sdsd


#ilnclude <iostream>
using namespace std;
int main()
{
double year,money,slary;
cout<<"欢迎使用利息计算器!\n"
cout<<"请输入存款金额:"
cin>>money;
cout<<"======存款期限======\n1.3个月\n2.6个月\n3.一年\n4.二年\n5.三年\n6.五年\n请输入存款期限的代号:";
cin>>year;
switch(year)
case 0.25:slary=money*0.031*0.5;cout<<"到期利息为:"<<slary;break;
case 0.5:slary=money*0.033*0.5;cout<<"到期利息为:"<<slary;break;
case 1:slary=money*0.035*0.5;cout<<"到期利息为:"<<slary;break;
case 2:slary=money*0.044*0.5;cout<<"到期利息为:"<<slary;break;
case 3:slary=money*0.050*0.5;cout<<"到期利息为:"<<slary;break;
case 5:slary=money*0.055*0.5;cout<<"到期利息为:"<<slary;break;
default:cout<<"错误\n"

  return 0;
}

pip install lightning Collecting lightning Downloading lightning-2.5.2-py3-none-any.whl (821 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 821.1/821.1 kB 12.8 kB/s eta 0:00:00 Requirement already satisfied: tqdm<6.0,>=4.57.0 in d:\anaconda\envs\sdsd_torch\lib\site-packages (from lightning) (4.62.2) Requirement already satisfied: typing-extensions<6.0,>=4.4.0 in d:\anaconda\envs\sdsd_torch\lib\site-packages (from lightning) (4.12.2) Collecting lightning-utilities<2.0,>=0.10.0 Downloading lightning_utilities-0.14.3-py3-none-any.whl (28 kB) Collecting pytorch-lightning Downloading pytorch_lightning-2.5.2-py3-none-any.whl (825 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 825.4/825.4 kB 13.7 kB/s eta 0:00:00 Collecting torch<4.0,>=2.1.0 Downloading torch-2.7.1-cp39-cp39-win_amd64.whl (216.0 MB) ━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 41.7/216.0 MB ? eta -:--:-- ERROR: Exception: Traceback (most recent call last): File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_vendor\urllib3\response.py", line 438, in _error_catcher yield File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_vendor\urllib3\response.py", line 561, in read data = self._fp_read(amt) if not fp_closed else b"" File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_vendor\urllib3\response.py", line 527, in _fp_read return self._fp.read(amt) if amt is not None else self._fp.read() File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_vendor\cachecontrol\filewrapper.py", line 90, in read data = self.__fp.read(amt) File "D:\anaconda\envs\sdsd_torch\lib\http\client.py", line 463, in read n = self.readinto(b) File "D:\anaconda\envs\sdsd_torch\lib\http\client.py", line 507, in readinto n = self.fp.readinto(b) File "D:\anaconda\envs\sdsd_torch\lib\socket.py", line 704, in readinto return self._sock.recv_into(b) File "D:\anaconda\envs\sdsd_torch\lib\ssl.py", line 1242, in recv_into return self.read(nbytes, buffer) File "D:\anaconda\envs\sdsd_torch\lib\ssl.py", line 1100, in read return self._sslobj.read(len, buffer) socket.timeout: The read operation timed out During handling of the above exception, another exception occurred: Traceback (most recent call last): File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_internal\cli\base_command.py", line 160, in exc_logging_wrapper status = run_func(*args) File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_internal\cli\req_command.py", line 247, in wrapper return func(self, options, args) File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_internal\commands\install.py", line 419, in run requirement_set = resolver.resolve( File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_internal\resolution\resolvelib\resolver.py", line 92, in resolve result = self._result = resolver.resolve( File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_vendor\resolvelib\resolvers.py", line 481, in resolve state = resolution.resolve(requirements, max_rounds=max_rounds) File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_vendor\resolvelib\resolvers.py", line 373, in resolve failure_causes = self._attempt_to_pin_criterion(name) File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_vendor\resolvelib\resolvers.py", line 213, in _attempt_to_pin_criterion criteria = self._get_updated_criteria(candidate) File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_vendor\resolvelib\resolvers.py", line 204, in _get_updated_criteria self._add_to_criteria(criteria, requirement, parent=candidate) File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_vendor\resolvelib\resolvers.py", line 172, in _add_to_criteria if not criterion.candidates: File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_vendor\resolvelib\structs.py", line 151, in __bool__ return