Intelligent test equipment for computer software development of radiation detector

Abstract

The invention provides intelligent testing equipment for computer software development of a radiation detector, and relates to the technical field of software development testing. The intelligent test equipment for computer software development of the radiation detector comprises the radiation detector, a computer and an Internet communication module, a detector CPU chip and computer software used for controlling the radiation detector are fixedly arranged in the radiation detector, an Internet card is further arranged in the radiation detector, and the computer software is used for controlling the Internet card. The internet card is used for being in network data connection with the internet communication module, and the computer is connected with the internet communication module through an Ethernet mode. According to the intelligent test equipment, the computer software data packet of the detector can be directly and remotely downloaded through the Internet for testing, data parameters after testing can be remotely and online changed, the application range is wide, and the parameters of the radiation detector can be rapidly changed according to different detected objects.

Description

Technical Field

[0001] This invention relates to the fields of software development, testing, and technology, specifically to an intelligent testing device for computer software development of radiation detectors. Background Technology

[0002] Computer software for radiation detectors is typically used to process and analyze the radiation data acquired by the detector. Below are some common computer software programs for radiation detectors:

[0003] RadCal is a widely used radiation detector calibration software that can be used to calibrate various types of radiation detectors, including Geiger counters, scintillation counters, and semiconductor detectors. RadCal can generate calibration curves and calibration reports, and supports multiple data formats such as ASCII and Excel.

[0004] WinRad is a radiation detector data analysis software for the Windows platform. It supports various radiation detectors and their interfaces, such as USB and RS-232. WinRad can display radiation dose rate, count rate, and other data in real time, and supports data logging and export functions.

[0005] GeMPI: GeMPI is a high-performance radiation detector data processing software that can be used to process large amounts of radiation detector data. GeMPI supports parallel computing and multi-core processing, enabling it to quickly process large amounts of detector data and perform data analysis and statistics.

[0006] RadEye is a software specifically designed for radiation detector calibration and monitoring. It can calibrate various types of radiation detectors, including Geiger counters, scintillation counters, and semiconductor detectors. RadEye can generate calibration curves and calibration reports, and supports real-time monitoring and data logging.

[0007] GammaWorks is a professional software for measuring radioactivity. It can be used to calibrate and measure various types of radiation detectors, including Geiger counters, scintillation counters, and semiconductor detectors. GammaWorks supports multiple data formats and data export, and can perform data analysis and statistics.

[0008] After designing the software, software operators conduct software testing to ensure its proper functioning. Software testing is a crucial part of software engineering, and effective testing methods are essential for ensuring software quality. The following are common computer software development testing methods:

[0009] Unit testing: Unit testing refers to testing the smallest testable unit in a software system, such as functions, methods, and classes. Unit testing helps developers find and fix errors in a timely manner during the development process, improving the quality and stability of the software system.

[0010] Integration testing: Integration testing refers to testing the interactions between different modules in a software system to ensure collaboration and compatibility between the modules. Integration testing is usually performed after unit testing is completed.

[0011] System testing: System testing refers to testing the entire software system to verify whether it conforms to the specifications and user requirements. System testing typically includes functional testing, performance testing, security testing, and other aspects.

[0012] However, existing computer software development and testing methods for radiation detectors cannot remotely download software data parameters online. Changing parameters requires importing the detector's software package into the computer, testing it through the computer's testing components, making further modifications, and finally importing it back into the detector's computer software. Therefore, those skilled in the art provide an intelligent testing device for computer software development of radiation detectors to solve the problems mentioned in the background art. Summary of the Invention

[0013] (a) Technical problems to be solved

[0014] To address the shortcomings of existing technologies, this invention provides an intelligent testing device for computer software development of radiation detectors. This intelligent testing device can directly and remotely download computer software data packages of the detector via the internet for testing, enabling remote online modification of data parameters after testing. It has a wide range of applications, allowing the radiation detector to quickly change parameters according to different objects being detected. Furthermore, it integrates multiple testing tools into the testing system and employs two different learning algorithms in the intelligent testing decision-making process, improving the efficiency and accuracy of intelligent testing for computer software development of the radiation detector.

