Performance test method, device and equipment of a board card

Abstract

The present disclosure provides a method, device and equipment for testing performance of a board card. The method comprises: obtaining a standard data file generated by testing the board card; parsing the standard data file to obtain a target test suite name and a measurement data set; determining a target board card type to which a target test suite represented by the target test suite name belongs; determining a target configuration file corresponding to the target board card type based on an association file representing a correspondence between board card types and configuration files; parsing the target configuration file to obtain a performance standard value corresponding to the target test suite name and a data analysis method; and determining a performance test result of the board card based on the measurement data set and the performance standard value by using the data analysis method. Thus, the performance test efficiency of the board card can be improved.

Description

Technical Field

[0001] This disclosure relates to the field of chip testing equipment technology, and in particular to a method, apparatus and equipment for testing the performance of circuit boards. Background Technology

[0002] The circuit board is the core hardware component of a semiconductor testing system. It is mainly used to perform electrical performance testing on semiconductor devices (such as chips and integrated circuits), and undertakes the functions of generating, transmitting, acquiring and preliminarily processing test signals. It is a key carrier to ensure the accuracy and stability of semiconductor device testing.

[0003] In related technologies, each type of board has its own independent configuration file. For test suites with the same function on different boards, the naming of test suites varies slightly due to different engineers' personal habits. Furthermore, the data analysis methods for different test suites in different projects differ slightly. Traditional methods cannot use a single software to suit all projects; different software needs to be designed and developed for different projects, resulting in a large workload and low testing efficiency. Summary of the Invention

[0004] This disclosure provides a method, apparatus, and device for testing the performance of circuit boards, thereby improving the efficiency of circuit board performance testing.

[0005] A first aspect of this disclosure provides a method for testing the performance of a circuit board, comprising:

[0006] Obtain the standard data file generated from testing the board, and parse the standard data file to obtain the target test suite name and measurement data set;

[0007] Determine the target board type to which the target test suite, indicated by the target test suite name, belongs;

[0008] Based on the associated file representing the correspondence between board type and configuration file, determine the target configuration file corresponding to the target board type;

[0009] Parse the target configuration file to obtain the performance standard values ​​and data analysis methods corresponding to the target test suite name;

[0010] The performance test results of the board are determined by using data analysis methods based on the measurement data set and performance standard values.

[0011] In some possible implementations, the target configuration file includes a first table showing the correspondence between keywords in the test suite name and performance standard values ​​and data analysis methods; and

[0012] Parse the target configuration file to obtain the performance standard values ​​and data analysis methods corresponding to the target test suite name, including:

[0013] Determine the first keyword in the name of the target test suite;

[0014] For any keyword in the first table, determine the similarity between any keyword and the first keyword to obtain the similarity corresponding to any keyword; determine whether the similarity corresponding to any keyword meets the preset similarity conditions; if the similarity corresponding to any keyword meets the similarity conditions, then determine any keyword as the second keyword;

[0015] The performance standard value and data analysis method corresponding to the second keyword in the first table are respectively determined as the performance standard value and data analysis method corresponding to the target test suite name.

[0016] In some possible implementations, the target configuration file also includes a second table showing the correspondence between test suite names, performance benchmark values, and data analysis methods; and

[0017] Before determining the first keyword in the target test suite name, the method also includes:

[0018] Determine whether the target test suite name is included in the second table;

[0019] If the target test suite name is not contained in the second table, proceed with the step of determining the first keyword in the target test suite name;

[0020] If the second table contains the name of the target test suite, then the performance standard value and data analysis method corresponding to the name of the target test suite in the second table will be determined as the performance standard value and data analysis method corresponding to the name of the target test suite, respectively.

[0021] In some possible implementations, determining whether the similarity corresponding to any keyword meets a preset similarity condition includes:

[0022] Perform case-insensitive string matching between any keyword and the first keyword;

[0023] If any keyword matches the first keyword, then the similarity of any keyword is determined to meet the similarity condition.

[0024] If any keyword does not match the first keyword, then the similarity of any keyword does not meet the similarity criteria.

[0025] In some possible implementations, the data analysis methods include upper specification limit calculation formulas, lower specification limit calculation formulas, and accuracy calculation formulas. The measurement data set contains at least two measurement data sets, each including the actual measurement value, the upper measurement limit value, the lower measurement limit value, and the measurement range; and

[0026] The performance test results of the board are determined through data analysis, based on the measurement data set and performance standard values, including:

[0027] From at least two sets of measurement data, determine the set of measurement data used to calculate the specification limits to obtain the target set of measurement data;

[0028] Determine the target upper specification limit and the target lower specification limit; wherein, the target upper specification limit is obtained by the upper specification limit calculation formula based on the measurement range and the upper limit value in the target measurement data set; the target lower specification limit is obtained by the lower specification limit calculation formula based on the measurement range and the lower limit value in the target measurement data set.

[0029] For any actual measurement value in at least two sets of measurement data, based on any actual measurement value, the upper limit of measurement, the lower limit of measurement, and the measurement range in the target set of measurement data, determine the calculation result of the accuracy calculation formula to obtain the target accuracy corresponding to any actual measurement value;

[0030] The performance test results of the board are determined based on the target upper specification limit, the target lower specification limit, and the target accuracy and performance standard values ​​corresponding to multiple actual measurement values.

[0031] In some possible implementations, the measurement data set used to calculate the specification limit is determined from at least two measurement data sets, including:

[0032] For the measurement range in any of at least two measurement data sets:

[0033] Based on any measurement range and the upper limit value of the measurement data group to which any measurement range belongs, determine the calculation result of the upper specification limit calculation formula, and obtain the upper specification limit value corresponding to any measurement range.

[0034] Determine whether the upper limit value of the specification corresponding to any measurement range falls within the preset upper limit value range;

[0035] If the upper limit value of the specification corresponding to any measurement range is within the upper limit range, then the measurement data group to which any measurement range belongs is determined as the measurement data group used to calculate the specification limit value.

[0036] In some possible implementations, the performance standard values ​​include a process accuracy standard value, a first process capability index standard value that does not consider offset, and a second process capability index standard value that considers offset; and

[0037] Based on the target upper specification limit, the target lower specification limit, and the target accuracy and performance standard values ​​corresponding to multiple measured actual values, the performance test results of the board are determined, including:

[0038] Based on the target upper specification limit, the target lower specification limit and the target accuracy corresponding to multiple actual measurement values, the process accuracy test value of the board, the first process capability index test value without considering offset and the second process capability index test value considering offset are determined respectively.

[0039] The performance test results of the board are determined based on the process accuracy test value and the process accuracy standard value, the first process capability index test value and the first process capability index standard value, and the second process capability index test value and the second process capability index standard value.

[0040] In some possible implementations, after determining the board's performance test results through data analysis based on a set of measurement data and performance standard values, the method further includes any of the following:

[0041] Based on the performance test results, perform same-channel data analysis on the board;

[0042] Based on the performance test results, data analysis was performed on the inter-channel data of the board.

[0043] A second aspect of this disclosure provides a performance testing apparatus for a circuit board, comprising:

[0044] The acquisition unit is configured to acquire the standard data file generated for testing the board, and parse the standard data file to obtain the target test suite name and measurement data set;

[0045] The first determining unit is configured to determine the target board type to which the target test suite, represented by the target test suite name, belongs;

[0046] The second determining unit is configured to determine the target configuration file corresponding to the target board type based on the association file representing the correspondence between the board type and the configuration file.

[0047] The parsing unit is configured to parse the target configuration file to obtain the performance standard value and data analysis method corresponding to the target test suite name;

[0048] The third determining unit is configured to determine the performance test results of the board based on the measurement data set and performance standard values ​​through data analysis.

