A method and device for charting display of machine data and a storage medium

Abstract

The application relates to the field of semiconductor manufacturing, and particularly relates to a chart display method and device of machine data and a storage medium. The method comprises the following steps: obtaining to-be-charted data of a target machine, historical browsing records corresponding to the target machine, and preset browsing suggestions corresponding to a process executed by the target machine; determining initial chart attributes based on the preset browsing suggestions; determining a plurality of candidate chart attributes based on the historical browsing records; rendering and displaying the to-be-charted data based on the initial chart attributes to obtain a current display chart; and in response to a scale adjustment operation of a user on the current display chart, updating the current display chart in real time. In the industrial data scene with extremely large view space, the adjustment intention of the user in the interaction process, which may be ambiguous or incomplete, is accurately responded based on the multi-source prior information, the efficiency and effectiveness of data visualization are improved, the user experience is optimized, and the efficiency of real-time monitoring, fault early warning and root cause analysis in semiconductor manufacturing is improved.

Description

Technical Field

[0001] This application relates to the field of semiconductor manufacturing, and more particularly to a method, apparatus, and storage medium for graphical display of machine data. Background Technology

[0002] In the semiconductor manufacturing industry, as process nodes continue to evolve towards advanced processes, the complexity of chip production increases exponentially. Modern wafer fabs involve hundreds or even thousands of process steps, covering multiple key stages such as photolithography, etching, thin film deposition, ion implantation, and chemical mechanical polishing. These processes are typically completed collaboratively by a wide variety of specialized machines with diverse functions. To ensure yield and achieve precise process control, each machine integrates numerous high-precision sensors to monitor hundreds or even thousands of process parameters in real time, including temperature, pressure, gas flow rate, RF power, and vibration frequency.

[0003] To achieve refined monitoring, fault prediction, and root cause analysis of the production process, the industry tends to adopt high-frequency (millisecond or even microsecond-level sampling) and large dynamic range data acquisition strategies. Faced with the resulting massive amounts of high-dimensional data, highly flexible visualization capabilities are required.

[0004] For example, patent application CN121120905A discloses a semiconductor SPC system data multi-threaded rendering and display device and method, belonging to the field of semiconductor intelligent manufacturing. Specifically, it includes: acquiring sampling point data; displaying an interactive interface for user selection of interface content to be displayed; determining rendering tasks based on the business operations corresponding to the selected interface content; performing business calculations on the sampling point data using a multi-task concurrent mode and outputting task processing results; determining the rendering mode for the task processing results based on the data volume of the sampling point data and the business operations corresponding to the selected interface content, and generating a result to be rendered; decomposing the rendering task into multiple frame-by-frame rendering stages based on the result to be rendered; performing frame-by-frame rendering on the task processing results according to the stage tasks, and displaying the rendered page.

[0005] For example, the invention patent application with publication number CN118550616A discloses a data display method for semiconductor process equipment and semiconductor process equipment. The method adopts a single-page display mode. When the data volume is large, only the data that can be displayed on the screen is instantiated on a single page, and some data is instantiated and stored in the cache. When the user continues to view the data, it is preferentially retrieved from the cache.

[0006] For example, patent application CN116431255A discloses a chart data processing method, apparatus, electronic device, and computer program product, relating to the field of data processing technology. The method includes: downsampling the full chart data to obtain a downsampling point array; segmenting the full chart data to obtain a two-dimensional array; calculating the mean of the meta-arrays of the two-dimensional array and forming a mean array; correcting elements in the downsampling point array whose actual error rate is greater than a preset error threshold based on the mean array; updating the values ​​of corresponding elements in the downsampling point array according to the proportion of outliers in each meta-array; and rendering and displaying the chart data based on the downsampling point array after correction and numerical update.

[0007] However, high-dimensional and high-frequency data leads to an explosion of view configuration combinations, with a massive number of possible chart presentation methods for users to choose from. Users find it difficult to quickly locate the truly effective view with analytical value and often have to repeatedly try different configurations to obtain a suitable visualization effect, resulting in low analysis efficiency and affecting the timeliness and accuracy of fault warnings and root cause tracing. Summary of the Invention

[0008] The main objective of this application is to provide a method, device, and storage medium for graphical display of machine data. To solve the aforementioned technical problems, this application specifically adopts the following technical solution:

[0009] A first aspect of this application is to provide a method for graphically displaying machine data, the method comprising:

[0010] S201, Obtain the data to be visualized from the target machine, as well as the historical browsing records corresponding to the target machine and the preset browsing suggestions corresponding to the process performed by the target machine;

