smyx-sunjinhui

Supports uploading local MP4 videos or network video URLs to call the server-side API for facial diagnosis. It returns structured TCM facial diagnosis result...

Using a fixed camera in the home of a solitary-living elderly person or in a private nursing-home room, the system analyzes daily activity video and detects loneliness-related behaviors: prolonged solitude (no social interaction), static gazing (long-time fixation with no purposeful activity), sighing (rapid chest/abdomen rise-fall with exhale), and talking-to-self (mouth activity with no conversation partner). | 通过独居老人家中或养老院单人房的固定摄像头，分析老人日常行为视频，检测孤独相关行为：长时间独处（无社交互动）、静止发呆（长时间凝视一处无目的活动）、叹气（胸腹快速起伏伴呼气）、自言自语（口部活动但无对话对象）等。

Detects quality issues in camera footage such as black/white screens, color cast, stripes, noise, and blurriness. Suitable for security surveillance and camera self-check scenarios. | 图像质量检测分析工具，检测摄像头画面出现的全黑、全白、偏色、条纹、雪花、模糊等质量问题，适用于安防监控、摄像头自检等场景

AI-powered cage cleanliness detection via fixed cameras in boarding kennels/pet shops; analyzes floor images to detect feces/urine coverage area ratio, triggers cleaning alerts when exceeding preset threshold (e.g. 5%). Scenarios: pet boarding centers, pet shops, animal hospitals, breeding facilities. | 通过寄养中心或宠物店笼舍内的固定摄像头，定时分析地面图像，识别粪便、尿液等排泄物的覆盖面积占比，当超过预设阈值（如5%）时自动触发清洁提醒。该技能可帮助管理人员及时清理笼舍，维持环境卫生，预防疾病传播，并提升宠物福利。应用场景：宠物寄养中心、宠物店、动物医院住院部、宠物繁育基地。

Triggers when a user provides a pet ear/scratching/head-shaking video URL or file for analysis; uses smart camera to monitor head-shaking and ear-scratching actions, automatically captures HD ear-canal images, and applies AI vision analysis to identify ear-canal color (pink/red/dark red), presence of black granular discharge (ear mites), and degree of earwax accumulation. When redness, large discharge or suspected ear mites are detected, outputs abnormality alerts and recommends owner check-up or veterinary visit. Application: pet families, boarding centers, pet hospital pre-screening. Helps early detection of ear-canal diseases and prevents deterioration. | 当用户提供宠物甩耳、挠耳或耳道抓拍视频URL或文件时，触发本技能进行耳道健康监测分析；通过智能摄像头实时监测甩耳/挠耳动作，自动触发高清抓拍耳道内部图像，利用AI视觉分析识别耳道颜色（粉红/红/暗红）、有无黑色颗粒状分泌物（耳螨）、耳垢堆积程度等异常指标；当检测到红肿、大量分泌物或疑似耳螨时，输出异常提示，建议主人进一步检查或就医，仅输出基于视觉的客观描述与建议，不提供医疗诊断。应用场景：宠物家庭日常健康监测、寄养中心批量监控、宠物医院预检。

Identifies sleep states like deep sleep, light sleep, waking, and restlessness. Generates daily sleep reports and schedule analysis to help parents understand their baby's sleep patterns. | 婴儿睡眠状态监测技能，识别熟睡、浅睡、苏醒、躁动不同睡眠状态，生成每日睡眠报告和作息分析，帮助家长掌握宝宝睡眠规律

Using a fixed home camera to capture frontal facial images or short videos of the elderly, the system uses AI facial-landmark detection to analyze features such as the height difference between left/right mouth corners, the symmetry of nasolabial folds (smile lines), and the asymmetry of eyebrow lifts, and computes a facial asymmetry index (0-100%). | 通过家庭固定摄像头拍摄老年人正面面部图像或视频，利用AI面部关键点检测技术分析左右嘴角的高度差、鼻唇沟（法令纹）的对称性、眉毛抬高的差异等特征，计算面部不对称指数（0-100%）。该技能可作为脑卒中（中风）前兆的辅助筛查工具，提示家属或护理人员关注老年人是否存在面瘫、口角歪斜等神经系统异常，及时就医。

Using an in-cabin DMS camera, the system analyzes the driver's facial video in real time, detecting skin color variation (flush index, derived from red-channel ratio in RGB or skin-color models) and sweat-droplet / reflective area (via image texture and reflection features). | 通过车载DMS摄像头实时分析驾驶员面部视频，检测面部肤色变化（潮红指数，通过RGB色空间中的红色分量比例或肤色模型）以及汗珠/反光面积（通过图像纹理和反射特征）。当潮红指数显著升高（可能提示血压升高、发热或情绪激动）或出汗区域面积超过阈值（可能提示热应激、低血糖或心脏问题）时，输出健康风险提醒，建议驾驶员停车休息或就医。

Recognizes cat and dog barks through pet voiceprint AI, translates and outputs emotions and behavioral intentions such as happiness, excitement, anger, anxiety, pain, vigilance, and attention-seeking, enabling human-pet smart interaction. | 宠物叫声情绪解析技能，通过宠物声纹AI识别猫狗叫声，翻译输出开心、兴奋、愤怒、焦虑、痛苦、警惕、求关注等情绪与行为意图，实现人宠智能交互

AI-powered pet separation anxiety detection & relief when the owner leaves home. Real-time monitoring via smart camera detects typical anxiety signs—continuous vocalization, pacing, scratching doors/windows, destructive chewing. When anxiety reaches preset thresholds, the system auto-triggers comfort actions (play owner's pre-recorded voice, dispense treats via smart feeder, activate interactive toys) to reduce anxiety and destructive behavior, improving pet welfare. Scenarios: pet households (especially office workers / frequent travelers), pet boarding centers. | 通过智能家居摄像头（宠物摄像头）实时监测主人离家后宠物的行为，检测持续性发声（哀嚎、嚎叫）、来回踱步、抓挠门窗或破坏家具等分离焦虑典型表现。当焦虑行为达到预设阈值时，自动触发安抚动作，包括播放主人预录的安抚语音、联动智能零食机投掷零食、或启动互动玩具（如自动逗猫棒），减轻宠物独处时的焦虑，减少破坏行为，提升宠物福利。应用场景：宠物家庭（尤其上班族、经常出差的主人）、宠物寄养中心。

Triggers when a user provides a cat litter box area video URL or file for analysis; supports local video uploads or network URLs to call server-side APIs for waste characteristic recognition, analyzing feces morphology (loose stool, bloody stool, dry hard stool) and urine clump size to identify urinary and digestive system abnormalities, outputting health risk alerts (without diagnosing diseases). Application scenarios: smart litter boxes, multi-cat household health monitoring. Development reason: early signs of urinary diseases, a pain point for multi-cat households. | 当用户提供猫砂盆区域的视频URL或文件时，触发本技能进行排泄物性状分析；支持通过上传本地视频或网络视频URL，调用服务端API进行排泄物识别，分析粪便形态（稀便、血便、干硬便）和尿团大小，识别泌尿系统与消化系统异常，输出健康风险提示（不诊断疾病）。应用场景：智能猫砂盆、多猫家庭健康监测。

Uses a fixed home camera to detect prolonged standing, bending, and related posture of a pregnant woman, track standing duration and bending frequency, and assess fatigue risk. It sends rest reminders via speaker or app when tiring behavior is found. Suitable for homes, prenatal schools, community health centers, and smart-home or pregnancy apps. For health reference only, not medical diagnosis. | 通过家庭固定摄像头识别孕妇久站、弯腰等姿态，统计连续站立时长和弯腰频次，评估孕期劳累风险。发现久站、频繁弯腰或疑似重体力活动时，生成休息提醒，并可通过智能音箱或手机App推送。适用于家庭、孕妇学校、社区健康中心，可接入智能家居或孕期管理应用；结果仅供健康参考，不替代医生诊断。

Monitors restricted area intrusions, climbing on dining tables, and rummaging through trash cans, and issues real-time alerts, suitable for home pet monitoring scenarios. | 宠物禁区预警技能，监测禁止区域闯入、攀爬餐桌、翻垃圾桶行为并实时报警，适用于家庭宠物监控场景

