Eye diagram data compression processing method and apparatus, and storage medium

Abstract

This application discloses an eye diagram data compression processing method, apparatus, and storage medium. The method includes: acquiring time-domain waveform data; segmenting the time-domain waveform data according to a preset time segmentation rule to determine multiple waveform segments, and mapping the waveform segments to a unified reference period interval; extracting waveform feature information from the multiple waveform segments, and determining heatmap compression parameters based on the waveform feature information, the heatmap compression parameters including: discretization parameters of the heatmap in the time and amplitude directions; discretizing and mapping the waveform trajectory corresponding to the waveform segments according to the heatmap compression parameters to determine the mapping result of the waveform segments in the heatmap; accumulating and statistically analyzing multiple mapping results to generate heatmap data characterizing the waveform distribution characteristics within the reference period interval. This application has the technical effect of improving the processing efficiency of eye diagram data.

Description

Technical Field

[0001] This disclosure relates to the field of image data processing technology, and in particular to an eye diagram data compression processing method, apparatus, and storage medium. Background Technology

[0002] Electronic design automation (EDA) software typically outputs waveform files, providing users with waveform data display and interactive functions. Waveform display not only reflects simulation results but also helps users determine the correctness of design calculations or parameter configurations. As the amount of waveform data continues to increase, memory usage and computational efficiency during waveform processing are becoming increasingly important factors affecting user experience. In signal integrity simulation scenarios, users often need to superimpose periodic time-domain waveforms according to a preset time segmentation method to form eye diagrams. In such applications, users are more concerned with the overall periodic distribution characteristics of the waveform than the specific values ​​of individual data points. However, since the original time-domain waveforms usually contain a large amount of periodic data, directly storing, drawing, and calculating them as linear waveforms can easily lead to high memory usage and low processing efficiency. Therefore, it is necessary to propose more efficient data compression methods for this type of eye diagram processing scenario. Summary of the Invention

[0003] In view of this, the present disclosure provides an eye diagram data compression processing method, apparatus and storage medium to improve the processing efficiency of eye diagram data.

[0004] Firstly, an eye diagram data compression processing method is provided, comprising: acquiring time-domain waveform data; segmenting the time-domain waveform data according to a preset time segmentation rule to determine multiple waveform segments, and mapping the waveform segments to a unified reference period interval; extracting waveform feature information from multiple waveform segments, and determining heatmap compression parameters based on the waveform feature information, the heatmap compression parameters including: discretization parameters of the heatmap in the time direction and amplitude direction; discretizing and mapping the waveform trajectory corresponding to the waveform segment according to the heatmap compression parameters to determine the mapping result of the waveform segment in the heatmap; accumulating and statistically analyzing multiple mapping results to generate heatmap data characterizing the waveform distribution characteristics within the reference period interval.

[0005] The above eye diagram data compression method first converts the original time-domain waveform into multiple waveform segments within a unified reference period interval. Then, it determines the heatmap compression parameters based on waveform feature information and performs discretization mapping and cumulative statistics on the waveform trajectory to generate heatmap data characterizing the waveform distribution features. Compared with directly processing the original linear waveform data, this application compresses multiple periods of original point data into statistically significant heatmap data, effectively reducing memory usage. Furthermore, since heatmap data is more suitable for expressing the periodic overall distribution characteristics of interest in eye diagrams, it can improve processing efficiency in subsequent eye diagram display and related calculations.

[0006] Optionally, the preset time segmentation rules include: segmenting the time-domain waveform data based on a preset fixed period; or, extracting period information from a reference signal and segmenting the time-domain waveform data based on the period information.

[0007] Optionally, the multiple waveform segments include multiple first waveform segments; mapping the waveform segments to a unified reference period interval includes: determining the end time of the previous period corresponding to the multiple first waveform segments; subtracting the end time of the previous period from the time values ​​of the multiple first waveform segments respectively to obtain multiple second waveform segments after period zeroing processing; and mapping the multiple second waveform segments to the reference period interval.

[0008] Optionally, waveform feature information can be extracted from multiple waveform segments, including: extracting a preset proportion of waveform segments from multiple waveform segments as sampled waveform segments, and extracting waveform feature information based on the sampled waveform segments.

[0009] Optionally, the waveform feature information includes at least one of the following: the slope distribution of line segments in the waveform segment; and the deviation information between the endpoints of the waveform segment and the discretized values ​​of the heat map.

