·
收录于
当前文章被以下社区和专栏收录：
标签
#信息可视化
#数据分析
#数据挖掘
#数据采集
#PLC
注塑机数据采集网关，注塑协议标准化
例如，发那科的数据采集软件起价为10万元，住友的软件起价高达30万元，而东芝、恩格尔等品牌的授权费用同样不菲，给行业带来了一定的障碍。面对注塑机行业联网的这一挑战，我们凭借多年的控制系统开发经验和与各种方案的对接经历，提出了自己的解决方案：设计了一个适用于90%情况的注塑行业协议标准化网关。这个注塑机数据采集网关简化了协议的对接过程，无论是弘讯、科强、KEBA、长飞亚还是东芝的数据，都能通过标准化的MQTT协议传输，变量命名也得到了统一，从而让上层MES系统无需关心数据来源的差异。
阅读详情
相关推荐
破解住友注塑机数据采集难题：RS232通讯全流程指南（含Python示例代码）
本文提供了一套完整的住友注塑机数据采集解决方案，通过RS232串口通讯协议，结合串口服务器硬件与Python编程，实现了对设备运行参数的实时读取与监控。指南详细解析了硬件连接、协议帧结构，并附有实战代码，帮助工程师以低成本实现技术自主，破解数据采集壁垒，助力智能制造升级。
h9j8k7l6m5n的博客
813
【产品体系】【会计领域】【成本会计】第十九篇 GPU服务器的成本会计01
从内到外依次为：界面氧化层（SiO₂，~0.5nm）、高K介电层（HfO₂或HfZrOₓ，等效氧化层厚度EOT < 0.5nm）、功函数金属层（TiN、TiAlC等多层，总厚度~2-3nm）、钨（W）或钴（Co）栅极填充金属。：服务器操作系统（如Linux发行版）、GPU驱动程序、集群管理软件（如Kubernetes）、虚拟化软件（如VMware）、AI框架（如CUDA、TensorFlow）的商用许可。随后进行绝缘层（SiO2）沉积、阻挡层（Ta/TaN）和种子层（Cu）沉积，电镀填充铜。
weixin_49199313的博客
814
Anaconda/Miniconda的删除和安装
要在 MacBook 上删除 Anaconda 或 Miniconda，并重新安装它，您可以按照以下步骤进行操作。
qq_27390023的博客
3018
银河麒麟v10离线安装docker二进制包
因为工作需要在银河麒麟v10系统上离线部署k8s集群，k8s部署的前提是具有docker环境，因此离线安装docker成为必然。
1.下载docker包：Index of linux/static/stable/x86_64/
选择自己合适的版本，我下载的是20.10.7
选择自己放置docker包的路径进行解压：
cd /home/huawei/zq
tar -zxvf docker-20.10.7.tgz
2. 给docker包解压出来的docker文件夹下的二进制文件赋..
q_hsolucky的博客
7673
AIDD机器学习深度学习药物发现与设计教程总结
AIDD（AI Drug Discovery & Design）：是近年来非常火热的技术应用，且已经介入到新药设计到研发的大部分环节当中，为新药发现与开发带来了极大的助力。倾向于机器对数据库信息的自我学习，可以对数据进行提取和学习，一定程度上避免了化合物设计过程中的试错路径，同时还会带来很多全新的结构，为药物发现打破常规的结构壁垒。覆盖了机器学习和深度学习，能与新药研发相结合并实质性的应用到靶点预测、高通量筛选、药物设计、药物的ADMET性质预测等；且算力方面，由早期应用于药物发现领域的决策树、随机
wutianlin0723的博客
6798
YOLOv11改进 | Conv篇 | DWRSeg扩张式残差助力小目标检测 （附创新修改后的C3k2DWRSeg）
本文内容给大家带来的DWRSeg中的DWR模块来改进YOLOv11中的C2f和Bottleneck模块，主要针对的是小目标检测，主要创新点可以总结如下：多尺度特征提取机制的深入研究和创新的DWR模块和SIR模块的提出这种方法使得网络能够更灵活地适应不同尺度的特征，从而更准确地识别和分割图像中的物体。DWRSeg的基本原理和框架，并且能够在你自己的网络结构中进行添加DWRSeg需要增加一定的计算量一个DWR模块大概增加0.4GFLOPsYOLOv11改进系列专栏——本专栏持续复习各种顶会内容——科研必备。
Snu77的博客
4157
在移动固态上安装ubuntu系统，重启后卡住报错：blk_update_request: I/O error, dev sda, sector ... op 0x0 (READ)如何解决？
🏆本文收录于 《全栈 Bug 调优（实战版）》 专栏。专栏聚焦真实项目中的各类疑难 Bug，从成因剖析 → 排查路径 → 解决方案 → 预防优化全链路拆解，形成一套可复用、可沉淀的实战知识体系。无论你是初入职场的开发者，还是负责复杂项目的资深工程师，都可以在这里构建一套属于自己的「问题诊断与性能调优」方法论，助你稳步进阶、放大技术价值 。
**My Coding Family**
1767
华为OD机考真题 新系统 9月23号 【世界杯徽章换购】多语言题解
华为OD机试真题 新系统【世界杯徽章换购】，提供C语言、Java、C++、Python、Go、JavaScript源码实现，并提供完整思路讲解，以及在线OJ模拟做题。华为OD机试真题 【世界杯徽章换购】多语言题解。
qq_45776114的博客
612
多线程开发的基本概念
在Android开发中，我们常常会遇到多线程开发的需求。由于Android中的，我们不能在主线程处理耗时逻辑，否则会导致卡顿，甚至出现ANR（应用无响应）等异常情况。因此，我们需要将网络请求等耗时操作放到中去执行，完成后再更新UI。多线程开发因此成为Android开发中的一个重要部分。随着Android开发技术的不断进步，从早期的使用Handler，到后来的RxJava，再到如今的Kotlin协程，切换线程以及并发编程的代码写起来是越来越简洁了。
喻志强的博客
1003
Python爬虫实战：扒取专利数据库核心信息，我用这招搞定反爬+数据结构化（附完整可运行代码）
这套爬虫方案从「实际需求」出发，解决了专利数据库抓取的核心痛点：反爬突破、数据结构化、标题创作。核心价值不在于代码多复杂，而在于「实用」——我用这套代码半天就抓取了800+条Python爬虫相关专利，比手动效率提升了几十倍。最后想说：爬虫技术的核心是「解决问题」，而不是炫技。专利数据能帮我们快速了解行业技术趋势，尤其是程序员，从专利中扒出的核心逻辑，远比零散的博客文章更系统。希望这篇实战分享能帮到有需要的小伙伴，也欢迎在评论区交流专利爬虫的更多技巧！
专注于Python爬虫开发，分享爬虫技巧、项目实战与反爬经验，使用Scrapy、BeautifulSoup等工具，解决数据抓取难题。
2117
怀旧版电脑GBC模拟器
再现原版GBC掌机的界面，电脑运行连游戏界面都一样大小，很怀旧，很真实，（英文版）强烈推荐，很小的文件，很难才找到的。
Statistics and Machine Learning Toolbox
Statistics and Machine Learning Toolbox（带书签），MATLAB统计学习工具
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")
汉书HE8，好用的汇编代码编写工具
汉书HE8，好用的汇编代码编写工具早期编写汇编代码所用功能强大
上一篇：
从零开始了解数据采集（九）——数据采集的艺术：精度与实时性的完美平衡
下一篇：
从零开始了解数据采集（十一）——数控机床的采集与运用