Using fixed cameras in enterprise office areas (open-plan workstations), the system anonymously analyzes multiple employees' facial expressions (e.g., frowning, downturned mouth corners) and postures (e.g., rigid sitting, forward leaning, frequent eye rubbing), and applies a group-level stress assessment model to compute a real-time stress index for each workstation zone, generating a stress distribution heatmap of the entire. | 通过企业办公区（开放式工位）的固定摄像头，匿名分析多名员工的面部表情（如皱眉、嘴角下垂）和姿态（如僵坐、前倾、频繁揉眼），使用群体压力评估模型计算每个工位区域的实时压力指数，生成整个办公区的压力分布热力图（颜色从绿到红，绿色代表低压力，红色代表高压力）。系统定期（如每小时）生成压力热力图，用于组织健康度监测。

Diagnoses plant nutrient deficiency or excess based on computer vision and plant physiology, outputs targeted fertilization suggestions for precision nutrient management. | 植物营养诊断技能，基于计算机视觉与植物生理学，通过叶片特征诊断氮磷钾及微量元素缺乏或过剩，输出精准施肥建议

AI-powered plant nutrient deficiency diagnosis from leaf images. Detects leaf color, morphology changes (pale green/yellow-green/purple-red, marginal scorch, interveinal chlorosis) via computer vision, matches against common deficiency symptom databases, and outputs the most likely deficient nutrient element (nitrogen, phosphorus, potassium, iron, magnesium, zinc, etc.) with confidence score. Enables precision fertilization, avoids blind over-fertilization. Scenarios: smart planters, home gardening, agricultural greenhouses, plant factories. | 通过智能花盆、农业大棚或手机拍摄的植物叶片高清图像，利用AI视觉分析技术识别叶片颜色、形态变化（如叶色浅绿/黄绿/紫红、叶缘焦枯、叶脉间失绿等），与常见营养缺乏症特征库比对，输出最可能缺乏的营养元素（氮、磷、钾、铁、镁、锌等）及置信度。有助于精准施肥，避免盲目用肥造成浪费或伤害。应用场景：智能花盆、家庭园艺、农业大棚、植物工厂。

Accurately identifies plant species from images based on deep learning and computer vision, outputs structured information including species name, family, gr...

Using a smart mirror or fixed camera, the system analyzes high-resolution adult facial images or short videos to detect physiological features such as under-eye bag area (puffiness/shadow under the lower eyelid), dark-circle grayscale (darkness around the eyes), mouth-corner drop angle (angle between corner of mouth and horizontal), and glabellar frown lines (vertical lines between the brows), and computes a comprehensive. | 通过智能镜子或固定摄像头，分析成人面部的高清图像或视频，检测眼袋面积（下眼睑区域的浮肿或阴影面积）、黑眼圈灰度（眼眶区域的暗沉程度）、嘴角下垂角度（口角与水平线的夹角）以及皱眉纹（眉间川字纹）等生理特征，综合计算疲劳/压力指数（0-100分）。该技能可用于职场健康管理、个人状态监测，辅助了解自身精力水平。

Based on computer vision, automatically detects employees playing with phones during work hours, supports real-time video stream and image detection, counts the duration and frequency of phone usage, helps enterprises standardize office order, and improves work efficiency. | 职场玩手机智能监测技能，基于计算机视觉自动检测工作时间员工玩手机行为，支持视频流和图片实时检测，统计玩手机时长与频次，帮助企业规范办公秩序，提升工作效率

Through a fixed camera in a smart-home living room, the system analyzes office worker behavior in the first 30 minutes after coming home, detecting slumped sitting / reclining (relaxed posture, back-to-sofa angle > 120°), facial fatigue features (visible eye bags, downturned mouth corners, frequent blinking), and sighing frequency (rapid chest/abdomen rise-fall with audible exhale). | 通过智能家居客厅的固定摄像头，分析上班族回家后30分钟内的行为，检测瘫坐/斜躺（姿态放松、背部与沙发夹角>120°）、面部疲惫特征（眼袋明显、嘴角下垂、频繁眨眼）、叹气频次（胸腹快速起伏伴呼气声）。当疲劳指数超过阈值时，通过智能音箱主动播报关怀语音（如'辛苦了，喝杯水休息一下'），并播放舒缓音乐。

Using a plant-monitoring platform that periodically (e.g., daily) collects plant images, environmental data, and growth metrics (new bud count, leaf-area change, leaf color), an AI evaluation model fuses leaf color (chlorophyll index), morphology (spread, leaf size), and growth dynamics (new buds, leaf-area growth rate) to output an overall vitality score from 0-100 along with a trend (rising / stable / declining). | 通过植物监测平台定期（如每天）采集的植物图像、环境数据以及生长指标（如新芽数、叶片面积变化、叶色），利用AI综合评估模型融合叶片颜色（叶绿素指数）、形态（舒展度、叶片大小）、生长动态（新芽萌发数、叶面积增长率），输出0-100的整体活力评分，并给出活力趋势（上升/稳定/下降）。

Combines facial blood flow and emotional characteristics to analyze stress index, anxiety tendency, and depression tendency, suitable for mental health monitoring scenarios. | 心理压力评估技能，结合面部血流与情绪特征，分析压力指数、焦虑倾向、抑郁倾向，适用于心理健康监测场景

Using fixed cameras in a smart greenhouse to analyze plant morphology in real time (e.g., leaf wilting angle, stem uprightness, leaf color changes) combined with environmental sensors (light intensity, temperature, humidity, soil moisture), an AI decision model outputs climate control commands including irrigation (pump/solenoid valve), shade-net opening, fan/wet-curtain on-off, heater on-off, etc. | 通过智能温室中的固定摄像头实时分析植物的形态（如叶片萎蔫角度、茎秆挺直度、叶色变化）以及结合环境传感器（光照强度、温度、湿度、土壤水分），利用AI决策模型输出环境调控指令，包括灌溉（水泵/电磁阀）、遮阳网开度、风机/湿帘启停、加热器开关等。

Based on computer vision, automatically detects and recognizes cats and dogs appearing in the target area from the perspective of feeder/IPC cameras, and supports pet identity recognition and database entry, suitable for pet identification management in smart feeding scenarios. | 智能喂食器宠物检测识别技能，基于计算机视觉从喂食器/IPC摄像头视角自动检测识别目标区域出现的猫、狗宠物，并支持宠物身份识别和底库录入，适用于智能喂养场景的宠物识别管理

Monitors infant behavior via visual AI, automatically identifying high-risk actions like rolling over, mouth/nose obstruction, climbing, or falling from bed, and triggers instant safety warnings. | 婴儿智能安全看护技能，基于视觉AI实时监测婴儿行为，自动识别翻身、口鼻遮挡、攀爬、翻越围栏、坠床等高风险动作，即时触发安全预警，守护婴幼儿睡眠与活动安全

Using an in-cabin DMS camera, the system analyzes the driver's facial video in real time, detects eye open/closed state, calculates blink rate per minute (normal range 15-20 blinks/min), and identifies single-blink closure duration. | 通过车载DMS摄像头实时分析驾驶员面部视频，检测眼部开闭状态，计算每分钟眨眼频率（正常约为15-20次/分钟），并识别闭眼持续时间。当眨眼频率异常降低（如<10次/分钟）或出现单次闭眼超过2秒（微睡眠前兆）时，输出疲劳驾驶预警，联动车内语音提醒或震动座椅，预防因疲劳导致的事故。

Triggers when a user provides a cat scratch post area video URL or file for analysis; supports local video uploads or network URLs to call server-side APIs for scratch behavior recognition, analyzing scratch frequency, single-session duration, and intensity (estimated via vibration amplitude), outputting standardized observation data on stress level and claw health (without diagnosing diseases or prescribing behavior correction). Application scenarios: smart scratch post, multi-cat household stress management. Development reason: stress-induced abnormal scratch, early signs of behavioral issues. | 当用户提供猫抓板区域的视频URL或文件时，触发本技能进行抓挠行为分析；支持通过上传本地视频或网络视频URL，调用服务端API进行抓挠动作识别，分析抓挠频率、单次持续时间、力度（通过振动幅度估算），评估宠物压力水平和爪子健康状况，输出标准化观察结果（不诊断疾病、不提供行为矫正建议）。应用场景：智能猫抓板、宠物行为监测、多猫家庭压力管理。

