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What is interesting though is the details they decided to store on a person ( despite it being a mock-up with fantasy names ): /** * 单人全息档案系统 - 标签数据 v2.0 * 310项标签,8大维度,以单人为核心 */ // ==================== 完整标签体系 ==================== const PersonArchiveData = { // 基础信息 basic: { name: '张伟', formerNames: ['张强'], gender: '男', birthDate: '1988-03-15', age: 36, nation: '汉族', politicalStatus: '群众', bloodType: 'O型', height: 175, weight: 70, appearance: '方脸,短发,左臂有纹身', accent: '贵州口音', // 证件信息 idCard: '520102198803151234', idCardIssueDate: '2018-05-20', idCardExpireDate: '2038-05-20', idCardIssueOrg: '贵阳市公安局南明分局', passport: 'E12345678', passportType: '普通', passportIssueDate: '2019-03-10', passportExpireDate: '2029-03-09', hkMacauPermit: 'C12345678', taiwanPermit: 'T12345678', driverLicense: '520101198803151234', driverLicenseType: 'C1', driverLicenseIssueDate: '2010-08-15', birthCertificate: '', marriageCertificate: 'J520102-2015-001234', divorceCertificate: '', // 户籍居住 householdType: '居民户口', householdStatus: '正常', nativePlace: '贵州省贵阳市', birthPlace: '贵州省贵阳市', registeredAddress: '贵州省贵阳市南明区花果园大街1号', registeredProvince: '贵州省', registeredCity: '贵阳市', registeredDistrict: '南明区', registeredPoliceStation: '花果园派出所', currentAddress: '贵州省贵阳市云岩区北京路123号', currentProvince: '贵州省', currentCity: '贵阳市', currentDistrict: '云岩区', currentCommunity: '北京路社区', currentGrid: '第一网格', housingType: '租赁', residenceDuration: '2年', // 联系方式 mobilePhones: ['13800123456', '13900567890'], fixedPhone: '0851-12345678', emergencyContact: '李芳(配偶)', emergencyPhone: '13700111111', email: 'zhangwei1988@qq.com', qq: '123456789', wechat: 'zhangwei88', weibo: '', dingtalk: '', workPhone: '0851-87654321' }, // 社会属性 social: { // 职业信息 occupationCategory: '个体经营', occupationDetail: '建材批发', workUnit: '贵阳市南明区伟强建材经营部', workUnitType: '个体工商', workUnitCode: '92520102MA6JXXXXXX', department: '', position: '经营者', jobTitle: '', employmentDate: '2015-06-01', workYears: 9, monthlyIncome: 15000, annualIncome: 180000, incomeSource: ['经营收入', '投资收益'], workAddress: '贵州省贵阳市南明区花果园建材市场A区12号', workPhone: '0851-12345678', isKeyPosition: false, industry: '批发零售', // 教育背景 educationLevel: '高中', degree: '无', graduateSchool: '贵阳市第一中学', schoolType: '普通', major: '', enrollmentDate: '2003-09-01', graduationDate: '2006-06-30', studentId: '', academicPerformance: '一般', certificates: ['驾驶证C1', '营业执照'], // 家庭关系 maritalStatus: '已婚', marriageDate: '2015-05-01', spouseName: '李芳', spouseIdCard: '520102199005152345', spouseOccupation: '家庭主妇', childrenCount: 1, childrenInfo: [ { name: '张小明', gender: '男', birthDate: '2016-08-20', relation: '儿子' } ], fatherName: '张建国', fatherIdCard: '520102196001011111', motherName: '王秀英', motherIdCard: '520102196202022222', siblingsCount: 1, siblingsInfo: [ { name: '张强', relation: '弟弟', occupation: '公司职员' } ], familyStructure: '核心家庭', familyAssets: '一般', // 社交网络 socialCircles: ['生意伙伴', '老乡', '车友'], closeContacts: ['李强', '王鹏', '赵明'], friendCount: 328, communityParticipation: '低', religiousBelief: '无', politicalParticipation: '无', volunteerActivities: [], onlineCommunities: ['建材交流群', '贵阳车友会'] }, // 行为特征 behavior: { // 通信行为 callFrequency: '频繁', callDurationAvg: '3.5分钟', monthlyCallCount: 450, monthlyCallDuration: 1575, contactCount: 186, frequentContacts: ['李强(建材商)', '王鹏(客户)', '赵明(老乡)'], nightCallRatio: '15%', roamingFrequency: '偶尔', internationalCall: false, smsFrequency: '较少', // 消费行为 consumptionLevel: '品质型', monthlyConsumption: 8500, consumptionCategories: { 餐饮: 2000, 交通: 1500, 购物: 3000, 娱乐: 1000, 其他: 1000 }, paymentPreference: ['微信支付', '支付宝', '银行卡'], creditCards: 3, creditLimit: 150000, loanStatus: [ { type: '房贷', amount: 500000, status: '还款中' } ], shoppingPlatforms: ['淘宝', '京东', '拼多多'], gamblingTendency: '高风险', overseasConsumption: false, // 上网行为 onlineTimeDaily: '6小时', onlineTimeDistribution: { morning: '1小时', afternoon: '2小时', evening: '3小时' }, favoriteApps: ['微信', '抖音', '淘宝', '支付宝', '高德地图'], contentPreference: ['新闻', '娱乐', '汽车', '财经'], socialActivity: '高', gamingBehavior: '偶尔玩手游', liveStreaming: '偶尔观看', vpnUsage: '偶尔', darkWebAccess: false, onlineShopping: '频繁', // 出行行为 travelMode: ['私家车', '出租车'], hasVehicle: true, vehicleCount: 2, vehicleInfo: [ { plate: '贵A·12345', brand: '奥迪', model: 'A6L', color: '黑色' }, { plate: '贵A·67890', brand: '五菱', model: '宏光', color: '银色' } ], driverBehavior: '有超速记录', activityRadius: '市内', frequentRoutes: ['家-建材市场', '家-客户公司'], borderCrossing: '无', hotelFrequency: '偶尔', transportationPreference: ['自驾', '打车'] }, // 轨迹刻画 trajectory: { // 空间轨迹 frequentLocations: [ { name: '家', address: '云岩区北京路123号', type: '住宅', frequency: '每天' }, { name: '建材市场', address: '南明区花果园建材市场', type: '工作', frequency: '每天' }, { name: '客户公司', address: '观山湖区金融城', type: '业务', frequency: '每周3次' }, { name: '某酒店', address: '南明区中华南路', type: '娱乐', frequency: '每月2次' } ], homeLocation: { lat: 26.6470, lng: 106.6302 }, workLocation: { lat: 26.5732, lng: 106.7145 }, activityHotspots: [ { name: '花果园商圈', lat: 26.5732, lng: 106.7145, intensity: '高' }, { name: '北京路', lat: 26.6470, lng: 106.6302, intensity: '高' } ], sensitiveAreasVisited: [ { area: '边境地区', visitCount: 0 }, { area: '机场', visitCount: 5 } ], borderAreasVisited: [], placeTypes: ['住宅', '工作场所', '餐饮', '娱乐场所', '交通枢纽'], // 时间轨迹 dailyRoutine: { wakeUp: '07:00', leaveHome: '08:30', arriveWork: '09:00', lunch: '12:00', leaveWork: '18:00', arriveHome: '19:00', sleep: '23:30' }, wakeUpTime: '07:00', sleepTime: '23:30', workHours: { start: '09:00', end: '18:00' }, anomalyTimeActivity: [ { time: '02:00-04:00', activity: '通话', frequency: 3 } ], overnightFrequency: '偶尔', weekendActivity: '外出应酬', // 轨迹关联 companions: [ { name: '李强', relation: '生意伙伴', coincidenceCount: 45 }, { name: '王鹏', relation: '客户', coincidenceCount: 23 } ], spatiotemporalCoincidence: [ { location: '某酒店', time: '2024-02-15', persons: ['李强', '赵明'] } ], gatheringEvents: [ { location: '花果园酒楼', time: '2024-02-10', participants: 8, type: '聚餐' } ], meetingPatterns: '定期与固定人员会面', groupActivities: ['建材商会', '老乡聚会'] }, // 风险研判 risk: { // 人员类型 focusLevel: '重点人员', personCategory: ['涉毒人员', '前科人员'], criminalRecord: [ { caseType: '贩卖毒品', caseNo: '(2018)黔01刑初123号', sentence: '有期徒刑3年', prison: '贵州省第一监狱', entryDate: '2018-06-15', releaseDate: '2021-06-14', status: '已释放' } ], drugHistory: '吸毒史', drugDetail: { drugType: '冰毒', firstUseDate: '2017-03-01', treatmentHistory: ['强制戒毒2年'], currentStatus: '社区康复' }, terroristRelated: '无', gangRelated: '无', fraudHistory: [], wantedStatus: '无', controlStatus: '已布控', entryBan: false, // 行为预警 abnormalGathering: [ { date: '2024-02-10', location: '花果园酒楼', participants: 8, alertLevel: '中' } ], sensitiveAreaAlert: [], suddenWealth: '可疑', suddenWealthDetail: '近期账户有大额资金流入,来源不明', frequentBorderCrossing: '无', communicationAnomaly: ['夜间频繁通话', '与重点人员联系'], consumptionAnomaly: ['大额现金消费', '频繁出入娱乐场所'], travelAnomaly: ['频繁更换居住地点'], // 关联分析 caseInvolved: [ { caseNo: 'A5201022024020001', caseType: '毒品案件', role: '嫌疑人', date: '2024-02-01' } ], gangMembership: [], keyContacts: [ { name: '李强', relation: '同案人员', contactType: '频繁通话' }, { name: '赵明', relation: '涉毒人员', contactType: '资金往来' } ], moneyLaundering: '中风险', undergroundBank: false, illegalOrganization: [] }, // 感知数据 perception: { // 生物特征 faceFeature: 'FACE_3A7B9C2D4E5F6G7H8I9J0K1L2M3N4O5P', facePhoto: 'available', fingerprint: 'FP_5F6G7H8I9J0K1L2M3N4O5P6Q7R8S9T0', fingerprintCount: 10, dna: 'DNA_A1B2C3D4E5F6G7H8I9J0K1L2M3N4O5P6Q7R8', iris: '', voiceprint: 'VP_9J0K1L2M3N4O5P6Q7R8S9T0U1V2W3X4', gaitFeature: '', palmPrint: '', // 设备特征 macAddresses: ['00:1A:2B:3C:4D:5E', '00:6F:7G:8H:9I:0J'], imei: ['860000011234567', '860000098765432'], imsi: ['460001234567890', '460009876543210'], deviceFingerprint: ['FP_device_001', 'FP_device_002'], phoneBrand: 'iPhone', phoneModel: 'iPhone 14 Pro', osVersion: 'iOS 17.1', installedApps: ['微信', '支付宝', '抖音', '淘宝', '高德地图', '百度'], // 位置特征 cellTower: [ { lac: '12345', ci: '67890', time: '2024-02-22 10:30:00' } ], wifiFingerprint: [ { ssid: 'ChinaNet-5G', bssid: '00:1A:2B:3C:4D:5E', signal: '-65dBm' } ], gpsTrace: [ { lat: 26.6470, lng: 106.6302, time: '2024-02-22 10:30:00' } ], bluetoothId: [], nfcRecords: [] }, // 资产信息 asset: { // 不动产 propertyCount: 1, propertyInfo: [ { address: '贵州省贵阳市南明区花果园大街1号2栋3单元401', area: 89.5, type: '住宅', value: 850000, ownership: '共有(配偶)', mortgage: true } ], propertyValue: 850000, landOwnership: [], // 车辆资产 vehicleCount: 2, vehicleDetails: [ { plate: '贵A·12345', brand: '奥迪', model: 'A6L', year: 2020, value: 350000 }, { plate: '贵A·67890', brand: '五菱', model: '宏光', year: 2018, value: 50000 } ], vehicleValue: 400000, // 金融资产 bankAccounts: [ { bank: '工商银行', account: '6222********1234', balance: 50000 }, { bank: '建设银行', account: '6227********5678', balance: 120000 } ], deposits: 170000, investments: [ { type: '股票', value: 80000 }, { type: '基金', value: 50000 } ], stocks: [ { code: '600519', name: '贵州茅台', shares: 100, value: 150000 } ], funds: [ { name: '某某混合基金', value: 50000 } ], insurance: [ { type: '寿险', value: 200000 } ], // 经营资产 companyShares: [ { company: '伟强建材', share: 100, value: 500000 } ], businessLicense: ['92520102MA6JXXXXXX'], intellectualProperty: [] }, // 健康医疗 health: { // 医疗信息 medicalInsurance: '居民医保', medicalHistory: ['骨折(2015年)'], chronicDisease: [], disabilityStatus: '无', mentalHealth: '正常', hospitalRecords: [ { date: '2015-08-10', hospital: '贵阳市第一人民医院', reason: '右腿骨折' } ], medicationRecords: [], // 健康状况 physicalCondition: '良好', psychologicalStatus: '正常', addictionStatus: ['毒品成瘾(已戒断)'], vaccination: ['新冠疫苗(3针)', '乙肝疫苗'] } }; // 维度配置 const DimensionConfig = { identity: { name: '基础身份', icon: '🆔', color: '#1890ff', categories: { demographic: { name: '人口信息', count: 12 }, document: { name: '证件信息', count: 16 }, residence: { name: '户籍居住', count: 12 }, contact: { name: '联系方式', count: 5 } } }, social: { name: '社会属性', icon: '👥', color: '#52c41a', categories: { occupation: { name: '职业信息', count: 17 }, education: { name: '教育背景', count: 10 }, family: { name: '家庭关系', count: 15 }, network: { name: '社交网络', count: 10 } } }, behavior: { name: '行为特征', icon: '📊', color: '#faad14', categories: { communication: { name: '通信行为', count: 12 }, consumption: { name: '消费行为', count: 14 }, internet: { name: '上网行为', count: 12 }, travel: { name: '出行行为', count: 10 } } }, trajectory: { name: '轨迹刻画', icon: '🗺️', color: '#f5222d', categories: { spatial: { name: '空间轨迹', count: 12 }, temporal: { name: '时间轨迹', count: 10 }, association: { name: '轨迹关联', count: 16 } } }, risk: { name: '风险研判', icon: '⚠️', color: '#722ed1', categories: { personType: { name: '人员类型', count: 14 }, behaviorAlert: { name: '行为预警', count: 14 }, relationAnalysis: { name: '关联分析', count: 14 } } }, perception: { name: '感知数据', icon: '👁️', color: '#eb2f96', categories: { biometric: { name: '生物特征', count: 10 }, device: { name: '设备特征', count: 10 }, location: { name: '位置特征', count: 15 } } }, asset: { name: '资产信息', icon: '💰', color: '#13c2c2', categories: { realEstate: { name: '不动产', count: 8 }, vehicle: { name: '车辆资产', count: 6 }, financial: { name: '金融资产', count: 10 }, business: { name: '经营资产', count: 4 } } }, health: { name: '健康医疗', icon: '🏥', color: '#ff4d4f', categories: { medical: { name: '医疗信息', count: 10 }, healthStatus: { name: '健康状况', count: 12 } } } }; // 统计数据 const ArchiveStats = { totalTags: 310, filledTags: 287, fillRate: '92.6%', riskLevel: '重点人员', riskTags: 