Test tool based on ICD configuration generation

Abstract

The invention discloses a test tool based on ICD configuration generation, relates to the technical field of ICD configuration testing, and aims to solve the problem that the test process is not perfect when ICD configuration is used for tool testing. By utilizing monitoring equipment such as a sensor and a log recorder, test data can be accurately acquired, errors caused by human intervention are reduced, and analyzed real-time data are converted and presented in an image-text form, so that a test result is more intuitive and easy to understand, and the test efficiency is improved. An alarm mechanism set during real-time data analysis can automatically trigger an alarm when an abnormality is detected or a key index exceeds a preset threshold value, so that a tester is reminded to pay attention to potential problems in time, the hardware cost of a test tool is reduced by reducing dependence on physical equipment, and the test efficiency is improved. Test data and logs are automatically collected, the comprehensiveness and accuracy of data collection are improved, and test evaluation is more objective and scientific through analysis of test results and comparison with preset test tool results.

Description

Technical Field

[0001] The present invention relates to the technical field of ICD configuration testing, and in particular to generating a testing tool based on ICD configuration. Background Art

[0002] ICD configuration testing refers to the process of verifying and testing the interfaces defined in the interface control document.

[0003] The patent application with announcement number CN106980560B discloses a multi-bus protocol test and simulation method and platform based on ICD database. It mainly improves the versatility and maintainability of bus data protocol configuration and test simulation through unified configuration management of multi-bus protocol test and simulation. It can be widely used in simulation and testing of multi-bus protocol configuration. It is easy to manage, edit and distribute. It simplifies and transforms the existing multi-bus test simulation platform on an industrial control computer, making it suitable for bus test simulation tasks of on-board equipment. It can not only reduce the cost of test tasks, but also greatly improve the convenience of bus test tasks and ensure the successful completion of flight missions. This solution can be widely used in bus testing and simulation work in the fields of communications, manned spaceflight, etc. Although the above patent solves the problem of ICD configuration, the following problems still exist in actual operation: 1. The basic data of each ICD configuration was not further analyzed and designed, resulting in inaccurate basic ICD data.

[0004] 2. The test process of the test tooling was not effectively simulated and trained, which resulted in abnormalities in the tooling process when the tooling test was directly carried out.

[0005] 3. The testing process of the ICD test tooling was not monitored in real time, resulting in the inability to promptly detect any abnormalities that occurred during the tooling process. Summary of the invention

[0006] The purpose of the present invention is to provide a test tooling generated based on ICD configuration. By utilizing monitoring equipment such as sensors and log recorders, test data can be accurately collected, errors caused by human intervention are reduced, and the analyzed real-time data is converted and presented in the form of graphics and text, so that the test results are more intuitive and easy to understand. The alarm mechanism set during real-time data analysis can automatically trigger an alarm when an abnormality is detected or a key indicator exceeds a preset threshold, thereby promptly reminding the tester to pay attention to potential problems. By reducing the dependence on physical equipment, the hardware cost of the test tooling is reduced. The automatic collection of test data and logs improves the comprehensiveness and accuracy of data collection. The analysis of the test results and the comparison with the preset test tooling results make the test evaluation more objective and scientific, which can solve the problems in the prior art.

[0007] To achieve the above object, the present invention provides the following technical solutions: Generate test fixtures based on ICD configuration, including: ICD reading and parsing unit, used for: Read the ICD file from the database, extract and parse the interface information in the ICD file, and obtain the ICD basic data after the parsing is completed; Test fixture design unit for: Design the structure and function of the test tooling according to the basic data of ICD, and obtain the ICD tooling design data after the design is completed; Test fixture generation unit, used to: Generate executable test jigs according to ICD jig test data, and obtain ICD jig test data after the test jigs are generated; The test environment builds a simulation unit for: The test environment is built according to the test requirements, a virtual model is constructed for the built test environment, and a simulated tooling test is performed based on the ICD tooling test data and the test environment virtual model, and the simulated test results are evaluated. After the evaluation is completed, the ICD test tooling implementation data is obtained; Test execution monitoring unit, used to: The test is implemented according to the ICD test tooling implementation data, and the test implementation process is monitored in real time. Finally, the test implementation process is evaluated and the evaluation results are transmitted to the display terminal.

[0008] Preferably, the ICD reading and parsing unit is further used for: Read the interface information in the ICD file, which includes interface identifier, data format, communication protocol, message type, message ID, data field, timing information, error handling, status information, security requirements, physical layer information, constraints and version information; After the interface information is read, it is extracted to the memory for source file storage; Parse the stored interface information source file, the parsing is divided into file format parsing and file content parsing; File format parsing includes parsing the XML or JSON format files in the interface information using the XML parsing library; parsing the CSV format files in the interface information using the CSV parsing library; and parsing the text format files in the interface information using regular expressions. File content parsing is to parse the interface information using a syntax analyzer, and obtain the key feature data of the large hole interface information after parsing; After all interface information is parsed, the basic ICD data is obtained.

[0009] Preferably, the test tool design unit is also used for: Design the test fixture structure in the ICD basic data, which includes hardware design, software design and safety design; Hardware design is to design the physical connection part of the interface according to the physical layer information. The physical connection part includes connectors, cables and adapters. After the physical connection part equipment is completed, the hardware components are selected. The hardware components include processors, memory and communication interface cards. The circuit is designed according to the physical connection part and hardware components. The software device is to design the timing logic of the software according to the timing information, and implement the software logic of error handling and status information according to the timing logic; The security design is to perform integrated encryption and authentication based on the hardware design and software design, and finally obtain the design data of the test tooling structure design in the ICD basic data.

