要将窗口按时间属性推送到流中，可以使用流处理框架，如Apache Flink，下面是一个示例代码：

import org.apache.flink.api.common.functions.AggregateFunction;
import org.apache.flink.api.common.functions.ReduceFunction;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.timestamps.AscendingTimestampExtractor;
import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;

import java.util.Properties;

public class WindowPush {
    public static void main(String[] args) throws Exception {
        // 设置执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        // 设置时间属性
        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);

        // 配置Kafka消费者
        Properties properties = new Properties();
        properties.setProperty("bootstrap.servers", "localhost:9092");
        properties.setProperty("group.id", "test-group");

        // 创建Kafka消费者
        FlinkKafkaConsumer kafkaConsumer = new FlinkKafkaConsumer<>("topic", new SimpleStringSchema(), properties);

        // 从Kafka读取数据流
        DataStream input = env.addSource(kafkaConsumer);

        // 提取时间戳，并分配水印
        DataStream> timestampedInput = input
                .map(line -> Tuple2.of(line, extractTimestamp(line)))
                .assignTimestampsAndWatermarks(new AscendingTimestampExtractor>() {
                    @Override
                    public long extractAscendingTimestamp(Tuple2 element) {
                        return element.f1;
                    }
                });

        // 按时间属性进行窗口操作
        DataStream> windowed = timestampedInput
                .keyBy(0)
                .window(TumblingEventTimeWindows.of(Time.seconds(5)))
                .aggregate(new SumAggregator(), new SumWindowFunction());

        // 输出结果
        windowed.print();

        // 执行任务
        env.execute("WindowPush");
    }

    // 自定义聚合函数，将输入累加到状态中
    public static class SumAggregator implements AggregateFunction, Integer, Integer> {
        @Override
        public Integer createAccumulator() {
            return 0;
        }

        @Override
        public Integer add(Tuple2 value, Integer accumulator) {
            return accumulator + 1;
        }

        @Override
        public Integer getResult(Integer accumulator) {
            return accumulator;
        }

        @Override
        public Integer merge(Integer a, Integer b) {
            return a + b;
        }
    }

    // 自定义窗口函数，输出窗口中的结果
    public static class SumWindowFunction implements ReduceFunction> {
        @Override
        public Tuple2 reduce(Tuple2 value1, Tuple2 value2) {
            return Tuple2.of(value1.f0, value1.f1 + value2.f1);
        }
    }

    // 提取时间戳的方法
    public static long extractTimestamp(String line) {
        // 从数据行中提取时间戳
        // 示例：line格式为 "data,timestamp"，使用逗号分隔
        String[] parts = line.split(",");
        return Long.parseLong(parts[1]);
    }
}

以上代码使用Apache Flink处理从Kafka读取的数据流，将数据流按时间属性进行窗口操作。示例代码中使用了一个自定义的AggregateFunction和一个自定义的ReduceFunction来实现窗口的聚合操作。在示例中，时间戳是从数据行中提取出来的，你可以根据实际情况修改extractTimestamp方法来提取时间戳。