[0011] S202, determine initial chart attributes based on the preset browsing suggestions; determine multiple candidate chart attributes based on the historical browsing records; wherein, the chart attributes include coordinate axis scale, and the coordinate axis scale includes data granularity and / or data range;

[0012] S203, Render and display the data to be charted based on the initial chart attributes to obtain the currently displayed chart;

[0013] S204, in response to the user's scaling operation on the currently displayed chart, updates the currently displayed chart in real time, including:

[0014] S2041, Obtain the user's input adjustment intention, the adjustment intention including one or both of the following: adjustment object, adjustment trend, and adjustment range; the adjustment trend includes a refinement trend or a coarsening trend;

[0015] S2042, based on the adjustment intention and the current chart attribute of the currently displayed chart, find the target chart attribute with the highest matching degree with the adjustment intention among the multiple candidate chart attributes;

[0016] S2043, Based on the target chart attributes, render and display the data to be charted, so as to update the currently displayed chart.

[0017] In some embodiments, S204 includes: arranging the initial chart attribute and a plurality of candidate chart attributes according to the increasing or decreasing trend of the coordinate axis scale to obtain a chart call sequence; locating the current chart attribute in the chart call sequence, and selecting a chart attribute that precedes or follows the current chart attribute as the target chart attribute according to the adjustment trend.

[0018] In some embodiments, the chart call sequence is arranged in order of data granularity from fine to coarse and / or data range from short to long; the refinement trend corresponds to the data granularity becoming finer or the data range becoming shorter; the coarsening trend corresponds to the data granularity becoming coarser or the data range becoming longer.

[0019] The method further includes: when the adjustment trend is a refinement trend, selecting a chart attribute in the chart call sequence that precedes the current chart attribute; when the adjustment trend is a coarsening trend, selecting a chart attribute in the chart call sequence that follows the current chart attribute.

[0020] In some embodiments, the method further includes: obtaining a first historical browsing record of the target device; performing statistical analysis on the first historical browsing record of the target device to extract a number of high-frequency chart attributes whose usage popularity is higher than a first preset threshold; and using the high-frequency chart attributes as candidate chart attributes of the target device.

[0021] In some embodiments, the method further includes: constructing a machine profile of the machine, the machine profile including at least one of machine type and process; clustering multiple machines into one or more machine groups based on the machine profile; obtaining the second historical browsing records of each machine in the machine group to which the target machine belongs, and performing statistical analysis on the second historical browsing records to extract several high-frequency chart attributes with usage popularity higher than a second preset threshold; wherein the second preset threshold is greater than or equal to a first preset threshold; and using the high-frequency chart attributes as candidate chart attributes of the target machine.

[0022] In some embodiments, the data to be charted includes several data points associated with timestamps; the method includes: S102, determining key data points from the several data points, wherein the key data points are process concerns or data anomalies; S103, determining a downsampling ratio based on the current window's browsing memory capacity, and performing downsampling processing on the data to be charted based on the downsampling ratio to obtain a downsampled dataset; S104, merging the downsampled dataset with the key data points to form a target data point set for generating a chart.

[0023] In some embodiments, the method further includes: determining an identification threshold for abnormal data points based on a preset mapping relationship and the downsampling ratio, wherein the identification threshold is negatively correlated with the downsampling ratio.

[0024] In some embodiments, the method further includes: determining a preset coordinate axis scale for each key data point based on the distribution characteristics or time span of the key data points; generating at least one key chart attribute based on the preset coordinate axis scale of the key data points so that the chart fully covers or displays the key data points; and, in response to a user's selection operation on a key data point in the currently displayed chart, calling the key chart attribute corresponding to the selected key data point to render and display the data to be charted.

[0025] A second aspect of this application is to provide a computer device, the device comprising:

[0026] Memory, used to store computer programs;

[0027] A processor is configured to execute the computer program and, in executing the computer program, implement the steps of the graphical display method for machine data as provided in any embodiment of this application.

[0028] A third aspect of this application is that a computer-readable storage medium is also provided, the computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the graphical display method for machine data provided in any embodiment of this application.

[0029] Beneficial technical effects:

[0030] This application provides a method, device, and storage medium for graphical display of machine data. Specifically, it provides an intelligent recommendation and retrieval mechanism for visual charts. In industrial data scenarios with extremely large view spaces, it accurately responds to users' ambiguous or incomplete adjustment intentions that may exist during the interaction process based on multi-source prior information, thereby improving the efficiency and effectiveness of data visualization, optimizing the user's visualization experience in complex data environments, and thus improving the efficiency of real-time monitoring, fault warning, and root cause analysis in semiconductor manufacturing.