[0010] Preferably, the test tool design unit is also used for: Design the test tooling functions in the ICD basic data; The test tooling function is designed to detect functional abnormalities of the basic ICD data; When there is an abnormality in the function abnormality detection, the device is replaced or repaired. When there is no abnormality in the function abnormality monitoring, the device of each interface information is matched with the design data of the test tooling structure design; After the matching is completed, the design data of the test tooling function design is obtained; The design data of the matched test tooling functional design is marked as ICD tooling design data.

[0011] Preferably, the test tool generating unit is further used for: Obtain test fixture generation rules from the database, and confirm the design specifications of hardware and software according to the test fixture generation rules; Integrate the hardware and software that have been confirmed to meet the design specifications, test the functions and performance after integration, and make adjustments based on the test results of functions and performance; At the same time, prepare the tools, materials and staff required for the test tooling according to the test tooling generation rules; A test plan is generated based on the integrated hardware and software, as well as the tools, materials and staff required for the test tooling, and the ICD tooling test data is obtained after the generation.

[0012] Preferably, the test environment construction simulation unit is also used for: Layout the test equipment according to the test site and connect the test equipment according to the ICD tooling test data. At the same time, control the environment according to the test requirements, including temperature and humidity; After the test equipment is connected, configure the network topology, IP address, subnet mask and routing information, and finally complete the test environment construction; The constructed test environment is simulated and modeled, wherein a consistent virtual device model is created according to the function and structure of the test device; Map key data in ICD tooling test data to the virtual equipment model for model parameterization; After the model parameterization is completed, a virtual model of the test environment is obtained in which the virtual model construction is completed.

[0013] Preferably, the test environment construction simulation unit is also used for: Use the virtual model of the test environment to simulate tooling testing; Before conducting the simulated tooling test, start and load the constructed virtual model of the test environment, define and configure specific test cases according to the test requirements and objectives in the ICD tooling test data, and execute the configured test cases in the virtual model; During the test process, collect and record all relevant test data and logs, including network traffic, message delivery, device status changes and errors; Analyze the test results based on the collected data logs, compare the analysis results with the preset test fixture results, and evaluate and judge the simulated fixture test of the test environment virtual model based on the comparison results; The evaluation results are divided into qualified, qualified and unqualified; The qualified and up-to-standard assessment results are marked as ICD test tool implementation data.

[0014] Preferably, the test execution monitoring unit is further used for: According to the ICD test fixture implementation data, the test fixture configured by ICD is tested and implemented; During the test implementation, the monitoring platform is used to capture the test data generated during the actual test process in real time; During the test, sensors, loggers and other monitoring equipment are used to collect test data in real time; Use data analysis tools or algorithms to process and interpret the collected data; And convert and present the key indicator data in the analyzed real-time data in the form of graphics and text; At the same time, when conducting real-time data analysis, an alarm mechanism is set up to automatically trigger an alarm when an anomaly is detected or a key indicator exceeds a preset threshold; Finally, the graphic data of alarm data and key indicator data are transmitted to the display terminal for display.

[0015] Preferably, monitoring the operating state of the sensor during the process of the sensor collecting test data in real time includes: During the process of real-time data collection by the sensor, the data collection response time of each single data collection by the sensor is monitored in real time; Comparing the data collection response time with its corresponding data collection response time threshold; When the data acquisition response time exceeds a preset data acquisition response time threshold, the sensor corresponding to the data acquisition response time exceeding the preset data acquisition response time threshold is used as a target sensor; Extracting sensors whose correlation coefficients with the target sensor exceed a preset correlation coefficient threshold as object sensors; Extracting the current data collection response time of the object sensor; Using the current data collection response time of all object sensors to obtain the first collection operation stability index coefficient; The first acquisition operation stability index coefficient is obtained by the following formula: Among them, K 01 represents the first acquisition operation stability index coefficient; m represents the total number of object sensors; T i represents the current data collection response time of the i-th object sensor; T y Indicates the preset data collection response time threshold; T m Indicates the data collection response time corresponding to the target sensor; T g Indicates the current data collection response time of the object sensor corresponding to the maximum value of the correlation coefficient; T r Indicates the current data collection response time of the object sensor corresponding to the minimum value of the correlation coefficient; s i represents the correlation coefficient between the i-th object sensor and the target sensor; The first acquisition operation stability index coefficient is used to determine whether to issue a sensor acquisition abnormality alarm.

[0016] Preferably, using the first acquisition operation stability index coefficient to determine whether to issue a sensor acquisition abnormality alarm includes: Comparing the first acquisition operation stability index coefficient with a preset first index coefficient threshold; When the first acquisition operation stability index coefficient exceeds a preset first index coefficient threshold, it is determined that there is no need to issue a sensor acquisition abnormality alarm; When the first acquisition operation stability index coefficient does not exceed a preset first index coefficient threshold, retrieving a correlation coefficient between the target sensor and the object sensor; Obtaining a second acquisition operation stability index coefficient using a correlation coefficient between the target sensor and the object sensor; The second acquisition operation stability index coefficient is obtained by the following formula: Among them, K 02 Represents the second acquisition operation stability index coefficient; s b represents the standard deviation of the correlation coefficient between m object sensors and the target sensor; T b represents the standard deviation of the current data collection response time of m object sensors; s max Indicates the correlation coefficient corresponding to the object sensor with the maximum response time of the current data collection; s min The correlation coefficient corresponding to the object sensor representing the minimum response time of the current data collection; P 01 The sensor sensitivity of the object sensor indicating the maximum response time of the current data collection; P 02 The sensor sensitivity of the object sensor representing the minimum response time of the current data collection; The second acquisition operation stability index coefficient and the first acquisition operation stability index coefficient are used to comprehensively determine whether to issue a sensor acquisition abnormality alarm.

