2026年4月1日起，华为OD机试正式切换至新系统模式！采用力扣模式，无需自己写输入！难度大幅度降低。正在备考华为OD机试的同学，优先刷新系统机试真题，可大幅提升通过率。新系统考题考点与旧版一致，无需重新适应。本专栏所有算法题均配有详细思路解析，并提供C语言、JS、C++、Python、Go、Java多语言源码实现。订阅后私信即可开通专属OJ刷题权限，高效备考一次上岸！

算法大师！ 45万+

华为OD机试新系统双机位C卷往年真题机考题库（持续收录中）以及算法考点详解（Python/JS/C/C++）

刷的越多，抽中的概率越大，每一题都有详细的答题思路、详细的代码注释发现新题目，随时更新。

学Java，找哪吒 5万+

华为OD机试 - 最优的投资方式 - 贪心算法（Python/JS/C/C++ 2025 A卷 200分）

本专栏收录于《华为OD机试真题（Python/JS/C/C++）》。刷的越多，抽中的概率越大，私信哪吒，备注华为OD，加入华为OD刷题交流群，每一题都有详细的答题思路、详细的代码注释、3个测试用例、为什么这道题采用XX算法、XX算法的适用场景，发现新题目，随时更新。在一家虚拟游戏中生活，你必须进行投资以增强在虚拟游戏中的资产以免被淘汰出局。拥有一家Bank，它提供有若干理财产品 M 个，风险及投资回报不同，你有 N(元)进行投资，能接收的总风险值为X 你要在可接受范围内选择最优的投资方式获得最大回报。备注：

学Java，找哪吒 1365

华为OD机试新系统双机位C卷往年真题机考题库（持续收录中）以及算法考点详解

每一题都有详细的答题思路、详细的代码注释、样例测试，发现新题目，随时更新，全天优快云在线答疑。

学Java，找哪吒 7万+

2025华为OD机试真题目录【E卷+A卷+B卷+C卷+D卷】持续收录中

本专栏提供2025最新最全的华为OD机试真题库（E+A+B+C+D卷），包括100分和200分题型。题目包含题目描述、输入描述、用例、备注和解题思路、多种语言解法（Java/JS/Py/C/C++）。希望小伙伴们认真学习、顺利通过。

耀耀zz的博客 852

2023华为od机试C卷【最优的投资方式】Java 实现

在一款虚拟游戏中生活，你必须进行投资以增强在虚拟游戏中的资产以免被淘汰出局。现有一家Bank，它提供有若干理财产品 M个，风险及投资回报不同，你有 N(元)进行投资，能接收的总风险值为X.你要在可接受范围内选择最优的投资方式获得最大回报

MISAYAONE的博客 1552

现有一家Bank，它提供有若干理财产品 m 个，风险及投资回报不同，你有 N（元）进行投资，能接收的总风险值为X。在一款虚拟游戏中生活，你必须进行投资以增强在虚拟游戏中的资产以免被淘汰出局。投资第二项30个单位，第四项40个单位，总的投资风险为两项相加为4+6=10。第四行：最大投资额度序列，输入为整数，取值范围[1, 10000]第二行：产品投资回报率序列，输入为整数，取值范围[1,60]第三行：产品风险值序列，输入为整数，取值范围[1, 100]可接受的总风险（整数，取值范围[1,200]）

算法算法算法！！！ 1032

Nangate 45nm FreePDK开源工艺库：从RTL到GDSII全流程实战

在数字IC设计中，标准单元库是连接逻辑综合与物理实现的基石，它定义了每个单元的逻辑功能、时序延迟与物理尺寸。开源EDA工具链的成熟，使得个人也能借助免费工艺套件完整跑通芯片设计流程。Nangate 45nm FreePDK作为一套经典的开源工艺库，凭借结构清晰、配套完善的特性，成为学习数字后端和时序收敛的理想训练场。从综合工具Yosys读取Liberty文件进行逻辑映射，到OpenROAD执行布局布线与时钟树综合，再到使用OpenSTA分析关键路径延迟，这套流程涵盖了数字设计最核心的环节。通过亲手操作，开发

weixin_29168393的博客 270

交流异步机VVVF变频控制simulink/matlab仿真分析

VVVF，是Variable Voltage and Variable Frequency的缩写，意为：可变电压、可变频率，也就是变频调速系统。 VVVF控制的逆变器连接电机，通过同时改变频率和电压，达到磁通恒定（可以用反电势/频率近似表征）和控制电机转速（和频率成正比）的目的，所以多应用在变频器中，属于工业自动化领域。

