Intelligent cloud testing system and method based on cloud and edge computing

Abstract

The invention provides an intelligent cloud testing system and method based on cloud and edge computing, and particularly relates to the technical field of automatic testing, and the system comprises a cloud service platform, an edge unit, an instrument discovery unit and message middleware. The cloud service platform manages test cases and test data in a centralized manner, provides a code-free programming tool, saves test records, and supports multiple report formats and data online analysis. The edge unit is deployed on a user site, automatically identifies test instruments and equipment, receives test cases, autonomously completes testing, pushes test data to the cloud service platform in real time, and supports offline testing. An instrument discovery unit automatically detects and configures an access instrument. And the message middleware improves the data throughput, realizes module decoupling, and is convenient for independent deployment and upgrade. The system solves the technical problems that an automatic test system is long in research and development period, poor in flexibility and difficult to deploy, and rapid construction, flexible adjustment and low-cost deployment of the automatic test system are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of automated testing, and more particularly, to an intelligent cloud testing system and method based on cloud and edge computing. Background Art

[0002] With the rapid development of technology, the complexity of products has increased day by day, and the testing work has become more complex and time-consuming. Manual testing can no longer meet the requirements of rapid iteration and high-quality assurance. A large number of production and R & D enterprises are upgrading from manual testing to automated testing. However, when dealing with rapidly changing market demands, the traditional automated testing mode exposes problems such as long R & D cycles, poor flexibility, difficult deployment, and neglect of data value. These problems seriously restrict the improvement of enterprise production efficiency and product quality, and there is an urgent need for a more efficient, flexible, and cost-controlled automated testing solution.

[0003] Traditional automated testing usually uses development languages and tools such as Labview, CVI, and C# to meet the testing requirements of specific products through customized development. Although this customized development can precisely match the current testing process and instruments, problems such as long R & D cycles, poor flexibility, difficult deployment, and data dispersion are becoming increasingly prominent. Although the existing technologies can solve some problems in specific scenarios, they are unable to cope when faced with rapidly iterating product lines, frequently changing testing processes and instruments, diverse report format requirements, and the need for centralized data analysis. Therefore, the existing solutions have obvious deficiencies in dealing with the complexity and variability in modern production environments.

[0004] In summary, how to solve the technical problems of long R & D cycles, poor flexibility, and difficult deployment of automated testing systems is an urgent problem to be solved. Summary of the Invention

[0005] The main objective of the present invention is to provide an intelligent cloud testing system and method based on cloud and edge computing to solve the technical problems of long R & D cycles, poor flexibility, and difficult deployment of automated testing systems, thereby realizing the rapid construction, flexible adjustment, low-cost deployment of automated testing systems, and centralized management and in-depth analysis of test data, significantly improving testing efficiency and reducing enterprise operation costs.

[0006] To achieve the above objective, the present invention provides an intelligent cloud testing system and method based on cloud and edge computing.

[0007] In a first aspect, the present invention provides an intelligent cloud testing system based on cloud and edge computing, the system comprising:

[0008] A cloud service platform, which is deployed in the test system. The cloud service platform is used for centralized management of test cases and test data. The cloud service platform is also used to provide a no-code programming tool to build and debug test cases. The cloud service platform is also used to centrally save test records and provide multiple optional report formats and online data analysis functions;

[0009] An edge unit, which is deployed at the user site and connected to the cloud service platform. The edge unit is used to access test instrument devices and automatically identify the models of the test instrument devices for collecting raw data. The edge unit is also used to receive the test cases issued by the cloud service platform and independently complete test tasks according to the raw data and test cases and generate the test data. The edge unit is also used to push the test data to the cloud service platform in real time. The edge unit is also used to complete test tasks when the network connection with the cloud service platform is interrupted and upload the test data after the network is restored;

[0010] An instrument discovery unit, which is set in the edge unit. The instrument discovery unit is used to automatically detect and identify the connected instruments and automatically configure the test environment;

[0011] A message middleware, which is connected to the cloud service platform and the edge unit. The message middleware is used to improve the throughput of the system for collecting raw data. The message middleware is also used to decouple the modules in the test system so that each module can be independently deployed and upgraded.

[0012] Optionally, the cloud service platform further includes a test rule engine, which is installed and deployed in the test system. The test rule engine is used to intelligently schedule the entire test process according to the test process instructions issued by the cloud service platform for automated execution of test activities.

[0013] Optionally, the edge unit further includes:

[0014] An instrument connector, which is connected to the test instrument device and the product under test. The instrument connector is used to establish and maintain the connection between the test instrument device and the product under test;

[0015] An operator service module, which is connected to the cloud service platform. The operator service module is used to process and process the raw data collected from the test instrument device to convert it into product index data.

[0016] Optionally, the edge unit also has a local caching function, which is used to cache the generated data locally during the test process.

[0017] Optionally, the system further includes an application terminal, which is connected to the edge unit. The application terminal is used to control the test instrument and equipment by directly dragging function icons instead of complex language programming, so that users can control the test instrument and equipment without programming to perform basic analysis.

[0018] Optionally, the cloud service platform further includes a data encryption module, which is used to encrypt test data during the transmission process of test data.

[0019] In a second aspect, the present application provides an intelligent cloud testing method based on cloud and edge computing. The testing method is applied to the testing system described in the first aspect, and the testing method includes:

[0020] Centralize the management of test cases and test data, and provide a no-code programming tool to build and debug test cases;

[0021] Centralize the storage of test records and provide multiple optional report formats and online data analysis functions;

[0022] Connect to the test instrument and equipment and automatically identify the model of the test instrument and equipment to collect raw data;

[0023] Receive the test cases issued by the cloud service platform and independently complete the test tasks according to the raw data and test cases to generate the test data;

[0024] Push the test data to the cloud service platform in real time. The edge unit completes the test tasks when the network connection with the cloud service platform is interrupted and uploads the test data after the network is restored;

[0025] Automatically detect and identify the connected instruments and automatically configure the test environment;

[0026] Improve the throughput of the system for collecting raw data, and decouple the modules in the test system so that each module can be independently deployed and upgraded.