[0049] In some possible implementations, the target configuration file includes a first table showing the correspondence between keywords in the test suite name and performance standard values ​​and data analysis methods; and

[0050] The parsing unit includes:

[0051] The first determining subunit is configured to determine the first keyword in the name of the target test suite;

[0052] The second determining subunit is configured to, for any keyword in the first table, determine the similarity between any keyword and the first keyword, and obtain the similarity corresponding to any keyword; determine whether the similarity corresponding to any keyword meets the preset similarity conditions; if the similarity corresponding to any keyword meets the similarity conditions, then determine any keyword as the second keyword;

[0053] The third determining subunit is configured to determine the performance standard value and data analysis method corresponding to the second keyword in the first table as the performance standard value and data analysis method corresponding to the target test suite name, respectively.

[0054] In some possible implementations, the target configuration file also includes a second table showing the correspondence between test suite names, performance benchmark values, and data analysis methods; and

[0055] The above-mentioned device also includes:

[0056] The fourth determining unit is configured to determine whether the target test suite name is contained in the second table before determining the first keyword in the target test suite name;

[0057] The fifth determining unit is configured to perform the step of determining the first keyword in the target test suite name if the second table does not contain the target test suite name;

[0058] The sixth determining unit is configured to, if the second table contains the name of the target test suite, determine the performance standard value and data analysis method corresponding to the name of the target test suite in the second table as the performance standard value and data analysis method corresponding to the name of the target test suite, respectively.

[0059] In some possible implementations, the second determining subunit includes:

[0060] The matching module is configured to perform case-insensitive string matching between any keyword and the first keyword;

[0061] The first determining module is configured to determine that if any keyword matches the first keyword, the similarity of any keyword meets the similarity condition.

[0062] The second determining module is configured to determine that if any keyword does not match the first keyword, the similarity of that keyword does not meet the similarity condition.

[0063] In some possible implementations, the data analysis methods include upper specification limit calculation formulas, lower specification limit calculation formulas, and accuracy calculation formulas. The measurement data set contains at least two measurement data sets, each including the actual measurement value, the upper measurement limit value, the lower measurement limit value, and the measurement range; and

[0064] The third determining unit includes:

[0065] The fourth determining subunit is configured to determine, from at least two sets of measurement data, a set of measurement data used to calculate the specification limit, to obtain the target set of measurement data;

[0066] The fifth determining subunit is configured to determine the target upper specification limit and the target lower specification limit; wherein, the target upper specification limit is obtained by the upper specification limit calculation formula based on the measurement range and the upper limit value in the target measurement data set; the target lower specification limit is obtained by the lower specification limit calculation formula based on the measurement range and the lower limit value in the target measurement data set.

[0067] The sixth determining subunit is configured to determine the calculation result of the accuracy calculation formula based on any actual measurement value in at least two measurement data sets, the upper limit of measurement, the lower limit of measurement, and the measurement range in the target measurement data set, so as to obtain the target accuracy corresponding to any actual measurement value.

[0068] The seventh determination subunit is configured to determine the performance test results of the board based on the target upper specification limit, the target lower specification limit, and the target accuracy and performance standard values ​​corresponding to multiple actual measurement values.

[0069] In some possible implementations, the fourth determining subunit includes:

[0070] The processing module is configured to target the measurement range in any of at least two sets of measurement data:

[0071] Based on any measurement range and the upper limit value of the measurement data group to which any measurement range belongs, determine the calculation result of the upper specification limit calculation formula, and obtain the upper specification limit value corresponding to any measurement range.

[0072] Determine whether the upper limit value of the specification corresponding to any measurement range falls within the preset upper limit value range;

[0073] If the upper limit value of the specification corresponding to any measurement range is within the upper limit range, then the measurement data group to which any measurement range belongs is determined as the measurement data group used to calculate the specification limit value.

[0074] In some possible implementations, the performance standard values ​​include a process accuracy standard value, a first process capability index standard value that does not consider offset, and a second process capability index standard value that considers offset; and

[0075] The seventh defined subunit includes:

[0076] The third determination module is configured to determine the process accuracy test value of the board, the first process capability index test value without considering offset, and the second process capability index test value considering offset, based on the target upper specification limit, the target lower specification limit, and the target accuracy corresponding to multiple actual measurement values, respectively.

[0077] The fourth determination module is configured to determine the performance test results of the board based on the process accuracy test value and the process accuracy standard value, the first process capability index test value and the first process capability index standard value, and the second process capability index test value and the second process capability index standard value.

[0078] In some possible implementations, the above-described apparatus further includes any one of the following:

[0079] The first analysis unit is configured to perform same-channel data analysis on the board after determining the board's performance test results based on the measurement data set and performance standard values ​​through data analysis.

[0080] The second analysis unit is configured to perform inter-channel data analysis on the board after determining the board's performance test results based on the measurement data set and performance standard values ​​through data analysis.

[0081] A third aspect of this disclosure provides an electronic device, comprising:

[0082] Memory, used to store computer programs;

[0083] A processor is configured to execute a computer program stored in the memory, wherein, when the computer program is executed, it implements the method of any embodiment of the board performance testing method of the first aspect of this disclosure.

[0084] A fourth aspect of this disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method of any embodiment of the board performance testing method of the first aspect described above.

[0085] A fifth aspect of this disclosure provides a computer program including computer-readable code, wherein when the computer program instructions are executed by a processor, they implement any embodiment of the board performance testing method of the first aspect described above.

[0086] In this embodiment, a standard data file generated during board testing can be obtained. The standard data file is parsed to obtain the target test suite name and measurement data set. Then, the target board type to which the target test suite represented by the target test suite name belongs is determined. Then, based on the association file representing the correspondence between the board type and the configuration file, the target configuration file corresponding to the target board type is determined. Subsequently, the target configuration file is parsed to obtain the performance standard value and data analysis method corresponding to the target test suite name. Finally, the performance test result of the board is determined based on the measurement data set and the performance standard value through the data analysis method. Therefore, by acquiring and parsing standard data files, the target test suite name and measurement data set are extracted, establishing a connection between the raw test data and analysis requirements. By determining the target board type and associating it with the corresponding configuration file, a matching relationship between different board types and data analysis methods is achieved. By parsing the configuration file to obtain performance standard values ​​and data analysis methods, a unified and adaptable processing rule is provided for the measurement data of different boards. By processing the measurement data using the data analysis methods provided in the configuration file, performance test results are obtained. This solves the problems of test suite naming differences and incompatible formulas. There is no need to package software separately for different projects. Only the mapping relationship between the new board type and the configuration file needs to be added to the associated file, and the performance standard values ​​and data analysis methods in the configuration file need to be updated. This automatically adapts to the different test requirements of different board types, realizes the standardization and automation of the board testing process, thereby improving the board testing efficiency. Furthermore, it can improve the matching degree between the board performance test results and the performance standard values ​​and data analysis methods defined in the configuration file.

[0087] The technical solutions of this disclosure will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0088] The accompanying drawings, which form part of this specification, illustrate embodiments of this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0089] This disclosure will become clearer with reference to the accompanying drawings and the following detailed description, wherein:

[0090] Figure 1 A flowchart illustrating a performance testing method for a circuit board provided in this embodiment of the present disclosure;

[0091] Figure 2 This is a flowchart illustrating the parsing of the target configuration file in a board performance testing method provided in this embodiment of the disclosure;

[0092] Figure 3 This is a flowchart illustrating the process of determining performance test results in a board performance testing method provided in this embodiment of the disclosure;

[0093] Figure 4 A flowchart illustrating another performance testing method for a circuit board provided in this embodiment of the present disclosure;

[0094] Figure 5 This is a schematic diagram of the structure of a performance testing device for a circuit board provided in an embodiment of the present disclosure;

[0095] Figure 6 This is a schematic diagram of the structure of the parsing unit in a board performance testing device provided in an embodiment of the present disclosure;

[0096] Figure 7 This is a schematic diagram of the structure of an application embodiment of an electronic device provided in this disclosure. Detailed Implementation

[0097] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present disclosure.

[0098] Those skilled in the art will understand that the terms "first," "second," etc., in the embodiments of this disclosure are only used to distinguish different steps, devices, or modules, and do not represent any specific technical meaning, nor do they indicate a necessary logical order between them.

