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原因：
mybatis-plus的xml中自带的xml解析器不支持当前写法，这个错误是JSQLParser报出来的。
解决：
1.简单的方法：
添加mybaitis-plus-extension的依赖，和自己mybatis-plus-starter的以来版本保持一致，然后在mapper文件中定义的sql接口上加上注解：
@SqlParser(filter = true)
@Update(“xxxxxx你的sqlxxxxx”)
这样就可以绕过xml的sql解析器了
2.麻烦的方法
写原生SQL，用SQL session template提交执行，这种适用于第一种方法行不通的情况，比如我就是因为公司的依赖库强制排除了starter里的extension依赖，我没法加，所以就只能注入了sqlSessionTemplate提交原生sql来实现，两种方法本质都是绕过sql解析器，第一种优雅一点，第二种使用时要注意sql注入问题。
【阿里云大模型高级工程师ACP习题集】2.1 用大模型构建新人答疑机器人
【摘要】这是我精心整理出对应的习题集，里面足足有 30 道题目，从基础概念到深度拓展，全方位覆盖章节重点、难点。不管你是想检验自己的学习成果，还是想针对薄弱环节进行强化训练，它都能完美适配！
caused by: net.sf.jsqlparser.parser.parseexception
json转换异常
IFIX往Mysql数据库同步数据方法
通过IFix数据库向Mysql数据库同步数据
解决：net.sf.jsqlparser.parser.ParseException: Encountered unexpected token: “=“ “=“
网上看了很多，说是因为MyBatis_Plus框架中，有多租户的功能，MP会进行数据权限的过滤，但是都试了试发现并不生效，记着之前遇到过这个问题，是因为那个包的版本原因导致的，但是忘了，这次就记下来吧，免得自己又忘了。1.排除其他依赖中的jsqlparser的包，引入新的jsqlparser，也可以只排除在其中一个的，使用另一个的jsqlparser包，不用重新导入。mybatis-plus包与原本存在的pagehelper存在com.github.jsqlparser:jsqlparser冲突。
C++11并发与多线程笔记（2） 线程启动、结束，创建线程多法、join，detach
第二节 线程启动、结束，创建线程多法、join，detach 一、范例演示线程运行的开始 程序运行起来，生成一个进程，该进程所属的主线程开始自动运行；当主线程从main（）函数返回，则整个进程执行完毕 主线程从main（）开始执行，那么我们自己创建的线程，也需要从一个函数开始运行（初始函数），一旦这个函数运行完毕，线程也结束运行 整个进程是否执行完毕的标志是：主线程是否执行完，如果主线程执行完毕了，就代表整个进程执行完毕了，此时如果其他子线程还没有执行完，也会被强行终止【此条有例外，以后会解释】 创建
军品研制过程-转阶段
介绍军品转阶段评审
解决：net.sf.jsqlparser.parser.ParseException: Encountered unexpected token: “xx“ “xx“
有的博客说@InterceptorIgnore(tenantLine = "true")的作用是对某个Mapper方法 忽略多租户处理，从这次报错来看不仅是这个作用，感觉应该还有忽略mysql关键词解析的作用,因为我的sql包含关键词database
QT实现OPCUA服务端客户端（附完整源码）
QT实现OPCUA服务端客户端（附完整源码）
MyBatis报错 Caused by: net.sf.jsqlparser.parser.ParseException: Encountered unexpected token: “(“ “(“
MyBatis 使用报错 Caused by: net.sf.jsqlparser.parser.ParseException: Encountered unexpected token: "(" "("at line 12, column 24.
n8n AI高效工作流自动化 部署使用教程
摘要： n8n是一款开源可视化工作流自动化工具，结合AI能力可高效解决重复性任务。文章详细介绍了从Docker部署到实战应用的完整流程：1）通过Docker Compose自托管n8n，确保数据可控；2）以AI客服日报机器人为例，演示如何串联工单系统、ChatGPT和邮件通知，实现定时自动化；3）进阶搭建交互式AI助手，利用ToolsAgent节点调用业务API；4）整合向量数据库构建RAG知识库问答。关键建议包括：拆分复杂工作流、严格管理凭据、区分开发与生产环境，并建议从高频刚性需求（如日报生成）切入，逐
2026年最值得学习的十大 SAP 开发技术
在这辞旧迎新之际，我想和大家聊聊我心中的2026年我仍然愿意持续投入时间去学习的一些 SAP 开发技术。因为笔者水平和视野局限，加上这份清单有笔者主观兴趣爱好在内，可能大家对清单上罗列的技术有不同意见，欢迎大家在评论区发表自己的见解。下面是笔者列出的十个技术方向，排名不分先后。因为精力有限，2026年我很可能只会选择其中一两个自己最感兴趣的方向去深耕。每项技术后面都提供了链接，方便大家看完就开始学习。
net.sf.jsqlparser.parser.ParseException: Encountered unexpected token: “(“ “(“ at line 10, colum
报错： Cause: com.baomidou.mybatisplus.core.exceptions.MybatisPlusException: Failed to process, please exclude the tableName or statementId. Error SQL: select id,field_name,image_url from field WHERE 1=1...
