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国家基站整点数据分析系统- 简介
基于大数据的国家基站整点数据分析系统是一套综合运用Hadoop+Spark大数据框架技术的气象数据分析平台，系统采用HDFS分布式文件存储配合Spark SQL进行海量气象数据的高效处理与分析。技术架构上支持Python+Django和Java+SpringBoot两种开发方案，前端采用Vue+ElementUI+Echarts实现数据可视化展示。系统核心聚焦于国家基站整点采集的气象数据，通过Spark的分布式计算能力实现气象数据时间序列特征分析、关键气象要素关联性深度挖掘、风况时空分布特征分析以及应用气象专题分析四大维度共计17项专业分析功能。具体功能涵盖年度气温变化趋势分析、季节性气象差异统计、24小时日变化规律挖掘、温湿负相关性验证、风玫瑰图绘制、人体舒适度指数计算、低能见度事件风险评估等多个实用场景,利用Pandas和NumPy进行数据清洗与科学计算,最终通过MySQL数据库存储分析结果并以可视化图表形式呈现给用户,为气象研究、农业生产、城市规划等领域提供数据支撑。
国家基站整点数据分析系统-技术 框架
开发语言：Python或Java（两个版本都支持）
大数据框架：Hadoop+Spark（本次没用Hive，支持定制）
后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)（两个版本都支持）
前端：Vue+ElementUI+Echarts+HTML+CSS+JavaScript+jQuery
详细技术点：Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy
数据库：MySQL
国家基站整点数据分析系统- 背景
选题背景 随着气象观测技术的不断进步,全国各地的气象基站每天都在产生海量的整点观测数据,这些数据包含了温度、湿度、气压、风速风向、能见度等多维度气象信息。传统的数据库技术在处理这种量级的时序数据时往往面临查询效率低下、存储成本高昂的困境,而大数据技术的成熟恰好为解决这一问题提供了新的思路。Hadoop分布式存储系统能够将PB级别的气象数据分散存储在多个节点上,Spark计算引擎则可以并行处理这些数据并在秒级完成复杂的统计分析任务。当前气象部门和科研机构虽然积累了大量的基站观测数据,但这些数据的深度价值挖掘还远远不够,很多潜在的气象规律和要素关联关系并没有被充分发现。这就需要借助大数据分析技术来构建一套专门针对基站整点数据的分析系统,通过对历史数据的深度挖掘来揭示气象变化的内在规律,为相关决策提供科学依据。
选题意义 开发这套基于大数据的国家基站整点数据分析系统,能够帮助气象工作者更高效地从海量数据中提取有价值的信息。系统通过Spark SQL实现的年度气温趋势分析和季节性差异统计,可以让研究人员快速掌握某个地区的气候变化特征,这对于农业部门制定种植计划、能源部门预测用电负荷都有一定的参考价值。再说风况分析这块,系统生成的风玫瑰图能够直观展示主导风向和风力分布,这在城市规划选址、风电场建设评估时就能派上用场。另外系统设计的温湿关联分析、气压风速关系挖掘等功能,虽然是基于基础的气象学原理,但通过大数据技术的验证能够让这些理论在具体地区得到量化印证,对于教学演示和科普宣传也算是个不错的工具。从技术实现层面来看,这个项目整合了Hadoop、Spark、HDFS等主流大数据组件,能够为后续开发类似的数据分析系统积累一些经验,毕竟在实际操作中踩过的坑和解决方案都是宝贵的技术资产。
国家药品采集药品数据可视化分析系统-图片展示
国家药品采集药品数据可视化分析系统-代码展示
from pyspark.sql.functions import year, month, season, hour, avg, max, min, col, corr, count, when
from pyspark.sql.types import StructType, StructField, StringType, FloatType, IntegerType
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views import View
spark = SparkSession.builder.appName("WeatherDataAnalysis").config("spark.sql.warehouse.dir", "/user/hive/warehouse").config("spark.executor.memory", "4g").config("spark.driver.memory", "2g").getOrCreate()
def load_weather_data_from_hdfs(hdfs_path):
schema = StructType([
StructField("station_id", StringType(), True),
StructField("data_time", StringType(), True),
StructField("air_temperature", FloatType(), True),
StructField("relative_humidity", FloatType(), True),
StructField("station_pressure", FloatType(), True),
StructField("two_minute_avg_wind_speed", FloatType(), True),
StructField("ten_minute_avg_wind_speed", FloatType(), True),
StructField("ten_minute_avg_wind_direction_degree", IntegerType(), True),
StructField("visibility", FloatType(), True),
StructField("ground_temperature", FloatType(), True),
StructField("sunshine_duration", FloatType(), True)
])
df = spark.read.option("header", "true").schema(schema).csv(hdfs_path)
return df
def annual_temperature_trend_analysis(df):
df_with_year = df.withColumn("year", year(col("data_time")))
annual_avg_temp = df_with_year.groupBy("year").agg(
avg("air_temperature").alias("avg_temperature"),
max("air_temperature").alias("max_temperature"),
min("air_temperature").alias("min_temperature"),
count("*").alias("record_count")
).orderBy("year")
annual_trend_df = annual_avg_temp.toPandas()
trend_coefficient = np.polyfit(annual_trend_df['year'], annual_trend_df['avg_temperature'], 1)
trend_direction = "上升" if trend_coefficient[0] > 0 else "下降"
annual_trend_df['trend_slope'] = trend_coefficient[0]