[0010] Optionally, the discretization parameters of the heatmap in the time direction and the discretization parameters in the amplitude direction are equally spaced parameters; according to the heatmap compression parameters, the waveform trajectory corresponding to the waveform segment is discretized and mapped, including: interpolation based on the discretization parameters of the heatmap in the time direction, and determining the corresponding discretization values ​​of the heatmap in the amplitude direction based on the interpolation results.

[0011] Optionally, multiple mapping results are accumulated and statistically analyzed to generate heat map data representing the waveform distribution characteristics within the reference period interval, including: for each mapping position of each waveform segment in the heat map, the probability weight is accumulated according to the reciprocal of the total number of periods.

[0012] Optionally, it also includes: generating a cached image based on heatmap data and using the cached image to draw an eye diagram, wherein when scaling the eye diagram, the cached image is scaled as a whole; when adjusting the delay parameters of the eye diagram, the cached image is cut and stitched for display; and it also includes: calculating eye diagram parameters based on heatmap data.

[0013] Secondly, an eye diagram data compression processing apparatus is provided, comprising: an acquisition unit for acquiring time-domain waveform data; a segmentation unit for segmenting the time-domain waveform data according to a preset time segmentation rule, determining multiple waveform segments, and mapping the waveform segments to a unified reference period interval; an extraction unit for extracting waveform feature information from the multiple waveform segments, and determining heatmap compression parameters based on the waveform feature information, the heatmap compression parameters including: discretized value parameters of the heatmap in the time direction and amplitude direction; a mapping unit for discretizing and mapping the waveform trajectory corresponding to the waveform segment according to the heatmap compression parameters, and determining the mapping result of the waveform segment in the heatmap; and an accumulation unit for accumulating and statistically analyzing multiple mapping results, and generating heatmap data characterizing the waveform distribution characteristics within the reference period interval.

[0014] Thirdly, a computer-readable storage medium is provided, on which instructions are stored, which, when read by a processor, implement the eye diagram data compression processing method provided in the first aspect above. Attached Figure Description

[0015] The accompanying drawings used in the description of the embodiments of this disclosure are briefly introduced below: Figure 1 A flowchart illustrating an eye diagram data compression processing method provided in some embodiments of this application is shown. Figure 2 A schematic diagram of the structure of an eye diagram data compression processing apparatus provided in some embodiments of this application is shown. Detailed Implementation

[0016] To more clearly illustrate the technical solutions in the embodiments of this disclosure, examples of implementation methods of this disclosure will be described below with reference to the accompanying drawings. The accompanying drawings described below are merely some embodiments of this disclosure. For those skilled in the art, other drawings and other implementation methods can be obtained based on these drawings without creative effort. Adjustments and improvements made without departing from the concept of this disclosure are all within the protection scope of this disclosure.

[0017] To keep the drawings simple, each figure only schematically shows the parts relevant to the embodiment, and they do not represent the actual structure of the product. In addition, for the sake of clarity and ease of understanding, some figures only schematically show parts of components with the same structure or function, and there may actually be more or fewer components with the same structure or function.

[0018] In this disclosure, unless otherwise expressly specified and limited, ordinal numbers, such as “first”, “second”, etc., are used only to distinguish and describe related objects, and should not be construed as indicating or implying the relative importance or order between related objects; furthermore, they do not represent the quantity of related objects. “Multiple” includes two or more, and other quantifiers are similar. “ / ” is used to describe the relationship between related objects, indicating an “or” relationship between them. “And / or” is used to describe the relationship between related objects, including any combination relationship between them, such as “a and / or b” including: “a alone”, “b alone”, or “a and b”. “One or more” or “at least one” of multiple objects refers to any object or any combination of multiple objects, such as “one or more of a1, a2, a3” or “at least one of a1, a2, a3” including: “a1 alone”, “a2 alone”, “a3 alone”, “a1 and a2”, “a1 and a3”, “a2 and a3”, or “a1, a2 and a3”.

[0019] With the widespread application of electronic design automation (EDA) software in circuit design, simulation analysis, and result verification, waveform display has become an important component of related post-processing stages. Typically, EDA software outputs waveform files and presents users with waveform information showing how signals change over time through interactive display functions. This allows users to assess the correctness of parameter configurations, simulation results, or related calculations during the design process. Therefore, waveform display functionality must not only ensure the accuracy of the displayed content and calculation results but also consider data reading performance, computational efficiency, and memory usage efficiency to meet the user's operational needs during post-processing.