[0031] Multiple alternative candidate views are generated using multi-source prior information, including process-preset browsing suggestions and historical browsing records (including historical preferences of the local machine and similar machine groups). An initial view that is highly compatible with the current process is automatically presented upon first loading, which greatly reduces the entry barrier.

[0032] During the user interaction phase, even if the user only provides partial adjustment intentions (such as specifying only the adjustment object, refining the trend, or only specifying a coarsening trend), the system can intelligently match the target view with the highest fit from the backup icons based on the current chart state, transforming the user's rough, tentative operations into precise visual responses. To further improve navigation efficiency, candidate chart attributes can be logically structured into an ordered call sequence according to data granularity from fine to coarse and time range from short to long, supporting rapid adjacent jumps based on the user's adjustment trend (refining / coarsening), balancing efficiency and controllability.

[0033] Meanwhile, by identifying key process points and retaining them during downsampling, it ensures that important information is always visible in the charts, avoiding the omission of key information due to the application of preferences from other machine groups; users can also focus on the best display scale customized for them with one click on abnormal areas.

[0034] By identifying key process data points (such as outliers or process concerns) and forcibly retaining them during downsampling, important information is ensured to be clearly visible in any chart, avoiding the neglect of differences in each data point due to over-reliance on browsing preferences. Furthermore, users can click on key data points in the chart to switch to a personalized optimal display scale for that key data point, providing flexible and personalized charts outside of ordered sequences, achieving a balance between intelligent guidance and free interaction. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. The elements or parts in the drawings are not necessarily drawn to scale. Obviously, the drawings described below are some embodiments of this application; for those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0036] Figure 1 This is a schematic flowchart illustrating a method for graphically displaying machine data provided in an embodiment of this application;

[0037] Figure 2 This is a schematic flowchart illustrating a response to a fuzzy adjustment intent provided in an embodiment of this application;

[0038] Figure 3 This application provides an embodiment of a currently displayed chart generated based on initial chart attributes;

[0039] Figure 4 This application provides an embodiment of a currently displayed chart generated based on target chart attributes;

[0040] Figure 5 This is yet another method for graphically displaying machine data provided in the embodiments of this application;

[0041] Figure 6 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0043] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0044] In this document, suffixes such as “module,” “part,” or “unit” used to denote elements are used only for illustrative purposes and have no specific meaning in themselves. Therefore, “module,” “part,” or “unit” may be used interchangeably.

[0045] In this document, the terms "upper," "lower," "inner," "outer," "front," "rear," "one end," and "the other end," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0046] In this document, unless otherwise explicitly specified and limited, the terms "installed," "equipped with," and "connected," etc., should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, a direct connection, or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0047] In this document, the term “and / or” includes any and all combinations of one or more of the listed related items.

[0048] In this article, the term "multiple" means two or more, that is, it includes two, three, four, five, etc.

[0049] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0050] In this article, "machine" refers to equipment on a semiconductor manufacturing production line that performs specific process steps. Specifically, in a semiconductor wafer fab, this can refer to lithography machines, etching machines, thin film deposition equipment, ion implanters, or chemical mechanical polishing equipment. Data is collected and recorded during the operation of each machine for real-time monitoring, fault diagnosis, yield analysis, and predictive maintenance.

[0051] For example, machine data may include engineering data on the health status of the equipment itself (such as time-series data such as temperature, pressure, gas flow, motor speed, vibration spectrum, etc.), process parameters of the processing (such as formula settings, actual execution values, alarm logs, event records), and contextual information related to production results (such as wafer ID, batch number, process step identifier, process sequence label, timestamp, etc.), etc., without limitation.

[0052] In this paper, the machine data includes multiple data points, each representing the measured value of a key process parameter at a specific moment or process node. Each data point can be associated with clear contextual information, including but not limited to: the data acquisition timestamp (i.e., the target acquisition time), the process step it is in (i.e., the target process step), the process time in that step, and the corresponding product type or batch identifier, enabling it to be accurately located and traced back throughout the entire manufacturing process.

[0053] To support engineers in performing detailed analysis of machine operation status and troubleshooting, the aforementioned machine data needs to be visualized. However, due to the high-dimensionality (multiple parameters, multiple machines, multiple batches) and high-frequency (sampling frequency can reach milliseconds or even microseconds) characteristics of semiconductor manufacturing data, the supported visualization configuration combinations are extremely rich, and the space of visualization views that can be generated is extremely large. For example, visualization configuration involves multiple degrees of freedom, including but not limited to: data range (such as the selection of time windows), dimensional combinations (such as single-parameter trend or multi-parameter correlation analysis), data granularity (such as raw sampling points or aggregation by minute), aggregation method (such as mean, maximum value, standard deviation, etc.), and display mode (line chart, scatter matrix, etc.).

