Research Report on the In-Depth Development and Future Trends of LabVIEW in the Test and Measurement

Comprehensive Research Report on the Comprehensive Exploration of LabVIEW's Role in Advancing Test and Measurement Technology during its Evolution Prospects (2025-2030)

Introduction

Since ever since its 1986 release by National Instruments (NI), LabVIEW (Laboratory Virtual Instrument Engineering Workbench) has been established as an irreplaceable tool for engineers and scientists worldwide across the domains of test, measurement, and control. The software's distinctive graphical programming paradigm has demonstrated robustness in integrating hardware components, thereby achieving consistent operation across different platforms. It has greatly reduced development cycles while simultaneously supporting deployment on desktop computers as well as real-time embedded systems.

本报告旨在探讨LabVIEW在测试与测量领域的深入应用情况，并对其未来1至5年（即2025至2030年）的技术发展与应用趋势进行分析。本研究将涵盖工业自动化、航空航天、汽车测试以及科学实验等多个关键领域，并结合最新的技术革新与行业动态。报告将深入阐述实时系统、驱动开发与硬件集成等核心技术方向的具体实现细节及其架构设计。此外，本研究还将进一步探讨其生态系统整合与兼容性的发展演变过程。

LabVIEW's Current Development Status in In Depth Applications for Test and Measurement

LabVIEW boasts key features that include an intuitive graphical interface and seamless integration with NI hardware and third-party devices. This empowers engineers to rapidly develop advanced testing and measurement systems without requiring a deep understanding of traditional text-based programming languages.

In the industrial automation sector, LabVIEW is commonly employed for plantwide automation systems, process control applications, quality assurance, and machine monitoring. It combines robust data acquisition and processing capabilities with real-time control modules to meet precise performance standards in industrial environments. For instance, an intelligent monitoring system built using LabVIEW integrates sensors and data acquisition cards to achieve real-time monitoring and analysis of environmental parameters, thereby enhancing accuracy and response speed.

Automated test systems in the aerospace sector are typically developed using LabVIEW. It offers a broad range of reliable solutions for testing from simple voltage measurements to complex testing of aircraft components and systems. LabVIEW serves as a typical example in this context, enabling fast, efficient, and traceable testing of power distribution units through its integration with the CPCI modular hardware architecture.

The demand for LabVIEW in the automotive testing sector is continuously rising. It covers a wide range of traditional powertrain testing, controller unit testing, as well as innovative ADAS/autonomous driving and new energy vehicle testing. LabVIEW provides versatile and high-performance test platforms. For example, a LabVIEW-based battery performance test system for new energy vehicles can offer real-time monitoring and analysis of key battery parameters.

The scientific research experimental domain where LabVIEW originated serves as a significant application area. In the domains of high-energy physics, biomedicine, and materials science,... automation of experimental procedures,... precision data acquisition,... image processing,... as well as equipment control are among its primary applications. Thanks to its versatility and customization features,... it can be tailored to meet diverse experimental requirements effectively. In particular,... laboratory systems can be developed using this technology for specialized purposes. In the field of materials science,... real-time visualization of microstructural changes during tensile testing is possible through this system.

LabVIEW的主要特点及其优势包括缩短开发时间、良好的平台一致性以及强大的硬件集成能力，并为这些领域奠定了坚实的基础。

LabVIEW在测试与测量领域的技术发展预测（2025-2030）

During the next five to one years, the advancement of LabVIEW’s technical capabilities in the test and measurement sector will emphasize intelligent evolution within an open ecosystem that provides high-performance solutions through expanding ecosystems. This strategic focus aims to address emerging challenges while capitalizing on opportunities from technologies such as Industry 4.0, IoT-enabled systems, and AI advancements.

2.1 Real-Time Systems and Driver Development Integration

Real-time systems hold a central position in various test and measurement applications particularly those necessitating deterministic responses and precise timing including industrial control and aerospace testing. The LabVIEW Real-Time module enables the construction of real-time systems. In the foreseeable future there will be an intensified demand for real-time performance capabilities this will necessitate LabVIEW's continued advancements in fundamental operating system support task scheduling optimization strategies and resource management techniques.

