个人信息日志：公园游记揭示数据追踪全景

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"《你的数据的一天：一次公园之旅的隐私窥探》\n\n在这份报告中，作者探讨了现代个人数据收集的深度和广泛性。自2010年以来，随着数字时代的加速发展，一个庞大的、隐藏在背后的个人数据产业正在悄然膨胀。这个由各种在线平台、应用程序、社交媒体公司、数据代理商和广告技术公司组成的网络，不仅在线上追踪用户，也在线下的日常生活中渗透，如李先生和他女儿迎迎在公园游玩的一天中所体现的那样。\n\n在李先生查询天气和交通信息的过程中，他使用的地图应用和多个后台运行的app实际上内置了跟踪器，这些追踪器可能来自第三方代码。这些应用不仅收集位置数据，还可能与开发者共享，使得数据代理商得以获取并销售这些匿名化但其实可以被关联的个人信息。数据代理商并不直接与用户接触，而是作为中介，将个人数据出售给那些对这些信息感兴趣的第三方，不论他们是广告商、研究机构还是其他商业实体。\n\n在迎迎玩耍的途中，她的活动同样被记录。通过跨app和多来源的跟踪，即使是看似简单的游戏也可能成为构建个人档案的一部分，这些档案详尽地记录了用户的日常生活习惯和行为模式，尽管这可能并未得到用户的明确知情和同意。Steve Jobs曾经强调，保护用户数据隐私的重要性，强调在使用数据时必须征得用户的明确同意，并告知他们数据的具体用途。\n\n这份报告揭示了我们在日常活动中数据收集的隐蔽性，提醒我们，尽管技术带来了便利，但同时也带来了个人隐私的新挑战。它强调了数据所有权和透明度的问题，呼吁公众和监管机构更加关注数据隐私和安全，以确保我们的数字生活不会被过度侵犯。"

eq_data_30_day_m1.rar

2021-06-21 上传
标题中的"eq_data_30_day_m1.rar"是一个压缩文件，通常用于存储多个相关文件或数据集。根据描述，这个文件与《Python:从入门到实践》这本书的第16章有关，这意味着它可能包含了一个与Python编程、数据分析或者可视化...

eq_data_30_day_m1.json

2021-03-16 上传
30天全球地震数据集

优化sql语句update jc_content set views_day=(select contentcou1_.views_day from jc_content_count contentcou1_ where jc_content.content_id=contentcou1_.content_id), comments_day=(select contentcou2_.comments_day from jc_content_count contentcou2_ where jc_content.content_id=contentcou2_.content_id), downloads_day=(select contentcou3_.downloads_day from jc_content_count contentcou3_ where jc_content.content_id=contentcou3_.content_id), ups_day=(select contentcou4_.ups_day from jc_content_count contentcou4_ where jc_content.content_id=contentcou4_.content_id)

2023-05-26 上传
SET jc_content.views_day = jc_content_count.views_day, jc_content.comments_day = jc_content_count.comments_day, jc_content.downloads_day = jc_content_count.downloads_day, jc_content.ups_day = jc_...

SELECT bs.sample_id, bs.item_id, bs.report_id, bs.order_no, bs.order_id, bs.order_business_type, bs.commission_date, bs.customer_name, bs.applicant, bs.phone, bs.receive_user_name, bs.contract_no, bs.special_requirements, bs.report_org_name, bs.report_org_address, bs.sample_name, bs.standard_instrument_name, bs.complete_day, bs.sample_remark AS remark, bs.standard_instrument_id, bs.sample_no, bs.factory_number, bs.item_name, /*bs.item_quantity,*/ bs.inspection_type, bs.mandatory_flag, bs.test_quantity, bs.sample_state, bs.current_site, bs.plan_complete_date, bs.affix, bs.ranges, bs.grade, bs.factory, bs.calibrat_point, bs.apply_dept, bs.specification, bs.final_fee, bs.service_type, CASE WHEN bs.actual_complete_date IS NOT NULL THEN DATEDIFF( bs.plan_complete_date, bs.actual_complete_date ) ELSE datediff( bs.plan_complete_date, now()) END AS surplus_days, bs.report_no, bs.is_report_back, bs.back_reason AS report_back_reason, bs.is_just_certificate, bs.report_state, bs.temper, bs.humidity, bs.test_result, bs.test_date, bs.next_test_date, bs.test_cycle, bs.test_address, bs.generate_time, bs.point_report_id, bs.is_merge, bs.circulation_flag, bs.item_proposal_fee AS proposal_fee, bs.change_price_reason, bs.test_user_name, bs.group_id, bs.group_name, bs.charging_num, bs.other_fee, bs.receivable_fee, bs.affix_quantity, bs.test_org, bs.out_org_order_no, bs.out_org_sample_no, bs.business_user_name, bs.pdf_path, bs.settlement_state, bs.result_describe, bsa.attach_id FROM view_sample_info bs JOIN bus_sample_report bsr ON bs.report_id = bsr.id JOIN bus_sample sa ON bsr.sample_id = sa.id JOIN bus_sample_attr bsa ON sa.id = bsa.id 根据bs.commission_date 进行排序最近的排上面 bs.commission_date