[0015] (II) Technical Solution

[0016] To achieve the above objectives, the present invention is implemented through the following technical solution: an intelligent testing device for computer software development of a radiation detector, comprising a radiation detector, a computer and an Internet communication module, wherein a detector CPU chip and computer software for controlling the radiation detector are fixedly installed inside the radiation detector, and an Internet card is also installed inside the radiation detector for network data connection with the Internet communication module, and the computer is connected to the Internet communication module via Ethernet.

[0017] The above technical solution enables the radiation detector to connect to a computer via an internet communication module. The computer software data parameters of the radiation detector are then downloaded remotely for testing. After the test is completed, the parameters can be modified remotely online.

[0018] Preferably, an intelligent testing system for computer software development of a radiation detector includes a data reading module, an internet connection, and a computer. The data reading module is fixedly connected to a data packet at its front end. The internet connection is connected to the data reading module and the computer. The computer is fixedly connected to a modification module and a testing system at its back end. The testing system is fixedly connected to a feedback module at its back end.

[0019] Preferably, the data packet contains data parameters from the computer software of the radiation detector, mainly including the detector's sensitivity parameters, accuracy parameters, and stability parameters. The data reading module is used to read the data from the computer software data packet. The Internet serves as a connection bridge between the data reading module and the computer, acting as a medium for network data transmission between the two. The testing system is used to test the data parameters in the radiation detector's computer software data packet. The modification module is used to modify and compile the erroneous parameters that occur after the test. The feedback module is used to provide feedback on the data after the test by the testing system.

[0020] The above technical solution involves using a data reading module to read and download computer software data packages, transmitting the data to the computer via the internet, testing the transmitted data parameters through a testing system, and then providing feedback on the tested data. Testers can then modify the feedback data parameters by writing software.

[0021] Preferably, the testing system includes:

[0022] Test plan generation module: Automatically generates test plans based on the software requirements specification and test objectives;

[0023] Test case generation module: Automatically generates test cases based on the test plan and test case library;

[0024] Test execution module: Executes test cases according to the test plan and test cases, and records test results and logs;

[0025] Defect Management Module: Manages defects discovered during the testing process;

[0026] Intelligent Test Decision Module: Based on machine learning and deep learning algorithms, it performs intelligent analysis and decision-making on test results, including anomaly detection, fault diagnosis and performance evaluation.

[0027] Test tool integration module: Integrates multiple test tools into the test system;

[0028] Test data management module: Manages test data, displays the test data, and generates reports.

[0029] Preferably, the test plan generation module includes test scope, test objectives, test methods, test time, and test resource information in the test plan; the test case generation module includes test cases under normal conditions and test cases under abnormal conditions in the test cases; the test execution module records test results and logs including test time, test results, and test data information in the logs; the defect management module includes defect recording, classification, priority, and status information in the defect management; the test tool integration module includes various test tools, including static code analysis tools, automated testing tools, and performance testing tools; and the test data management module generates and displays test data mainly through charts, tables, and text descriptions. The test data includes test case data, test result data, and test report data.

[0030] Preferably, the intelligent testing decision module mainly adopts the following two methods based on machine learning and deep learning algorithms:

[0031] Linear regression algorithm: An algorithm used to establish a linear relationship between input and output variables. It predicts the value of the output variable by finding a best-fit line. During training, the algorithm uses known input and output data to calculate the best-fit line, and then uses this line to predict the unknown output value. The formula for linear regression is:

[0032] y = β0 + β1x1 + β2x2 + ... + β n x n

[0033] Where y is the dependent variable, x1, x2, ..., x n The independent variables are β0, β1, β2, ..., β n It is the regression coefficient;

[0034] The backpropagation algorithm for convolutional neural networks (CNNs) works as follows: Input data is fed into the neural network, and the weighted input values ​​and activation function output values ​​of each layer are calculated sequentially until the output layer is obtained. The output of the neural network is compared with the target value, the loss function is calculated, and the gradient of each parameter is calculated using the chain rule. Starting from the output layer, the backpropagation proceeds layer by layer towards the input layer. Based on the calculated gradients and parameters such as the learning rate, the weights and biases of the neural network are updated using gradient descent. The formula for backpropagation in a convolutional neural network is as follows:

[0035]

[0036] Where C is the loss function, a i,jIt is the pixel value in the i-th row and j-th column of the input feature map, δ m,n It is the label y and the predicted value The difference between them is that f′ is the derivative of the activation function, and h m,p It is the output of the previous layer, w p,n It is the weight of the current layer, b n It is the bias of the current layer;

[0037] Using the above technical solution, the computer software data parameters of the radiation detector are tested through a testing system.