Driver development serves as an essential component in hardware integration efforts. As new hardware platforms and custom devices continue to emerge, streamlining driver development and deployment becomes increasingly vital. LabVIEW currently provides plug-and-play instrument drivers along with an intuitive Instrument Driver Development Wizard. In light of these evolving processor architectures like ARM or RISC-V, along with high-speed interfaces such as PCIe Gen4/5 or Ethernet TSN, more robust solutions are necessary to meet growing demands.

Technical Implementation Details and Trends:

Unlike LabVIEW Real-Time, which is built upon dedicated real-time operating systems, the future might offer broader support for general-purpose RTOSs or introduce more versatile approaches for incorporating user-defined real-time code.

Enhanced Simplified Custom Driver Development Frameworks: Enhanced abstraction layers and wizards may not only streamline but also significantly reduce the complexity involved in developing drivers for conventional or specialized hardware, thereby minimizing reliance on intricate low-level hardware details.

Model-Based Driver Development: 采用基于Model-Based Systems Engineering（MBSE）的原则，在模型中描述硬件接口和行为以自动生成部分代码，并从而提高开发效率和代码质量。

Advanced Low-Level Communication Protocol Support: Offering advanced support that caters to a user-centric approach for emerging industrial Ethernet protocols such as EtherNet/IP and PROFINET, along with tools designed to facilitate the construction and management of protocol stacks.

Heterogeneous Computing Driver Integration: With the growing application of heterogeneous computing architectures such as CPUs, GPUs, FPGAs, and DSPs in test and measurement systems,...

The LabVIEW Real-Time module’s core originates from its deterministic execution characteristics. The determinacy of a real-time application can be assessed through the consistency and reliability of its responses to external stimuli.

Ideal determinism implies a consistent response period that remains unaffected by variations in system load. LabVIEW Real-Time utilizes preemptive multitasking and time-shared scheduling to achieve deterministic performance.

2.2 New Hardware Platforms and Heterogeneous Computing

LabVIEW has historically maintained a strong coupling relationship with NI's range of hardware platforms (including PXI and CompactRIO), offering a robust and scalable hardware architecture. In the future, LabVIEW will continue to evolve towards becoming increasingly compatible with diverse advancements in computing technology, such as third-party solutions and new processor designs.

Heterogeneous computing serves as the core technology for enhancing operational efficiency in test and measurement systems. Optimally assigning computing tasks to the most appropriate processing units, such as placing high-speed signal processing onto an FPGA and complex algorithm calculations onto a GPU or multi-core CPU, can lead to significant improvements in efficiency. LabVIEW's advancements in heterogeneous computing will represent a crucial direction for its future development.

Technical Implementation Details and Trends:

Enhanced Support for Third-Party Hardware: Additionally, LabVIEW requires offering more flexible mechanisms for integrating a variety of third-party hardware, such as embedded systems built on emerging processor architectures.

更强大的异构计算程序模型：提供了更加直观的图形界面工具以将任务分配给不同的处理单元，并对数据流动与同步操作进行管理。这些程序模型降低了程序设计的复杂性。LabVIEW FPGA模块现提供了一种基于图形化的FPGA程序设计方法，并可能在未来扩展至GPU和其他加速器。

Model-Based Hardware Integration Methods: Employing models to document the hardware composition and interconnection relationships of the entire system, automatically generating portions of integration code, thereby reducing the effort required for manual configuration and interconnection.

Providing support for high-speed interface standards, staying aligned with advancements in technologies like PCIe Gen4/5 and Ethernet TSN, offering corresponding drivers and toolkits to meet the needs of high-bandwidth data acquisition and transmission.

Specific Domain Custom Hardware Integration: In domains requiring specialized hardware customization, such as high-energy physics and advanced manufacturing, LabVIEW must offer more versatile interfaces and tools to enable the integration of user-created hardware. The SLSC architecture stands out as an exemplary solution, empowering users and partners to effortlessly design custom circuit boards and seamlessly integrate them with the NI platform.

2.3 AI Integration

Artificial Intelligence (AI) and Machine Learning (ML) are currently among the most sought-after technological trends in today's landscape. They hold significant potential for application in the testing and measurement domain, encompassing areas such as predictive maintenance, anomaly detection, intelligent control systems, and automated data analysis. LabVIEW is actively adopting AI to integrate it into current workflows.

