{"id":1186216,"date":"2025-01-15T19:50:02","date_gmt":"2025-01-15T11:50:02","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1186216.html"},"modified":"2025-01-15T19:50:06","modified_gmt":"2025-01-15T11:50:06","slug":"%e5%a6%82%e4%bd%95%e9%80%9a%e8%bf%87python%e5%86%99%e7%a7%bb%e5%8a%a8%e8%8d%b7%e8%bd%bd","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1186216.html","title":{"rendered":"\u5982\u4f55\u901a\u8fc7python\u5199\u79fb\u52a8\u8377\u8f7d"},"content":{"rendered":"

\"\u5982\u4f55\u901a\u8fc7python\u5199\u79fb\u52a8\u8377\u8f7d\"<\/p>\n

\u901a\u8fc7Python\u5199\u79fb\u52a8\u8377\u8f7d\uff0c\u53ef\u4ee5\u4f7f\u7528pandas\u3001matplotlib\u3001numpy\u7b49\u5e93\u6765\u5904\u7406\u6570\u636e\u3001\u7ed8\u5236\u56fe\u8868\u548c\u8fdb\u884c\u6570\u503c\u8ba1\u7b97\u3002\u4f7f\u7528pandas\u53ef\u4ee5\u8f7b\u677e\u5730\u5904\u7406\u6570\u636e\u96c6\u5e76\u8fdb\u884c\u5206\u6790\uff0cmatplotlib\u7528\u4e8e\u7ed8\u5236\u79fb\u52a8\u8377\u8f7d\u7684\u56fe\u8868\uff0cnumpy\u5219\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\u548c\u6570\u636e\u5904\u7406<\/strong>\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u8be6\u7ec6\u6b65\u9aa4\u5305\u62ec\u6570\u636e\u5bfc\u5165\u4e0e\u9884\u5904\u7406\u3001\u5b9a\u4e49\u79fb\u52a8\u8377\u8f7d\u6a21\u578b\u3001\u8ba1\u7b97\u79fb\u52a8\u8377\u8f7d\u5f71\u54cd\u3001\u7ed8\u5236\u79fb\u52a8\u8377\u8f7d\u6548\u679c\u56fe\u7b49\u3002\u6211\u4eec\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5982\u4f55\u7528Python\u5b9e\u73b0\u8fd9\u4e9b\u6b65\u9aa4\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u6570\u636e\u5bfc\u5165\u4e0e\u9884\u5904\u7406<\/h3>\n<\/p>\n

\u8981\u5b9e\u73b0\u79fb\u52a8\u8377\u8f7d\u5206\u6790\uff0c\u9996\u5148\u9700\u8981\u5bfc\u5165\u548c\u9884\u5904\u7406\u6570\u636e\u3002\u6570\u636e\u53ef\u4ee5\u6765\u81eaCSV\u6587\u4ef6\u3001Excel\u6587\u4ef6\u6216\u5176\u4ed6\u6570\u636e\u6e90\u3002\u6211\u4eec\u4f7f\u7528pandas\u5e93\u6765\u8bfb\u53d6\u548c\u5904\u7406\u6570\u636e\u3002\u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2aCSV\u6587\u4ef6\uff0c\u5176\u4e2d\u5305\u542b\u65f6\u95f4\u3001\u4f4d\u7f6e\u548c\u8377\u8f7d\u7684\u6570\u503c\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u5bfc\u5165\u6570\u636e\uff1a<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
\u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
data = pd.read_csv('load_data.csv')<\/p>\n
\u663e\u793a\u524d\u51e0\u884c\u6570\u636e<\/strong><\/h2>\n