[0017] Preferably, the second acquisition operation stability index coefficient and the first acquisition operation stability index coefficient are used to comprehensively determine whether to issue a sensor acquisition abnormality alarm, including: Retrieve the first acquisition operation stability index coefficient; Retrieve the second acquisition operation stability index coefficient; Retrieving the historical data collection operation record corresponding to the target sensor; Retrieving the data collection response time corresponding to each data collection from the historical data collection operation record corresponding to the target sensor; Obtain a comprehensive index coefficient by combining the data collection response time corresponding to each data collection with the first collection operation stability index coefficient and the second collection operation stability index coefficient; The comprehensive index coefficient is obtained by the following formula: Among them, K z represents the comprehensive index coefficient; n represents the number of data collection times in the historical data collection operation record of the target sensor; T ci represents the data collection response time corresponding to the i-th data collection of the target sensor; T m Indicates the data collection response time corresponding to the target sensor; K 01Indicates the first acquisition operation stability index coefficient; K 02 represents the second acquisition operation stability index coefficient; Comparing the comprehensive index coefficient with a preset second index coefficient threshold; When the comprehensive index coefficient is lower than a preset second index coefficient threshold, it is determined that a sensor acquisition abnormality alarm is required.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. The test fixture generated based on ICD configuration provided by the present invention has hardware design, software design and safety design clearly divided into different modules. This modular design makes the structure of the test fixture clearer and easier to modify and upgrade. When a module needs to be updated or replaced, it will not affect other modules, thereby improving the flexibility and maintainability of the entire test fixture.

[0019] 2. The test tooling generated based on ICD configuration provided by the present invention reduces the dependence on physical equipment and the hardware cost of the test tooling. The automated collection of test data and logs improves the comprehensiveness and accuracy of data collection. The analysis of test results and comparison with preset test tooling results make the test evaluation more objective and scientific.

[0020] 3. The test tooling generated based on ICD configuration provided by the present invention can accurately collect test data by using monitoring equipment such as sensors and log recorders, reducing errors caused by human intervention, and converting and presenting the analyzed real-time data in the form of graphics and text, making the test results more intuitive and easy to understand. The alarm mechanism set during real-time data analysis can automatically trigger an alarm when an anomaly is detected or a key indicator exceeds a preset threshold, thereby promptly reminding testers to pay attention to potential problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic diagram of a test fixture unit for configuring an ICD of the present invention; Figure 2 It is a schematic diagram of the ICD configuration test tooling process of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] In order to solve the problem that the basic data of each ICD configuration is not further analyzed and designed in the prior art, resulting in inaccurate basic data of ICD, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions: Generate test fixtures based on ICD configuration, including: ICD reading and parsing unit, used for: Read the ICD file from the database, extract and parse the interface information in the ICD file, and obtain the ICD basic data after the parsing is completed; Test fixture design unit for: Design the structure and function of the test tooling according to the basic data of ICD, and obtain the ICD tooling design data after the design is completed; Test fixture generation unit, used to: Generate executable test jigs according to ICD jig test data, and obtain ICD jig test data after the test jigs are generated; The test environment builds a simulation unit for: The test environment is built according to the test requirements, a virtual model is constructed for the built test environment, and a simulated tooling test is performed based on the ICD tooling test data and the test environment virtual model, and the simulated test results are evaluated. After the evaluation is completed, the ICD test tooling implementation data is obtained; Test execution monitoring unit, used to: Carry out the test according to the ICD test fixture implementation data, monitor the test implementation process in real time, and finally evaluate the test implementation process and transmit the evaluation results to the display terminal; Specifically, the integrity and consistency of ICD basic data are ensured through the ICD reading and parsing unit. Accurate interface information is the basis for ensuring that the test fixture can correctly simulate and test the airborne electronic system. The test fixture design unit matches the equipment of each interface information with the design data of the test fixture structure design, which can further verify the correctness and completeness of the test fixture. The test fixture generation unit can adopt a modular approach to facilitate rapid adjustment according to different test requirements. A comprehensive test plan can ensure that the test covers all key functions and performance indicators. The analysis of the test results by the test environment simulation unit and the comparison with the preset test fixture results make the test evaluation more objective and scientific. The precise data collection and analysis by the test execution monitoring unit can ensure the accuracy of the test results and improve the test quality.

[0024] ICD reading and parsing unit is also used for: Read the interface information in the ICD file, which includes interface identifier, data format, communication protocol, message type, message ID, data field, timing information, error handling, status information, security requirements, physical layer information, constraints and version information; After the interface information is read, it is extracted to the memory for source file storage; Parse the stored interface information source file, the parsing is divided into file format parsing and file content parsing; File format parsing includes parsing the XML or JSON format files in the interface information using the XML parsing library; parsing the CSV format files in the interface information using the CSV parsing library; and parsing the text format files in the interface information using regular expressions. File content parsing is to parse the interface information using a syntax analyzer, and obtain the key feature data of the large hole interface information after parsing; After all interface information is parsed, the basic ICD data is obtained.