[0020] In the field of signal integrity simulation, users typically focus more on the overall periodicity of waveforms, such as statistical information like eye diagram contours and edge distributions, rather than the specific characteristics of individual data points within the original linear waveform. That is, during eye diagram analysis, a large number of data points in the original time-domain waveform are primarily used to reflect the overall distribution of the waveform, rather than all participating in subsequent display and calculation with equal importance. However, in existing processing methods, periodic time-domain waveforms usually contain a large amount of periodic data, resulting in a large data volume for the original linear waveform. When the original linear waveform data is still the primary processing object, it consumes significant memory resources. Furthermore, eye diagram-related displays or calculations often require processing a large amount of periodic data, thus impacting overall processing efficiency. Especially with a continuously increasing number of periods, the related memory consumption and computational overhead will further amplify, adversely affecting the software's post-processing performance and the user's operational fluency. Therefore, how to perform more efficient compression processing on the original waveform data while preserving the overall periodic distribution characteristics of the waveform, in order to reduce memory consumption and improve computational efficiency, has become a technical problem to be solved in this field. The following description, in conjunction with the accompanying figures, illustrates this point: Figure 1 This document illustrates a flowchart of an eye diagram data compression method provided in some embodiments of this application. This eye diagram data compression method can be applied to waveform post-processing scenarios in electronic design automation software. In such scenarios, users are typically more concerned with the overall distribution characteristics of the waveform in a periodic sense, rather than the specific values ​​of each individual data point in the original linear waveform. Therefore, this embodiment, by compressing the original linear waveform data into heatmap data, can improve waveform data processing efficiency and reduce memory usage while preserving the overall periodic characteristics of the waveform. The method includes at least the following steps: S110: Acquire time-domain waveform data; S120: Based on a preset time segmentation rule, the time-domain waveform data is segmented to determine multiple waveform segments, and the waveform segments are mapped to a unified reference period interval; S130: Extract waveform feature information from multiple waveform segments and determine heatmap compression parameters based on the waveform feature information. The heatmap compression parameters include: discretization parameters of the heatmap in the time direction and amplitude direction. S140: According to the heatmap compression parameters, the waveform trajectory corresponding to the waveform segment is discretized and mapped to determine the mapping result of the waveform segment in the heatmap; S150: Accumulate and statistically analyze multiple mapping results to generate heat map data representing waveform distribution characteristics within a reference period interval.

[0021] In this embodiment, time-domain waveform data can be obtained by reading a waveform file, thus reflecting the signal's changes in the time dimension. This includes, for example, the waveform's time information and the corresponding amplitude information. Furthermore, since the original time-domain waveform unfolds continuously along the time axis and often covers a long time range, it can be segmented according to a preset time segmentation rule before compression, resulting in multiple waveform segments. The preset time segmentation rule can be used to determine the segmentation boundaries of the waveform, dividing the long time-domain waveform into multiple data segments corresponding to different periods. After segmentation, the waveform segments are mapped to a unified reference period interval, which can be used to uniformly carry the interval range of each period waveform segment. Through this mapping process, multiple periodic data originally distributed at different absolute time positions are folded into the same period range, transforming the subsequent processing object from the entire long time-domain waveform into multiple waveform segments arranged under a unified periodic coordinate semantics. This not only meets the requirement of eye diagram processing to focus on the overall periodic distribution characteristics but also provides a unified data foundation for subsequent compression and statistical processing. Furthermore, waveform feature information can be extracted from multiple waveform segments, and heatmap compression parameters can be determined based on this feature information. Since the original waveform segments still exhibit a continuously changing linear trajectory within the reference period interval, while the heatmap data uses a discretized statistical form, the value settings of the heatmap in the time and amplitude directions can be determined first to convert the continuous waveform into a data form suitable for statistics and compression. If the discretization value settings are too coarse, the waveform will experience significant information loss during compression, affecting the overall expression of the eye diagram features; if the discretization value settings are too dense, it will increase the size of the heatmap data, exacerbate memory pressure, and reduce processing efficiency. This embodiment can determine the heatmap compression parameters based on the waveform feature information inherent in the waveform segments themselves after the waveform segments are formed, so that the discretization method of the heatmap matches the distribution characteristics of the current waveform data, thereby balancing feature expression capability and compression processing effect.

