TMMI_测试过程改进框架_已测量级别7_测试测量3

本文详细介绍了软件测试中测量与分析的重要步骤,包括收集、分析测试数据,沟通结果及存储数据。强调了数据完整性、准确性的重要性,并提供了子实践示例。

SG 2 Provide Test Measurement Results 提供测试测量结果

Test measurement results that address identified information needs and objectives are provided.

测试测量结果来解决验证的信息需求和提供目标。

SP 2.1 Collect test measurements data 搜集测试测量数据

The test measurement data necessary for analysis are obtained and checked for completeness and integrity. Example work products 1. Test measurement data sets 2. Results of data integrity tests Sub-practices 1. Gather test measurement data from project records or from elsewhere in the organization 2. Generate the data for derived test measures and calculate their values 3. Perform data integrity checks as close to the source of the data as possible All measurements are subject to error in specifying or recording data. It is always better to identify such errors and to identify sources of missing data early in the measurement and analysis cycle. Checks can include scans for missing data, out-of-bounds data values, and unusual patterns and lack of correlation across measures.

测试测量数据对分析师必须的,被获得和检查完整性和完成度。

例子工作产品:测试测量数据集,数据完整性测试结果。

子实践:1.从项目记录或者组织其他地方搜集测量数据。2.生成数据来提取测试测量方法和计算他们的价值。3.执行数据完整性检查,尽可能的靠近数据源。所有的测量都受到指出和记录的数据错误的影响。通常指出这些错误和早期指出错误数据源会更好一些。检查会被包含扫描丢失数据,超过域的数据,不寻常的格式和不同测量项目工作的缺乏。

SP 2.2 Analyze test measurements data 分析测试测量数据

The collected test measurement data are analyzed as planned and additional analysis is conducted as necessary. Example work products 1. Analysis results 2. Draft test measurement reports Sub-practices 1. Conduct initial analysis, interpret the results, and draw preliminary conclusions 2. Conduct additional measurement and analysis as necessary and prepare results for presentation The results of planned analysis may suggest (or require) additional, unanticipated analysis. 3. Review the initial results with relevant stakeholders It is appropriate to review initial interpretations of the results and the way in which they are presented before disseminating and communicating them more widely. Reviewing the initial results before their release may prevent needless misunderstandings and lead to improvements in the data analysis and communication.

搜集的测试测量数据被按照计划分析,额外的分析被执行是必须的。

例子工作产品:1. 分析结果。2,写测试测量报告

子实践:1. 执行初始分析,解释结果,和得到初步的结论。 2.执行额外的测量和分析,准备报告的结果。3. 重审初始结果。

SP 2.3 Communicate results 交流结果

Results of test measurement activities are communicated to all relevant stakeholders. Example work products 1. Test measurement reports and related analysis results Sub-practices 1. Keep relevant stakeholders informed of test measurement results on a timely basis 2. Assist relevant stakeholders in understanding the results Examples of actions to assist in understanding of results include the following:  Discussing the results with the relevant stakeholders in feedback sessions  Providing a transmittal memo that provides background and explanation  Briefing users on the results  Providing training on the appropriate use and understanding of test measurement results 3. Define corrective and improvement actions based on the analyzed test measurement results

测试测量行为的结果被交流,在所有利益相关人之间。

例子工作产品:1. 测试测量报告和相关的分析结果。

子实践:1. 保持利益相关人了解测试测量的情况。2协助相关利益人理解结果。例子:讨论结果,提供demo,简介人员,提供培训。3.定义正确的,改进的行动,基于分析测试测量结果。

SP 2.4 Store data and results 存储数据和结果

The test measurement data, measurement specification and analysis results are stored and managed. Example work products 1. Stored test measurement data inventory, including measurement plans, specifications of measures, sets of data that have been collected and analysis reports and presentations Sub-practices 1. Review the measurement data to ensure their completeness, integrity, accuracy, and currency 2. Store the test measurement data according to the data storage procedures 3. Restrict access to the data to the appropriate groups and personnel 4. Prevent the stored information from being used inappropriately, e.g. by controlling access to the test measurement data

测试测量数据,测量说明书和分析结果被存储和管理。

例子工作产品:1. 存储测试测量数据,报告测量计划,测量说明书,搜集的数据集和分析报告以及陈述。

子实践:1. 重审测量数据,保证完成度和完整度,正确性,实时性。 2.存储测试测量数据,基于数据存储流程。3.严格团队和个人访问数据权限。4,组织错误使用的数据存储。

基于TROPOMI高光谱遥感仪器获取的大气成分观测资料,本研究聚焦于大气污染物一氧化氮(NO₂)的空间分布与浓度定量反演问题。NO₂作为影响空气质量的关键指标,其精确监测对环境保护与大气科学研究具有显著价值。当前,利用卫星遥感数据结合先进算法实现NO₂浓度的高精度反演已成为该领域的重要研究方向。 本研究构建了一套以深度学习为核心的技术框架,整合了来自TROPOMI仪器的光谱辐射信息、观测几何参数以及辅助气象数据,形成多维度特征数据集。该数据集充分融合了不同来源的观测信息,为深入解析大气中NO₂的时空变化规律提供了数据基础,有助于提升反演模型的准确性与环境预测的可靠性。 在模型架构方面,项目设计了一种多分支神经网络,用于分别处理光谱特征与气象特征等多模态数据。各分支通过独立学习提取代表性特征,并在深层网络中进行特征融合,从而综合利用不同数据的互补信息,显著提高了NO₂浓度反演的整体精度。这种多源信息融合策略有效增强了模型对复杂大气环境的表征能力。 研究过程涵盖了系统的数据处理流程。前期预处理包括辐射定标、噪声抑制及数据标准化等步骤,以保障输入特征的质量与一致性;后期处理则涉及模型输出的物理量转换与结果验证,确保反演结果符合实际大气浓度范围,提升数据的实用价值。 此外,本研究进一步对不同功能区域(如城市建成区、工业带、郊区及自然背景区)的NO₂浓度分布进行了对比分析,揭示了人类活动与污染物空间格局的关联性。相关结论可为区域环境规划、污染管控政策的制定提供科学依据,助力大气环境治理与公共健康保护。 综上所述,本研究通过融合TROPOMI高光谱数据与多模态特征深度学习技术,发展了一套高效、准确的大气NO₂浓度遥感反演方法,不仅提升了卫星大气监测的技术水平,也为环境管理与决策支持提供了重要的技术工具。 资源来源于网络分享,仅用于学习交流使用,请勿用于商业,如有侵权请联系我删除!
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