(四)操作Libcurl下载文件,处置无信号中缀,下载中掉电复原后断点

(四)使用Libcurl下载文件,解决无信号中断,下载中掉电恢复后断点续传问题的源代码 源代码附上:   //全局变量 bool resumeDownload = false; //是否需要下载的标记位 long downloadFileLenth = 0; //需要下载的总大小, 远程文件的大小/* 得到本地文件大小的函数, 若不是续传则返回0, 否则返回指定路径地址的文件大小 */ long getLocalFileLenth(const char* localP 阅读详情

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//全局变量

bool resumeDownload = false;             //能否需求下载的标记位

long downloadFileLenth = 0;                 //需求下载的总大小,长途文件的大小

/* 失去当地文件大小的函数,若不是续传则前往0, 不然前往指定门路地址的文件大小 */

long getLocalFileLenth(const char* localPath){

      if (!resumeDownload){

             return 0;

}

return fs_open(localPath).fs_size();

}

/* 失去长途文件的大小,要下载的文件大小 */

long getDownloadFileLenth(const char *url){

long downloadFileLenth = 0;

CURL *handle = curl_easy_init();

curl_easy_setopt(handle, CURLOPT_URL, url);

curl_easy_setopt(handle, CURLOPT_HEADER, 1);    //只要求header头

curl_easy_setopt(handle, CURLOPT_NOBODY, 1);    //不需求body

if (curl_easy_perform(handle) == CURLE_OK) {

curl_easy_getinfo(handle, CURLINFO_CONTENT_LENGTH_DOWNLOAD, &downloadFileLenth);

} else {

downloadFileLenth = -1;

}

return downloadFileLenth;

}

/* scomoDownload回调的计算进度条的函数 */

void getProgressValue(const char* localSize, double dt, double dn, double ult, double uln){

     double showTotal, showNow;

showTotal = downloadFileLenth;

int localNow = atoi (localSize.c_str());

showNow = localNow + dn;

showProgressBar(showTotal, showNow);

}

/* 间接停止下载的函数 */

public CurlCode scomoDownload(long timeout) {

      long localFileLenth = getLocalFileLenth();

      const char *localFileLenthStr;

      sprint(localFileLenthStr, %ld, localFileLenth);

curl_easy_setopt(handle, CURLOPT_URL, mUrl);

curl_easy_setopt(handle, CURLOPT_HEADER, 0);

curl_easy_setopt(handle, CURLOPT_TIMEOUT, timeout);

curl_easy_setopt(handle, CURLOPT_CONNECTIONTIMEOUT, 0);

curl_easy_setopt(handle, CURLOPT_WRITEFUNCTION, &writeDataCallback);

curl_easy_setopt(handle, CURLOPT_WRITEDATA, this);

curl_easy_setopt(handle, CURLOPT_RESUME_FROM_LARGE, localFileLenth);

curl_easy_setopt(handle, CURLOPT_NOPROGRESS, 0);

curl_easy_setopt(handle, CURLOPT_ PROGRESSFUNCTION, getProgressValue);

curl_easy_setopt(handle, CURLOPT_PROGRESSDATA, localFileLenthStr);

if (curl_easy_perform) {

       resumeDownload = true;

       return DS_FAILED;

} else {

       resumeDownload = false;

       return DS_FINISHED;

}

}

/* downloadControl函数用来管制整个下载进程的节拍,管制下载的次数, 每次期待的工夫等 */

public void downloadControler(){

      downloadFileLenth = getDownloadFileLenth();                 //下载前失去要下载的文件大小赋值给全局变量

      int times = 605;                                      //600次*50ms=5分钟,以此确保5分钟内的重试次数,而5次是反常下载的中缀次数,意思即是5次内能反常完成下载.

      int count = 0;

      int timeout = 30;

      DSTATUS dstatus = DS_FAILED;

      while (count++ < times){

             status = scomoDownload(timeout);

             if (dstatus == DS_FINISHED){

                    break;

             }


http://www.nnwnn.com/a/shoujibiancheng/2012/0920/17756.html

突破NER性能瓶颈:BERT与LLM协同的混合架构实践 性能卓越:在多个测试集上,F1分数比单一模型提升5-15%成本可控:相比纯LLM方案,成本降低60%以上易于落地:架构清晰,便于调试和优化这种"专才+通才"的组合思路可以扩展到其他NLP任务中,如关系抽取、事件提取、文本分类等。更精细的流水线优化多模型投票机制在线学习与自适应调整不追求单一模型的万能,而是通过精心设计的组合,让每个组件发挥其独特优势,最终实现系统性能的最优化。欢迎在评论区分享你在NER任务中的实践经验,或者对这个混合架构的改进建议! 阅读详情

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