湖南省第八届程序设计竞赛F

SN74HC148DR 8-3优先编码器:实测12.8ns延迟与TTL电平兼容性分析 本文深入分析了SN74HC148DR 8-3优先编码器的性能特点,包括其12.8ns的典型传播延迟和TTL电平兼容性。通过实测数据和工业应用案例,展示了该编码器在中断优先级管理、多片级联扩展以及抗干扰设计中的关键作用,为嵌入式系统设计提供了实用参考。 阅读详情

Problem F. Kingdoms

 

Akingdom has n cities numbered 1 to n, and some bidirectional roads connectingcities. The capital is always city 1.

 

After awar, all the roads of the kingdom are destroyed. The king wants to rebuild someof the roads to connect the cities, but unfortunately, the kingdom is runningout of money. The total cost of rebuilding roads should not exceed K.

 

Giventhe list of m roads that can be rebuilt (other roads are severelydamaged and cannot be rebuilt), the king decided to maximize the total populationin the capital and all other cities that are connected (directly or indirectly)with the capital (we call it "accessible population"), can you helphim?

 

Input

The first line of input contains a single integer T (T<=20), the numberof test cases. Each test case begins with three integers n(4<=n<=16), m(1<=m<=100) and K(1<=K<=100,000).The second line contains npositive integers pi(1<=pi<=10,000),the population of each city. Each of the following m lines contains three positive integers u, v, c (1<=u,v<=n, 1<=c<=1000), representing a destroyed roadconnecting city u and v, whose rebuildingcost is c. Note that two cities canbe directly connected by more than one road, but a road cannot directlyconnect a city and itself.

 

Output

For each test case, print the maximalaccessible population.

 

Sample Input                           Output for Sample Input

2

4 6 6

500 400 300 200

1 2 4

1 3 3

1 4 2

4 3 5

2 4 6

3 2 7

4 6 5

500 400 300 200

1 2 4

1 3 3

1 4 2

4 3 5

2 4 6

3 2 7

1100

1000

 

代码:

// Rujia Liu
#include<cstdio>
#include<cstring>
#include<algorithm>
using namespace std;

const int maxn = 16 + 5;
const int maxm = 100 + 5;
const int INF = 1000000000;
int n, m;

int pa[maxn];
int findset(int x) { return pa[x] != x ? pa[x] = findset(pa[x]) : x; } 

struct Edge {
  int u, v, c;
  bool operator < (const Edge& rhs) const {
    return c < rhs.c;
  }
};

Edge e[maxm];

int MST() {
  sort(e, e+m);
  for(int i = 0; i < n; i++) pa[i] = i;
  int cnt = 0;
  int ans = 0;
  for(int i = 0; i < m; i++) {
    int u = e[i].u, v = e[i].v;
    u = findset(u);
    v = findset(v);
    int c = e[i].c;
    if(u != v) {
      pa[u] = v;
      ans += c;
      if(++cnt == n-1) return ans;
    }
  }
  return INF;
}

int in, im, K;
Edge ie[maxm];
int p[maxn];
int id[maxn];

int main() {
  int T;
  scanf("%d", &T);
  while(T--) {
    scanf("%d%d%d", &in, &im, &K);
    int p[maxn];
    Edge ie[maxn*maxn];
    for(int i = 0; i < in; i++) scanf("%d", &p[i]);
    for(int i = 0; i < im; i++) {
      int u, v, c;
      scanf("%d%d%d", &u, &v, &c);
      ie[i] = (Edge){ u-1, v-1, c };
    }

    int ans = p[0];
    int bestn = 1;
    for(int S = 0; S < (1<<(in-1)); S++) {
      n = 1;
      m = 0;
      memset(id, -1, sizeof(id));
      id[0] = 0; // capital
      for(int i = 0; i < in-1; i++)
        if(S & (1<<i)) id[i+1] = n++;
      for(int i = 0; i < im; i++) {
        int u = id[ie[i].u];
        int v = id[ie[i].v];
        if(u >= 0 && v >= 0) e[m++] = (Edge) { u, v, ie[i].c };
      }
      if(MST() <= K) {
        int tot = p[0];
        for(int i = 0; i < in-1; i++)
          if(S & (1<<i)) tot += p[i+1];
        if(tot > ans) { bestn = n; }
        ans = max(ans, tot);        
      }
    }

    printf("%d\n", ans);
  }
  return 0;
}


 