42, lastUpdate: '2024-02-22 15:30:00', dataSource: ['公安人口', '通信运营商', '银行', '互联网'] }; // 时间轴数据 const TimelineData = [ { time: '2024-02-22 14:30', type: '通话', content: '与李强通话 5分32秒', location: '云岩区' }, { time: '2024-02-22 12:15', type: '消费', content: '餐饮消费 ¥268', location: '南明区花果园酒楼' }, { time: '2024-02-22 09:00', type: '出行', content: '驾驶贵A·12345从家到建材市场', location: '云岩区→南明区' }, { time: '2024-02-21 22:30', type: '上网', content: '使用抖音 45分钟', location: '云岩区家中' }, { time: '2024-02-21 20:00', type: '消费', content: '超市购物 ¥156', location: '南明区沃尔玛' }, { time: '2024-02-21 14:00', type: '通话', content: '与王鹏通话 12分08秒', location: '南明区建材市场' } ]; export { PersonArchiveData, DimensionConfig, ArchiveStats, TimelineData };
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🚀 New paper out in Transportation Research Part B: Methodological - ISTTT26 Special Issue 📄 "Capturing Behavioral Heterogeneity for Traffic Flow: A Scalable and Personalized Decomposition Approach" Agent heterogeneity has long been a defining characteristic of traffic flow systems, yet it remains one of the hardest to understand and to model. We show in this work how approaches of learning traffic models under heterogeneous data can mislead inference. We then propose a modeling pipeline that gives each agent its own behavioral signature while preserving population-level structure. The framework opens doors to behavioral transfer across agents (think personalizing #AutonomousVehicles behavior using learned human driving styles), feature-targeted clustering, and richer interpretation of traffic dynamics from the bottom up. 🎤 We will be presenting this work as a lectern presentation at the International Symposium on Transportation and Traffic Theory (#ISTTT26) in Munich, Germany this July! 👥 Joint work with the amazing team: Yongju Kim, Xinzhi Zhong, and Soyoung Ahn 🔗 Read it here: sciencedirect.com/science/ar… #TransportationResearch #TrafficFlow #DriverBehavior #AutomatedVehicles #Personalization #Heterogeneity

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Drivers Struggle to Navigate Around Parked Tesla. #Tesla #raybanmeta #ParkingLot #DriverBehavior #sanfrancisco
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📢 #SpecialIssue Road Markings: Technologies, Materials, and Traffic Safety 📅 20 January 2026 👨‍🔬 Guest Editors: Prof. Darko Babic from University of Zagreb Dr. Tomasz E. Burghardt from M. Swarovski GmbH 🔗mdpi.com/journal/applsci/spe… #roadmarkings #pavementmarkings #thermoplasticmarkings #roadmarkingrobots #markingdegradation #markingdetection #machinevision #maintenancecoatings #akidresistance #colordurability #roadwaysafety #trafficsafety #driverbehavior #roadwayinfrastructure
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🚚 Revolutionizing Fleet Safety with AI Camera Systems The future of fleet management is here! Explore how AI-driven camera systems are transforming road safety, reducing accidents, and enhancing driver behavior: Real-Time Alerts for immediate action. AI-Powered Insights for proactive maintenance. Comprehensive Coverage for all vehicle angles highways.today/2024/12/18/fl… #FleetManagement #AISafety #DriverBehavior #FleetCameras #RoadSafety
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How can a driver's eye movements influence the design of tomorrow's vehicles? 🚗👁️ Our latest eBook explores how #eyetracking technology is changing the way we study #driverbehavior, human-machine interaction (#HMI), and mobility trends. 📚 Download now: hubs.ly/Q02WMjF-0
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Nexdata's vast library of off-the-shelf DMS and OMS data covers head orientation, facial expression, gaze, gestures, and more. Learn more: nexdata.ai/datasets/computer… #DMS #OMS #Incabin #driverbehavior #adas #OTSdata #deeplearning
