Link data imaging method, device and medium for chip design verification

Abstract

The present application provides a link data visualization method, device and medium for chip design verification, and can be applied to high-speed serial communication link characteristic analysis in the field of chip design verification technology. The method includes: obtaining multiple data sets of the communication link; determining the target independent variable parameter dimension from at least two independent variable parameter dimensions, and determining the target dependent variable parameter dimension from at least one dependent variable parameter dimension; determining the target subset of each data set in the multiple data sets according to the target independent variable parameter dimension and the target dependent variable parameter dimension; constructing a parameter image according to the target subset of each data set; and displaying the parameter image. Based on the scheme of the present application, the law of the interaction between the signal transmission influencing parameters and the quality evaluation parameters can be intuitively displayed through the parameter image, thereby improving the efficiency of screening and optimizing the signal transmission influencing parameters.

Description

Technical Field

[0001] The present application relates to the field of chip design verification technology, and in particular to a link data visualization method, device and medium applied to chip design verification. Background Art

[0002] The communication link between chips will show different characteristics under the influence of different signal transmission influencing parameters. In order to find suitable signal transmission influencing parameters to improve the communication link, relevant technicians need to screen the signal transmission influencing parameters one by one, which is undoubtedly inefficient. Summary of the invention

[0003] The present application provides a link data visualization method, device and medium for chip design verification. The parameter image provided by the method can effectively guide relevant technical personnel to find suitable signal transmission influencing parameters, thereby optimizing communication link performance and improving the efficiency of screening and optimizing signal transmission influencing parameters.

[0004] In a first aspect, a link data visualization method for chip design verification is provided, comprising:

[0005] Acquire multiple data sets of the communication link, wherein the data sets include at least two independent variable parameter dimensions and at least one dependent variable parameter dimension, the parameters corresponding to the independent variable parameter dimensions are signal transmission influencing parameters, the parameters corresponding to the dependent variable parameter dimensions are quality evaluation parameters, and the quality evaluation parameters are determined according to a test signal corresponding to the signal transmission influencing parameters transmitted in the communication link;

[0006] determining a target independent variable parameter dimension from at least two independent variable parameter dimensions, and determining a target dependent variable parameter dimension from at least one dependent variable parameter dimension;

[0007] Determine a target subset of each of the multiple data sets according to the target independent variable parameter dimension and the target dependent variable parameter dimension;

[0008] Construct parameter images based on the target subset of each data set;

[0009] Displays the parameter image.

[0010] In combination with the first aspect, in some possible implementations, at least two independent variable parameter dimensions of each data set contain at least one high-level independent variable parameter dimension other than the target independent variable parameter dimension; determining a target subset of each data set in the multiple data sets according to the target independent variable parameter dimension and the target dependent variable parameter dimension, including: determining a directory hierarchy structure according to at least two independent variable parameter dimensions and at least one dependent variable parameter dimension of the data set, wherein the directory hierarchy structure includes directory hierarchies corresponding to each high-level independent variable parameter dimension in at least one high-level independent variable parameter dimension, and directory hierarchies corresponding to both the target independent variable parameter dimension and the target dependent variable parameter dimension, and the directory hierarchy structure corresponding to both the target independent variable parameter dimension and the target dependent variable parameter dimension is the last directory hierarchy structure; determining non-repeating parameters corresponding to each high-level independent variable parameter dimension according to each data set; recursively expanding the directory according to the non-repeating parameters corresponding to each high-level independent variable parameter dimension, each data set, and the directory hierarchy structure to create directories corresponding to each directory hierarchy in the directory hierarchy structure; determining the target subset of each data set according to the directories corresponding to the last directory hierarchy of each data set.

[0011] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, there is at least one high-level independent variable parameter dimension other than the target independent variable parameter dimension in at least two independent variable parameter dimensions of each data set; according to the target independent variable parameter dimension and the target dependent variable parameter dimension, a target subset of each data set in the multiple data sets is determined, including: converting the parameters of the high-level independent variable parameter dimension in each data set into the annotation information of each data set respectively to eliminate the parameters of the high-level independent variable parameter dimension in each data set; and determining each data set with the annotation information attached and the parameters of the high-level independent variable parameter dimension eliminated as the target subset of each data set.

[0012] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, constructing a parameter image according to a target subset of each data set includes: determining at least one image construction task, and a target subset of the data set corresponding to each image construction task in at least one image construction task; and constructing a parameter image corresponding to each image construction task according to the target subset of the data set corresponding to each image construction task.

[0013] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, the parameter image is a three-dimensional stereo image; the parameter image corresponding to each image construction task is constructed according to the target subset of the data set corresponding to each image construction task, including: determining the coordinate points of each image construction task in the horizontal direction according to the signal transmission influence parameters corresponding to the target independent variable parameter dimension in the target subset of the data set corresponding to the image construction task; determining the coordinate points of each image construction task in the vertical direction according to the quality evaluation parameters corresponding to the target dependent variable parameter dimension in the target subset of the data set corresponding to each image construction task; rendering the three-dimensional stereo image corresponding to each image construction task according to the coordinate points of each image construction task in the horizontal direction and the coordinate points in the vertical direction.

[0014] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, the parameter image is a three-dimensional stereo image; the parameter image corresponding to each image construction task is constructed according to the target subset of the data set corresponding to each image construction task, including: determining the coordinate points of each image construction task in the horizontal direction according to the signal transmission influence parameters corresponding to the target independent variable parameter dimension in the target subset of the data set corresponding to the image construction task; determining the coordinate points of each image construction task in the vertical direction and the visual effect information of the coordinate points of each image construction task in the vertical direction according to the quality evaluation parameters corresponding to the target dependent variable parameter dimension in the target subset of the data set corresponding to each image construction task; rendering the three-dimensional stereo image corresponding to each image construction task according to the coordinate points of each image construction task in the horizontal direction, the coordinate points of each image construction task in the vertical direction, and the visual effect information of the coordinate points of each image construction task in the vertical direction.

[0015] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, the parameter image is a contour image; and the parameter image corresponding to each image construction task is constructed according to the target subset of the data set corresponding to each image construction task, including: determining the coordinate points of each image construction task in the horizontal direction according to the signal transmission influence parameters corresponding to the target independent variable parameter dimension in the target subset of the data set corresponding to the image construction task; determining the elevation value of the coordinate point of each image construction task in the horizontal direction according to the quality evaluation parameters corresponding to the target dependent variable parameter dimension in the target subset of the data set corresponding to each image construction task; and rendering the contour images corresponding to each image construction task according to the coordinate points of each image construction task in the horizontal direction and the elevation values ​​of the coordinate points of each image construction task in the horizontal direction.

[0016] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, after determining the target independent variable parameter dimension from at least two independent variable parameter dimensions, and determining the target dependent variable parameter dimension from at least one dependent variable parameter dimension, before determining the target subset of each data set in multiple data sets based on the target independent variable parameter dimension and the target dependent variable parameter dimension, it also includes: if there is an empty value in the parameter corresponding to the target dependent variable parameter dimension, obtaining a non-empty default value; assigning the empty value in the parameter corresponding to the target dependent variable parameter dimension according to the default value.

[0017] In a second aspect, an electronic device is provided, the electronic device comprising:

[0018] A memory for storing executable program codes;

[0019] The processor is used to call and run the executable program code from the memory, so that the electronic device executes any one of the link data imaging methods as described above.

[0020] In a third aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed, any of the link data visualization methods described above is implemented.

[0021] The beneficial effects brought by the technical solutions provided by some embodiments of the present application include at least: first, by obtaining multiple data sets of the communication link and clarifying the independent variable parameter dimensions (i.e., signal transmission influencing parameters) and dependent variable parameter dimensions (i.e., quality evaluation parameters) in the data set, a data basis is provided for subsequent in-depth analysis. Secondly, the target independent variable parameter dimensions and the target dependent variable parameter dimensions are determined from multiple parameter dimensions, so that the analysis can focus on the most critical influencing factors, improving the pertinence and efficiency of the analysis. Furthermore, by extracting the target subset of each data set, the amount of data is further streamlined, making subsequent processing and image construction more efficient. Finally, according to the target subset of each data set, the parameter image is constructed and displayed, which can intuitively show the association between the signal transmission influencing parameters and the quality evaluation parameters, as well as the law of the interaction and change between the two parameters. In this way, the parameter image can effectively guide relevant technical personnel to find suitable signal transmission influencing parameters, thereby optimizing the performance of the communication link and improving the efficiency of screening and optimizing the signal transmission influencing parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0023] Figure 1 It is a flowchart of a link data imaging method for chip design verification provided by an embodiment of the present application;

[0024] Figure 2 It is a schematic diagram of a process of determining a target subset based on a directory provided in an embodiment of the present application;

[0025] Figure 3 This is an example schematic diagram of a directory hierarchy structure provided in an embodiment of the present application;

[0026] Figure 4 It is a schematic diagram of a process of determining a target subset based on annotations provided in an embodiment of the present application;

[0027] Figure 5 It is a schematic diagram of a process of constructing a parameter image according to an image construction task provided in an embodiment of the present application;

[0028] Figure 6 It is a schematic diagram of a process of constructing a three-dimensional image provided by an embodiment of the present application;

[0029] Figure 7 is an example schematic diagram of a three-dimensional stereoscopic image provided in an embodiment of the present application;

[0030] Figure 8 It is a schematic diagram of a process of constructing a three-dimensional image with visual effects provided by an embodiment of the present application;

[0031] Fig. 9 is an exemplary schematic diagram of a three-dimensional image with visual effects provided in an embodiment of the present application;

[0032] Fig.10 It is a schematic diagram of a process of constructing a contour image provided by an embodiment of the present application;

[0033] Fig.11 is an example schematic diagram of a contour image provided in an embodiment of the present application;

[0034] Fig.12 is an exemplary schematic diagram of multiple three-dimensional stereoscopic images provided in an embodiment of the present application;

[0035] Fig.13 is an example schematic diagram of multiple contour line images provided in an embodiment of the present application;

[0036] Fig.14 This is a schematic diagram of a process for assigning a null value provided in an embodiment of the present application;

[0037] Fig.15 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to make the features and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0039] When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are only examples of electronic devices and methods consistent with some aspects of the present application as detailed in the attached claims.