[0022] After determining the heatmap compression parameters, the waveform trajectories corresponding to the waveform segments can be discretized and mapped. The waveform trajectory is the path formed by the waveform segment's changes in time and amplitude within a reference period interval. Since the heatmap represents the distribution of waveforms in both the time and amplitude directions using discretized data, the waveform trajectories need to be transformed according to the heatmap compression parameters to form their corresponding distribution results in the heatmap. Through this processing, the linear waveform segments, originally described by a large amount of raw point data, are transformed into heatmap mapping results suitable for cumulative statistics, laying the foundation for subsequent generation of heatmap data.

[0023] After obtaining the mapping results corresponding to multiple waveform segments, these mapping results can be accumulated and statistically analyzed to generate heatmap data representing the waveform distribution characteristics within a reference period interval. This transforms the original linear waveform from a single period into a statistical result of multiple periods superimposed within a unified reference period interval. The resulting heatmap data reflects the overall distribution of the waveform in a periodic sense, making it particularly suitable for expressing the overall contour and distribution characteristics of the waveform in eye diagram scenarios. Since the statistical results are already concentrated in the heatmap data, subsequent eye diagram display or related analysis does not require displaying and analyzing all the original periodic data itself, thus reducing processing overhead and improving post-processing efficiency.

[0024] This embodiment first converts the original time-domain waveform into multiple waveform segments within a unified reference period interval. Then, it determines the heatmap compression parameters based on waveform feature information. On this basis, it performs discretization mapping and cumulative statistics of the waveform trajectory, thereby generating heatmap data characterizing the waveform distribution features. Compared to directly processing the original linear waveform data, this embodiment compresses multiple periods of original point data into statistically significant heatmap data, effectively reducing memory usage. Furthermore, since heatmap data is more suitable for expressing the periodic overall distribution characteristics of the eye diagram, it can improve processing efficiency in subsequent eye diagram display and related calculations. Taking a time-domain waveform with a start time of 0s, an end time of 200ns, and a step size of 1p as an example, assuming the number of original points is approximately 2 × 10⁻⁶... 5 If the data is superimposed into an eye diagram at a fixed frequency and then compressed into a heatmap, the data size required to be stored in the heatmap will be significantly smaller than that of the original linear graph. Furthermore, as the number of cycles increases, the advantages in terms of memory usage and computation speed will be further amplified.

[0025] In some embodiments of this application, the preset time segmentation rules include: segmenting the time-domain waveform data based on a preset fixed period; or, extracting period information from a reference signal and segmenting the time-domain waveform data based on the period information.

[0026] The segmentation method of time-domain waveform data can affect the alignment of subsequent waveform segments within a unified reference period interval and the overall distribution characteristics reflected by the heatmap data. Therefore, when segmenting time-domain waveform data, different time segmentation rules can be adopted according to the specific characteristics of the waveform to be processed. For waveform data with relatively stable periods, a preset fixed period can be directly used as the segmentation basis, that is, the time-domain waveform data is continuously divided along the time axis according to a predetermined period length to obtain multiple waveform segments. When using this method, each waveform segment can have a consistent period length in the time range, the processing method is relatively direct, and it is suitable for waveform processing scenarios with clear periodic patterns and small periodic variations.

[0027] Furthermore, for waveform data with non-fixed periods or those difficult to accurately describe directly by a fixed period, periodic information can be extracted using a reference signal, and the time-domain waveform data can be segmented based on this periodic information. The reference signal can be a signal that reflects the waveform's periodic changes or the position of periodic boundaries, while the periodic information can characterize the boundary positions, period lengths, or boundary relationships between adjacent periods. In this segmentation method, the segmentation of the time-domain waveform data no longer depends on a single fixed period, but rather the boundaries of each waveform segment can be determined based on the periodic information reflected by the reference signal, making the resulting waveform segments more consistent with the periodic variation characteristics of the waveform itself. Thus, when multiple waveform segments are subsequently mapped to a unified reference period interval, the overall distribution of the periodic waveform under different periodic conditions can be more accurately reflected, thereby improving the adaptability of subsequent heatmap compression processing.

[0028] In some embodiments of this application, multiple waveform segments include multiple first waveform segments; mapping the waveform segments to a unified reference period interval includes: determining the end time of the previous period corresponding to the multiple first waveform segments; subtracting the end time of the previous period from the time values ​​of the multiple first waveform segments to obtain multiple second waveform segments after period zeroing processing; and mapping the multiple second waveform segments to the reference period interval.