[0054] Based on this, embodiments of this application provide a method, device, and storage medium for graphical display of machine data. Specifically, it provides an intelligent recommendation and retrieval mechanism for visual charts. In industrial data scenarios with extremely large view spaces, it accurately responds to the user's ambiguous or incomplete adjustment intentions that may exist during the interaction process based on multi-source prior information, thereby improving the efficiency and effectiveness of data visualization, optimizing the user's visualization experience in complex data environments, and thus improving the efficiency of real-time monitoring, fault warning, and root cause analysis in semiconductor manufacturing.

[0055] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0056] Please see Figure 1 , Figure 1 This is a schematic flowchart illustrating a method for graphically displaying machine data provided in an embodiment of this application, such as... Figure 1As shown in the figure, this application provides a method for graphical display of machine data.

[0057] S201, acquire the data to be visualized from the target machine, as well as the historical browsing records corresponding to the target machine and the preset browsing suggestions corresponding to the process performed by the target machine.

[0058] Among them, browsing history refers to the set of chart attributes used by users when viewing data charts in the past, which is used to reflect the operating habits and analysis preferences under specific machine or process scenarios.

[0059] Among them, the preset browsing suggestions refer to a set of recommended chart attributes pre-configured by process experts or based on specific process steps, which are used to guide the data views that should be prioritized at this process stage.

[0060] In some embodiments, the chart attribute set includes, but is not limited to, one or more of the following configuration parameters: data range, dimension combination, data granularity, aggregation method, and display mode.

[0061] S202, determine initial chart attributes based on the preset browsing suggestions; determine multiple candidate chart attributes based on the historical browsing records.

[0062] S203, Render and display the data to be charted based on the initial chart attributes to obtain the currently displayed chart.

[0063] Specifically, based on the preset browsing suggestions corresponding to the process currently being performed by the target machine, initial chart attributes (such as the time window of focus for each axis and appropriate data granularity and display mode) that align with the current process analysis focus are determined. This ensures that the chart presents a default view with analytical value upon initial loading, providing users with reliable initial visual feedback. This facilitates users in providing relatively accurate fuzzy adjustment intentions based on this chart during subsequent interactions (such as "more detailed" or "view for a longer period"). Simultaneously, based on the machine's historical browsing records, frequently used chart configurations are extracted to generate multiple candidate chart attributes for intelligent recommendation and quick switching during subsequent interactions. This approach balances process standardization with user personalization, effectively reducing initial cognitive burden and improving subsequent interaction efficiency.

[0064] In some embodiments, chart attributes include axis scale, which includes data granularity and / or data range. Axis scale is a core attribute affecting the visual effect of a chart, and its flexibility is mainly reflected in the two configuration dimensions of data granularity and data range.

[0065] Data granularity refers to the time or logical interval between adjacent data points on the chart's coordinate axis, reflecting the sampling density and data accuracy in visualization. For example, data granularity can be expressed as "one data point per hour," "one data point per day," or "one aggregate value per batch." The finer the data granularity, the richer the details presented; the coarser the data granularity, the more emphasis is placed on trend overview.

[0066] The data range refers to the analysis period or process window corresponding to the chart's coordinate axis, used to determine the subset of data selected to generate the current chart. This range can be set based on absolute time (e.g., from 2026-01-01 to 2026-01-31) or based on the process window of the manufacturing process, such as a single process batch, continuous equipment operation cycle, or the entire execution period of a certain lithography process.

[0067] It should be understood that each chart contains at least two axes, typically including a time axis (such as the X-axis) and one or more process parameter axes (such as the Y-axis, representing temperature, voltage, pressure, etc.). Each axis has an independent scale, meaning that its corresponding data range and / or data granularity can be configured and adjusted separately. For simplicity, the following examples describe the scaling of a single axis (such as the time axis), but the method is also applicable to scenarios involving multi-axis collaborative or independent adjustments, allowing for the parsing of the adjustment intent for each axis and updating the chart properties of the corresponding dimensions.

[0068] S204, responding to the user's scaling operation on the currently displayed chart, updates the currently displayed chart in real time.

[0069] Specifically, scale adjustment refers to the user's behavior of changing the scale of the chart's coordinate axes through interface interaction. The user's adjustment intention can be parsed from this, and the chart attributes corresponding to the adjustment intention can be obtained to update the currently displayed chart.

[0070] For example, scaling operations include precise operations, which can provide a relatively complete adjustment intent, such as the user explicitly specifying the adjustment object (e.g., time axis or value axis), the adjustment trend (refinement or coarsening), and the adjustment magnitude (e.g., a specific scaling ratio). In this case, the adjustment intent corresponds to clear chart attributes, and the currently displayed chart can be updated directly.

