除了其他答案之外,您可能会考虑使用笛卡尔坐标(x、y 和 z)而不是 lat/lng 进行 db 存储,因为生成的查询表达式在 db 服务器上的负载/时间方面比可能的更简单查询纬度/经度距离。
可以在以下位置找到 PHP 实现的示例:
http://headers-already-sent.com/geodistance/
“getCartesian”方法将 lat/lng 转换为笛卡尔坐标,“getDistanceByCartesian”方法显示如何计算实际距离。您需要做的是将这个距离计算从 PHP 转移到 SQL 查询(这不应该那么复杂)。
编辑,因为我有时间给出一个更实际的例子
根据您可以在上述链接下找到的课程,我为我的公司位置和附近的所有 MC Donalds 餐厅设置了 2 个演示表,并将 lat/lng 从 Google 地图转换为笛卡尔 x、y、z:
CREATE TABLE `locations` (
`id` int(11) unsigned NOT NULL AUTO_INCREMENT,
`title` varchar(255) NOT NULL DEFAULT '',
`lat` double NOT NULL,
`lng` double NOT NULL,
`x` double NOT NULL,
`y` double NOT NULL,
`z` double NOT NULL,
PRIMARY KEY (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8;
INSERT INTO `locations` (`id`, `title`, `lat`, `lng`, `x`, `y`, `z`)
VALUES
(1,'Ida-Ehre-Platz 10, 20095 Hamburg',53.55053,9.99949,3727600.05477,657242.251356,5124712.81705),
(2,'Kieler Straße 191-193, 22525 Hamburg',53.57731,9.93686,3725956.4981,652753.812254,5126481.40905),
(3,'Reeperbahn 42, 20359 Hamburg',53.549951,9.964937,3728046.74189,655003.113578,5124674.56664),
(4,'Theodor-Heuss-Platz 3, 20354 Hamburg',53.56083,9.99038,3726797.15378,656489.722425,5125393.17725),
(5,'Mundsburger Damm 67, 22087 Hamburg',53.57028,10.02642,3725550.98379,658686.623655,5126017.24553),
(6,'Paul-Nevermann-Platz 1, 22765 Hamburg',53.552602,9.936678,3728135.78521,653123.397726,5124849.69505),
(7,'Friedrich-Ebert-Damm 101, 22047 Hamburg',53.58753,10.08958,3723303.02881,662522.688778,5127156.05819),
(8,'Amsinckstraße 73, 20097 Hamburg',53.54271,10.02654,3727978.07563,659123.791421,5124196.16112),
(9,'Eiffestraße 440, 20537 Hamburg',53.55214,10.04638,3726919.13256,660267.521487,5124819.17553);
CREATE TABLE `user` (
`id` int(11) unsigned NOT NULL AUTO_INCREMENT,
`name` varchar(255) NOT NULL DEFAULT '',
`lat` double NOT NULL,
`lng` double NOT NULL,
`x` double NOT NULL,
`y` double NOT NULL,
`z` double NOT NULL,
PRIMARY KEY (`id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8;
INSERT INTO `user` (`id`, `name`, `lat`, `lng`, `x`, `y`, `z`)
VALUES
(1,'Ministry.BBS, Cremon 36, 20457 Hamburg',53.545943,9.988761,3728127.10678,656615.385203,5124409.77226),
(2,'BBS, Dorotheenstraße 60, 22301 Hamburg',53.583231,10.008315,3724617.80169,657307.963226,5126872.28974);
基于这两个表,用于查找到每个用户(我们公司办公室)一定距离(2000 年,在本例中以米为单位)内的所有位置(餐厅)的 SQL 查询将是:
SELECT locations.*,
2 * 6371000.785 *
asin(
sqrt(
pow(locations.x - user.x, 2)
+ pow(locations.y - user.y, 2)
+ pow(locations.z - user.z, 2)
) / (2 * 6371000.785)
) AS distance
FROM locations, user
HAVING distance < 2000
ORDER BY distance ASC
如果您需要“米”以外的其他内容,则必须将地球半径更改为大约。 6371000.785(以米为单位)更改为您需要的任何值,并将所需距离 2000 更改为您喜欢的任何值或存储在每个用户的用户表中。