[0029] After the time-domain waveform data is segmented according to a preset time division rule, the resulting multiple first waveform segments, although corresponding to data content of different periods, still retain the absolute time position in the original time-domain waveform in terms of their time dimension values. If these first waveform segments are directly used for superposition processing, it is difficult to align waveform segments of different periods within a unified period range. Therefore, this embodiment further normalizes the values ​​of multiple first waveform segments in the time direction, so that waveform segments of different periods can be uniformly mapped to the same reference period interval. For each first waveform segment, the end time of the previous period corresponding to the first waveform segment is first determined. The end time of the previous period is used to characterize the starting reference position of the current first waveform segment on the original time axis. Subsequently, the end time of the corresponding previous period is subtracted from each value of the first waveform segment in the time direction. After this processing, the multiple first waveform segments originally located at different positions on the absolute time axis are re-translated in the time direction, so that their starting positions uniformly return to the vicinity of the same period reference starting point, thereby obtaining multiple second waveform segments after period zeroing processing. After obtaining multiple second waveform segments, the multiple second waveform segments are then mapped to the reference period interval. Since each second waveform segment has undergone time-direction unification by subtracting the end time of the previous cycle, data segments corresponding to different cycles can be aligned and arranged under the same cycle semantics. After this processing, waveform data from multiple cycles are uniformly organized into the same reference cycle interval, transforming multiple first waveform segments originally scattered at different absolute time positions into multiple second waveform segments within the reference cycle interval. This aligns the periodic waveform data in the time direction, which is beneficial for subsequently reflecting the overall distribution characteristics of the waveform within a unified cycle range.

[0030] In some embodiments of this application, extracting waveform feature information from multiple waveform segments includes: extracting a preset proportion of waveform segments from multiple waveform segments as sampled waveform segments, and extracting waveform feature information based on the sampled waveform segments.

[0031] Considering that time-domain waveforms in eye diagram processing scenarios typically exhibit good periodicity and often contain a large amount of periodic data, directly extracting waveform feature information from all waveform segments one by one would increase the processing overhead of the feature analysis process itself, hindering the improvement of overall compression efficiency. Therefore, this application does not require complete feature extraction from all waveform segments. Instead, it first extracts a predetermined proportion of waveform segments as sampled waveform segments from multiple waveform segments, and then extracts waveform feature information based on these sampled waveform segments. Since multiple waveform segments already correspond to periodic waveform data within the same reference period interval, the sampled waveform segments can reflect the main changing characteristics of the overall waveform at a lower processing cost. The waveform feature information extracted from these sampled waveform segments can serve as the basis for subsequently determining heatmap compression parameters, ensuring that the discretization settings of the heatmap in the time and amplitude directions are adapted to the overall distribution of the current waveform data. In this way, representative feature information can still be obtained without traversing all waveform segments, thus balancing the accuracy of parameter determination with the efficiency of the processing. The above approach establishes the acquisition of waveform feature information based on representative sampled waveform segments, which not only helps reduce the computational burden in the feature extraction process but also improves the execution efficiency of the entire eye diagram data compression process, providing effective support for the determination of subsequent heatmap compression parameters.

[0032] In some embodiments of this application, the waveform feature information includes at least one of the following: the slope distribution of line segments in the waveform segment; and the deviation information between the endpoints of the waveform segment and the discretized values ​​of the heat map.

[0033] To ensure that the subsequently determined heatmap compression parameters better reflect the actual changes in the current waveform data, this application further extracts waveform feature information based on factors affecting the accuracy of discretization mapping. The main factors influencing errors in heatmap compression processing are the slope of line segments in the waveform fragments and whether the endpoints correspond to the target interpolation points. Therefore, this application considers the slope distribution of line segments in the waveform fragments and the deviation information between the endpoints of the waveform fragments and the discretized values ​​of the heatmap as important components of waveform feature information. The slope distribution of line segments reflects the changing trend of the waveform fragments in the time and amplitude directions. Since the waveform trajectory needs to be converted according to the value settings of the heatmap in the time and amplitude directions during the discretization mapping process, different line segment slopes will have different effects on the mapped results. When there are many line segments with large slope changes in the waveform fragment, if the discretization value settings of the heatmap in the corresponding directions are insufficient, it is easy for the waveform trajectory to deviate from the original changing trend during the mapping process, thus affecting the heatmap's display of the overall waveform outline. By extracting the slope distribution information of line segments, the overall characteristics of waveform changes can be fully considered when determining the heatmap compression parameters, making the discretization value settings more consistent with the changes in the waveform trajectory.

