折线图数据获取及处理
Controller层
public SegmentChartVo getChartInfo(@PathVariable("id") Long id) {
Tuple2 tuple2 = getDT(id);//获得开始熏蒸任务开始时间，若结束时间未到获取当前时间，否者则end获取结束时间
LocalDateTime[] localDateTimes = (LocalDateTime[]) tuple2.second;
Long segmentId = (Long) tuple2.first;
SegmentChartVo chartVo = new SegmentChartVo();
Tuple5 axisData = chartService.getTHMAxisDataById(segmentId, localDateTimes[0], localDateTimes[1], "hour", 1l, 1);//将室内温湿度，ppm浓度，水份按照每一小时为一个间隔返回
Tuple3 axisData2 = chartService.getWeatherAxisData(localDateTimes[0], localDateTimes[1]);//将室外温湿度按照每五分钟为一个单位返回
chartVo.setSegment(axisData);
chartVo.setWeather(axisData2);
return chartVo;
}
室内坐标轴获取
因为室内传感器较多，获取数据时间不规律，所以按照整小时为x坐标，对其内数据进行相关处理。
public Tuple5 getTHMAxisDataById(Long segmentId, LocalDateTime start, LocalDateTime end, String type, Long step,Integer typo) {
DateTimeFormatter dateTimeFormatter = DateTimeFormatter.ofPattern(DATE_FORMAT_FULL);
List xAxis = generatorXAxis(start, end, type, step);//获取x坐标
List xAxisFormatter =//获得日期的标准格式
xAxis.stream().map(localDateTime -> {
return parseLocalDateTime(localDateTime, dateTimeFormatter);
}).collect(Collectors.toList());
Map> data =
aggregateService.getDataBetweenDateBySegmentId(segmentId, DateUtils.convertDate(start), DateUtils.convertDate(end),null,typo);//将数据按照deviceid分类
Tuple4 tuple4 = getY(data,xAxis,"max");//获取y坐标
return Tuple5.with(xAxisFormatter,tuple4.first,tuple4.second,tuple4.third,tuple4.fourth);//将x，y坐标返回
}
按照时间间隔取得Y轴坐标
public Tuple4 getY(Map> data,List xAxis,String aggregateType){
if (data != null && data.size() > 0) {
List tempAndTempTuple = new ArrayList<>();
for (int i = 0; i < xAxis.size() - 1; i++) {
LocalDateTime intervalStart = xAxis.get(i);
LocalDateTime intervalEnd = xAxis.get(i + 1);
Tuple4 t = preAchieveYAxis(data, intervalStart, intervalEnd);//获得间隔时间的起始值和终止值，并根据此查出数据
tempAndTempTuple.add(t);
}
Map maxTupleList = realAchieveYAxis(tempAndTempTuple, aggregateType);//获取每个间隔区间的最大Y值
List tempY = new ArrayList<>();
List humiY = new ArrayList<>();
List hzzppmY = new ArrayList<>();
List moistureY = new ArrayList<>();
xAxis.forEach(x -> {
Tuple4 tuple4 = maxTupleList.get(x);
if (tuple4 != null) {
Float temp = (Float) tuple4.first;
Float humi = (Float) tuple4.second;
Float hzzppm = (Float) tuple4.second;
Float moisture = (Float) tuple4.second;
tempY.add(temp);
humiY.add(humi);
hzzppmY.add(hzzppm);
moistureY.add(moisture);
} else {
tempY.add(null);
humiY.add(null);
hzzppmY.add(null);
moistureY.add(null);
}
});
return Tuple4.with(tempY, humiY, hzzppmY, moistureY);
}
return Tuple4.with(new ArrayList<>(), new ArrayList<>(), new ArrayList<>(), new ArrayList<>());
}
取出间隔时间内的数据
public Tuple4 preAchieveYAxis(Map> data, LocalDateTime intervalStart, LocalDateTime intervalEnd) {
List tempList = new ArrayList<>();
List humiList = new ArrayList<>();
List hzsppmList = new ArrayList<>();
List moistureList = new ArrayList<>();
// temp.put(intervalStart,new ArrayList<>());
data.forEach((k, v) -> {
v.forEach(infoHis -> {
LocalDateTime collectDT = DateUtils.convertLocalDateTime(infoHis.getCollectTime());
if (collectDT.isBefore(intervalEnd) && collectDT.isAfter(intervalStart)) {//如果收集时间在时间间隔内
Float temp = infoHis.getTemp();
Float humi = infoHis.getHumidity();
Float hzsppm = infoHis.getHzsppm();
Float moisture = infoHis.getMoisture();