Automatically detects smoking behavior in target areas based on computer vision; supports real-time detection of video streams, images, and video files; identifies violation smoking behavior and triggers violation alerts, assisting in smoking control safety management for parks/communities/units. | 公共场所吸烟行为智能检测技能，基于计算机视觉自动检测目标区域内的吸烟行为，支持视频流、图片、视频文件实时检测，识别违规吸烟行为，触发违规预警，助力园区/社区/单位控烟安全管理

Triggers when a user provides a litter-box area video URL or file for analysis; uses object detection and tracking to identify each cat's entry/exit times at the litter box, records daily usage frequency and per-visit duration (entry → exit), and compares against the historical baseline. When frequency rises/falls significantly, or per-visit duration is abnormally long/short, outputs a urinary-disease (cystitis, urinary obstruction, kidney disease) early-warning alert. Especially useful for individualized health management in multi-cat households. Application: multi-cat homes, catteries, vet hospital inpatient wards, boarding centers. Does NOT provide medical diagnosis — only outputs behavior-statistics-based alerts. | 当用户提供猫砂盆区域视频URL或文件时，触发本技能进行使用频次与时长分析；通过智能猫砂盆或固定摄像头分析视频，利用目标检测和跟踪技术识别每只猫咪进出猫砂盆的时刻，记录每日使用频次、单次停留时长（从进入至离开），并与历史基线对比；若频次显著增减或单次时长异常，则输出泌尿系统疾病（膀胱炎、尿闭、肾病等）预警，有助于多猫家庭的个体化管理及早发现健康问题。应用场景：多猫家庭、猫舍、宠物医院住院部、宠物寄养中心。仅输出基于行为统计的提示，不提供医疗诊断。

AI-powered pet grooming effect assessment: detects mat residue area, dandruff coverage, and coat smoothness from post-grooming images, outputs a 0-100 grooming score with targeted re-grooming suggestions. Scenarios: daily home grooming, pet salon quality check, long-hair cat/dog shedding season management. | 通过智能梳毛器或普通摄像头拍摄梳毛后的宠物皮肤和毛发高清图像，利用AI图像识别技术检测毛结团块残留面积、皮屑覆盖率以及毛发顺滑度，自动评估本次梳毛效果，并提示是否需要进行二次梳理或进一步护理。有助于宠物主人判断梳毛是否彻底，预防毛球症和皮肤问题。应用场景：宠物家庭日常梳理、宠物美容店服务质检、长毛猫/犬换毛期管理。

Analyzes videos of vehicle drivers to identify unsafe driving behaviors. It generates professional analysis reports to help enhance road safety awareness. | 安全驾驶行为分析工具，针对机动车驾驶人员的驾驶行为进行视频分析，识别不安全驾驶行为，输出专业分析报告，提升道路交通安全意识

Supports identifying high-risk behaviors and health risks through video/images, including elderly falls, precursors to heart attacks and strokes, and abnorma...

Using a fixed thermal-imaging camera installed in public areas (e.g., living room, dining room), the system automatically analyzes each person's skin-surface temperature (usually forehead or facial region) when multiple people gather, and computes the difference between an individual's temperature and the average temperature of others in the scene. | 通过安装于公共区域（如客厅、餐厅）的固定热成像摄像头，在多人聚集时自动分析每个人的体表温度（通常为额头或面部区域），计算个体温度与场景内其他人平均温度的差值。当某个人温度显著高于周边人群（差值超过预设阈值，如1.5℃）时，输出'体温相对异常'提醒，建议使用额温枪复测。

Triggers when a user provides multi-angle pet videos (standing/side/top-down view) URL or files for analysis; supports local video uploads or network URLs to call server-side APIs for 3D body shape reconstruction and Body Condition Score (BCS, 1-9) evaluation, classifying body type as underweight, ideal, or overweight/obese, outputting standardized observation data (without diagnosing diseases or prescribing treatment). Application scenarios: smart feeders, pet cameras, pet health management platforms. Development reason: early warning for obesity-related diseases, scientific weight management. | 当用户提供宠物多角度视频（站立、侧身、俯视）的URL或文件时，触发本技能进行3D体型构建与BCS体况评分；支持通过上传本地视频或网络视频URL，调用服务端API进行体态分析，自动评估体况评分（1-9分），判断偏瘦、正常或肥胖，输出标准化体态观察结果（不诊断疾病、不提供治疗建议）。应用场景：智能喂食器、宠物摄像头、宠物健康管理平台。

Conducts video safety risk analysis for participants in outdoor sports competitions, long-distance running, marathons, etc.; identifies sports injuries and sudden health risks, outputs professional analysis reports, and provides timely warnings to ensure sports safety. | 户外体育赛事风险分析工具，针对户外体育比赛、长跑马拉松等运动项目的参赛人员进行视频安全风险分析，识别运动损伤和突发健康风险，输出专业分析报告，及时预警保障运动安全

AI-powered flowering and fruit-set rate analysis for tomato / chili plants. From home grow-box or mobile phone images of flowering/fruit clusters, uses object-detection models to count open flowers (fully-opened corolla with visible stamens) and successfully-set young fruits (enlarged ovary, ~0.5-1cm green baby fruits), and computes fruit-set rate = young fruits / flowers × 100%. Helps growers evaluate pollination, nutrition and environmental adaptability, and guides hand-assisted pollination or water/fertilizer adjustment. Scenarios: home smart grow-boxes, greenhouses, balcony vegetable gardens. | 通过家庭种植箱或手机拍摄的植株花果图像（包含花穗、果实区域），利用AI视觉目标检测模型识别开放花朵（花冠完全展开、雄蕊可见）的数量以及已坐果的小果（子房膨大、直径约0.5-1cm的绿色幼果）数量，计算坐果率（小果数/花朵数 × 100%）。该技能帮助种植者评估授粉效果、营养状况及环境适应性，指导人工辅助授粉或调整水肥管理。应用场景：家庭智能种植箱、温室大棚、阳台菜园。

Identifies babies kicking off blankets or exposing their bodies during sleep and alerts parents to cover them up to prevent catching a cold. | 婴儿蹬被监测技能，识别婴儿夜间睡觉踢开被子、身体裸露，及时提醒家长给宝宝盖被保暖，预防着凉感冒

Automatically identifies wet clothing and abnormal excretion via visual AI. Instantly notifies caregivers to improve care for incontinent elderly, bedridden patients, and infants, reducing skin issues and complications. | 智能失禁状态提醒技能，基于视觉AI自动识别衣物潮湿、排泄异常等状况，第一时间推送通知给看护人员，提升失能老人、卧床病人、婴幼儿的护理质量，减少皮肤问题和并发症

Automatically detects personnel in target areas based on computer vision. Supports real-time video stream detection and is suitable for monitoring personnel access in parks, offices, and restricted areas. | 区域人形检测技能，基于计算机视觉自动检测目标区域内出现的人员，支持视频流实时检测，适用于园区、办公室、禁入区域等人员出入监测场景

Analyzes incoming call content for multi-dimensional risk, intelligently identifies scam scripts, determines if a call is fraudulent, assesses risk levels, and generates an Anti-Fraud Guardian analysis report. | 诈骗电话识别分析工具，针对来电通话内容进行多维度风险分析，智能识别诈骗话术，判断是否为诈骗电话并评估风险等级，输出反诈卫士分析报告

Supports identifying high-risk behaviors and health risks through video/images, including elderly falls, precursors to heart attacks and strokes, and abnormal behaviors, issuing timely warning alerts. | 高风险行为识别分析工具，支持通过视频/图片识别高危行为和健康风险，包括老人跌倒、心梗脑梗前兆、异常行为等，及时发出预警提示