[0071] For example, scale adjustment operations include vague or incomplete operations, such as zooming with the mouse wheel, clicking the "zoom in / zoom out" button, or dragging the axis slider. These operations can provide limited adjustment intentions, such as one or two of the adjustment object, adjustment trend, and adjustment range. Since they only provide the user's directional and exploratory analysis needs without specifying specific parameters, it is necessary to combine multi-source prior information to achieve effective view updates.

[0072] In some embodiments, please refer to Figure 2 , Figure 2 This is a schematic flowchart illustrating a response to a fuzzy adjustment intent provided in an embodiment of this application, such as... Figure 2 As shown, S204 includes:

[0073] S2041, Obtain the user's input adjustment intention, the adjustment intention including one or both of the following: adjustment object, adjustment trend and adjustment range;

[0074] S2042, based on the adjustment intention and the current chart attribute of the currently displayed chart, find the target chart attribute with the highest matching degree with the adjustment intention among the multiple candidate chart attributes;

[0075] S2043, Based on the target chart attributes, render and display the data to be charted, so as to update the currently displayed chart.

[0076] Among them, the adjustment object refers to the coordinate axis dimension that the user intends to adjust, such as the XY axis corresponding to time series data, and you can choose to adjust a single coordinate axis or multiple coordinate axes; the adjustment trend refers to the direction of view change that the user expects; the adjustment magnitude refers to the scale or degree of adjustment, such as scaling from the "month" level to the "week" or "day" level on the time axis, or changing the proportion of the display range on the numerical axis, reflecting the user's specific needs for the intensity of view scaling.

[0077] Specifically, the system acquires the adjustment intent input by the user through interface operations. This intent may only include one or two of the adjustment object, adjustment trend, and adjustment magnitude, reflecting the user's vague or directional interaction needs. Combining the existing chart attributes of the currently displayed chart (such as current data granularity and current time range), the system calculates the degree of fit between each pre-generated candidate chart attribute and the user's intent, selects the target chart attribute with the highest matching degree, and re-renders the data to be charted based on the target chart attribute, updating the currently displayed chart in real time.

[0078] In some embodiments, the degree of matching between the adjustment intent and the chart attribute can be determined by assessing the granularity level, scope inclusion relationship, and process context consistency of the chart attribute based on the principle of minimum change and the consistency of operational semantics. If multiple axes are adjusted simultaneously, the differences can be comprehensively evaluated. Specific matching determination rules can be preset, and are not limited here. For example, if the difference between a candidate chart attribute and the current chart attribute matches the user's adjustment intent (such as a trend towards refinement or coarsening), it is considered a valid candidate. Among multiple valid candidates, the candidate chart attribute with the smaller the difference from the current chart attribute and the smoother the change, the higher the degree of matching.

[0079] For example, when a user simply scrolls the mouse wheel to perform a "zoom in" operation, without specifying the axis or adjustment range, only implying a refinement trend, and the currently displayed time axis data range is "1 month" with a data granularity of "one data point per day," candidate chart attributes A (data range "1 month", data granularity "one data point per hour"), B (data range "1 week", data granularity "one data point per hour"), and C (data range "1 day", data granularity "one data point per minute") all satisfy the refinement trend. The option with the highest matching degree, A, is preferred because it only increases the granularity while maintaining the original time range, resulting in minimal change and the strongest contextual continuity, thus achieving a precise and smooth response to intent. In contrast, B and C not only change the range but may also skip the focus interval not explicitly specified by the user, jumping to irrelevant parameter axes or an overly scaled "minute granularity" view, easily causing analysis interruptions.

[0080] For example, if the current chart shows the etching rate curve for a certain batch, and the user simply clicks the "zoom in" button without specifying the axis or adjustment range, only implying a more refined trend, then identifying the main analysis dimension under the current process as the time axis, with a data range of "the entire batch (e.g., 8 hours)" and a data granularity of "one data point every 10 minutes," and given that the process is in a critical etching window, candidate charts with a data range of "2 hours" and a data granularity of "one data point every 1 minute" have a higher match to the adjustment intent.

[0081] For example, if the timeline data range is "1 day" and the data granularity is "one data point per hour", and the user manually changes the displayed data range from "1 day" to "1 hour" on the timeline, specifying the axis and adjustment range but not the adjustment trend, the system will automatically identify the implicit refinement trend by comparing the old and new ranges and finding that the data range has significantly decreased. Then, it will select the configuration that best matches the "1 hour" data range from the candidate chart attributes as the target chart attribute, such as a data range of "1 hour" and a data granularity of "one data point per minute".