[0038] A smart testing method for computer software development of radiation detectors includes the following steps:

[0039] S1. Data Download and Read

[0040] The network enables remote downloading and reading of computer software development data packages for radiation detectors;

[0041] S2. Define the test objectives and test scope.

[0042] The test objectives and test scope are determined based on the software requirements specification and design documents. The test objectives refer to the functions and characteristics of the software that need to be tested, and the test scope refers to the software modules, functions and scenarios to be tested.

[0043] S3. Develop test plans and test cases.

[0044] Based on the test objectives and test scope, develop a detailed test plan and test cases. The test plan refers to the test tasks and test methods that need to be completed, and the test cases refer to the test steps and test data that need to be executed.

[0045] S4. Execute test cases

[0046] Execute tests according to test cases, and record test results and logs, including test time, test results, and test data information;

[0047] S5. Defect Management

[0048] Manage defects discovered during testing, including defect recording, classification, priority, and status information;

[0049] S6. Intelligent Test Decision

[0050] Based on machine learning and deep learning algorithms, intelligent analysis and decision-making are performed on test results, including anomaly detection, fault diagnosis, and performance evaluation.

[0051] S7. Data Presentation and Report Generation

[0052] Based on the test results and analysis, write test reports and summaries. Test reports include test execution status, test coverage, test results and recommendations. Test summaries include test experiences, lessons learned and improvement plans, presented through charts, tables and text descriptions.

[0053] S8. Parameter Modification

[0054] The computer software parameters of the radiation detector can be directly modified via a remote computer after testing.

[0055] Through the above technical solution, the intelligent testing system integrates multiple testing tools into the testing system, and adopts two different learning algorithms in the intelligent testing decision-making, thereby improving the efficiency and accuracy of intelligent testing for the computer software development of the radiation detector.

[0056] Working Principle: When using this intelligent testing equipment for computer software development of radiation detectors, it remotely downloads and reads the computer software development data package of the radiation detector via a network. Then, based on the software requirements specification and design documents, it determines the test objectives and scope. Based on the test objectives and scope, it formulates detailed test plans and test cases. Next, it executes the tests according to the test cases, records the test results and logs, manages the defects found during the test, and then performs intelligent analysis and decision-making on the test results based on machine learning and deep learning algorithms. After the test is completed, it writes and summarizes the test results and presents them. Finally, it allows direct modification of the computer software parameters of the radiation detector after the test via a remote computer.

[0057] (III) Beneficial Effects

[0058] This invention provides an intelligent testing device for computer software development of radiation detectors.

[0059] It has the following beneficial effects:

[0060] 1. This invention provides an intelligent testing device for computer software development of a radiation detector. The intelligent testing device of this invention can directly and remotely download the computer software data package of the detector via the Internet for testing, realize remote online modification of the data parameters after testing, has a wide range of applications, and enables the radiation detector to quickly change parameters according to different objects being detected.

[0061] 2. This invention provides an intelligent testing device for computer software development of a radiation detector. This intelligent testing system integrates multiple testing tools into the testing system, and employs two different learning algorithms in intelligent testing decision-making, thereby improving the efficiency and accuracy of intelligent testing for computer software development of the radiation detector. Attached Figure Description

[0062] Figure 1 This is a schematic diagram of the intelligent testing device for computer software development of a radiation detector according to the present invention.

[0063] Figure 2 This is a flowchart of the intelligent testing system for computer software development of a radiation detector according to the present invention.

[0064] Figure 3 This is a schematic diagram of the intelligent testing system for computer software development of a radiation detector according to the present invention.

[0065] Figure 4 This is a flowchart of the intelligent testing method for computer software development of radiation detectors according to the present invention.

[0066] Among them, 101 is the radiation detector; 102 is the detector CPU chip; 103 is the detector computer software; 104 is the internet card; 105 is the computer; and 106 is the internet communication module. Detailed Implementation

[0067] To provide a more detailed understanding of the features and technical content of the embodiments of this disclosure, the implementation of the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this disclosure. In the following technical description, for ease of explanation, several details are used to provide a full understanding of the disclosed embodiments. However, one or more embodiments may still be implemented without these details. In other cases, well-known structures and devices may be simplified in their depiction to simplify the drawings.