[0099] It should also be understood that in the embodiments disclosed herein, "a plurality of" may refer to two or more, and "at least one" may refer to one, two or more.

[0100] It should also be understood that any component, data or structure mentioned in the embodiments of this disclosure can generally be understood as one or more unless expressly defined or given to the contrary in the context.

[0101] Furthermore, the term "and / or" in this disclosure is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this disclosure generally indicates that the preceding and following related objects have an "or" relationship.

[0102] It should also be understood that the description of the various embodiments in this disclosure emphasizes the differences between the various embodiments, and the similarities or similarities can be referred to each other. For the sake of brevity, they will not be described in detail.

[0103] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0104] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this disclosure or its application or use.

[0105] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0106] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0107] It should be noted that, unless otherwise specified, different embodiments and features described in this disclosure can be combined with each other. Furthermore, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0108] The acquisition and processing of data (such as data in standard data files and target configuration files) in this disclosure comply with relevant laws, regulations, social ethics, and provisions concerning public interests. For example, the acquisition, storage, and other processing of data in this disclosure are all carried out with the knowledge and consent of the users and have obtained full authorization from the relevant users.

[0109] The embodiments disclosed herein can be applied to electronic devices such as terminal devices, computer systems, and servers, and can operate together with a wide range of other general-purpose or special-purpose computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments, and / or configurations suitable for use with electronic devices such as terminal devices, computer systems, and servers include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments including any of the above systems, etc.

[0110] Electronic devices such as terminal devices, computer systems, and servers can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in distributed cloud computing environments, where tasks are executed by remote processing devices linked through communication networks. In distributed cloud computing environments, program modules can reside on local or remote computing system storage media, including storage devices.

[0111] In related technologies, each type of board has its own independent configuration file. Because different boards may have different test suites for the same function, the naming of these test suites may vary slightly due to the different personal habits of the Application Engineers (AEs).

[0112] The aforementioned test suite is a general term for a type of test item, and a test suite typically contains multiple test items. For example, the test suite "FV_Single_Rapid_TEST" may include the following test items: "FV_Single_Rapid_-1V", "FV_Single_Rapid_-2V", "FV_Single_Rapid_OV", "FV_Single_Rapid_1V", and "FV_Single_Rapid_2V", etc., each corresponding to a specific voltage test condition. For example, the test item "FV_Single_Rapid_-1V" can be used to test the relevant performance (such as accuracy) of the board under the "-1V" voltage condition, and generate corresponding data such as the actual measured value, the upper limit of the measurement, and the lower limit of the measurement at that voltage.

[0113] In addition, the calculation formulas for each board are fixed and pre-written into the software, which means that different projects need to package the software independently.

[0114] For example, the formula for calculating the number of boards to be tested for project A could be:

[0115] Accuarcy=Result-(Lowlimit+HighLimit) / 2

[0116] Formula (1) can be used to calculate the required test boards for Project B:

[0117] Accuarcy=Result-(Lowlimit+HighLimit)

[0118] Formula (2)

[0119] In formulas (1) and (2) above, Accuarcy represents the accuracy value, Result represents the actual measured value, LowLimit represents the lower limit of measurement, and HighLimit represents the upper limit of measurement.

[0120] In the above use cases, the formulas used for data analysis differ slightly between different test suites for different projects, and traditional methods cannot make a single software suitable for all projects.

[0121] Figure 1 This is a flowchart illustrating a performance testing method for a circuit board according to an embodiment of this disclosure. This embodiment can be applied to electronic devices, such as terminal devices, servers, cloud computing, etc., for performance testing of circuit boards in a semiconductor testing system. Figure 1 As shown, the method specifically includes:

[0122] Step 101: Obtain the standard data file generated for testing the board, and parse the standard data file to obtain the target test suite name and measurement data set.

[0123] In this embodiment, a board is a printed circuit board, typically containing integrated circuit chips, interface circuits, connectors, and other components. It can be inserted into a device slot to achieve specific hardware function expansion or data processing capabilities. Here, the board is the object of performance testing; testing it yields standard data files. As an example, the aforementioned board could be used for multi-channel digital signal and power supply testing, or it could be used for level drive performance testing.

[0124] A standard data file is a file generated according to a preset format during board testing, recording raw data related to the test. This raw data may include information such as the name of the test suite and multiple actual measurement values.

[0125] The target test suite name can be the test suite name parsed from the standard data file. As an example, the target test suite name could be DIO256_PMU_FIMV_MV_Rapid_TEST.

[0126] A measurement data set can be a group of data related to board testing, parsed from a standard data file. It can contain multiple measurement data points, recording various actual values ​​of measurements during the testing process, such as voltage and current. Alternatively, a measurement data set can be a collection of measurement data obtained by testing the target test suite indicated by the aforementioned target test suite name.

[0127] The target test suite can be the test suite represented by the target test suite name.

[0128] Here, a standard data file can be obtained from the local storage path of the aforementioned execution entity or the connected server storage directory. Then, based on the format of the standard data file, the standard data file is parsed to obtain the target test suite name and measurement data set.

[0129] Step 102: Determine the target board type to which the target test suite, indicated by the target test suite name, belongs.

[0130] In this embodiment, the board type can be a category categorized based on the board's function, testing purpose, and hardware characteristics. Boards of the same type have the same testing requirements and analysis rules. As an example, the board type can be a board type used for testing digital input / output functions; or it can be a board type used for testing the performance of power management units.

[0131] The target board type can be the board type to which the target test suite belongs.

[0132] Step 103: Based on the association file that represents the correspondence between board type and configuration file, determine the target configuration file corresponding to the target board type.

[0133] In this embodiment, the configuration file is a pre-defined file containing test rules for a specific board type. For example, the configuration file can be used to represent the correspondence between test suite names, performance standard values, and data analysis methods. The test suite names, performance standard values, data analysis methods, and other content in the configuration file can be configured by users or other objects. Furthermore, the configuration file can store the correspondence between test suite names, performance standard values, and data analysis methods according to a preset format.

[0134] Performance standard values ​​are used to measure whether the measured data meet the expected requirements. For example, performance standard values ​​may include at least one of the following: voltage range, current threshold, process accuracy (CA) standard value, first process capability index (CP) standard value without considering offset, and second process capability index (CPK) standard value considering offset.

[0135] Data analysis methods are used to define algorithms or logic for processing and evaluating measurement data. For example, data analysis methods can be accuracy calculation formulas, mean calculation formulas, extreme value detection methods, or waveform fitting methods.

[0136] The association file can represent the correspondence between board types and configuration files, and is used to establish a mapping between different board types and configuration files.

[0137] The target configuration file is a configuration file in the association files that corresponds to the target board type.

[0138] Step 104: Parse the target configuration file to obtain the performance standard value and data analysis method corresponding to the target test suite name.

[0139] In this embodiment, the target configuration file can be parsed according to a preset format to extract performance standard values ​​and data analysis methods that correspond to the target test suite name.

[0140] Step 105: Determine the performance test results of the board based on the measurement data set and performance standard values ​​through data analysis.

[0141] In this embodiment, the performance test results can be a set of indicators and / or evaluation conclusions that reflect the performance status of the board, calculated based on measurement data and data analysis methods.

[0142] As an example, performance test results can indicate whether the board's performance is up to standard, or they can also indicate the board's accuracy, stability, and other performance parameters.