湖南省第八届大学生计算机程序设计竞赛试题 湖南省第八届ACM大学生计算机程序设计竞赛试题,湖南省第八届ACM大学生计算机程序设计竞赛试题;湖南省第八届ACM大学生计算机程序设计竞赛试题; 立即下载

相关推荐

香橙派5B-RK3588-NPU内核升级至0.9.8:RKLLM多模态部署实战

本文详细介绍了香橙派5B-RK3588设备NPU内核升级至0.9.8版本的完整流程,重点讲解了升级的必要性、两种升级方案(预编译包安装与自行编译内核)以及RKLLM SDK的安装配置。通过实战演示多模态模型转换与推理,显著提升AI计算性能,为嵌入式AI部署提供实用指南。

salt9的博客 1027

CSUOJ 1116 Kingdoms

题目链接:http://acm.csu.edu.cn/OnlineJudge/problem.php?id=1116、 1116: Kingdoms Time Limit: 3 Sec  Memory Limit: 64 MB Submit: 441  Solved: 145 Description A kingdom has n cities numbered

关于完全听不懂神犇在说什么的事 651

自动驾驶入门到进阶完整指南:感知、预测、规划、端到端、仿真测评与场景挖掘全解析

对小白来说,不建议一开始直接冲端到端大模型。先学数据和坐标系:否则所有结果都看不懂。再学 LiDAR 3D 检测:最容易建立空间感。再学 BEV/多相机感知:这是当前主流。再学轨迹预测:理解交互和多模态。再学规划控制:理解车为什么这样开。再学仿真和闭环评测:理解 open-loop 和 closed-loop 差异。最后学端到端/VLM/VLA:这时候你才知道它到底替代了哪些模块。贯穿始终学习场景挖掘和安全测评:这是工业界最核心的能力之一。

u013250861的博客 394

UVa 12507 - Kingdoms(状压dp)

A kingdom has n cities numbered 1 to n, and some bidirectional roads connecting cities. The capital is always city 1. After a war, all the roads of the kingdom are destroyed. The king wants to rebui

Wabrush的博客 484

ODrive0.5.5源码分析(1) ADC的处理

odrive 0.5.5 ADC

danger的博客 4307

用C++手把手教你连接SimNow期货模拟盘:从CTP API封装到第一个订单(附完整代码)

本文详细介绍了如何使用C++连接SimNow期货模拟盘,从CTP API的基础架构解析到实现第一个订单的完整流程。内容涵盖CTP API的封装、SimNow环境配置、交易通道实现、行情接收与处理等核心环节,并提供完整代码示例,帮助开发者快速掌握量化交易中的期货模拟盘连接技术。

weixin_27215337的博客 330

湖南省第八届程序设计竞赛K

Problem K. Cross-ShapedTests   There isan n*n matrix. Each number can be increased or decreased. If weincrease a number by x (which may be non-integer), the cost is c*x;If we decrease a number by

住在半山腰的螃蟹专栏 849

嘉立创EDA vs Altium Designer:新手如何选择?从界面到实战全对比

本文全面对比了嘉立创EDA与Altium Designer(AD)两大PCB设计工具,从设计哲学、界面交互、封装库管理、设计规则引擎、协作功能到成本生态六大维度进行深入分析。嘉立创EDA以极简设计和云端协作为特色,适合学生和创客快速上手;而AD则凭借专业级精准控制,成为高端电子设计的行业标准。通过实战案例和详细对比表格,帮助新手根据项目需求选择合适的EDA工具。

weixin_30296405的博客 721

[BZOJ 4057] Kingdoms

Link: BZOJ 4057 传送门 Solution: 一道比较基础的状压DP 看到$n<=20$,状态转移与顺序相关,就可以开心地状压了 1表示破产,0表示未破产,不断扩展破产的集合即可 Code: #include <bits/stdc++.h> using namespace std; const int MAXN=105; int a[...

