Solutions

面向现场问题的落地方案Field-Problem Deployment Solutions

不把方案写成产品说明书。先按现场问题选择方向，再用一个工位、一台设备或一条产线做快速验证。 Solutions are organized by field problems, not product brochures. Start with one station, one machine, or one production line for fast validation.

小型化部署Compact deployment 快速验证Fast validation 少改造接入Light retrofit 对话式上手Conversational onboarding

How to Choose

先按现场问题选方向Choose by Field Problem First

视觉检测解决“看得见但人工难稳定判断”的问题；预测性维护解决“设备还没停，但风险已经在积累”的问题。 Visual monitoring handles visible issues that are hard to judge consistently; predictive maintenance handles risks that build up before equipment stops.

Visual Inspection

外观、漏装、异物、人员行为Appearance, Missing Parts, Foreign Objects, Behavior

Predictive Maintenance

振动、温度、压力、电流、健康趋势Vibration, Temperature, Pressure, Current, Health Trends

视觉检测试点包Visual Pilot Package

一处工位先验证，异常样本持续回流。Validate one station first, then keep samples flowing back.

工位快装Fast station setup 少样本Few-shot 边缘推理Edge inference 报警联动Alert linkage

Visual Inspection

视觉检测：先拦截高风险缺陷Visual Inspection: Intercept High-Risk Defects First

适合包装、装配、外观、漏装和安全行为等场景。先用小型化设备跑通一个点，再复制到更多工位。 Fit for packaging, assembly, appearance, missing-part, and safety-behavior scenarios. Start with one compact device and then scale to more stations.

Case 01

苏州 xx 化妆品灌装工厂Suzhou xx Cosmetics Filling Factory

痛点Pain: 液位、瓶口污染、漏装靠人工抽检不稳定。Fill level, contamination, and missing fills are unstable with manual sampling.
落地Deployment: 先覆盖灌装和包装工位，异常触发报警并回收样本。Cover filling and packing stations first, with alerts and sample feedback.

Case 02

常州 xx 汽车零部件质检Changzhou xx Auto Parts Inspection

痛点Pain: 外观缺陷、漏装、混料风险高，传统方案试点慢。Defects, missed parts, and mix-ups are high-risk, while traditional pilots are slow.
落地Deployment: 首检和复检先拦截高风险项，再扩展到更多型号。Use first-pass and re-check stations, then expand to more variants.

Predictive Maintenance

预测性维护：把停机风险提前暴露Predictive Maintenance: Surface Downtime Risk Earlier

适合空压机、机床、泵、风机等关键设备。先接入少量传感数据，输出健康评分、趋势异常和维护建议。 Fit for compressors, machine tools, pumps, fans, and other critical equipment. Start from limited sensor data, then output health scores, trend anomalies, and maintenance suggestions.

Case 03

苏州 xx 空压机预测性维护Suzhou xx Air Compressor Predictive Maintenance

痛点Pain: 温度、压力、振动先有趋势，等停机再处理成本高。Temperature, pressure, and vibration trends appear before shutdown, but late action is costly.
落地Deployment: 先选一台关键空压机，健康评分联动维护工单。Start with one critical compressor and link health scoring to work orders.

Case 04

苏州 xx 机床预测性维护Suzhou xx Machine Tool Predictive Maintenance

痛点Pain: 主轴、润滑和温升变化复杂，依赖老师傅经验。Spindle, lubrication, and temperature changes are complex and experience-dependent.
落地Deployment: 关键机床先做趋势预警，再同步给设备管理员和维修班组。Monitor key machine tools first, then sync risk alerts to managers and maintenance teams.

设备健康试点包Equipment Health Pilot

一台设备先跑趋势，再接工单闭环。Run trends on one machine first, then connect work orders.

传感接入Sensor input 健康评分Health score 趋势预警Trend alerts 维护建议Maintenance advice