[0143] Based on the embodiments of this disclosure, a standard data file generated for testing the board can be obtained. The standard data file is parsed to obtain the target test suite name and measurement data set. Then, the target board type to which the target test suite represented by the target test suite name belongs is determined. Then, based on the association file representing the correspondence between the board type and the configuration file, the target configuration file corresponding to the target board type is determined. Subsequently, the target configuration file is parsed to obtain the performance standard value and data analysis method corresponding to the target test suite name. Finally, the performance test result of the board is determined based on the measurement data set and the performance standard value through the data analysis method. Therefore, by acquiring and parsing standard data files, the target test suite name and measurement data set are extracted, establishing a connection between the raw test data and analysis requirements. By determining the target board type and associating it with the corresponding configuration file, a matching relationship between different board types and data analysis methods is achieved. By parsing the configuration file to obtain performance standard values ​​and data analysis methods, a unified and adaptable processing rule is provided for the measurement data of different boards. By processing the measurement data using the data analysis methods provided in the configuration file, performance test results are obtained. This solves the problems of test suite naming differences and incompatible formulas. There is no need to package software separately for different projects. Only the mapping relationship between the new board type and the configuration file needs to be added to the associated file, and the performance standard values ​​and data analysis methods in the configuration file need to be updated. This automatically adapts to the different test requirements of different board types, realizes the standardization and automation of the board testing process, thereby improving the board testing efficiency. Furthermore, it can improve the matching degree between the board performance test results and the performance standard values ​​and data analysis methods defined in the configuration file.

[0144] In some optional implementations of this embodiment, the target configuration file includes a first table, which represents the correspondence between keywords in the test suite name and performance standard values ​​and data analysis methods.

[0145] Here, the first table may include multiple keywords, multiple performance standard values, and multiple data analysis methods. Each keyword corresponds to at least one performance standard value and at least one data analysis method.

[0146] Based on this, such as Figure 2 As shown above, in the above Figure 1 Based on the illustrated embodiment, step 104 may include the following steps:

[0147] Step 1041: Determine the first keyword in the name of the target test suite.

[0148] In this embodiment, keywords can be representative core strings in the test suite name that can identify the core test type or function of the test suite.

[0149] The first keyword is a keyword extracted from the name of the target test suite. The first keyword can reflect the core characteristics of the target test suite. For example, for the target test suite name "DIO256_PMU_FIMV_MV_Rapid_TEST", the first keyword could be "FIMV".

[0150] Step 1042: For any keyword in the first table, determine the similarity between any keyword and the first keyword to obtain the similarity corresponding to any keyword; determine whether the similarity corresponding to any keyword meets the preset similarity conditions; if the similarity corresponding to any keyword meets the similarity conditions, then determine any keyword as the second keyword.

[0151] In this embodiment, similarity is an indicator that measures the degree of similarity between two keywords in terms of string composition, semantic association, etc. Here, the similarity between the first keyword and any other keyword can be calculated using algorithms such as edit distance, cosine similarity, or Jaccard similarity coefficient.

[0152] Here, the similarity between each keyword in all or part of the keywords in the first table and the first keyword can be calculated, thus obtaining the similarity score for each keyword. For example, each keyword in the first table can be iterated through, and its similarity score with the first keyword can be calculated one by one. Alternatively, the similarity scores between keywords and the first keyword can be calculated sequentially according to a preset order (e.g., random, or the order in which the keywords appear in the first table) until a keyword that meets the similarity criteria is found.

[0153] Similarity criteria are preset thresholds or rules used to determine whether two keywords meet matching requirements. For example, a similarity criterion could be that the similarity between any two keywords is greater than or equal to a preset similarity threshold. Alternatively, the similarity criterion could be set so that the top N (e.g., 1) similarity keywords are considered to meet the criteria.

[0154] The second keyword can be any keyword in the first table that meets the similarity criteria to the first keyword.

[0155] Step 1043: Determine the performance standard value and data analysis method corresponding to the second keyword in the first table as the performance standard value and data analysis method corresponding to the target test suite name, respectively.

[0156] In this embodiment, the performance standard value corresponding to the second keyword in the first table can be determined as the performance standard value corresponding to the target test suite name, and the data analysis method corresponding to the second keyword in the first table can be determined as the data analysis method corresponding to the target test suite name.

[0157] Based on the embodiments of this disclosure, by recording the correspondence between keywords, performance standard values, and data analysis methods in a first table set in the target configuration file, the first keyword is extracted and its similarity is calculated with the keywords in the first table. Keywords that meet the similarity conditions are filtered out, and then the corresponding performance standard values ​​and data analysis methods are obtained. Thus, by calculating similarity, fuzzy matching between keywords in the first table is achieved, solving the matching failure problem caused by differences in test suite names, expanding the adaptability of the configuration file, and ensuring that test suites with different names but similar keywords can all find corresponding performance standard values ​​and data analysis methods without needing to design different test schemes for different boards, thereby improving the universality of the board functional test scheme.

[0158] In some application scenarios of the above optional implementation methods, the target configuration file also includes a second table, which shows the correspondence between the test suite name, performance standard value, and data analysis method.

[0159] The second table is a table in the target configuration file used to record the correspondence between the complete test suite name, performance standard value, and data analysis method.

[0160] Based on this, before determining the first keyword in the target test suite name, the following steps can also be performed:

[0161] The first step is to determine whether the target test suite name is included in the second table.

[0162] Here, the presence of the target test suite name in the second table can be determined by performing a precise string comparison between the test suite name in the second table and the target test suite name.

[0163] The second step is to determine the first keyword in the target test suite name if the second table does not contain the target test suite name; if the second table contains the target test suite name, the performance standard value and data analysis method corresponding to the target test suite name in the second table are determined as the performance standard value and data analysis method corresponding to the target test suite name, respectively.

[0164] Understandably, in the above application scenario, the second table in the target configuration file allows for precise matching of the target test suite name with the test suite names in the second table. Before performing fuzzy matching, the system prioritizes checking whether the second table contains the target test suite name. This way, for test suites with clearly defined performance standards and data analysis methods, precise matching can directly retrieve the corresponding performance standards and data analysis methods, avoiding unnecessary fuzzy matching calculations and improving matching efficiency. Simultaneously, precise matching is based on the complete test suite name, avoiding potential biases from fuzzy matching and improving the accuracy of analysis rule matching. When precise matching fails, fuzzy matching is then initiated, achieving a complementary relationship between precise and fuzzy matching, balancing efficiency and the applicability of the test plan.

[0165] In some application scenarios of the above optional implementation methods, the following methods can be used to determine whether the similarity corresponding to any keyword meets the preset similarity conditions:

[0166] The first step is to perform case-insensitive string matching between any keyword and the first keyword.

[0167] String matching refers to the process of comparing two strings to see if they have consistency or a specific relationship according to preset rules. Case-insensitive string matching means that the difference between uppercase and lowercase characters is ignored when making the comparison.

[0168] As an example, to perform a case-insensitive match between the first keyword "FIMV" and the keyword "fimv" in the first table, we can either convert the strings to be of the same case and then compare them character by character, or use a case-insensitive regular expression for matching and comparison, thus determining that they match. Similarly, to perform a case-insensitive match between the first keyword "EIMV" and the keyword "fimv" in the table, we can determine that they do not match.

[0169] The second step is to determine whether any keyword matches the first keyword, and whether the similarity of any keyword meets the similarity condition. If any keyword does not match the first keyword, it is determined that the similarity of any keyword does not meet the similarity condition.

[0170] As an example, for the target test suite name "DIO256_PMU_FIMV_MV_Rapid_TEST" in the standard data file, we can first check in the second table whether there is a completely matching test suite name. If it exists, we directly obtain its corresponding performance standard value and data analysis method; if it does not exist, we extract the keyword "FIMI" from the target test suite name as the first keyword and perform keyword matching in the first table. We compare "FIMI" with each keyword in the first table in a case-insensitive string comparison. If there is a keyword with the same character sequence (case-insensitive) (e.g., fimi), it is determined that the similarity condition is met, and then the corresponding performance standard value and data analysis method are associated; if there is no completely matching item, it is considered that the similarity condition is not met.

[0171] In addition, if the number of identified second keywords is greater than or equal to 2, the first identified second keyword can be used as the standard, and a prompt message can be generated to warn of ambiguity caused by multiple matching items.