[0025] Specifically, the interface information in the ICD file is read in an automated manner and extracted to the memory for source file storage, which significantly improves the efficiency of data processing. The automated process reduces human intervention and reduces the risk of inaccurate data due to human errors. For interface information in different file formats, special parsing libraries are used for parsing, such as XML parsing library, CSV parsing library, and regular expressions, etc., to ensure the accuracy and efficiency of the parsing. File content parsing uses a syntax analyzer to further extract key feature data of the interface information, improves the readability and usability of the data, and covers all aspects of the interface information, including interface identifiers, data formats, communication protocols, etc., to ensure the integrity and consistency of the basic ICD data. Accurate interface information is the basis for ensuring that the test tooling can correctly simulate and test the airborne electronic system.

[0026] The test fixture design unit is also used for: Design the test fixture structure in the ICD basic data, which includes hardware design, software design and safety design; Hardware design is to design the physical connection part of the interface according to the physical layer information. The physical connection part includes connectors, cables and adapters. After the physical connection part equipment is completed, the hardware components are selected. The hardware components include processors, memory and communication interface cards. The circuit is designed according to the physical connection part and hardware components. The software device is to design the timing logic of the software according to the timing information, and implement the software logic of error handling and status information according to the timing logic; The security design is to perform integrated encryption and authentication based on the hardware design and software design, and finally obtain the design data of the test tooling structure design in the ICD basic data.

[0027] Design the test tooling functions in the ICD basic data; The test tooling function is designed to detect functional abnormalities of the basic ICD data; When there is an abnormality in the function abnormality detection, the device is replaced or repaired. When there is no abnormality in the function abnormality monitoring, the device of each interface information is matched with the design data of the test tooling structure design; After the matching is completed, the design data of the test tooling function design is obtained; The design data of the matched test tooling functional design is marked as ICD tooling design data.

[0028] Specifically, hardware design, software design and security design are clearly divided into different modules. This modular design makes the structure of the test fixture clearer and easier to modify and upgrade. When a module needs to be updated or replaced, it will not affect other modules, thereby improving the flexibility and maintainability of the entire test fixture. The hardware design part considers the interface connection of physical layer information in detail, including connectors, cables and adapters, etc., to ensure that the test fixture can be compatible with various types of devices. This compatibility enables the test fixture to be applicable to a wider range of test scenarios and improves its practical value. The software design part designs the timing logic of the software based on the timing information and implements the software logic of error handling and status information. This rigorous software design ensures that the test fixture can accurately and reliably capture and process various data when performing test tasks, improving the accuracy and stability of the test. The security design part ensures the data security and communication security of the test fixture by integrating encryption and authentication mechanisms. This security design is of great significance for protecting sensitive information and preventing malicious attacks, especially in test scenarios involving important systems or equipment. The functional design part of the test fixture includes a functional anomaly detection mechanism, which can detect and report abnormal situations in a timely manner during the test process. At the same time, when an abnormality is detected, it can also guide the user to perform replacement or repair operations to ensure the continued availability of the test fixture. In addition, by matching the device of each interface information with the design data of the test fixture structure design, the correctness and integrity of the test fixture can be further verified.

[0029] In order to solve the problem that there is no effective simulation test training for the test process of the test tool in the prior art, which causes the tool process to be abnormal when the tool test is directly performed, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions: The test fixture generation unit is also used to: Obtain test fixture generation rules from the database, and confirm the design specifications of hardware and software according to the test fixture generation rules; Integrate the hardware and software that have been confirmed to meet the design specifications, test the functions and performance after integration, and make adjustments based on the test results of functions and performance; At the same time, prepare the tools, materials and staff required for the test tooling according to the test tooling generation rules; A test plan is generated based on the integrated hardware and software, as well as the tools, materials and staff required for the test tooling, and the ICD tooling test data is obtained after the generation.

[0030] Specifically, by obtaining the test tooling generation rules from the database, the test tooling can be automatically generated, which greatly improves the generation efficiency. The design specifications of the hardware and software are confirmed according to the test tooling generation rules to ensure the precise match between the test tooling and the equipment to be tested. The hardware and software with confirmed design specifications are integrated to form a complete test system. The integrated system can more conveniently perform functional and performance testing and improve test efficiency. The design of the test tooling can be modular, which is convenient for rapid adjustment according to different test requirements. A comprehensive test plan can ensure that the test covers all key functions and performance indicators and improve the comprehensiveness of the test. The required tools, materials and staff are prepared according to the test tooling generation rules to ensure the smooth progress of the test. By recording various data and parameters during the test process, the test results can be traceable.

[0031] The test environment builds a simulation unit and is also used for: Layout the test equipment according to the test site and connect the test equipment according to the ICD tooling test data. At the same time, control the environment according to the test requirements, including temperature and humidity; After the test equipment is connected, configure the network topology, IP address, subnet mask and routing information, and finally complete the test environment construction; The constructed test environment is simulated and modeled, wherein a consistent virtual device model is created according to the function and structure of the test device; Map key data in ICD tooling test data to the virtual equipment model for model parameterization; After the model parameterization is completed, a virtual model of the test environment is obtained in which the virtual model construction is completed.

[0032] Use the virtual model of the test environment to simulate tooling testing; Before conducting the simulated tooling test, start and load the constructed virtual model of the test environment, define and configure specific test cases according to the test requirements and objectives in the ICD tooling test data, and execute the configured test cases in the virtual model; During the test process, collect and record all relevant test data and logs, including network traffic, message delivery, device status changes and errors; Analyze the test results based on the collected data logs, compare the analysis results with the preset test fixture results, and evaluate and judge the simulated fixture test of the test environment virtual model based on the comparison results; The evaluation results are divided into qualified, qualified and unqualified; The qualified and up-to-standard assessment results are marked as ICD test tool implementation data.

