How to use Workerman to implement a real-time location-based recommendation system

With the development of mobile Internet and people's increasing demand for personalized recommendations, real-time location-based recommendation systems are becoming more and more important. As a high-performance framework for PHP, Workerman can easily build a real-time recommendation system. This article will mainly introduce how to use Workerman to implement a real-time location-based recommendation system and provide specific code examples.

1. Determine the system architecture

When implementing a location-based real-time recommendation system, we need to consider the following issues:

(1) How to obtain the user’s location information?

(2) How to store location information in the database?

(3) How to calculate the distance between two users?

(4) How to update the recommendation results in real time?

To address the above problems, we can adopt the following system architecture:

(1) Use the geolocation API of HTML5 to obtain the user's location information.

(2) Store location information in the MySQL database.

(3) Calculate the distance between two users by using the haversine formula.

(4) Calculate the recommendation results in real time on the server side and return them to the client.

2. Client implementation

First, we need to use the geolocation API in HTML5 to obtain the user's location information:

if (navigator.geolocation) {
    navigator.geolocation.getCurrentPosition(showPosition);
} else {
    alert("Geolocation API is not supported in your browser.");
}
 
function showPosition(position) {
    var lat = position.coords.latitude;
    var lng = position.coords.longitude;
 
    // 将经纬度发送到服务器端进行处理
    var xhr = new XMLHttpRequest();
    xhr.open("POST", "http://localhost:2345/savePosition.php", true);
    xhr.setRequestHeader("Content-type", "application/x-www-form-urlencoded");
    xhr.send("lat=" + lat + "&lng=" + lng);
}

Here we pass the latitude and longitude through POST The request is sent to the savePosition.php file on the server side for processing.

On the server side, we can use Workerman's MySQL class to store location information into a MySQL database:

require_once __DIR__ . '/vendor/autoload.php';
use WorkermanMySQLConnection;
 
$db = new Connection('localhost', '3306', 'root', 'password', 'dbname');
 
$lat = $_POST['lat'];
$lng = $_POST['lng'];
 
$db->insert('user_position', array('lat' => $lat, 'lng' => $lng));

Here we store the user's location information in a table named user_position.

3. Server-side implementation

In order to calculate the distance between two users, we can use the haversine formula.

The implementation of the haversine formula is as follows:

DELTA_LATITUDE = LATITUDE_B - LATITUDE_A
DELTA_LONGITUDE = LONGITUDE_B - LONGITUDE_A
a = sin(DELTA_LATITUDE/2)^2 + cos(LATITUDE_A) * cos(LATITUDE_B) * sin(DELTA_LONGITUDE/2)^2
c = 2 * atan2(sqrt(a), sqrt(1-a))
DISTANCE = EARTH_RADIUS * c

In PHP, the code to implement the haversine formula is as follows:

function haversineDistance($lat1, $lng1, $lat2, $lng2)
{
    $earth_radius = 6371;
 
    $delta_latitude = deg2rad($lat2 - $lat1);
    $delta_longitude = deg2rad($lng2 - $lng1);
 
    $a = sin($delta_latitude / 2) * sin($delta_latitude / 2) + cos(deg2rad($lat1)) * cos(deg2rad($lat2)) * sin($delta_longitude / 2) * sin($delta_longitude / 2);
    $c = 2 * atan2(sqrt($a), sqrt(1 - $a));
    $distance = $earth_radius * $c;
 
    return $distance;
}

Through the above code, we can calculate the relationship between two users Based on the distance and the user's interests and hobbies information, we can calculate the recommendation results in real time and return them to the client. The code is implemented as follows:

function getRecommendations($user_id, $lat, $lng)
{
    $earth_radius = 6371;
    $max_distance = 20;
 
    $query = "SELECT id, lat, lng, interests FROM user_position WHERE id != '$user_id'";
    $result = $db->query($query);
 
    $recommendations = array();
 
    while ($row = mysqli_fetch_assoc($result)) {
        $distance = haversineDistance($lat, $lng, $row['lat'], $row['lng']);
 
        if ($distance <= $max_distance) {
            $interests = explode(",", $row['interests']);
            $common_interests = array_intersect($user_interests, $interests);
 
            if (count($common_interests) > 0) {
                $recommendations[] = $row['id'];
            }
        }
    }
 
    return $recommendations;
}

4. Summary

Through this article, we learned how to use Workerman to implement a real-time location-based recommendation system and provided specific code examples. The real-time recommendation system is a very practical application and has broad application prospects in the business field, social networks, etc. I hope this article can help you understand how to use Workerman to implement a real-time recommendation system.

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