[0040] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood as suggesting or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features.

[0041] The following are detailed descriptions respectively. It should be noted that the description order of the following embodiments is not intended to limit the preferred order of the embodiments.

[0042] The following will be combined Figure 1 - Fig.12 , a detailed introduction is given to the link data visualization method for chip design verification provided in an embodiment of the present application.

[0043] See also Figure 1 , Figure 1 A flowchart of a link data visualization method for chip design verification provided in an embodiment of the present application. Figure 1 As shown, the method of the embodiment of the present application may include the following steps S101-S105.

[0044] S101, obtaining multiple data sets of a communication link, wherein the data sets include at least three parameter dimensions, and the at least three parameter dimensions can be further subdivided into at least two independent variable parameter dimensions and at least one dependent variable parameter dimension. The parameters corresponding to the independent variable parameter dimension are signal transmission influencing parameters, and the parameters corresponding to the dependent variable parameter dimension are quality evaluation parameters, and the quality evaluation parameters are determined according to the test signals corresponding to the signal transmission influencing parameters transmitted in the communication link.

[0045] Specifically, the communication link involved in this embodiment is used for communication between the first chip and the second chip. Among them, the first chip and the second chip have different manifestations in different application scenarios. For example, in the Chip-to-Chip (C2C, chip to chip) application scenario, it is specifically manifested as a chip, in the Die-to-Die (D2D, bare die to bare die) application scenario, it is specifically manifested as a bare die, and in the Chiplet (core particle) application scenario, it is specifically manifested as a core particle (or small chip). It can be understood that the communication link involved in this embodiment can be a high-speed serial communication link, such as a serializer / deserializer link (Serializer / Deserializer link, referred to as SerDes link).

[0046] The communication link between the first chip and the second chip refers to the data transmission channel connecting the first chip and the second chip. The communication link realizes data transmission and interaction through a specific communication protocol to ensure that the signal is transmitted between the two chips accurately and efficiently. It is a key component of inter-chip communication, and its performance directly affects the overall operating efficiency and stability of the system.

[0047] This embodiment first needs to obtain multiple data sets of communication links, and any one of the multiple data sets includes at least two independent variable parameter dimensions and at least one dependent variable parameter dimension. Among them, the parameters corresponding to the independent variable parameter dimension are signal transmission impact parameters, and the parameters corresponding to the dependent variable parameter dimension are quality evaluation parameters.

[0048] For any one of the multiple data sets, the first chip sends a test signal to the second chip under the constraint of the signal transmission influencing parameter of the data set, and the quality evaluation parameter of the data set can be obtained by evaluating the quality of the test signal. Among them, the signal transmission influencing parameter can be a serial number of a printed circuit board assembly (PCBA SN for short), an identification code of a transmitter (or an ID number of a transmitter), ambient temperature, ambient humidity, multiple equalization parameters of a transmitter (such as a main coefficient main-cursor, a pre-emphasis coefficient pre-cursor, and a de-emphasis coefficient post-cursor), an identification code of a receiver (or an ID number of a receiver), and one or more parameters of multiple equalization parameters of a receiver [such as a decision feedback equalizer (DFE) parameter, a continuous time linear equalizer (CTLE) parameter], all of which may affect the quality of the test signal; the quality evaluation parameter can be one or more parameters such as the eye opening of the test signal, the mean value of the eye opening of the test signal, the bit error rate of the test signal, and the clock synchronization parameter of the test signal, all of which reflect the quality of the test signal.

[0049] It can be understood that the parameter dimensions of each acquired data set are the same, that is, each data set has the same data structure.

[0050] S102, determining a target independent variable parameter dimension from at least two independent variable parameter dimensions, and determining a target dependent variable parameter dimension from at least one dependent variable parameter dimension.

[0051] Specifically, in order to construct a parameter image, it is necessary to determine a certain number of target independent variable parameter dimensions from at least two independent variable parameter dimensions, and to determine a certain number of target dependent variable parameter dimensions from at least one dependent variable parameter dimension.

[0052] For example, if a two-dimensional parameter image needs to be constructed, a target independent variable parameter dimension may be determined from at least two independent variable parameter dimensions, and a target dependent variable parameter dimension may be determined from at least one dependent variable parameter dimension;

[0053] For another example, if a three-dimensional parameter image needs to be constructed, two target independent variable parameter dimensions may be determined from at least two independent variable parameter dimensions, and one target dependent variable parameter dimension may be determined from at least one dependent variable parameter dimension;

[0054] For another example, if a four-dimensional parameter image needs to be constructed, two target independent variable parameter dimensions may be determined from at least two independent variable parameter dimensions, and two target dependent variable parameter dimensions may be determined from at least one dependent variable parameter dimension.

[0055] It is understandable that for a three-dimensional parameter image, a four-dimensional parameter image or a higher-dimensional parameter image, the number of target independent variable parameter dimensions that need to be determined is two, while the number of target dependent variable parameter dimensions can be one or more. For a four-dimensional parameter image or a higher-dimensional parameter image, visual feedback similar to that of a three-dimensional parameter image is still provided, wherein the parameters of the target independent variable parameter dimension that are additional to the three-dimensional parameter image will be used to provide visual effect information. For example, adding visual effect information of the parameters of an additional target independent variable parameter dimension to the three-dimensional parameter image can constitute a four-dimensional parameter image, and the visual effect information can be expressed based on transparency, color, text annotation, etc.

[0056] S103, determining a target subset of each data set in the multiple data sets according to the target independent variable parameter dimension and the target dependent variable parameter dimension.

[0057] Specifically, the target subset is a subset consisting of the signal transmission impact parameters corresponding to the target independent variable parameter dimension and the quality evaluation parameters corresponding to the target dependent variable parameter dimension. Therefore, the target subset of each data set can be determined according to the target independent variable parameter dimension and the target dependent variable parameter dimension.

[0058] For any one of the multiple data sets, the number of parameter dimensions of the target subset corresponding to the data set can be less than or equal to the number of parameter dimensions of the data set. For example, for a data set with 5 parameter dimensions, the number of parameter dimensions of the target subset corresponding to the data set can be 3; for another example, for a data set with 3 parameter dimensions, the number of parameter dimensions of the target subset corresponding to the data set can be 3.

[0059] S104, constructing a parameter image according to the target subset of each data set.

[0060] Specifically, in order to intuitively display the relationship between the signal transmission influencing parameters and the quality evaluation parameters, this embodiment will perform visualization processing based on the target subset of each data set. According to the different parameter dimensions of the target subset of each data set, the visual features of the parameter image in different directions are determined, and then the parameter image is rendered.

[0061] In a possible implementation, the parameter image may be a two-dimensional parameter image, which displays the relationship between the signal transmission influencing parameter and the quality evaluation parameter in a planar manner, as well as the law of the interaction between the two parameters.

[0062] In a possible implementation, the parameter image may be a three-dimensional parameter image. The three-dimensional parameter image shows the relationship between the signal transmission influencing parameter and the quality evaluation parameter, as well as the law of the interaction between the two parameters in a three-dimensional or two-dimensional manner. Compared with the two-dimensional parameter image, the three-dimensional parameter image can present more information, such as the law of the quality evaluation parameter corresponding to a target dependent variable parameter dimension under the influence of the signal transmission influencing parameter corresponding to two target independent variable parameter dimensions.

[0063] In a possible implementation, the parameter image may be a four-dimensional parameter image or a higher-dimensional parameter image. For the four-dimensional parameter image or the higher-dimensional parameter image, visual feedback similar to that of the three-dimensional parameter image is still provided, wherein the parameters of the target independent variable parameter dimension that are additional to the three-dimensional parameter image are used to provide visual effect information. For example, adding visual effect information of the parameters of an additional target independent variable parameter dimension to the three-dimensional parameter image may constitute a four-dimensional parameter image, and the visual effect information may be expressed based on transparency, color, text annotation, and the like.

[0064] S105, displaying a parameter image.

[0065] Specifically, the parameter image can be displayed through a computer display screen, a projector or other visualization equipment. More specifically, different display modes and resolutions can be selected according to the properties of the parameter image and the needs of relevant technicians.

