18072937735

AI-powered pest & disease detection for roses (Rosa spp.). From garden cameras or mobile phone images of leaves, young shoots and flower buds, detects common rose enemies including black spot (black round/irregular spots with yellow halo), powdery mildew (white powdery layer on leaves/shoots), spider mites (tiny red/white dots on leaf back with webbing in severe cases) and aphids (green/black clustered tiny insects on shoots and buds). Outputs pest/disease type, severity grade and general control suggestions. Helps gardeners detect issues early and act in time. Scenarios: home gardens, rose specialty gardens, courtyard landscaping, cut-flower production bases. | 通过庭院摄像头或手机拍摄月季/玫瑰的叶片、嫩芽、花苞图像，利用AI视觉分析技术检测黑斑病（叶面黑色圆形或不规则斑点，周围黄晕）、白粉病（叶片、嫩芽表面白色粉状霉层）、红蜘蛛（叶片背面细小红色或白色点状螨虫，严重时结网）、蚜虫（嫩芽、花苞上绿色或黑色聚集的小虫）等常见病虫害，输出病虫害类型及严重程度，并提供防治建议。该技能有助于月季种植者早期发现问题，及时采取措施。应用场景：家庭花园、月季专类园、庭院绿化、切花生产基地。

Triggers when a user provides a cat climbing frame / cat tree area video URL or file for analysis; supports local video uploads or network URLs to call server-side APIs for multi-region activity recognition, analyzing dwell time on each layer, jump/transition counts, and generating a 2D activity heatmap to evaluate exercise volume and mental wellbeing (without diagnosing diseases). Application scenarios: cat climbing frame + camera, pet behavior monitoring, cat health management. Development reason: quantify cat activity distribution and enrichment usage. | 当用户提供猫爬架/猫树区域的视频URL或文件时，触发本技能进行活动热力图分析；支持通过上传本地视频或网络视频URL，调用服务端API进行多区域活动识别，分析各层停留时长、跳跃/转移次数，生成2D活动热力图，用于评估运动量与心理健康（不诊断疾病）。应用场景：猫爬架+摄像头、宠物行为监测、猫咪健康管理。

AI-powered plant root health analysis from transparent pots or smart seedling boxes. Uses fixed cameras to capture images/videos of plant roots, identifies root tip color (white=active, brown=aging, black=rotten), root hair density, branching structure, and detects root rot symptoms (softness, mucus, blackish-brown color). Outputs a root health score (0-100) and vitality grade (Healthy/Normal/Weak/Rotten). Helps early detection of root issues (overwatering rot, fertilizer burn, pathogen infection) and guides care adjustments. Scenarios: smart seedling boxes, transparent pots, plant factories, hydroponic systems. | 通过智能育苗箱或透明花盆的固定摄像头，拍摄植物根系图像或视频，利用AI视觉分析技术识别根尖颜色（白色为活性强、褐色为老化、黑色为腐烂）、根毛密度、根系分支情况以及是否存在根腐病（软烂、粘液、黑褐色）。综合评估根系健康评分（0-100分），输出根系活力等级（健康/一般/衰弱/腐烂）。该技能有助于及早发现根部问题（如浇水过多引起的烂根、肥害、病菌感染），指导用户调整养护措施。应用场景：智能育苗箱、透明花盆、植物工厂、水培系统。

Using a fixed indoor camera to continuously capture leaf images of houseplants from the same angle every day, AI vision techniques detect leaf color changes (green → yellow → brown), loss of glossiness (reduced surface reflectance), and formation of the abscission zone at the petiole base (angle change). | 通过室内绿植固定摄像头连续采集叶片图像（每天同一角度），利用AI视觉分析技术检测叶片颜色变化（从绿到黄再到褐）、光泽度下降（叶面反光减弱）、叶柄基部离层形成（角度变化）等老化进程，并基于历史图像序列的时间序列模型预测未来3-7天内叶片脱落的风险时段。系统每日生成老化报告，当预测即将落叶时推送提醒（如'富贵竹下位叶预计3天后脱落，可提前剪除以保持美观'）。

Real-time detection of gaze direction and facial pose to quantify states of focus, distraction, or mind-wandering. Suitable for scenarios such as classroom learning, office meetings, and driving attention monitoring. | 专注度分析技能，实时检测视线方向、面部姿态，量化专注/分心/走神状态，适用于课堂学习、办公会议、驾驶专注度监测等场景

Triggers when a user provides a pet grooming session video URL or file for analysis; supports local video uploads or network URLs to call server-side APIs for stress behavior recognition, detecting struggling, panting, tail tucking and other stress signals during grooming, outputting stress level grading to help groomers intervene promptly. Application scenarios: pet grooming shop cameras, veterinary clinics, pet care services. | 当用户提供宠物美容过程视频URL或文件时，触发本技能进行应激行为分析；支持通过上传本地视频或网络视频URL，调用服务端API进行识别，检测挣扎、张口喘气、尾巴夹紧等应激行为信号，输出应激等级，帮助美容师及时干预，减少宠物应激伤害，提升服务体验。应用场景：宠物美容店摄像头、宠物医院、宠物护理服务。

Identifies abnormal behaviors such as limb tremors, convulsions, stiffness, and gait abnormalities through video recognition, assisting in home risk monitoring for patients with chronic conditions. | 帕金森癫痫行为识别技能，通过视频识别肢体震颤、抽搐、僵硬、步态异常等异常行为，辅助慢性病患者居家风险监测

Detects morbid behavioral cues in poultry and pigs from continuous barn videos — such as difficulty standing, ruffled feathers/piloerection, isolation, drowsiness and appetite loss — and outputs behavior type with risk level up to 2-3 days ahead of visible clinical signs. | 识别站立困难、羽毛蓬松、离群、嗜睡等病态行为，比人工观察提前2-3天。

Using the camera built into a smart desk lamp or mounted above the desk, the system analyzes the child's sitting-posture video in real time, detecting spinal curvature angle (estimated Cobb angle) and head tilt angle. | 通过智能台灯内置摄像头或书桌上方摄像头，实时分析儿童学习时的坐姿视频，检测脊柱弯曲角度（Cobb角估算）以及头部倾斜度（侧倾角）。当驼背（Cobb角>10°）或歪头（头部侧倾角>15°）持续时间超过预设阈值（如5秒）时，触发语音提醒（如'请坐直'、'头抬正'），帮助儿童养成良好坐姿习惯，预防近视和脊柱侧弯。

Using fixed home cameras (bedroom and dining area), the system analyzes the multi-day behavior pattern of elderly people or solo-living individuals, detecting daily lying-in-bed duration (continuous lying > 20 hours per day) and a sharp drop in eating frequency / duration (e.g., daily eating-action count below 50% of personal baseline). | 通过家庭固定摄像头（卧室和餐厅区域），分析老年人或独居者连续多日的行为模式，检测卧床时长（连续卧床超过20小时/天）以及进食频次/时长骤减（如每日进食动作次数低于历史基线的50%）。当这些行为变化持续超过设定天数（如3天）时，输出行为变化报告，提醒家属或社区医生关注可能存在的抑郁倾向或其他健康问题。

