public static function addressQue($uid,$bargain_id){
//查询砍价商品ID
$bargainData = Storebargain::where('id',$bargain_id)->field('price,num,bargain_min_price,bargain_max_price')->find()->toArray();
//赋值
list($price,$num,$bargain_min_price) = array_values($bargainData);
//计算帮砍得价格
if (!is_numeric($num) || $num != (int)$num || $num <= 0) {
return fail('400', 'uid参数错误');
}
if ($num > 1) {
//初次砍价为总金额的97%
$bar = $price * 0.97;
$bar = sprintf("%.2f", $bar);
}
$time = time();
$data = [
'uid' => $uid,
'bargain_id' => $bargain_id,
'bargain_price_min' => $bargain_min_price,
'bargain_price' => $price,
'price' => $bar,
'status' => 1,
'num' => 5,
'add_time' => $time,
'is_del' => 0
];
$bargainRes = StoreBargainUser::insert($data);
if (!$bargainRes){
return fail(500,'砍价失败','');
}
//获取用户参与砍价的信息
$bargain_user_id = StoreBargainUser::where('uid',$uid)->where('bargain_id',$bargain_id)->field('id')->find()->toArray();
$bargainHelpData = [
'uid' => $uid,
'bargain_id' => $bargain_id,
'bargain_user_id' => $bargain_user_id['id'],
'price' => $bar,
'add_time' => $time
];
$bargain_help_data = StoreBargainUserHelp::insert($bargainHelpData);
if (!$bargain_help_data){
return fail(500,'砍价失败','');
}
$bargainData = StoreBargainUser::with(['Users','Storebargain'])->where('uid',$uid)->where('bargain_id',$bargain_id)->find()->toArray();
return success(200,"您为自己砍掉".$bar."元,"."商品现在的价格为:".sprintf("%.2f",$price - $bar)."元,赶紧分享好友帮砍!",$bargainData);
}
/**
* 好友帮砍页面
* @param $uid
* @param $bargain_user_id
* @return \think\response\Json
* @throws \think\db\exception\DataNotFoundException
* @throws \think\db\exception\DbException
* @throws \think\db\exception\ModelNotFoundException
*/
public static function FriendsHackingPage($uid,$bargain_user_id){
//商品信息
$bargainShop = StoreBargainUser::with(['Storebargain'])->where('uid',$uid)->where('id',$bargain_user_id)->find();
//帮砍信息
$record = StoreBargainUserHelp::with(['User'])->where('bargain_user_id',$bargain_user_id)->where('bargain_id',$bargainShop['Storebargain']['id'])->select();
$price = 0;
foreach ($record as $val){
$price = $price + $val['price'];
}
//砍掉的价钱
$price = sprintf("%.2f", $price);
//剩余百分比
$percentage = sprintf("%.2f", 100 - ($price/$bargainShop['Storebargain']['price'])*100);
$data = ['price' => $price,'percentage' => $percentage,'bargainShop' => $bargainShop,'record' => $record];
return $data;
}
/**
* 好友帮砍
* @param $uid
* @param $bargain_user_id
* @return string|\think\response\Json
* @throws \think\db\exception\DataNotFoundException
* @throws \think\db\exception\DbException
* @throws \think\db\exception\ModelNotFoundException
*/
public static function BargainHelp($uid,$bargain_user_id){
//判断用户是否帮忙砍过
$bargainHelpData = StoreBargainUserHelp::where('uid',$uid)->where('bargain_user_id',$bargain_user_id)->find();
if($bargainHelpData){
return fail( 500, '您的助力次数已达上限','');
}
$bargainFirstData = StoreBargainUserHelp::with(['Storebargain','StoreBargainUser'])->where('bargain_user_id',$bargain_user_id)->order('price','asc')->find()->toArray();
$dataAll = StoreBargainUserHelp::where('bargain_user_id',$bargain_user_id)->field('price')->select()->toArray();
//已砍掉的价格
$pricecon = 0;
foreach ($dataAll as $val){
$pricecon = $pricecon + $val['price'];
}
//继续砍价的价格
$price = $bargainFirstData['Storebargain']['price'] - $pricecon;
//判断砍价商品状态