Technical Implementation Details and Trends:

无缝整合主流AI框架： National Instruments（NI）已成功实现了Python与TensorFlow的结合，并将在未来继续深化与主流框架如PyTorch和ONNX的整合工作，在LabVIEW环境下为用户提供更加便捷的应用体验。具体来说，在LabVIEW环境下应用OpenVINO™ toolkit以及ONNX toolkit正是这一战略举措的最佳实践。

部署AI模型到实时与嵌入式平台上:** 投放训练好的AI模型至LabVIEW Real-Time与FPGA平台以实现边缘推理。这对于需要快速响应与低延迟的应用至关重要。LabVIEW借助FPGA加速技术在边缘执行AI算法展现出一项具有前瞻性的发展方向。

AI-Driven Development Tools: Leveraging AI technology to enhance the LabVIEW development environment, including features such as automated code generation, intelligent error detection, suggestions for performance optimization, and automated comments. AI-assisted code inheritance and automated comments/completion represent promising application avenues.

人工智能在数据分析中的应用：系统性地将人工智能（AI）与机器学习（ML）算法整合至LabVIEW的数据处理与分析工作流中，并通过该系统实现更为深入的数据见解提取功能。以识别大量测试数据中的模式、执行故障预测以及进行根本原因分析为例，在此过程中能够显著提升数据处理的智能化水平。此外，在LabVIEW的Analytics和Machine Learning Toolkit中已预先配置了包括异常检测、分类以及聚类算法在内的多种先进算法支持

通过人工智能驱动的测试创新方法的应用有望彻底改变工程工作流程。

NI's AI Principles Focus on Ensuring a Smooth Transition to LabVIEW with AI Technology NI prioritizes three core principles in its AI implementation strategy: Engineers Maintain Control, AI Facilitates Seamless Integration, and Industry Standards are Met. These principles collectively ensure that AI is effectively integrated into existing systems without compromising operational efficiency or compliance requirements.

The utilization of AI in the testing and measurement domain is typically characterized by its widespread integration into modern analytical systems.

Predictive Maintenance (PdM): Relies on analyzing data from monitoring devices and past performance records to utilize algorithms that forecast equipment failures, thereby enabling proactive measures to minimize equipment downtime.

Anomaly Detection: Real-time tracking of system data employing AI models to detect abnormal patterns, immediately identifying potential anomalies.

Intelligent Control: By applying cutting-edge AI technologies, including reinforce learning, to devise and optimize intelligent control systems, we can significantly boost their operational efficiency and adaptability in varying environments.

Automated Analysis: Relying on methods such as computer vision and natural language processing for the automation of analyzing non-structured information including images and text.

3. Test and Measurement System Architecture Design Trends

Building complex test and measurement systems requires adopting appropriate architectural design patterns to ensure system maintainability, scalability, robustness, and performance. As system scale and complexity increase, traditional monolithic architectures are becoming insufficient. In the future, LabVIEW test and measurement system architecture design will focus more on modularity, service-orientation (conceptually), and distribution.

Architecture Design Patterns and Trends:

Evolution and Promotion of the Actor Framework (AF): AF, emerging as a robust concurrent and distributed architectural paradigm, is poised for expanded application in complex systems. NI will further enhance AF's capabilities by providing comprehensive examples and best-in-class practices to lower its learning threshold effectively.

Modular and component-based design focuses on decomposing systems into independent, reusable modules or components that communicate via clear interfaces. This approach enhances code maintainability and reusability. The reliance on subVIs underpins LabVIEW's modular architecture.

Message Queue and Event-Driven Architectures: Using asynchronous message queues for communication between different components or processes can enhance a system's response speed and decouple its architecture. Event-driven architectures allow a system to respond swiftly to external events. The Message Queue pattern along with the Producer/Consumer pattern are typical implementations found in LabVIEW.

应用服务架构概念：尽管LabVIEW并非传统上以服务架构著称，在其内部通过将功能封装为独立的VI或库，并通过网络协议（如TCP/IP、HTTP）提供接口的方式仍可实现这一概念。这有助于构建分布式系统并与其他系统进行集成。

采用DevOps实践：将DevOps概念与工具引入LabVIEW开发流程中，例如通过使用Git进行版本控制并采用CI/CD管道线实现自动化构建与测试。NI对Git集成的持续改进以及TestStand对CI/CD的支持体现了这一实践的实施情况

Integration with Model-Based Systems Engineering (MBSE): Employing MBSE methods to construct test and measurement systems while evaluating their performance helps ensure robustness in both functionality and reliability. This approach significantly enhances the overall quality of integrated systems by fostering consistency across all technical aspects.