Accurately identifies cat and dog breeds and supports distinguishing between different individuals in multi-pet households; an essential assistant for intelligent pet butlers. | 宠物品种个体识别技能，精准识别猫狗宠物品种，支持多宠家庭区分不同独立个体，智能宠物管家好帮手

Uses visual AI on frontal faces to recognize multi-dimensional emotions like happiness, sadness, depression, calmness, anger, surprise, and fear in real-time. Supports emotion intensity quantification and abnormal emotion marking, suitable for human-computer interaction and mental health monitoring. | 人体视觉情绪识别技能，基于正面人脸视觉AI实时识别快乐、悲伤、抑郁、平静、愤怒、惊讶、恐惧等多维度情绪状态，支持情绪强度量化与异常情绪标记，适配人机交互、心理健康监测场景

Identifies prone sleeping positions, head covering, and occlusion of the mouth/nose by bedding or clothing; provides real-time high-risk alerts to safeguard infant sleep safety. | 婴儿趴睡窒息预警技能，识别俯卧睡姿、蒙头、口鼻被被褥/衣物遮挡，高风险实时报警，守护婴儿睡眠安全

Based on computer vision, analyzes pet health indicators such as feeding frequency, drinking frequency, excretion status, mental state, vomiting behavior, and limping abnormalities through camera/feeder monitoring videos, promptly detects abnormal pet health conditions, and outputs health monitoring reports. | 宠物日常健康监测分析技能，基于计算机视觉通过摄像头/喂食器监控视频分析宠物的进食频次、饮水频次、排泄状态、精神状态、呕吐行为、跛行异常等健康指标，及时发现宠物异常健康状况，输出健康监测报告

Non-contact detection of heart rate, respiration, blood oxygen, and heart rate variability. No wearable devices are required; monitoring is achieved solely through camera footage. | 无感生命体征监测分析技能，非接触检测心率、呼吸、血氧、心率变异性，无需穿戴设备，通过摄像头画面即可监测

Performs AI analysis on input video clips/image content and generates a smooth, natural scene description. | 视觉摘要智述技能，对传入的视频片段/图片内容进行AI分析，生成一段通顺自然的场景描述内容

Automatically triggers soothing mechanisms (playing relaxing sounds, activating laser toys) when pet anxiety, howling, or prolonged loneliness is detected; a smart companion for pet care. | 宠物安抚触发技能，检测到宠物焦虑、嚎叫、长时间孤独时，自动触发安抚机制（播放舒缓音效、开启激光逗宠），智能宠物陪伴好帮手

Real-time detection of flames and smoke in video and image scenes. Suitable for fire early warning in industrial parks, forests, warehouses, and other locations. | 火情烟雾检测技能，实时检测视频/图片场景中的火焰、烟雾，适用于工业园区、森林、仓库等场所火情预警

Using a fixed camera at the home entrance or kindergarten gate, the system analyzes pre-school videos and detects crying facial expressions (frowning, open-mouth crying, tearing), physical clinging actions (grabbing parent's clothes, hugging parent's leg, pulling door frame), and resistance behaviors (stepping back, lying on the ground), then comprehensively evaluates the separation-anxiety level (mild / moderate / severe). | 通过家庭或幼儿园门口固定摄像头，分析儿童上学前的视频，检测哭闹面部表情（皱眉、张嘴哭泣、流泪）、肢体抓拽动作（抓住家长衣服、抱住家长腿、拉扯门框）以及抗拒行为（后退、躺地）等，综合评估分离焦虑等级（轻度/中度/重度）。该技能可辅助家长和教师了解儿童情绪状态，及时进行安抚或心理疏导。

AI-powered pet sleep quality analysis from a fixed bed/rest-area camera. Uses motion detection and pose recognition to distinguish sleeping vs. awake states, accumulates total sleep duration, counts roll-overs / position changes and startle-awakenings, and outputs a 0-100 sleep-quality score. Helps owners spot potential pain, anxiety, or disease early. Scenarios: home nighttime monitoring, senior pet health management, animal hospital wards, pet boarding centers. | 通过宠物窝或休息区固定摄像头，在夜间（或宠物主要睡眠时段）持续分析视频，利用运动检测和姿态识别技术判断宠物处于静止（睡眠）或活动（觉醒）状态，累计睡眠总时长，并统计翻身次数、惊醒频次，输出睡眠质量评分（0-100分），帮助主人了解宠物的睡眠健康，识别潜在的疼痛、焦虑或疾病。应用场景：宠物家庭夜间监护、老年宠物健康管理、宠物医院住院观察、寄养中心。

Monitors farrowing and poultry hatching events from continuous videos of farrowing pens or hatching areas — detecting key milestones such as water breaking,...

Using an in-cabin DMS camera, the system analyzes the driver's head pose in real time, computing head pitch (down/up) and yaw (left/right turn). | 通过车载DMS摄像头实时分析驾驶员头部姿态，计算头部俯仰角（低头/抬头）和偏航角（左/右转头）。当低头角度超过阈值（默认>30°）且持续时间超过2秒（可能为看手机、查看物品），或侧视角度超过阈值（默认>45°）持续时间超过2秒（可能为与乘客聊天、看窗外）时，输出分心驾驶预警，联动语音提醒，预防交通事故。

Identifies common abnormal pet behaviors such as scratching, biting, destructive chewing, jumping, digging, chasing, and separation anxiety, helping owners understand their pet's habits. | 宠物行为识别技能，识别抓挠、啃咬、拆家、跳跃、刨地、追逐、独处焦虑等常见宠物异常行为，帮助主人了解宠物行为习惯

Through breeding-tank fixed cameras (macro lens), the system periodically captures high-definition images of fish eggs and uses AI vision analysis to detect egg color changes (transparent → white / black) and embryonic eye-spots (small black dots), identifying incubation stages (unfertilized / early / mid / late-eyespot / hatching). | 通过繁殖缸固定摄像头（微距镜头），定期拍摄鱼卵的高清图像，利用 AI 视觉分析技术检测鱼卵颜色变化（透明 → 发白/发黑）以及胚胎眼睛点（黑色小点）的出现，识别鱼卵的孵化阶段（未受精/早期/中期/晚期/破壳）。系统定时（如每 6 小时）自动分析，输出孵化阶段及建议（如'已出现眼睛点，预计 24 小时内孵化，准备丰年虾'）。

Through fixed cameras on aquariums or underwater cameras capturing high-definition fish images, the system uses AI vision analysis to detect abnormal symptoms on the fish body surface: white-spot disease (white spots of about 0.5-1mm in diameter, salt-grain like), hyperemia (red blood streaks or patches on skin or fin bases), and fin-rot (tail-fin edges turning white, ragged or rotting). | 通过鱼缸固定摄像头或水下摄像头拍摄鱼类高清图像，利用 AI 视觉分析技术检测鱼体表面的异常症状：白点病（白色点状物，直径约 0.5-1mm，类似盐粒）、充血（皮肤或鳍条基部出现红色血丝或斑块）、烂尾（尾鳍边缘发白、残缺、腐烂）。该技能有助于早期发现观赏鱼常见疾病，指导用户采取隔离、升温、用药（用药请咨询专业水族兽医）等措施。