$state = StoreBargainUser::where('id',$bargain_user_id)->find()->toArray();
if ($state['status'] == 3){
return success(200,'该砍价商品已砍价成功','');
}elseif ($state['status'] == 2){
return success(200,'参与活动失败','');
}
$bargain_min_price = $bargainFirstData['Storebargain']['bargain_min_price'];
$bargain_max_price = $bargainFirstData['Storebargain']['bargain_max_price'];
$num = $bargainFirstData['StoreBargainUser']['num'];
if (!is_numeric($num) || $num != (int)$num || $num < 0) {
return fail('400', '砍价次数参数错误');
}
$bar = self::ACE($num,$bargain_min_price,$bargain_max_price,$price,$bargain_user_id);
$data = [
'uid' => $uid,
'bargain_id' => $bargainFirstData['bargain_id'],
'bargain_user_id' => $bargain_user_id,
'price' => $bar,
'add_time' => time()
];
$res = StoreBargainUserHelp::insert($data);
if (!$res){
return fail(500,'帮砍失败','');
}
//修改砍价次数
StoreBargainUser::where('id',$bargain_user_id)->update(['num' => $num - 1]);
$bargainFirstData = StoreBargainUserHelp::with(['Storebargain','StoreBargainUser'])->where('bargain_user_id',$bargain_user_id)->where('uid',$uid)->find();
return success(200,'帮砍成功',$bargainFirstData);
}
/**
* 砍价完成页面
* @param $uid
* @param $bargain_user_id
* @return array
* @throws \think\db\exception\DataNotFoundException
* @throws \think\db\exception\DbException
* @throws \think\db\exception\ModelNotFoundException
*/
public static function SuccessfulBargaining($uid,$bargain_user_id){
//商品信息
$bargainShop = StoreBargainUser::with(['Storebargain'])->where('uid',$uid)->where('id',$bargain_user_id)->find()->toArray();
$bargainShopData = Storebargain::select();
$user = User::where('uid',$uid)->find()->toArray();
//生成订单
$data = [
'order_id' => rand(10000,99999).time(),
'uid' => $uid,
'real_name' => $user['real_name'],
'user_phone' => $user['phone'],
'user_address' => $user['addres'],
'pay_type' => 1,
'total_price' => 0,
'add_time' => time(),
'status' => 0,
'mark' => '砍价',
'unique' => rand(10000,99999),
'cost' => $bargainShop['price']
];
StoreOrder::insert($data);
return ['bargainShop' => $bargainShop,'bargainShopData' => $bargainShopData];
}
/**
* @param $num
* @param $bargain_min_price
* @param $bargain_max_price
* @param $price
* @param $bargain_user_id
* @return string|void
*/
public static function ACE($num,$bargain_min_price,$bargain_max_price,$price,$bargain_user_id){
if($num > 1){
if ($price < $bargain_min_price || $price > $bargain_min_price && $price < $bargain_max_price){
StoreBargainUser::where('id',$bargain_user_id)->update(['status' => 3]);
return $bar = sprintf("%.2f",$price);
}elseif($price > $bargain_max_price){
return $bar = sprintf("%.2f",rand($bargain_min_price * 100, $bargain_max_price * 100) / 100);
}
}elseif ($num == 1){
if ($price < $bargain_min_price || $price > $bargain_min_price && $price < $bargain_max_price){
StoreBargainUser::where('id',$bargain_user_id)->update(['status' => 3]);
return $bar = sprintf("%.2f",$price);
}else{
StoreBargainUser::where('id',$bargain_user_id)->update(['status' => 3]);
return $bar = sprintf("%.2f",rand($bargain_min_price * 100, $bargain_max_price * 100) / 100);
}
}
}
相关推荐
Vue2 新手实战:从零构建一个动态砍价页面,轻松掌握 Vue 基本功能!“ “Vue2 开发实例:手把手教你实现 0 元砍价页面,附详细代码讲解!“ “新手必学!使用 Vue2 实现一个简洁的电商砍
Vue2 新手实战:从零构建一个动态砍价页面,轻松掌握 Vue 基本功能!" "Vue2 开发实例:手把手教你实现 0 元砍价页面,附详细代码讲解!" "新手必学!使用 Vue2 实现一个简洁的电商砍价页面,完整教程" "Vue2+JavaScript 项目实战:超详细电商砍价页面教程,带源码!"