[0027] Optionally, after connecting to the test instrument and equipment and automatically identifying the model of the test instrument and equipment to collect raw data, it further includes: intelligently scheduling the entire test process according to the test cases issued by the cloud service platform to automate the execution of test activities.

[0028] Optionally, after connecting to the test instrument and equipment and automatically identifying the model of the test instrument and equipment to collect raw data, it further includes:

[0029] Establish and maintain the connection between the test instrument and equipment and the product under test;

[0030] Process and process the raw data collected from the test instrument and equipment to convert it into product index data.

[0031] Optionally, after receiving the test cases sent by the cloud service platform, autonomously completing the test tasks according to the original data and the test cases, and generating the test data, the following further includes: caching the generated test data locally during the test through the local caching function.

[0032] The intelligent cloud testing system and method based on cloud and edge computing provided by the present application, the system includes a cloud service platform, an edge unit, an instrument discovery unit, and a message middleware. The system centrally manages test cases, test data, and test records through the cloud service platform, provides a no-code programming tool, supports multiple report formats and online data analysis. The edge unit is deployed at the user site, automatically identifies and accesses test instrument devices, receives the test cases sent by the cloud service platform, autonomously completes the test tasks, and pushes the test data to the cloud service platform in real time. At the same time, the edge unit has the ability of offline testing to ensure that the test is not affected when the network connection is interrupted, and automatically uploads the data after the network is restored. The instrument discovery unit automatically detects and identifies the accessed instruments and configures the test environment. The message middleware improves the data throughput, realizes module decoupling, supports independent deployment and upgrade of each module, and ensures the efficient and stable operation of the system. The system solves the technical problems of long R & D cycle, poor flexibility, and difficult deployment of the automated testing system, thereby realizing the rapid construction, flexible adjustment, low-cost deployment of the automated testing system, and the centralized management and in-depth analysis of test data, significantly improving the test efficiency and reducing the enterprise operation cost. Description of the Drawings

[0033] The specification drawings forming a part of the present application are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0034] Figure 1 is a schematic diagram of the intelligent cloud testing system based on cloud and edge computing provided by the present application;

[0035] Figure 2 is a schematic flow chart of the intelligent cloud testing method based on cloud and edge computing provided by the present application.

[0036] Through the above drawings, the clear embodiments of the present application have been shown, and there will be more detailed descriptions hereinafter. These drawings and the textual descriptions are not intended to limit the scope of the concept of the present application in any way, but to explain the concept of the present application to those skilled in the art by referring to specific embodiments. Detailed Embodiments

[0037] To make the objectives, technical solutions, and advantages of this application clearer, the following will clearly and completely describe the technical solutions in this application with reference to the accompanying drawings in this application. Apparently, the described embodiments are some, but not all, of the embodiments of this application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts shall fall within the scope of protection of this application.

[0038] In the description of the present invention and the claims and the above-mentioned drawings, the terms "first", "second", "third", "fourth", etc. (if any) are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data used in this way can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here.

[0039] In the present invention, words such as "exemplary" or "for example" are used to represent examples, illustrations, or explanations. Any embodiment or design solution described as "exemplary" or "for example" in this application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary" or "for example" is intended to present relevant concepts in a specific manner.

[0040] The intelligent cloud testing system and method based on cloud and edge computing provided by this application, the system includes a cloud service platform, an edge unit, an instrument discovery unit, and a message middleware. The cloud service platform centrally manages test cases and data, provides a no-code programming tool, saves test records, and supports diverse reports and online analysis. The edge unit is deployed on-site, automatically identifies instruments, receives use cases from the cloud, conducts autonomous testing and uploads data in real-time, and supports offline testing. The instrument discovery unit automatically detects and configures instruments. The message middleware improves data throughput, realizes module decoupling, and facilitates independent deployment and upgrade. This system solves the technical problems of long R & D cycle, poor flexibility, and difficult deployment of the automated testing system, and realizes the rapid construction, flexible adjustment, and low-cost deployment of the automated testing system.

[0041] The following uses specific embodiments to detail the technical solutions of this application and how the technical solutions of this application solve the above technical problems. These several specific embodiments below can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The following will describe the embodiments of this application with reference to the accompanying drawings.

[0042] Figure 1 It is a schematic diagram of the intelligent cloud testing system based on cloud and edge computing provided by this application, as Figure 1 shown, the intelligent cloud testing system based on cloud and edge computing provided in this embodiment, the system includes:

[0043] A cloud service platform, which is deployed in the test system. The cloud service platform is used for centralized management of test cases and test data. The cloud service platform is also used to provide a no-code programming tool to build and debug test cases. The cloud service platform is also used to centrally save test records and provide multiple optional report formats and online data analysis functions;

[0044] An edge unit, which is deployed at the user site and connected to the cloud service platform. The edge unit is used to access test instrument devices and automatically identify the models of the test instrument devices to collect raw data. The edge unit is also used to receive test cases issued by the cloud service platform and independently complete test tasks based on the raw data and test cases to generate the test data. The edge unit is also used to push the test data to the cloud service platform in real time. The edge unit is also used to complete test tasks when the network connection with the cloud service platform is interrupted and upload the test data after the network is restored;

[0045] An instrument discovery unit, which is set in the edge unit. The instrument discovery unit is used to automatically detect and identify the connected instruments and automatically configure the test environment;

[0046] A message middleware, which is connected to the cloud service platform and the edge unit. The message middleware is used to improve the throughput of the system for collecting raw data. The message middleware is also used to decouple the modules in the test system so that each module can be independently deployed and upgraded.

[0047] Optionally, the cloud service platform further includes a test rule engine, which is installed and deployed in the test system. The test rule engine is used to intelligently schedule the entire test process according to the test process instructions issued by the cloud service platform for automated execution of test activities.