[0172] Understandably, in the above application scenarios, the similarity criteria are clearly defined as case-insensitive string matching, thus unifying the matching standard. In actual testing, test suite names from different projects may differ in capitalization (e.g., "FIMV" vs. "fimv"). This method avoids missing reasonable keyword matches due to capitalization issues, ensuring comprehensive matching. Simultaneously, the clear standard for exact string matching reduces errors caused by subjective judgment, lowers the probability of false matches, ensures the accuracy of matching results, and consequently guarantees the reliability of subsequent data analysis and performance testing results.

[0173] Alternatively, a combination of full-character keyword matching and length verification can be used. First, check if the character lengths of the two keywords are the same. If the lengths are the same, convert the first keyword and any keyword in the first table to the same case (e.g., all lowercase), and then compare them character by character. If the characters are identical and the lengths are the same, the similarity condition is met; if there are character differences or different lengths, the similarity condition is not met.

[0174] In some optional implementations of this embodiment, the data analysis method includes the upper specification limit calculation formula, the lower specification limit calculation formula, and the accuracy calculation formula. The measurement data set contains at least two measurement data groups, and each measurement data group includes the actual measurement value, the upper measurement limit value, the lower measurement limit value, and the measurement range.

[0175] The upper specification limit calculation formula is a mathematical expression used to calculate the upper specification limit of the board's performance indicators. As an example, the upper specification limit calculation formula can represent the correspondence between the upper limit of measurement, the lower limit of measurement, the measurement range, and the upper specification limit. For example, the upper specification limit calculation formula can be USL=((HL) / 2) / (2*R), where USL represents the upper specification limit, H represents the upper limit of measurement, L represents the lower limit of measurement, and R represents the measurement range; or, the upper specification limit calculation formula can also represent the correspondence between the upper limit of measurement, the measurement range, and the upper specification limit. For example, the upper specification limit calculation formula can be USL=H / 2 / (2*R), where USL represents the upper specification limit, H represents the upper limit of measurement, and R represents the measurement range.

[0176] The lower specification limit calculation formula is a mathematical expression used to calculate the lower specification limit of a circuit board's performance indicators. As an example, the lower specification limit calculation formula can represent the correspondence between the upper and lower measurement limits, measurement ranges, and the lower specification limit. For instance, the lower specification limit calculation formula could be LSL=-((HL) / 2) / (2*R), where LSL represents the lower specification limit, H represents the upper measurement limit, L represents the lower measurement limit, and R represents the measurement range. Alternatively, the lower specification limit calculation formula can also represent the correspondence between the lower measurement limit, measurement range, and the lower specification limit. For instance, the lower specification limit calculation formula could be LSL=-1*L / 2 / (2*R), where LSL represents the lower specification limit, L represents the lower measurement limit, and R represents the measurement range.

[0177] Accuracy calculation formulas are mathematical expressions used to calculate the measurement accuracy of circuit boards. As an example, an accuracy calculation formula can represent the correspondence between measurement data, upper limit measurement value, lower limit measurement value, measurement range, and accuracy. For instance, the accuracy calculation formula could be Accuancy = (T - (H + L) / 2) / (2 * R), where Accuancy represents accuracy, T represents measurement data, H represents the upper limit measurement value, L represents the lower limit measurement value, and R represents the measurement range.

[0178] A measurement data set is the basic unit in a measurement data collection, containing the actual measurement value, upper limit value, lower limit value, and measurement range for a single test item or test condition.

[0179] The measured actual value is the physical quantity value actually measured during the board testing process, reflecting the actual output state of the board under specific test conditions.

[0180] The upper limit of measurement is a preset maximum threshold that the actual measurement value of the board is allowed to reach, used to define the upper limit range of acceptable measurement results.

[0181] The measurement lower limit is a preset minimum threshold value that the actual measurement value of the board is allowed to reach, used to define the acceptable lower limit range of the measurement results.

[0182] Measurement ranges are levels of measurement ranges divided according to the required testing accuracy. Different ranges correspond to different measurement accuracies and ranges. For example, measurement ranges can be represented by the range of physical quantities such as current and voltage.

[0183] Based on this, such as Figure 3 As shown above, in the above Figure 1 Based on the illustrated embodiment, step 105 may include the following steps:

[0184] Step 1051: From at least two sets of measurement data, determine the set of measurement data used to calculate the specification limit, and obtain the target set of measurement data.

[0185] In this embodiment, the specification limits include an upper specification limit and a lower specification limit.

[0186] The target measurement data set is a set of measurement data selected from multiple measurement data sets and used to calculate the specification limits. The corresponding measurement range can keep the specification limits within a reasonable range.

[0187] Step 1052: Determine the target upper specification limit and the target lower specification limit; wherein, the target upper specification limit is obtained by the upper specification limit calculation formula based on the measurement range and the upper limit value in the target measurement data set; the target lower specification limit is obtained by the lower specification limit calculation formula based on the measurement range and the lower limit value in the target measurement data set.

[0188] In this embodiment, the target upper specification limit is an upper limit threshold for measuring the performance of the board, calculated based on the target measurement data set, and is one of the standards for judging whether the board performance meets the requirements.

[0189] The target lower specification limit is a lower threshold calculated based on the target measurement data set to measure the performance of the board. It is one of the standards for judging whether the performance of the board meets the target.

[0190] Step 1053: For any actual measurement value in at least two measurement data sets, based on any actual measurement value, the upper limit of measurement, the lower limit of measurement, and the measurement range in the target measurement data set, determine the calculation result of the accuracy calculation formula to obtain the target accuracy corresponding to any actual measurement value.

[0191] In this embodiment, the target accuracy is a numerical value reflecting the measurement precision of the circuit board, calculated based on the actual measurement value, the upper limit of measurement, the lower limit of measurement, and the measurement range of the target measurement data set. It can be used to quantify the deviation between the measured value and the reference value. Specifically, the accuracy calculation formula in the data analysis method can be used to determine the calculation result of the accuracy calculation formula based on any actual measurement value, the upper limit of measurement, the lower limit of measurement, and the measurement range in the target measurement data set. This calculation result is then used as the target accuracy corresponding to the actual measurement value.

[0192] Step 1054: Determine the performance test results of the board based on the target upper specification limit, the target lower specification limit, and the target accuracy and performance standard values ​​corresponding to multiple actual measurement values.

[0193] In this embodiment, the performance test results of the board can be determined by comparing the target accuracy corresponding to each actual measurement value with the performance standard value, and combining the range defined by the upper and lower specification limits of the target.

[0194] Based on the embodiments of this disclosure, three types of formulas and measurement data sets are defined in the data analysis method. The accuracy of specification limit calculation is ensured by screening the target measurement data set. The upper and lower specification limits of the target are calculated based on the target measurement data set, providing a more accurate threshold standard for performance evaluation. The measurement accuracy of the board is quantified by calculating the target accuracy corresponding to each actual measurement value. The performance test results are determined by combining the specification limits, target accuracy and performance standard values, so that the performance test results can more accurately reflect the actual performance of the board.

[0195] In some application scenarios of the above-mentioned optional implementation methods, the measurement data set used to calculate the specification limit can be determined from at least two measurement data sets in the following manner:

[0196] For any measurement setting in at least two measurement data sets, perform the following steps (including steps one through three) based on that measurement setting:

[0197] The first step is to determine the calculation result of the upper specification limit calculation formula based on any measurement range and the upper limit value of the measurement data group to which any measurement range belongs, and obtain the upper specification limit value corresponding to any measurement range.

[0198] The upper specification limit is a numerical value calculated based on the measurement range and upper limit value in the measurement data set, reflecting the upper limit reference value of performance at that measurement range. Each measurement range can correspond to one upper specification limit value.

[0199] The second step is to determine whether the upper limit value of any measurement range falls within the preset upper limit value range.

[0200] The upper limit range is a preset numerical range used to determine whether the upper limit of the specification is reasonable. The upper limit range can be determined based on industry standards, testing requirements, and board performance requirements.

