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Mybatis-Plus发生该报错：
'getBaseMapper()' in 'com.baomidou.mybatisplus.extension.service.impl.ServiceImpl' clashes with 'getBaseMapper()' in 'com.baomidou.mybatisplus.extension.service.IService'; attempting to use incompatible return type.
原因：
你的dao层接口文件名不是xxxMapper.java命名规范，MP中的dao层文件名必须是Mapper结尾。
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本文介绍了为什么需要维护cache一致性、一致性协议的本质和类别，以及如何设计一致性协议。
解决‘getBaseMapper()‘ in ‘com.baomidou.mybatisplus.extension.service.impl.ServiceImpl‘ clashes with问题
将 import com.baomidou.mybatisplus.mapper.BaseMapper;
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解决‘getBaseMapper()‘ in ‘com.baomidou.mybatisplus.extension.service.impl.ServiceImpl‘ 解决
改成 import com.baomidou.mybatisplus.core.mapper.BaseMapper;将 import com.baomidou.mybatisplus.mapper.BaseMapper;把 import com.baomidou.mybatisplus.mapper.BaseMapper;以及资源路径下的mapper文件是否是正确实体类路径。
Widar3.0复现日志(DFSExtractionCode)
需要使用的matlab基本功能
相机通用类之海康相机，软触发硬触发（飞拍），并输出halcon格式对象
如何显示图像：可以访问我文章开头的博客，通过编写相机通用类即可，程序中是有继承通用类，通过访问相机通用的获取图像数据，从而传输图像数据。具体可以查看相机通用类。整体过程其实比较简单，由于海康相机没有像一些品牌相机一样可以直接使用序列号开启相机，所以我们需要通过对相机信息进行索引，根据索引到的相机序列号来开启相机。
问题：‘getBaseMapper()‘ in ‘com.baomidou.mybatisplus.extension.service.impl.ServiceImpl‘ clashes with ‘
问题：'getBaseMapper()' in 'com.baomidou.mybatisplus.extension.service.impl.ServiceImpl' clashes with 'getBaseMapper()' in 'com.baomidou.mybatisplus.extension.service.IService'; attempting to use incompatible return type
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Vite开发服务器 WebSocket中的fetchModule方法未实施server.fs访问控制，导致攻击者可通过WebSocket连接并读取服务器上的任意文件（CVE-2026-39363）
报错：‘getBaseMapper()‘ in ‘com.baomidou.mybatisplus.extension.service.impl.ServiceImpl‘ clashes with ‘
Service层报错：'getBaseMapper()' in 'com.baomidou.mybatisplus.extension.service.impl.ServiceImpl' clashes with '
第4章-07-将WebDriver获取的Cookie传递给Requests
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华为 MetaERP 通过元数据驱动的多组织架构 + 内部交易协同 + 自动对账引擎 + 合并规则引擎，实现多组织内部往来与合并报表的全流程自动抵消，核心是源头标准化、过程自动化、结果可追溯
摘要： 华为MetaERP通过元数据驱动的多组织架构与主数据标准化，实现内部往来与合并报表全流程自动化。其核心包括：统一组织模型与主数据（确保数据口径一致）、内部交易协同（自动生成对称凭证）、智能对账引擎（多维度自动匹配差异）、合并规则引擎（配置化抵消模板）。依托GaussDB高性能数据库与流批一体架构，华为全球88家子公司合并报表效率提升400%，关账时间缩短至2小时，支持多准则、多币种自动抵消，审计全程可追溯。相比传统ERP，MetaERP在协同性、自动化与扩展性上优势显著，为跨国集团提供高效、准确的合
解决 getBaseMapper() in com.baomidou.mybatisplus.extension.service.impl.ServiceImpl
改成 import com.baomidou.mybatisplus.core.mapper.BaseMapper;1.查看接口、实现类、mapper，对应的实体类是否一致。
RFC 字符码转换深度解析：Unicode、非 Unicode 与 MDMP 系统的协奏
老版本 MDMP 系统不上传自定义映射时，Unicode 端会落回“预配置列表”。列表里可见西欧 1100、日文 8000、中文 8400 等经典编号。若需强制覆写映射，可在 SM59 ➡Unicode页签启用，或直接勾选作为兜底。字符编码问题常“潜伏”多年，一旦旧系统升级或新增语种便集中爆发。掌握 RFC code page 转换机理、灵活运用 SM59 选项、在早期对 LANG 字段和字段长度进行治理，是根治乱码与 short dump 的关键。
解决 getBaseMapper() in com.baomidou.mybatisplus.extension.service.impl.ServiceImpl clashes with