[0068] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure 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 can be interchanged where appropriate for the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0069] Unless otherwise stated, the term "multiple" means two or more.

[0070] In this embodiment of the disclosure, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0071] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0072] The term "correspondence" can refer to an association or binding relationship. The correspondence between A and B means that there is an association or binding relationship between A and B.

[0073] It should be noted that, unless otherwise specified, the embodiments and features described in the present disclosure can be combined with each other.

[0074] like Figure 1-4 As shown, this embodiment of the invention provides an intelligent testing device for computer software development of a radiation detector, including a radiation detector 101, a computer 105, and an internet communication module 106. The radiation detector 101 is internally equipped with a detector CPU chip 102 and computer software 103 for controlling the radiation detector 101. The radiation detector 101 is also internally equipped with an internet card 104, which is used for network data connection with the internet communication module 106. The computer 105 is connected to the internet communication module 106 via Ethernet.

[0075] The radiation detector 101 is connected to the computer 105 via the Internet communication module 106. The computer software 103 of the radiation detector 101 is downloaded remotely by the computer 105 to test the data parameters. After the test is completed, the data parameters can be modified directly online remotely.

[0076] This disclosure provides an intelligent testing device for computer software development of a radiation detector. The intelligent testing device of the present invention can directly and remotely download the computer software data package of the detector via the Internet for testing, realize remote online modification of the data parameters after testing, has a wide range of applications, and enables the radiation detector to quickly change parameters according to different objects being detected.

[0077] An intelligent testing system for computer software development of a radiation detector includes a data reading module, an internet connection, and a computer. The data reading module is fixedly connected to a data packet at its front end. The internet connection is connected to the data reading module and the computer. The computer is fixedly connected to a modification module and a testing system at its back end. The testing system is fixedly connected to a feedback module at its back end.

[0078] The data packet contains data parameters from the radiation detector's computer software, primarily its sensitivity, accuracy, and stability parameters. The data reading module reads the data from the computer software data packet. The internet serves as the connection bridge between the data reading module and the computer, acting as the medium for network data transmission. The test system is used to test the data parameters in the radiation detector's computer software data packet. The modification module modifies and recompiles any erroneous parameters that occur after testing. The feedback module provides feedback on the data after testing by the test system. The data reading module reads and downloads the computer software data packet, transmits the data to the computer via the internet, tests the transmitted data parameters through the test system, and then provides feedback on the tested data. Testers then write software to modify the feedback data parameters.

[0079] The testing system includes:

[0080] Test plan generation module: Automatically generates test plans based on the software requirements specification and test objectives;

[0081] Test case generation module: Automatically generates test cases based on the test plan and test case library;

[0082] Test execution module: Executes test cases according to the test plan and test cases, and records test results and logs;

[0083] Defect Management Module: Manages defects discovered during the testing process;

[0084] Intelligent Test Decision Module: Based on machine learning and deep learning algorithms, it performs intelligent analysis and decision-making on test results, including anomaly detection, fault diagnosis and performance evaluation.

[0085] Test tool integration module: Integrates multiple test tools into the test system;

[0086] Test data management module: Manages test data, displays the test data, and generates reports;

[0087] The test plan generation module includes test scope, test objectives, test methods, test time, and test resource information. The test case generation module includes test cases for normal and abnormal situations. The test execution module records test results and logs, including test time, test results, and test data information. The defect management module includes defect recording, classification, priority, and status information. The test tool integration module includes various test tools, including static code analysis tools, automated testing tools, and performance testing tools. The test data management module generates and displays test data, mainly through charts, tables, and text descriptions. Test data includes test case data, test result data, and test report data.

[0088] The intelligent testing decision-making module primarily employs the following two methods based on machine learning and deep learning algorithms:

[0089] Linear regression algorithm: An algorithm used to establish a linear relationship between input and output variables. It predicts the value of the output variable by finding a best-fit line. During training, the algorithm uses known input and output data to calculate the best-fit line, and then uses this line to predict the unknown output value. The formula for linear regression is:

[0090] y = β0 + β1x1 + β2x2 + ... + β n x n

[0091] Where y is the dependent variable, x1, x2, ..., x n The independent variables are β0, β1, β2, ..., β n It is the regression coefficient;