[0033] Specifically, by pre-laying out the test equipment according to the test site and connecting the equipment according to the ICD tooling test data, the efficiency of the test preparation phase can be significantly improved. The use of virtual models allows testing without the need for actual physical equipment, saving a lot of time and resources. Environmental control (including temperature and humidity) ensures the consistency of test conditions, thereby improving the accuracy of test results. The configuration flexibility of network topology, IP address, subnet mask and routing information enables the test environment to adapt to different test requirements. Virtual models and simulation tooling tests allow repeated testing under the same conditions to verify the stability and consistency of test results. By reducing the reliance on physical equipment, the hardware cost of test tooling is reduced. The automated collection of test data and logs (such as network traffic, messaging, device status changes and errors) improves the comprehensiveness and accuracy of data collection. The analysis of test results and comparison with the preset test tooling results make the test evaluation more objective and scientific. The definition and configuration of test cases can be easily completed in a virtual environment, reducing the possibility of manual intervention and errors.

[0034] In order to solve the problem in the prior art that the test process of the ICD test fixture is not monitored in real time, resulting in the problem that abnormalities in the fixture process cannot be discovered in time, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions: The test execution monitoring unit is also used to: According to the ICD test fixture implementation data, the test fixture configured by ICD is tested and implemented; During the test implementation, the monitoring platform is used to capture the test data generated during the actual test process in real time; During the test, sensors, loggers and other monitoring equipment are used to collect test data in real time; Use data analysis tools or algorithms to process and interpret the collected data; And convert and present the key indicator data in the analyzed real-time data in the form of graphics and text; At the same time, when conducting real-time data analysis, an alarm mechanism is set up to automatically trigger an alarm when an anomaly is detected or a key indicator exceeds a preset threshold; Finally, the graphic data of alarm data and key indicator data are transmitted to the display terminal for display.

[0035] Specifically, through real-time monitoring platforms and sensors and other equipment, data during the test process can be captured and recorded in real time, thereby ensuring the efficiency and real-time nature of the test. Using sensors, log recorders and other monitoring equipment, test data can be accurately collected, reducing errors caused by human intervention. The analyzed real-time data can be converted and presented in the form of graphics and text, making the test results more intuitive and easy to understand. The alarm mechanism set during real-time data analysis can automatically trigger an alarm when an anomaly is detected or key indicators exceed the preset threshold, thereby promptly reminding testers to pay attention to potential problems. Through a unified monitoring platform and data analysis tools, the standardization and normalization of the test process can be ensured. Real-time data analysis and visual presentation help testers quickly locate problems, thereby improving test efficiency. At the same time, through precise data collection and analysis, the accuracy of test results can be ensured and the test quality can be improved.

[0036] Specifically, monitoring the operating status of the sensor during the real-time collection of test data by the sensor includes: During the process of real-time data collection by the sensor, the data collection response time of each single data collection by the sensor is monitored in real time; Comparing the data collection response time with its corresponding data collection response time threshold; When the data acquisition response time exceeds a preset data acquisition response time threshold, the sensor corresponding to the data acquisition response time exceeding the preset data acquisition response time threshold is used as a target sensor; Extracting sensors whose correlation coefficients with the target sensor exceed a preset correlation coefficient threshold as object sensors; Extracting the current data collection response time of the object sensor; Using the current data collection response time of all object sensors to obtain the first collection operation stability index coefficient; The first acquisition operation stability index coefficient is obtained by the following formula: Among them, K 01 represents the first acquisition operation stability index coefficient; m represents the total number of object sensors; T i represents the current data collection response time of the i-th object sensor; T y Indicates the preset data collection response time threshold; T mIndicates the data collection response time corresponding to the target sensor; T g Indicates the current data collection response time of the object sensor corresponding to the maximum value of the correlation coefficient; T r Indicates the current data collection response time of the object sensor corresponding to the minimum value of the correlation coefficient; s i represents the correlation coefficient between the i-th object sensor and the target sensor; The first acquisition operation stability index coefficient is used to determine whether to issue a sensor acquisition abnormality alarm.

[0037] The technical effect of the above technical solution is: by monitoring the response time of each data collection in real time during the process of real-time data collection by the sensor, and comparing it with the preset threshold, the technical solution can quickly identify the sensor with abnormal response time (i.e., the target sensor). This real-time monitoring and rapid response mechanism helps to timely discover and solve problems in sensor operation, thereby ensuring the accuracy and continuity of data collection. When a sensor whose data collection response time exceeds the threshold is found, the technical solution further extracts the sensor whose correlation coefficient with the target sensor exceeds the preset threshold as the object sensor. This method of accurately locating abnormal sensors helps to narrow the scope of the problem and improve the efficiency of troubleshooting and solving problems. By calculating the first acquisition operation stability index coefficient, the technical solution comprehensively considers factors such as the current data collection response time of multiple object sensors, the data collection response time of the target sensor, and the response time of the object sensor corresponding to the maximum and minimum values ​​of the correlation coefficient. This comprehensive evaluation method can more comprehensively reflect the operating stability of the sensor and improve the accuracy of abnormal judgment. The first acquisition operation stability index coefficient is used to determine whether to perform a sensor acquisition abnormality alarm, thereby avoiding the false alarm caused by the abnormality of a single factor. This optimized alarm mechanism helps reduce unnecessary interference and misoperation, and improves the reliability and stability of the system. This technical solution provides strong support for system maintenance personnel by real-time monitoring and accurate positioning of abnormal sensors, as well as comprehensive stability assessment. Maintenance personnel can quickly locate problems and take corresponding measures based on the abnormal information and stability indicators provided, thereby improving maintenance efficiency. By real-time monitoring of sensor operating status and taking corresponding abnormal handling measures, this technical solution can promptly discover and solve potential problems, thereby enhancing the robustness and reliability of the system. This helps ensure that the system can operate stably in various complex environments and improve the accuracy and reliability of data collection.