Consideration of Cybersecurity in Architecture Design: When designing test and measurement system architectures, it is imperative to comprehensively address cybersecurity challenges through the adoption of secure communication protocols, implementation of access control measures, and application of data encryption techniques. LabVIEW possesses inherent memory safety benefits, yet further enhancement of system-level security measures is still required.

A based on simplified message queue, the architecture of a LabVIEW test and measurement system can be represented as:

graph TD
A[数据采集模块]-->B(消息队列)
C[控制模块]-->B
D[用户界面模块]-->B
B-->E(数据处理模块)
B-->F(报警模块)
B-->G(数据存储模块)
E-->D
F-->D

This diagram displays diverse components interacting via a shared messaging system, enabling separation of concerns and non-blocking operations.

3.1 Distributed Test and Measurement Systems

As the growth of the geographic scope of test and measurement systems expands, distributed architectures are becoming more critical. LabVIEW exhibits strengths in constructing distributed test and measurement systems, such as leveraging network communication functions and real-time platforms.

Technical Implementation Details and Trends:

Advanced Inter-Node Communication Mechanisms: By enhancing the efficiency and dependability of inter-node communication mechanisms, RT-LAB supports a variety of network protocols and ensures effective data synchronization across distributed systems. RT-LAB provides high-performance solutions for synchronizing data across distributed systems.

Scalable Task Scheduling and Workload Distribution: Offering a suite of tools and frameworks, this approach effectively handles task distribution and workload management in scalable systems, ensuring optimal system performance. In doing so, it ensures efficient system operation.

故障检测与恢复机制：

Edge Computing & Cloud Computing Collaboration: Deploying certain data processing operations and control functions on edge devices alongside intensive computational processes in the cloud facilitates collaborative efforts between edge computing and cloud computing. LabVIEW's foundational role in supporting both cloud services and edge computing devices underpins this objective.

3.2 Containerization Technology (Docker)

打包LabVIEW应用程序到Docker容器中可以显著提升其可移植性、可扩展性和维护性。尽管目前关于LabVIEW与Docker整合的公开信息有限，在这一方向仍有较大的潜力作为未来的发展重点。

Potential Applications and Challenges (Speculation):

简化部署与管理：通过使用Docker技术可以在不同环境中（包括云环境或边缘设备）有效地简化LabVIEW应用程序的部署与管理过程。

Improved Portability: Docker-based applications host all required components, enabling deployment across any platform that supports Docker containers, thus eliminating the need for complex setup steps.

Deployment of Microservices Architecture: Breaking down LabVIEW applications into miniature, self-contained microservices and hosting them in Docker containers can enhance the system's ability to be flexible and scalable.

Difficulties: Assembling LabVIEW's graphical interface code and its run-time environment into Docker containers may involve significant technical challenges, particularly when dealing with real-time and embedded systems that have strict performance requirements.

4. Ecosystem Integration and Interoperability

LabVIEW's power extends beyond its inherent capabilities, encompassing its open ecosystem and compatibility with a variety of other software and hardware solutions. Looking ahead, LabVIEW will continue to enhance its capacity for ecosystem integration to effectively address the needs of users in complex system integrations.

Integration and Interoperability Trends:

深入集成Enterprise Systems（MES, ERP）： 尽管LabVIEW不适于直接开发MES/ERP系统，但作为用于采集与控制底层测试与测量数据的平台，在实现高效的数据交换与过程整合方面仍需努力以适应这些企业系统的需求。这些标准协议包括TCP/IP、OPC以及HTTP协议，并采用XML等数据格式以确保有效通信与数据管理。

**无缝融入云端平台：**LabVIEW持续深化与主流云端平台（包括亚马逊云服务、微软Azure以及谷歌云端服务）的融合，并提供更加直观易用的工具包及API功能，并支持将测试与测量数据上传至云端用于存储、分析以及可视化展示。这些创新功能旨在提升数据处理效率并优化资源利用情况。目前可用解决方案包括LabVIEW云工具包以及ThingsSpot IIoT工具包等现有产品

Connection to Data Lakes: 面对测试和测量数据急剧增长的趋势, 将数据存储在数据湖中以便集中管理和分析变得愈发关键. LabVIEW应提供便捷的接口以直接向各类数据湖存储系统写入数据.