Conducts video analysis of learning behavior for children/students, identifies poor learning habits, provides structured analysis reports and family education improvement suggestions, focusing on learning habit cultivation and behavior correction. | A comprehensive tool designed to analyze video footage of children's and students' learning behaviors. It identifies poor study habits and provides structured analysis reports along with actionable suggestions for family education improvements. The tool is dedicated to fostering positive study habits and facilitating behavioral correction. | 分析孩子学习行为 孩子学习行为分析工具，针对孩子/学生的学习行为进行视频分析，识别不良学习习惯，提供结构化分析报告和家庭教育改善建议，专注学习习惯培养和行为矫正

Professional discernment of subtle cues! It performs detailed analysis and recognition of facial micro-expressions, outputs precise emotional state reports, and unveils true inner emotional activities. | 微观情绪（微表情）识别分析工具，专业察言观色！针对人物面部微表情进行细致分析识别，输出精准的情绪状态分析报告，揭示真实内心情绪活动

Analyzes videos to evaluate human eating behaviors, habits, and dietary patterns. It identifies tendencies towards unhealthy eating and provides structured analysis reports along with nutritional improvement recommendations. | 饮食行为健康分析工具，针对人的饮食行为、进食习惯、饮食结构进行视频分析，识别不良饮食行为倾向，提供结构化分析报告和营养改善建议

Triggers diagnostic analysis when users provide video URLs or files for reptiles such as lizards, snakes, and spiders. Supports uploading local videos or network URLs to call the server-side API for health checks. It analyzes features like scales, skin, and body appearance to identify potential diseases and generate a Pet Safety Guardian health report. | 爬行类宠物健康诊断分析工具，当用户提供蜥蜴、蛇、蜘蛛等爬行宠物的视频 URL 或文件需要分析时，触发本技能进行爬行宠物健康诊断分析；支持通过上传本地视频或网络视频 URL，调用服务端 API 进行爬行宠物健康检查，分析鳞片、皮肤、身体外观等特征，识别潜在疾病并输出宠安卫士健康报告

When a user provides a video URL or file of aquatic pets such as goldfish, koi, betta, shrimp, crab, etc. for analysis, this skill is triggered to perform aquatic pet health diagnosis analysis. Supports uploading local videos or online video URLs, calls server-side API for aquatic pet health examination, analyzes features such as scales, fins, body color, activity level, identifies potential diseases and outputs a pet health report. | 鱼类水族宠物健康诊断分析工具，当用户提供金鱼、锦鲤、斗鱼、虾、蟹等水族宠物的视频 URL 或文件需要分析时，触发本技能进行水族宠物健康诊断分析；支持通过上传本地视频或网络视频 URL，调用服务端 API 进行水族宠物健康检查，分析鳞片、鱼鳍、体色、活跃度等特征，识别潜在疾病并输出宠安卫士健康报告

设备管理工具，用于管理摄像头、监控等实时设备，支持设备增删改查、实时画面查看、直播流地址获取。当用户提及摄像头、监控、设备管理、查看实时画面、直播流、监控地址等关键词时使用。

生命涌现支付技能，提供技能账户增值服务（充值/续费）、账户查询（余额/使用次数）、余额检查、支付宝支付集成和支付页面生成。🔴【强制性要求】每次展示套餐列表时，必须完整显示套餐 1-4，包括专属定制套餐，禁止省略定制套餐。当用户需要查询账户状态、检查余额、进行充值续费、或处理支付流程时使用此技能。自动触发关键词：...

Accurately identifies plant diseases based on computer vision and deep learning, supports both image and video input, outputs structured diagnostic reports including disease type, cause and prevention suggestions. | 植物病害识别技能，基于计算机视觉与深度学习，支持视频/图片输入，精准识别植物病害类型，输出包含病害名称、致病原因、防治建议的结构化诊断报告，为农业生产和园艺养护提供病害预警

Early monitoring of plant wilting based on hyperspectral imaging and computer vision, captures early wilting signs before visible symptoms, provides early warning for precision irrigation and disease control. | 植物枯萎监测技能，基于高光谱成像与计算机视觉，在肉眼可见症状前捕捉早期枯萎迹象，为精准灌溉和病害防控提供早期预警

Accurately identifies key growth stages of plants from germination to fruiting based on computer vision and deep learning, provides structured data for precision agriculture decision support. | 植物生长阶段识别技能，基于计算机视觉与深度学习算法，精准识别植物从发芽到结果的全生命周期关键生长阶段，为精准农业提供科学决策支持

Conducts intelligent video search based on target and semantic descriptions; supports conventional target retrieval, natural language description retrieval,...

Identifies sleep stages including falling asleep, light sleep, deep sleep, and REM; monitors body movement, nighttime awakenings, and sleep apnea, suitable for sleep monitoring scenarios. | 睡眠质量分析技能，识别入睡、浅睡、深睡、快速眼动阶段，监测体动、夜间觉醒、睡眠呼吸暂停，适用于睡眠监测场景

Detects baby cries via audio AI in real-time, analyzes causes, and precisely identifies needs like hunger, tiredness, pain, discomfort, or irritability to assist new parents. | 婴儿哭声智能解析技能，通过音频AI实时检测婴儿哭声，自动解析哭声成因，精准识别饥饿、困倦、疼痛、身体不适、情绪烦躁等不同需求，辅助新手爸妈科学育婴

AI-powered fruit ripeness grading for tomatoes / strawberries. From smart grow-boxes or mobile phone images, uses AI vision to detect fruit color (green / light green / orange / red / dark red), colored-area ratio, gloss, and relative fruit size (against a reference object), and outputs a ripeness grade (Mature-Green / Turning / Ripe / Over-Ripe) based on preset standards. Helps growers identify the optimal harvest window and ensures flavor and shelf quality. Scenarios: smart grow-boxes, greenhouses, home vegetable gardens, fruit & vegetable cooperatives. | 通过智能种植箱或手机拍摄的果实图像，利用AI视觉分析技术检测果实的颜色（绿/浅绿/橙/红/暗红）、着色面积比例、光泽度以及果实大小（相对于参照物），根据预设的成熟度分级标准输出等级（青熟期/转色期/成熟期/过熟期）。该技能帮助种植者确定最佳采收时机，保证果实口感和商品性。应用场景：智能种植箱、温室大棚、家庭菜园、果蔬合作社。

Detects and analyzes indoor plant light stress from images and optional lux data, identifying low or excessive light symptoms and suggesting adjustments.

AI-powered plant growth stage auto-detection from periodic full-plant images via smart pot / greenhouse fixed cameras. Recognizes key phenological features—cotyledon emergence, true-leaf count, flower bud differentiation, blooming, fruit setting, fruit ripening—and identifies the current developmental stage (germination, seedling, vegetative, flowering, fruiting, ripening), enabling precision irrigation/fertilization/lighting control and personalized growing guidance. Scenarios: smart pots, home grow boxes, greenhouses, plant factories. | 通过智能花盆或温室内固定摄像头，定期拍摄植物整体图像，利用AI视觉分析技术识别子叶展开、真叶数量、花芽分化、开花、结果、果实成熟等关键物候特征，自动判定植物当前所处的生长发育阶段（如发芽期、幼苗期、生长期、开花期、结果期、成熟期）。有助于精准农业管理，实现自动化灌溉、施肥、光照调节，并为用户提供种植指导。应用场景：智能花盆、家庭种植机、温室大棚、植物工厂。

AI-powered pet social interaction analysis for multi-pet households. Uses pose recognition and behavior classification to detect cat-cat, dog-dog, and cat-dog interactions—sniffing, chasing, biting, fleeing, hiding, playing—then records duration, frequency, initiator and receiver to generate a social-behavior report. Helps owners understand pet relationships, spot aggression or stress sources, and promote harmonious cohabitation. Scenarios: multi-pet homes, pet boarding centers, pet daycare, animal behavior clinics. | 通过多宠家庭固定摄像头，分析宠物之间（猫-猫、狗-狗、猫-狗等）的互动视频，利用姿态识别和行为分类模型检测嗅闻、追逐、撕咬、逃跑、躲避、玩耍等行为类型，记录每种行为的持续时间、频次以及发起者，生成社交行为报告。帮助主人了解宠物间的社交关系，识别潜在的攻击行为或压力源，促进多宠和谐共处。应用场景：多宠家庭（多猫/多狗/猫狗混养）、宠物寄养中心、宠物日托班、宠物行为诊所。

Triggers when a user provides a pet side-view walking video URL or file for analysis; uses AI pose estimation to track limb joint trajectories, analyzes stri...