print(data.head())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u6570\u636e\u5bfc\u5165\u4e4b\u540e\uff0c\u6211\u4eec\u53ef\u80fd\u9700\u8981\u8fdb\u884c\u4e00\u4e9b\u9884\u5904\u7406\uff0c\u4f8b\u5982\u5904\u7406\u7f3a\u5931\u503c\u3001\u6570\u636e\u8f6c\u6362\u7b49\u3002\u5177\u4f53\u7684\u9884\u5904\u7406\u6b65\u9aa4\u53d6\u51b3\u4e8e\u6570\u636e\u7684\u5177\u4f53\u60c5\u51b5\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u5b9a\u4e49\u79fb\u52a8\u8377\u8f7d\u6a21\u578b<\/h3>\n<\/p>\n
\u5728\u79fb\u52a8\u8377\u8f7d\u5206\u6790\u4e2d\uff0c\u6211\u4eec\u9700\u8981\u5b9a\u4e49\u79fb\u52a8\u8377\u8f7d\u6a21\u578b\u3002\u79fb\u52a8\u8377\u8f7d\u6a21\u578b\u53ef\u4ee5\u662f\u7b80\u5355\u7684\u70b9\u8377\u8f7d\uff0c\u4e5f\u53ef\u4ee5\u662f\u590d\u6742\u7684\u5206\u5e03\u8377\u8f7d\u3002\u6211\u4eec\u5c06\u4ee5\u4e00\u4e2a\u7b80\u5355\u7684\u70b9\u8377\u8f7d\u6a21\u578b\u4e3a\u4f8b\uff0c\u5b9a\u4e49\u4e00\u4e2a\u79fb\u52a8\u8377\u8f7d\u7c7b\uff1a<\/p>\n<\/p>\n
class MovingLoad:<\/p>\n
    def __init__(self, load_value, position, time):<\/p>\n
        self.load_value = load_value<\/p>\n
        self.position = position<\/p>\n
        self.time = time<\/p>\n
    def __repr__(self):<\/p>\n
        return f'MovingLoad(load_value={self.load_value}, position={self.position}, time={self.time})'<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u8ba1\u7b97\u79fb\u52a8\u8377\u8f7d\u5f71\u54cd<\/h3>\n<\/p>\n
\u8ba1\u7b97\u79fb\u52a8\u8377\u8f7d\u5f71\u54cd\u662f\u79fb\u52a8\u8377\u8f7d\u5206\u6790\u7684\u6838\u5fc3\u6b65\u9aa4\u3002\u6211\u4eec\u9700\u8981\u6839\u636e\u79fb\u52a8\u8377\u8f7d\u7684\u4f4d\u7f6e\u548c\u65f6\u95f4\uff0c\u8ba1\u7b97\u5176\u5bf9\u7ed3\u6784\u7684\u5f71\u54cd\u3002\u4f8b\u5982\uff0c\u5047\u8bbe\u6211\u4eec\u9700\u8981\u8ba1\u7b97\u6865\u6881\u5728\u4e0d\u540c\u4f4d\u7f6e\u548c\u65f6\u95f4\u4e0b\u7684\u5e94\u529b\u54cd\u5e94\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
def calculate_stress(load, position):<\/p>\n
    # \u8fd9\u91cc\u4f7f\u7528\u4e00\u4e2a\u7b80\u5355\u7684\u7ebf\u6027\u6a21\u578b\u6765\u8ba1\u7b97\u5e94\u529b<\/p>\n
    stress = load.load_value \/ (1 + np.abs(load.position - position))<\/p>\n
    return stress<\/p>\n
\u793a\u4f8b\u8ba1\u7b97<\/strong><\/h2>\n
load = MovingLoad(100, 10, 0)<\/p>\n
position = np.arange(0, 20, 1)<\/p>\n
stress = calculate_stress(load, position)<\/p>\n
print(stress)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u7ed8\u5236\u79fb\u52a8\u8377\u8f7d\u6548\u679c\u56fe<\/h3>\n<\/p>\n