使用了guns二次开发,但由于是开源版本没有集成代码生成,于是自己下载了一个代码生成生成了代码,把dao mapper啥的手动放进去然后就出现了'getBaseMapper()' in 'com.baomidou.mybatisplus.extension.service.impl.ServiceImpl' clashes with 'getBaseMapper()' in 'com.baomidou.mybatisplus.extension.service.IService'; attempting t
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")
老版QQ原生表情gif动态
这是老版的QQ表情，不过全是动态的的，Windows照片查看器是不会动的，可以右击用浏览器打开查看，再说一遍是老版的，只是分享给宁愿用老版也要动态的人。
关于getBaseMapper()‘ in ‘com.baomidou.mybatisplus.extension.service.impl.ServiceImpl‘ clashes with的问题
使用generator代码生成器生成了基本的增删改查的代码，但是在service层的实现类serviceImpl却出现了getBaseMapper()’ in ‘com.baomidou.mybatisplus.extension.service.impl.ServiceImpl’ clashes with ‘getBaseMapper()’ in ‘com.baomidou.mybatisplus.extension.service.IService’; attempting to use incompa
TMS_WebGMaps_1.9.8.0_Full_Source
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getBaseMapper()‘ in ‘com.baomidou.mybatisplus.extension.service.impl.ServiceImpl‘ clashes with ‘getB
原因：dao层接口没有实现BaseMapper<>,或者是<>内的实体类导包导错了 修改后如下： 在运行就不报错了。
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‘getBaseMapper()‘ in ‘com.baomidou.mybatisplus.extension.service.impl.ServiceImpl‘ clashes with ‘get
我目前遇到的这种报错, 是因为 用的Mybatis-Plus 自动生成的插件来生成的service层的数据, 此处因为导包的问题,看一下 Mapper层 对应的导包问题, 此处来说 检查导包问题, 导包删除即可。
关于'getBaseMapper()' in 'com.baomidou.mybatisplus.extension.service.impl.ServiceImpl' clashes with...
使用MybatisPlus时代码下报错’getBaseMapper()’ in ‘com.baomidou.mybatisplus.extension.service.impl.ServiceImpl’ clashes with ‘getBaseMapper()’ in ‘com.baomidou.mybatisplus.extension.service.IService’; attemptin...
【MP报错】MP：‘getBaseMappe ServiceImpl’ clashes
ServiceImp报错 ‘getBaseMapper()’ in ‘com.baomidou.mybatisplus.extension.service.impl.ServiceImpl’ clashes with ‘getBaseMapper()’ in ‘com.baomidou.mybatisplus.extension.service.IService’; attempting to use incompatible return type 在MyBatis-plus中，一般引用包为 c.
‘com.baomidou.mybatisplus.extension.service.impl.ServiceImpl‘ 中的 ‘getBaseMapper()‘ 与 ‘com.baomidou.m
'com.baomidou.mybatisplus.extension.service.impl.ServiceImpl' 中的 'getBaseMapper()' 与 'com.baomidou.mybatisplus.extension.service.IService' 中的 'getBaseMapper()' 冲突; 尝试使用不兼容的返回类型（）已解决
getBaseMapper()‘ in ‘com.baomidou.mybatisplus.extension.service.impl.Service
getBaseMapper()' in 'com.baomidou.mybatisplus.extension.service.impl.Service
attempting to use incompatible return type问题解决思路
使用generator代码生成器生成代码，引用类后报"‘getBaseMapper()’ in ‘com.baomidou.mybatisplus.extension.service.impl.ServiceImpl’ clashes with ‘getBaseMapper()’ in ‘com.baomidou.mybatisplus.extension.service.IService’;attempting to use incompatible return type "
使用mybatisPlus启动失败问题
Field baseMapper in com.baomidou.mybatisplus.extension.service.impl.ServiceImpl required a single bean, but 15 were found:
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