随着城市化进程的加速和工业化水平的提高，空气污染已成为全球性的环境问题之一，严重威胁人类健康和生态环境。在这种情况下，精准地预测和监测空气质量变化变得尤为重要。传统的空气质量预测方法往往面临着数据量大、复杂性高等挑战，难以有效应对大数据时代带来的需求。因此，开展基于大数据的空气质量预测研究具有迫切的现实意义。本案例结合大数据技术和人工智能算法，建立空气质量监测数据的预测模型。本案例选取了华北地区的西安、华中地区的武汉以及华南地区的深圳这三个城市为研究对象，覆盖了从2021年1月到2024年3月期间的数据，分别运用主要选择BP神经网络、LSTM和SVR 三种常见的神经网络进行空气质量指数预测，比较预测的准确性。使学生掌握空气质量指数分析与预测的一般方法，培养学生运用Python语言进行数据分析的能力。
"
["minPrice"]=>
string(4) "0.00"
["maxPrice"]=>
string(4) "0.00"
["discountId"]=>
string(1) "0"
["images"]=>
array(3) {
["large"]=>
string(75) "http://www.chinadatacase.com/files/course/2025/03-20/10122264f3a4534165.jpg"
["middle"]=>
string(75) "http://www.chinadatacase.com/files/course/2025/03-20/1012226560db018296.jpg"
["small"]=>
string(75) "http://www.chinadatacase.com/files/course/2025/03-20/1012226567ce738626.jpg"
}
["ratingNum"]=>
string(1) "0"
["rating"]=>
string(1) "0"
["hitNum"]=>
string(3) "112"
["hotSeq"]=>
string(1) "0"
["maxPriceObj"]=>
array(2) {
["currency"]=>
string(3) "RMB"
["amount"]=>
string(4) "0.00"
}
["minPriceObj"]=>
array(2) {
["currency"]=>
string(3) "RMB"
["amount"]=>
string(4) "0.00"
}
["minDisplayPrice"]=>
string(4) "0.00"
["maxDisplayPrice"]=>
string(4) "0.00"
["minDisplayPriceObj"]=>
array(2) {
["currency"]=>
string(3) "RMB"
["amount"]=>
string(4) "0.00"
}
["maxDisplayPriceObj"]=>
array(2) {
["currency"]=>
string(3) "RMB"
["amount"]=>
string(4) "0.00"
}
["canManage"]=>
bool(false)
["peopleShowNum"]=>
string(3) "105"
["isMember"]=>
bool(false)
["status"]=>
string(7) "created"
["orgId"]=>
string(1) "1"
["orgCode"]=>
string(2) "1."
["recommendWeight"]=>
string(1) "0"
["recommendedTime"]=>
string(1) "0"
["createdTime"]=>
string(25) "2025-01-13T17:01:15+08:00"
["updatedTime"]=>
string(25) "2025-12-03T22:26:08+08:00"
["product"]=>
array(7) {
["id"]=>
string(4) "2070"
["targetType"]=>
string(6) "course"
["title"]=>
string(66) "基于大数据分析技术的空气质量指数预测实验案例"
["owner"]=>
string(1) "3"
["createdTime"]=>
string(10) "1736758875"
["updatedTime"]=>
string(10) "1742436784"
["target"]=>
array(17) {
["id"]=>
string(4) "2191"
["type"]=>
string(6) "normal"
["title"]=>
string(66) "基于大数据分析技术的空气质量指数预测实验案例"
["subtitle"]=>
string(0) ""
["summary"]=>
string(1018) "随着城市化进程的加速和工业化水平的提高，空气污染已成为全球性的环境问题之一，严重威胁人类健康和生态环境。在这种情况下，精准地预测和监测空气质量变化变得尤为重要。传统的空气质量预测方法往往面临着数据量大、复杂性高等挑战，难以有效应对大数据时代带来的需求。因此，开展基于大数据的空气质量预测研究具有迫切的现实意义。本案例结合大数据技术和人工智能算法，建立空气质量监测数据的预测模型。本案例选取了华北地区的西安、华中地区的武汉以及华南地区的深圳这三个城市为研究对象，覆盖了从2021年1月到2024年3月期间的数据，分别运用主要选择BP神经网络、LSTM和SVR 三种常见的神经网络进行空气质量指数预测，比较预测的准确性。使学生掌握空气质量指数分析与预测的一般方法，培养学生运用Python语言进行数据分析的能力。
"
["cover"]=>
array(3) {
["large"]=>
string(75) "http://www.chinadatacase.com/files/course/2025/03-20/10122264f3a4534165.jpg"
["middle"]=>
string(75) "http://www.chinadatacase.com/files/course/2025/03-20/1012226560db018296.jpg"
["small"]=>
string(75) "http://www.chinadatacase.com/files/course/2025/03-20/1012226567ce738626.jpg"
}
["status"]=>
string(9) "published"
["studentNum"]=>
string(3) "105"
["discountType"]=>
string(8) "discount"
["discount"]=>
string(2) "10"
["minCoursePrice"]=>
string(4) "0.00"