Conducts open-ended Q&A on image content based on computer vision and large language models, supporting any questions to receive natural language responses. | 大模型视觉问答（VQA）技能，基于计算机视觉和大语言模型对图片内容进行开放式问答，支持任意提问得到自然语言回答

Using a fixed camera installed above or beside the home medication area, the system monitors the elderly person's full medication process in real time. With pose estimation and object detection, it recognizes three key steps: (1) picking up — hand takes a tablet/capsule out of the pill box; (2) to-mouth — hand brings the medication to the lips; (3) swallowing — throat/jaw movement indicating a swallow. | 通过家庭药箱区域上方或侧方的固定摄像头，实时监测老年人取药、服药的全过程，利用姿态估计和目标检测技术识别以下三个关键步骤：①取药（手从药盒中取出药片/胶囊）、②送入口中（手部将药物送至嘴边）、③吞咽（喉部运动或颈部吞咽动作）。当系统检测到缺步骤（例如取药后未送入口中，或送入口中后无吞咽）时，记录为'未完成服药'，并向家属或护理人员推送提醒。

AI-powered flowering-date prediction for ornamental/cut-flower plants. From fixed greenhouse cameras or drones, captures images of flower-bud developmental stages, combines environmental sensor data — cumulative temperature (Growing Degree Days, GDD) and accumulated light (PAR or daylight hours) — and uses a pre-trained phenology model to predict the full-bloom date within the next 3-7 days. Helps growers precisely schedule pollination, harvesting and tourism activities. Scenarios: smart-agriculture greenhouses, cut-flower production bases, botanical gardens, flower tourism parks. | 通过智慧农业温室中的固定摄像头或无人机拍摄植物花蕾发育阶段的图像，并结合环境传感器提供的温度累积（生长度日，GDD）、光照累积（光合有效辐射或日照时长）等数据，利用预训练的物候模型预测未来3-7天内的开花日期（花朵完全开放）。该技能有助于温室种植者精准安排授粉、采收或观光活动。应用场景：智慧农业温室、切花生产基地、植物园、花卉观光园区。

Triggers when a user provides an image/video URL or file of dog toilet area or outdoor dog-walking path for analysis; supports local uploads or network URLs...

Using fixed cameras in malls, exhibition halls, scenic areas and other public places, the system analyzes facial expressions of multiple people in the scene in real time (with anonymized expression recognition only), aggregates the distribution of emotions (happy, calm, irritated, surprised, sad, fearful, etc.), and computes an overall group-emotion index (0-100; higher = more positive). | 通过商场、展览馆、景区等公共场所的固定摄像头，实时分析场景中多人的面部表情（使用匿名化表情识别），统计各类情绪（愉悦、平静、烦躁、惊讶、悲伤等）的分布比例，计算整体情绪指数（0-100，数值越高代表群体情绪越积极）。该技能可帮助运营方了解顾客满意度、优化服务布局，或用于公共安全预警（如烦躁情绪比例过高可能预示冲突风险）。

Identifies obesity, emaciation, external injuries, skin abnormalities, and abnormal mental states, helping pet owners detect health issues promptly. | 宠物体态健康分析技能，识别肥胖、消瘦、外伤、皮肤异常、精神状态异常，帮助宠物主人及时发现宠物健康问题

Using everyday cameras (laptop camera, smartphone front camera, smart mirror), the system records 30-60 seconds of facial video and uses remote photoplethysmography (rPPG) to extract subtle color variations from facial skin micro-circulation, from which it computes heart-rate-variability (HRV) metrics including SDNN (standard deviation of all normal sinus RR intervals) and RMSSD (root mean square of successive RR-interval. | 通过日常摄像头（如电脑摄像头、手机前置摄像头）拍摄面部视频（30-60秒），利用光电容积描记技术（远程光电容积描记术，rPPG）提取面部皮肤微循环的微弱色度变化，从中计算心率变异性（HRV）指标，包括SDNN（全部正常窦性心搏间期的标准差）、RMSSD（相邻心搏间期差值的均方根）等。该技能可用于压力评估、心血管健康监测及疲劳管理。

AI-powered pet vomiting and regurgitation detection from indoor fixed-camera video. Identifies rhythmic abdominal contractions, head-forward extension, and mouth opening actions, plus detects vomitus on the floor (food, hairball, bile). Records event time, frequency, and vomitus characteristics for early digestive issue discovery. Scenarios: daily home health monitoring, multi-pet households, senior pet care, animal hospital inpatient observation. | 通过室内固定摄像头分析宠物活动区域的连续视频，利用动作识别技术检测宠物的呕吐或反流行为（包括腹部节律性收缩、口部张合、头部前伸等典型动作），同时识别地面是否出现呕吐物（食物残渣、毛球、黄色胆汁等），记录发生时间、频次以及呕吐物特征。有助于主人及早发现宠物的消化系统问题，避免延误治疗。应用场景：宠物家庭日常健康监护、多宠家庭、老年宠物护理、宠物医院住院观察。

Triggers when a user provides a training-area video of a pet for analysis; supports local uploads or network URLs to call server-side APIs for command-execution recognition, detecting whether the pet's body posture matches the issued commands (Sit / Down / Stay), comparing posture timing against command timestamps, and judging execution success. When the command is not executed, the result can trigger an external voice repeat-prompt signal (not a medical / behavior-therapy advice). Application scenarios: smart dog-training devices, remote pet training, behavior correction. | 当用户提供训练区域视频时，触发本技能进行姿态-指令匹配分析；支持通过上传本地视频或网络视频URL，调用服务端API检测宠物身体姿态是否符合“坐/卧/等”指令标准，对比指令发出时间，判断是否执行成功；未执行时可由外部设备触发声控重复提示信号（不提供疾病诊断或行为治疗方案）。应用场景：智能训狗设备、宠物远程训练、行为矫正。

Using multispectral or high-resolution RGB cameras mounted on agricultural UAVs to capture orthophotos or mosaics of farmland, AI models compute vegetation indices (e.g., NDVI, NDRE) and generate a farm health-index heatmap, where colors distinguish crop vigor (red = poor, yellow = medium, green = healthy). | 通过农业无人机平台搭载的多光谱或高分辨率RGB相机，采集农田的正射影像或拼接图，利用AI模型计算植被指数（如归一化植被指数NDVI、归一化红边指数NDRE等），生成农田健康指数热力图，用颜色区分作物长势（红色代表健康差、黄色代表中等、绿色代表健康）。该技能可快速识别问题区域（如缺肥、缺水、病虫害、杂草），指导精准变量施肥或植保作业。

AI-powered plant leaf disease identification from high-resolution leaf images. Detects disease lesion features (color, shape, distribution, surface deposits) such as white powdery patches (powdery mildew), rust-colored spore pustules (rust), brown necrotic spots (leaf spot), and outputs the most likely disease type with confidence score. Helps users quickly diagnose plant diseases and take timely measures. Scenarios: plant factories, greenhouses, home gardening, farm inspection. | 通过拍摄植物叶片的高清图像，利用AI视觉分析技术识别叶片上的病斑特征（颜色、形状、分布），检测是否有白色粉状物（白粉病）、锈色孢子堆（锈病）、褐色坏死斑（叶斑病）等典型症状，输出最可能的病害类型及置信度。帮助用户快速诊断植物病害，采取防治措施。应用场景：植物工厂、温室大棚、家庭盆栽、园艺养护。