[0034] On the other hand, the deviation information between the endpoints of the waveform segment and the discretized values ​​of the heatmap reflects the degree of fit between the original waveform data points and the heatmap values. During the mapping of a waveform segment to the heatmap, if the endpoint position deviates significantly from the discretized values ​​of the heatmap in the time or amplitude direction, that portion of the waveform is more prone to positional errors during discretization mapping, thus affecting the overall heatmap data's representation of the original waveform's distribution characteristics. By extracting the deviation information between the endpoints and the discretized values ​​of the heatmap, the correspondence between the original data points and the discretized value system can be considered when determining the heatmap compression parameters, thereby reducing the distortion caused by discretization mapping. By using the slope distribution of the line segment and the deviation information between the endpoints and the discretized values ​​of the heatmap as waveform feature information, this application can provide a basis for determining the heatmap compression parameters from two aspects: waveform change trend and discretization mapping error. This helps to ensure that the subsequently generated heatmap data maintains high compression efficiency while more accurately representing the distribution characteristics of periodic waveforms within the reference period interval.

[0035] In some embodiments of this application, the discretization parameters of the heatmap in the time direction and the discretization parameters in the amplitude direction are equally spaced parameters; according to the heatmap compression parameters, the waveform trajectory corresponding to the waveform segment is discretized and mapped, including: interpolation based on the discretization parameters of the heatmap in the time direction, and determining the corresponding discretization value of the heatmap in the amplitude direction based on the interpolation result.

[0036] In the above embodiments, for ease of use later, the values ​​of the heat map in the time direction and amplitude direction can be set to be taken at equal intervals. This is beneficial for the discretization mapping of waveform trajectories, as well as for the subsequent statistics, storage and retrieval of heat map data.

[0037] Building upon this, when discretizing the waveform trajectory corresponding to a waveform segment, interpolation can be performed using the discretized parameters of the heatmap in the time direction as a reference. That is, first, based on the discretized parameters of the heatmap in the time direction, the corresponding interpolation positions are determined on the waveform trajectory. Then, based on the interpolation results at each interpolation position, the corresponding discretized values ​​of the heatmap in the amplitude direction are determined. Through this processing method, the continuous change trajectory in the original waveform segment can be converted into a discrete distribution result under the heatmap value system, thus adapting the continuous curve waveform data to the data representation method of the heatmap.

[0038] After adopting the above mapping method, the changes in the waveform trajectory in the time direction can be gradually unfolded according to the discretization process of the heatmap, and the corresponding amplitude changes are interpolated into the corresponding values ​​in the amplitude direction of the heatmap. The resulting discretization mapping result can be converted into a heatmap data format suitable for cumulative statistics while maintaining the overall trend of waveform changes. Furthermore, combined with the aforementioned processing method of determining heatmap compression parameters based on waveform feature information, the discretization mapping process can be adapted to the current waveform distribution characteristics, thereby achieving a good balance between compression efficiency and feature representation.

[0039] In some embodiments of this application, multiple mapping results are accumulated and statistically analyzed to generate heat map data characterizing waveform distribution features within a reference period interval, including: for each mapping position of each waveform segment in the heat map, the probability weight is accumulated according to the reciprocal of the total number of periods.

[0040] To further generate heatmap data that reflects the overall periodic distribution characteristics, this application can accumulate and statistically analyze multiple mapping results. After the waveform segment corresponding to each period is mapped to the heatmap, the points appearing in the heatmap can be recorded as corresponding probability values ​​according to the reciprocal of the total number of periods. These probabilities are then superimposed after processing multiple periods to form the final heatmap data. For any waveform segment, after its discretization mapping, it will correspond to several mapping positions in the heatmap. For each mapping position of the waveform segment in the heatmap, a probability weight is accumulated according to the reciprocal of the total number of periods. After this processing, the contribution of each waveform segment to the heatmap data is uniformly converted into a weight value related to the total number of periods. The accumulated results of multiple waveform segments at each mapping position then constitute the heatmap data within the reference period interval. As the mapping results corresponding to different waveform segments are continuously superimposed, the values ​​corresponding to each position in the heatmap can reflect the degree to which the corresponding position is traversed by the waveform trajectory in multiple periods. When the value corresponding to a certain position is large, it indicates that the waveform trajectory has traversed that position in many periods; when the value corresponding to a certain position is small, it indicates that the frequency of the waveform trajectory traversing that position is low. For example, when the probability value for a certain position is 100%, it means that the position is passed through in every cycle; when the probability value is 0%, it means that the position is not passed through in any cycle. Thus, the final heatmap data not only retains the overall distribution characteristics of the original periodic waveform, but also transforms a large amount of point information in the original linear graph into a data form that can characterize statistical features.