2023-07-15 上传
bs.complete_day, bs.sample_remark AS remark, bs.standard_instrument_id, bs.sample_no, bs.factory_number, bs.item_name, /*bs.item_quantity,*/ bs.inspection_type, bs.mandatory_flag, bs.test_...

select case when current_data.role_id is not null and rete_data.role_id is not null then concat(current_data.role_id,current_data.server) else null end as rete_role_id, concat(rete_data.role_id,rete_data.server) as b_role_id, c.is_ugc, rete_data.server, rete_data.dt from ( select distinct dt,role_id,server from ods_p1_day where dt between 20210715 and 20210812 and server in (21001,21002) ) rete_data left join ( select distinct dt,role_id,server from ods_p1_day where dt between 20210721 and 20210818 and server in (21001,21002) ) current_data on current_data.role_id=rete_data.role_id and current_data.server =rete_data.server and current_data.dt = date_add(rete_data.dt,6)有什么错误

2023-06-01 上传
该 SQL 语句使用两个子查询来获取 `ods_p1_day` 表中特定日期范围内的独特的 `role_id` 和 `server` 组合，然后使用左连接将这些组合连接起来。其中 `current_data` 子查询使用 `date_add` 函数将 `rete_data` 子...

INSERT INTO target_table (biz_date, county_name, depart_name, person_uuid, cust_code, cust_name, terminal_level_name, data_gather_type_name, sc_type_name, sc_score, sc_type_name_old, sc_score_old, up) SELECT a.biz_date, a.county_name, a.depart_name, a.person_uuid, a.cust_code, a.cust_name, a.terminal_level_name, a.data_gather_type_name, a.sc_type_name, a.sc_score, b.sc_type_name AS sc_type_name_old, b.sc_score AS sc_score_old, a.sc_score - b.sc_score AS up FROM ads_yh_yx_sctwo a LEFT JOIN ads_yh_yx_sctwo b ON a.cust_code = b.cust_code AND b.biz_date = DATE_FORMAT(DATE_SUB(NOW(), INTERVAL 5 DAY), '%Y%m%d') AND b.person_uuid = '00000000001389361632876_5304_371' WHERE a.biz_date = DATE_FORMAT(DATE_SUB(NOW(), INTERVAL 1 DAY '%Y%m%d') AND a.person_uuid = '00000000001389361632876_5304_371' AND a.sc_type_name != b.sc_type_name AND (a.sc_score - b.sc_score) <> 0 查找此sql的问题

2023-07-15 上传
a.data_gather_type_name, a.sc_type_name, a.sc_score, b.sc_type_name AS sc_type_name_old, b.sc_score AS sc_score_old, a.sc_score - b.sc_score AS up FROM ads_yh_yx_sctwo a LEFT JOIN ads_yh_yx_...

SELECT t.*, c.manage_person_mobile as phone FROM ( SELECT a.biz_date, a.county_name, a.depart_name, a.person_uuid, a.cust_code, a.cust_name, a.terminal_level_name, a.data_gather_type_name, a.sc_type_name, a.sc_score, b.sc_type_name AS sc_type_name_old, b.sc_score AS sc_score_old, a.sc_score - b.sc_score AS up FROM ads_yh_yx_sctwo a LEFT JOIN ads_yh_yx_sctwo b ON a.cust_code = b.cust_code AND b.biz_date = DATE_FORMAT(DATE_SUB(NOW(), INTERVAL 2 DAY), '%Y%m%d') AND b.person_uuid = '00000000001389361632876_5304_371' WHERE a.biz_date = DATE_FORMAT(DATE_SUB(NOW(), INTERVAL 1 DAY), '%Y%m%d') AND a.person_uuid = '00000000001389361632876_5304_371' AND a.sc_type_name <> b.sc_type_name AND (a.sc_score - b.sc_score) <> 0 ) t LEFT JOIN ads_yh_yx_customer_login c ON t.cust_code = c.cust_code; 优化以上代码

2023-07-16 上传
a.data_gather_type_name, a.sc_type_name, a.sc_score, b.sc_type_name AS sc_type_name_old, b.sc_score AS sc_score_old, a.sc_score - b.sc_score AS up FROM ads_yh_yx_sctwo a LEFT JOIN ads_yh_yx...

console.log("list"); console.log(list); console.log(list.length); for (let i = 0; i < list.length; i++) { const state = list[i].DATE_TYPE; const info = list[i].INFO;//时间 for (let z = 0; z < list[i].INFO.length; z++) { for (let j = 0; j < this.report_day_data.series.length; j++) { if (this.report_day_data.series[j].name == state || (this.report_day_data.series[j].name === "OEE" && state === "PROD")) { this.report_day_data.series[j].data.push(info[z].BASE_DATA); } } }