weixin_30593261的博客 195

《手把手教你YOLOv9实战》,改进专栏目录

为了提供友好的操作效果,本专栏将会手把手搭建可视化界面,我将用PyQt5 搭建一个可视化界面,可视化界面能够实现基本的图像加载与检测功能,如支持视频、摄像头,还支持更换不同的 YOLOv9 模型,并适应不同领域(如口罩检测系统、人脸检测系统、工业缺陷检测系统等),界面灵活、功能强大。通过YOLOv9加以改进设计,形成新的算法框架,一起水科研和论文,专栏会一直持续更新中,本专栏适合目标检测、分割、分类。

在职AI算法工程师,擅长计算机视觉,YOLO目标检测、分割等,擅长web、pyqt界面可视化,好内容持续更新中,来这里跟大家一起学习,共同进步 2158

3D医学影像开发入门<一>:VS2019+DCMTK3.6.8编译及环境配置

3D医学影像开发入门:VS2019+DCMTK3.6.8编译及环境配置

欧特GO 1308

第八届湖南省大学生程序设计大赛 - 笑不语@USC 随笔,感想,解题报告

第八届湖南省大学生程序设计大赛原题+最终榜单:  http://pan.baidu.com/share/link?shareid=87473&uk=1124339433/         南华大学终于在省里拿到了一等奖!继续向着省内一流队伍迈进!    其实这一向水题的手感都怪怪的..包括赛前一天的试机..状态一直都不好..直到试机中有道题..晚上想通了..瞬间感觉豁然开朗...    解题

zzyzzy12 5067

135、高通 ChromaTix 调优工具入门:参数含义、调优流程与效果对比

本文介绍了高通ChromaTix色彩调优工具的核心使用要点:1. ChromaTix是位于ISP管线中的色彩处理系统,核心任务是将原始色彩映射为符合人眼感知的"舒适"效果;2. 重点解析了饱和度、色调、亮度相关饱和度等关键参数的实际含义和调试陷阱;3. 分享了四步调优流程,强调需要基于多场景数据验证;4. 通过案例展示了参数调整带来的色彩改善效果;5. 总结了实用建议,包括保持单一变量调试、量化评估色差、关注极端场景表现等。文章指出ChromaTix调优是平衡不同场景需求的"妥协艺术",需要结合客观数据和主

sundong_81的博客 109

湖南省第八届大学生计算机程序设计竞赛C题 Updating a Dictionary

湖南省第八届大学生计算机程序设计竞赛C题 Updating a Dictionary(题目链接)。 Problem C     Updating a Dictionary In this problem, a dictionary is collection of key-value pairs, where keys are lower-case letters, and values

yjf_victor的专栏 4316

数学建模模型总结

这是本人在两年内准备参加数学建模竞赛搜集到的模型资料,已经经过了本人的筛选和整理,希望能够对准备参加数学建模竞赛的学弟学妹们提供一些帮助!

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")

湖南省第八届程序设计竞赛J

题目J. 病毒   你有一个日志文件,里面记录着各种系统事件的详细信息。自然的,事件的时间戳按照严格递增顺序排列(不会有两个事件在完全相同的时刻发生)。   遗憾的是,你的系统被病毒感染了,日志文件中混入了病毒生成的随机伪事件(但真实事件的相对顺序保持不变)。备份的日志文件也被感染了,但由于病毒采用的随机感染方法,主日志文件和备份日志文件在感染后可能会变得不一样。   给出被感染的主日

住在半山腰的螃蟹专栏 956

Probability and Statistics - 4th edition

Probability and Statistics - 4th edition

湖南省第八届程序设计竞赛C

Problem C. Updating a Dictionary   In this problem, a dictionary is collectionof key-value pairs, where keys are lower-case letters, and values are non-negativeintegers. Given an old dictionary and