Using a fixed home camera to record video of an elderly person's hand at rest (placed on a table or armrest with no voluntary movement), AI video-motion analysis detects periodic shaking, extracts tremor frequency (Hz) and amplitude (pixel displacement), and identifies the presence of resting tremor (commonly associated with Parkinson's disease and other neurological conditions). | 通过家庭固定摄像头拍摄老年人手部（置于桌面或自然静止）的视频，利用AI视频分析技术检测手部在静止状态下的周期性抖动频率（Hz）和幅度（像素位移），识别是否存在静止性震颤（常见于帕金森病等神经系统疾病）。该技能可作为早期筛查工具，提示家属或护理人员关注老年人神经系统健康，及时就医。

Customizes safety zones, identifies babies crawling out or approaching dangerous areas such as bedsides/windowsills, and immediately alerts to protect baby safety. | 虚拟围栏越界预警技能，自定义安全区域，识别婴儿爬出、靠近床边/窗台危险区域立即报警，守护宝宝安全

Based on facial video, identifies abnormal rhythms such as premature beats, atrial fibrillation, tachycardia/bradycardia, assists in early detection of heart health risks. | 心律失常早期预警技能，基于面部视频识别早搏、房颤、心动过速/心动过缓等异常节律，辅助心脏健康风险早发现

Detects targets such as people, vehicles, non-motorized vehicles, and pets within target areas; supports batch image analysis, suitable for outdoor surveillance scenarios like courtyards, orchards, and farms. | 户外看护智能监测分析技能，检测目标区域内的人、车、非机动车、宠物等目标，支持批量图片分析，适用于庭院、果园、养殖场等户外区域看护场景

Detects fire and smoke in video scenes. Supports both video stream and image analysis. Suitable for fire early warning scenarios such as security surveillance, forest fire prevention, and industrial parks. | 烟火检测技能，对视频场景中火情和烟雾进行检测，支持视频流和图片检测，适用于安防监控、森林防火、工业园区等火灾预警场景

Detects the appearance of strangers near minors and actively issues safety reminder alerts to protect minor safety, suitable for homes, schools, childcare centers, and other scenarios. | 陌生人靠近预警技能，检测未成年人身边出现陌生人员，主动发出安全提醒预警，守护未成年人安全，适用于家庭、学校、托管场所等场景

Dentifies specific cats by comparing images or videos of their faces. It supports distinguishing between different individuals in multi-cat households, ensuring accurate recognition once the data is registered in the database. | 猫脸识别技能，通过猫脸图片/视频比对识别具体是哪只猫咪，支持多猫家庭区分不同个体，录入底库后可准确识别

AI-powered plant wilting quantification from full-plant images via smart pots or fixed cameras. Detects leaf-stem angle (leaf droop), stem straightness, and leaf turgidity to quantify wilting severity (0-100%). Optionally fuses soil-moisture sensor data to discriminate dehydration (underwatering) vs. waterlogging (root hypoxia), and auto-triggers watering or drainage prompts for precision irrigation. Scenarios: smart pots, home gardening, greenhouses, plant factories. | 通过智能花盆或固定摄像头拍摄植物整体图像，利用AI视觉分析技术检测叶片与茎秆的夹角（叶片下垂角度）、茎秆挺直程度以及叶片舒展度，量化萎蔫程度（0-100%）。可选结合土壤湿度传感器数据，综合判断萎蔫原因是缺水还是水涝（根部缺氧导致）。可自动触发灌溉或排水提示，帮助用户精准浇水。应用场景：智能花盆、家庭园艺、温室大棚、植物工厂。

Using fixed cameras (and optional microphones) at home or in kindergartens, AI multimodal analysis recognizes a child's facial expressions (eyebrow/eye shape, mouth-corner curvature), cry-sound features (pitch, frequency, duration), and body-motion amplitude (waving, stomping, curling up) in real time, and jointly identifies the child's typical emotional state: happy, sad, angry, fearful, etc. | 通过家庭或幼儿园内的固定摄像头（及可选麦克风），利用AI多模态分析技术实时分析儿童的面部表情（如眉眼形态、嘴角弧度）、哭声音频特征（音调、频率、持续时间）以及肢体动作幅度（挥手、跺脚、蜷缩等），综合识别出儿童当前的典型情绪状态：快乐、悲伤、愤怒、恐惧等。

Detects cats, dogs, and birds appearing in the target area; supports video stream and image detection, suitable for home pet monitoring scenarios. | 宠物检测技能，检测出目标区域内出现的猫、狗、鸟，支持视频流和图片检测，适用于家庭宠物监控场景

Utilizes vision and radar technology for contactless detection of falls. It triggers alarms within seconds and is suitable for home safety monitoring of elderly people living alone. | 老人跌倒检测技能，视觉/雷达无感识别摔倒倒地，秒级触发报警，适用于独居老人居家安全监测场景

Through fixed cameras (and optional microphones) at the pregnant woman's home or prenatal exam waiting room, the system analyzes facial expressions (sudden crying, frowning, anxiety), prolonged silent sitting (≥ 30 consecutive minutes without social interaction or activity), and tone of conversations with family members (rapid, impatient). | 通过孕妇家中或产检候诊室的固定摄像头（及可选麦克风），分析孕妇的面部表情（突然哭泣、皱眉、焦虑）、长时间静坐不语（连续超过30分钟无社交互动或活动）、以及与家人对话的语气（急促、不耐烦）。当检测到显著情绪波动时，自动触发安抚动作：通过智能音箱播放孕期舒缓音乐或正念引导音频，或向丈夫手机APP推送提醒（'妻子情绪波动，请打电话关心'）。

Identifies strangers appearing in surveillance areas through facial comparison; supports video stream and image detection, suitable for stranger warnings in residential communities, units, access control, and other scenarios. | 陌生人识别技能，通过人脸比对识别监控区域出现的陌生人员，支持视频流和图片检测，适用于小区、单位、门禁等场景的陌生人预警

Using a fixed classroom camera, the system analyzes students' facial expressions (focused, confused, happy, frustrated, bored, etc.), computes a class-level overall engagement score (0-100), and can identify low-engagement student positions (no identity stored — for real-time teacher reminders only). Real-time analysis provides engagement heatmaps and abnormal alerts. | 通过教室固定摄像头，分析学生面部表情（专注、困惑、开心、沮丧、无聊等），计算班级整体参与度评分（0-100分），并可识别出参与度较低的学生个体（不存储身份，仅用于实时提醒）。该技能可辅助教师调整教学节奏，关注学习困难学生。

Detects the presence of delivery packages within the target surveillance area; suitable for inventory checks and unattended alerts at community stations, residential entrances, and office building lobbies. | 包裹检测技能，检测目标监控区域内是否出现快递包裹，适用于社区驿站、小区门口、写字楼前台快递盘点和无人值守提醒

Detects climbing, playing with fire, touching power sources, and dangerous actions near windows, providing real-time alerts. It is suitable for child safety supervision in scenarios such as homes, kindergartens, and nurseries. | 儿童危险行为识别技能，识别攀爬、玩火、触电源、窗边危险动作并实时预警，适用于家庭、幼儿园、托儿所等场景儿童安全监护