[0066] In some cases, if only one parameter image needs to be displayed, it can be directly displayed in full screen or in a window so that relevant technicians can clearly see all the details of the parameter image.

[0067] In some cases, if multiple parameter images need to be displayed simultaneously, a split-screen approach can be used to place each parameter image in a different window; or, multiple parameter images can be displayed simultaneously by overlaying, side by side, etc.; or, multiple parameter images can be displayed simultaneously in a tree structure for comparison and analysis.

[0068] In some cases, the technician may only be interested in a certain part of the parameter image. Therefore, a selective display function may be provided to allow the technician to select and display a specific parameter image or a part of the parameter image as needed.

[0069] In some cases, in order to meet different observation and analysis requirements, the relevant technicians can switch different parameter images or adjust the display mode of the parameter images through manual operation. The manual operation may include zooming in, zooming out, rotating, adjusting color mapping, etc., so that the relevant technicians can understand and analyze the information in the parameter image more deeply.

[0070] Regarding the parameters of the parameter dimension in the data set of this embodiment, some optional examples are provided below:

[0071] (1) Signal transmission influencing parameters of independent variable parameter dimension:

[0072] The signal transmission influencing parameter of the independent variable parameter dimension 1 is the pre-emphasis coefficient of the transmitter: the pre-emphasis coefficient of the transmitter is one of the equalization parameters of the transmitter, which is used to adjust the signal output of the transmitter to optimize the quality of the signal during the transmission process.

[0073] The signal transmission influencing parameter of the independent variable parameter dimension 2 is the de-emphasis coefficient of the transmitter: the de-emphasis coefficient of the transmitter is one of the equalization parameters of the transmitter, which is used to adjust the signal output of the transmitter to optimize the quality of the signal during the transmission process.

[0074] The signal transmission influencing parameter of independent variable parameter dimension 3 is the main coefficient of the transmitter: the main coefficient of the transmitter is one of the equalization parameters of the transmitter, which is used to adjust the signal output of the transmitter to optimize the quality of the signal during the transmission process.

[0075] The signal transmission influencing parameter of independent variable parameter dimension 4 is the continuous time linear equalizer (CTLE) parameter of the receiver: the continuous time linear equalizer parameter of the receiver is one of the equalization parameters of the receiver, which is used to perform equalization processing on the received signal at the receiver end to restore the original quality of the signal.

[0076] The signal transmission influencing parameter of independent variable parameter dimension 5 is the decision feedback equalizer (DFE) parameter of the receiver: the decision feedback equalizer parameter of the receiver is one of the equalization parameters of the receiver, which is used to perform equalization processing on the received signal at the receiver end to restore the original quality of the signal.

[0077] The signal transmission influencing parameter of independent variable parameter dimension 6 is the identification code of the transmitter (or the ID number of the transmitter): it represents one or more information such as the manufacturer, model, version, etc. of the transmitter. Different manufacturers, models, and versions may have different effects on the transmission quality of the signal.

[0078] The signal transmission influencing parameter of independent variable parameter dimension 7 is the identification code of the receiver (or the ID number of the receiver): similar to the transmitter, it characterizes one or more information such as the manufacturer, model, version, etc. of the receiver, which may also affect the signal reception quality.

[0079] The signal transmission influencing parameter of independent variable parameter dimension 8 is ambient temperature: changes in ambient temperature may affect the transmission and reception quality of the signal, so it is also a signal transmission influencing parameter that needs to be considered.

[0080] The signal transmission influencing parameter of independent variable parameter dimension 9 is ambient humidity: in addition to temperature, humidity may also have a certain impact on the transmission quality of the signal, especially in certain specific application scenarios.

[0081] The signal transmission influencing parameter of independent variable parameter dimension 10 is the serial number of the printed circuit board assembly (PCBASN for short): the printed circuit board assembly is equipped with a transmitter and a receiver. The serial number of the printed circuit board assembly is a unique identifier of the printed circuit board assembly, which is used to track and identify a specific printed circuit board assembly. Different serial numbers may represent different production batches, production processes or quality control standards, all of which may have a certain impact on the transmission quality and reception quality of the signal. Therefore, considering the serial number of the printed circuit board assembly in the signal transmission influencing parameters helps to more comprehensively evaluate and optimize the signal transmission performance.

[0082] (2) Quality evaluation parameters regarding the dependent variable parameter dimension:

[0083] The quality evaluation parameter of dependent variable parameter dimension 1 is the eye opening of the test signal: the eye diagram is a graphical representation method used to describe the quality of digital signals. The eye opening reflects the amplitude variation range of the signal within a specific time window and is one of the important indicators for evaluating signal quality.

[0084] The quality evaluation parameter of dependent variable parameter dimension 2 is the average eye opening of the test signal: the average value of the eye opening of the test signal over a period of time or at multiple test points, which is used to more comprehensively evaluate the overall quality of the signal.

[0085] The quality evaluation parameter of dependent variable parameter dimension 3 is the eye diagram quality evaluation parameter of the test signal: the eye diagram quality evaluation factor is a quantitative value determined based on the eye diagram opening, the clock synchronization state during the test signal reception process, and the error reception state during the test signal reception process, and is used to more comprehensively evaluate the overall quality of the signal.

[0086] The quality evaluation parameter of dependent variable parameter dimension 5 is the bit error rate of the test signal: the bit error rate refers to the ratio of the number of received erroneous code elements to the total number of transmitted code elements during signal transmission, and is one of the important indicators for evaluating signal transmission quality.

[0087] The quality evaluation parameter of dependent variable parameter dimension 6 is the clock synchronization parameter of the test signal: the clock synchronization parameter reflects the clock synchronization between the transmitter and the receiver, which is of great significance for ensuring the accurate transmission and reception of the signal. The quality of the clock synchronization parameter directly affects the transmission quality of the signal and the overall performance of the system.

[0088] It should be noted that the above-mentioned transmitter refers to the first chip, and the receiver refers to the second chip.

[0089] In a possible implementation, a database containing the above-mentioned multiple data sets can be constructed, that is, the database stores the parameters of all the above-mentioned independent variable parameter dimensions and dependent variable parameter dimensions. On the basis of constructing the database, a relevant operation interface can be provided, and relevant technical personnel can perform dimension selection operations in the operation interface. Accordingly, in response to the dimension selection operation of the relevant technical personnel in the operation interface, the target independent variable parameter dimension is determined from the independent variable parameter dimensions stored in the database, and the target dependent variable parameter dimension is determined from the dependent variable parameter dimensions stored in the database.

[0090] Further, in combination with the scheme of this embodiment, the target subset of the data set in the multiple data sets can be determined according to the target independent variable parameter dimension and the target dependent variable parameter dimension; according to the target subset of each data set, the parameter image is constructed and displayed. In this way, it is equivalent to providing a link data graphical query method for relevant technical personnel. The relevant technical personnel can flexibly select the target independent variable parameter dimension and the target dependent variable parameter dimension, and construct and display the parameter image in combination with the data provided by the above database. Through the displayed parameter image, the relevant technical personnel can intuitively see the law of the interaction and change between the signal transmission influencing parameters and the quality evaluation parameters, and then find a combination of signal transmission influencing parameters that meets the needs.

[0091] In this embodiment, first, by obtaining multiple data sets of the communication link and clarifying the independent variable parameter dimensions (i.e., signal transmission influencing parameters) and dependent variable parameter dimensions (i.e., quality evaluation parameters) in the data sets, a data basis is provided for subsequent in-depth analysis. Secondly, the target independent variable parameter dimensions and the target dependent variable parameter dimensions are determined from multiple parameter dimensions, so that the analysis can focus on the most critical influencing factors, improving the pertinence and efficiency of the analysis. Furthermore, by extracting the target subset of each data set, the amount of data is further streamlined, making subsequent processing and image construction more efficient. Finally, according to the target subset of each data set, the parameter image is constructed and displayed, which can intuitively show the association between the signal transmission influencing parameters and the quality evaluation parameters, as well as the law of the interaction between the two parameters. In this way, the parameter image can effectively guide relevant technical personnel to find suitable signal transmission influencing parameters, thereby optimizing the performance of the communication link and improving the efficiency of screening and optimizing the signal transmission influencing parameters.

[0092] See also Figure 2 , which is a schematic diagram of a process for determining a target subset based on a directory according to an embodiment of the present application. Among the at least two independent variable parameter dimensions of each data set, there is at least one high-level independent variable parameter dimension other than the target independent variable parameter dimension. Figure 2 As shown, the method of the embodiment of the present application may include the following steps S201-S203, and steps S201-S203 may be used as Figure 1 The refinement of step S103 in the illustrated embodiment.