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Diesel prices are skyrocketing! 😱 How are you keeping your fleet costs under control? What's your secret weapon for optimizing fuel efficiency? 🤔 Have you considered the impact of driver behavior on fuel consumption? 🤔 Let's share ideas and learn together! Drop your top strategies in the comments below. 👇 #dieselprices #fuelcosts #transportation #logistics #trucking #costcontrol #fleetmanagement #efficiency #driverbehavior #optimization
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🚗🔊 Excited to share our latest paper with @annnhuang, @Dernshadi, @konigpeter on autonomous vehicles! We explored how warning signals impact driver behavior and situational awareness. Check it out here: mdpi.com/2624-8921/6/3/76 #AutonomousVehicles #Safety #DriverBehavior #Tech

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New paper with an incredible team @annnhuang & @JohnMadrid92 , on the role of warning in enhancing safety towards the widespread adoption of AV tech. Full read: mdpi.com/2624-8921/6/3/76 Special 🙏 @FarbodNezami & Dr. Maximilian Wächter, & supervisors Gordon Pipa & @konigpeter
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7 Mar 2024
✂ Cut the number of accidents and fatalities under your watch. We help you optimize #driverbehavior, safety, and operational efficiency by leveraging #AI and #behaviorchange tech, leading to a significant reduction in incidents and insurance claims. 👇shorturl.at/itAC4

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📱🚐 Discover how to revolutionize #FleetSafety when you leverage proven scoring tech to minimize vehicle downtime. Experience smart simplicity with #DriverCareCoPilot. Learn more @ElementFleet: lnkd.in/gxQu9_Hb #FleetManagement #FleetTech #DriverBehavior
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📱Discover how to revolutionize #FleetSafety when you leverage proven scoring tech to minimize vehicle downtime. Experience smart simplicity with #DriverCareCoPilot. Learn more: lnkd.in/gxQu9_Hb #ElementDrivesResults #FleetManagement #FleetTech #DriverBehavior
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A modern car has: #IoT #Sensors collecting data on vehicle condition & #DriverBehavior #MachineLearning algorithms converting the data to insightful reports Apps using the data to segment customers & provide individualized offers See #AI use cases in #Automotive industry⬇️
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Discover how remote fleet management solutions prioritize safety by monitoring driver behaviour, reducing accidents, and protecting valuable cargo. Safety first, always! Call 0711333555 to book your installation. #SafetyFirst #DriverBehavior #FleetSafety
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21 Jul 2023
In Session 1 of #OneWeekOneLab, Dr. Ch. Ravi Sekhar from @CSIRCRRI presented a fascinating examination of #DriverBehavior using DMS Data. The session was enlightening & insightful, attendees were captivated by Dr. Ravi's expertise & in-depth knowledge in field of #DriverBehavior.
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4 Jul 2023
Did you know that MSRB utilizes cutting-edge technology to analyze driver behavior and promote safe driving habits? Stay tuned for more details! #MSRB #SafeDriving #DriverBehavior #Innovation
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19 Jun 2023
🚀✨ Introducing our New Release: AI Dual DashCam Protect Your Fleet, Protect Your Future with HyperNet AI Dual Dashcam! Elevate fleet safety and efficiency with our cutting-edge AI Dashcam. #aidashcam #fleetmanagement #safetyfirst #security #video #ai #safety #driverbehavior
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