毕业设计：2023-2024年计算机专业毕业设计选题汇总（建议收藏）

毕业设计：2023-2024年最新最全计算机专业毕设选题推荐汇总

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1、项目介绍

技术栈：
Python语言、Flask框架、MySQL数据库、requests爬虫、前程无忧全国招聘信息爬虫

2、项目界面

（1）岗位行业分析

（2）岗位应聘要求分析

（3）互联网岗位分析

（4）各地区平均薪资分析

（5）首页注册登录界面

（6）招聘数据展示

3、项目说明

Flask前程无忧数据采集分析可视化系统是一个基于Flask框架开发的数据处理工具。它可以帮助用户采集、分析和可视化前程无忧网站上的就业数据。

该系统具有以下特点：

1. 数据采集：系统通过爬虫技术，从前程无忧网站上获取就业数据。用户可以根据自己的需求，选择不同的搜索条件和筛选规则，获取特定的就业信息。

2. 数据分析：系统提供了多种数据分析功能，帮助用户深入了解就业市场的趋势和变化。用户可以通过系统提供的统计图表和数据报告，分析不同行业、地区和职位的就业情况，从而做出更明智的职业决策。

3. 可视化展示：系统通过可视化技术，将采集到的数据以图表和图形的形式展示出来。这样用户可以更直观地了解就业市场的状况，并发现潜在的就业机会。同时，系统还支持用户自定义展示方式，满足不同用户的需求。

4. 用户友好性：系统注重用户体验，提供了简洁直观的界面和操作流程。用户可以快速上手，轻松完成数据采集、分析和可视化的工作。

总之，Flask前程无忧数据采集分析可视化系统是一个功能强大、易于使用的工具，帮助用户更好地了解就业市场，做出职业规划和决策。无论是求职者还是招聘方，都可以从中获得有价值的信息和洞察。

4、核心代码

#!/usr/bin/python
# coding=utf-8

import sqlite3
import pandas as pd
from flask import Flask, render_template, jsonify, request
import numpy as np
import json
import jieba

app = Flask(__name__)
app.config.from_object('config')

@app.route('/register/<name>/<password>')
def register(name, password):
    conn = sqlite3.connect('user_info.db')
    cursor = conn.cursor()

    check_sql = "SELECT * FROM sqlite_master where type='table' and name='user'"
    cursor.execute(check_sql)
    results = cursor.fetchall()
    # 数据库表不存在
    if len(results) == 0:
        # 创建数据库表
        sql = """
                CREATE TABLE user(
                    name CHAR(256), 
                    password CHAR(256)
                );
                """
        cursor.execute(sql)
        conn.commit()
        print('创建数据库表成功！')

    sql = "INSERT INTO user (name, password) VALUES (?,?);"
    cursor.executemany(sql, [(name, password)])
    conn.commit()
    return jsonify({'info': '用户注册成功！', 'status': 'ok'})


@app.route('/login/<name>/<password>')
def login(name, password):
    global login_name
    conn = sqlite3.connect('user_info.db')
    cursor = conn.cursor()

    check_sql = "SELECT * FROM sqlite_master where type='table' and name='user'"
    cursor.execute(check_sql)
    results = cursor.fetchall()
    # 数据库表不存在
    if len(results) == 0:
        # 创建数据库表
        sql = """
                CREATE TABLE user(
                    name CHAR(256), 
                    password CHAR(256)
                );
                """
        cursor.execute(sql)
        conn.commit()
        print('创建数据库表成功！')

    sql = "select * from user where name='{}' and password='{}'".format(name, password)
    cursor.execute(sql)
    results = cursor.fetchall()

    login_name = name
    if len(results) > 0:
        print(results)
        return jsonify({'info': name + '用户登录成功！', 'status': 'ok'})
    else:
        return jsonify({'info': '当前用户不存在！', 'status': 'error'})


# 省份与城市的映射
shengfen_city_dict = json.load(open('dili_fengqu.json', 'r', encoding='utf8'))
# 分区与城市的映射
dili_fengqu_cities_maps = {}

for fengqu in dili_fengqu_shengfen_maps:
    cities = []
    for shengfen in dili_fengqu_shengfen_maps[fengqu]:
        # 省份下的所有城市
        if shengfen in shengfen_city_dict:
            cities.extend(shengfen_city_dict[shengfen])

    dili_fengqu_cities_maps[fengqu] = set(cities)

# 城市 与 分区的映射
city_fenqu_maps = {}
for fengqu in dili_fengqu_shengfen_maps:
    for shengfen in dili_fengqu_shengfen_maps[fengqu]:
        if shengfen in shengfen_city_dict:
            # 省份下的所有城市
            for city in shengfen_city_dict[shengfen]:
                city_fenqu_maps[city] = fengqu

