Smart Port IoT Dashboard - Quay Crane and Berth Scheduling URL: https://dc3.site/en/demo/smart-port

This dashboard demonstrates IoT DC3 for smart ports: quay cranes, yard flows and berth turns on one screen, with 3D yard capacity and a berth-operation Gantt showing the whole port cadence and live crane efficiency for lean dispatch.

## Dashboard highlights - Berth-crane operation Gantt: vessel berthing and quay-crane progress at a glance. - Tide windows visualized to support berthing decisions. - Yard block profiles, throughput trends and KPIs, STS efficiency ranking, crane moves gauge and autonomous-truck dispatch view. ## Business value - Berth and crane efficiency benchmarked, bottlenecks located. - Better tide-window utilization cuts waiting time. - Yard turnover and equipment efficiency on one scorecard. ## How to build this on IoT DC3 Terminal operating systems, crane PLCs, gate and positioning data onboard through protocol drivers and ODBC. Operations events and equipment states share one time-series model, with efficiency indicators computed live by the rule engine. See the [documentation](https://docs.dc3.site/en/) and the [GitHub repository](https://github.com/pnoker/iot-dc3). ## FAQ ### Is the data real? This page runs on mock data; connect your TOS and equipment feeds to reproduce it. ### Can existing TOS data be ingested? Yes — the ODBC driver pulls operational data from TOS databases, while PLC drivers add real-time equipment state.
--- # Smart Traffic IoT Dashboard - Congestion and Adaptive Signals URL: https://dc3.site/en/demo/smart-traffic

This dashboard demonstrates IoT DC3 across urban traffic: network congestion heatmaps and intersection channelization, green-wave time-distance diagrams and adaptive signals tuning intersection timing, with the Traffic Performance Index fully traceable.

## Dashboard highlights - Network congestion heatmap: citywide arterial TPI pulsing in real time. - Intersection channelization with approach queue lengths. - Green-wave time-space diagram to evaluate arterial coordination. - Saturation ranking, signal-controller online rate, spillback alerts and event timeline. ## Business value - Ranking plus heatmap gives congestion management a concrete grip. - Green-wave plans can be quantified before and after deployment. - Signal-controller outages are sensed within seconds. ## How to build this on IoT DC3 Signal controllers, magnetic detectors, checkpoints and radar feed IoT DC3 through custom TCP/UDP drivers or MQTT into an intersection-point model. Queues, saturation and other indicators are computed by the rule engine, with anomalies pushed through the alarm channel. See the [documentation](https://docs.dc3.site/en/) and the [GitHub repository](https://github.com/pnoker/iot-dc3). ## FAQ ### Is the data real? This page runs on mock data with morning/evening peaks simulated; connect real detectors to reproduce it. ### Can it control signals? IoT DC3 provides a command channel for issuing timing plans to controllers; the decision loop belongs to your traffic system while the platform supplies data and execution.
--- # Water Network IoT Dashboard - Distribution Digital Twin URL: https://dc3.site/en/demo/water-network

This dashboard demonstrates IoT DC3 across an urban water network: a digital twin from source works to end taps, with a GIS overview, DMA four-quadrant leakage analysis and a flow Sankey that localize pipe losses, while pressure compliance is tracked in real time.

## Dashboard highlights - GIS digital twin of the network: source, treatment and distribution on one map. - DMA quadrant view: night minimum flow vs. pressure to localize leaky district zones. - Flow Sankey: water balance from intake to sales, making non-revenue water visible. - Pump gauges, reservoir levels, quality heatmaps and pressure/flow trends. ## Business value - Leak reduction with DMA night-flow analysis, cutting non-revenue water. - Real-time pressure-drop alerts support burst localization and response. - Continuous water-quality records satisfy regulatory reporting. ## How to build this on IoT DC3 Plant and pump-station PLCs, RTUs and pressure transmitters connect via Modbus, MQTT and other drivers. Edge gateways buffer during link outages and resume on recovery, while the rule engine raises tiered alarms on pressure and flow anomalies. See the [documentation](https://docs.dc3.site/en/) and the [GitHub repository](https://github.com/pnoker/iot-dc3). ## FAQ ### Is the data real? This page runs on mock data; in production the source-to-tap pipeline looks exactly like what is shown here. ### How do I estimate leakage? District metering with night-minimum-flow analysis is the common approach — the platform provides zone-metered points and history, and the quadrant rules evaluate leakage continuously.
--- # IoT DC3 · Connect the Physical World to AI URL: https://dc3.site/en/ --- # Vision · IoT DC3 URL: https://dc3.site/en/vision [Go to the IoT DC3 homepage](/en/) ---