Enhanced Interoperability with Other Engineering Software: Further enhance interoperability through the use of programming languages including MATLAB, Python, C/C++, .NET, and a variety of simulation tools. The integration of the Python Node and MATLAB Node alongside enhanced compatibility with RT-LAB and Moku will see further improvements in functionality.

开放性架构与第三方支持：NI将继续致力于LabVIEW生态系统的开发与完善，并积极邀请第三方开发者提供工具包、驱动程序及解决方案。LabVIEW社区推出的OpenG工具包及其日益增多的第三方设备驱动正成为该生态系统发展成熟的有力证明。

Implementation of Industry Standards: Across industries including automotive (ASAM), aerospace industries with DO-178C and DO-254 specifications, and industrial automation (ISA-95), LabVIEW must implement enhanced support for relevant industry standards to aid in the creation of systems compliant with industry regulations.

Incorporation of Model-Based Systems Engineering (MBSE): By integrating MBSE tools and methods into the LabVIEW development environment, we aim to establish a seamless workflow that starts with defining system requirements and concludes with their full realization within the LabVIEW environment.

Interoperability between LabVIEW and other software is supported by various techniques.

External Code Interfaces: Calling C/C++ DLLs, .NET assemblies, etc.

Script Nodes: Directly running Python, MATLAB scripts within LabVIEW.

Common Data Transfer Protocols include TCP/IP, UDP, Serial, GPIB, VISA, OPC, Modbus, and EtherNet/IP etc.

File I/O: Reading and writing various file formats (TDMS, CSV, XML, etc.).

Database Connectivity: Interacting with various databases.

5. Specific Manifestations in Industry Application Areas

LabVIEW's advanced applications and evolutionary paths in the field of test and measurement demonstrate varying focuses across distinct sub-fields.

5.1 Industrial Automation

Centralized Infrastructure for Industry 4.0 (IIoT): The LabVIEW RIO architecture will serve as a central hub for developing advanced monitoring and control systems within the Industrial Internet of Things (IIoT), enabling the integration of edge computing with cloud computing capabilities.

High Degree of Integration with PLC/DCS: LabVIEW will be integrated more closely with existing PLC/DCS systems through enhanced protocol compatibility and integration tools and techniques to achieve unified monitoring and control of industrial process complexity.

Applications in Smart Manufacturing: Within the domains of motion control, advanced machine vision systems, and high-precision coordinated operations, LabVIEW integrates with artificial intelligence to deliver highly intelligent automation solutions that are both efficient and adaptable. Notably, leveraging advanced machine vision technologies enables automated quality inspection coupled with comprehensive data analytics. The integration aims to enhance operational efficiency while laying a solid groundwork for ongoing innovations in smart manufacturing.

Large-Scale Sensor Data Processing: 面对大规模工业环境中的传感器数据流, LabVIEW需实现高效率的数据处理与存储方案,包括数据切片、并行运算以及硬件加速等技术手段,同时支持优化文件格式(TDMS)以提高处理效率。

Constructing a distributed control system requires the use of tools like NI VeriStand for building distributed HIL, testing cells, and implementing real-time monitoring systems. This approach will spread across the entire system architecture.

Comprehensive Support for Industrial Protocols: The support for industrial protocols, including Modbus, EtherNet/IP, and PROFINET, will be enhanced to become more comprehensive and user-centric.

5.2 Aerospace

核心自动化测试系统：LabVIEW 作为航空航天自动化测试系统的首选平台，在结合模块化硬件如CPCI/PXI后，能够满足高可靠性、高精度及可追溯性的测试需求

HIL仿真与验证流程：通过整合LabVIEW+ Suite与HIL技术，并支持实时仿真工具如RT-LAB的无缝集成，本方案将为航空航天系统提供强大的仿真支持环境；该环境将有助于提升控制算法验证效率与准确性

LabVIEW’s implementation of comprehensive technological solutions, automated testing processes, and data analytics capabilities enables "New Space" companies to efficiently shorten time-to-market while enhancing the quality and reliability of their satellite operations.

Compliance with Industry Standards: LabVIEW and its related tools, including TestStand, have always been designed to remain supportive of aerospace industry standards such as DO-178C and DO-254, ensuring that test systems meet these requirements.