[0048] Optionally, the edge unit further includes:

[0049] An instrument connector, which is connected to the test instrument device and the product under test. The instrument connector is used to establish and maintain the connection between the test instrument device and the product under test;

[0050] An operator service module, which is connected to the cloud service platform. The operator service module is used to process the raw data collected from the test instrument device to convert it into product index data.

[0051] Optionally, the edge unit also has a local caching function, which is used to cache the generated data locally during the test process.

[0052] Optionally, the system further includes an application terminal, which is connected to the edge unit. The application terminal is used to control the test instrument and equipment without programming by directly dragging function icons instead of complex language programming to perform basic analysis.

[0053] Optionally, the cloud service platform further includes a data encryption module, which is used to encrypt test data during the transmission process of test data.

[0054] Specifically, this embodiment provides an intelligent cloud test system based on cloud and edge computing. The system aims to improve test efficiency and quality through centralized management and automated test processes. The following is a detailed description of each component of the system and its specific implementation methods.

[0055] I. Cloud Service Platform

[0056] The cloud service platform is the core of the entire test system, deployed in the test system, and is responsible for centralized management of test cases and test data. Specifically, the cloud service platform includes the following functions:

[0057] Test Case and Test Data Management: The cloud service platform provides a centralized database for storing and managing all test cases and test data. Users can conveniently create, edit, retrieve, and delete test cases through the cloud service platform, as well as upload, download, and analyze test data.

[0058] No-Code Programming Tools: To reduce the difficulty of constructing test cases, the cloud service platform is built-in with no-code programming tools (such as Jotform Workflows, V0.dev, etc.). This tool allows users to quickly construct and debug test cases through a graphical interface and drag-and-drop operations without writing complex code.

[0059] Test Record and Report Generation: The cloud service platform centrally stores all test records and provides multiple optional report formats, such as PDF, Excel, etc. Users can select the report format according to their needs and analyze test data online to generate detailed test reports.

[0060] Data Encryption Module: During the transmission process of test data, the data encryption module of the cloud service platform encrypts the test data to ensure data security. During the transmission process of test data, the data encryption module of the cloud service platform ensures the security of test data during transmission and storage by selecting appropriate encryption algorithms (such as AES, RSA, etc.), applying encryption technologies such as SSL / TLS protocols, implementing strict key management strategies, and formulating comprehensive encryption strategies, thereby effectively preventing data leakage and tampering.

[0061] In addition, the cloud service platform also includes a test rule engine. According to the test process instructions issued by the cloud service platform, this engine intelligently schedules the entire test process to achieve the automated execution of test activities. The test rule engine can automatically select test cases, allocate test resources, monitor the test progress according to preset rules and conditions, and automatically analyze the results after the test is completed.

[0062] II. Edge Unit

[0063] The edge unit is deployed at the user site and remains connected to the cloud service platform. Its main functions include:

[0064] Instrument access and automatic identification: The edge unit is built with an instrument discovery unit that can automatically detect and identify the connected test instrument devices and automatically configure the test environment according to the instrument model. This enables users to quickly start testing without manually setting up the test instrument devices.

[0065] Test case execution and data generation: The edge unit receives the test cases sent by the cloud service platform and independently completes the test tasks according to the original data and test cases. During the test process, the edge unit will collect test data in real time and push it to the cloud service platform. This ensures the real-time and accuracy of test data.

[0066] Network interruption handling: In the case of a network connection interruption, the edge unit can continue to complete the test tasks and synchronize the test data with the cloud service platform after the network is restored. This improves the reliability and stability of the system.

[0067] Local caching function: To further improve the test efficiency, the edge unit also has a local caching function. During the test process, the edge unit will cache the generated data locally to reduce data transmission latency and bandwidth occupancy.

[0068] In addition, the edge unit also includes an instrument connector and an operator service module. The instrument connector is responsible for establishing and maintaining the connection between the test instrument devices and the product under test; the operator service module processes and processes the raw data collected from the test instrument devices to convert it into product index data. This enables the edge unit to process and analyze test data more efficiently.

[0069] III. Message Middleware

[0070] The message middleware connects the cloud service platform and the edge unit. Its main role is to improve the throughput of the system for collecting raw data and decouple the various modules in the test system. Through the message middleware, the cloud service platform and the edge unit can communicate asynchronously to achieve real-time data transmission and processing. At the same time, the message middleware also supports the independent deployment and upgrade of each module, improving the scalability and flexibility of the system.

[0071] IV. Application End

[0072] The application end is connected to the edge unit, providing users with an intuitive and convenient way to control test instrument devices. Users can directly drag function icons through the application end, replacing complex language programming, so that they can control test instrument devices without programming. This enables non-professionals to easily perform basic analysis and test operations.

[0073] In summary, the intelligent cloud test system of this embodiment realizes the rapid construction and debugging of test cases, the real-time collection and analysis of test data, the automated execution of test processes, and the flexible expansion and efficient operation of the test system through the collaborative work of the cloud service platform, edge unit, message middleware, and application end. This system solves the technical problems of long R & D cycle, poor flexibility, and difficult deployment of automated test systems, and realizes the rapid construction, flexible adjustment, and low-cost deployment of automated test systems.

[0074] Figure 2 It is a schematic flowchart of the intelligent cloud test method based on cloud and edge computing provided by this application. The intelligent cloud test method based on cloud and edge computing will be described in detail as follows Figure 2 As shown, the intelligent cloud test method based on cloud and edge computing provided in this embodiment includes:

[0075] S201: Centralize the management of test cases and test data, and provide a no-code programming tool to build and debug test cases.

[0076] In this embodiment, for step S201 in the intelligent cloud test method based on cloud and edge computing, that is, to centralize the management of test cases and test data and provide a no-code programming tool to build and debug test cases, the specific implementation method is as follows:

[0077] First, in order to centralize the management of test cases and test data, we adopt a specially designed test case and data management system. This system has a user-friendly interface that allows testers to upload, download, edit, and delete test cases. Test cases are stored in a structured format, including but not limited to detailed information such as test steps, expected results, and preconditions. At the same time, test data is also centrally stored in this system so that testers can easily access and use it.