bool(self._sequence) File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_internal\resolution\resolvelib\found_candidates.py", line 155, in __bool__ return any(self) File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_internal\resolution\resolvelib\found_candidates.py", line 143, in <genexpr> return (c for c in iterator if id(c) not in self._incompatible_ids) File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_internal\resolution\resolvelib\found_candidates.py", line 47, in _iter_built candidate = func() File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_internal\resolution\resolvelib\factory.py", line 206, in _make_candidate_from_link self._link_candidate_cache[link] = LinkCandidate( File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_internal\resolution\resolvelib\candidates.py", line 297, in __init__ super().__init__( File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_internal\resolution\resolvelib\candidates.py", line 162, in __init__ self.dist = self._prepare() File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_internal\resolution\resolvelib\candidates.py", line 231, in _prepare dist = self._prepare_distribution() File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_internal\resolution\resolvelib\candidates.py", line 308, in _prepare_distribution return preparer.prepare_linked_requirement(self._ireq, parallel_builds=True) File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_internal\operations\prepare.py", line 491, in prepare_linked_requirement return self._prepare_linked_requirement(req, parallel_builds) File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_internal\operations\prepare.py", line 536, in _prepare_linked_requirement local_file = unpack_url( File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_internal\operations\prepare.py", line 166, in unpack_url file = get_http_url( File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_internal\operations\prepare.py", line 107, in get_http_url from_path, content_type = download(link, temp_dir.path) File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_internal\network\download.py", line 147, in __call__ for chunk in chunks: File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_internal\cli\progress_bars.py", line 53, in _rich_progress_bar for chunk in iterable: File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_internal\network\utils.py", line 63, in response_chunks for chunk in response.raw.stream( File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_vendor\urllib3\response.py", line 622, in stream data = self.read(amt=amt, decode_content=decode_content) File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_vendor\urllib3\response.py", line 587, in read raise IncompleteRead(self._fp_bytes_read, self.length_remaining) File "D:\anaconda\envs\sdsd_torch\lib\contextlib.py", line 137, in __exit__ self.gen.throw(typ, value, traceback) File "D:\anaconda\envs\sdsd_torch\lib\site-packages\pip\_vendor\urllib3\response.py", line 443, in _error_catcher raise ReadTimeoutError(self._pool, None, "Read timed out.") pip._vendor.urllib3.exceptions.ReadTimeoutError: HTTPSConnectionPool(host='files.pythonhosted.org', port=443): Read timed out.
07-25
内容概要:该论文研究增程式电动汽车(REEV)的能量管理策略,针对现有优化策略实时性差的问题,提出基于工况识别的自适应等效燃油消耗最小策略(A-ECMS)。首先建立整车Simulink模型和基于规则的策略;然后研究动态规划(DP)算法和等效燃油最小策略;接着通过聚类分析将道路工况分为四类,并设计工况识别算法;最后开发基于工况识别的A-ECMS,通过高德地图预判工况类型并自适应调整SOC分配。仿真显示该策略比规则策略节油8%,比简单SOC规划策略节油2%,并通过硬件在环实验验证了实时可行性。 适合人群:具备一定编程基础,特别是对电动汽车能量管理策略有兴趣的研发人员和技术爱好者。 使用场景及目标:①理解增程式电动汽车能量管理策略的基本原理;②掌握动态规划算法和等效燃油消耗最小策略的应用;③学习工况识别算法的设计和实现;④了解基于工况识别的A-ECMS策略的具体实现及其优化效果。 其他说明:此资源不仅提供了详细的MATLAB/Simulink代码实现,还深入分析了各算法的原理和应用场景,适合用于学术研究和工业实践。在学习过程中,建议结合代码调试和实际数据进行实践,以便更好地理解策略的优化效果。此外,论文还探讨了未来的研究方向,如深度学习替代聚类、多目标优化以及V2X集成等,为后续研究提供了思路。
内容概要:论文《基于KANN-DBSCAN带宽优化的核密度估计载荷谱外推》针对传统核密度估计(KDE)载荷外推中使用全局固定带宽的局限性,提出了一种基于改进的K平均最近邻DBSCAN(KANN-DBSCAN)聚类算法优化带宽选择的核密度估计方法。该方法通过对载荷数据进行KANN-DBSCAN聚类分组,采用拇指法(ROT)计算各簇最优带宽,再进行核密度估计和蒙特卡洛模拟外推。实验以电动汽车实测载荷数据为对象,通过统计参数、拟合度和伪损伤三个指标验证了该方法的有效性,误差显著降低,拟合度R²>0.99,伪损伤接近1。 适合人群:具备一定编程基础和载荷数据分析经验的研究人员、工程师,尤其是从事汽车工程、机械工程等领域的工作1-5年研发人员。 使用场景及目标:①用于电动汽车载荷谱编制,提高载荷预测的准确性;②应用于机械零部件的载荷外推,特别是非对称载荷分布和多峰扭矩载荷;③实现智能网联汽车载荷预测与数字孪生集成,提供动态更新的载荷预测系统。 其他说明:该方法不仅解决了传统KDE方法在复杂工况下的“过平滑”与“欠拟合”问题,还通过自适应参数机制提高了方法的普适性和计算效率。实际应用中,建议结合MATLAB代码实现,确保数据质量,优化参数并通过伪损伤误差等指标进行验证。此外,该方法可扩展至风电装备、航空结构健康监测等多个领域,未来研究方向包括高维载荷扩展、实时外推和多物理场耦合等。
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