select 框条件化初始数据并能筛选

博客内容主要展示了一系列字段的配置信息,包括工厂选择、工序选择、机床选择等。每个字段有显示名称、类型、验证规则等属性,部分字段的编辑器通过 AJAX 请求获取数据,如根据工厂选择动态获取机床和工序数据。

field = [
                            {
                                display: "工厂选择", name: "FactoryCode", type: "select", newline: false, validate: { required: true },
                                editor: {
                                    url: 'getFactory',
                                    valueField: 'id',
                                    textField: 'text',
                                    onSelected: function (value, text) {
                                        $.ajax({
                                            url: "getJiChuang?FactoryCode=" + value,
                                            type: "POST",
                                            dataType: "json",
                                            success: function (data) {
                                                if (data.length > 0) {
                                                    liger.get("JCCode").setData(data);
                                                }
                                            }
                                        });
                                        $.ajax({
                                            url: "getGongXu?FactoryCode=" + value,
                                            type: "POST",
                                            dataType: "json",
                                            success: function (data) {
                                                if (data.length > 0) {
                                                    liger.get("GXCode").setData(data);
                                                }
                                            }
                                        });
                                    }
                                },
                            },
                            {
                                display: "工序选择", name: "GXCode", type: "select", newline: false, validate: { required: true },
                                editor: {
                                    url: 'getGongXu?FactoryCode=@ViewBag.FactoryCode',
                                    valueField: 'id',
                                    textField: 'text',
                                    autocomplete: true,
                                    keySupport: true,
                                },
                            },
                            {
                                display: "机床选择", name: "JCCode", type: "select", newline: false, validate: { required: true },
                                editor: {
                                    url: 'getJiChuang?FactoryCode=@ViewBag.FactoryCode',
                                    valueField: 'id',
                                    textField: 'text',
                                    autocomplete: true,
                                    keySupport: true,
                                },
                            },
                            { display: "产线顺序", name: "Order", type: "int", newline: false, validate: { required: true }, },
                            { display: "ID", name: "ID", type: "hidden", newline: false, },
                            
            ];

内容概要:本文介绍了ENVI Deep Learning V1.0的操作教程,重点讲解了如何利用ENVI软件进行深度学习模型的训练与应用,以实现遥感图像中特定目标(如集装箱)的自动提取。教程涵盖了从数据准备、标签图像创建、模型初始化与训练,到执行分类及结果优化的完整流程,并介绍了精度评价与通过ENVI Modeler实现一键化建模的方法。系统基于TensorFlow架,采用ENVINet5(U-Net变体)架构,支持通过点、线、面ROI或分类图生成标签数据,适用于多/高光谱影像的单一类别特征提取。; 适合人群:具备遥感图像处理基础,熟悉ENVI软件操作,从事地理信息、测绘、环境监测等相关领域的技术人员或研究人员,尤其是希望将深度学习技术应用于遥感目标识别的初学者与实践者。; 使用场景及目标:①在遥感影像中自动识别和提取特定地物目标(如车辆、建筑、道路、集装箱等);②掌握ENVI环境下深度学习模型的训练流程与关键参数设置(如Patch Size、Epochs、Class Weight等);③通过模型调优与结果反馈提升分类精度,实现高效自动化信息提取。; 阅读建议:建议结合实际遥感项目边学边练,重点关注标签数据制作、模型参数配置与结果后处理环节,充分利用ENVI Modeler进行自动化建模与参数优化,同时注意软硬件环境(特别是NVIDIA GPU)的配置要求以保障训练效率。
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