[0201] The third step is to determine the measurement data group to which any measurement range belongs as the measurement data group used to calculate the specification limit if the upper limit value of any measurement range is within the upper limit range.

[0202] As an example, we can first determine whether the measurement data sets corresponding to each measurement range meet the preset screening criteria. For instance, we can iterate through the 5uA to 50mA measurement ranges and determine which range makes the USL value calculated using the USL formula fall within the expected upper limit range (e.g., 1%-10%). The measurement range that makes the USL value fall within the expected upper limit range is designated as the optimal range value (R). The measurement data set corresponding to this optimal range value is then designated as the target measurement data set for subsequent specification limit calculations. The target measurement data set selected in this way can improve the accuracy of specification limit setting and avoid misjudgments caused by data deviations. Simultaneously, this method enhances the consistency of performance evaluation across different batches of boards, providing more reliable data support for production quality control.

[0203] Understandably, in the above application scenarios, for each measurement data set, the upper limit of the specification can be calculated based on the measurement upper limit value, and its accuracy within the preset upper limit range can be verified. Measurement data sets that meet the criteria are then identified as target measurement data sets. This solution ensures that the upper limit of the specification corresponding to the measurement data set used to calculate the specification limit is within a reasonable range, avoiding the problem of unreasonable specification limits due to improper gear selection. This provides a more reliable data foundation for the accurate calculation of subsequent target upper and lower specification limits, thereby improving the accuracy of performance index calculations and performance test results.

[0204] Optionally, a two-way verification and filtering method can also be used. In addition to calculating the upper limit value of the specification and verifying whether it is within the upper limit value range, the lower limit value of the specification corresponding to each measurement data group can also be calculated and verified whether it is within the preset lower limit value range. Only when the upper limit value of the specification of a certain measurement data group is within the upper limit value range and the lower limit value of the specification is within the lower limit value range, is that measurement data group determined as the measurement data group for calculating the specification limit value.

[0205] In some application scenarios of the above-mentioned optional implementation methods, the performance standard value includes the process accuracy standard value, the first process capability index standard value without considering the offset, and the second process capability index standard value considering the offset.

[0206] The process accuracy standard value is a preset benchmark threshold that measures the deviation between the center value and the specification center value during the measurement process of the board, reflecting the acceptable range of deviation of the process mean.

[0207] The first process capability index standard value, which does not consider offset, is a preset benchmark threshold that measures the degree to which the board's measured process capability meets the specification range. It does not consider the offset of the process mean and only reflects the potential capability of the process.

[0208] The second process capability index standard value, which takes into account the offset, is a preset benchmark threshold that measures the degree to which the board's measurement process capability meets the specification range. It takes into account the offset of the process mean and reflects the actual capability of the process.

[0209] Based on this, the performance test results of the board can be determined using the following method, based on the target upper specification limit, the target lower specification limit, and the target accuracy and performance standard values ​​corresponding to multiple measured actual values:

[0210] The first step is to determine the process accuracy test value, the first process capability index test value without considering offset, and the second process capability index test value considering offset, based on the target upper specification limit, the target lower specification limit, and the target accuracy corresponding to multiple actual measurement values.

[0211] The process accuracy test value is a numerical value that reflects the degree of deviation between the center value and the specification center value during the measurement process of the board, based on the target upper specification limit, the target lower specification limit and the accuracy of multiple targets.

[0212] Here, the following formula (3) can be used to determine the process accuracy test value of the board based on the target upper specification limit, the target lower specification limit, and the target accuracy corresponding to multiple actual measurement values:

[0213] CA=(mean-(USL+LSL) / 2) / (USL-LSL) / 2

[0214] Formula (3)

[0215] In the above formula (3), CA represents the process accuracy test value, mean represents the average of the target accuracy corresponding to multiple actual measurement values, USL represents the target upper specification limit, and LSL represents the target lower specification limit.

[0216] The first process capability index test value, which does not consider offset, is a value that reflects the potential capability of the board measurement process, calculated based on the standard deviation of the target upper specification limit, the target lower specification limit, and the target accuracy.

[0217] Here, the following formula (4) can be used to determine the first process capability index test value of the board without considering offset, based on the target upper specification limit, the target lower specification limit, and the target accuracy corresponding to multiple actual measurement values:

[0218] CP = (USL - LSL) / 6 * Sgma

[0219] Formula (4)

[0220] In the above formula (4), CP represents the first process capability index test value without considering the offset, USL represents the target upper specification limit, LSL represents the target lower specification limit, and Sgma represents the standard deviation of the target accuracy corresponding to multiple measured actual values.

[0221] The second process capability index test value, which takes offset into account, is a value that reflects the actual capability of the board measurement process, calculated based on the mean and standard deviation of the target upper specification limit, target lower specification limit, and target accuracy.

[0222] Here, the following formula (5) can be used to determine the second process capability index test value of the board, taking offset into account, based on the target upper specification limit, the target lower specification limit, and the target accuracy corresponding to multiple actual measurement values:

[0223] CPK=std::min((USL-mean) / 3*Sgma,(U-LSL) / 3*Sgma)

[0224] Formula (5)

[0225] In the above formula (5), CPK represents the test value of the second process capability index considering the offset, USL represents the target upper specification limit, LSL represents the target lower specification limit, mean represents the average value of the target accuracy corresponding to multiple actual measurement values, Sgma represents the standard deviation of the target accuracy corresponding to multiple actual measurement values, and std::min represents taking the minimum value of the two.

[0226] The second step is to determine the performance test results of the board based on the process accuracy test value and the process accuracy standard value, the first process capability index test value and the first process capability index standard value, and the second process capability index test value and the second process capability index standard value.

[0227] Here, the comparison results of the process accuracy test value with the process accuracy standard value, the comparison results of the first process capability index test value with the first process capability index standard value, and the comparison results of the second process capability index test value with the second process capability index standard value can be directly determined as the performance test results of the board; or, based on the above three comparison results, a weighted scoring or threshold judgment method can be used to further quantify the pass rate of the board performance, thereby obtaining the performance test results of the board.

[0228] As can be understood, in the above application scenarios, the performance standard values ​​include standard values ​​for three core indicators: CA, CP, and CPK. The corresponding three types of test values ​​are calculated based on the target specification limits and target precision, and then compared one by one with the standard values. The CA indicator reflects the degree of process deviation, CP reflects potential capability, and CPK reflects actual capability. The comprehensive comparison of these three indicators enables a multi-dimensional evaluation of the board's performance. This process solves the problem of lacking clear criteria for performance evaluation, enabling performance test results to accurately reflect whether the board's process deviation, potential capability, and actual capability meet the standards, thus improving the comprehensiveness and accuracy of performance evaluation.

[0229] In some optional implementations of this embodiment, such as Figure 4 As shown above, in the above Figure 1 Based on the illustrated embodiment, one of the following steps may be included after step 105:

[0230] Step 106: Based on the performance test results, perform same-channel data analysis on the board.

[0231] Same-channel data analysis is a comparative analysis of performance test results for the same channel of a circuit board under one or more test times or one or more test conditions. Its aim is to discover the stability and consistency of single-channel performance. For example, same-channel data analysis could be the analysis of CPK value fluctuations across multiple tests of a channel. For instance, trend analysis of CPK values ​​over multiple test cycles of the same circuit board could reveal a continuous decline in the CPK of a certain channel, even if it remains above the standard value. This indicates a potential for gradual deterioration in that channel, requiring early warning and investigation of the cause. This shifts from passive judgment to proactive prediction, enhancing the foresight and reliability of the testing system.

[0232] Step 107: Based on the performance test results, perform inter-channel data analysis on the board.

[0233] Inter-channel data analysis is a comparative analysis of performance test results for multiple channels of a circuit board under the same test conditions. Its purpose is to identify performance differences and consistency between different channels. For example, inter-channel data analysis can be an analysis of the differences in CPK values ​​across multiple channels. For instance, by comparing the CPK values ​​of each channel, it can analyze whether there are individual channels with lower performance or that deviate from the overall trend, thereby determining whether the consistency between circuit board channels meets design requirements. This analysis can effectively identify potential hardware imbalances or manufacturing deviations, improving the overall reliability of the circuit board and the stability of mass production.