Spring Boot 集成 Spring AI：实现可被大模型调用的 MCP Server
本文介绍了如何利用Spring AI框架构建MCP Server，使大模型能够调用业务系统的接口。主要内容包括：1）MCP协议简介，分为MCP Server和Client两部分；2）环境配置要求，需Java 17+和Spring Boot 3.5.0+；3）实现步骤，通过@McpTool注解将Service方法暴露给大模型调用；4）使用Cherry Studio作为MCP Client测试效果，成功实现订单查询和下单功能。本文仅实现MCP Server部分，后续将介绍如何开发MCP Client。
Caused by: net.sf.jsqlparser.parser.ParseException: Encountered unexpected token: “:“ “:“
先把我的SQL贴出来看一下： <select id="getAnnualReport" resultType="map"> SELECT b.* FROM ( SELECT @rownum := @rownum + 1 AS sort, a.* FROM (SELECT @rownum := 0) r, ( SELECT user_id AS userId, SUM(d
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")
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
解决 Caused by: net.sf.jsqlparser.parser.ParseException: Encountered unexpected token:XXXXXXX
解决 Caused by: net.sf.jsqlparser.parser.ParseException: Encountered unexpected token:XXXXXXX
ssm框架搭建
这个是一个在idea上成功跑起来的ssm框架项目，保存在这里方便以后使用直接下载
net.sf.jsqlparser.JSQLParserException
sql中case when语句导致报错net.sf.jsqlparser.JSQLParserException
Data-Structures-and-Algorithm-Analysis-in-Java.pdf
作者: Weiss, Mark A. 出版社: Addison Wesley Data Structures and Algorithm Analysis in Java is an “advanced algorithms” book that fits between traditional CS2 and Algorithms Analysis courses. In the old ACM Curriculum Guidelines, this course was known as CS7. This text is for readers who want to learn good programming and algorithm analysis skills simultaneously so that they can develop such programs with the maximum amo...
MybatisPlus执行sql语句报错：Caused by: net.sf.jsqlparser.parser.ParseException
mybatisplus多租户使用sql拦截导致的不能识别sql语句问题
net.sf.jsqlparser.parser.ParseException: Encountered unexpected token: “)“ “)“ at line 3, column
在使用mybatis-plus进行条件查询时，遇到一个需求：使用一个输入框查询多个属性。乍一看这不挺简单的嘛，于是一通操作代码如下： LambdaQueryWrapper<Student> queryWrapper = new LambdaQueryWrapper<Student>(); queryWrapper.and(wrapper -> wrapper.like(Strings.isNotEmpty(student.getStudentName()),
sql关键字与系统冲突报错 Caused by: net.sf.jsqlparser.parser.ParseException: Encountered unexpected token:
# sql关键字与系统冲突报错 Caused by: net.sf.jsqlparser.parser.ParseException: Encountered unexpected token: "ORDER" "order"
Caused by: net.sf.jsqlparser.parser.ParseException
错误异常: Caused by: net.sf.jsqlparser.parser.ParseException: Encountered " "(" "( "" at line 3, column 11. Was expecting one of: <EOF> "AS" ... "DO" ... "INTO" ... "FROM" ... "WHERE" ... "XML" ... "GROUP" ... "VALUE
在mybatis中使用@变量报错 Caused by: net.sf.jsqlparser.parser.ParseException: Encountered unexpected token: “
2.或者在mapper上打注解 @InterceptorIgnore(tenantLine = "true")起因是我写了一个复杂的sql,sql中使用了@这种自定义的变量,在查询sql的时候直接报错了,1.是升级mybatisplus版本到v3.5.1及以上,
执行查询sql报Encountered unexpected token: “(“ “(“
报错信息： Caused by: net.sf.jsqlparser.parser.ParseException: Encountered unexpected token: “(” “(” 问题： 使用jsqlparser解析sql时，sql中含有if条件 解决方案： 使用case when进行替代
net.sf.jsqlparser.parser.ParseException: Encountered unexpected token: “-＞＞“
该查询使用了mybatis-plus分页插件PaginationInnerInterceptor进行分页查询，默认自动优化 COUNT SQL，使用的mybatis-plus版本为3.4.3.2，其jsqlparser版本为4.0，解析时报错，无法优化会使用原sql，因此可以正常查询，只不过会打印错误日志。报错net.sf.jsqlparser.parser.ParseException: Encountered unexpected token: “->>”1.修改pom.xml文件。
net.sf.jsqlparser.parser.ParseException: Encountered unexpected token: “,“ “,“ 问题解决方案
在使用mybaits plus中的动态数据源配置使用时报以下错误！！！
SQL报错:Encountered unexpected token: “FUNCTION“ “FUNCTION“at line 5, column 10.Was expecting one of:
写代码SQL报错，做下记录： Caused by: net.sf.jsqlparser.parser.ParseException: Encountered unexpected token: "FUNCTION" "FUNCTION" at line 5, column 10. Was expecting one of: "ACTION" "ANY" "BYTE" "CASCADE" "CAST" "CHANGE" "CHAR"
MyBtisplus异常PaginationInnerInterceptor net.sf.jsqlparser.parser.ParseException: Encountered unexpect
PaginationInnerInterceptor异常提示 net.sf.jsqlparser.parser.ParseException: Encountered unexpected token: "," "," at line 1, column 143.
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