一、个人简介
- 💖💖作者：计算机编程果茶熊
- 💙💙个人简介：曾长期从事计算机专业培训教学，担任过编程老师，同时本人也热爱上课教学，擅长Java、微信小程序、Python、Golang、安卓Android等多个IT方向。会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！
- 💛💛想说的话：感谢大家的关注与支持！
- 💜💜
- 网站实战项目
- 安卓/小程序实战项目
- 大数据实战项目
- 计算机毕业设计选题
- 💕💕文末获取源码联系计算机编程果茶熊
二、系统介绍
- 大数据框架：Hadoop+Spark（Hive需要定制修改）
- 开发语言：Java+Python（两个版本都支持）
- 数据库：MySQL
- 后端框架：SpringBoot(Spring+SpringMVC+Mybatis)+Django（两个版本都支持）
- 前端：Vue+Echarts+HTML+CSS+JavaScript+jQuery 基于大数据的国家基站整点数据分析系统是一套面向通信行业基础设施数据处理的综合性分析平台，采用Hadoop+Spark大数据技术架构，结合Django后端框架和Vue前端技术栈构建而成。系统主要针对全国范围内基站设备的实时运行数据进行采集、存储、处理和可视化分析，通过HDFS分布式存储海量基站数据，利用Spark进行大规模数据计算和分析处理。系统核心功能涵盖可视化大屏展示、用户权限管理、国家基站信息管理、应用气象专题分析、气象要素关联分析、气象时间序列分析、风速风向综合分析、系统公告管理以及个人中心等九大模块。平台采用前后端分离架构，前端通过Vue+ElementUI构建交互界面，结合Echarts实现数据图表可视化展示，后端基于Django框架提供API接口服务，数据存储采用MySQL数据库，整体技术栈融合了大数据处理、Web开发、数据可视化等多个技术领域，为基站运营管理和气象数据分析提供了完整的解决方案。
三、基于大数据的国家基站整点数据分析系统-视频解说
大数据专业不做大数据项目就是浪费：基于Hadoop+Spark的国家基站数据分析系统技术要点
四、基于大数据的国家基站整点数据分析系统-功能展示
五、基于大数据的国家基站整点数据分析系统-代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import *
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
import mysql.connector
from datetime import datetime, timedelta
spark = SparkSession.builder.appName("BaseStationAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
@csrf_exempt
def weather_correlation_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
start_date = data.get('start_date')
end_date = data.get('end_date')
station_ids = data.get('station_ids', [])
weather_factors = data.get('weather_factors', ['temperature', 'humidity', 'wind_speed'])
query = f"""
SELECT station_id, record_time, temperature, humidity, wind_speed, wind_direction,
signal_strength, power_consumption, error_count
FROM base_station_data
WHERE record_time BETWEEN '{start_date}' AND '{end_date}'
"""
if station_ids:
station_list = ','.join([f"'{sid}'" for sid in station_ids])
query += f" AND station_id IN ({station_list})"
df = spark.sql(query)
correlation_results = {}
for factor in weather_factors:
if factor in df.columns:
correlation_with_signal = df.stat.corr(factor, 'signal_strength')
correlation_with_power = df.stat.corr(factor, 'power_consumption')
correlation_with_error = df.stat.corr(factor, 'error_count')
correlation_results[factor] = {
'signal_strength_correlation': round(correlation_with_signal, 4),
'power_consumption_correlation': round(correlation_with_power, 4),
'error_count_correlation': round(correlation_with_error, 4)
}
grouped_data = df.groupBy('station_id').agg(
avg('temperature').alias('avg_temperature'),
avg('humidity').alias('avg_humidity'),
avg('wind_speed').alias('avg_wind_speed'),
avg('signal_strength').alias('avg_signal_strength'),
count('*').alias('record_count')
).collect()
station_summary = []
for row in grouped_data:
station_summary.append({
'station_id': row['station_id'],
'avg_temperature': round(row['avg_temperature'], 2),
'avg_humidity': round(row['avg_humidity'], 2),
'avg_wind_speed': round(row['avg_wind_speed'], 2),
'avg_signal_strength': round(row['avg_signal_strength'], 2),
'record_count': row['record_count']
})
return JsonResponse({
'status': 'success',
'correlation_analysis': correlation_results,
'station_summary': station_summary,
'analysis_period': {'start': start_date, 'end': end_date}
})
@csrf_exempt
def time_series_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
station_id = data.get('station_id')