Triggers when a user provides a pet facial image or video URL/file for vaccination reminder analysis; uses AI facial recognition to confirm pet identity, automatically queries the linked vaccination records (last dose date, vaccine type) from the hospital management database, and compares with current date. When the gap since last vaccination exceeds 11 months (or the preset reminder cycle), outputs a due/overdue reminder and suggests re-vaccination. Helps pet hospitals automate client management, raise vaccination coverage and avoid missed doses. Application: hospital front-desk registration, boarding center check-in, pet insurance underwriting. Does NOT provide medical advice — only returns database-comparison results. | 当用户提供宠物面部图像或视频URL/文件时，触发本技能进行疫苗到期提醒分析；利用AI面部识别确认宠物个体身份，自动关联数据库中该宠物的疫苗接种记录（上次接种日期、疫苗类型）并与当前日期比对；若距离上次接种超过11个月（或预设的提醒周期），输出到期/逾期提醒，并建议尽快补种。该技能可帮助宠物医院实现自动化客户管理、提升疫苗接种率、防止漏种。应用场景：宠物医院前台登记、宠物寄养中心入住检查、宠物保险核保。仅输出基于数据库比对的结果，不提供医疗建议。

Triggers when a user provides a video of a pet feeding bowl area for analysis; supports local video uploads or network URLs to call server-side APIs for picky-eater behavior detection, identifying behaviors such as pushing kibble out of the bowl, picking only treats/freeze-dried bites, or sniffing then leaving without eating; records frequency and outputs feeding-adjustment suggestions to prevent malnutrition. Application scenarios: smart pet feeders, pet boarding centers, pet hospital inpatient wards. | 当用户提供宠物食盆区域视频时，触发本技能进行选择性拒食行为识别；支持通过上传本地视频或网络视频URL，调用服务端API识别宠物把主粮拨出食盆、只挑拣零食/冻干、嗅闻后离开等挑食行为，记录发生频率，连续异常时输出喂养调整建议，预防营养不均衡（不诊断疾病）。应用场景：智能喂食器、宠物寄养中心、宠物医院住院部。

Using a fixed camera at home or in the office, the system analyzes daily videos of an individual (e.g., adult, adolescent) and detects anxiety-related behaviors: hand rubbing (repeated rubbing of both hands), nail biting (hand approaching mouth with biting motion), and pacing (repeated back-and-forth walking in a small area). | 通过家庭或办公室的固定摄像头，分析个体（如成人、青少年）的日常行为视频，检测手部搓揉（双手反复摩擦）、指甲啃咬（手部靠近嘴部并有啃咬动作）、来回踱步（在狭小区域内反复折返行走）等焦虑相关行为。系统实时监测，当焦虑行为指数超过阈值时推送提醒（如'您今日焦虑行为较多，建议进行放松练习'）。

Using a fixed camera with microphone in the living room, the system analyzes audio and video in real time, detecting sound intensity (dB) and the intensity of body movements (e.g., rapid hand waving, finger pointing, pushing, throwing objects). It comprehensively evaluates the family conflict intensity level (low / medium / high). | 通过客厅固定摄像头（含麦克风），实时分析音频和视频，检测声音强度（分贝）和肢体动作激烈程度（如快速挥手、戳指、推搡、摔物等）。综合评估家庭争吵的冲突强度等级（低/中/高），当强度达到中或高时，通过手机APP推送提醒（如'检测到高强度冲突，建议冷静沟通或暂时分开'）。

Using fixed cameras at home (living room, bedroom) of elderly people living alone, the system analyzes daily videos and detects negative behavior indicators during solo time: dazing (long-duration motionless gazing without purposeful action), sighing (rapid chest rise-and-fall with audible expiration), and self-talking (mouth movement without any conversation partner). | 通过独居老人在家中的固定摄像头（如客厅、卧室），分析日常视频，检测独处期间的消极行为指标：发呆（长时间静止注视，缺乏目的性动作）、叹气（胸部快速起伏伴呼气声）、自言自语（口部活动但无对话对象）等。该技能可辅助家属或社区工作者了解老人心理状态，及时进行情感关怀或心理干预。

Using a fixed camera in the living room (aimed at the sofa and TV area), the system analyzes the elderly person's continuous sitting time while watching TV, detecting whether the body remains in a seated posture and the face is oriented toward the TV area (watching). | 通过客厅固定摄像头（对准沙发和电视区域），分析老年人连续观看电视的坐姿时长，检测人体是否持续处于坐姿且面部朝向电视区域（注视电视）。当连续坐姿观看电视超过预设阈值（默认2小时）且期间未起身活动时，输出'久坐活动提醒'，建议老年人起身走动、做伸展运动。

Using a fixed camera in the child's bedroom (infrared night vision), the system continuously captures video and audio at night to analyze the child's sleep behavior. It detects rollover frequency (rollovers per minute), cries (recognizing specific cry-sound features), and sleep talk (speech during sleep), and generates a sleep-quality report. When rollovers occur too often (e.g., > 3 per hour), strong crying is detected, or sleep talk is observed, the system pushes 'possible nightmare' or 'restless sleep' alerts to the parents. Application scenarios: child bedrooms, infant rooms. The system relays night-time monitoring to help parents understand the child's sleep quality and provide timely comfort. Skill features: improve sleep. | 通过儿童床或卧室的固定摄像头（红外夜视），在夜间连续采集视频及音频，分析儿童的睡眠行为。检测翻身次数（每分钟翻身频率）、哭声（识别特定的哭声音频特征）以及梦话（检测睡眠中的语音），生成睡眠质量报告。当翻身过于频繁（如>3次/小时）、出现强烈哭声或梦话时，推送给父母'可能做噩梦'或'睡眠不安'的预警。应用场景：儿童卧室、婴儿房。系统夜间接力监测，帮助家长了解儿童睡眠质量，及时安抚。技能特点：改善睡眠。

Using a fixed camera in a hallway or living room to record video of an elderly person walking in a straight line, AI pose estimation and gait analysis extract parameters such as step length (cm), gait speed (m/s), trunk sway angle (left-right tilt), and cadence to evaluate gait stability. | 通过走廊或客厅的固定摄像头拍摄老年人直线行走的视频，利用AI姿态估计和步态分析技术检测步幅长度（cm）、步速（m/s）、躯干摇摆角度（左右倾斜度）以及步频等参数，评估步态稳定性。当步幅过小（小碎步）、步速过慢、躯干摇摆幅度过大时，输出跌倒风险等级（低/中/高）。

Using a fixed kitchen camera (must be able to capture the stove area), the system analyzes video in real time to detect whether there is human activity in the kitchen area, and at the same time identifies stove flames or heat sources (e.g., thermal/infrared features) to determine whether the gas stove is on. | 通过厨房固定摄像头（需能拍摄到灶台区域）实时分析视频，检测厨房区域内是否有人体活动，同时识别灶台火焰或热源（如红外特征）以判断燃气灶是否处于开启状态。当检测到厨房无人连续超过预设时间（默认10分钟）且灶火仍处于开启状态时，输出'忘关火'预警，可联动智能燃气阀自动关闭阀门，并推送提醒至家属或护理人员手机，预防火灾和燃气泄漏事故。

Using a night-time camera (infrared or low-light) above the crib, the system analyzes in real time the coverage of the blanket on the infant's body. It checks whether the blanket coverage is below a preset threshold (e.g., 50%) or recognizes the kicking motions that cause the blanket to slip off, then outputs an alert. | 通过婴儿床夜间摄像头（红外或微光），实时分析婴儿身体及被子的覆盖情况。可联动智能家居设备（如自动调高室温、推送提醒至父母手机），预防婴儿着凉。