[0093] S201, determining a directory hierarchical structure according to at least two independent variable parameter dimensions and at least one dependent variable parameter dimension of a data set, wherein the directory hierarchical structure includes directory hierarchies corresponding to each high-level independent variable parameter dimension in at least one high-level independent variable parameter dimension, and directory hierarchies corresponding to both the target independent variable parameter dimension and the target dependent variable parameter dimension, and the directory hierarchical level corresponding to both the target independent variable parameter dimension and the target dependent variable parameter dimension is the last directory hierarchical level in the directory hierarchical structure;

[0094] S202, determining non-repetitive parameters corresponding to each advanced independent variable parameter dimension according to each data set;

[0095] S203, recursively expand the directory according to the non-repeated parameters corresponding to each high-level independent variable parameter dimension, each data set, and the directory hierarchy structure, so as to create a directory corresponding to each directory level in the directory hierarchy structure;

[0096] S204, determining a target subset of each data set according to the directory corresponding to each data set at the last directory level.

[0097] Specifically, first, a directory hierarchy is determined based on at least two independent variable parameter dimensions and at least one dependent variable parameter dimension of the data set. The directory hierarchy is the basis for organizing and accessing data in subsequent steps. The directory hierarchy not only includes the directory hierarchies corresponding to each high-level independent variable parameter dimension in at least one high-level independent variable parameter dimension, but also pays special attention to the target independent variable parameter dimension and the target dependent variable parameter dimension. The directory hierarchy corresponding to them is set as the last directory hierarchy in the directory hierarchy. In other words, at the bottom of the directory hierarchy, data directly related to the target independent variable parameter dimension and the target dependent variable parameter dimension will be found, and these data are exactly the target subsets that need to be further analyzed and visualized.

[0098] Next, determine the non-repeating parameters corresponding to each advanced independent variable parameter dimension. Non-repeating parameters refer to the non-repeating parameters that appear in different data sets in each advanced independent variable parameter dimension. They represent the shared parts between data sets on these advanced independent variable parameter dimensions. By identifying non-repeating parameters, data can be organized and managed more effectively, because data sets with parameters of the same advanced independent variable parameter dimension will be classified into the same directory.

[0099] For example, for a certain advanced independent variable parameter dimension, the parameters of all data sets under this advanced independent variable parameter dimension are A1, A2, A3, A3, A2, A1, A3, A2, A1, A2, etc., which do not include other parameters except A1, A2, and A3. Then, after excluding some repeated parameters, it can be obtained that there are 3 non-repeated parameters corresponding to this advanced independent variable parameter dimension, and these 3 non-repeated parameters are A1, A2, and A3.

[0100] Furthermore, the directory is recursively expanded according to the non-repeating parameters corresponding to each high-level independent variable parameter dimension, each data set, and the directory hierarchy structure. The directory recursive expansion is a process of creating directories step by step. It starts from the root directory of the directory hierarchy structure and creates corresponding directories in sequence according to the non-repeating parameters corresponding to each high-level independent variable parameter dimension. And according to the relationship between the directories under the directory hierarchy corresponding to each data set and each high-level independent variable parameter dimension, a directory containing the parameters of the target independent variable parameter dimension and the parameters of the target dependent variable parameter dimension is created at the last directory level of the directory hierarchy structure. This process ensures that the directory structure can fully reflect the classification of the data set in all parameter dimensions.

[0101] Finally, the target subset of each data set is determined according to the directory corresponding to each data set in the last directory level. Since the last directory level is determined by the target independent variable parameter dimension and the target dependent variable parameter dimension, the target subset containing these two key parameter dimensions can be determined in the directory corresponding to this directory level. The target subset is exactly the target subset that needs to be further analyzed and visualized because they are directly related to the signal transmission influencing parameters and quality evaluation parameters of interest. Through this step, the most critical data subset for analysis can be effectively extracted from the original large number of data sets, providing a solid foundation for the subsequent parameter image construction and communication link performance optimization.

[0102] For easier understanding of this embodiment, please refer to Figure 3 , Figure 3 This is an example schematic diagram of a directory hierarchy structure provided in an embodiment of the present application. There are 12 data sets involved, and the parameters contained in each data set are as follows:

[0103] Dataset 1 {A1, B1, C1, D1, E1};

[0104] Dataset 2 {A1, B1, C2, D2, E2};

[0105] Dataset 3 {A1, B1, C3, D3, E3};

[0106] Dataset 4 {A1, B2, C4, D4, E4};

[0107] Dataset 5 {A1, B2, C5, D5, E5};

[0108] Dataset 6 {A1, B2, C6, D6, E6};

[0109] Dataset 7 {A2, B1, C7, D7, E7};

[0110] Dataset 8 {A2, B1, C8, D8, E8};

[0111] Dataset 9 {A2, B1, C9, D9, E9};

[0112] Dataset 10 {A2, B2, C10, D10, E10};

[0113] Dataset 11{A2, B2, C11, D11, E11};

[0114] Dataset 12 {A2, B2, C12, D12, E12}.

[0115] In each of the above data sets, the first parameter starting with "A" corresponds to the first high-level independent variable parameter dimension, the second parameter starting with "B" corresponds to the second high-level independent variable parameter dimension, the third parameter starting with "C" corresponds to the first target independent variable parameter dimension, the fourth parameter starting with "D" corresponds to the second target independent variable parameter dimension, and the fifth parameter starting with "E" corresponds to the target dependent variable parameter dimension.

[0116] It can be seen that the non-repeating parameters of the first high-level independent variable parameter dimension are A1 and A2, and the non-repeating parameters of the second high-level independent variable parameter dimension are B1 and B2. Then, in the way of recursive directory expansion, two directories will be created after the root directory, corresponding to A1 and A2 respectively; then, two directories will be created after the directory corresponding to A1, corresponding to B1 and B2 respectively, and two directories will be created after the directory corresponding to A2, corresponding to B1 and B2 respectively.

[0117] Next, follow the directory path of A1 and B1 and create three directories after the directory corresponding to B1. One directory contains C1, D1, and E1; one directory contains C2, D2, and E2; and one directory contains C3, D3, and E3.

[0118] Following the directory paths of A1 and B1, create three directories after the directory corresponding to B1. One directory contains C4, D4, and E4; one directory contains C5, D5, and E5; and one directory contains C6, D6, and E6.

[0119] And so on, along the directory path of A2 and B2, three directories are created after the directory corresponding to B2, one directory contains C10, D10, and E10, one directory contains C11, D11, and E11, and one directory contains C12, D12, and E12.

[0120] In this way, the target subset of each data set can be determined according to the directory corresponding to each data set in the last directory level, as follows:

[0121] The target subset corresponding to dataset 1 is {C1, D1, E1};

[0122] The target subset corresponding to dataset 2 is {C2, D2, E2};

[0123] The target subset corresponding to dataset 3 is {C3, D3, E3};

[0124] The target subset corresponding to dataset 4 is {C4, D4, E4};

[0125] The target subset corresponding to dataset 5 is {C5, D5, E5};

[0126] The target subset corresponding to dataset 6 is {C6, D6, E6};

[0127] The target subset corresponding to dataset 7 is {C7, D7, E7};

[0128] The target subset corresponding to dataset 8 is {C8, D8, E8};

[0129] The target subset corresponding to dataset 9 is {C9, D9, E9};

[0130] The target subset corresponding to dataset 10 is {C10, D10, E10};

[0131] The target subset corresponding to dataset 11 is {C11, D11, E11};

[0132] The target subset corresponding to dataset 12 is {C12, D12, E12}.

[0133] In this embodiment, firstly, by determining the directory hierarchy structure, the parameter dimensions of the data set are orderly classified to build a clear data organization framework. Secondly, by identifying the non-repeating parameters corresponding to each high-level independent variable parameter dimension, and according to the non-repeating parameters corresponding to each high-level independent variable parameter dimension, each data set and the directory hierarchy structure, the target subset of each data set is determined by recursive directory expansion, which realizes the rapid positioning and extraction of key data and provides data support for the subsequent parameter image construction.

[0134] See also Figure 4 , which is a flow chart of determining a target subset based on annotations according to an embodiment of the present application. Among the at least two independent variable parameter dimensions of each data set, there is at least one high-level independent variable parameter dimension other than the target independent variable parameter dimension. Figure 4 As shown, the method of the embodiment of the present application may include the following steps S301-S302, and steps S301-S302 may be used as Figure 1 The refinement of step S103 in the illustrated embodiment.

[0135] S301, respectively converting the parameters of the high-level independent variable parameter dimension in each data set into the annotation information of each data set to eliminate the parameters of the high-level independent variable parameter dimension in each data set;

[0136] S302 , determining each data set with attached annotation information and after eliminating the parameters of the high-level independent variable parameter dimension as a target subset of each data set.

[0137] Specifically, the parameters of the high-level independent variable parameter dimensions in each data set are first converted into the annotation information of each data set. The purpose of this conversion process is to eliminate the high-level independent variable parameter dimensions in the data set, so that the data set is more concise in subsequent analysis and processing. High-level independent variable parameter dimensions refer to independent variable parameter dimensions other than the target independent variable parameter dimensions. Although they exist in the data set, they generally do not directly constitute the content of the subsequent parameter image. By converting the parameters of these high-level independent variable parameter dimensions into annotation information, the focus of the data set can be focused on the target independent variable parameter dimensions and the target dependent variable parameter dimensions without worrying about the interference of other non-critical parameter dimensions.