\u4e3a\u4e86\u66f4\u597d\u5730\u7406\u89e3\u548c\u5c55\u793a\u79fb\u52a8\u8377\u8f7d\u7684\u5f71\u54cd\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528matplotlib\u5e93\u7ed8\u5236\u56fe\u8868\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff0c\u5c55\u793a\u5982\u4f55\u7ed8\u5236\u79fb\u52a8\u8377\u8f7d\u5bf9\u7ed3\u6784\u7684\u5e94\u529b\u54cd\u5e94\uff1a<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u7ed8\u5236\u5e94\u529b\u54cd\u5e94\u56fe<\/strong><\/h2>\n
plt.plot(position, stress, label='Stress Response')<\/p>\n
plt.xlabel('Position')<\/p>\n
plt.ylabel('Stress')<\/p>\n
plt.title('Moving Load Stress Response')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u6269\u5c55\u4e0e\u4f18\u5316<\/h3>\n<\/p>\n
\u4e0a\u8ff0\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u57fa\u672c\u7684\u79fb\u52a8\u8377\u8f7d\u5206\u6790\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6211\u4eec\u53ef\u80fd\u9700\u8981\u5904\u7406\u66f4\u590d\u6742\u7684\u60c5\u51b5\uff0c\u4f8b\u5982\u591a\u4e2a\u79fb\u52a8\u8377\u8f7d\u3001\u975e\u7ebf\u6027\u54cd\u5e94\u7b49\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u6269\u5c55\u548c\u4f18\u5316\u4ee3\u7801\u6765\u5904\u7406\u8fd9\u4e9b\u590d\u6742\u60c5\u51b5\uff1a<\/p>\n<\/p>\n
1\u3001\u5904\u7406\u591a\u4e2a\u79fb\u52a8\u8377\u8f7d<\/h4>\n<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u5b9a\u4e49\u4e00\u4e2a\u79fb\u52a8\u8377\u8f7d\u96c6\u5408\uff0c\u5e76\u8ba1\u7b97\u6240\u6709\u79fb\u52a8\u8377\u8f7d\u7684\u603b\u5e94\u529b\u54cd\u5e94\uff1a<\/p>\n<\/p>\n
loads = [<\/p>\n
    MovingLoad(100, 10, 0),<\/p>\n
    MovingLoad(200, 15, 1),<\/p>\n
    MovingLoad(150, 20, 2)<\/p>\n
]<\/p>\n
def total_stress(loads, position):<\/p>\n
    stress = np.zeros_like(position)<\/p>\n
    for load in loads:<\/p>\n
        stress += calculate_stress(load, position)<\/p>\n
    return stress<\/p>\n
\u793a\u4f8b\u8ba1\u7b97<\/strong><\/h2>\n
total_stress_response = total_stress(loads, position)<\/p>\n
\u7ed8\u5236\u603b\u5e94\u529b\u54cd\u5e94\u56fe<\/strong><\/h2>\n
plt.plot(position, total_stress_response, label='Total Stress Response')<\/p>\n
plt.xlabel('Position')<\/p>\n
plt.ylabel('Stress')<\/p>\n
plt.title('Total Moving Load Stress Response')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u975e\u7ebf\u6027\u54cd\u5e94<\/h4>\n<\/p>\n
\u5bf9\u4e8e\u975e\u7ebf\u6027\u54cd\u5e94\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u66f4\u590d\u6742\u7684\u6a21\u578b\u548c\u6570\u503c\u65b9\u6cd5\u6765\u8ba1\u7b97\u3002\u4f8b\u5982\uff0c\u4f7f\u7528\u6709\u9650\u5143\u65b9\u6cd5\uff08FEM\uff09\u6765\u6a21\u62df\u7ed3\u6784\u7684\u975e\u7ebf\u6027\u54cd\u5e94\uff1a<\/p>\n<\/p>\n