[0038] In summary, this technical solution achieves efficient monitoring and processing of sensor acquisition anomalies through real-time monitoring of sensor operating status, precise positioning of abnormal sensors, comprehensive evaluation of stability, and optimization of alarm mechanisms, thereby improving the stability and reliability of the system.

[0039] Specifically, using the first acquisition operation stability index coefficient to determine whether to issue a sensor acquisition abnormality alarm includes: Comparing the first acquisition operation stability index coefficient with a preset first index coefficient threshold; When the first acquisition operation stability index coefficient exceeds a preset first index coefficient threshold, it is determined that there is no need to issue a sensor acquisition abnormality alarm; When the first acquisition operation stability index coefficient does not exceed a preset first index coefficient threshold, retrieving a correlation coefficient between the target sensor and the object sensor; Obtaining a second acquisition operation stability index coefficient using a correlation coefficient between the target sensor and the object sensor; The second acquisition operation stability index coefficient is obtained by the following formula: Among them, K 02 Represents the second acquisition operation stability index coefficient; s b represents the standard deviation of the correlation coefficient between m object sensors and the target sensor; T b represents the standard deviation of the current data collection response time of m object sensors; s max Indicates the correlation coefficient corresponding to the object sensor with the maximum response time of the current data collection; s min The correlation coefficient corresponding to the object sensor representing the minimum response time of the current data collection; P 01 The sensor sensitivity of the object sensor indicating the maximum response time of the current data collection; P 02 The sensor sensitivity of the object sensor representing the minimum response time of the current data collection; The second acquisition operation stability index coefficient and the first acquisition operation stability index coefficient are used to comprehensively determine whether to issue a sensor acquisition abnormality alarm.

[0040] The technical effect of the above technical solution is: by comparing the first acquisition operation stability index coefficient with the preset first index coefficient threshold, the stability of sensor acquisition can be preliminarily judged. Only when the index coefficient is lower than the threshold, the possible abnormality is further considered, which avoids unnecessary false alarms and improves the accuracy of the alarm. When the first acquisition operation stability index coefficient does not exceed the threshold, the system does not directly trigger the alarm, but further analyzes the correlation coefficient between the target sensor and multiple object sensors. This multi-level analysis method enhances the system's ability to identify abnormal situations and improves the robustness of the system. By introducing the second acquisition operation stability index coefficient, multiple factors such as the standard deviation of the correlation coefficient, the standard deviation of the data acquisition response time, and the sensor sensitivity corresponding to the maximum / minimum response time are comprehensively considered. This comprehensive evaluation method is more comprehensive and can more accurately reflect the stability and abnormal situation of sensor acquisition. When determining whether an alarm is needed, the system first performs a quick screening based on the first acquisition operation stability index coefficient, reducing unnecessary subsequent calculations. At the same time, the calculation of the second acquisition operation stability index coefficient also makes full use of existing data, avoids repeated acquisition and calculation, and improves the response efficiency of the system. The index coefficients and thresholds in this technical solution can be adjusted and optimized according to the actual application scenario, so that the system can adapt to different sensor types and acquisition requirements. This flexibility is conducive to the long-term maintenance and expansion of the system.

[0041] In summary, this technical solution improves the accuracy and efficiency of sensor acquisition abnormal alarms through multi-level and multi-factor comprehensive analysis, enhances the robustness and scalability of the system, and is of great significance for improving the stability and reliability of the overall system.

[0042] Specifically, using the second acquisition operation stability index coefficient and the first acquisition operation stability index coefficient to comprehensively determine whether to issue a sensor acquisition abnormality alarm includes: Retrieve the first acquisition operation stability index coefficient; Retrieve the second acquisition operation stability index coefficient; Retrieving the historical data collection operation record corresponding to the target sensor; Retrieving the data collection response time corresponding to each data collection from the historical data collection operation record corresponding to the target sensor; Obtain a comprehensive index coefficient by combining the data collection response time corresponding to each data collection with the first collection operation stability index coefficient and the second collection operation stability index coefficient; The comprehensive index coefficient is obtained by the following formula: Among them, K zrepresents the comprehensive index coefficient; n represents the number of data collection times in the historical data collection operation record of the target sensor; T ci represents the data collection response time corresponding to the i-th data collection of the target sensor; T m Indicates the data collection response time corresponding to the target sensor; K 01 Indicates the first acquisition operation stability index coefficient; K 02 represents the second acquisition operation stability index coefficient; Comparing the comprehensive index coefficient with a preset second index coefficient threshold; When the comprehensive index coefficient is lower than a preset second index coefficient threshold, it is determined that a sensor acquisition abnormality alarm is required.