AI-powered succulent special-state detection from HD images via plant cameras or smartphones. Identifies three critical conditions—black rot (stem base or leaves turning black and mushy), etiolation/melting (leaves becoming translucent and water-soaked), and stretching (elongated internodes, widened leaf spacing, loose rosette)—and outputs the anomaly type with severity grading, enabling early intervention such as beheading, water restriction, or increased light. Scenarios: home succulent care, succulent greenhouses, flower shops. | 通过多肉种植摄像头或手机拍摄的高清图像，利用AI视觉分析技术识别多肉植物的三种常见异常状态：黑腐病（茎基部或叶片变黑、腐烂）、化水（叶片透明化、水渍状）、徒长（茎节拉长、叶片间距增大、形态松散）。输出对应的异常状态类型及严重程度，帮助种植者及时采取处理措施（如砍头、控水、增加光照）。应用场景：多肉植物家庭养护、多肉大棚、花店。

Using fixed cameras in living rooms or child-activity areas (aimed at windows or balconies), AI pose estimation and object detection analyze the video in real time to recognize whether a child is climbing windows, leaning out, or gripping window-sill edges. | 通过家庭客厅或儿童活动区域的固定摄像头（需对准窗户或阳台），利用AI姿态估计和目标检测技术实时分析视频，识别儿童是否发生攀爬窗户、身体探出窗外、抓握窗台边缘等危险行为，当检测到危险动作时立即输出预警，联动手机APP推送警报或触发声光报警器。该技能可有效预防儿童坠楼事故。

Using agricultural cameras to capture high-resolution images of plant leaves, AI vision techniques detect leaf curling direction (up-curling or down-curling) and the distribution of leaf-margin scorch (old vs new leaves, tip vs margin). | 通过农业摄像头拍摄植物叶片的高清图像，利用AI视觉分析技术检测叶片卷曲方向（上卷或下卷）、焦边（叶缘干枯）的分布特征（老叶/新叶、叶尖/叶缘），并可结合土壤湿度传感器数据（可选），综合判断卷叶/焦边的主要原因（干旱胁迫、病害如白粉病/病毒病、药害、肥害等）。系统定期巡检，发现卷叶或焦边时自动分析原因，输出诊断及建议（如'叶片上卷、叶缘焦枯，土壤湿度偏低，可能干旱，建议灌溉'）。

Triggers when a user provides a full-body image/video of a pet (multi-angle preferred) for analysis; supports local uploads or network URLs to call server-side APIs for breed/body-type recognition and fur-density estimation (sparse / medium / dense), then outputs a recommended drying temperature curve (temperature in ℃ + time in minutes) for personalized care to reduce scald risk (not a medical recommendation). Application scenarios: pet drying boxes, pet grooming salons, smart pet care devices. | 当用户提供宠物全身图像/视频（多角度最佳）时，触发本技能进行品种识别与毛发密度估算（稀疏/中等/浓密），输出个性化烘干温度曲线（温度 ℃ + 时长 分钟）参数，实现个性化护理，减少烫伤风险（不提供医疗建议）。应用场景：宠物烘干箱、宠物美容店、智能宠物护理设备。

AI-powered transpiration rate estimation for indoor plants. From smart planters or fixed cameras, uses thermal infrared images of leaves (preferred) — or regular RGB images combined with ambient temperature/humidity — to estimate the leaf-to-air temperature difference, combines radiation/humidity parameters (sensor or model-inferred), and computes a relative transpiration rate index (0-100%). Transpiration rate correlates with root water-uptake activity, indirectly reflecting root health and water transport capacity. Helps determine whether the plant is water-stressed, has damaged roots, or is under environmental stress. Scenarios: smart planters, indoor green plant care, plant factories, research greenhouses. | 通过智能花盆或固定摄像头采集植物叶片的红外热成像图像（或普通RGB图像结合环境温湿度数据），利用AI模型估算叶片温度与空气温度的差值，结合辐射、湿度等参数（可由传感器提供或模型内估），计算植物蒸腾速率的相对值（0-100%）。蒸腾速率与根系吸水活力正相关，可间接反映根系健康及水分输送能力。该技能有助于判断植物是否缺水、根系受损或环境胁迫。应用场景：智能花盆、室内绿植养护、植物工厂、科研温室。

Using fixed cameras in kindergartens or early-education centers, the system analyzes multi-person video to detect social-interaction behaviors among children, including approach (distance < 1 m), conversation (face-to-face with mouth movement), and cooperative play (collaborative play, chasing, etc.). | 通过幼儿园或早教中心的固定摄像头，分析多人视频，检测儿童之间的社交互动行为，包括接近（距离<1米）、对话（面对面且嘴部运动）、共同游戏（合作玩耍、追逐等）。系统定期生成社交互动热力图，为教师提供参考。

Using a fixed camera in rehabilitation centers or homes, the system analyzes children's behavior videos with pose estimation and temporal action detection to recognize repetitive stereotyped behaviors, including spinning (body rotation ≥ 360°), hand flapping (non-functional repetitive arm movement), body rocking (rhythmic forward-backward or side-to-side trunk motion), etc. | 通过康复机构或家庭固定摄像头，分析儿童行为视频，利用姿态估计和时序动作检测技术识别重复性刻板动作，包括转圈（身体旋转360°以上）、摆手（手臂非功能性重复摆动）、摇晃（躯干前后或左右有节律摆动）等。该技能可辅助康复师和家长客观记录行为变化，评估干预效果。

Using a camera installed at the bathroom doorway (or inside the bathroom only detecting human silhouettes, without capturing private details), the system uses human detection and entry/exit tracking to identify when an elderly person enters or leaves the toilet and calculates the continuous occupancy time. | 通过在卫生间门口（或内部仅检测人体，不采集隐私细节）安装的摄像头，利用人体检测和进出跟踪技术，识别老年人进入和离开卫生间的时刻，计算连续占用时间。当占用时间超过预设安全阈值（默认30分钟）时，输出异常预警，通知家属或护理人员及时查看，预防老年人因跌倒、突发疾病（如中风、心梗）或体力不支导致的无法自主移动等意外。

Determines nine TCM constitution types including Yin deficiency, Yang deficiency, Qi deficiency, phlegm-dampness, and blood stasis through facial features and physical signs, and provides personalized health preservation and conditioning suggestions. | 中医体质识别分析技能，通过面部特征与体征判别阴虚、阳虚、气虚、痰湿、血瘀等九种中医体质类型，给出个性化养生调理建议

Through fixed cameras in rehabilitation centers or home rehab areas, the system analyzes video of patients during rehabilitation training to detect frustration / giving-up tendency behaviors: sighing (rapid chest-abdomen rise-fall with exhalation), training interruption (actively stopping before reaching preset reps or duration), head-down silence (head lowered, avoiding eye contact, long-term silence), sluggish or. | 通过康复中心或家庭康复区的固定摄像头，分析患者在进行康复训练时的视频，检测沮丧/放弃倾向行为：叹气（胸腹快速起伏伴呼气声）、中断训练（在未达到预设次数或时间前主动停止动作）、低头不语（头部低垂，避免眼神接触，长时间无言语）、动作迟缓或敷衍（关节活动范围明显小于前期），以及长时间无进展（连续多日同一训练项目的表现停滞或下降）。