[0138] Specifically, annotation information is a kind of metadata attached to a dataset, which is used to provide additional information or description about the dataset. By adding the parameter values ​​of the advanced independent variable parameter dimension to the dataset as annotation information, the parameters of the original advanced independent variable parameter dimension are replaced by the annotation information and are no longer part of the dataset. This conversion method makes it possible to simplify the structure of the dataset to only contain the target independent variable parameter dimension and the target dependent variable parameter dimension while retaining the advanced independent variable parameter dimension information, which facilitates subsequent analysis and visualization.

[0139] Next, each data set that is accompanied by annotation information and has eliminated the parameters of the high-level independent variable parameter dimension is determined as the target subset of each data set. The target subset refers to the part of the data set that contains the target independent variable parameter dimension and the target dependent variable parameter dimension of interest, and is the focus of subsequent analysis and visualization. Through the above-mentioned conversion process, the parameters of the high-level independent variable parameter dimension in the data set have been converted into annotation information, and these parameter dimensions have been eliminated. Therefore, in step S302, only those parts of the data set that are accompanied by annotation information and only contain the parameters of the target independent variable parameter dimension and the parameters of the target dependent variable parameter dimension need to be determined. These parts are the target subsets of the data set. Since the target subsets only contain the key parameter dimensions of interest, they can more directly reflect the relationship between the signal transmission influencing parameters and the quality evaluation parameters of interest.

[0140] To facilitate understanding of the present embodiment, an example is provided below: Assuming there are 12 data sets, the parameters included in each data set are as follows:

[0141] Dataset 1 {A1, B1, C1, D1, E1};

[0142] Dataset 2 {A1, B1, C2, D2, E2};

[0143] Dataset 3 {A1, B1, C3, D3, E3};

[0144] Dataset 4 {A1, B2, C4, D4, E4};

[0145] Dataset 5 {A1, B2, C5, D5, E5};

[0146] Dataset 6 {A1, B2, C6, D6, E6};

[0147] Dataset 7 {A2, B1, C7, D7, E7};

[0148] Dataset 8 {A2, B1, C8, D8, E8};

[0149] Dataset 9 {A2, B1, C9, D9, E9};

[0150] Dataset 10 {A2, B2, C10, D10, E10};

[0151] Dataset 11{A2, B2, C11, D11, E11};

[0152] Dataset 12 {A2, B2, C12, D12, E12}.

[0153] In each of the above data sets, the first parameter starting with "A" corresponds to the first high-level independent variable parameter dimension, the second parameter starting with "B" corresponds to the second high-level independent variable parameter dimension, the third parameter starting with "C" corresponds to the first target independent variable parameter dimension, the fourth parameter starting with "D" corresponds to the second target independent variable parameter dimension, and the fifth parameter starting with "E" corresponds to the target dependent variable parameter dimension.

[0154] For data set 1, the corresponding annotation information can be determined based on {A1, B1}, and the target subset {C1, D1, E1} corresponding to data set 1 can be obtained (the annotation information is "A1, B1");

[0155] For dataset 2, the corresponding annotation information can be determined based on {A1, B1}, and the target subset {C2, D2, E2} corresponding to dataset 1 can be obtained (the annotation information is "A1, B1");

[0156] And so on, for data set 12, the corresponding annotation information can be determined based on {A2, B2}, and the target subset {C12, D12, E12} corresponding to data set 12 can be obtained (the annotation information is "A2, B2").

[0157] In this embodiment, the parameters of the high-level independent variable parameter dimensions in the data set are first converted into annotation information, thereby eliminating the influence of these non-critical parameter dimensions on the subsequent parameter image construction. At the same time, by determining the target subset of the data set with annotation information, the key data is located and extracted, providing data support for the subsequent parameter image construction.

[0158] See also Figure 5 , which provides a schematic diagram of a process for constructing a parameter image according to an image construction task in an embodiment of the present application, such as Figure 5 As shown, the method of the embodiment of the present application may include the following steps S401-S402, and steps S401-S402 may be used as Figure 1 The refinement of step S104 in the illustrated embodiment.

[0159] S401, determining at least one image construction task and a target subset of a data set corresponding to each image construction task in the at least one image construction task;

[0160] S402 , constructing parameter images corresponding to each image construction task according to the target subset of the data set corresponding to each image construction task.

[0161] Specifically, at least one image construction task and a target subset of a data set corresponding to each image construction task in at least one image construction task are first determined. The image construction task is used to indicate that corresponding parameters are extracted from a data set according to specific requirements or goals, and then an image that can reflect the characteristics and relationships of the parameters is constructed. The target subset of the data set is the most critical data part for subsequent analysis and visualization extracted from the original data set through a series of processing and screening in the previous steps.

[0162] Furthermore, parameter images corresponding to each image construction task are constructed according to the target subset of the data set corresponding to each image construction task. A parameter image is an image that can intuitively display parameter characteristics and relationships. It displays the key parameter dimensions (i.e., target independent variable parameter dimensions and target dependent variable parameter dimensions) in the data set in the form of an image, so that relevant technicians can understand and analyze the data more clearly. In this step, various image construction techniques and tools can be used to construct parameter images that meet the requirements of the image construction task based on the data in the target subset of the data set.

[0163] The following are two optional implementations for determining at least one image building task:

[0164] In a possible implementation, the corresponding image construction task is determined according to the directory in the second-to-last directory level in the directory hierarchy structure. In other words, the number of image construction tasks is the same as the number of directories in the second-to-last directory level in the directory hierarchy structure. In this way, the parameter image can be constructed using the parameters of the target independent variable parameter dimension and the parameters of the target dependent variable parameter dimension in the last directory level in the directory hierarchy structure. The advantage of this implementation is that it can effectively organize and manage image construction tasks through the directory hierarchy structure, so that each image construction task corresponds to a parameter combination of the high-level independent variable parameter dimension, and the parameter combinations on the high-level independent variable parameter dimension corresponding to different image construction tasks are different, thereby improving the diversity and richness of parameter image construction.

[0165] In another possible implementation, the corresponding image construction task is determined based on the target subset of the data set with the same annotation information. That is to say, the target subsets of the data set with the same annotation information are all assigned to the same image construction task. The advantage of this implementation is that it can effectively divide and classify the target subsets of the data set through the annotation information, so that the target subsets of the data set with the same annotation information can correspond to the same image construction task, thereby improving the consistency and comparability of the image construction tasks. At the same time, this implementation can also ensure that each image construction task corresponds to a parameter combination of the high-level independent variable parameter dimension, and the parameter combinations on the high-level independent variable parameter dimension corresponding to different image construction tasks are different, thereby improving the diversity and richness of parameter image construction.

[0166] In this embodiment, at least one image construction task and the target subset of the data set corresponding to each task are first determined, providing a clear direction and data basis for the subsequent parameter image construction. By constructing a parameter image based on the target subset of the data set corresponding to each image construction task, the parameter characteristics and relationships can be intuitively displayed, improving the effect of data analysis and visualization.

[0167] See also Figure 6 , which is a schematic diagram of a process for constructing a three-dimensional stereoscopic image according to an embodiment of the present application. In which, the parameter image is a three-dimensional stereoscopic image. Figure 6 As shown, the method of the embodiment of the present application may include the following steps S501-S502, and steps S501-S502 may be used as Figure 5 The refinement of step S402 in the illustrated embodiment.

[0168] S501, determining the coordinate points of each image construction task in the horizontal direction according to the signal transmission influence parameter corresponding to the target independent variable parameter dimension in the target subset of the data set corresponding to the image construction task; determining the coordinate points of each image construction task in the vertical direction according to the quality evaluation parameter corresponding to the target dependent variable parameter dimension in the target subset of the data set corresponding to each image construction task;

[0169] S502 : Rendering to obtain a three-dimensional image corresponding to each image construction task according to the coordinate points of each image construction task in the horizontal direction and the coordinate points of each image construction task in the vertical direction.

[0170] Specifically, this embodiment aims to demonstrate the relationship between the signal transmission influencing parameters and the quality evaluation parameters through a three-dimensional stereoscopic image.

[0171] The three-dimensional image is rendered by the coordinate points of the X-axis, Y-axis, and Z-axis, where the X-axis and Y-axis correspond to the horizontal direction, and the Z-axis corresponds to the vertical direction.

[0172] In this embodiment, the number of target independent variable parameter dimensions is two, and the number of target dependent variable parameter dimensions is one. Based on the signal transmission influence parameter corresponding to the first target independent variable parameter dimension, the coordinate point on the X-axis is determined; based on the signal transmission influence parameter corresponding to the second target independent variable parameter dimension, the coordinate point on the Y-axis is determined; based on the quality evaluation parameter corresponding to the target dependent variable parameter dimension, the coordinate point on the Z-axis is determined.

[0173] Furthermore, after the coordinate points of the X-axis, Y-axis, and Z-axis are determined, the three-dimensional image corresponding to the image construction task can be rendered based on these coordinate points. The three-dimensional image construction method can be extended to each image construction task, so the three-dimensional image corresponding to each image construction task can be constructed.