[0092] The backpropagation algorithm for convolutional neural networks (CNNs) works as follows: Input data is fed into the neural network, and the weighted input values ​​and activation function output values ​​of each layer are calculated sequentially until the output layer is obtained. The output of the neural network is compared with the target value, the loss function is calculated, and the gradient of each parameter is calculated using the chain rule. Starting from the output layer, the backpropagation proceeds layer by layer towards the input layer. Based on the calculated gradients and parameters such as the learning rate, the weights and biases of the neural network are updated using gradient descent. The formula for backpropagation in a convolutional neural network is as follows:

[0093]

[0094] Where C is the loss function, a i,j It is the pixel value in the i-th row and j-th column of the input feature map, δ m,n It is the label y and the predicted value The difference between them is that f′ is the derivative of the activation function, and h m,p It is the output of the previous layer, w p,nIt is the weight of the current layer, b n It is the bias of the current layer, and the computer software data parameters of the radiation detector are tested through the test system.

[0095] A smart testing method for computer software development of radiation detectors includes the following steps:

[0096] S1. Data Download and Read

[0097] The network enables remote downloading and reading of computer software development data packages for radiation detectors;

[0098] S2. Define the test objectives and test scope.

[0099] The test objectives and test scope are determined based on the software requirements specification and design documents. The test objectives refer to the functions and characteristics of the software that need to be tested, and the test scope refers to the software modules, functions and scenarios to be tested.

[0100] S3. Develop test plans and test cases.

[0101] Based on the test objectives and test scope, develop a detailed test plan and test cases. The test plan refers to the test tasks and test methods that need to be completed, and the test cases refer to the test steps and test data that need to be executed.

[0102] S4. Execute test cases

[0103] Execute tests according to test cases, and record test results and logs, including test time, test results, and test data information;

[0104] S5. Defect Management

[0105] Manage defects discovered during testing, including defect recording, classification, priority, and status information;

[0106] S6. Intelligent Test Decision

[0107] Based on machine learning and deep learning algorithms, intelligent analysis and decision-making are performed on test results, including anomaly detection, fault diagnosis, and performance evaluation.

[0108] S7. Data Presentation and Report Generation

[0109] Based on the test results and analysis, write test reports and summaries. Test reports include test execution status, test coverage, test results and recommendations. Test summaries include test experiences, lessons learned and improvement plans, presented through charts, tables and text descriptions.

[0110] S8. Parameter Modification

[0111] The computer software parameters of the radiation detector can be directly modified via a remote computer after testing.

[0112] This disclosure provides an intelligent testing system and method for computer software development of a radiation detector. The intelligent testing system integrates multiple testing tools into the testing system and employs two different learning algorithms in the intelligent testing decision-making process, thereby improving the efficiency and accuracy of intelligent testing for computer software development of the radiation detector.

[0113] The foregoing description and accompanying drawings fully illustrate embodiments of the present disclosure to enable those skilled in the art to practice them. Other embodiments may include structural and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and features of some embodiments may be included or substituted for parts and features of other embodiments. Embodiments of the present disclosure are not limited to the structures described above and shown in the accompanying drawings, and various modifications and changes may be made without departing from its scope. The scope of the present disclosure is limited only by the appended claims.

Claims

1. An intelligent testing device for computer software development of a radiation detector, comprising a radiation detector (101), a computer (105), and an internet communication module (106), characterized in that: The radiation detector (101) is internally equipped with a detector CPU chip (102) and computer software (103) for controlling the radiation detector (101). The radiation detector (101) is also internally equipped with an Internet card (104), which is used to connect to the Internet communication module (106) for network data connection. The computer (105) is connected to the Internet communication module (106) via Ethernet.

2. The intelligent testing system for computer software development of a radiation detector according to claim 1, characterized in that: The system includes a data reading module, an internet connection, and a computer. The data reading module is fixedly connected to a data packet at its front end. The internet connection is connected to the data reading module and the computer. The computer is fixedly connected to a modification module and a testing system at its back end. The testing system is fixedly connected to a feedback module at its back end.

3. The intelligent testing system for computer software development of a radiation detector according to claim 2, characterized in that: The data packet contains data parameters from the computer software of the radiation detector, mainly including the detector's sensitivity, accuracy, and stability parameters. The data reading module is used to read the data from the computer software data packet. The Internet serves as a connection bridge between the data reading module and the computer, acting as a medium for network data transmission between the two. The testing system is used to test the data parameters in the radiation detector's computer software data packet. The modification module is used to modify and compile the erroneous parameters that occur after the test. The feedback module is used to provide feedback on the data after the test by the testing system.