住在半山腰的螃蟹专栏 878

VB摄像头监控

该VB模块可对摄像头捕获的画面进行录像等,可在创建的窗口进行控制

2012年湖南省第八届大学生计算机程序设计竞赛 F 题 Kingdoms

题目大意: 给你n个城市,m条道路,每个城市有P【i】个人,

u012659423的专栏 834

湖南省第八届大学生程序设计大赛原题 B

B - 机器人的指令 Time Limit:1000MS Memory Limit:131072KB 64bit IO Format:%lld & %llu Submit Status Practice CSU 1112 Description 数轴原点有一个机器人。该机器人将执行一系列指令,你的任务是预测所有指令执行完毕之后它的位置。 ·L...

weixin_33774615的博客 114

2012湖南大学第八届程序设计竞赛 Incredible[公式]

题目地址:http://acm.hnu.cn/online/?action=problem&type=show&id=12312&courseid=215 Incredible Time Limit: 2000ms, Special Time Limit:5000ms, Memory Limit:65536KB Total submit ...

weixin_30897079的博客 86

CSU OJ 三家人(湖南省第八届大学生计算机程序设计竞赛)

题目链接:http://acm.csu.edu.cn/OnlineJudge/problem.php?id=1111     这是道简单的数学题。 #include int main() { int t,z,d;; float x,y,k,s; scanf("%d",&t); int i; while (t--) { sc

旧时相识的专栏 1227

湖南省第八届大学生程序设计大赛原题 C

C - Updating a Dictionary Time Limit:1000MS Memory Limit:131072KB 64bit IO Format:%lld & %llu Submit Status Practice CSU 1113 Description In this problem, a dictionary is co...

weixin_34343689的博客 119

2012年湖南省第八届大学生程序设计大赛 F题 “Kingdoms ” CSG - 1062 (最小生成树,二进制枚举)

题目数据给的很有启发性,n最大为16。再将只含枚举点的边取出来,做最小生成树,求出建好该图的最小费用,然后和所给费用k比较,若小于等于则可行,将获得的价值与结果进行比较更新。给n个点和m条边,每个点都有价值ai,每条边费用为bi,现在给你k费用,让你建一个图,求该图中与1直接或间接相连的点的总价值最大为多少。题目要求的是与1直接或间接相连的点,所以最开始1必在所建图中,最后得出的最小生成树的点必等于边+1,不然则不是树,不全部相连。有时想不出来好的方案可以根据数据猜做法。

saintjasper的博客 142

CSU 1111: 三家人(水题)

1111: 三家人 Submit Page     Summary    Time Limit: 1 Sec     Memory Limit: 128 Mb     Submitted: 3085     Solved: 1213     Description 有三户人家共拥有一座花园,每户人家的太太均需帮忙整理花园。A 太太工作了5 天,B 太太则

qq_37164003的博客 358

CSU1115-最短的名字

最短的名字 Time Limit: 5 Sec  Memory Limit: 64 MB Submit: 1234  Solved: 465 [Submit][Status][Web Board] Description 在一个奇怪的村子中,很多人的名字都很长,比如aaaaa, bbb and abababab。 名字这么长,叫全名显然起来很不方便。所以村民之间一般只叫名字

wang_128的博客 474

csu1116 Kingdoms 最小生成树-枚举状态

题目链接: csu 1116 题意: 有一幅双向图连接N个城市(标号1~n,1表示首都)  每个城市有一个价值W. 地震摧毁了所有道路,现给出可修复的m条道路并给出修复每条道路所需的费用 问在总费用不超过k的情况下,使得  与  首都连通的所有城市  的价值和 最大 解题思路: 点的数量不超过16 ,2^16次方枚举所有城市是否在连通的集合类

旧时光 1249
上一篇: 湖南省第八届程序设计竞赛E
下一篇: 湖南省第八届程序设计竞赛G
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付:10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值