Using a baby monitor (smart camera) fixed above the crib, the system analyzes infant sleep video in real time to detect sleep posture (supine, side, prone) and whether the mouth/nose area is occluded by a blanket, pillow, plush toy or other object. | 通过婴儿监护器（智能摄像头）固定于婴儿床上方，实时分析婴儿睡眠视频，检测婴儿的睡姿（仰卧、侧卧、俯卧）以及口鼻区域是否被被子、枕头、玩偶等异物遮挡。当检测到俯卧或口鼻被遮挡时，输出风险等级（中风险/高风险），并立即向父母手机APP推送警报，预防婴儿猝死综合征（SIDS）和窒息意外。

Using fixed cameras on a hydroponic growing rack to capture high-resolution images of plant roots (in transparent containers) and leaves (young and old), AI vision analysis identifies root color (white = healthy, yellow = early stress, brown = severe stress, black = rotting) and leaf morphology (tip burn, leaf-margin scorch, yellowing, curling) to judge whether the nutrient solution is too concentrated or too dilute, and. | 通过水培种植架的固定摄像头拍摄植物根系（透明容器）和叶片（新叶、老叶）的高清图像，利用AI视觉分析技术识别根须颜色（白色健康、黄色初期胁迫、褐色严重胁迫、黑色腐烂）、叶片形态（叶尖灼伤、叶缘焦枯、叶片黄化、卷曲）等特征，判断营养液浓度是否过浓或过稀，并输出调整建议（增加清水稀释或补充浓缩营养液）。

Using fixed cameras in enterprise office areas (with employee consent and anonymization), the system performs long-term monitoring of employees' facial expressions and posture features, building per-person historical baselines (smile frequency, sigh count, frown level, etc.). | 通过企业办公区固定摄像头（需征得员工同意并匿名化处理），长期监测员工的面部表情和姿态特征，建立个人历史基线（如笑容频率、叹气次数、皱眉程度等）。当检测到某员工近期的笑容频率显著下降（例如比基线降低40%）、叹气次数增加（例如比基线增加50%）或与其他异常行为（社交回避、长时间独自静坐）时，输出情绪波动预警，提醒HR或管理者进行关怀沟通。

Through fixed cameras on aquariums, the system analyzes fish swimming videos and computes the angle between the fish body axis and the horizontal plane (normal fish bodies stay nearly horizontal). | 通过鱼缸固定摄像头，分析鱼类的游动视频，检测鱼体轴线与水平面的夹角（正常鱼体基本保持水平），当鱼体倾斜角度超过阈值（默认 > 30°）或出现倒立（头部向下 > 45°）、旋转（绕自身纵轴连续翻转）等异常游姿时，标记为异常，并记录异常时长占观察总时长的比例。该技能有助于早期发现鱼鳔失调、神经系统疾病或水质中毒等健康问题，提醒养鱼爱好者及时干预。

Recognizes various poses such as standing, sitting, lying down, bending, raising hands, running, and falling. Supports abnormal pose recognition and fall warnings, suitable for security monitoring and elderly care. | 人体姿态识别技能，识别站立、坐姿、躺卧、弯腰、举手、奔跑、摔倒等多种人体姿态，支持肢体异常姿态识别和摔倒预警，适用于安防监测、老人看护等场景

Triggers when a user provides a pet vocalization audio/video URL or file for analysis; extracts acoustic features such as frequency, duration, interval, and harmonic structure via AI audio analysis, and classifies the vocalization into 6+ emotion categories (howling, growling, excitement, loneliness, fear, whining/coaxing) with confidence scores. Helps owners understand pet emotional states, improve human-pet interaction, and detect potential stress or health issues early. Application: daily companionship (smart camera / collar), boarding center mood monitoring, vet clinic calming assessment, behavior training assistance. Does NOT provide medical or behavior-modification advice — only outputs audio-based emotion classification results with confidence. | 当用户提供宠物（犬/猫）叫声音频或视频URL/文件时，触发本技能进行叫声情绪深度分类分析；利用AI音频分析技术提取频率、时长、间隔、谐波结构等声学特征，将叫声分类为哀嚎、低吼、兴奋、孤独、恐惧、撒娇等6种以上情绪类别，并输出置信度；帮助宠物主人理解宠物情绪状态，改善人宠互动，及时发现潜在压力或健康问题。应用场景：宠物家庭日常陪伴（智能摄像头/项圈）、寄养中心情绪监测、宠物医院安抚评估、行为训练辅助。仅输出基于音频的情绪分类结果及置信度，不提供医疗或行为矫正建议。

AI-powered pet eye anomaly detection from close-up facial images/video. Detects conjunctival redness, abnormal tearing/tear stains, and pupil/cornea opacity (cataract / corneal edema), then outputs anomaly alerts to help owners catch eye disease risks early. Scenarios: daily home health self-check, boarding center routine inspection, animal hospital triage, senior pet cataract monitoring. | 通过宠物摄像头捕捉宠物面部近景视频，利用AI视觉分析技术检测眼部充血（结膜颜色发红）、异常流泪（泪痕严重或持续性溢泪）、瞳孔区域浑浊（可能为白内障或角膜水肿）等异常征象，输出异常提示，帮助主人及早发现眼部疾病风险。适用于日常健康监测、老年宠物护理及宠物医院预检。应用场景：宠物家庭日常健康自检、宠物寄养中心巡检、宠物医院门诊初筛、老年宠物白内障监测。

Conducts intelligent video search based on target and semantic descriptions; supports conventional target retrieval, natural language description retrieval,...

Through fixed cameras on aquariums, the system continuously tracks the 2D position of each fish in the school, computes the school centroid (center of mass), and measures the distance from each fish to the centroid (in units of fish body length). | 通过鱼缸固定摄像头，持续跟踪鱼群中每条鱼的二维位置，计算鱼群中心（质心），并检测每条鱼与中心的距离（以鱼体长为单位）。当某条鱼偏离鱼群中心超过 10 倍体长，且持续时间超过 1 小时（或用户设定阈值）时，输出'离群行为'提示。

Through fixed cameras on aquariums, the system analyzes fish gill-cover opening / closing motion video, detects periodic gill opening and closing, and calculates respiratory rate (breaths per minute). | 通过鱼缸固定摄像头，分析鱼类的鳃盖开合运动视频，检测鳃盖的周期性开启和闭合，计算呼吸频率（次/分钟）。当呼吸频率超过正常阈值（例如 > 80 次/分钟，具体依品种和水温而定）时，输出'缺氧预警'，提示用户检查水质（溶氧量）、水温或鱼的健康状态。

Through fixed cameras (and optional microphones) in dementia care facilities or homes, the system analyzes behaviors of people with dementia to identify confusion/disorientation states: sudden activity stops (interrupting ongoing actions such as eating or walking for ≥ 5 seconds), gaze drifting (eyes wandering without focus), looking around (frequent head turning), and repeated disorientation questions ('Where is this?'. | 通过失智照护机构或家庭固定摄像头（及可选麦克风），分析失智老人的行为，识别困惑/迷惘状态：突然停止活动（中断正在进行的动作，如吃饭、行走 ≥ 5 秒）、眼神游离（视线漫无目的漂移、不聚焦）、四处张望（头部频繁转动）、反复询问'这是哪''现在几点''你是谁'等定向障碍问题（需配合声纹或语音识别）。

Determines when elderly people living alone have no interaction or visitors for extended periods, and actively pushes care reminders to family members, suitable for remote care scenarios for elderly people living alone at home. | 无人陪伴监测技能，判定独居老人长时间无人互动来访，主动推送关怀提醒给家属，适用于居家独居老人远程关怀场景

Analyzes human mental health and psychological behavior, supports identifying common psychological problem tendencies through video analysis, and provides structured mental health analysis reports and improvement suggestions. | 心理健康分析工具，针对人的心理健康和心理行为进行分析，支持通过视频分析识别常见心理问题倾向，提供结构化心理健康分析报告和改善建议

Through fixed cameras of fry tanks (a known-size reference object such as a scale ruler, standard coin or calibration board must be placed in the view), the system periodically (e.g. daily or weekly) captures fry images and uses AI vision analysis to measure body length (from snout to tail-fin tip, in mm), record individual growth rate (mm/day) and draw the growth curve. | 通过鱼苗缸固定摄像头（需放置已知尺寸的参照物，如刻度尺、标准硬币或标定板），定期（如每天或每周）拍摄鱼苗图像，利用 AI 视觉分析技术测量鱼苗体长（从吻端到尾鳍末端，单位 mm），记录个体的生长速率（mm/天），并绘制生长曲线。系统自动采集图像，生成生长报告，异常时提示（如生长停滞）。