需求对软件硬件一体化人才: 在航空航天领域中对具备软件与硬件综合技能的人才的需求将会增加，并且LabVIEW工程师必须具备深入理解硬件驱动器以及低层通信协议的能力。

5.3 Automotive Testing

Driver of "Shift Left" Testing:** In the V-model development cycle, LabVIEW serves as a crucial tool for implementing earlier-stage testing strategies. By integrating advanced module-based hardware with TestStand automation tools, it enhances both efficiency and reliability in test execution.

New Energy Vehicle Testing Solutions: LabVIEW offers diverse testing solutions for a range of critical components within electric vehicles, including batteries with high energy density, We brushless DC motors, and advanced battery management systems (BMS). The system supports high-speed data acquisition and storage capabilities.

ADAS/Autonomous Driving Test Platform: The LabVIEW system, integrated with high-integrity simulation capabilities (VeriStand), advanced vehicle radar and sensor systems (VRTS by Konrad Technologies), and robust data acquisition and playback infrastructure, will be equipped with a comprehensive suite of tools to establish cutting-edge ADAS/autonomous driving test platforms.

In-Vehicle Network Testing:** LabVIEW will maintain support for in-vehicle network protocols, including CAN and Automotive Ethernet, offering interconnection interfaces and testing tools to ensure robust network functionality during vehicle development. These features contribute to enhanced system reliability and performance evaluation.

Data Management and Analysis: Leveraging LabVIEW’s robust data handling features in conjunction with effective integration into cloud-based infrastructure solutions, the incorporation of advanced AI/ML technologies is employed to enhance the efficiency and accuracy of automotive test data analysis.

Alignment with Industrial Standards: centered on the alignment between ASAM and various automotive industry standards using LabVIEW technology.

5.4 Scientific Research Experiments

Platform for Cross-Disciplinary Technology Integration: LabVIEW will continue to act as an effective platform that integrates advanced technologies across diverse domains. Specifically, it will integrate mature technologies by employing techniques such as integration with game engines to visualize data and leveraging IoT technology through remote monitoring systems.

Automated Experimental Procedures: LabVIEW's utilization in achieving fully automated control over experimental equipment, ensuring precise data collection and ensuring precise monitoring of experimental setups will be implemented with greater depth, thereby enhancing both the efficiency and precision of experimental processes.

High-precision data acquisition and analysis: To tackle issues including a large number of channels, extremely high sampling frequencies, and the need for precise measurement, LabVIEW must invest in more efficient data capture and processing techniques, leveraging AI/ML technologies for advanced data analysis tasks.

Customized Experimental System Development: The flexibility of LabVIEW enables it to sustain diverse scientific research requirements while continuously creating tailored experimental setups and software solutions.

Laboratory Networking and Remote Experiments: By leveraging LabVIEW's networking features, it is possible to enable remote operation of experimental equipment as well as the sharing of data.

Continued Promotion in Education: LabVIEW is dedicated to advancing its initiatives in the education sector, aiming to produce a larger pool of skilled engineers and scientists specializing in test and measurement systems.

6. Challenges and Countermeasures

Despite its significant advantages in the test and measurement related fields, LabVIEW still encounters difficulties in its future development.

Technology Updates and Compatibility: A system must be geared towards steadily evolving with new technologies and standards. It should ensure seamless integration across a wide range of hardware and software platforms while also providing backward compatibility for older software applications and library versions.

Countermeasure: NI must consistently allocate resources to R&D initiatives, regularly update and refresh drivers and toolkits, and provide a comprehensive suite of migration tools along with robust compatibility support services.

High-Performance Demands: High performance demands, including real-time processing tasks and high-precision measurements, call for continuous optimization of execution speed and resource utilization.

Countermeasure: 通过优化算法、提升内存管理能力、加强并行计算能力和充分利用异构计算硬件。

Data Integrity and System Confidentiality: When accessing cloud and edge devices, robust security protocols must be implemented to ensure the protection of sensitive information and system integrity.

防护措施: 加强身份验证、访问控制、数据加密等功能，并遵循行业最佳实践和安全标准的具体要求.

Cross-Platform and Cloud Integration: Delivering a range of adaptable and high-efficiency cross-platform solutions while enhancing collaboration with cloud service providers.

对策: 不断优化跨平台支持，并开发更为强大的云集成工具包及API以满足开发者的需求。

User Experience and Interface Design: When the system's functionality increases, ensuring an intuitive design becomes increasingly important.