# 加载经纬度数据
districts = json.load(open('china_region.json', 'r', encoding='utf8'))['districts']

city_region_dict = {}
for province in districts:
    cities = province['districts']
    for city in cities:
        city_region_dict[city['name']] = {'longitude': city['center']['longitude'],
                                          'latitude': city['center']['latitude']}


# ------------------ ajax restful api -------------------
@app.route('/query_spidered_data')
def query_spidered_data():
    conn = sqlite3.connect(DATABASE)
    cursor = conn.cursor()

    check_sql = "SELECT * FROM job"
    cursor.execute(check_sql)
    jobs = cursor.fetchall()

    hotjobs = []
    for job in jobs:
        job_name, hangye, company, location, salary, jingyan, xueli, zhaopin_counts, pub_time = job

        try:
            tmp = float(jingyan)
            jingyan = '{}年工作经验'.format(jingyan)
        except:
            pass

        hotjobs.append((job_name, hangye, company, location, salary, jingyan, xueli, zhaopin_counts, pub_time))

    return jsonify(hotjobs[:40])


@app.route('/job_hangye_analysis')
def job_hangye_analysis():
    """行业分析"""
    conn = sqlite3.connect(DATABASE)
    cursor = conn.cursor()

    check_sql = "SELECT hangye, salary FROM job"
    cursor.execute(check_sql)
    jobs = cursor.fetchall()

    # 行业的个数
    hangye_counts = {}
    hangye_salary = {}
    for job in jobs:
        hangye, salary = job
        if hangye not in hangye_counts:
            hangye_counts[hangye] = 0
        hangye_counts[hangye] += 1

        if not salary.endswith('/月'):
            continue

        if salary.endswith('千/月'):
            scale = 1000
        elif salary.endswith('万/月'):
            scale = 10000
        else:
            continue

        salary = salary[:-3]
        # 计算平均薪资
        salary = sum(map(float, salary.split('-'))) / 2 * scale

        if hangye not in hangye_salary:
            hangye_salary[hangye] = []
        hangye_salary[hangye].append(salary)

    hangye_counts = list(zip(list(hangye_counts.keys()), list(hangye_counts.values())))
    hangye_counts = sorted(hangye_counts, key=lambda k: k[1], reverse=True)

    # 过滤掉一些在招岗位很少的行业
    hangye_counts = [v for v in hangye_counts if v[1] > 10]
    hangye1 = [v[0] for v in hangye_counts][:40]
    counts = [v[1] for v in hangye_counts][:40]

    # 计算行业的平均薪资
    for hangye in hangye_salary:
        hangye_salary[hangye] = np.mean(hangye_salary[hangye])

    hangye_salary = list(zip(list(hangye_salary.keys()), list(hangye_salary.values())))
    hangye_salary = sorted(hangye_salary, key=lambda k: k[1], reverse=False)
    hangye2 = [v[0] for v in hangye_salary][:40]
    salary = [v[1] for v in hangye_salary][:40]
    return jsonify({'行业': hangye1, '岗位数': counts, '行业2': hangye2, '平均薪资': salary})


@app.route('/dili_fengqu_analysis/<fengqu>')
def dili_fengqu_analysis(fengqu):
    conn = sqlite3.connect(DATABASE)
    cursor = conn.cursor()

    check_sql = "SELECT hangye, location, salary FROM job"
    cursor.execute(check_sql)
    jobs = cursor.fetchall()

    # 行业的个数
    hangye_counts = {}
    hangye_salary = {}
    for job in jobs:
        hangye, location, salary = job
        if location + '市' not in city_fenqu_maps:
            continue
        if city_fenqu_maps[location + '市'] != fengqu:
            continue

        if hangye not in hangye_counts:
            hangye_counts[hangye] = 0
        hangye_counts[hangye] += 1

        if not salary.endswith('/月'):
            continue

        if salary.endswith('千/月'):
            scale = 1000
        elif salary.endswith('万/月'):
            scale = 10000
        else:
            continue

        salary = salary[:-3]
        # 计算平均薪资
        salary = sum(map(float, salary.split('-'))) / 2 * scale

        if hangye not in hangye_salary:
            hangye_salary[hangye] = []
        hangye_salary[hangye].append(salary)

    hangye_counts = list(zip(list(hangye_counts.keys()), list(hangye_counts.values())))
    hangye_counts = sorted(hangye_counts, key=lambda k: k[1], reverse=True)

    # 过滤掉一些在招岗位很少的行业
    hangye1 = [v[0] for v in hangye_counts][:20]
    counts = [v[1] for v in hangye_counts][:20]

    # 计算行业的平均薪资
    for hangye in hangye_salary:
        hangye_salary[hangye] = np.mean(hangye_salary[hangye])

    hangye_salary = list(zip(list(hangye_salary.keys()), list(hangye_salary.values())))
    hangye_salary = sorted(hangye_salary, key=lambda k: k[1], reverse=False)
    hangye2 = [v[0] for v in hangye_salary][:20]
    salary = [v[1] for v in hangye_salary][:20]

    high_salary_hangyes = ' > '.join(hangye2[::-1][:3])
    return jsonify({'行业': hangye1, '岗位数': counts, '行业2': hangye2, '平均薪资': salary,
                    '高薪行业推荐': high_salary_hangyes})