2023-06-13 上传
这一行代码将数组 `info` 中当前遍历到的元素的 `BASE_DATA` 属性值添加到数组 `report_day_data` 中当前遍历到的 `series` 对象的 `data` 属性值中。 总的来说，这段代码的作用是将数组 `list` 中的数据转换为数组...

sql1 = " SELECT a.biz_date, a.county_name, a.depart_name, a.person_uuid, a.cust_code, a.cust_name, a.terminal_level_name, a.data_gather_type_name, a.sc_type_name, a.sc_score, b.sc_type_name AS sc_type_name_old, b.sc_score AS sc_score_old, a.sc_score-b.sc_score AS up FROM ads_yh_yx_sctwo a LEFT JOIN ads_yh_yx_sctwo b ON a.cust_code = b.cust_code AND b.biz_date = DATE_FORMAT(DATE_SUB(NOW(), INTERVAL 5 DAY), '%Y%m%d') AND b.person_uuid = '00000000001389361632876_5304_371' WHERE a.biz_date = DATE_FORMAT(DATE_SUB(NOW(), INTERVAL 1 DAY), '%Y%m%d') AND a.person_uuid = '00000000001389361632876_5304_371' AND a.sc_type_name != b.sc_type_name AND (a.sc_score - b.sc_score) <> 0; "; $result1 = mysqli_query($conn1, $sql1); 如何把查出来的多条记录再插入对应的表里

2023-07-15 上传
a.data_gather_type_name, a.sc_type_name, a.sc_score, b.sc_type_name AS sc_type_name_old, b.sc_score AS sc_score_old, a.sc_score - b.sc_score AS up FROM ads_yh_yx_sctwo a LEFT JOIN ads_yh_yx_...

import pandas as pd import numpy as np import os from pprint import pprint from pandas import DataFrame from scipy import interpolate data_1_hour_predict_raw = pd.read_excel('./data/附件1 监测点A空气质量预报基础数据.xlsx' ) data_1_hour_actual_raw = pd.read_excel('./data/附件1 监测点A空气质量预报基础数据.xlsx' ) data_1_day_actual_raw = pd.rea df_1_predict = data_1_hour_actual_raw df_1_actual = data_1_day_actual_raw df_1_predict.set_axis( ['time', 'place', 'so2', 'no2', 'pm10', 'pm2.5', 'o3', 'co', 'temperature', 'humidity', 'pressure', 'wind', 'direction'], axis='columns', inplace=True) df_1_actual.set_axis(['time', 'place', 'so2', 'no2', 'pm10', 'pm2.5', 'o3', 'co'], axis='columns', inplace=True) modeltime_df_actual = df_1_actual['time'] modeltime_df_pre = df_1_predict['time'] df_1_actual = df_1_actual.drop(columns=['place', 'time']) df_1_predict = df_1_predict.drop(columns=['place', 'time']) df_1_predict = df_1_predict.replace('—', np.nan) df_1_predict = df_1_predict.astype('float') df_1_predict[df_1_predict < 0] = np.nan # 重新插入time列 df_1_actual.insert(0, 'time', modeltime_df_actual) df_1_predict.insert(0, 'time', modeltime_df_pre) # 线性插值的方法需要单独处理最后一行的数据 data_1_actual = df_1_actual[0:-3] data_1_predict = df_1_predict data_1_predict.iloc[-1:]['pm10'] = 22.0 data_1_actual_knn = df_1_actual[0:-3] data_1_predict_knn: DataFrame = df_1_predict for indexs in data_1_actual.columns: if indexs == 'time': continue data_1_actual['rownum'] = np.arange(data_1_actual.shape[0]) df_nona = data_1_actual.dropna(subset=[indexs]) f = interpolate.interp1d(df_nona['rownum'], df_nona[indexs]) data_1_actual[indexs] = f(data_1_actual['rownum']) data_1_actual = data_1_actual.drop(columns=['rownum']) for indexs in data_1_predict.columns: if indexs == 'time': continue data_1_predict['rownum'] = np.arange(data_1_predict.shape[0]) df_nona = data_1_predict.dropna(subset=[indexs]) f = interpolate.interp1d(df_nona['rownum'], df_nona[indexs]) data_1_predict[indexs] = f(data_1_predict['rownum']) data_1_predict = data_1_predict.drop(columns=['rownum']) writer = pd.E

2023-06-01 上传
data_1_day_actual_raw = pd.read_excel('./data/附件1 监测点A空气质量预报基础数据.xlsx' ) ``` 3. 对读取的数据进行处理： ``` df_1_predict = data_1_hour_actual_raw df_1_actual = data_1_day_actual_raw df...
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