from scipy.integrate import solve_ivp<\/p>\n
def nonlinear_stress(load, position):<\/p>\n
    # \u8fd9\u91cc\u4f7f\u7528\u4e00\u4e2a\u7b80\u5355\u7684\u975e\u7ebf\u6027\u6a21\u578b<\/p>\n
    stress = load.load_value \/ (1 + np.abs(load.position - position)2)<\/p>\n
    return stress<\/p>\n
\u793a\u4f8b\u8ba1\u7b97<\/strong><\/h2>\n
nonlinear_stress_response = nonlinear_stress(load, position)<\/p>\n
\u7ed8\u5236\u975e\u7ebf\u6027\u5e94\u529b\u54cd\u5e94\u56fe<\/strong><\/h2>\n
plt.plot(position, nonlinear_stress_response, label='Nonlinear Stress Response')<\/p>\n
plt.xlabel('Position')<\/p>\n
plt.ylabel('Stress')<\/p>\n
plt.title('Nonlinear Moving Load Stress Response')<\/p>\n
plt.legend()<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Python\u8fdb\u884c\u79fb\u52a8\u8377\u8f7d\u5206\u6790\uff0c\u5305\u62ec\u6570\u636e\u5bfc\u5165\u4e0e\u9884\u5904\u7406\u3001\u5b9a\u4e49\u79fb\u52a8\u8377\u8f7d\u6a21\u578b\u3001\u8ba1\u7b97\u79fb\u52a8\u8377\u8f7d\u5f71\u54cd\u3001\u7ed8\u5236\u79fb\u52a8\u8377\u8f7d\u6548\u679c\u56fe\u7b49\u3002\u5177\u4f53\u5b9e\u73b0\u8fc7\u7a0b\u4e2d\uff0c\u53ef\u4ee5\u6839\u636e\u5b9e\u9645\u9700\u6c42\u5bf9\u4ee3\u7801\u8fdb\u884c\u6269\u5c55\u548c\u4f18\u5316\uff0c\u4f8b\u5982\u5904\u7406\u591a\u4e2a\u79fb\u52a8\u8377\u8f7d\u3001\u8003\u8651\u975e\u7ebf\u6027\u54cd\u5e94\u7b49\u3002Python\u7684pandas\u3001numpy\u3001matplotlib\u7b49\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u5904\u7406\u548c\u6570\u503c\u8ba1\u7b97\u529f\u80fd\uff0c\u4f7f\u5f97\u79fb\u52a8\u8377\u8f7d\u5206\u6790\u66f4\u52a0\u9ad8\u6548\u548c\u7075\u6d3b\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u8fdb\u884c\u79fb\u52a8\u8377\u8f7d\u6a21\u62df\uff1f<\/strong>
\u5728\u8fdb\u884c\u79fb\u52a8\u8377\u8f7d\u6a21\u62df\u65f6\uff0c\u9996\u5148\u9700\u8981\u5b9a\u4e49\u8377\u8f7d\u7684\u7279\u6027\uff0c\u5305\u62ec\u8377\u8f7d\u7684\u5927\u5c0f\u3001\u901f\u5ea6\u4ee5\u53ca\u4f5c\u7528\u8def\u5f84\u3002\u53ef\u4ee5\u5229\u7528Python\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u5982NumPy\u548cSciPy\uff0c\u6765\u521b\u5efa\u8377\u8f7d\u6a21\u578b\u5e76\u8fdb\u884c\u6570\u503c\u8ba1\u7b97\u3002\u540c\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u7b49\u53ef\u89c6\u5316\u5e93\u6765\u5c55\u793a\u6a21\u62df\u7ed3\u679c\uff0c\u4ee5\u4fbf\u66f4\u76f4\u89c2\u5730\u7406\u89e3\u8377\u8f7d\u5bf9\u7ed3\u6784\u7684\u5f71\u54cd\u3002<\/p>\n
\u6709\u54ea\u4e9bPython\u5e93\u9002\u5408\u7528\u4e8e\u8377\u8f7d\u5206\u6790\uff1f<\/strong>
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