analysis_type = data.get('analysis_type', 'daily')
time_range = data.get('time_range', 30)
metrics = data.get('metrics', ['temperature', 'humidity', 'signal_strength'])
end_date = datetime.now()
start_date = end_date - timedelta(days=time_range)
df = spark.sql(f"""
SELECT station_id, record_time, temperature, humidity, wind_speed,
signal_strength, power_consumption, error_count
FROM base_station_data
WHERE station_id = '{station_id}'
AND record_time BETWEEN '{start_date.strftime('%Y-%m-%d')}' AND '{end_date.strftime('%Y-%m-%d')}'
ORDER BY record_time
""")
if analysis_type == 'hourly':
time_format = "yyyy-MM-dd HH:00:00"
elif analysis_type == 'daily':
time_format = "yyyy-MM-dd"
else:
time_format = "yyyy-MM"
aggregated_df = df.withColumn('time_bucket', date_format('record_time', time_format)).groupBy('time_bucket').agg(
avg('temperature').alias('avg_temperature'),
max('temperature').alias('max_temperature'),
min('temperature').alias('min_temperature'),
avg('humidity').alias('avg_humidity'),
avg('wind_speed').alias('avg_wind_speed'),
avg('signal_strength').alias('avg_signal_strength'),
avg('power_consumption').alias('avg_power_consumption'),
sum('error_count').alias('total_error_count'),
count('*').alias('record_count')
).orderBy('time_bucket')
time_series_data = aggregated_df.collect()
result_data = []
for row in time_series_data:
result_data.append({
'time_bucket': row['time_bucket'],
'avg_temperature': round(row['avg_temperature'] if row['avg_temperature'] else 0, 2),
'max_temperature': round(row['max_temperature'] if row['max_temperature'] else 0, 2),
'min_temperature': round(row['min_temperature'] if row['min_temperature'] else 0, 2),
'avg_humidity': round(row['avg_humidity'] if row['avg_humidity'] else 0, 2),
'avg_wind_speed': round(row['avg_wind_speed'] if row['avg_wind_speed'] else 0, 2),
'avg_signal_strength': round(row['avg_signal_strength'] if row['avg_signal_strength'] else 0, 2),
'avg_power_consumption': round(row['avg_power_consumption'] if row['avg_power_consumption'] else 0, 2),
'total_error_count': row['total_error_count'] if row['total_error_count'] else 0,
'record_count': row['record_count']
})
trend_analysis = {}
if len(result_data) > 1:
for metric in metrics:
if f'avg_{metric}' in result_data[0]:
values = [item[f'avg_{metric}'] for item in result_data]
trend_slope = np.polyfit(range(len(values)), values, 1)[0]
trend_analysis[metric] = {
'trend': 'increasing' if trend_slope > 0.1 else 'decreasing' if trend_slope < -0.1 else 'stable',
'slope': round(trend_slope, 4)
}
return JsonResponse({
'status': 'success',
'time_series_data': result_data,
'trend_analysis': trend_analysis,
'analysis_config': {
'station_id': station_id,
'analysis_type': analysis_type,
'time_range': time_range
}
})
@csrf_exempt
def wind_comprehensive_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
station_ids = data.get('station_ids', [])
analysis_period = data.get('analysis_period', 'month')
wind_speed_threshold = data.get('wind_speed_threshold', 10.0)
query = """
SELECT station_id, record_time, wind_speed, wind_direction, temperature, humidity,
signal_strength, power_consumption, error_count
FROM base_station_data
WHERE wind_speed IS NOT NULL AND wind_direction IS NOT NULL
"""
if station_ids:
station_list = ','.join([f"'{sid}'" for sid in station_ids])
query += f" AND station_id IN ({station_list})"
df = spark.sql(query)
