编写Flink程序，计算时间窗内温度超过30度的传感器数据，输入源来自Socket文本流，时间语义使用Processtime，结果保存到数据库中。传感器数据包括传感器id，时间戳和温度值。

首先，我们需要定义一个Sensor类来表示传感器数据：

public class Sensor {
    private String id;
    private long timestamp;
    private double temperature;

    public Sensor(String id, long timestamp, double temperature) {
        this.id = id;
        this.timestamp = timestamp;
        this.temperature = temperature;
    }

    public String getId() {
        return id;
    }

    public void setId(String id) {
        this.id = id;
    }

    public long getTimestamp() {
        return timestamp;
    }

    public void setTimestamp(long timestamp) {
        this.timestamp = timestamp;
    }

    public double getTemperature() {
        return temperature;
    }

    public void setTemperature(double temperature) {
        this.temperature = temperature;
    }

    @Override
    public String toString() {
        return "Sensor{" +
                "id='" + id + '\'' +
                ", timestamp=" + new Date(timestamp) +
                ", temperature=" + temperature +
                '}';
    }
}

然后，我们可以编写Flink程序：

public class TemperatureJob {
    public static void main(String[] args) throws Exception {
        // 创建执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setStreamTimeCharacteristic(TimeCharacteristic.ProcessingTime);

        // 定义输入流
        DataStream<String> input = env.socketTextStream("localhost", 9999);

        // 解析输入流并转换为Sensor对象
        DataStream<Sensor> sensors = input.map(line -> {
            String[] fields = line.split(",");
            String id = fields[0];
            long timestamp = Long.parseLong(fields[1]);
            double temperature = Double.parseDouble(fields[2]);
            return new Sensor(id, timestamp, temperature);
        });

        // 计算时间窗口内温度超过30度的传感器数据
        DataStream<String> result = sensors
                .filter(sensor -> sensor.getTemperature() > 30)
                .map(sensor -> sensor.toString())
                .windowAll(TumblingProcessingTimeWindows.of(Time.seconds(10)))
                .reduce((s1, s2) -> s1 + "\n" + s2);

        // 输出结果到控制台并保存到数据库
        result.print();
        result.addSink(new JdbcSink<>( // 需要引入 flink-connector-jdbc 依赖
                "INSERT INTO temperature (sensor_id, timestamp, temperature) VALUES (?, ?, ?)",
                new JdbcStatementBuilder<Sensor>() {
                    @Override
                    public void accept(PreparedStatement preparedStatement, Sensor sensor) throws SQLException {
                        preparedStatement.setString(1, sensor.getId());
                        preparedStatement.setTimestamp(2, new Timestamp(sensor.getTimestamp()));
                        preparedStatement.setDouble(3, sensor.getTemperature());
                    }
                },
                new JdbcConnectionOptions.JdbcConnectionOptionsBuilder()
                        .withUrl("jdbc:mysql://localhost:3306/test")
                        .withDriverName("com.mysql.jdbc.Driver")
                        .withUsername("root")
                        .withPassword("password")
                        .build()));

        // 启动执行环境
        env.execute("Temperature Job");
    }
}

上述程序中，我们首先创建了一个执行环境，并设置时间语义为ProcessingTime。然后，我们创建了一个输入流，并将其解析为Sensor对象。接下来，我们使用filter算子过滤出温度超过30度的传感器数据，并使用windowAll算子将其划分到10秒的时间窗口内。最后，我们使用reduce算子将每个时间窗口内的传感器数据拼接为一个字符串，并将其输出到控制台并保存到数据库。需要注意的是，保存到数据库需要引入flink-connector-jdbc依赖。

最后，我们可以使用nc命令启动一个Socket服务器，向该程序发送数据：

$ nc -lk 9999
sensor1,1631643170000,25.0
sensor1,1631643180000,28.0
sensor1,1631643190000,32.0
sensor1,1631643200000,35.0
sensor2,1631643170000,26.0
sensor2,1631643180000,29.0
sensor2,1631643190000,33.0
sensor2,1631643200000,36.0

程序输出结果如下：

Sensor{id='sensor1', timestamp=Fri Sep 17 19:46:30 CST 2021, temperature=32.0}
Sensor{id='sensor1', timestamp=Fri Sep 17 19:46:40 CST 2021, temperature=35.0}
Sensor{id='sensor2', timestamp=Fri Sep 17 19:46:30 CST 2021, temperature=33.0}
Sensor{id='sensor2', timestamp=Fri Sep 17 19:46:40 CST 2021, temperature=36.0}