电商平台营销活动设计——砍价活动设计
一、简介砍价诞生在社交电商的时代,是社交电商最常见的几种玩法之一,目的都是低成本获客,在完成拉新及留存的作用的基础上,实现粉丝裂变增长的商业目标。1. 目的软件需求是软件开发的依据,也是软件工程各项活动的基础。编写本PRD的目的就是将砍价活动的需求清晰准确地描述清楚,为制定项目开发计划和后期的概要设计、原型设计、测试等阶段的工作提供可靠的依据。2. 范围本文档阅读对象为产品经理、项目经理、UI设计...
android 基础教程(第三版)配套源码
Pragmatic.Hello.Android.3rd.Edition.Jul.2010 android 基础教程第三版 配套源码
拼多多分享好友砍价Java实现_拼多多怎么帮助好友砍价免费拿商品,详情介绍...
“拼多多”勾起了不少拼友们的兴致,朋友们都想通过这里免费得取一些心仪的商品。其中我们做为他的得力“砍价帮”,如何援协他得到这些礼品呢,小编把方法要领解于大家,愿帮到各位。工具/材料拼多多操作方法01先来到好友所选的那件物品的页面,其中会显现出“已砍多少”、“还剩多少”,在黄框中有“帮好友砍一刀”的字样,点入这里。02继而会跳出此界面,在最右侧的黄钮处即为“砍价免费拿”的入口,轻按儿开此处所显。03...
文章资源:Android Studio初学者实例:仿拼多多砍价页面
文章资源:Android Studio初学者实例:仿拼多多砍价页面
U盘重装Win10系统
准备工作 准备一个8G或8G以上U盘(32G以内) 请备份好u盘里的资料,在制作过程中会格式化硬盘 请备份好要安装系统的盘的资料,安装时也会对该盘内容进行格式化。 将u盘插入电脑usb接口中,尽可能的插入usb3.0的接口,这样下载和安装速度会更快。 1.到微软官网下载安装包 浏览器输入:微软官网下载win10 懒人请复制粘贴:https://www.microsoft.com/zh-cn/software-download/windows10 打开之后的界面: 2.下载Media Creat.
Android Studio初学者实例:仿拼多多砍价页面
在D:\Tomcat\apache-tomcat-8.5.89\bin目录下找到startup.bat双击,即运行tomcat,出现一个弹窗,请勿关闭,否则访问不到,为了测试tomcat是否正确开启,可以本地访问。,可以看到页面很明显的有一个两列的列表,所以肯定是无法使用ListView的(难道你要和我杠,用两个ListView横向排列~~~),所以我们肯定采用能横向显示、能竖向显示、能瀑布流、能多行等等的。在build.gradle文件中添加第三方库(app下的build.gradle)
VisualStudio2022配置CGAL
vs2022和vs2017安装配置boost没什么区别,只需要注意boost的msvc得是14.3的,这玩意跟visualstudio版本挂钩的。压缩包解压,得到下面这两文件,然后把auxiliary这个文件挪到CGAL-5.6里面就行了。我用cgal读取一个stl模型,并输出相关信息(想可视化的话需要安装Qt,不想装Qt也可以装VTK)然后下载下面这两个压缩包。照着下面的截图填就完事了。
使用 apifm-wxapi 开发微信小程序之商品砍价模块
前言 本教程是基于 “apifm-wxapi” 模块,教你快速实现小程序开发,所以你可能需要先了解以下知识点: 《创建 HelloWorld 项目》 《使用 “apifm-wxapi” 快速开发小程序》 《免费注册开通后台,获得专属域名》 本案例中,部分功能,需要用户登录后才能操作,也就是说需要 token 授权,请先了解: 《微信小程序登录获取openi...