[0174] In an exemplary implementation, see Figure 7 , Figure 7 It is an example schematic diagram of a three-dimensional stereoscopic image provided by an embodiment of the present application. The signal transmission influencing parameter corresponding to the first target independent variable parameter dimension is the pre-emphasis coefficient of the transmitter, and the value range is 0 to 7; the signal transmission influencing parameter corresponding to the second target independent variable parameter dimension is the de-emphasis coefficient of the transmitter, and the value range is 0 to 14; the quality evaluation parameter corresponding to the target dependent variable parameter dimension is the eye diagram quality evaluation parameter of the test signal. Then, the three-dimensional stereoscopic image finally constructed will reflect the law that the eye diagram quality evaluation parameter of the test signal changes under the influence of the pre-emphasis coefficient of the transmitter and the de-emphasis coefficient of the transmitter. The higher the fluctuation in the vertical direction, the larger the eye diagram quality evaluation parameter of the test signal, and the better the quality of the corresponding test signal.

[0175] In this embodiment, by constructing a three-dimensional image, the variation pattern between the signal transmission influencing parameters and the quality evaluation parameters can be displayed in a very intuitive way, so that relevant technicians can quickly complete the screening and optimization of the signal transmission influencing parameters according to the content of the three-dimensional image.

[0176] See also Figure 8 , provides a schematic diagram of a process for constructing a three-dimensional image with visual effects in an embodiment of the present application. Wherein, the parameter image is a three-dimensional image. Figure 8 As shown, the method of the embodiment of the present application may include the following steps S601-S602, and steps S601-S602 may be used as Figure 5 The refinement of step S402 in the illustrated embodiment.

[0177] S601, determining the coordinate points of each image construction task in the horizontal direction according to the signal transmission influence parameter corresponding to the target independent variable parameter dimension in the target subset of the data set corresponding to the image construction task; determining the coordinate points of each image construction task in the vertical direction according to the quality evaluation parameter corresponding to the target dependent variable parameter dimension in the target subset of the data set corresponding to each image construction task, and the visual effect information of the coordinate points of each image construction task in the vertical direction;

[0178] S602, rendering to obtain a three-dimensional stereoscopic image corresponding to each image construction task according to the coordinate points of each image construction task in the horizontal direction, the coordinate points of each image construction task in the vertical direction, and the visual effect information of the coordinate points of each image construction task in the vertical direction.

[0179] Specifically, this embodiment aims to demonstrate the relationship between the signal transmission influencing parameters and the quality evaluation parameters through a three-dimensional stereoscopic image. The three-dimensional stereoscopic image is rendered by coordinate points of the X-axis, Y-axis, and Z-axis, where the X-axis and Y-axis correspond to the horizontal direction, and the Z-axis corresponds to the vertical direction.

[0180] In this embodiment, the number of target independent variable parameter dimensions is two, and the number of target dependent variable parameter dimensions is two or more. Based on the signal transmission influence parameter corresponding to the first target independent variable parameter dimension, the coordinate point on the X-axis is determined; based on the signal transmission influence parameter corresponding to the second target independent variable parameter dimension, the coordinate point on the Y-axis is determined; based on the quality evaluation parameter corresponding to the first target dependent variable parameter dimension, the coordinate point on the Z-axis is determined; based on the quality evaluation parameters corresponding to other target dependent variable parameter dimensions other than the first target dependent variable parameter dimension, the visual effect information of the coordinate point on the Z-axis is determined.

[0181] Furthermore, after determining the coordinate points of the X-axis, Y-axis, and Z-axis and the visual effect information of the coordinate points on the Z-axis, the three-dimensional image corresponding to the image construction task can be rendered based on the visual effect information of these coordinate points and the coordinate points on the Z-axis. The three-dimensional image construction method can be extended to each image construction task, so that the three-dimensional image corresponding to each image construction task can be constructed.

[0182] It should be noted that the above-mentioned visual effect information may be color effect information, transparency effect information or mouse pointer hover effect information.

[0183] Specifically, the color effect information means that different colors can be assigned to the coordinate points on the Z axis, and the colors are used to represent the quality evaluation parameters corresponding to the target dependent variable parameter dimensions other than the first target dependent variable parameter dimension. For example, if the quality evaluation parameters corresponding to the target dependent variable parameter dimensions other than the first target dependent variable parameter dimension perform better, they can be represented by colors with lower recognition (such as gray); if the quality evaluation parameters corresponding to the target dependent variable parameter dimensions other than the first target dependent variable parameter dimension perform poorly, they can be represented by colors with higher recognition (such as red). This enables the coordinate points on the Z axis to display different colors, making it easier for relevant technicians to identify different parameter performances based on color visual feedback.

[0184] Transparency effect information means that different transparencies can be set for the coordinate points on the Z axis, and the transparency is used to characterize the quality evaluation parameters corresponding to the other target dependent variable parameter dimensions other than the first target dependent variable parameter dimension. For example, if the quality evaluation parameters corresponding to the other target dependent variable parameter dimensions other than the first target dependent variable parameter dimension perform better, they can be represented by a lower transparency (for example, 20% transparency, where 0% transparency means completely opaque and 100% transparency means completely transparent); if the quality evaluation parameters corresponding to the other target dependent variable parameter dimensions other than the first target dependent variable parameter dimension perform poorly, they can be represented by a higher transparency (for example, 70% transparency). This enables the coordinate points on the Z axis to display different transparencies, making it easier for relevant technical personnel to identify different parameter performances based on the visual feedback of transparency.

[0185] It should be noted that the quality evaluation parameters corresponding to the other target dependent variable parameter dimensions other than the first target dependent variable parameter dimension above perform better / poorly, which can be judged based on the following method: first, the quality evaluation parameters corresponding to the other target dependent variable parameter dimensions other than the first target dependent variable parameter dimension are normalized to obtain a normalized value, and the size of the normalized value is positively correlated with the performance of the parameter. Further, the normalized value is compared with a given threshold. If the normalized value is greater than or equal to the given threshold, it is determined that the quality evaluation parameters corresponding to the other target dependent variable parameter dimensions other than the first target dependent variable parameter dimension perform better; if the normalized value is less than the given threshold, it is determined that the quality evaluation parameters corresponding to the other target dependent variable parameter dimensions other than the first target dependent variable parameter dimension perform poorly.

[0186] The mouse pointer hovering effect information means that when the relevant technical personnel hover the mouse pointer over a coordinate point on the Z axis of the three-dimensional stereoscopic image, additional information can be displayed. The additional information is determined based on the quality evaluation parameters corresponding to the other target dependent variable parameter dimensions other than the first target dependent variable parameter dimension. In this way, the coordinate point on the Z axis can hide or display the quality evaluation parameters corresponding to the other target dependent variable parameter dimensions other than the first target dependent variable parameter dimension, which is convenient for the relevant technical personnel to distinguish different parameter performances based on the visual feedback of the additional information. At the same time, this mouse pointer hovering effect setting allows the relevant technical personnel to obtain additional information without clicking, which greatly reduces the complexity of the operation. In the case of a large number of parameters, the relevant technical personnel can also quickly complete the acquisition of relevant information.

[0187] In an exemplary implementation, see Fig. 9 , Fig. 9It is an example schematic diagram of a three-dimensional image with visual effects provided by an embodiment of the present application. The signal transmission influencing parameter corresponding to the first target independent variable parameter dimension is the pre-emphasis coefficient of the transmitter, and the value range is 0 to 7; the signal transmission influencing parameter corresponding to the second target independent variable parameter dimension is the de-emphasis coefficient of the transmitter, and the value range is 0 to 14; the quality evaluation parameter corresponding to the first target dependent variable parameter dimension is the eye diagram quality evaluation parameter of the test signal; the quality evaluation parameters corresponding to other target dependent variable parameter dimensions other than the first target dependent variable parameter dimension can be the eye diagram opening of the test signal, the eye diagram opening mean of the test signal, the bit error rate of the test signal, the clock synchronization parameter of the test signal, etc., which are not limited to this. Then, the three-dimensional image finally constructed will reflect the law that the eye diagram quality evaluation parameter of the test signal changes under the influence of the pre-emphasis coefficient of the transmitter and the de-emphasis coefficient of the transmitter. The higher the fluctuation in the vertical direction, the larger the eye diagram quality evaluation parameter of the test signal, and the better the quality of the corresponding test signal. When the mouse pointer 001 hovers over a coordinate point on the Z axis, a display box 002 containing additional information corresponding to the coordinate point on the Z axis will be displayed, and the additional information contains the content of quality evaluation parameters corresponding to other target dependent variable parameter dimensions. When the mouse pointer 001 leaves the coordinate point on the Z axis, the display box 002 will be hidden.

[0188] In this embodiment, by constructing a three-dimensional image, the change rules between the signal transmission influencing parameters and the quality evaluation parameters can be displayed in a very intuitive way. At the same time, the visual effect information can provide higher-dimensional information, so that relevant technicians can quickly complete the screening and optimization of the signal transmission influencing parameters according to the content of the three-dimensional image.

[0189] See also Fig.10 , provides a schematic diagram of a process for constructing a contour image in an embodiment of the present application, such as Fig.10 As shown. The parameter image is a contour image. The method of the embodiment of the present application may include the following steps S701-S702, and steps S701-S702 may be used as Figure 5 The refinement of step S402 in the illustrated embodiment.