[0043] The technical effect of the above technical solution is: by combining the first acquisition operation stability index coefficient and the second acquisition operation stability index coefficient, and introducing the historical data acquisition operation record of the target sensor, the technical solution can more comprehensively evaluate the acquisition stability of the sensor. This helps to reduce false alarms and missed alarms and improve the accuracy of judgment. The technical solution takes into account the historical data acquisition response time of the target sensor, which enables the system to adapt to the characteristics of different sensors and acquisition environments. Through the analysis of historical data, the system can better understand the normal behavior pattern of the sensor, thereby more accurately identifying abnormal behavior. By calculating the comprehensive index coefficient, the technical solution integrates multiple evaluation indicators into a single value, simplifying the judgment process. This enables the system to make decisions more quickly and improves the judgment efficiency. The technical solution sets a preset second index coefficient threshold. When the comprehensive index coefficient is lower than the threshold, the system will determine that a sensor acquisition abnormality alarm is required. This mechanism ensures that the alarm is triggered only when there is indeed an abnormality, avoiding unnecessary interference and misoperation. By recording and analyzing the historical data acquisition operation records of the sensor, the technical solution helps system maintenance personnel understand the operating status of the sensor, discover potential problems in a timely manner and deal with them. This helps to extend the service life of the sensor and improve the overall reliability of the system. The index coefficients and thresholds in this technical solution can be adjusted and optimized according to the actual application scenarios, so that the system can adapt to different sensor types and collection requirements. In addition, this technical solution can be integrated with other monitoring and management systems to achieve a wider range of functions and applications.

[0044] In summary, this technical solution achieves accurate judgment and optimization of sensor acquisition abnormal alarms by introducing comprehensive index coefficients. This helps to improve the stability, reliability and maintainability of the system, and provides strong support for sensor data acquisition and monitoring.

[0045] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0046] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. Generate test fixture based on ICD configuration, characterized in that: include: ICD reading and parsing unit, used for: Read the ICD file from the database, extract and parse the interface information in the ICD file, and obtain the ICD basic data after the parsing is completed; Test fixture design unit for: Design the structure and function of the test tooling according to the basic data of ICD, and obtain the ICD tooling design data after the design is completed; Test fixture generation unit, used to: Generate executable test jigs according to ICD jig test data, and obtain ICD jig test data after the test jigs are generated; The test environment builds a simulation unit for: The test environment is built according to the test requirements, a virtual model is constructed for the built test environment, and a simulated tooling test is performed based on the ICD tooling test data and the test environment virtual model, and the simulated test results are evaluated. After the evaluation is completed, the ICD test tooling implementation data is obtained; Test execution monitoring unit, used to: The test is implemented according to the ICD test tooling implementation data, and the test implementation process is monitored in real time. Finally, the test implementation process is evaluated and the evaluation results are transmitted to the display terminal.

2. The ICD-based configuration test fixture according to claim 1, characterized in that: The ICD reading and parsing unit is further used for: Read the interface information in the ICD file, which includes interface identifier, data format, communication protocol, message type, message ID, data field, timing information, error handling, status information, security requirements, physical layer information, constraints and version information; After the interface information is read, it is extracted to the memory for source file storage; Parse the stored interface information source file, the parsing is divided into file format parsing and file content parsing; File format parsing includes parsing the XML or JSON format files in the interface information using the XML parsing library; parsing the CSV format files in the interface information using the CSV parsing library; and parsing the text format files in the interface information using regular expressions. File content parsing is to parse the interface information using a syntax analyzer, and obtain the key feature data of the large hole interface information after parsing; After all interface information is parsed, the basic ICD data is obtained.

3. The ICD-based configuration test fixture according to claim 2, characterized in that: The test fixture design unit is also used for: Design the test fixture structure in the ICD basic data, which includes hardware design, software design and safety design; Hardware design is to design the physical connection part of the interface according to the physical layer information. The physical connection part includes connectors, cables and adapters. After the physical connection part equipment is completed, the hardware components are selected. The hardware components include processors, memory and communication interface cards. The circuit is designed according to the physical connection part and hardware components. The software device is to design the timing logic of the software according to the timing information, and implement the software logic of error handling and status information according to the timing logic; The security design is to perform integrated encryption and authentication based on the hardware design and software design, and finally obtain the design data of the test tooling structure design in the ICD basic data.

4. The ICD-based configuration test fixture according to claim 3, characterized in that: The test fixture design unit is also used for: Design the test tooling functions in the ICD basic data; The test tooling function is designed to detect functional abnormalities of the basic ICD data; When there is an abnormality in the function abnormality detection, the device is replaced or repaired. When there is no abnormality in the function abnormality monitoring, the device of each interface information is matched with the design data of the test tooling structure design; After the matching is completed, the design data of the test tooling function design is obtained; The design data of the matched test tooling functional design is marked as ICD tooling design data.

5. The ICD-based configuration test fixture according to claim 4, characterized in that: The test fixture generation unit is further used for: Obtain test fixture generation rules from the database, and confirm the design specifications of hardware and software according to the test fixture generation rules; Integrate the hardware and software that have been confirmed to meet the design specifications, test the functions and performance after integration, and make adjustments based on the test results of functions and performance; At the same time, prepare the tools, materials and staff required for the test tooling according to the test tooling generation rules; A test plan is generated based on the integrated hardware and software as well as the tools, materials and staff required for the test tooling, and the ICD tooling test data is obtained after the generation.

6. The ICD-based configuration test fixture according to claim 5, characterized in that: The test environment builds a simulation unit and is also used for: Layout the test equipment according to the test site and connect the test equipment according to the ICD tooling test data. At the same time, control the environment according to the test requirements, including temperature and humidity; After the test equipment is connected, configure the network topology, IP address, subnet mask and routing information, and finally complete the test environment construction; The constructed test environment is simulated and modeled, wherein a consistent virtual device model is created according to the function and structure of the test device; Map key data in ICD tooling test data to the virtual equipment model for model parameterization; After the model parameterization is completed, a virtual model of the test environment is obtained in which the virtual model construction is completed.