Using a neonatal monitor or baby camera, the system captures high-resolution facial images of the newborn and uses AI visual analysis to detect sclera color (white in normal babies, yellow when jaundiced) and facial skin yellowness index (based on skin-color chromatic spaces, e.g., mapping the skin region to estimated clinical bilirubin levels). It outputs a jaundice-risk hint (low / medium / high risk). | 通过新生儿监护器或婴儿摄像头拍摄新生儿面部高清图像，利用AI视觉分析技术检测巩膜（眼白）的颜色（正常白色，黄疸时呈黄色）以及面部皮肤的黄染指数（基于肤色色度空间，如将皮肤区域映射到临床胆红素水平估算），输出黄疸风险提示（低风险/中风险/高风险）。该技能可辅助家长及医护人员早期发现新生儿高胆红素血症，及时就医干预。

Identifies abnormal behaviors such as getting out of bed at night, prolonged wandering, and remaining motionless for extended periods. It is suitable for night-time safety monitoring in nursing homes and for elderly people living alone. | 老人离床徘徊监测技能，识别夜间起床离床、长时间徘徊、长时间静止不动异常行为，适用于养老院、独居老人夜间安全监测

Using a fixed camera in the living room or bedroom of a person living alone, the system continuously analyzes night video (typically 22:00-06:00) to detect lights-off time (when light sources turn off) and early-morning activity (human movement or body motion between 0-6 AM). It builds a personal historical baseline (e.g., average lights-off time and early-morning activity frequency over the past 7-14 days). | 通过家庭客厅或卧室固定摄像头，夜间（通常指22:00-6:00）连续分析视频，检测熄灯时间（光源关闭的时刻）、凌晨活动（0-6点期间的人体移动或肢体动作）。建立个人历史基线（如过去7-14天的平均熄灯时间和凌晨活动频率），当当前熄灯时间比基线延迟超过2小时，或凌晨活动频次显著增加（如超出基线2个标准差）时，输出'作息规律异常'提醒。

Triggers when a user provides a pet grooming area video or image URL/file for analysis; supports local uploads or network URLs to call server-side APIs for coat condition and shed hair recognition, detecting matting area ratio and shed hair volume to output hairball risk level, helping prevent hairball syndrome. Application scenarios: smart grooming tools, long-haired pet care, pet health management. | 当用户提供梳毛器区域的视频/图像URL或文件时，触发本技能进行毛发表面状态分析；支持通过上传本地视频/图片或网络URL，调用服务端API进行识别，检测打结面积占比、梳下毛发量（堆积面积），输出毛球风险等级，帮助预防毛球症。应用场景：智能梳毛器、长毛宠物护理、宠物健康管理。

Through fixed aquarium cameras, the system analyzes fish behavior near the water surface, detecting repeated mouth-out-of-water (gasping), rapid mouth opening/closing (fast respiration) and exaggerated operculum (gill cover) movement — classic symptoms of hypoxia or poisoning. | 通过鱼缸固定摄像头，分析鱼类在水面附近的行为，检测鱼嘴反复探出水面（浮头）、张口快速开合（类似喘气）、鳃盖运动加剧等缺氧或中毒典型症状。当多条鱼同时出现上述行为且持续时间超过设定阈值（默认 60 秒）时，输出'氨氮中毒或缺氧风险预警'，提醒用户立即检测水质、换水或增氧。

Identifies acquaintances in videos or images through face photo comparison. Supports database enrollment, and the recognition results tell you who is at which location. Suitable for identity verification in homes and office areas. | 熟人识别分析技能，通过人脸图片比对识别视频/图片中的熟人，支持底库录入，识别结果告诉你哪个位置是谁，适用于家庭、办公区域身份核验

Triggers when a user provides a video of a pet inside an airline carrier/crate for analysis; supports local uploads or network URLs to call server-side APIs for respiratory rate monitoring, detecting chest/abdomen rise-fall cycles to calculate resting breathing frequency (breaths/min), and outputting an alert when the rate exceeds the safety threshold (>40 bpm), helping early detection of hypoxia, anxiety, or health abnormalities during pet air transport to reduce transport risks (without diagnosing diseases). Application scenarios: pet airline carriers, pet cargo transport, long-distance pet transport. | 当用户提供航空箱内宠物视频时，触发本技能进行呼吸频率监测分析；支持通过上传本地视频或网络视频URL，调用服务端API检测胸腹起伏运动，计算静息呼吸频率（次/分），超过安全阈值（>40次/分）时输出预警，帮助托运过程中早期发现缺氧、焦虑或健康异常，降低托运风险（不诊断疾病）。应用场景：宠物航空箱、宠物托运、宠物长途运输。

AI-powered pet treadmill exercise intensity analysis combined with optional heart-rate band data. Detects stride frequency, limb extension, and respiratory r...

Triggers when a user provides a pet toy area video URL or file for analysis; supports local video uploads or network URLs to call server-side APIs for toy in...

Triggers when a user provides a pet drying box area video URL or file for analysis; supports local video uploads or network URLs to call server-side APIs for pet heat stress signal detection, analyzing open-mouth panting intensity, tongue color (pink/cyanotic), and body movement frequency to identify early heat stress signals, outputting risk levels and supporting auto-cooling or stopping drying. Application scenarios: pet drying boxes, pet grooming stores, pet hospitals. Development reason: prevent heatstroke and improve safety. | 当用户提供宠物烘干箱区域的视频URL或文件时，触发本技能进行烘干箱内热应激预警分析；支持通过上传本地视频或网络视频URL，调用服务端API进行热应激信号识别，分析张口喘气强度、舌体颜色（粉红/紫绀）、身体移动频率，识别热应激早期信号，输出风险等级，支持自动降温或停止烘干。应用场景：宠物烘干箱、宠物美容店、宠物医院。

Generates condensed album highlights based on specified keywords or targets. It extracts specific target segments from long videos and compiles them into an exciting summary. | 时光相册自定义浓缩分析技能，基于指定关键词/目标生成时光相册浓缩集锦，从长视频中提取特定目标片段，整理生成精彩汇总

Triggers when a user provides a video of the pet food-bowl area for analysis; supports local uploads or network URLs to call server-side APIs for eating-speed detection, recording start/end timestamps of feeding, estimating eating speed (g/s and seconds-per-bowl), and when the speed falls below the safety threshold (e.g. < 30 sec/bowl) emitting an intervention signal (slow-feed baffle pop-up or voice prompt) to prevent choking and vomiting (without diagnosing diseases). Application scenarios: smart slow-feeder bowls, pet health management, canine care. | 当用户提供食盆区域视频时，触发本技能进行进食速度检测分析；支持通过上传本地视频或网络视频URL，调用服务端API记录进食开始/结束时间，计算进食速度（克/秒），当低于安全阈值（例如 < 30 秒/碗）时触发外部干预信号（智能慢食碗隔板弹出、语音提醒），预防噎食与呕吐（不诊断疾病）。应用场景：智能慢食碗、宠物健康管理、犬类护理。

