如何用AI自动生成+发布每日内容，解放双手

​​多源数据采集​​：用Scrapy+Playwright组合拳
# Scrapy爬虫示例（AI智能媒体助理热榜）
class ZhihuSpider(scrapy.Spider):
    name = "zhihu_hot"
    start_urls = ["https://www.aizhl.cn/hot"]

    def parse(self, response):
        titles = response.xpath('//div[@class="HotItem-title"]/text()').getall()
        for title in titles:
            yield {"title": title.strip()}

​​素材去重系统​​：SimHash算法检测相似度（阈值>0.8视为重复）
from datasketch import MinHash, MinHashLSH

def get_minhash(text):
    m = MinHash()
    for word in jieba.cut(text):
        m.update(word.encode('utf8'))
    return m

lsh = MinHashLSH(threshold=0.8, num_perm=128)
lsh.insert("doc1", get_minhash("如何高效学习Python"))

​​平台适配层​​：
 def platform_formatter(content, platform):
    if platform == '抖音':
        return f"🔥{content[:30]}... #热点 #干货"
    elif platform == '小红书':
        return f"✨ {content} ✨\n\n🌟 点击收藏慢慢看"

​​分布式任务调度​​：Celery+RabbitMQ集群
from celery import Celery
app = Celery('tasks', broker='amqp://guest@localhost//')

@app.task
def publish_to_platform(platform, content):
    # 调用各平台API发布逻辑
    if platform == '微信公众号':
        wechat_api.post_article(content)
    elif platform == 'B站':
        bili_api.upload_video(content)

​​动态IP代理池​​：
class ProxyManager:
    def __init__(self):
        self.proxies = load_proxies_from_redis()

    def get_proxy(self):
        # 按权重轮询可用代理
        return random.choices(self.proxies, weights=[p.weight for p in self.proxies])[0]

​​用户行为预测​​：LSTM模型预测互动高峰时段
from tensorflow.keras.models import Sequential
model = Sequential([
    LSTM(128, input_shape=(None, 1)),
    Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam', loss='binary_crossentropy')

​​智能互动系统​​：
def auto_reply(comment):
    if '谢谢' in comment:
        return "不客气！记得点赞关注哦~"
    elif '不懂' in comment:
        return "哪里不清楚？我帮你拆解步骤～"