@app.route('/fengqu_salary_analysis')
def fengqu_salary_analysis():
    conn = sqlite3.connect(DATABASE)
    cursor = conn.cursor()

    check_sql = "SELECT hangye, location, salary FROM job"
    cursor.execute(check_sql)
    jobs = cursor.fetchall()

    fengqu_high_salary = {'华东': [], '华北': [], '华中': [], '华南': [], '西南': [], '西北': [], '东北': []}
    fengqu_low_salary = {'华东': [], '华北': [], '华中': [], '华南': [], '西南': [], '西北': [], '东北': []}

    for job in jobs:
        hangye, location, salary = job
        if location + '市' not in city_fenqu_maps:
            continue

        if not salary.endswith('/月'):
            continue

        if salary.endswith('千/月'):
            scale = 1000
        elif salary.endswith('万/月'):
            scale = 10000
        else:
            continue

        fengqu = city_fenqu_maps[location + '市']
        salary = salary[:-3]
        low_salary, high_salary = map(float, salary.split('-'))
        fengqu_high_salary[fengqu].append(high_salary * scale)
        fengqu_low_salary[fengqu].append(low_salary * scale)

    fengqu = ['华东', '华北', '华中', '华南', '西南', '西北', '东北']
    high_salary = [np.mean(fengqu_high_salary[fq]) for fq in fengqu]
    low_salary = [np.mean(fengqu_low_salary[fq]) for fq in fengqu]

    return jsonify({'fengqu': fengqu, 'high_salary': high_salary, 'low_salary': low_salary})


@app.route('/query_yingpin_yaoqiu/<search>')
def query_yingpin_yaoqiu(search):
    conn = sqlite3.connect(DATABASE)
    cursor = conn.cursor()

    check_sql = "SELECT jingyan, xueli, salary, job_name FROM job"
    cursor.execute(check_sql)
    jobs = cursor.fetchall()

    jingyan_salary = {}
    xueli_salary = {}

    for job in jobs:
        jingyan, xueli, salary, job_name = job

        if search != '无':
            if search.lower() not in job_name.lower():
                continue

        try:
            jingyan = int(jingyan)
            jingyan = '{}年经验'.format(str(jingyan))
        except:
            pass

        if jingyan not in jingyan_salary:
            jingyan_salary[jingyan] = []
        if xueli not in xueli_salary:
            xueli_salary[xueli] = []

        if not salary.endswith('/月'):
            continue

        if salary.endswith('千/月'):
            scale = 1000
        elif salary.endswith('万/月'):
            scale = 10000
        else:
            continue

        salary = salary[:-3]
        # 计算平均薪资
        salary = sum(map(float, salary.split('-'))) / 2 * scale
        jingyan_salary[jingyan].append(salary)
        xueli_salary[xueli].append(salary)

    jingyan_job_counts = {}
    for jingyan in jingyan_salary:
        jingyan_job_counts[jingyan] = len(jingyan_salary[jingyan])
        jingyan_salary[jingyan] = np.mean(jingyan_salary[jingyan])

    jingyan_salary = list(zip(list(jingyan_salary.keys()), list(jingyan_salary.values())))
    jingyan_salary = sorted(jingyan_salary, key=lambda k: k[1], reverse=True)
    jingyan = [v[0] for v in jingyan_salary]
    jingyan_salary = [v[1] for v in jingyan_salary]
    jingyan_job_counts = [jingyan_job_counts[jy] for jy in jingyan]

    xueli_job_counts = {}
    for xueli in xueli_salary:
        xueli_job_counts[xueli] = len(xueli_salary[xueli])
        xueli_salary[xueli] = np.mean(xueli_salary[xueli] + [0])

    xueli_salary = list(zip(list(xueli_salary.keys()), list(xueli_salary.values())))
    xueli_salary = sorted(xueli_salary, key=lambda k: k[1], reverse=True)
    xueli = [v[0] for v in xueli_salary if '人' not in v[0]]
    xueli_salary = [v[1] for v in xueli_salary if '人' not in v[0]]
    xueli_job_counts = [xueli_job_counts[xl] for xl in xueli]

    results = {'经验': jingyan, '经验平均薪资': jingyan_salary, '经验岗位数': jingyan_job_counts,
               '学历': xueli, '学历平均薪资': xueli_salary, '学历岗位数': xueli_job_counts}
    return jsonify(results)



if __name__ == "__main__":
    app.run(host='127.0.0.1')

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