[0082] This avoids the reliance on complete parameter input. Even if the user only expresses directional or tentative rough analysis needs, it can be intelligently mapped to the view configuration that best matches their analysis goals, achieving efficient and smooth visual navigation.

[0083] In some embodiments, the adjustment trend includes a refinement trend or a coarsening trend.

[0084] The "refinement trend" refers to a user's desire to focus on details, which can be achieved by using finer data granularity (e.g., changing from monthly to weekly) or narrowing the data range (e.g., changing from "last 90 days" to "last 7 days"). For example, the refinement trend corresponds to a finer data granularity or a shorter data range, allowing for clearer observation of local features or abnormal behaviors.

[0085] The coarsening trend refers to a user's desire for a comprehensive overview, which can be achieved by using coarser data granularity (e.g., changing from hours to days) or expanding the data range (e.g., changing from "the most recent day" to "the most recent 30 days"). For example, the coarsening trend corresponds to coarsening the data granularity or lengthening the data range to understand the overall trend or long-term fluctuation pattern.

[0086] It should be understood that scale adjustment operations may originate from users' analytical purposes such as anomaly investigation, trend observation, or process verification. At the very least, they can reflect the adjustment trend expected by the user. By identifying the explicit or implicit adjustment trend, and combining it with the current chart attributes, the system can intelligently match and switch to the chart configuration under the corresponding trend, achieving an efficient mapping from fuzzy interaction to precise visualization.

[0087] In some embodiments, when responding to a refined trend (e.g., narrowing an 8-month time range to 6 months) and the user has not explicitly specified a target interval, the target interval length (e.g., the first 6 months) can be automatically selected by aligning with the start point of the current time range; or a data window symmetrically cropped to the target interval length (e.g., 6 months) before and after the user's interaction location (e.g., mouse hover or click point) can be used. This effectively avoids sudden changes in view or interruptions in analysis due to missing location information. If the user wants to view other sub-intervals (e.g., the end or middle segment), they can dynamically adjust them using lightweight interactions such as sliding, dragging, or panning, without needing to re-enter parameters.

[0088] Please see Figures 3 to 4 , Figure 3 This application provides an embodiment of a currently displayed chart generated based on initial chart attributes. Figure 4 This is a currently displayed chart generated based on the target chart attributes, as provided in this application embodiment. Figure 3 , Figure 4 The horizontal axis represents time (in hours: minutes: seconds), and the vertical axis represents voltage (in volts).

[0089] For machines performing dry etching processes, the default browsing suggestion is to specify the initial chart attributes as follows: X-axis: time, data range approximately 2 minutes, data granularity 1 second; Y-axis: voltage (unit: volts), data range -500 to 2500, data granularity 1. Based on the initial chart attributes, the data to be charted is rendered and displayed, resulting in the following: Figure 3 The currently displayed chart is shown below.

[0090] When a user clicks the "Zoom In" button, it can be inferred that their adjustment intention is to refine the trend. Among multiple candidate chart attributes, the target chart attribute with the highest matching degree to this intention is: X-axis: time, data range 2 minutes, data granularity 0.1 seconds; Y-axis: voltage, data range -500 to 2500, data granularity 0.1. Based on the target chart attribute, the data to be charted is rendered to update the currently displayed chart, such as... Figure 4 As shown, both axes automatically switch to a view with finer data granularity, making the originally clustered data points appear more dispersed, which facilitates the identification of outliers and improves analysis efficiency while maintaining ease of interaction.

[0091] like Figure 3 , Figure 4 As shown, different colors can be used to represent different data types. For example, one color can represent the machine's actual data, while another color can represent the machine's threshold data (such as voltage thresholds). Alternatively, different colored data can be used to display data from different machines in the same window. Figure 3 , Figure 4 This is merely an illustration of the display format of the window interface, and the present invention does not impose any limitations on it.

[0092] In some embodiments, S204 includes: arranging the initial chart attribute and a plurality of candidate chart attributes according to the increasing or decreasing trend of the coordinate axis scale to obtain a chart call sequence; locating the current chart attribute in the chart call sequence, and selecting a chart attribute that precedes or follows the current chart attribute as the target chart attribute according to the adjustment trend.

[0093] Specifically, an increasing or decreasing trend refers to an orderly change in chart attributes. For example, in terms of data range, a time window changing from 1 hour to 1 day to 1 week is an increasing trend (i.e., the data range is expanding), and vice versa; in terms of data granularity, a sampling interval changing from 1 second to 1 minute to 1 hour is a decreasing trend (i.e., the data granularity is becoming coarser), and vice versa.