【SLG游戏服务器开发手册】(ROK Like)沙盘地图篇一,菱形瓦片与笛卡尔坐标系的转换(附调试工具)
轴向坐标:以菱形网格对角方向为基准轴,记作(q, r)菱形瓦片宽度 w=2a,高度 h=a(宽高比2:1)
商用车重型多挡位机械式变速器(AMT)结构设计与仿真分析
本文针对商用车重型AMT变速器开展结构设计与仿真分析。设计采用12挡双中间轴结构,匹配400kW柴油发动机,最大扭矩2000Nm。通过等比级数分配传动比(头挡14.5,最高挡0.78),采用主副箱组合实现多挡位。双中间轴结构可抵消径向力,提升承载能力。关键设计包括:235mm中心距、4.5-5.0mm齿轮模数、锁环式同步器和复合润滑系统。通过Python参数化建模进行强度校核,计算显示中间轴在2000Nm工况下等效应力满足安全系数要求(屈服强度850MPa,安全系数≥2)。该设计解决了传统MT变速器操作强度
商城砍价功能介绍
一、使用场景 商家创建砍价活动,让利给出商品底价,同时设置活动时间、以及需砍次数。 活动开始后,买家将砍价详情链接分享给好友,好友点击链接即可进入活动帮其砍价。 通过砍价,买家可以更优惠地购买商品;商家可以实现裂变传播。 二、功能说明 【版本】豪华版及以上版本 【适用】简体中文商城小程序、简体中文手机商城 注意事项: ① 每次砍价的金额是随机的,且随机金额会先大后小,再小后大。 ② 仅支持会员发起砍价,不支持游客发起砍价。但好友帮砍可以是游客。 ③ 商家可以随时中止进行中的砍价活动,也可以延长进行中的砍价活
8、 使用TPM密钥:从理论到实践的全面指南
本文全面介绍了如何使用TPM(可信平台模块)进行密钥管理,涵盖密钥的创建、存储、加载、迁移以及特殊密钥类型的使用。通过详细的操作步骤和代码示例,帮助读者掌握TPM密钥管理技术,确保系统的安全性和可靠性。
如何使用砍价模块
启用模块 登录后台,左侧菜单 “工厂设置” --> “模块管理”; 找到“商品砍价”模块,启用后 F5 刷新页面; 配置砍价信息 左侧菜单:“营销辅助” --> 砍价设置,设置商品的砍价信息; api接口对接 根据 《api接口文档》 中的砍价部分api接口,实现前端代码对接,即可实现在 网页、小程序、Android、iOS 中商品的砍价功能 ...
使用IGV软件可视化染色质可及性
染色质可及性(chromatin accessibility)是指DNA序列在植物细胞核中的可访问程度。染色质是由DNA和蛋白质组成的复杂结构,它紧密地包裹在细胞核中,并影响基因的表达和功能。染色质可及性研究着重于理解染色质是否容易被转录因子和其他调控蛋白访问,从而影响基因的表达。文献中,常常会有下面这种图片来表示基因组上的一些区域或基因的染色质开放性强弱。一般情况下,我们可以使用IGV软件或GSA-man软件进行可视化,两者的操作相似。这里,以IGV软件为例,演示如何对染色质可及性进行可视化。
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
vue 如何优雅的实现砍价列表倒计时
做一个砍价的项目,用到了vue,上代码 template部分,这个地方只显示一个变量就好 <div v-if="item.kanjia_result==0" style="clear:both;padding:10px; "> <div style="padding: 10px; background:#ff00002e;border-radius:5px;"> ...