[0078] To build and debug test cases, we provide a no-code programming tool. This tool uses a graphical interface that allows testers to create test cases by dragging and configuring components. Specifically, this tool includes the following components and functions:

[0079] Test Step Component: Testers can select test step components from the component library and drag them into the test case editor. Each test step component corresponds to a specific test operation, such as sending an HTTP request, verifying the response status code, etc. Testers can define the specific parameters of the test step by configuring the properties of the component, such as the request URL, request method, request headers, etc.

[0080] Data Binding Function: Testers can bind test data to the properties of test step components to achieve dynamic data replacement. In this way, during test execution, the test step components will use the bound test data to perform test operations.

[0081] Condition Judgment Component: To support complex test logic, we provide a condition judgment component. Testers can add a condition judgment component to the test case and decide whether to execute subsequent test steps based on the results of the test steps. The condition judgment component supports multiple logical operators, such as AND, OR, NOT, etc.

[0082] Debugging Function: To facilitate testers in debugging test cases, we provide a debugging function. Testers can set breakpoints in the test case editor and pause at the breakpoints during test execution. At this time, testers can view information such as the execution results of test steps and variable values for problem location and repair.

[0083] Version Management Function: The test case and data management system supports the version management function, allowing testers to create versions of test cases and record the content of each modification. In this way, testers can roll back to a previous version at any time to ensure the stability and traceability of testing.

[0084] Through the above no-code programming tool, testers can easily build and debug test cases without writing complex test scripts. This not only lowers the testing threshold but also improves testing efficiency and accuracy.

[0085] In summary, in this embodiment, by designing a dedicated test case and data management system and providing a no-code programming tool, centralized management of test cases and test data is achieved, as well as the construction and debugging of test cases. This enables testers to perform testing work more conveniently and efficiently, thereby improving the overall testing quality.

[0086] Optionally, after centrally managing test cases and test data and providing a no-code programming tool to build and debug test cases, it further includes: intelligently scheduling the entire test process according to the test cases issued by the cloud service platform for automated execution of test activities.

[0087] After step S201, "Centralize the management of test cases and test data, and provide a no-code programming tool to build and debug test cases" is completed, the system automatically enters the intelligent scheduling phase. At this time, the cloud service platform acts as the control center, responsible for receiving and parsing the test case requirements defined by users or within the system.

[0088] The cloud service platform first uses a rule-based scheduling algorithm (such as specific algorithm names like Round Robin, priority scheduling, etc. In this embodiment, priority scheduling is taken as an example) to intelligently schedule the entire test process. This algorithm assigns appropriate execution order and resources to each test case according to factors such as the priority of the test case, resource requirements, and execution time.

[0089] The specific implementation steps are as follows:

[0090] Test case parsing: After receiving the test case, the cloud service platform first parses it to extract information such as the specific content, expected results, and execution conditions of the test case.

[0091] Priority evaluation: Assign a priority to each test case according to factors such as the urgency and importance of the test case. The priority can be in numerical form, and the smaller the value, the higher the priority.

[0092] Resource allocation: The cloud service platform allocates appropriate execution environments for test cases according to the resource requirements of the test cases (such as CPU, memory, storage, etc.) and the current available resource situation. This includes selecting appropriate resources such as virtual machines, containers, or physical servers.

[0093] Scheduling decision: Based on the results of priority and resource allocation, the cloud service platform uses the priority scheduling algorithm to determine the execution order of test cases. High-priority test cases will be executed first, while ensuring the efficient use of resources.

[0094] Automated execution: Once the test case is scheduled to the execution stage, the system will automatically trigger the execution of the test case. This includes steps such as loading the test case, configuring the test environment, executing the test steps, and collecting the test results. The entire execution process requires no manual intervention, achieving the automation of test activities.

[0095] Status monitoring and feedback: During the execution process, the cloud service platform will monitor the execution status of the test case in real time, including execution progress, resource usage, etc. Once an abnormal situation (such as execution failure, insufficient resources, etc.) is detected, the system will issue an alarm in a timely manner and provide corresponding handling suggestions.

[0096] Through the above intelligent scheduling mechanism, we have achieved the automated execution of the test process, improving the test efficiency and quality. At the same time, this mechanism also has flexibility and scalability, and can adapt to test requirements of different scales and complexities.

[0097] S202: Centralize the storage of test records and provide multiple optional report formats and online data analysis functions.

[0098] In this embodiment, for step S202 of the intelligent cloud testing method based on cloud and edge computing, that is, centralize the storage of test records and provide multiple optional report formats and online data analysis functions, the specific implementation method is as follows:

[0099] First, in order to centralize the storage of test records, we designed and implemented a test record management system. This system has a central database for storing test records of all test tasks. These test records include but are not limited to basic information of test tasks (such as task name, execution time, executor, etc.), execution results of test cases, detailed information of test data, etc. To ensure the security and reliability of the data, this database adopts a redundant storage and backup mechanism to prevent data loss or damage.

[0100] Next, in order to meet the requirements of multiple optional report formats, we provide a report generation module. This module can automatically generate test reports in multiple formats based on the data in the test record management system. These report formats include but are not limited to:

[0101] Text report: Displays the basic information and test results of the test task in plain text, facilitating quick browsing and printing.

[0102] HTML report: Displays the test task in the form of a web page, including rich styles and interactive functions, facilitating online viewing and sharing.

[0103] Excel report: Displays test data in the form of a spreadsheet, facilitating subsequent data analysis and processing.

[0104] PDF report: Displays the test task in the form of a printable PDF document, including complete test information and results, facilitating archiving and distribution.

[0105] The report generation module allows users to select a suitable report format according to actual needs and generate the corresponding test report through simple configuration.

[0106] In addition, in order to meet the requirements of the online data analysis function, we provide a data analysis module. This module can access the data in the test record management system in real time and provide a variety of data analysis tools and methods so that testers can conduct in-depth analysis and mining of test data. These data analysis tools include but are not limited to:

[0107] Trend analysis: By plotting a trend chart, display the change of test data over time, helping testers identify potential performance problems or abnormal behaviors.