[0190] S701, determining the coordinate points of each image construction task in the horizontal direction according to the signal transmission influence parameter corresponding to the target independent variable parameter dimension in the target subset of the data set corresponding to the image construction task; determining the elevation value of the coordinate points of each image construction task in the horizontal direction according to the quality evaluation parameter corresponding to the target dependent variable parameter dimension in the target subset of the data set corresponding to each image construction task;

[0191] S702 : Rendering to obtain contour images corresponding to each image construction task according to the coordinate points of each image construction task in the horizontal direction and the elevation values ​​of the coordinate points of each image construction task in the horizontal direction.

[0192] Specifically, a three-dimensional image is an image format that directly presents multi-dimensional information. However, under certain viewing angles, a three-dimensional image may have an observation blind spot (for example, a peak visually blocks the background). Therefore, this embodiment provides a contour image to overcome the shortcomings of a three-dimensional image in the above situation and flexibly meet the needs of relevant technicians for image observation.

[0193] The contour image is rendered by the coordinate points of the X-axis and Y-axis and the elevation values ​​corresponding to the coordinate points of the X-axis and Y-axis, where the X-axis and Y-axis correspond to the horizontal direction and the Z-axis corresponds to the vertical direction.

[0194] In this embodiment, the number of target independent variable parameter dimensions is two, and the number of target dependent variable parameter dimensions is one. Based on the signal transmission influence parameter corresponding to the first target independent variable parameter dimension, the coordinate point on the X-axis is determined; based on the signal transmission influence parameter corresponding to the second target independent variable parameter dimension, the coordinate point on the Y-axis is determined; based on the quality evaluation parameter corresponding to the target dependent variable parameter dimension, the elevation value corresponding to the coordinate point on the X-axis and Y-axis is determined.

[0195] Furthermore, after determining the coordinate points of the X-axis and Y-axis and the elevation values ​​corresponding to the coordinate points of the X-axis and Y-axis, the contour line image corresponding to the image construction task can be rendered based on these coordinate points and elevation values. The method of constructing the contour line image can be extended to each image construction task, so the contour line image corresponding to each image construction task can be constructed.

[0196] In an exemplary implementation, see Fig.11 , Fig.11 It is an example schematic diagram of a contour image provided in an embodiment of the present application. The signal transmission influencing parameter corresponding to the first target independent variable parameter dimension is the pre-emphasis coefficient of the transmitter, and the value range is 0 to 7; the signal transmission influencing parameter corresponding to the second target independent variable parameter dimension is the de-emphasis coefficient of the transmitter, and the value range is 0 to 14; the quality evaluation parameter corresponding to the target dependent variable parameter dimension is the eye diagram quality evaluation parameter of the test signal. Then, the contour image finally constructed will reflect the law that the eye diagram quality evaluation parameter of the test signal changes under the influence of the pre-emphasis coefficient of the transmitter and the de-emphasis coefficient of the transmitter. The higher the elevation value represented by the contour line, the larger the eye diagram quality evaluation parameter of the test signal, and the better the quality of the corresponding test signal.

[0197] In this embodiment, by constructing a contour image, the variation pattern between the signal transmission influencing parameters and the quality evaluation parameters can be displayed in a very intuitive way, so that relevant technicians can quickly complete the screening and optimization of the signal transmission influencing parameters according to the content of the contour image.

[0198] Based on the above Figure 2 , Figure 6 - Fig.10 In some possible implementations of the illustrated embodiment, if a directory-based approach is used to determine a target subset of each data set, the ultimately constructed three-dimensional image or contour image can be displayed in a high-dimensional manner.

[0199] Specifically, Figure 3 The directory hierarchy of the data set is shown, where an image processing task can be determined along the directory path of A1 and B1, and then the corresponding parameter image 1 can be constructed; an image processing task can be determined along the directory path of A1 and B2, and then the corresponding parameter image 2 can be constructed; an image processing task can be determined along the directory path of A2 and B1, and then the corresponding parameter image 3 can be constructed; an image processing task can be determined along the directory path of A2 and B2, and then the corresponding parameter image 4 can be constructed. In other words, based on Figure 3 The directory hierarchy structure of the data set is shown, and four parameter images can be constructed. In some cases, multiple parameter images need to be displayed simultaneously so that relevant technicians can obtain more information. In this case, the constructed parameter images can be displayed in a high-dimensional manner according to the directory hierarchy structure.

[0200] For example, parameter image 1 and parameter image 2 corresponding to the A1 directory may be displayed simultaneously, so that relevant technicians can determine the law of the interaction between the signal transmission influencing parameters and the quality evaluation parameters under the influence of A1 from the displayed parameter image 1 and parameter image 2. For another example, parameter image 3 and parameter image 4 corresponding to the A2 directory may be displayed simultaneously, so that relevant technicians can determine the law of the interaction between the signal transmission influencing parameters and the quality evaluation parameters under the influence of A2 from the displayed parameter image 3 and parameter image 4.

[0201] Based on this principle, see Fig.12 and Fig.13 , Fig.12 is an example schematic diagram of multiple three-dimensional images provided in an embodiment of the present application. Fig.13 This is an example schematic diagram of multiple contour line images provided in an embodiment of the present application.

[0202] exist Fig.12In the figure, multiple three-dimensional images are displayed at the same time. The three-dimensional images are constructed based on the transmitter's pre-emphasis coefficient, the transmitter's de-emphasis coefficient, and the eye diagram quality evaluation parameters in the data set, and different three-dimensional images correspond to different transmitter's main coefficients. Based on this, relevant technicians can determine the law of the interaction and change between the transmitter's pre-emphasis coefficient, the transmitter's de-emphasis coefficient, and the eye diagram quality evaluation parameters from a single three-dimensional image, and determine the influence of the transmitter's main coefficient on the eye diagram quality evaluation parameters from different three-dimensional images.

[0203] In Figure GGG, multiple contour images are displayed simultaneously. The contour images are constructed based on the transmitter's pre-emphasis coefficient, transmitter's de-emphasis coefficient, and eye quality evaluation parameters in the data set, and different contour images correspond to different transmitter's main coefficients. Based on this, relevant technicians can determine the law of interaction and change between the transmitter's pre-emphasis coefficient, transmitter's de-emphasis coefficient, and eye quality evaluation parameters from a single contour image, and determine the influence of the transmitter's main coefficient on the eye quality evaluation parameters from different contour images.

[0204] See also Fig.14 , provides a schematic diagram of a process for assigning a null value for an embodiment of the present application, such as Fig.14 As shown, the method of the embodiment of the present application may include the following steps S801-S802, and steps S801-S802 may be Figure 1 In the illustrated embodiment, it is performed after step S102 and before step S103.

[0205] S801, if there is a null value in the parameter corresponding to the parameter dimension of the target dependent variable, obtain a non-null default value;

[0206] S802, assigning a value to the null value in the parameter corresponding to the parameter dimension of the target dependent variable according to the default value.

[0207] Specifically, the aforementioned embodiment has mentioned that the quality evaluation parameter is determined according to the test signal corresponding to the signal transmission influencing parameter transmitted in the communication link, that is, the signal transmission influencing parameter can be set according to the demand, and the quality evaluation parameter depends on the test signal corresponding to the signal transmission influencing parameter. Then, if some signal transmission influencing parameters are limited by the receiver / transmitter in the communication link and the corresponding test signal cannot be generated, it is impossible to determine the corresponding quality evaluation parameter. Based on the above reasons, it can be seen that in the actual data processing and analysis process, the parameters corresponding to the independent variable parameter dimension in the data set are complete, but the parameters corresponding to the dependent variable parameter dimension in the data set may be incomplete. This incompleteness is reflected in the possibility of null values ​​in the parameters corresponding to the dependent variable parameter dimension.

[0208] More specifically, the parameters corresponding to the parameter dimension of the target dependent variable are used to construct the parameter image. If there are null values ​​in the parameters corresponding to the parameter dimension of the target dependent variable, it may affect the subsequent construction of the parameter image. Therefore, it is necessary to properly handle the null values.

[0209] Specifically, when there is an empty value in the parameter corresponding to the target dependent variable parameter dimension, it is necessary to obtain a non-empty default value, and assign a value to the empty value in the parameter corresponding to the target dependent variable parameter dimension according to the default value. In some cases, the default value is set to 0. In the parameter image constructed later, the default value of 0 can provide appropriate visual feedback, so that the relevant technicians can know that the parameter corresponding to the target dependent variable parameter dimension is unreasonable, and then exclude the parameter corresponding to the corresponding target independent variable parameter dimension.

[0210] To facilitate understanding of this embodiment, an example is provided below:

[0211] Assume that there are n data sets, and the target subset of the i-th data set is recorded as ,as follows:

[0212]

[0213] in, The corresponding pre-emphasis coefficient of the transmitter corresponding to the independent variable parameter dimension 1, represents the de-emphasis coefficient of the transmitter corresponding to the independent variable parameter dimension 2, Represents the eye diagram quality evaluation parameter of the test signal corresponding to the dependent variable parameter dimension 1.