Through fixed cameras (with microphones) in the family living room or kitchen, the system monitors conflict events among family members in real time, identifying high-decibel arguments (sound intensity exceeding a threshold and lasting more than 10 seconds), door slams (object impact sound + door-frame vibration), and aggressive arm-swing actions. | 通过家庭客厅或厨房的固定摄像头（含麦克风），实时监测家庭成员间的冲突事件，识别高分贝争吵（声音强度超过阈值且持续时间>10秒）、摔门（物体撞击声+门框振动）、甩手等激烈肢体动作。当冲突事件结束后（音频和视频均平静超过预设时间，默认10分钟）且无新冲突，系统自动输出缓和提示：通过智能音箱播放轻柔音乐，或通过手机APP推送关怀语（如'需要一杯茶吗？

Using the camera built into a smart desk lamp or a tabletop camera, the system analyzes video of the child's study area in real time, detecting behavioral indicators such as face orientation (whether it deviates from the book/screen), eye gaze direction, and fidgeting hand actions (playing with a pen, touching the face, fiddling with objects), and computes a per-minute focus score (0-100) while recording distraction periods. The skill helps parents and teachers understand the child's learning state and optimize study habits. Application scenarios: smart study lamps, home study rooms, classrooms. The system monitors in real time, generates focus reports, and pushes alerts when focus stays persistently low. Skill features: improve learning efficiency. | 通过智能台灯内置摄像头或桌面摄像头，实时分析儿童学习区域的视频，检测面部朝向（是否偏离书本/屏幕）、眼部注视方向、手部小动作（玩笔、摸脸、摆弄物品）等行为指标，计算每分钟专注得分（0-100分），并记录走神时段。该技能可帮助家长和教师了解儿童学习状态，优化学习习惯。应用场景：智能学习台灯、家庭书房、教室。系统实时监测，生成专注度报告，当专注度持续偏低时推送提醒。技能特点：提升学习效率。

Triggers when a user provides a pet water fountain area video URL or file for analysis; supports local video uploads or network URLs to call server-side APIs for water intake behavior recognition, tracking drinking frequency, single-session duration, and estimated daily intake per pet, comparing against historical baselines to detect sudden drops or spikes in water consumption, outputting early warning alerts for potential kidney disease or diabetes risk. Application scenarios: smart water fountains, multi-pet household health monitoring, pet chronic disease management. | 当用户提供饮水机区域视频URL或文件时，触发本技能进行饮水量行为分析；支持通过上传本地视频或网络视频URL，调用服务端API进行饮水行为识别，统计每只宠物的饮水次数、单次时长、日总饮水量，对比历史基线，对饮水骤降或骤升进行预警，辅助早期发现肾病或糖尿病风险。应用场景：智能饮水机、多宠家庭健康监测、宠物慢性病管理。

AI-powered lawn health assessment from drone or fixed-camera top-down images. Uses semantic segmentation to distinguish healthy turf (green), wilting/yellow turf (yellow-brown), bare soil and weeds (off-species color/texture), then computes wilting area ratio and weed coverage ratio, and outputs a composite lawn health score (0-100). Helps managers of golf courses, courtyards or municipal greenways quantify turf quality and guide irrigation, fertilization and weeding operations. Scenarios: home courtyards, golf courses, municipal park lawns, sports fields. | 通过无人机或固定摄像头拍摄草坪的俯视图像，利用AI语义分割技术区分健康草坪（绿色）、枯黄草坪（黄/褐色）、裸土以及杂草（非目标草种，颜色和纹理不同），计算枯黄面积占比和杂草覆盖面积占比，综合评估草坪健康评分（0-100分）。该技能有助于高尔夫球场、庭院或市政绿地管理者量化草坪质量，指导灌溉、施肥及除草作业。应用场景：家庭庭院、高尔夫球场、市政公园草坪、运动场。

In a plant factory, a fixed thermal imaging camera continuously captures thermal images of the plant canopy leaves at night (no-light period), analyzes leaf temperature trends (respiration releases heat, causing leaf temperature to be slightly higher than air temperature), and combined with optional ambient CO₂ sensor data (respiration releases CO₂ raising concentration), an AI model estimates the relative respiration. | 通过植物工厂内的固定热成像摄像头，在夜间（无光照时段）连续采集植物冠层叶片的热图像，分析叶片温度变化趋势（呼吸作用释放热量导致叶片温度略高于气温），结合可选的环境CO₂传感器数据（呼吸作用释放CO₂浓度升高），利用AI模型估算整株或群体的呼吸强度相对值（0-100%）。

Using a fixed camera above the baby-changing table or a smartphone, the system captures high-resolution images of the diaper area (or the stool itself), and uses AI visual analysis to identify stool color: normal yellow / yellow-green, abnormal clay-pale (white/clay-like, suggesting biliary obstruction), bright red (lower-GI bleeding), dark red / tarry black (upper-GI bleeding), etc. | 通过婴儿护理台上方固定摄像头或手机拍摄尿不湿区域（或直接拍摄排泄物）的高清图像，利用AI视觉分析技术识别大便颜色，包括正常黄色/黄绿色、异常陶土色（白陶土样，提示胆道梗阻）、鲜红色（下消化道出血）、暗红色/黑色（上消化道出血）等。当检测到异常颜色时，输出风险提醒，建议家长及时就医。

Performs special video analysis on behavioral characteristics of children with autism, identifies core symptom features, provides structured analysis reports and intervention recommendations. | 孤独症谱系障碍行为分析工具，针对儿童孤独症行为特征进行专项视频分析，识别核心症状特征，提供结构化分析报告和干预建议

Triggers when a user provides a video URL or file of a pet (cat/dog/bird) for analysis; supports local video uploads or network URLs to call server-side APIs for health checks, analyzing features like fur, body, and face to identify potential diseases and generate a Pet Safety Guardian health report. | 猫狗宠物健康诊断分析工具，当用户提供猫/狗/鸟等宠物的视频 URL 或文件需要分析时，触发本技能进行宠物健康诊断分析；支持通过上传本地视频或网络视频 URL，调用服务端 API 进行宠物健康检查，分析毛发、身体、脸部等特征，识别潜在疾病并输出宠安卫士健康报告

AI-powered aquatic plant health monitoring from aquarium camera images. Detects leaf color changes (yellowing, bleaching, blackening), morphological anomalies (melting, curling, holes), and iron deficiency symptoms in submerged plants. Outputs health assessment and care suggestions (e.g., supplement iron fertilizer, adjust lighting, increase CO₂). Helps early detection of aquatic plant issues and maintains aquarium ecological balance. Scenarios: smart fish tanks, aquascaping tanks, aquarium shops. | 通过智能鱼缸或水下摄像头拍摄水草的图像，利用AI视觉分析技术识别水草叶片的颜色变化（黄化、白化、发黑）、形态异常（溶叶、卷曲、穿孔）以及缺铁等典型症状，输出健康评估及养护建议（如补充铁肥、调整光照、增加CO₂）。有助于及早发现水草生长问题，维持水族箱生态平衡。应用场景：智能鱼缸、水族箱、水草造景缸、水族店。

Using fixed cameras in multiple zones of a solo-living elder's home (living room, bedroom, kitchen, bathroom, etc.), the system continuously analyzes the video streams to detect human activity (movement, limb actions, gestures, etc.). If no activity is detected within a configured time window (default 12 hours), the system outputs a 'long-term no activity' alert and can notify emergency contacts via app or phone. | 通过独居老人家中的多个区域（客厅、卧室、厨房、卫生间等）固定摄像头，连续分析视频流，检测人体活动（包括移动、肢体动作、手势等）。若在设定的时间窗口内（默认12小时）未检测到任何活动，则输出'长期无活动'预警，并可通过APP或电话通知紧急联系人。