[0108] Statistical analysis: Use statistical methods (such as mean, variance, median, etc.) to perform statistical analysis on the test data to quantitatively evaluate the performance and stability of the system.

[0109] Correlation analysis: By analyzing the correlation relationships between different test data, reveal potential system problems or performance bottlenecks.

[0110] Anomaly detection: Use machine learning algorithms (such as clustering, classification, etc.) to perform anomaly detection on the test data, automatically identify and mark the abnormal data points for further investigation and processing by testers.

[0111] The data analysis module provides an intuitive data visualization interface to help testers quickly understand and analyze the test data. At the same time, this module also supports the data export function, allowing testers to export the analysis results to common file formats (such as Excel, CSV, etc.) for subsequent data processing and analysis.

[0112] In summary, in this embodiment, by designing and implementing the test record management system, report generation module, and data analysis module, the centralized storage of test records, the provision of multiple optional report formats, and the online data analysis function are realized. This enables testers to more conveniently and efficiently manage and analyze the test data, thereby improving the overall test quality and efficiency.

[0113] S203: Connect to the test instrument and automatically identify the model of the test instrument to collect raw data.

[0114] In this embodiment, we will describe in detail the steps of "S203: Connect to the test instrument and automatically identify the model of the test instrument to collect raw data". To achieve this goal, we will adopt a series of specific technical means and devices.

[0115] I. Connection of Test Instruments

[0116] Physical connection: First, physically connect the test instrument to the edge unit through standardized interfaces (such as USB, Ethernet, GPIB, etc.). These interfaces ensure that different models of test instruments can communicate with the edge unit.

[0117] Communication Protocol: The edge unit is built with drivers for multiple communication protocols, such as VISA (Virtual Instrument Software Architecture), SCPI (Standard Commands for Programmable Instruments), etc. These drivers can identify and communicate with different models of test instrument devices.

[0118] II. Automatic Identification of Test Instrument Devices

[0119] Device Detection: When a test instrument device is connected to the edge unit, the edge unit will actively detect the connected device. This is usually achieved by sending specific detection signals or query commands.

[0120] Model Identification: Based on the received response signals or returned information, the edge unit uses the built-in device database for model matching. The device database contains information such as the models, manufacturers, and communication protocols of various test instrument devices.

[0121] Driver Loading: Once the model of the test instrument device is identified, the edge unit will automatically load the corresponding driver for that model. These drivers contain all the instruction sets and function libraries required to communicate with the test instrument device.

[0122] III. Acquisition of Raw Data

[0123] Configuration Parameters: After successfully loading the driver, the edge unit configures the parameters of the test instrument device according to the requirements in the test case. These parameters may include sampling rate, range, trigger conditions, etc.

[0124] Data Acquisition: After configuration, the edge unit sends a data acquisition command to the test instrument device through the driver. After receiving the command, the test instrument device starts to acquire raw data and transmits the data back to the edge unit in real-time.

[0125] Data Verification: After receiving the raw data, the edge unit performs preliminary data verification to ensure the integrity and accuracy of the data. This usually includes checking the data format, range, outliers, etc.

[0126] IV. Specific Implementation Details

[0127] Interface Standardization: To ensure that different models of test instrument devices can communicate with the edge unit, we adopt standardized interfaces and communication protocols.

[0128] Device Database: The edge unit is built with a device database that contains information such as the models, manufacturers, and communication protocols of various test instrument devices to facilitate automatic identification of device models.

[0129] Driver modularization: We have modularized the drivers for test instrument devices of different models to facilitate subsequent maintenance and upgrades.

[0130] Data verification algorithm: In the data verification stage, we adopt the cyclic redundancy check (CRC) algorithm to ensure data integrity.

[0131] In summary, through a series of specific technical means such as physical connection, communication protocol, device detection, model identification, driver loading, configuration parameters, data acquisition, and data verification, this embodiment realizes the function of accessing test instrument devices and automatically identifying their models for collecting raw data. These specific measures ensure the automation and intelligence of the test process, improving test efficiency and accuracy.

[0132] Optionally, after accessing the test instrument device and automatically identifying the model of the test instrument device for collecting raw data, the following steps are further included:

[0133] Establish and maintain a connection between the test instrument device and the product under test;

[0134] Process and handle the raw data collected from the test instrument device to convert it into product index data.

[0135] In the intelligent cloud testing method based on cloud and edge computing, after step S203, we provide optional steps to establish and maintain a connection between the test instrument device and the product under test, and process and handle the raw data collected from the test instrument device to convert it into product index data. The following are the specific implementation details of this step:

[0136] I. Establish and maintain a connection between the test instrument device and the product under test

[0137] Physical connection: Physically connect the test instrument device to the product under test using standardized interfaces (such as USB, Ethernet, GPIB, etc.). These interfaces ensure that test instrument devices of different models can be compatible with various products under test.

[0138] Communication protocol: Configure corresponding communication parameters according to the communication protocols of the test instrument device and the product under test (such as SCPI, VISA, Modbus, etc.) to ensure unobstructed data transmission between the two.

[0139] Connection verification: Verify whether the connection between the test instrument device and the product under test is successfully established by sending test signals or query commands. If the connection fails, troubleshoot and repair the faults.

[0140] II. Processing and handling of raw data

[0141] Data reception: The test instrument and equipment collect the original data from the product under test according to the preset sampling rate and format. This data may include physical quantities such as voltage, current, temperature, and pressure.

[0142] Data preprocessing: Preprocess the received original data, including steps such as denoising, filtering, and calibration. These steps aim to improve the accuracy and reliability of the data.

[0143] For example, use the Kalman filtering algorithm to smooth the original data to reduce noise interference. The Kalman filter is a recursive algorithm that uses a series of measurement values to estimate the state of a dynamic system.

[0144] Data conversion: According to the definition and calculation formula of the product indicators, convert the preprocessed original data into product indicator data. These indicators may include power, efficiency, reliability, etc.