[0094] The initial chart attribute and multiple candidate chart attributes are arranged in logical order according to the coordinate axis scale to form a structured chart call sequence. In the chart call sequence, the current chart attribute corresponding to the currently displayed chart is located, and based on the adjustment trend, the adjacent previous attribute (i.e., the previous chart attribute) or next attribute (i.e., the subsequent chart attribute) in the sequence is automatically selected as the target chart attribute.

[0095] For example, the chart call sequence is arranged from finest to coarsest data granularity as follows: 1 second, 10 seconds, 1 minute, 10 minutes, 1 hour, corresponding to time ranges of 1 hour, 6 hours, 1 day, 1 day, 7 days, when the data granularity of the current chart attribute is "one data point per minute" and the data range is "1 day".

[0096] If the user inputs a more refined trend, the system automatically switches to the previous data granularity in the sequence, "one data point every 10 seconds," obtaining a data range of "6 hours." If the user inputs a more coarse trend, the system automatically switches to the previous data granularity in the sequence, "one data point every 10 minutes," obtaining a data range of "1 day." Thus, by organizing discrete views into ordered navigation paths through a structured sequence, users can easily navigate between semantically coherent views, improving interaction efficiency and analytical fluency.

[0097] In some embodiments, the chart call sequence is arranged in order of data granularity from fine to coarse and / or data range from short to long. The method further includes: when the adjustment trend is a refining trend, selecting a chart attribute in the chart call sequence that precedes the current chart attribute; when the adjustment trend is a coarsening trend, selecting a chart attribute in the chart call sequence that follows the current chart attribute.

[0098] In other embodiments, the chart call sequence may follow the reverse logic, arranged in order of data granularity from coarse to fine and / or data range from long to short. The method further includes: when the adjustment trend is a refining trend, selecting the chart attribute in the chart call sequence that follows the current chart attribute; when the adjustment trend is a coarsening trend, selecting the chart attribute in the chart call sequence that precedes the current chart attribute.

[0099] It should be understood that, based on the semantic consistency between the sequence structure and the adjustment trend, the user's refinement or coarsening trend is correctly mapped to the forward or backward jump in the sequence, and the adjacent jump direction is dynamically determined to ensure that the view switching logic is clear and the operation response is reliable.

[0100] In some embodiments, the method further includes: constructing a machine profile of the equipment, the machine profile including at least one of machine type and process technology; and clustering multiple machines into one or more machine groups based on the machine profile. The machine profile may optionally include one or more of the following: basic equipment attributes (such as equipment manufacturer, specific model, core hardware architecture version, etc.), executed process attributes (such as the process node to which it belongs, the specific process steps executed, key process parameter windows, etc.), and operating environment attributes (such as temperature, humidity, etc.).

[0101] In some embodiments, machines are clustered based on their basic equipment attributes, process attributes, and operating environment attributes to obtain different types of machine groups. The machines in a group can be distributed across one or more production lines, and their physical attributes and process functions are highly homogeneous. Furthermore, the inclusion of machines with similar functions but significant differences in internal structure or control logic is avoided, ensuring that the operating performance of each machine within the group has good comparability and reference value.

[0102] In some embodiments, a target machine group is obtained, which includes the target machine and several other machines of the same type as the target machine.

[0103] For example, the other machines include several concurrent machines whose usage time differs from the target machine by no more than a preset time. These include machines in the same production batch or at the same life cycle stage as the target machine, such as those all operating between the 30th and 40th day after production commencement. The preset time can be flexibly set according to the actual application scenario, such as 10 days.

[0104] For example, the other machines include those with a longer service life than the target machine. It should be understood that the prior machines refer to other machines that belong to the same equipment type and process steps as the target machine, and whose cumulative operating years (or the length of the data lifecycle) are longer than that of the target machine. Because the prior machines have a longer service life, their historical databases contain complete historical browsing records from when the machine was put into production to its current aging stage, which have high reference value.

[0105] In some embodiments, the method further includes: obtaining the second historical browsing records of each machine in the machine group to which the target machine belongs, and performing statistical analysis on the second historical browsing records to extract a number of high-frequency chart attributes whose usage popularity is higher than a second preset threshold; wherein the second preset threshold is greater than or equal to a first preset threshold; and using the high-frequency chart attributes as candidate chart attributes of the target machine.

[0106] In some embodiments, the method further includes: obtaining a first historical browsing record of the target device; performing statistical analysis on the first historical browsing record of the target device to extract a number of high-frequency chart attributes whose usage popularity is higher than a first preset threshold; and using the high-frequency chart attributes as candidate chart attributes of the target device.