Triggers when a user provides a pet racing track start/finish video URL or file for analysis; uses HD cameras at the starting line and finish line to analyze race video in real time, detecting each pet's (greyhounds, racehorses, etc.) start time, finish order, and lane assignment, automatically determining false starts (start before the signal) or lane crossing (deviating from own lane into an adjacent lane) fouls and outputting judgment results. Assists referee decisions and improves race fairness. Application: pet racing (greyhound, horse, obstacle course), pet sports events, professional track training. Does NOT provide race advice — only returns objective video-based judgment results. | 当用户提供宠物赛道起点/终点视频URL或文件时，触发本技能进行竞赛犯规检测分析；通过架设在赛道起点和终点线的高清摄像头，实时分析比赛视频，检测每只宠物（赛犬、赛马等）的起跑时间、通过终点线的顺序以及所在道次，自动判定是否存在抢跑（起跑时间早于发令信号）或窜道（偏离自身赛道进入邻道）等犯规行为，并输出判定结果。辅助裁判决策，提高赛事公平性。应用场景：宠物竞速比赛（灵缇赛跑、赛马、宠物障碍赛）、宠物运动会、专业赛道训练。仅输出基于视频的客观判定结果，不提供赛事建议。

AI-powered non-contact pet respiratory rate monitoring at rest. Detects thoracic-abdominal motion via a fixed camera, calculates breaths-per-minute, and compares against species/body-size resting norms; triggers early-warning when abnormal (e.g. dog >30 bpm, cat >40 bpm, or <8 bpm). Helps detect cardiopulmonary, respiratory or heat-stress risks early. Scenarios: home night monitoring, animal hospital wards, pet boarding centers. | 通过宠物窝或休息区固定摄像头，在宠物静息状态下分析其胸腹部起伏运动，自动计算呼吸频率（次/分钟），并与该物种/体型的正常静息呼吸范围进行对比；若检测到呼吸过快（如犬>30次/分钟，猫>40次/分钟）或过慢（<8次/分钟），则输出健康预警，建议主人观察或就医。有助于早期发现呼吸系统、心脏或热应激等潜在问题。应用场景：宠物家庭夜间监护、宠物医院住院部、宠物寄养中心。

AI-powered pet over-excitement detection & calming guidance. Real-time camera analysis tracks movement speed, jump height, spin laps, and jumping-on-people actions to score excitement level. When the score exceeds safety thresholds, the system auto-issues calming cues (play owner's voice command like 'sit'/'slow down', soft prompt tone, release calming pheromone, dim lights). Helps prevent injuries from over-excitement and keeps the household safe. Scenarios: lively pet households, pet boarding centers, pet daycare, dog training schools. | 通过宠物活动区的固定摄像头实时分析宠物的运动状态，检测狂跳、高速转圈、反复扑人等极度兴奋行为，评估兴奋等级。当兴奋等级超过安全阈值时，自动输出冷静引导指令，包括播放主人的语音口令（如"坐下"、"慢下来"）、发出柔和提示音，或联动环境设备（如释放宠物镇静信息素、调暗灯光），预防宠物因过度兴奋而撞伤、摔倒或伤人，维护家庭安全。应用场景：宠物家庭（尤其活泼好动的犬猫）、宠物寄养中心、宠物日托班、宠物训练学校。

AI-powered orchid growth-status detection from HD images (including roots visible through transparent pots) via orchid cameras or smartphones. Measures new-shoot count, flower-spike length, and root color/condition (white = healthy, brown = aged, black = rotten) to deliver a holistic vitality assessment (vigorous / normal / weak) plus care guidance such as 'three new shoots, healthy roots, increase phosphorus-potassium to promote spike growth'. Helps orchid hobbyists pinpoint repotting and feeding timing. Scenarios: home orchid care, orchid greenhouses, horticulture studios. | 通过兰花栽培专用摄像头或手机拍摄的高清图像（包括透明兰盆内的根系），利用AI视觉分析技术检测兰花新芽萌发数量、花梗（花箭）生长长度以及根系颜色（白色健康、褐色老化、黑色腐烂），综合输出兰花的生长状态评估（旺盛/正常/衰弱）及养护建议（如"新芽萌发3个，根系健康，可适当增加磷钾肥促进花梗生长"）。有助于兰花爱好者精准掌握植株生长节奏，及时调整水肥管理。应用场景：兰花家庭养护、兰花大棚、兰花园艺工作室。

Using a fixed camera in the classroom or above the home desk, the system analyzes the child's (student's) facial video in real time, detecting eye closure ratio (PERCLOS — the proportion of time eyes are closed more than 80% within a unit time), head-nodding frequency (rapid downward nod followed by lift), and changes in eye-region glossiness, and computes a comprehensive fatigue index (0-100). | 通过教室或家庭书桌的固定摄像头，实时分析儿童（学生）的面部视频，检测眼部闭合比例（PERCLOS，单位时间内眼睛闭合超过80%的时间占比）、点头动作频率（头部快速下点后抬起）以及眼部区域的光泽度变化，综合计算疲劳指数（0-100）。该技能可帮助教师或家长及时发现儿童困倦状态，调整学习安排或提醒休息。

Triggers when a user provides a dog toilet / pet defecation-zone video URL or file for analysis; uses a fixed camera on the dog toilet or designated defecation area to monitor video in real time, detects whether the pet is defecating (presence of feces) and, once the pet leaves the area, automatically outputs a cleaning trigger signal that can drive a robot vacuum to that spot for cleanup. Enables fully automated pet-waste handling, reduces owner workload, and keeps the home hygienic. Application: indoor dog toilets, balcony defecation zones, pet kennels. Does NOT provide medical diagnosis — only outputs vision-based event detection results. | 当用户提供狗厕所或宠物固定排便区域视频URL或文件时，触发本技能进行排便事件检测分析；通过安装在狗厕所或宠物固定排便区域的摄像头实时分析视频，识别宠物是否在该区域排便（粪便出现），并在宠物离开该区域后自动输出清扫触发信号，联动扫地机器人前往清理；实现宠物排泄物的即时自动化处理，减轻主人清理负担，保持居家环境卫生。应用场景：宠物家庭室内狗厕所、阳台排便区、宠物笼舍。仅输出基于视觉的事件检测结果，不进行医疗诊断。

AI-powered UV disinfection safety monitor for pets. Real-time camera analysis detects whether a pet enters an active UV-C disinfection zone and whether the UV lamp is on (via blue-purple glow recognition or smart-home API linkage). When both conditions are met, it auto-triggers a high-risk alert, recommends shutting off the UV lamp, and logs the event to prevent corneal burns or skin damage. Scenarios: smart homes, pet households, pet boarding facilities. | 通过智能家居摄像头实时识别宠物是否进入正在进行紫外线消毒的区域，自动关闭UV灯并推送提醒，防止宠物因误入消毒区而受到紫外线伤害。结合目标检测（宠物识别）与UV灯状态感知（可通过画面蓝紫色光晕/光谱特征或智能家居API联动），实现主动式安全防护。应用场景：智能家居、宠物家庭、宠物寄养场所。

AI-powered non-invasive rooting-stage detection for plant cuttings in transparent containers. From smart propagation boxes or transparent cutting containers, periodically captures images of the cutting base (water roots or substrate roots visible through the transparent wall), uses AI vision to detect white root primordia (tiny white callus dots) and count/locate them, and outputs the rooting stage (No Roots / Few Primordia / Many Primordia / Well-Developed Roots). Helps propagators monitor cutting-rooting progress without disturbing the cutting and choose the optimal transplant time. Scenarios: smart propagation boxes, home cutting propagation, tissue-culture rooms, agricultural research. | 通过智能育苗箱或透明扦插容器的固定摄像头，定期拍摄枝条基部的水生根或土培根系（通过透明壁观察），利用AI视觉分析技术识别枝条基部是否出现白色凸起根点（根原基发育形成的白色小点）以及根点的数量和分布，输出生根阶段（无生根/少量根点/大量根点/根系发达）。该技能帮助育苗者在不干扰枝条的情况下，无创监测扦插生根进度，判断移栽时机。应用场景：智能育苗箱、家庭扦插繁殖、植物组培室、农业科研。