[0145] For example, for the power indicator, the formula P = UI (where P is power, U is voltage, and I is current) can be used for calculation.

[0146] Data verification: Verify the converted product indicator data to ensure that it meets the expected range and accuracy requirements. If the data is abnormal, error handling or re - collection of data is performed.

[0147] Data storage: Store the verified product indicator data in a local database or a cloud service platform for subsequent analysis and use.

[0148] Through the above steps, we have successfully established a connection between the test instrument and equipment and the product under test, processed the original data collected from the test instrument and equipment, and finally converted it into product indicator data. These data provide an important basis for subsequent test analysis and product optimization. At the same time, this embodiment also demonstrates how to use specific algorithms and technical means to achieve the goal of data processing in practical applications.

[0149] S204: Receive the test cases issued by the cloud service platform and independently complete the test tasks according to the original data and the test cases, and generate the test data.

[0150] In this embodiment, for step S204 of the intelligent cloud testing method based on cloud and edge computing, that is, receiving the test cases issued by the cloud service platform and independently completing the test tasks according to the original data and the test cases, and generating the test data, the specific implementation method is as follows:

[0151] First, the edge computing node establishes a connection with the cloud service platform through a secure and reliable communication protocol (such as HTTPS, MQTT, etc.). Once the connection is established, the edge computing node can receive the test cases sent down by the cloud service platform. These test cases are transmitted in a structured format (such as JSON, XML, etc.) and contain all the information required for testing, including but not limited to test steps, expected results, references to test data, etc.

[0152] After receiving the test cases, the edge computing node will parse these test cases and obtain the required raw data from local or remote data sources according to the instructions in the test cases. The raw data may include various types of information, such as sensor readings, user inputs, system logs, etc., depending on the requirements of the test cases.

[0153] Next, the edge computing node will use a specially designed test execution engine to autonomously complete the test tasks. The test execution engine is a highly modular and extensible system that can execute the corresponding test operations one by one according to the test steps in the test cases. These test operations may include data verification, logical judgment, interface call, etc., depending on the definition of the test cases.

[0154] During the execution of the test operations, the test execution engine will record the test data in real time, including the execution results of the test steps, the processing results of the raw data, the changes in the system state, etc. These test data are crucial for subsequent test result analysis and problem location.

[0155] Once the test tasks are completed, the test execution engine will generate a final test data report. The report is organized in a structured format and contains the execution results of the test cases, the detailed information of the test data, and any possible error or exception information. The test data report can be pushed back to the cloud service platform in real time through a secure communication protocol so that testers can remotely monitor and analyze the test results.

[0156] In addition, to cope with possible network connection interruptions between the cloud service platform and the edge computing node, the edge computing node also has the ability to perform offline tests. When the network connection is interrupted, the edge computing node will continue to execute the received test cases and cache the generated test data. Once the network connection is restored, the edge computing node will immediately upload the cached test data to the cloud service platform to ensure the integrity and accuracy of the test results.

[0157] In summary, in this embodiment, by designing and implementing a highly modular and extensible test execution engine and offline test capabilities, the function of receiving test cases issued by the cloud service platform and autonomously completing test tasks and generating test data based on the original data and test cases is realized. This enables the edge computing node to efficiently and reliably complete test tasks and feedback the test results to the cloud service platform in real time.

[0158] Optionally, after receiving the test cases issued by the cloud service platform and autonomously completing the test tasks and generating the test data based on the original data and test cases, the following steps are further included: caching the generated test data locally during the test process through the local caching function.

[0159] In the intelligent cloud testing method based on cloud and edge computing, after step S204, we provide an optional step, that is, caching the generated test data locally during the test process through the local caching function. The following are the specific implementation details of this step:

[0160] I. Implementation of the local caching function

[0161] Cache mechanism design: Design an efficient local caching mechanism that adopts cache replacement policies such as First In First Out (FIFO) or Least Recently Used (LRU) to ensure that the latest and most frequently used test data is stored in the cache.

[0162] Cache storage location: Determine the storage location of the local cache. Usually, a dedicated area is selected on the local hard disk or solid-state drive (SSD) of the edge device as the cache space.

[0163] Cache size configuration: Reasonably configure the size of the cache according to the storage capacity of the edge device and the generation rate of test data to ensure that data will not be lost due to cache overflow during the test process.

[0164] II. Caching process of test data

[0165] Data generation and capture: In step S204, when the test system autonomously completes the test tasks and generates test data according to the test cases and original data issued by the cloud service platform, the local cache module captures these data in real time.

[0166] Data formatting: Perform formatting processing on the captured test data and convert it into a format suitable for local cache storage. This includes operations such as data encoding, compression, and packaging.

[0167] Data writing into the cache: Write the formatted test data into the local cache according to the cache replacement policy. If the cache is full, replace the earliest or least recently used data according to policies such as FIFO or LRU.

[0168] Cache status monitoring: Real-time monitoring of the status of the local cache, including indicators such as cache utilization rate, remaining space, read and write speeds, etc. When the cache utilization rate approaches the threshold, a warning is issued in a timely manner and corresponding measures are taken (such as expanding the cache space, optimizing the cache replacement policy, etc.).

[0169] III. Reading and Uploading of Cached Data

[0170] Data reading: When test data needs to be analyzed or processed, relevant data can be read from the local cache. The reading process should follow the cache replacement policy to ensure that the data read is the latest and valid data.

[0171] Data uploading: In step S205, when the network connection between the edge unit and the cloud service platform is restored, the test data in the local cache is uploaded to the cloud service platform. The uploading process should consider the integrity, accuracy, and security of the data.

[0172] Cache cleaning: After the data uploading is completed, old data in the local cache is cleaned according to actual needs to free up storage space and prepare for caching the next test data.

[0173] Through the above steps, we have implemented the function of caching the generated test data locally during the test process. This function improves the reliability and availability of the test data, while reducing the dependence on the network connection of the cloud service platform. When the network connection between the edge device and the cloud service platform is interrupted, the local cache can ensure the integrity and continuity of the test data, providing strong support for subsequent data analysis and product optimization.

