【Java 动态数据统计图】动态数据统计思路案例（动态，排序，数组）四（116）

news2025/1/19 14:27:21

需求：：前端根据后端的返回数据：画统计图；

1.动态获取地域数据以及数据中的平均值，按照平均值降序排序；
说明：
X轴是动态的，有对应区域数据则展示；
X轴区域数据降序排序；
在这里插入图片描述

代码：

	@Override
    public List<Map<String,Object>> getTimeData(HttpServletRequest request, Map<String, Object> paramMap) {
        List<Map<String,Object>> list = getCountData(request,paramMap);
        list = list.stream().filter(e -> (e.get("status").equals("关闭"))).collect(Collectors.toList());
        List<Map<String,Object>> resultList = new ArrayList<>();
        String[] reginArr = {"安徽","安顺","北京","北美","长春","长沙","成都","承德","大连","武汉"};
        int[] count = new int[reginArr.length];
        double[] processTimeCount = new double[reginArr.length];
        double[] processTimeAvg = new double[reginArr.length];
        for (int i = 0; i < list.size(); i++) {
            String processTime = String.valueOf(list.get(i).get("processTime"));
            String region = String.valueOf(list.get(i).get("region")).trim();
            if(!processTime.contains(".")){
                list.get(i).put("processTime","0.0");
            }
            String processTime2 = String.valueOf(list.get(i).get("processTime"));
            for (int j = 0; j < reginArr.length; j++) {
                if(region.equals(reginArr[j].trim())){
                    count[j] = count[j] + 1;  // 统计次数
                    processTimeCount[j] = processTimeCount[j] + Double.parseDouble(processTime2);  // 统计合并值
                }
            }
        }
        // 计算平均值
        for (int i = 0; i < processTimeCount.length; i++) {
            processTimeAvg[i] =  processTimeCount[i]/count[i];
            // double类型的数据当分母的数值趋近0的时候，返回来的数值就是一个NAN:
            if(Double.isNaN(processTimeAvg[i])){
                processTimeAvg[i] = 0.0;
            }
        }
        // 将数据封装集合
        for (int i = 0; i < reginArr.length; i++) {
            Map<String,Object> mapResult = new HashMap<>();
            String avg = String.valueOf(processTimeAvg[i]);
            mapResult.put("title",reginArr[i]);
            mapResult.put("value",avg.substring(0,avg.indexOf(".") +2));
            resultList.add(mapResult);
        }
        // 数据过滤
        List<Map<String,Object>> finalList = new ArrayList<>();
        // 过滤不需要的数据；
        for (int i = 0; i < resultList.size(); i++) {
            String value = String.valueOf(resultList.get(i).get("value"));
            if(!value.equals("0.0")){
                finalList.add(resultList.get(i));
            }
        }
        // 排序后最终输出集合：
        List<Map<String,Object>> finalList2= new ArrayList<>();
        Double[] valueSort = new Double[finalList.size()];
        String[] titleSort = new String[finalList.size()];
        // 用数组存储需要排序的元素；
        for (int i = 0; i < finalList.size(); i++) {
            valueSort[i] = Double.parseDouble(String.valueOf(finalList.get(i).get("value")));
            titleSort[i] = String.valueOf(finalList.get(i).get("title"));
        }
        // 多个数组排序；
        BubblSortUtils.bubbleSortDescMultipleDouble(valueSort,titleSort);
        // 根据排序结果封装List；
        for (int i = 0; i < valueSort.length; i++) {
            String ch = String.valueOf(titleSort[i]);
            for (int j = 0; j < finalList.size(); j++) {
                String value = String.valueOf(finalList.get(j).get("title"));
                if(ch.equals(value)){
                    finalList2.add(finalList.get(j));
                }
            }
        }
        // 添加排序字段：sortValue
        for (int i = 0; i < finalList2.size(); i++) {
            finalList2.get(i).put("sortValue",i+1);
        }
        return finalList2;
    }