annual_trend_df['trend_direction'] = trend_direction
result_dict = annual_trend_df.to_dict(orient='records')
spark_stats = annual_avg_temp.describe().toPandas().to_dict()
return {
'annual_data': result_dict,
'trend_slope': float(trend_coefficient[0]),
'trend_intercept': float(trend_coefficient[1]),
'trend_direction': trend_direction,
'spark_statistics': spark_stats,
'total_years': len(result_dict)
}
def meteorological_correlation_mining(df):
numeric_columns = ['air_temperature', 'relative_humidity', 'station_pressure',
'two_minute_avg_wind_speed', 'visibility', 'ground_temperature']
correlation_matrix = {}
for col1 in numeric_columns:
correlation_matrix[col1] = {}
for col2 in numeric_columns:
if col1 != col2:
corr_value = df.stat.corr(col1, col2)
correlation_matrix[col1][col2] = round(corr_value, 4)
else:
correlation_matrix[col1][col2] = 1.0
temp_humidity_corr = df.stat.corr("air_temperature", "relative_humidity")
pressure_windspeed_corr = df.stat.corr("station_pressure", "two_minute_avg_wind_speed")
visibility_humidity_corr = df.stat.corr("visibility", "relative_humidity")
ground_air_temp_corr = df.stat.corr("ground_temperature", "air_temperature")
high_humidity_df = df.filter(col("relative_humidity") > 80)
avg_visibility_high_humidity = high_humidity_df.agg(avg("visibility")).collect()[0][0]
low_pressure_df = df.filter(col("station_pressure") < df.approx_quantile("station_pressure", [0.25], 0.01)[0])
avg_windspeed_low_pressure = low_pressure_df.agg(avg("two_minute_avg_wind_speed")).collect()[0][0]
temp_diff_df = df.withColumn("temp_difference", col("ground_temperature") - col("air_temperature"))
avg_temp_difference = temp_diff_df.agg(avg("temp_difference")).collect()[0][0]
max_temp_difference = temp_diff_df.agg(max("temp_difference")).collect()[0][0]
min_temp_difference = temp_diff_df.agg(min("temp_difference")).collect()[0][0]
correlation_insights = []
if temp_humidity_corr < -0.3:
correlation_insights.append("温度与湿度呈现明显负相关,符合气象学基本规律")
if pressure_windspeed_corr < -0.2:
correlation_insights.append("低压系统伴随较强风速,动力气象特征显著")
if visibility_humidity_corr < -0.4:
correlation_insights.append("高湿度环境下能见度显著降低,雾霾形成风险增加")
return {
'correlation_matrix': correlation_matrix,
'key_correlations': {
'temp_humidity': round(temp_humidity_corr, 4),
'pressure_windspeed': round(pressure_windspeed_corr, 4),
'visibility_humidity': round(visibility_humidity_corr, 4),
'ground_air_temp': round(ground_air_temp_corr, 4)
},
'special_conditions': {
'avg_visibility_high_humidity': round(avg_visibility_high_humidity, 2) if avg_visibility_high_humidity else 0,
'avg_windspeed_low_pressure': round(avg_windspeed_low_pressure, 2) if avg_windspeed_low_pressure else 0,
'avg_temp_difference': round(avg_temp_difference, 2) if avg_temp_difference else 0,
'max_temp_difference': round(max_temp_difference, 2) if max_temp_difference else 0,
'min_temp_difference': round(min_temp_difference, 2) if min_temp_difference else 0
},
'insights': correlation_insights,
'analysis_record_count': df.count()
}
def wind_rose_seasonal_analysis(df):
df_with_season = df.withColumn("season",
when(month(col("data_time")).isin([3,4,5]), "春季")
.when(month(col("data_time")).isin([6,7,8]), "夏季")
.when(month(col("data_time")).isin([9,10,11]), "秋季")
.otherwise("冬季")
)
df_with_direction_category = df_with_season.withColumn("direction_category",
when(col("ten_minute_avg_wind_direction_degree").between(0, 22.5), "北风")