Based on computer vision, automatically detects coughing, phlegm, and wheezing frequency, counts the frequency of episodes, used for early health anomaly alerts, helping to detect respiratory diseases in a timely manner. | 呼吸道症状智能识别技能，基于计算机视觉自动检测咳嗽、咳痰、喘息频率，统计发作频次，用于健康异常早期提醒，帮助及时发现呼吸道疾病

Through built-in cameras of smart feeders or fixed cameras on aquariums, the system captures fish feeding videos after feeding. Using AI object detection and motion analysis, it identifies the number of fish gathering for food, feeding intensity (fish swimming speed, feeding action frequency), and remaining feed amount, and computes a comprehensive feeding activity score (0-100). | 通过智能喂食器内置摄像头或鱼缸固定摄像头，在投喂后拍摄鱼群摄食视频，利用 AI 目标检测和运动分析技术，识别鱼群聚集抢食的数量、摄食强度（鱼只游动速度、摄食动作频率）以及剩余饲料量，综合计算摄食活跃度评分（0-100 分）。当活跃度评分低于阈值时，输出'食欲下降'提示，可能预示疾病、水质恶化或应激反应。

Using a fixed bedroom camera (infrared or low-light), the system analyzes chest/abdominal motion of the elderly at rest (sleeping or quietly lying down) and computes the respiratory rate (breaths per minute). | 通过卧室固定摄像头（红外或微光），在老年人静息（睡眠或静卧）状态下分析其胸腹部起伏运动，计算呼吸频率（次/分钟）。当呼吸频率超过正常上限（默认24次/分，老年人静息正常值一般为12-20次/分）时，输出'呼吸急促'预警，提示家属或护理人员关注老年人是否有心肺疾病、发热或呼吸系统感染等潜在问题。

Combines TCM facial feature recognition with physiological indicator information to provide early warnings of high-risk stroke conditions such as cerebral infarction and cerebral hemorrhage, and provides lifestyle intervention suggestions and medical guidance. | 脑卒中风险筛查技能，结合中医面部特征辨识结合生理指标信息，提前预警脑梗塞、脑出血等脑卒中高危状态，给出生活干预建议和就医指引

Using fixed cameras (infrared night vision) in nursing-home or home bedrooms, the system continuously monitors elderly bed-exit status and activity trajectory at night. | 通过养老院或居家卧室的固定摄像头（红外夜视），夜间连续监测老年人的离床状态和活动轨迹。输出异常预警，可联动护理人员手机或护士站大屏，防止老人走失、跌倒或发生意外。

Through fixed aquarium cameras, the system analyzes fish behavior videos and detects abnormal frictional actions between fish bodies and tank walls, substrate, or rockwork — 'flashing' (fish flipping sideways and brushing tank walls rapidly) and 'scraping' (fish belly/flank rubbing on substrate). The system counts abnormal contact frequency per minute. | 通过鱼缸固定摄像头，分析鱼类的行为视频，检测鱼体与缸壁、底砂、造景石等物体的异常摩擦动作（擦缸：鱼体侧身快速蹭过缸壁；蹭底：鱼体腹部或侧面贴底砂摩擦）。统计每分钟的异常接触频次，当频次超过阈值（默认 5 次/分钟）且持续时间超过 10 秒时，输出'外寄风险提示'，提醒用户检查是否有寄生虫（如小瓜虫、车轮虫、三代虫）感染或皮肤不适。

Using fixed cameras at home, kindergartens, or playgrounds, the system analyzes children's behavior and expressions in real time to identify happy moments: big laughter (mouth corners sharply raised, eyes squinted into crescents, teeth showing), jumping (both feet off the ground), clapping (rhythmic hand clapping), and joyful reactions to praise or rewards. | 通过家庭、幼儿园或游乐场的固定摄像头，实时分析儿童的行为和表情，识别开心瞬间：大笑（面部表情：嘴角大幅度上翘、眼睛眯成月牙、露出牙齿）、蹦跳（双脚离地跳跃）、拍手（双手有节奏地拍击）、以及接收到表扬或奖励时的愉悦反应。当检测到开心事件时，自动抓拍高清图片或短视频（前后2秒），生成'开心日记'推送至家长手机APP，并播放鼓励音效（如'你真棒！

Real-time monitoring of personnel on-duty status in specific areas based on computer vision and human pose estimation, automatically detects abnormal statuses such as leaving posts and absent from work, supports custom threshold settings, and triggers early warning immediately when abnormality is detected. | 人员离岗实时监测技能，基于计算机视觉与人体姿态估计算法，实时监测特定区域内人员的在岗状态，自动判断离岗、缺岗等异常状态，支持自定义判定阈值，异常发生立即触发预警，适用于工厂车间、监控室、服务窗口等岗位监管场景

Triggers when a user provides a pet hospital waiting area video URL or file for analysis; supports local video uploads or network URLs to call server-side APIs for anxiety-related behavior recognition, detecting open-mouth panting intensity, limb/torso trembling amplitude, ear-flattening degree and other stress signals, outputting a standardized anxiety level (1-5) to help medical staff identify high-stress pets and prioritize care or comfort (without diagnosing diseases or prescribing treatment). Application scenarios: pet hospital waiting areas, veterinary clinics, pet care institutions. Development reason: optimize visit workflow and reduce stress-related harm. | 当用户提供候诊区宠物视频的URL或文件时，触发本技能进行焦虑行为信号分析；支持通过上传本地视频或网络视频URL，调用服务端API检测张口喘气强度、四肢/躯干颤抖幅度、耳朵后贴程度等应激信号，综合输出标准化焦虑等级（1-5级），帮助医护人员识别高应激宠物并优先安排就诊或安抚（不诊断疾病、不提供治疗方案）。应用场景：宠物医院候诊区、动物诊所、宠物护理机构。

Triggers when a user provides an indoor camera video for analysis; supports local uploads or network URLs to call server-side APIs for pet pica-behavior recognition, detecting contact between the pet's mouth and non-food hazardous items (electric wires, plastic bags, socks, tissues, toy fragments, etc.); when the contact lasts ≥ 2 seconds, outputs a warning signal to help prevent intestinal obstruction, electric shock and other dangers (without diagnosing diseases). Application scenarios: indoor cameras, pet safety monitoring, smart-home security. | 当用户提供室内监控视频时，触发本技能进行异食行为识别；支持通过上传本地视频或网络视频URL，调用服务端API检测宠物嘴部与电线、塑料袋、袜子、纸巾、玩具碎片等非食物物品的接触动作；持续接触 ≥ 2 秒时输出预警信号，预防肠梗阻、触电等危险（不诊断疾病）。应用场景：室内摄像头、宠物安全监控、智能家居安防。

Using the built-in microphone of a baby monitor or smart camera to capture infant cry audio, AI acoustic analysis extracts cry features such as frequency, pitch, rhythm, and duration, and classifies the possible causes behind the cry (hunger, sleepiness, pain/discomfort, boredom/need for comfort, fear, etc.), outputting the most likely cause and its confidence. | 通过婴儿监护器或智能摄像头的内置麦克风采集婴儿哭声音频，利用AI声学分析技术提取哭声的频率、音调、节奏、持续时间等特征，分类识别婴儿哭声背后的可能原因（饥饿、困倦、疼痛/不适、无聊/需要安抚、恐惧等），输出最可能的原因类别及置信度。系统实时监测哭声，当检测到哭声时自动分析并在父母手机APP上推送结果（如'宝宝可能是饿了，建议喂奶'）。