Ezekiel_Mok的博客 4209

YOLOv10改进策略【模型轻量化】| 替换骨干网络为MoblieNetV2，含模型详解和完整配置步骤

本文记录的是。采用深度可分离卷积将标准卷积分解为深度卷积和1×1卷积。同时，引入来，避免非线性层导致的性能下降问题。本文将应用到YOLOv10中，借助其高效的结构和特性，在保持一定精度的前提下，显著降低YOLOv10的计算复杂度和内存占用。

Limiiiing的博客 532

uniapp(全端兼容) - 最新实现全局设置修改字体大小功能，统一修改全部页面所有文字的大小（标准/小/中/大字号切换，用户修改后整个项目立即生效）设置应用字体的大小（详细示例代码，一键复制开箱即用

uni-app，支付宝微信小程序，h5，手机端H5网页，移动端网站，安卓app，ios苹果app，nvue，全局设置字体大小，退出后还能记录字号大小，uniApp将全部页面的字体字号进行修改，uniapp设置改变全局字体大小功能，动态调整应用字体大小，详细教程，动态显示字体大小，可调整显示字体大小，如何解决不同端字体大小的问题，除了rem还有什么方法，设置全局字体大小，用户设置字号大小，应用字体大中小，实现用户全局设置应用字体大小，老年模式字体设置思路，h5端不行怎么办，移动端不生效，修改全局字号，一键改整

✅ 精选推荐文章 8884

微信小程序(云开发) - 实现微信小程序拼团功能+前后台源码/数据库设计，开团参团、自动成团、微信分享链接URL参与拼团、组团人数/倒计时/实时数据、设置拼团人数限制等（拼团退款处理逻辑、订单同步等）

实现微信小程序云开发拼团功能，云开发小程序--微信拼团设计实现，微信小程序实现秒杀、拼团等效果，微信小程序云开发拼团源码完整版包含前后台和数据库，拼团商城小程序开发，分销，拼团，社群团购，微信小程序—判断参团人数（设置拼团剩余人数数组）拼多多拼团小程序开发，微信云开发教程怎么实现微信小程序云开发拼团功能？云开发如何实现拼团，拼团小程序怎么弄，微信小程序案例：拼团模块，需要朋友助力，详细源码demo示例，如何做拼团功能，云开发小程序如何做拼团功能，外卖电商拼团，仿拼多多拼团，拼团后退款怎么办，拼团后订单管理

🏆 前端领域优质创作者 4627

从入门到精通！Arduino 全栈开发深度指南

本文围绕 Arduino 开发撰写全攻略，从基础入门到高级应用层层递进。开篇介绍 Arduino 概念、优势及硬件家族，引导读者完成开发环境搭建与首个 LED 闪烁项目；深入讲解编程语法、I/O 控制及通信协议；详细阐述传感器、模块应用与调试优化技巧；最后以自定义库开发、低功耗设计等高级内容收尾，并给出综合项目案例与产品化思路。无论新手入门，还是开发者进阶，均可从中系统学习 Arduino 知识，掌握从理论到实践的全流程开发技能。

邓邓子的博客 2754

MicroChip的SmartFusion2的M2S090芯片的全流程开发环境搭建和Demo设计

qq_45806640的博客 455

用 CDS 作为 Referenced Data Source 开发 SAP Gateway OData 服务，一条更轻的 SEGW 路线

本文介绍了SAP Gateway项目中从传统手工建模到基于CDS View的现代化开发方式转变。主要内容包括：传统方式需要在SEGW中手动创建Entity Type、Entity Set等元素，并在DPC_EXT中编写大量查询逻辑，存在模型层和业务逻辑重复表达的问题。 7.50版本后，SAP Gateway与ABAP CDS深度集成，可通过SEGW直接引用CDS View作为数据源，自动生成数据模型和SADL读取逻辑，支持完整的OData查询功能。最佳实践建议通过Consumption View而非直接

2007 年 ~ 2025 年，深耕 SAP 技术 18 年 700

IMSL CNL 7.0.0 (x86-64)Crack

IMSL CNL 7.0.0 (x86-64)Crack intel的算法库破解文件，这是C++版本的破解文件

SAP B1中文培訓教材_SDK

公司内部培训资料，对开发工具，DIAPI， UIAPI，Add-ON有详细解说

TMS_WebGMaps_1.9.8.0_Full_Source

TMS_WebGMaps_1.9.8.0_Full_Source 全源码 delphi cbuilder

深度学习工具包 DeepLearningToolbox

深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")