tempList.add(temp);
humiList.add(humi);
hzsppmList.add(hzsppm);
moistureList.add(moisture);
}
});
});
Tuple2 tempTuple = Tuple2.with(intervalStart, tempList);
Tuple2 humiTuple = Tuple2.with(intervalStart, humiList);
Tuple2 hzzppmTuple = Tuple2.with(intervalStart, hzsppmList);
Tuple2 moistureTuple = Tuple2.with(intervalStart, moistureList);
return Tuple4.with(tempTuple, humiTuple, hzzppmTuple, moistureTuple);//将在时间间隔内的数据封装并返回
}
得到间隔时间内数据的最大值
public Map realAchieveYAxis(List data, String type) {
if ("max".equals(type)) {
Map maxTupleList = new HashMap<>();
data.forEach(tuple4 -> {
Tuple2 tempTuple = (Tuple2)tuple4.first;
LocalDateTime intervalStart = (LocalDateTime) tempTuple.first;
maxTupleList.put(intervalStart, compute(tuple4,type));//取得区间最大值
});
return maxTupleList;
} else if ("average".equals(type)) {
Map averTupleList = new HashMap<>();
data.forEach(tuple4 -> {
Tuple2 tempTuple = (Tuple2)tuple4.first;
LocalDateTime intervalStart = (LocalDateTime) tempTuple.first;
averTupleList.put(intervalStart, compute(tuple4,type));
});
return averTupleList;
} else {
return null;
}
}
private Tuple4 compute(Tuple4 data, String type){
Tuple2 tempTuple = (Tuple2)data.first;
Tuple2 humiTuple = (Tuple2) data.second;
Tuple2 hzTuple = (Tuple2)data.third;
Tuple2 moiTuple = (Tuple2)data.fourth;
LocalDateTime intervalStart = (LocalDateTime) tempTuple.first;
List tempList = (List) tempTuple.second;//获取区间内所有数值
List humiList = (List) humiTuple.second;
List hzsppmList = (List) hzTuple.second;
List moistureList = (List) moiTuple.second;
if("max".equals(type)){
return Tuple4.with(
(tempList != null && tempList.size() > 0) ? Collections.max(tempList) : null,//列表不为空取最大值，为空则值为空
(humiList != null && humiList.size() > 0) ? Collections.max(humiList) : null,
(hzsppmList != null && hzsppmList.size() > 0) ? Collections.max(hzsppmList) : null,
(moistureList != null && moistureList.size() > 0) ? Collections.max(moistureList) : null
);
}else if("average".equals(type)){
Double averTemp = tempList.stream().mapToDouble(Float::doubleValue).average().getAsDouble();
Double humiTemp = humiList.stream().mapToDouble(Float::doubleValue).average().getAsDouble();
Double hzsppmTemp = hzsppmList.stream().mapToDouble(Float::doubleValue).average().getAsDouble();
Double moistureTemp = moistureList.stream().mapToDouble(Float::doubleValue).average().getAsDouble();
return Tuple4.with(averTemp,humiTemp,hzsppmTemp,moistureTemp);
}else {
return null;
}
}
室外数据获取
public Tuple3 getWeatherAxisData(Date start, Date end) {
List weatherInfos = weatherInfoService.getWeatherDataBetweenData(start, end);//查询在时间段内的所有天气数据
List xAxisFormatter =//获取所有记录的时间，作为x值
weatherInfos.stream().map(weatherInfo -> {
return DateUtils.parseDateToString(weatherInfo.getWeatherDate());
}).collect(Collectors.toList());
//weatherInfos的weatherdate十分有规律不考虑再重新x坐标，用原来的就好
if (weatherInfos != null && weatherInfos.size() > 0) {
List tempY =
weatherInfos.stream().map(weatherInfo -> {
return Float.valueOf(weatherInfo.getTempOut());
}).collect(Collectors.toList());
List humiY =
weatherInfos.stream().map(weatherInfo -> {
return Float.valueOf(weatherInfo.getOutHum());
}).collect(Collectors.toList());
return Tuple3.with(xAxisFormatter, tempY, humiY);//返回数据坐标的x，y值
}
return Tuple3.with(xAxisFormatter, new ArrayList<>(), new ArrayList<>());
}