.when(col("ten_minute_avg_wind_direction_degree").between(22.5, 67.5), "东北风")
.when(col("ten_minute_avg_wind_direction_degree").between(67.5, 112.5), "东风")
.when(col("ten_minute_avg_wind_direction_degree").between(112.5, 157.5), "东南风")
.when(col("ten_minute_avg_wind_direction_degree").between(157.5, 202.5), "南风")
.when(col("ten_minute_avg_wind_direction_degree").between(202.5, 247.5), "西南风")
.when(col("ten_minute_avg_wind_direction_degree").between(247.5, 292.5), "西风")
.when(col("ten_minute_avg_wind_direction_degree").between(292.5, 337.5), "西北风")
.otherwise("北风")
)
seasonal_wind_stats = df_with_direction_category.groupBy("season", "direction_category").agg(
count("*").alias("frequency"),
avg("ten_minute_avg_wind_speed").alias("avg_wind_speed"),
max("ten_minute_avg_wind_speed").alias("max_wind_speed")
)
total_records_per_season = df_with_season.groupBy("season").agg(count("*").alias("total_count"))
wind_rose_data = seasonal_wind_stats.join(total_records_per_season, "season")
wind_rose_data = wind_rose_data.withColumn("frequency_percentage",
(col("frequency") / col("total_count") * 100))
wind_rose_result = wind_rose_data.toPandas()
seasonal_dominant_wind = {}
for season in ["春季", "夏季", "秋季", "冬季"]:
season_data = wind_rose_result[wind_rose_result['season'] == season]
if not season_data.empty:
dominant_direction = season_data.loc[season_data['frequency'].idxmax(), 'direction_category']
dominant_percentage = season_data.loc[season_data['frequency'].idxmax(), 'frequency_percentage']
avg_speed_dominant = season_data.loc[season_data['frequency'].idxmax(), 'avg_wind_speed']
seasonal_dominant_wind[season] = {
'direction': dominant_direction,
'percentage': round(dominant_percentage, 2),
'avg_speed': round(avg_speed_dominant, 2)
}
strong_wind_threshold = 10.8
strong_wind_events = df_with_season.filter(col("two_minute_avg_wind_speed") > strong_wind_threshold)
strong_wind_by_season = strong_wind_events.groupBy("season").agg(
count("*").alias("strong_wind_count"),
avg("two_minute_avg_wind_speed").alias("avg_strong_wind_speed")
).toPandas()
return {
'wind_rose_data': wind_rose_result.to_dict(orient='records'),
'seasonal_dominant_wind': seasonal_dominant_wind,
'strong_wind_statistics': strong_wind_by_season.to_dict(orient='records'),
'total_analysis_records': df.count(),
'strong_wind_threshold': strong_wind_threshold
}
class AnnualTrendView(View):
def get(self, request):
hdfs_path = "hdfs://localhost:9000/weather_data/station_records.csv"
weather_df = load_weather_data_from_hdfs(hdfs_path)
trend_result = annual_temperature_trend_analysis(weather_df)
return JsonResponse(trend_result, safe=False)
class CorrelationAnalysisView(View):
def get(self, request):
hdfs_path = "hdfs://localhost:9000/weather_data/station_records.csv"
weather_df = load_weather_data_from_hdfs(hdfs_path)
correlation_result = meteorological_correlation_mining(weather_df)
return JsonResponse(correlation_result, safe=False)
class WindRoseView(View):
def get(self, request):
hdfs_path = "hdfs://localhost:9000/weather_data/station_records.csv"
weather_df = load_weather_data_from_hdfs(hdfs_path)
wind_result = wind_rose_seasonal_analysis(weather_df)
return JsonResponse(wind_result, safe=False)
国家基站整点数据分析系统-结语
导师最爱的大数据毕设：国家基站整点数据分析系统Spark SQL实战案例
做过大数据毕设的都懂：国家基站数据分析系统用Spark SQL有多香
大数据毕设为何都选Spark而不选Hive？国家基站数据分析系统揭秘
如果遇到具体的技术问题或计算机毕设方面需求，主页上咨询我，我会尽力帮你分析和解决问题所在，支持我记得一键三连，再点个关注，学习不迷路！
⚡⚡如果遇到具体的技术问题或计算机毕设方面需求，你也可以在主页上咨询我~~