4. The intelligent testing system for computer software development of a radiation detector according to claim 2, characterized in that: The testing system includes: Test plan generation module: Automatically generates test plans based on the software requirements specification and test objectives; Test case generation module: Automatically generates test cases based on the test plan and test case library; Test execution module: Executes test cases according to the test plan and test cases, and records test results and logs; Defect Management Module: Manages defects discovered during the testing process; Intelligent Test Decision Module: Based on machine learning and deep learning algorithms, it performs intelligent analysis and decision-making on test results, including anomaly detection, fault diagnosis and performance evaluation. Test tool integration module: Integrates multiple test tools into the test system; Test data management module: Manages test data, displays the test data, and generates reports.

5. The intelligent testing system for computer software development of a radiation detector according to claim 4, characterized in that: The test plan generation module includes test scope, test objectives, test methods, test time, and test resource information. The test case generation module includes test cases for normal and abnormal situations. The test execution module records test results and logs, including test time, test results, and test data information. The defect management module includes defect recording, classification, priority, and status information. The test tool integration module includes various test tools, such as static code analysis tools, automated testing tools, and performance testing tools. The test data management module generates and displays test data mainly through charts, tables, and text descriptions. Test data includes test case data, test result data, and test report data.

6. The intelligent testing system for computer software development of a radiation detector according to claim 4, characterized in that: The intelligent testing decision-making module, based on machine learning and deep learning algorithms, mainly adopts the following two methods: Linear regression algorithm: An algorithm used to establish a linear relationship between input and output variables. It predicts the value of the output variable by finding a best-fit line. During training, the algorithm uses known input and output data to calculate the best-fit line, and then uses this line to predict the unknown output value. The formula for linear regression is: y=β0+β1x1+β2x2+…+β n x n Where y is the dependent variable, x1, x2, ..., x n The independent variables are β0, β1, β2, ..., β n It is the regression coefficient; The backpropagation algorithm for convolutional neural networks (CNNs) works as follows: Input data is fed into the neural network, and the weighted input values ​​and activation function output values ​​of each layer are calculated sequentially until the output layer is obtained. The output of the neural network is compared with the target value, the loss function is calculated, and the gradient of each parameter is calculated using the chain rule. Starting from the output layer, the backpropagation proceeds layer by layer towards the input layer. Based on the calculated gradients and parameters such as the learning rate, the weights and biases of the neural network are updated using gradient descent. The formula for backpropagation in a convolutional neural network is as follows: Where C is the loss function, a i,j It is the pixel value in the i-th row and j-th column of the input feature map, δ m,n It is the label y and the predicted value The difference between them is that f′ is the derivative of the activation function, and h m,p It is the output of the previous layer, w p,n It is the weight of the current layer, b n It is the bias of the current layer.

7. The intelligent testing method for computer software development of a radiation detector according to claim 1, characterized in that: Includes the following steps: S1. Data Download and Read The network enables remote downloading and reading of computer software development data packages for radiation detectors; S2. Define the test objectives and test scope. The test objectives and test scope are determined based on the software requirements specification and design documents. The test objectives refer to the functions and characteristics of the software that need to be tested, and the test scope refers to the software modules, functions and scenarios to be tested. S3. Develop test plans and test cases. Based on the test objectives and test scope, develop a detailed test plan and test cases. The test plan refers to the test tasks and test methods that need to be completed, and the test cases refer to the test steps and test data that need to be executed. S4. Execute test cases Execute tests according to test cases, and record test results and logs, including test time, test results, and test data information; S5. Defect Management Manage defects discovered during testing, including defect recording, classification, priority, and status information; S6. Intelligent Test Decision Based on machine learning and deep learning algorithms, intelligent analysis and decision-making are performed on test results, including anomaly detection, fault diagnosis, and performance evaluation. S7. Data Presentation and Report Generation Based on the test results and analysis, write test reports and summaries. Test reports include test execution status, test coverage, test results and recommendations. Test summaries include test experiences, lessons learned and improvement plans, presented through charts, tables and text descriptions. S8. Parameter Modification The computer software parameters of the radiation detector can be directly modified via a remote computer after testing.

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