[0174] S205: Push the test data to the cloud service platform in real time. The edge unit completes the test task when the network connection with the cloud service platform is interrupted and uploads the test data after the network is restored.

[0175] In this embodiment, step S205 of the intelligent cloud testing method based on cloud and edge computing, that is, "push the test data to the cloud service platform in real time. The edge unit completes the test task when the network connection with the cloud service platform is interrupted and uploads the test data after the network is restored", will be implemented by the following specific technical means:

[0176] First, to ensure the real-time push of test data, the edge unit will adopt an efficient data transmission protocol, such as MQTT (Message Queuing Telemetry Transport) or WebSocket, to establish a stable communication connection with the cloud service platform. These protocols support low-latency and highly reliable data transmission, and are very suitable for test scenarios with high real-time requirements.

[0177] During the execution of the test task, the edge unit will encapsulate the generated test data into a message format at preset time intervals or according to an event-trigger mechanism, and push it to the cloud service platform in real time through the above data transmission protocol. The message format includes the metadata of the test data (such as the test task ID, data generation timestamp, etc.) and the actual test data content, so that the cloud service platform can accurately parse and process it.

[0178] Meanwhile, to cope with possible network connection interruptions, the edge unit is built with a local data storage module for temporarily storing the generated test data when the network connection is unavailable. This storage module can be a disk-based database system or a data structure in memory, depending on the hardware resources and performance requirements of the edge device.

[0179] When it detects that the network connection to the cloud service platform is restored, the edge unit will automatically start the data synchronization mechanism and upload the test data stored locally to the cloud service platform in the order of generation time. To ensure data consistency and integrity, a data checksum and retransmission mechanism will be adopted during the upload process, and the data packets that fail to be transmitted will be retried until all data is successfully uploaded.

[0180] In addition, to further improve the reliability and efficiency of data transmission, the edge unit can also adopt data compression and encryption technologies. Before pushing the data, the test data is compressed to reduce the occupancy of network bandwidth; at the same time, the compressed data is encrypted to ensure the security during data transmission. After receiving the encrypted data, the cloud service platform will perform corresponding decryption and decompression operations to restore the original test data.

[0181] In summary, in this embodiment, by adopting an efficient data transmission protocol, a local data storage module, a data synchronization mechanism, and data compression and encryption technologies, the real-time push of test data and the functions of data caching and resumed upload during network connection interruptions are realized. These technical means ensure the real-time, integrity, and security of test data.

[0182] S206: Automatically detect and identify the connected instruments and automatically configure the test environment.

[0183] In this embodiment, step S206 of the intelligent cloud test method based on cloud and edge computing, that is, "automatically detect and identify the connected instruments and automatically configure the test environment", will be realized by the following specific technical means:

[0184] First, the edge unit has a built-in hardware interface recognition module that supports multiple hardware interface standards, such as USB, Ethernet, serial communication interfaces (such as RS-232, RS-485), etc. When a new test instrument device is connected to the edge unit, the hardware interface recognition module will automatically detect the interface type of the instrument and establish a communication connection with the instrument through the corresponding driver.

[0185] To identify the specific model and functions of the connected instrument, the edge unit also includes an instrument information database. This database stores information such as the models, communication protocols, and configuration parameters of various common test instrument devices. After establishing a communication connection, the edge unit will send a query command to the instrument, requesting its model and configuration information. After receiving the query command, the instrument will return the corresponding response data, and the edge unit will identify the specific model and functions of the connected instrument by comparing the information in the instrument information database.

[0186] Once the instrument is successfully identified, the edge unit will automatically configure the test environment according to the preset test environment and instrument configuration rules. These rules may include the setting of the instrument's communication parameters (such as baud rate, data bits, stop bits, etc.), the generation and acquisition parameters of test signals, the selection of data processing algorithms, etc. The edge unit will obtain the corresponding configuration parameters from the instrument information database based on the identified instrument model and functions, and apply these parameters to configure the test environment.

[0187] In addition, to address possible instrument compatibility issues, the edge unit also includes an adaptive configuration module. This module can dynamically adjust the configuration parameters according to the actual responses and feedback of the instrument to ensure the stability and accuracy of the test environment. For example, if the data format returned by the instrument does not match the expectation, the adaptive configuration module will automatically analyze the data format and adjust the data processing algorithm to adapt to the new data format.

[0188] During the process of configuring the test environment, the edge unit will also generate a configuration log file, recording the identification information of the instrument, configuration parameters, and any possible configuration adjustment records. This log file is very useful for subsequent test problem location and troubleshooting.

[0189] In summary, through specific technical means such as the hardware interface recognition module, instrument information database, adaptive configuration module, and configuration log file, this embodiment realizes the functions of automatically detecting and identifying the connected instrument and automatically configuring the test environment. These technical means ensure the rapid setup of the test environment and the compatibility of the instrument.

[0190] S207: Improve the throughput of the test system for collecting raw data, and decouple the modules in the test system so that each module can be independently deployed and upgraded.

[0191] In this embodiment, for the step of "improving the throughput of the system for collecting raw data and decoupling the modules in the test system so that each module can be independently deployed and upgraded", we will achieve it through the following specific technical means:

[0192] I. Improving the throughput of the system for collecting raw data

[0193] To improve the throughput of the system for collecting raw data, we have adopted the following measures:

[0194] 1. Parallel acquisition technology:

[0195] At the hardware level, we have adopted a multi-channel data acquisition card, which supports the simultaneous acquisition of multiple analog or digital signals, thus realizing the parallel processing of data.

[0196] At the software level, we have designed a parallel data acquisition task scheduler, which can dynamically allocate acquisition tasks to different acquisition channels according to the requirements of data acquisition, ensuring that each channel can be efficiently utilized.