[0234] Based on the embodiments of this disclosure, after obtaining the performance test results, further data analysis can be performed within the same channel or between channels. Same-channel data analysis, by comparing multiple sets of performance results for the same channel, can identify performance fluctuations within the channel itself (such as excessively large deviations in CA values ​​between different batches of a certain channel), pinpointing stability issues in a single channel. Inter-channel data analysis, by comparing the performance results of different channels, can identify differences between channels (such as significantly lower CPK values ​​for some channels than others), pinpointing consistency issues between channels. This provides a more precise basis for troubleshooting, targeted repair, and performance optimization of the circuit board.

[0235] Figure 5 This is a schematic diagram of a board performance testing apparatus provided in an embodiment of this disclosure. The board performance testing apparatus of this disclosure can be used to implement the board performance testing method of any of the above embodiments. Figure 5 As shown, the performance testing device for the circuit board in this embodiment includes:

[0236] Acquisition unit 501 is configured to acquire a standard data file generated for testing the board, and parse the standard data file to obtain the target test suite name and measurement data set;

[0237] The first determining unit 502 is configured to determine the target board type to which the target test suite represented by the target test suite name belongs;

[0238] The second determining unit 503 is configured to determine the target configuration file corresponding to the target board type based on the association file representing the correspondence between the board type and the configuration file.

[0239] Parsing unit 504 is configured to parse the target configuration file to obtain the performance standard value and data analysis method corresponding to the target test suite name;

[0240] The third determining unit 505 is configured to determine the performance test results of the board based on the measurement data set and performance standard values ​​through data analysis.

[0241] In some optional implementations of this embodiment, the target configuration file includes a first table, which represents the correspondence between keywords in the test suite name and performance standard values ​​and data analysis methods. Figure 6 This is a schematic diagram of the structure of the parsing unit in a performance testing device for a circuit board provided in an embodiment of this disclosure. Figure 6 As shown, the parsing unit 504 includes:

[0242] The first determining subunit 5041 is configured to determine the first keyword in the name of the target test suite;

[0243] The second determining subunit 5042 is configured to determine the similarity between any keyword and the first keyword for any keyword in the first table, thereby obtaining the similarity corresponding to any keyword; determine whether the similarity corresponding to any keyword meets the preset similarity conditions; if the similarity corresponding to any keyword meets the similarity conditions, then determine any keyword as the second keyword.

[0244] The third determining subunit 5043 is configured to determine the performance standard value and data analysis method corresponding to the second keyword in the first table as the performance standard value and data analysis method corresponding to the target test suite name, respectively.

[0245] In some possible implementations, the target configuration file also includes a second table showing the correspondence between test suite names, performance benchmark values, and data analysis methods; and

[0246] The above-mentioned device also includes:

[0247] The fourth determining unit (not shown in the figure) is configured to determine whether the target test suite name is contained in the second table before determining the first keyword in the target test suite name;

[0248] The fifth determining unit (not shown in the figure) is configured to perform the step of determining the first keyword in the target test suite name if the target test suite name is not contained in the second table;

[0249] The sixth determining unit (not shown in the figure) is configured to determine the performance standard value and data analysis method corresponding to the target test suite name in the second table as the performance standard value and data analysis method corresponding to the target test suite name, respectively, if the second table contains the target test suite name.

[0250] In some possible implementations, the second determining subunit 5042 includes:

[0251] The matching module (not shown in the figure) is configured to perform case-insensitive string matching between any keyword and the first keyword;

[0252] The first determining module (not shown in the figure) is configured to determine that the similarity of any keyword meets the similarity condition if any keyword matches the first keyword.

[0253] The second determining module (not shown in the figure) is configured to determine that the similarity of any keyword does not meet the similarity condition if any keyword does not match the first keyword.

[0254] In some possible implementations, the data analysis methods include upper specification limit calculation formulas, lower specification limit calculation formulas, and accuracy calculation formulas. The measurement data set contains at least two measurement data sets, each including the actual measurement value, the upper measurement limit value, the lower measurement limit value, and the measurement range; and

[0255] The third determining unit 505 includes:

[0256] The fourth determining subunit (not shown in the figure) is configured to determine the measurement data set used to calculate the specification limit from at least two measurement data sets to obtain the target measurement data set;

[0257] The fifth determining subunit (not shown in the figure) is configured to determine the target upper specification limit and the target lower specification limit; wherein, the target upper specification limit is obtained by the upper specification limit calculation formula based on the measurement range and the upper limit value in the target measurement data set; the target lower specification limit is obtained by the lower specification limit calculation formula based on the measurement range and the lower limit value in the target measurement data set.

[0258] The sixth determining subunit (not shown in the figure) is configured to determine the calculation result of the accuracy calculation formula based on any actual measurement value in at least two measurement data sets, the upper limit of measurement, the lower limit of measurement, and the measurement range in the target measurement data set, so as to obtain the target accuracy corresponding to any actual measurement value.

[0259] The seventh determining subunit (not shown in the figure) is configured to determine the performance test results of the board based on the target upper specification limit, the target lower specification limit, and the target accuracy and performance standard values ​​corresponding to multiple actual measurement values.

[0260] In some possible implementations, the fourth determining subunit includes:

[0261] The processing module (not shown in the figure) is configured to target the measurement range in any of at least two sets of measurement data:

[0262] Based on any measurement range and the upper limit value of the measurement data group to which any measurement range belongs, determine the calculation result of the upper specification limit calculation formula, and obtain the upper specification limit value corresponding to any measurement range.

[0263] Determine whether the upper limit value of the specification corresponding to any measurement range falls within the preset upper limit value range;

[0264] If the upper limit value of the specification corresponding to any measurement range is within the upper limit range, then the measurement data group to which any measurement range belongs is determined as the measurement data group used to calculate the specification limit value.

[0265] In some possible implementations, the performance standard values ​​include a process accuracy standard value, a first process capability index standard value that does not consider offset, and a second process capability index standard value that considers offset; and

[0266] The seventh defined subunit includes:

[0267] The third determining module (not shown in the figure) is configured to determine the process accuracy test value of the board, the first process capability index test value without considering the offset, and the second process capability index test value considering the offset, based on the target upper specification limit, the target lower specification limit, and the target accuracy corresponding to multiple actual measurement values.

[0268] The fourth determining module (not shown in the figure) is configured to determine the performance test results of the board based on the process accuracy test value and the process accuracy standard value, the first process capability index test value and the first process capability index standard value, and the second process capability index test value and the second process capability index standard value.

[0269] In some possible implementations, the above-described apparatus further includes any one of the following:

[0270] The first analysis unit (not shown in the figure) is configured to perform same-channel data analysis on the board after determining the board's performance test results based on the measurement data set and performance standard values ​​through data analysis.

[0271] The second analysis unit (not shown in the figure) is configured to perform inter-channel data analysis on the board after determining the board's performance test results based on the measurement data set and performance standard values ​​through data analysis.

[0272] The board performance testing device provided in this embodiment can execute the corresponding steps of the board performance testing methods described above, thereby achieving the technical effects of the board performance testing methods described above. The board performance testing device and the board performance testing methods can refer to and cite each other in terms of specific implementation and technical effects. For the sake of brevity, they will not be elaborated here.

[0273] In addition, this disclosure also provides an electronic device, including:

[0274] Memory, used to store computer programs;

[0275] A processor is configured to execute a computer program stored in the memory, and when the computer program is executed, to implement the board performance testing method described in any of the above embodiments of this disclosure.

[0276] Figure 7 This is a schematic diagram illustrating the structure of an application embodiment of the electronic device disclosed herein. Below, reference is made to… Figure 7 This describes an electronic device according to embodiments of the present disclosure. The electronic device may be either or both of a first device and a second device, or a standalone device independent of them, which may communicate with the first device and the second device to receive acquired input signals from them.