["maxCoursePrice"]=>
string(4) "0.00"
["defaultCourseId"]=>
string(4) "2197"
["productId"]=>
string(4) "2070"
["goodsId"]=>
string(4) "2069"
["minCoursePrice2"]=>
array(2) {
["currency"]=>
string(3) "RMB"
["amount"]=>
string(4) "0.00"
}
["maxCoursePrice2"]=>
array(2) {
["currency"]=>
string(3) "RMB"
["amount"]=>
string(4) "0.00"
}
}
}
["extensions"]=>
array(3) {
[0]=>
string(8) "teachers"
[1]=>
string(14) "recommendGoods"
[2]=>
string(10) "isFavorite"
}
["specs"]=>
array(1) {
[0]=>
array(26) {
["id"]=>
string(4) "2096"
["goodsId"]=>
string(4) "2069"
["targetId"]=>
string(4) "2197"
["title"]=>
string(0) ""
["seq"]=>
string(1) "1"
["status"]=>
string(9) "published"
["price"]=>
string(4) "0.00"
["coinPrice"]=>
string(4) "0.00"
["usageMode"]=>
string(7) "forever"
["usageDays"]=>
string(1) "0"
["usageStartTime"]=>
string(1) "0"
["usageEndTime"]=>
string(1) "0"
["buyableStartTime"]=>
string(1) "0"
["buyableEndTime"]=>
string(1) "0"
["buyableMode"]=>
NULL
["buyable"]=>
string(1) "1"
["maxJoinNum"]=>
string(1) "0"
["services"]=>
array(0) {
}
["priceObj"]=>
array(2) {
["currency"]=>
string(3) "RMB"
["amount"]=>
string(4) "0.00"
}
["displayPrice"]=>
string(4) "0.00"
["displayPriceObj"]=>
array(2) {
["currency"]=>
string(3) "RMB"
["amount"]=>
string(4) "0.00"
}
["isMember"]=>
bool(false)
["access"]=>
array(2) {
["code"]=>
string(14) "user.not_login"
["msg"]=>
string(15) "用户未登录"
}
["hasCertificate"]=>
bool(false)
["learnUrl"]=>
string(43) "http://www.chinadatacase.com/my/course/2197"
["teachers"]=>
array(1) {
[0]=>
array(6) {
["id"]=>
string(1) "3"
["nickname"]=>
string(5) "admin"
["title"]=>
string(1) " "
["uuid"]=>
string(40) "634d3b58166bfafd4069119be97ee6bfee064c52"
["destroyed"]=>
string(1) "0"
["avatar"]=>
array(3) {
["small"]=>
string(75) "http://www.chinadatacase.com/files/user/otherform/1711697442_1722994936.png"
["middle"]=>
string(75) "http://www.chinadatacase.com/files/user/otherform/1711697442_1722994936.png"
["large"]=>
string(75) "http://www.chinadatacase.com/files/user/otherform/1711697442_1722994936.png"
}
}
}
}
}
}
{
"i18nChapterName": "章",
"i18nUnitName": "节",
"i18nLessonName": "课时",
"i18nTaskName":"任务"
}
{
"text": {
"icon": "es-icon es-icon-graphic",
"name": "图文"
}
,
"video": {
"icon": "es-icon es-icon-video",
"name": "视频"
}
,
"audio": {
"icon": "es-icon es-icon-audio",
"name": "音频"
}
,
"live": {
"icon": "es-icon es-icon-entry-live",
"name": "直播"
}
,
"discuss": {
"icon": "es-icon es-icon-discuss",
"name": "讨论"
}
,
"flash": {
"icon": "es-icon es-icon-flash",
"name": "Flash"
}
,
"doc": {
"icon": "es-icon es-icon-document",
"name": "文档"
}
,
"ppt": {
"icon": "es-icon es-icon-ppt",
"name": "PPT"
}
,
"testpaper": {
"icon": "es-icon es-icon-examination",
"name": "考试"
}
,
"homework": {
"icon": "es-icon es-icon-task",
"name": "作业"
}
,
"exercise": {
"icon": "es-icon es-icon-exercise",
"name": "练习"
}
,
"download": {
"icon": "es-icon es-icon-downloadfile",
"name": "下载资料"
}
,
"cases": {
"icon": "es-icon es-icon-school",
"name": "实验"
}
}
1765608822