wind_direction_bins = df.withColumn('direction_sector',
when(col('wind_direction') >= 337.5, 'N')
.when(col('wind_direction') < 22.5, 'N')
.when((col('wind_direction') >= 22.5) & (col('wind_direction') < 67.5), 'NE')
.when((col('wind_direction') >= 67.5) & (col('wind_direction') < 112.5), 'E')
.when((col('wind_direction') >= 112.5) & (col('wind_direction') < 157.5), 'SE')
.when((col('wind_direction') >= 157.5) & (col('wind_direction') < 202.5), 'S')
.when((col('wind_direction') >= 202.5) & (col('wind_direction') < 247.5), 'SW')
.when((col('wind_direction') >= 247.5) & (col('wind_direction') < 292.5), 'W')
.when((col('wind_direction') >= 292.5) & (col('wind_direction') < 337.5), 'NW')
.otherwise('Unknown')
)
direction_stats = wind_direction_bins.groupBy('direction_sector').agg(
count('*').alias('frequency'),
avg('wind_speed').alias('avg_wind_speed'),
max('wind_speed').alias('max_wind_speed'),
avg('signal_strength').alias('avg_signal_strength_by_direction'),
avg('error_count').alias('avg_error_count_by_direction')
).collect()
wind_rose_data = []
for row in direction_stats:
wind_rose_data.append({
'direction': row['direction_sector'],
'frequency': row['frequency'],
'avg_wind_speed': round(row['avg_wind_speed'], 2),
'max_wind_speed': round(row['max_wind_speed'], 2),
'avg_signal_strength': round(row['avg_signal_strength_by_direction'] if row['avg_signal_strength_by_direction'] else 0, 2),
'avg_error_count': round(row['avg_error_count_by_direction'] if row['avg_error_count_by_direction'] else 0, 2)
})
high_wind_events = df.filter(col('wind_speed') > wind_speed_threshold).groupBy('station_id').agg(
count('*').alias('high_wind_count'),
avg('wind_speed').alias('avg_high_wind_speed'),
avg('signal_strength').alias('signal_during_high_wind'),
sum('error_count').alias('errors_during_high_wind')
).collect()
high_wind_impact = []
for row in high_wind_events:
high_wind_impact.append({
'station_id': row['station_id'],
'high_wind_events': row['high_wind_count'],
'avg_wind_speed_during_events': round(row['avg_high_wind_speed'], 2),
'signal_strength_impact': round(row['signal_during_high_wind'] if row['signal_during_high_wind'] else 0, 2),
'error_increase': row['errors_during_high_wind'] if row['errors_during_high_wind'] else 0
})
wind_speed_distribution = df.withColumn('speed_range',
when(col('wind_speed') < 2, '0-2 m/s')
.when((col('wind_speed') >= 2) & (col('wind_speed') < 5), '2-5 m/s')
.when((col('wind_speed') >= 5) & (col('wind_speed') < 8), '5-8 m/s')
.when((col('wind_speed') >= 8) & (col('wind_speed') < 12), '8-12 m/s')
.otherwise('12+ m/s')
).groupBy('speed_range').agg(
count('*').alias('count'),
avg('signal_strength').alias('avg_signal_by_speed_range')
).collect()
speed_distribution = []
for row in wind_speed_distribution:
speed_distribution.append({
'speed_range': row['speed_range'],
'frequency': row['count'],
'avg_signal_strength': round(row['avg_signal_by_speed_range'] if row['avg_signal_by_speed_range'] else 0, 2)
})
return JsonResponse({
'status': 'success',
'wind_rose_data': wind_rose_data,
'high_wind_impact': high_wind_impact,
'speed_distribution': speed_distribution,
'analysis_summary': {
'total_records_analyzed': df.count(),
'wind_speed_threshold': wind_speed_threshold,
'analysis_period': analysis_period
}
})
六、基于大数据的国家基站整点数据分析系统-文档展示
七、END
- 💛💛想说的话：感谢大家的关注与支持！
- 💜💜
- 网站实战项目
- 安卓/小程序实战项目
- 大数据实战项目
- 计算机毕业设计选题
- 💕💕文末获取源码联系计算机编程果茶熊