[0277] like Figure 7 As shown, the electronic device includes one or more processors and memory.

[0278] A processor can be a central processing unit (CPU) or other form of processing unit with data processing and / or instruction execution capabilities, and can control other components in an electronic device to perform desired functions.

[0279] The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and a processor may execute the program instructions to implement the board performance testing methods and / or other desired functions of the various embodiments of this disclosure described above.

[0280] In one example, the electronic device may also include input devices and output devices, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0281] In addition, the input device may include, for example, a keyboard, a mouse, etc.

[0282] This output device can output various information to the outside, including determined distance information, direction information, etc. The output device may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0283] Of course, for the sake of simplicity, Figure 7Only some of the components of the electronic device relevant to this disclosure are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device may include any other suitable components depending on the specific application.

[0284] In addition to the methods and apparatus described above, embodiments of this disclosure may also be computer program products, including computer program instructions that, when executed by a processor, cause the processor to perform the steps in the board performance testing methods according to various embodiments of this disclosure as described in the foregoing sections of this specification.

[0285] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this disclosure. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on a user's computing device, partially on a user's computing device, as a standalone software package, partially on a user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0286] Furthermore, embodiments of this disclosure may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps in the board performance testing methods according to various embodiments of this disclosure as described in the foregoing portion of this specification.

[0287] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0288] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0289] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0290] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus embodiments, since they largely correspond to the method embodiments, the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0291] The block diagrams of devices, apparatuses, devices, and systems disclosed herein are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0292] The methods and apparatus of this disclosure may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of this disclosure are not limited to the order specifically described above unless otherwise specifically stated. Furthermore, in some embodiments, this disclosure may also be implemented as a program recorded on a recording medium, the program including machine-readable instructions for implementing the methods according to this disclosure. Thus, this disclosure also covers recording media storing programs for performing the methods according to this disclosure.

[0293] It should also be noted that in the apparatus, devices, and methods of this disclosure, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions to this disclosure.

[0294] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0295] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for testing the performance of a circuit board, characterized in that, include: Obtain the standard data file generated by testing the board, and parse the standard data file to obtain the target test suite name and measurement data set; Determine the target board type to which the target test suite, represented by the target test suite name, belongs; Based on the associated file representing the correspondence between board type and configuration file, the target configuration file corresponding to the target board type is determined; Parse the target configuration file to obtain the performance standard value and data analysis method corresponding to the target test suite name; The performance test results of the board are determined based on the measured data set and the performance standard values ​​using the data analysis method described above.

2. The method according to claim 1, characterized in that, The target configuration file includes a first table, which represents the correspondence between keywords in the test suite name and performance standard values ​​and data analysis methods. as well as The process of parsing the target configuration file to obtain the performance standard value and data analysis method corresponding to the target test suite name includes: Determine the first keyword in the name of the target test suite; For any keyword in the first table, determine the similarity between the keyword and the first keyword to obtain the similarity corresponding to the keyword; determine whether the similarity corresponding to the keyword meets the preset similarity condition; if the similarity corresponding to the keyword meets the similarity condition, then determine the keyword as the second keyword; The performance standard value and data analysis method corresponding to the second keyword in the first table are respectively determined as the performance standard value and data analysis method corresponding to the name of the target test suite.

3. The method according to claim 2, characterized in that, The target configuration file also includes a second table, which shows the correspondence between test suite names, performance standard values, and data analysis methods; as well as Before determining the first keyword in the name of the target test suite, the method further includes: Determine whether the target test suite name is contained in the second table; If the second table does not contain the target test suite name, then proceed with the step of determining the first keyword in the target test suite name; If the second table contains the name of the target test suite, then the performance standard value and data analysis method corresponding to the name of the target test suite in the second table are respectively determined as the performance standard value and data analysis method corresponding to the name of the target test suite.

4. The method according to claim 2, characterized in that, Determining whether the similarity corresponding to any keyword meets the preset similarity condition includes: Perform case-insensitive string matching between any of the keywords and the first keyword; If any of the keywords matches the first keyword, then the similarity corresponding to any of the keywords is determined to satisfy the similarity condition; If any of the keywords does not match the first keyword, then it is determined that the similarity corresponding to any of the keywords does not meet the similarity condition.

5. The method according to any one of claims 1-4, characterized in that, The data analysis method includes the upper specification limit calculation formula, the lower specification limit calculation formula, and the accuracy calculation formula. The measurement data set contains at least two measurement data groups, and each measurement data group includes the actual measurement value, the upper measurement limit value, the lower measurement limit value, and the measurement range. as well as The process of determining the performance test results of the board based on the measurement data set and the performance standard values ​​through the data analysis method includes: From at least two sets of measurement data, determine the set of measurement data used to calculate the specification limits to obtain the target set of measurement data; Determine the target upper specification limit and the target lower specification limit; wherein, the target upper specification limit is obtained by the upper specification limit calculation formula based on the measurement range and the upper limit value of the target measurement data set; the target lower specification limit is obtained by the lower specification limit calculation formula based on the measurement range and the lower limit value of the target measurement data set; For any actual measurement value in at least two measurement data sets, based on the actual measurement value, the upper limit of measurement, the lower limit of measurement, and the measurement range in the target measurement data set, the calculation result of the accuracy calculation formula is determined to obtain the target accuracy corresponding to the actual measurement value. The performance test results of the board are determined based on the target upper specification limit, the target lower specification limit, the target accuracy corresponding to multiple measured actual values, and the performance standard value.

6. The method according to claim 5, characterized in that, The step of determining the measurement data set for calculating the specification limit from at least two measurement data sets includes: For the measurement range in any of at least two measurement data sets: Based on any of the measurement gears and the upper limit value of the measurement data group to which any of the measurement gears belong, the calculation result of the upper specification limit calculation formula is determined, and the upper specification limit value corresponding to any of the measurement gears is obtained. Determine whether the upper limit value of the specification corresponding to any of the measurement gears falls within the preset upper limit value range; If the upper limit value of the specification corresponding to any of the measurement gears is within the upper limit value range, then the measurement data group to which any of the measurement gears belongs is determined as the measurement data group used to calculate the specification limit value.

7. The method according to claim 5, characterized in that, The performance standard values ​​include process accuracy standard values, a first process capability index standard value without considering offset, and a second process capability index standard value considering offset. as well as The process of determining the performance test results of the board based on the target upper specification limit, the target lower specification limit, the target accuracy corresponding to multiple measured actual values, and the performance standard value includes: Based on the target upper specification limit, the target lower specification limit and the target accuracy corresponding to multiple actual measured values, the process accuracy test value, the first process capability index test value without considering offset and the second process capability index test value considering offset of the board are determined respectively. The performance test results of the board are determined based on the process accuracy test value and the process accuracy standard value, the first process capability index test value and the first process capability index standard value, and the second process capability index test value and the second process capability index standard value.

8. The method according to any one of claims 1-4, characterized in that, After determining the performance test results of the board based on the measurement data set and the performance standard value through the data analysis method, the method further includes any one of the following: Based on the performance test results, the board was subjected to same-channel data analysis; Based on the performance test results, inter-channel data analysis was performed on the board.

9. A performance testing device for circuit boards, characterized in that, include: The acquisition unit is configured to acquire a standard data file generated for testing the board, and parse the standard data file to obtain the target test suite name and measurement data set. The first determining unit is configured to determine the target board type to which the target test suite represented by the target test suite name belongs; The second determining unit is configured to determine the target configuration file corresponding to the target board type based on an association file representing the correspondence between the board type and the configuration file. The parsing unit is configured to parse the target configuration file to obtain the performance standard value and data analysis method corresponding to the target test suite name; The third determining unit is configured to determine the performance test results of the board based on the measurement data set and the performance standard value through the data analysis method.

10. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing a computer program stored in the memory, wherein when the computer program is executed, it implements the method described in any one of claims 1-8.

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