[0041] By employing the aforementioned method of accumulating probability weights based on the reciprocal of the total number of periods, this application can uniformly integrate the mapping results of multiple periodic waveform segments into the same heatmap data. This allows the values ​​at each position in the heatmap to directly reflect the distribution degree of the waveform within the reference period interval. The heatmap data generated in this way is more suitable for expressing the statistical features of overall contour, edge distribution, etc., which are of interest in eye diagram processing. It also helps to reduce the scale of original data processing while improving the processing efficiency in subsequent display and calculation processes.

[0042] In some embodiments of this application, the method further includes: generating a cached image based on heatmap data, and using the cached image to draw an eye diagram, wherein the cached image is scaled as a whole when the eye diagram is scaled; the cached image is cut and spliced ​​for display when the delay parameters of the eye diagram are adjusted; and the method further includes: calculating eye diagram parameters based on heatmap data.

[0043] After generating the heatmap data, the resulting data already characterizes the overall distribution of the waveform within the reference period interval. Therefore, subsequent eye diagram display and related calculations can be directly performed based on the heatmap data, without having to revert to the original linear waveform data for cycle-by-cycle processing. Based on this heatmap data, a fixed cached image can be created for drawing and directly used in eye diagram calculations. Furthermore, since the heatmap data itself compresses the waveform distribution information from multiple cycles into statistical results within a unified reference period interval, using the cached image as the drawing object during eye diagram display avoids re-traversing the original waveform data for each display. When the user zooms in or out of the eye diagram, the cached image can be directly scaled and drawn as a whole. Thus, zooming in and out during the display process no longer depends on regenerating the entire waveform trajectory, but rather on adjusting the overall scale based on the already generated cached image, thereby improving the response speed of the display process.

[0044] In the process of eye diagram display, in addition to scaling operations, it may also be necessary to adjust the delay parameters of the eye diagram. For this type of operation, this application can complete the corresponding processing by segmenting and stitching the cached image. Since the cached image corresponds to the heatmap results within a unified reference period interval, when adjusting the delay parameters, the display position can be changed by segmenting and re-stitching the corresponding areas of the cached image, without having to re-segment, overlay, and redraw the original waveform data, further reducing the processing burden during the interactive eye diagram display process.

[0045] In addition to eye diagram drawing, this application can also be used to calculate eye diagram parameters based on heatmap data. Since heatmap data already characterizes the overall statistical distribution of the waveform within the periodic range, parameter analysis related to the eye diagram can also be directly performed based on the heatmap data. For example, in conventional calculations such as finding the eye margin, heatmap data has a significant advantage in computational speed, unlike linear data. Figure 1 The entire cycle is traversed. Furthermore, eye diagram calculation can share algorithms with statistical eye diagram data, eliminating the need to maintain an additional set of line diagram calculation processes. Therefore, this application uses heatmap data as both the display and calculation basis, enabling eye diagram drawing and eye diagram parameter calculation to revolve around the same data results. This simplifies post-processing and improves computational efficiency.

[0046] Figure 2The diagram illustrates a structural schematic of an eye diagram data compression processing apparatus provided in some embodiments of this application. The eye diagram data compression processing apparatus 200 includes: an acquisition unit 210 for acquiring time-domain waveform data; a segmentation unit 220 for segmenting the time-domain waveform data according to a preset time segmentation rule, determining multiple waveform segments, and mapping the waveform segments to a unified reference period interval; an extraction unit 230 for extracting waveform feature information from the multiple waveform segments and determining heatmap compression parameters based on the waveform feature information, the heatmap compression parameters including: discretized value parameters of the heatmap in the time and amplitude directions; a mapping unit 240 for discretizing and mapping the waveform trajectory corresponding to the waveform segments according to the heatmap compression parameters, determining the mapping result of the waveform segments in the heatmap; and an accumulation unit 250 for accumulating and statistically analyzing multiple mapping results to generate heatmap data characterizing the waveform distribution features within the reference period interval.