7. The ICD-based configuration test fixture according to claim 6, characterized in that: The test environment builds a simulation unit and is also used for: Use the virtual model of the test environment to simulate tooling testing; Before conducting the simulated tooling test, start and load the constructed virtual model of the test environment, define and configure specific test cases according to the test requirements and objectives in the ICD tooling test data, and execute the configured test cases in the virtual model; During the test process, collect and record all relevant test data and logs, including network traffic, message delivery, device status changes and errors; Analyze the test results based on the collected data logs, compare the analysis results with the preset test fixture results, and evaluate and judge the simulated fixture test of the test environment virtual model based on the comparison results; The evaluation results are divided into qualified, up to standard and unqualified; Mark the qualified and up-to-standard evaluation results as ICD test fixture implementation data; The test execution monitoring unit is further used for: According to the ICD test fixture implementation data, the test fixture configured by ICD is tested and implemented; During the test implementation, the monitoring platform is used to capture the test data generated during the actual test process in real time; During the test, sensors, loggers and other monitoring equipment are used to collect test data in real time; Use data analysis tools or algorithms to process and interpret the collected data; And convert and present the key indicator data in the analyzed real-time data in the form of graphics and text; At the same time, when conducting real-time data analysis, an alarm mechanism is set up to automatically trigger an alarm when an anomaly is detected or a key indicator exceeds a preset threshold; Finally, the graphic data of alarm data and key indicator data are transmitted to the display terminal for display.

8. The ICD configuration-based test fixture generation method according to claim 7, characterized in that: Monitor the sensor's operating status during the sensor's real-time test data collection process, including: During the process of real-time data collection by the sensor, the data collection response time of each single data collection by the sensor is monitored in real time; Comparing the data collection response time with its corresponding data collection response time threshold; When the data acquisition response time exceeds a preset data acquisition response time threshold, the sensor corresponding to the data acquisition response time exceeding the preset data acquisition response time threshold is used as a target sensor; Extracting sensors whose correlation coefficients with the target sensor exceed a preset correlation coefficient threshold as object sensors; Extracting the current data collection response time of the object sensor; Using the current data collection response time of all object sensors to obtain the first collection operation stability index coefficient; The first acquisition operation stability index coefficient is obtained by the following formula: Among them, K 01 represents the first acquisition operation stability index coefficient; m represents the total number of object sensors; T i represents the current data collection response time of the i-th object sensor; T y Indicates the preset data collection response time threshold; T m Indicates the data collection response time corresponding to the target sensor; T g T represents the current data collection response time of the object sensor corresponding to the maximum value of the correlation coefficient; r Indicates the current data collection response time of the object sensor corresponding to the minimum value of the correlation coefficient; s i represents the correlation coefficient between the i-th object sensor and the target sensor; The first acquisition operation stability index coefficient is used to determine whether to issue a sensor acquisition abnormality alarm.

9. The ICD-based configuration test fixture generation method according to claim 8, characterized in that: Determining whether to issue a sensor acquisition abnormality alarm by using the first acquisition operation stability index coefficient includes: Comparing the first acquisition operation stability index coefficient with a preset first index coefficient threshold; When the first acquisition operation stability index coefficient exceeds a preset first index coefficient threshold, it is determined that there is no need to issue a sensor acquisition abnormality alarm; When the first acquisition operation stability index coefficient does not exceed a preset first index coefficient threshold, retrieving a correlation coefficient between the target sensor and the object sensor; Obtaining a second acquisition operation stability index coefficient using a correlation coefficient between the target sensor and the object sensor; The second acquisition operation stability index coefficient is obtained by the following formula: Among them, K 02 Represents the second acquisition operation stability index coefficient; s b represents the standard deviation of the correlation coefficient between m object sensors and the target sensor; T b represents the standard deviation of the current data collection response time of m object sensors; s max Indicates the correlation coefficient corresponding to the object sensor with the maximum response time of the current data collection; s min The correlation coefficient corresponding to the object sensor representing the minimum response time of the current data collection; P 01 The sensor sensitivity of the object sensor indicating the maximum response time of the current data collection; P 02 The sensor sensitivity of the object sensor representing the minimum response time of the current data collection; The second acquisition operation stability index coefficient and the first acquisition operation stability index coefficient are used to comprehensively determine whether to issue a sensor acquisition abnormality alarm.

10. The ICD-based configuration test fixture generation method according to claim 9, characterized in that: Comprehensively determining whether to issue a sensor acquisition abnormality alarm by using the second acquisition operation stability index coefficient and the first acquisition operation stability index coefficient includes: Retrieve the first acquisition operation stability index coefficient; Retrieve the second acquisition operation stability index coefficient; Retrieving the historical data collection operation record corresponding to the target sensor; Retrieving the data collection response time corresponding to each data collection from the historical data collection operation record corresponding to the target sensor; Obtain a comprehensive index coefficient by combining the data collection response time corresponding to each data collection with the first collection operation stability index coefficient and the second collection operation stability index coefficient; The comprehensive index coefficient is obtained by the following formula: Among them, K z represents the comprehensive index coefficient; n represents the number of data collection times in the historical data collection operation record of the target sensor; T ci represents the data collection response time corresponding to the i-th data collection of the target sensor; T m Indicates the data collection response time corresponding to the target sensor; K 01 Indicates the first acquisition operation stability index coefficient; K 02 represents the second acquisition operation stability index coefficient; Comparing the comprehensive index coefficient with a preset second index coefficient threshold; When the comprehensive index coefficient is lower than a preset second index coefficient threshold, it is determined that a sensor acquisition abnormality alarm is required.

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