AI-powered pet sneeze/cough detection from real-time camera (optional audio fusion). Analyzes head and thoracic-abdominal motion plus sound features to distinguish single occasional events (normal airway clearing) from continuous bursts (e.g. ≥3 sneezes/min, frequent dry/wet coughing) and records event time and frequency. Helps catch respiratory infection, allergy, or foreign-body irritation early. Scenarios: home health monitoring, animal hospital wards, pet boarding centers. | 通过宠物摄像头实时分析宠物头部和胸腹部的动作，结合可选的声音分析，识别宠物是否发生打喷嚏或咳嗽行为。区分单次偶发（可能是正常清理呼吸道）与连续发作（如频繁打喷嚏、干咳、湿咳等异常模式），并记录发生时间及频率。有助于早期发现宠物呼吸道感染、过敏或异物刺激。应用场景：宠物家庭日常健康监测、宠物医院住院观察、宠物寄养中心。

Using a fixed camera at the balcony door or home entrance, the system detects how many times the child enters/exits the home or balcony. With person-tracking and region-entry/exit logic, it records the timestamps of each 'leaving indoor (outdoor)' and 'returning indoor' event, and accumulates the daily total outdoor-activity duration. | 通过家庭阳台门或入户门口的固定摄像头，检测儿童进出家门或阳台的次数，利用人体跟踪和区域进出判定技术，记录每次离开室内（外出）和返回室内（归来）的时间点，累计每日户外活动总时长。当当日总时长低于预设推荐值（默认建议学龄儿童每天至少1小时户外活动）时，输出'户外活动不足'提醒，建议家长带孩子增加户外时间。

Triggers when a user provides a seedling tray image or video for analysis; uses AI object detection to identify emerged seedlings (cotyledons breaking through soil or fully expanded), counts germinated seeds, and compares with total sown seeds to estimate germination rate. Application scenarios: smart seedling incubators, greenhouse nursery trays, home planting pots, seed company germination tests. | 通过育苗盘上方的固定摄像头，播种后连续采集土壤表面图像，利用AI目标检测模型识别出土幼苗，计数已发芽的种子数量，并与播种总数对比估算发芽率。应用场景：智能育苗箱、温室育苗盘、家庭播种盆、种子公司发芽试验。

Identifies bird species in images/videos of target areas. Supports recognition of no less than 500 common bird species, supports customized model training, suitable for ecological observation, garden bird watching and other scenarios. | 鸟类识别工具，识别目标区域图片/视频中的鸟类种类，支持不低于500种常见鸟类识别，支持定制化模型训练，适用于生态观测、庭院观鸟等场景

Using fixed cameras in emergency shelters, the system analyzes video of disaster-affected crowds to detect typical acute stress reactions: stupor (prolonged motionless state with no response to external stimulation), tremor (involuntary shaking of body or limbs), unresponsiveness (no orientation or avoidance reaction to calls or sounds), and hypervigilance (frequent scanning of surroundings, startle reactions). | 通过应急避难所内的固定摄像头，分析受灾人群的行为视频，检测急性应激反应下的典型行为：木僵（长时间静止不动，对外界刺激无反应）、颤抖（身体或四肢不自主抖动）、无反应（对呼唤、声响等刺激没有定向或回避反应）以及过度警觉（频繁环顾四周、惊跳反应）。当检测到上述行为时，输出心理危机预警，提示现场心理救援团队及时介入，提供紧急心理支持，预防急性应激障碍或创伤后应激障碍。

Automatically detects electric motorcycles and e-bikes in restricted areas based on computer vision. It supports real-time detection for both video streams and images, counts the number of illegal parking or driving instances, and triggers violation alerts to assist with safety management in parks, communities, and organizations. | 电动车智能检测技能，基于计算机视觉自动检测禁行区域内的电动摩托车/电动车，支持视频流和图片实时检测，统计违规停放/行驶数量，触发违规预警，助力园区/社区/单位安全管理

Detects people, vehicles, non-motorized vehicles, pets, and parcels appearing in the target area. Supports video stream and image detection, suitable for general security surveillance scenarios. | 基础目标检测技能，检测出目标区域内出现的人、车、非机动车、宠物、包裹，支持视频流和图片检测，适用于通用安防监控场景

Detects whether anyone has fallen within a specified target area. Supports both image and short video analysis. Suitable for scenarios such as home care for elderly people living alone and safety monitoring in nursing homes. | 检测目标区域内是否有人跌倒，支持图片和短视频检测，适用于独居老人居家看护、养老院安全监测等场景

Using a fixed bedroom camera (infrared night vision + microphone), the system analyzes elderly nighttime sleep video and detects abnormal events such as sudden sitting-up (quick lying-to-sitting transition), screams (high-pitched short cries), and arm-thrashing (purposeless rapid arm movements), and records the occurrence time, frequency and duration of each event. | 通过卧室固定摄像头（红外夜视），分析老年人夜间睡眠视频，检测突然坐起（快速从躺卧变为坐立）、惊叫声音（高频短促叫声）以及挥舞手臂（无目的性的快速手臂动作）等行为，记录发生时间、频次及持续时间。该技能可帮助家属或护理人员了解老人夜间睡眠质量，识别可能的梦魇、快速眼动期睡眠行为障碍等异常现象，为医疗评估提供参考。

Using a fixed camera in the living room or kitchen, the system analyzes video of the water-cup placement area (e.g., coffee table, dining table), detects hand-to-cup contact actions (pickup, putdown), and counts daily cup-pickup events (an indirect proxy for water intake). | 通过客厅或厨房固定摄像头，分析水杯放置区域（如茶几、餐桌）的视频，检测手部与水杯的接触动作（拿起、放下），统计每日水杯拿起次数（间接反映饮水量）。当每日拿起次数低于预设阈值（如每天少于6次）时，输出'脱水风险'提醒，建议家属或护理人员督促老人增加饮水。

Combines frontal facial image capture with multimodal physiological feature analysis to provide early risk screening and alerts for chronic and acute conditions such as heart attack, stroke, hypertension, and hyperlipidemia. | 非接触式健康风险识别技能，通过正面人像采集结合多模态生理特征分析，提供心梗、脑梗、高血压、高血脂等慢病急症早期风险筛查预警

Using a fixed camera in the office (aimed at the workstation), the system analyzes office workers' sitting-posture video in real time, detecting continuous sitting duration, neck forward angle (head offset relative to shoulders), and back curvature (hunchback degree). | 通过办公区固定摄像头（对准工位）实时分析办公人员的坐姿视频，检测连续坐姿时间、颈部前倾角度（头部相对于肩部的偏移）、背部弯曲度（驼背程度）。当久坐时间超过预设阈值（默认1小时）且未起身活动时，输出'久坐提醒'；当颈部前倾角>20°或背部弯曲超过阈值时，输出'姿态异常提醒'。

Detects whether anyone has fallen within a target area. Supports video stream analysis and is suitable for real-time safety monitoring of elderly people living alone. | 跌倒检测视频版技能，检测目标区域内是否有人跌倒，支持视频流检测，适用于独居老人居家安全监测