[0047] Based on the same technical concept, a computer-readable storage medium is provided, on which instructions are stored, which, when read by a processor, implement the eye diagram data compression processing method provided in the above embodiments.

[0048] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not described in detail or in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Furthermore, the above embodiments can be freely combined as needed.

Claims

1. A method for compressing eye diagram data, characterized in that, include: Acquire time-domain waveform data; The time-domain waveform data is segmented based on a preset time segmentation rule to determine multiple waveform segments, and the waveform segments are mapped to a unified reference period interval. Waveform feature information is extracted from multiple waveform segments, and heatmap compression parameters are determined based on the waveform feature information. The heatmap compression parameters include: discretization parameters of the heatmap in the time direction and amplitude direction. According to the heatmap compression parameters, the waveform trajectory corresponding to the waveform segment is discretized and mapped to determine the mapping result of the waveform segment in the heatmap; By accumulating and statistically analyzing multiple mapping results, heatmap data characterizing the waveform distribution features is generated within the reference period interval.

2. The eye diagram data compression processing method according to claim 1, characterized in that, The preset time segmentation rule includes: segmenting the time-domain waveform data based on a preset fixed period; Alternatively, periodic information can be extracted from the reference signal, and the time-domain waveform data can be segmented based on the periodic information.

3. The eye diagram data compression processing method according to claim 1, characterized in that, The plurality of waveform segments include a plurality of first waveform segments; Mapping the waveform segment to a uniform reference period interval includes: Determine the end time of the previous cycle corresponding to multiple first waveform segments; Subtract the end time of the previous cycle from the time values ​​of the multiple first waveform segments to obtain multiple second waveform segments after cycle zeroing. Multiple second waveform segments are mapped to the reference period interval.

4. The eye diagram data compression processing method according to claim 3, characterized in that, Extracting waveform feature information from multiple waveform segments, including: A preset proportion of waveform segments are extracted from multiple waveform segments as sampled waveform segments, and waveform feature information is extracted based on the sampled waveform segments.

5. The eye diagram data compression processing method according to claim 4, characterized in that, The waveform feature information includes at least one of the following: The slope distribution of line segments in the waveform segment; The deviation information between the endpoints of the waveform segment and the discretized values ​​of the heatmap.

6. The eye diagram data compression processing method according to any one of claims 1 to 5, characterized in that, The discretized parameters of the heatmap in the time direction and the discretized parameters in the amplitude direction are equally spaced parameters. The step of discretizing the waveform trajectory corresponding to the waveform segment according to the heatmap compression parameters includes: interpolating based on the discretization parameters of the heatmap in the time direction, and determining the corresponding discretization values ​​of the heatmap in the amplitude direction based on the interpolation results.

7. The eye diagram data compression processing method according to any one of claims 1 to 5, characterized in that, The cumulative statistical analysis of multiple mapping results generates heatmap data characterizing waveform distribution features within the reference period interval, including: For each of the waveform segments at each mapping position in the heatmap, a probability weight is accumulated according to the reciprocal of the total number of cycles.

8. The eye diagram data compression processing method according to any one of claims 1 to 5, characterized in that, Also includes: A cached image is generated based on the heatmap data, and an eye diagram is drawn using the cached image. When scaling the eye diagram, the cached image is scaled as a whole for drawing. When adjusting the delay parameters of the eye diagram, the cached image is cut and stitched for display. It also includes: calculating eye diagram parameters based on the heatmap data.

9. An eye diagram data compression processing apparatus, characterized in that, include: The acquisition unit is used to acquire time-domain waveform data; The segmentation unit is used to segment the time-domain waveform data based on a preset time segmentation rule, determine multiple waveform segments, and map the waveform segments to a unified reference period interval. An extraction unit is used to extract waveform feature information from multiple waveform segments and determine heatmap compression parameters based on the waveform feature information. The heatmap compression parameters include: discretized value parameters of the heatmap in the time direction and amplitude direction. The mapping unit is used to discretize and map the waveform trajectory corresponding to the waveform segment according to the heat map compression parameters, and determine the mapping result of the waveform segment in the heat map; The accumulation unit is used to accumulate and statistically analyze multiple mapping results to generate heat map data characterizing the waveform distribution features within the reference period interval.

10. A computer-readable storage medium, characterized in that, It stores instructions that, when read by a processor, implement the eye diagram data compression processing method as described in any one of claims 1 to 8.

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