[0214] remember The set of values ​​for , The set of values ​​for ;based on The value of determines the X-axis, based on The value of determines the Y axis, based on Determine the Z axis. The following are the definitions of the X-axis value set X and the Y-axis value set Y:

[0215]

[0216]

[0217] Then, the complete set of the transmitter's pre-emphasis coefficient and the transmitter's de-emphasis coefficient on the plane formed by the X-axis and the Y-axis is It is expressed as follows:

[0218]

[0219] In the actual communication link test process, some combinations of transmitter pre-emphasis coefficients and transmitter de-emphasis coefficients are not within the transmitter FFE (Feed-Forward Equalizer) setting range. Therefore, the combination of transmitter pre-emphasis coefficients and transmitter de-emphasis coefficients used in the actual communication link test process is It is expressed as follows:

[0220]

[0221] in, It is a set of transmitter pre-emphasis coefficients and transmitter de-emphasis coefficients that do not exist due to the FFE limitation of the transmitter. Non-existent parameters are represented by null values.

[0222] As mentioned above, in the actual communication link test process, there are some sets that do not exist due to transmitter limitations. , so a supplementary dataset based on default values ​​needs to be generated , which is defined as follows:

[0223]

[0224] To simplify the representation, It can be regarded as the union of the target subsets of n data sets. Then the full set of parameters required to construct the parameter image It can be defined as:

[0225]

[0226] Based on the above processing, it is equivalent to using a data set representing the default value For datasets representing null values Copy and get the final data set , The eye diagram quality evaluation parameters of the test signal corresponding to the dependent variable parameter dimension 1 in are all non-empty and can be used to construct the parameter image later.

[0227] In this embodiment, by obtaining non-empty default values ​​and assigning empty values ​​in the parameters corresponding to the parameter dimensions of the target dependent variable, the integrity of the data set is ensured, providing a reliable data basis for the subsequent construction of the parameter image.

[0228] The present application embodiment also provides an electronic device 900. Fig.15 , Fig.15 1 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device 900 includes a processor 901 and a memory 902. The processor 901 is electrically connected to the memory 902.

[0229] Processor 901 is the control center of electronic device 900. It uses various interfaces and lines to connect various parts of the entire electronic device, executes various functions of the electronic device and processes data by running or calling computer programs stored in memory 902, and calling data stored in memory 902, thereby monitoring the electronic device as a whole.

[0230] The memory 902 may be used to store software programs and modules, and the processor 901 executes various functional applications and image construction by running computer programs and modules stored in the memory 902. The memory 902 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, a computer program required for at least one function, etc.; the data storage area may store data created according to the use of the electronic device, etc.

[0231] In addition, the memory 902 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 902 may also include a memory controller to provide the processor 901 with access to the memory 902.

[0232] In this embodiment, the processor 901 in the electronic device 900 will load instructions corresponding to the processes of one or more computer programs into the memory 902 according to the following steps, and the processor 901 will run the computer programs stored in the memory 902 to implement various functions.

[0233] The memory 902 involved in this embodiment is used to store executable program code; the processor 901 is used to call and run executable program code from the memory, so that the electronic device 900 can execute the steps in any link data visualization method provided in the embodiments of the present application. Therefore, the beneficial effects that can be achieved by any link data visualization method provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0234] This embodiment also provides a computer-readable storage medium, in which a computer program code is stored. When the computer program code is executed on a computer, the computer executes the above-mentioned related method steps to implement any one of the link data visualization methods provided in the above-mentioned embodiments.

[0235] The storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0236] Since the instructions stored in the storage medium can execute the steps in any link data visualization method provided in the embodiments of the present application, the beneficial effects that can be achieved by any link data visualization method provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0237] The above is a detailed introduction to the link data visualization method, device and medium for chip design verification provided by the embodiments of the present application. The principles and implementation methods of the present application are described in detail using specific examples. The above examples are only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A link data imaging method for chip design verification, characterized in that: include: Acquire multiple data sets of the communication link, wherein the data sets include at least two independent variable parameter dimensions and at least one dependent variable parameter dimension, the parameters corresponding to the independent variable parameter dimensions are signal transmission influencing parameters, the parameters corresponding to the dependent variable parameter dimensions are quality evaluation parameters, and the quality evaluation parameters are determined according to a test signal corresponding to the signal transmission influencing parameters transmitted in the communication link; determining a target independent variable parameter dimension from the at least two independent variable parameter dimensions, and determining a target dependent variable parameter dimension from the at least one dependent variable parameter dimension; Determining a target subset of each of the data sets in the plurality of data sets according to the target independent variable parameter dimension and the target dependent variable parameter dimension; constructing a parameter image based on a target subset of each of the data sets; displaying the parameter image; At least two of the independent variable parameter dimensions of each of the data sets include at least one high-level independent variable parameter dimension other than the target independent variable parameter dimension; and determining a target subset of each of the plurality of data sets according to the target independent variable parameter dimension and the target dependent variable parameter dimension comprises: Determine a directory hierarchical structure according to at least two independent variable parameter dimensions and at least one dependent variable parameter dimension of the data set, wherein the directory hierarchical structure includes directory hierarchies corresponding to each of the high-level independent variable parameter dimensions in at least one high-level independent variable parameter dimension, and directory hierarchies corresponding to both the target independent variable parameter dimension and the target dependent variable parameter dimension, and the directory hierarchical level corresponding to both the target independent variable parameter dimension and the target dependent variable parameter dimension is the last directory hierarchical level in the directory hierarchical structure; Determine, according to each of the data sets, non-repetitive parameters corresponding to each of the advanced independent variable parameter dimensions; Recursively expand the directory according to the non-repeating parameters corresponding to each of the high-level independent variable parameter dimensions, each of the data sets, and the directory hierarchy structure to create a directory corresponding to each of the directory levels in the directory hierarchy structure; The target subset of each of the data sets is determined according to the directory corresponding to each of the data sets at the last directory level.

2. The method according to claim 1, characterized in that The step of constructing a parameter image according to the target subset of each of the data sets comprises: Determine at least one image construction task and a target subset of a data set corresponding to each of the image construction tasks in at least one of the image construction tasks; A parameter image corresponding to each of the image construction tasks is constructed according to the target subset of the data set corresponding to each of the image construction tasks.

3. The method according to claim 2, characterized in that The parameter image is a three-dimensional stereo image; and constructing the parameter image corresponding to each of the image construction tasks according to the target subset of the data set corresponding to each of the image construction tasks, comprises: Determine the coordinate points of each image construction task in the horizontal direction according to the signal transmission influence parameter corresponding to the target independent variable parameter dimension in the target subset of the data set corresponding to the image construction task; determine the coordinate points of each image construction task in the vertical direction according to the quality evaluation parameter corresponding to the target dependent variable parameter dimension in the target subset of the data set corresponding to the image construction task; According to the coordinate points of each image construction task in the horizontal direction and the coordinate points in the vertical direction, a three-dimensional stereoscopic image corresponding to each image construction task is rendered.

4. The method according to claim 2, characterized in that: The parameter image is a three-dimensional stereo image; and constructing the parameter image corresponding to each of the image construction tasks according to the target subset of the data set corresponding to each of the image construction tasks, comprises: Determine the coordinate points of each image construction task in the horizontal direction according to the signal transmission influence parameters corresponding to the target independent variable parameter dimension in the target subset of the data set corresponding to the image construction task; determine the coordinate points of each image construction task in the vertical direction according to the quality evaluation parameters corresponding to the target dependent variable parameter dimension in the target subset of the data set corresponding to the image construction task, as well as the visual effect information of the coordinate points of each image construction task in the vertical direction; According to the coordinate points of each image construction task in the horizontal direction, the coordinate points of each image construction task in the vertical direction, and the visual effect information of the coordinate points of each image construction task in the vertical direction, a three-dimensional stereoscopic image corresponding to each image construction task is rendered.

5. The method according to claim 2, characterized in that: The parameter image is a contour image; and constructing the parameter image corresponding to each of the image construction tasks according to the target subset of the data set corresponding to each of the image construction tasks, comprises: Determine the coordinate points of each image construction task in the horizontal direction according to the signal transmission influence parameter corresponding to the target independent variable parameter dimension in the target subset of the data set corresponding to the image construction task; determine the elevation value of the coordinate point of each image construction task in the horizontal direction according to the quality evaluation parameter corresponding to the target dependent variable parameter dimension in the target subset of the data set corresponding to the image construction task; According to the coordinate points of each image construction task in the horizontal direction and the elevation values ​​of the coordinate points of each image construction task in the horizontal direction, the contour line images corresponding to each image construction task are rendered.

6. The method according to claim 2, characterized in that After determining a target independent variable parameter dimension from the at least two independent variable parameter dimensions, and determining a target dependent variable parameter dimension from the at least one dependent variable parameter dimension, and before determining a target subset of each of the plurality of data sets according to the target independent variable parameter dimension and the target dependent variable parameter dimension, the method further includes: If there is an empty value in the parameter corresponding to the parameter dimension of the target dependent variable, a non-empty default value is obtained; The null values ​​in the parameters corresponding to the parameter dimensions of the target dependent variable are assigned values ​​according to the default values.

7. An electronic device, characterized in that: The electronic device comprises: A memory for storing executable program codes; A processor, configured to call and run the executable program code from the memory, so that the electronic device executes the method according to any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the method according to any one of claims 1 to 6 is implemented.

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