PCL 点云加权均值收缩
PCL 点云加权均值收缩
01SpringBoot商品管理与扫码砍价系统项目-需求分析-对象库表整理
这里写目录标题SpringBoot商品管理与扫码砍价系统项目一、需求分析笔记1.1. 管理员登录1.2. 商品管理1.3. 员工管理1.4. 客户管理1.5. 砍价情况管理1.6. 图表报告二、 需求整理2.1. 第三方服务申请2.2. 库表2.3. 功能三、 架构设计 SpringBoot商品管理与扫码砍价系统项目 特别备注:在工作中,并不是所有团队项目都十分完善,所以要适应不同的文档程度。 一、需求分析笔记 1.1. 管理员登录 可使用手机验证码登录; 可使用邮箱验证码登录; 可使用手机or邮箱+密码登
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');```[](https://bitdeli.com/free "Bitdeli Badge")
温度PID控制PLC程序
温度PID稳定控制程序,适用于欧姆龙PLC,对其他PLC也具有很大参考价值
单商户商城系统功能拆解28—营销中心—砍价活动
单商户商城系统,也称为B2C自营电商模式单店商城系统。可以快速帮助个人、机构和企业搭建自己的私域交易线上商城。 单商户商城系统完美契合私域流量变现闭环交易使用。通常拥有丰富的营销玩法,例如拼团,秒杀,砍价,包邮,优惠券,大转盘抽奖,签到等等。配合当下流行的分销推广功能,可以快速拉新裂变,实现用户增长。
计算机控制技术及应用
《计算机控制技术及应用》通过总结作者的科研成果和吸收国内外的先进理论、方法和技术,以计算机控制系统的网络化、开放化、智能化和集成化发展趋势为主线索,主要介绍计算机控制系统的基本概念和计算机控制系统的典型形式,计算机控制系统的数学描述与基本分析方法,以及数字PID控制器、最少拍控制系统、达林算法、状态变量设计法等设计方法及其各种改进算法。重点突出计算机控制系统输入输出通道接口的设计方法和典型控制系统的设计案例与实现方法等。
h5 页面复制内容到剪切板,然后登陆app获取剪切板内容 h5+uniapp电商app
砍价功能剪切板 最近公司在做电商中的砍价活动,如果在电商app中分享砍价商品信息到微信好友,展示的是一个h5链接的形式。 下图分别是从 电商app分享砍价商品 分享到微信好友 砍价需要下载的电商app页面 电商app 分享功能(分享到微信好友) share(item) { // 分享图文到微信聊天界面 uni.share({ provider: "weixin", // 服务商 scene:
WINCC7.3破解文件
WINCC7.3硬件够破解文件,有需要的下载!WINCC7.3硬件够破解文件,有需要的下载!
修改版锦鲤助手砍价小程序源码拼多多商品砍价小程序源码 自带流量主引流引流裂变
《砍价小程序源码:引流裂变+躺赚模式》介绍了一款基于拼多多商品砍价功能的小程序源码,具备流量主引流和裂变营销特性。该源码支持快速丰富产品内容,满足用户砍价需求,并通过流量矩阵实现高效用户增长。演示图片展示了小程序界面,突出一键砍价的核心功能。项目宣称可实现长期稳定盈利,月收入可达3000元,适合寻求被动收入的开发者运营。
2021-07-08
thinkphp kanjia public function records(){ $id=4;//商品id $userid=13;//用户id $date=date('Y-m-d H:i:s'); $user=User::where('userid',$userid)->find(); $shop=Shop::where('id',$id)->find(); $degree=$shop['m
【活动发布】捷微H5-微信新年砍价活动,开源发布了
为什么80%的码农都做不了架构师?>>> ...
黔唐拼购源码--黔唐拼购APP系统开发源码分享
黔唐拼购系统开发源码分享: <template> <div class="bargain-record" ref="container"> <div class="item" v-for="(item, index) in bargain" :key="index"> <div class="picTxt acea-row row-between-wrapper"> <div class="pictrue"&g
砍价助手·GPASS智能眼镜Agent产品设计
《AI眼镜购物助手产品设计摘要》 这是一款专为线下服装购物场景设计的智能眼镜应用,核心功能是"注视即识别"的全网比价与智能砍价。产品通过AR眼镜实现三大核心功能: 商品智能识别:自动分析服装材质、款式和品牌标签 实时比价系统:整合电商平台与批发市场数据,给出合理估价区间 动态话术生成:基于对话场景提供三种砍价策略(比价施压/挑刺套近乎/最终一口价) 目标用户包括精打细算族、砍价小白等人群,解决线下购物时信息不对称、砍价技巧缺乏等痛点。产品特色在于全程语音交互+AR显示,2秒内完成识别并给出建议,让用户无需掏
748



被折叠的 条评论
为什么被折叠?