Countermeasure: Steadily enhance the user experience interface and interaction design by drawing on low-code/no-code platforms.

Complexity profile and learning challenges: Sophisticated architectural designs, such as the Actor Framework, and heterogeneous computing programming exhibit a certain level of complexity and involve a higher learning threshold.

Countermeasure: Supply more comprehensive documentation, tutored materials, and training resources. Simplify the process of utilizing tools and frameworks. Potentially incorporate AI-assisted development tools.

Driver Development Challenges:** Creating drivers for new or custom hardware continues to present a struggle.**

Countermeasure: Be enhanced with more advanced driver development frameworks and wizards, enabling the use of model-based approaches.

Data Fusion and Processing: 需解决多模态数据融合与大规模数据处理的问题

Countermeasure: Optimize data processing techniques, and integrate diverse computing resources to enhance the efficiency of data handling operations.

Industry Standard Compliance: Constructing systems that meet the requirements of specific industries needs more work.

Countermeasure: Offer toolkits, templates, and certification support in accordance with industry standards.

Safety Functionality: Within highly critical safety applications, the IEC 61508 standard is required to be implemented.

Countermeasure: Offer functional safety-related tools, testing methodologies, and certification assistance, focusing on a modular approach to ensure isolated safety functions.

7. Conclusion and Outlook

In the test and measurement domain as a leading entity, LabVIEW is projected to maintain its pivotal position over the next 1-5 years. The primary driver of its future evolution lies within intelligence integration with advanced AI capabilities; moreover it will leverage cloud-native computing for enhanced scalability; open ecosystems for seamless connectivity; high-performance computing for superior efficiency; deep FPGA integration for flexible hardware-software co-design; smart development tools for tailored automation; comprehensive real-time control systems for precise operation; along with expanded ecosystems that foster innovation through collaboration across industries.

In various fields such as industrial automation, aerospace engineering, automotive testing, and scientific research experiments, LabVIEW empowers engineers and scientists to tackle increasingly complex testing and measurement challenges through advanced real-time systems, simplified driver development, support for newer hardware platforms, integrated AI capabilities, and versatile architectural designs.

Despite facing challenges including technology updates, performance optimization, security concerns, and complexity increases, NI's unwavering dedication to advancing LabVIEW and fostering strong ties with industry partners and the broader community underscores its sustained leadership in the test and measurement landscape.

Suggested Future Research Directions:

In-Depth Implementation and Optimization of Specific Industrial Protocols in LabVIEW: Detailed in-depth research into the high-performance implementation of EtherNet/IP and PROFINET protocols on the LabVIEW real-time platform, encompassing protocol stack optimization and deterministic communication assurance.

Investigating the methods of building efficient AI inference systems and control mechanisms utilizing LabVIEW on resource-limited edge devices to collaborate with cloud-based solutions.

Influence and Application of Functional Safety Standards on the LabVIEW Development Process: Investigating the distinctive demands of functional safety standards such as IEC 61508 and ISO 26262 in terms of LabVIEW programming, testing, and validation processes, with the aim of establishing specific development guidelines alongside requisite technical aids.

Investigating the potential of LabVIEW in Automated Scientific Discovery: By integrating LabVIEW with AI, robotics, and data analysis, we explore how it can be combined with these technologies to automate and integrate intelligent experimental processes, thereby accelerating the scientific discovery process.

LabVIEW Application Deployment and Management Based on Containerization Technology: 深入研究涵盖技术可行性、最佳实践以及部署策略在不同平台上的应用策略以实现LabVIEW应用程序打包到Docker容器中。

Examining its advancements in LabVIEW toolkits, drivers, and application development has become a focal point for understanding its influence on the broader LabVIEW ecosystem. Assessing its impact on this ecosystem requires a detailed analysis of how these innovations contribute to system integration. Investigating collaboration frameworks between NI...

Enhancing LabVIEW-Based Solutions for Specific Industry Testing (e.g., EV, ADAS): To address specific requirements including electric vehicle battery management, charging station testing, and ADAS sensor fusion testing, it is necessary to conduct in-depth research into customized and high-performance test solutions based on LabVIEW.

The future of LabVIEW will be a transparent, integrated, networked future, one that will empower the next generation of engineers and scientists, paving the way for continuous advancements in testing and measurement technologies.