soapui加载天气预报接口报错Characters larger than 4 bytes are not supported: byte 0xb1 implies a length of more than 4 byte的解决办法

【soapui加载天气预报接口报错Characters larger than 4 bytes are not supported: byte 0xb1 implies a length of more than 4 byte的解决办法】的更多相关文章

soapui加载天气预报接口报错Characters larger than 4 bytes are not supported: byte 0xb1 implies a length of more than 4 byte的解决办法

soapui加载天气预报接口时报错如下: Error loading [http://www.webxml.com.cn/WebServices/WeatherWebService.asmx?wsdl]: org.apache.xmlbeans.XmlException: java.io.CharConversionException: Characters larger than 4 bytes are not supported: byte 0xb1 implies a length of…

worker中加载本地文件报错的解决方案

如果在一个swf的主线程中加载文件时,报安全沙箱的错误, 网上有诸多的解决方案.但是如果在一个worker中加载本地文件报类似如下的错误: *** 安全沙箱冲突 *** SecurityError: Error #2148 不能访问本地资源. 网上给出的方案几乎都不能有效的解决问题, 之所以说几乎, 是因为如果以后读者搜到这篇文章时, 有效的解决方案就在这里. 即在本地架设一个http资源服务器, 加载的时候不要用本地地址作为加载路径,而是使用http 地址作为加载地址即可正确加载.…

ztree插件异步加载使用RESTEasy报错 Only resource methods using @FormParam will work as expected. Resource methods consuming the request body by other means will not work as expected.

在使用ztree插件实现异步加载时遇到后台RESTEasy接收参数问题,查看后台报错: A servlet request to the URI http://localhost:8080/area/query contains form parameters in the request body but the request body has been consumed by the servlet or a servlet filter accessing the request par…

加载rocksdb实例报错：java.lang.UnsatisfiedLinkError: C:\Users\Administrator\AppData\Local\Temp\librocksdbjni3696928169151614297.dll

项目的缓存中用到了rocksdb,实例化时报错了: Related cause: org.springframework.beans.factory.BeanCreationException: Error creating bean with name 'org.springframework.beans.factory.config.MethodInvokingFactoryBean#1' defined in class path resource [spring-core.xml]: C…

(转帖)关于easyui中的datagrid在加载数据时候报错：无法获取属性"Length"的值，对象为null或未定义

结贴说明: 很感谢sp1234等人的热心帮忙和提醒,现在我主要说明下问题所在: 首先我在独立的js文件中,直接把测试数据loaddata进去datagrid是没有问题的.var kk = {"total":2,"rows":[{"INSTANCE_ID":"BI00000011","BUSINESS_NAME":"专项检查文档资料报送","INSTANCE_STATUS&quo…

pytorch 加载mnist数据集报错not gzip file

利用pytorch加载mnist数据集的代码如下 import torchvision import torchvision.transforms as transforms from torch.utils.data import DataLoader train_data = torchvision.datasets.MNIST( root='./mnist/', train=True, # this is training data transform=torchvision.transf…