[0197] 2. Data compression algorithm:

[0198] During the data acquisition process, we have adopted a lossless data compression algorithm (such as the LZW algorithm or Huffman coding) to compress the raw data in real time to reduce the load of data transmission and storage.

[0199] The compressed data is decompressed when needed to restore the raw data, ensuring the integrity and accuracy of the data.

[0200] 3. Cache mechanism:

[0201] To further reduce the latency of data processing, we have introduced a cache mechanism for temporarily storing the acquired data.

[0202] This cache mechanism manages data according to the first-in, first-out (FIFO) principle, ensuring that newly acquired data can replace old data in a timely manner while maintaining the real-time nature of the data.

[0203] II. Decoupling and independent deployment and upgrade of modules in the test system

[0204] To achieve the decoupling and independent deployment and upgrade of each module in the test system, we have adopted the following strategies:

[0205] 1. Microservices architecture:

[0206] We have refactored the test system into a microservices architecture, and each module is encapsulated as an independent microservice.

[0207] Microservices interact with each other through lightweight communication protocols (such as RESTful API or gRPC), achieving loose coupling between modules.

[0208] 2. Containerization technology:

[0209] Each microservice is deployed in an independent container (such as a Docker container), and the containers are isolated from each other and do not interfere with each other.

[0210] Containerization technology makes the deployment and upgrade of modules more flexible and efficient, and only requires replacing or updating the corresponding container image.

[0211] 3. Configuration center and version management:

[0212] We introduced a configuration center to centrally manage the configuration information of each module.

[0213] At the same time, we adopted a version management system (such as Git) to track and record the version change history of each module.

[0214] When a certain module needs to be upgraded, only need to obtain the latest version of the code or container image from the version management system, and update the corresponding configuration information in the configuration center.

[0215] In summary, through specific technical means such as parallel acquisition technology, data compression algorithm, cache mechanism, microservice architecture, containerization technology, and configuration center and version management, this embodiment realizes the improvement of the throughput of the system for collecting raw data and the decoupling and independent deployment and upgrade of each module in the test system. These measures ensure the efficient operation and flexibility of the test system, providing strong support for the intelligent cloud testing method based on cloud and edge computing.

[0216] Those of ordinary skill in the art can understand that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, and their appropriate combinations.

[0217] After considering the specification and practicing the invention disclosed herein, those skilled in the art will readily think of other embodiments of the present application. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include the common general knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of the present application are pointed out by the following claims.

[0218] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. An intelligent cloud testing system based on cloud and edge computing, characterized in that, Including: A cloud service platform, which is used to centrally manage test cases and test data, and is also used to provide a no-code programming tool to build and debug test cases. The cloud service platform is further used to centrally save test records and provide multiple optional report formats and online data analysis functions; An edge unit, which is deployed at the user site and connected to the cloud service platform. The edge unit is used to access and automatically identify the models of test instrument devices to collect raw data, and is also used to receive the test cases sent by the cloud service platform and independently complete test tasks and generate the test data according to the raw data and the test cases. The edge unit is further used to push the test data to the cloud service platform in real time, and is also used to complete test tasks when the network connection with the cloud service platform is interrupted and upload the test data after the network is restored; An instrument discovery unit, which is set in the edge unit and is used to automatically detect the connected test instrument devices and automatically configure the test environment; A message middleware, which is connected to the cloud service platform and the edge unit. The message middleware is used to improve the throughput of the edge unit for collecting raw data, and is also used to decouple the edge unit and the cloud service platform so that the edge unit and the cloud service platform can be independently deployed and upgraded.

2. The intelligent cloud testing system according to claim 1, wherein The cloud service platform further includes a test rule engine, which is used to intelligently schedule the entire test process according to the test cases issued by the cloud service platform for the automated execution of test activities.

3. The intelligent cloud testing system according to claim 1, wherein The edge unit further includes: An instrument connector, which is connected to the test instrument device and the product under test, and is used to establish and maintain the connection between the test instrument device and the product under test; An operator service module, which is connected to the cloud service platform and is used to process and process the raw data collected from the test instrument device to convert it into product index data.

4. The intelligent cloud testing system according to claim 1, characterized in that The edge unit also has a local caching function, which is used to cache the generated test data locally during the test process.

5. The intelligent cloud testing system according to claim 1, wherein The test system further includes an application end, which is connected to the edge unit and is used to control the test instrument device by directly dragging function icons instead of complex language programming.

6. The intelligent cloud testing system according to claim 1, wherein The cloud service platform further includes a data encryption module, which is used to encrypt the test data during the test data transmission process.

7. An intelligent cloud testing method based on cloud and edge computing, characterized in that, The test method is applied to the test system according to any one of claims 1-6, and the test method includes: Centrally manage test cases and test data, and provide a no-code programming tool to build and debug test cases; Centrally save test records and provide multiple optional report formats and online data analysis functions; Access the test instrument device and automatically identify the model of the test instrument device to collect raw data; Receive the test cases sent by the cloud service platform, independently complete the test tasks according to the original data and the test cases, and generate the test data; Push the test data to the cloud service platform in real time. When the network connection between the edge unit and the cloud service platform is interrupted, complete the test tasks and upload the test data after the network is restored; Automatically detect and identify the connected instruments and automatically configure the test environment; Improve the throughput of the test system for collecting the original data, and decouple the modules in the test system so that each module can be independently deployed and upgraded.

8. The test method according to claim 7, wherein After centrally managing the test cases and test data, providing a no-code programming tool to build and debug the test cases, it further includes: intelligently scheduling the entire test process according to the test cases issued by the cloud service platform for automated execution of the test activities.

9. The test method according to claim 7, wherein After accessing the test instrument device and automatically identifying the model of the test instrument device for collecting the original data, it further includes: Establish and maintain the connection between the test instrument device and the product under test; Process and process the original data collected from the test instrument device to convert it into product index data.

10. The test method according to claim 7, characterized in that, After receiving the test cases sent by the cloud service platform, independently completing the test tasks according to the original data and the test cases, and generating the test data, it further includes: caching the generated test data locally during the test process through the local caching function.

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