[0107] The first preset threshold is used to filter high-frequency chart attributes in the target machine's own browsing history to reflect individual machine preferences; the second preset threshold is used to filter common chart attributes in the historical records of a group of machines of the same type to reflect group machine preferences. In some embodiments, the second preset threshold is greater than or equal to the first preset threshold to make the introduced group preferences more consistent and reliable, and to avoid mistaking low-frequency or occasional browsing behaviors in the group as chart attributes with general reference value for promotion.

[0108] In some embodiments, popularity is determined based on the number of times the chart is loaded in a valid analysis session, the duration of a single session, or the number of interactive operations (such as zooming, annotation, exporting, etc.) to characterize the actual value and preference intensity of the chart attribute for the user. Specific calculation rules can be flexibly set according to actual needs; for example, popularity can be the sum of the above items, normalized, and then compared with a preset threshold.

[0109] For example, for a specific etching machine, statistical analysis of its historical browsing records reveals that the chart attribute with a time range of "7 days" and a data granularity of "one data point per minute" has a usage frequency exceeding a first preset threshold and is included in the candidate chart attribute set. Simultaneously, by combining the machine profile to identify the same group of etching machines, analysis of the group's historical browsing records shows that the chart attribute with a time range of "30 days" and a data granularity of "one data point per 10 minutes" has a usage frequency exceeding a second preset threshold and appears frequently within the group. This reflects the common need for medium- to long-term trend monitoring at this process stage, and therefore, this combination is also included in the candidate chart attribute set.

[0110] In some embodiments, high-frequency chart attributes in the second historical browsing record (also known as the group browsing record) based on the group include two types: chart attributes based on absolute time and chart attributes based on the process window.

[0111] For chart attributes based on absolute time, if the browsing history of a group shows that a large number of users are concentrated in viewing a certain fixed absolute time interval (such as "2026-03-05 10:00–12:00"), and this period spans multiple machines within the group, it may indicate the existence of common external disturbances or systemic anomalies, such as sudden changes in cleanroom temperature and humidity, power fluctuations, or abnormal gas supply in a certain batch. In this case, the absolute time window itself becomes a valuable analytical clue and has promotional value.

[0112] For chart attributes based on process windows, if the group browsing history shows that similar machines generally view data within the relative time window of "1 hour to 3 hours after formula start-up", it indicates that this period has inherent process regularity (such as key reaction stages, parameter stabilization period, etc.) and has promotional value.

[0113] In some embodiments, to efficiently present high-dimensional, high-frequency machine data under limited system resources, an intelligent downsampling mechanism for preserving critical information is introduced. Please refer to... Figure 5 , Figure 5 This application provides another method for graphical display of machine data, the method comprising:

[0114] S101, Obtain the data to be charted from the target machine, wherein the data to be charted includes several data points associated with timestamps;

[0115] S102, Identify key data points from a number of data points;

[0116] S103, determine the downsampling ratio based on the current window's browsing memory capacity, and perform downsampling processing on the data to be charted based on the downsampling ratio to obtain the downsampled dataset;

[0117] S104, the downsampled dataset is merged with the key data points to form a target data point set, which is used to generate a chart.

[0118] Specifically, based on the maximum amount of memory that the current user terminal (such as a browser or client) can safely use when rendering a chart, i.e., the browser's memory capacity, for example, by estimating through performance testing that the current window can smoothly render a maximum of 5,000 data points, the downsampling ratio can be calculated based on the total number of data points in the data to be charted and the number of data points that can be rendered, and downsampling processing can be performed to reduce the number of data points. Common methods include mean aggregation, extreme value preservation, or equal-interval frame extraction.

[0119] Furthermore, several key data points are identified through process rules or anomaly detection algorithms, and these data points are forcibly retained in the target dataset. These key data points are either process concern points or data anomaly points. Process concern points are data points at important moments or events defined by process specifications or operating procedures, such as recipe start / end, equipment start / stop, batch switching, etc.; data anomaly points are data points that deviate from normal thresholds, identified by various anomaly detection algorithms, such as parameter values ​​exceeding control limits, sudden changes, or oscillation signals.

[0120] For example, assuming the original data to be charted contains 1 million timestamped data points, and the current window's memory capacity is 50MB, the calculated downsampling ratio is 1:500, retaining 2000 points after downsampling; simultaneously, 15 key data points are identified (including 8 process-related points and 7 anomalies). Ultimately, these 2015 points serve as the target data point set, subsequently used as the charting data obtained in step S201 to participate in the final chart generation. It should be noted that the specific anomaly detection and downsampling algorithms can be implemented using relevant technologies, which will not be elaborated upon here.

[0121] This avoids interface lag caused by data overload and ensures that engineers can clearly identify abnormal events or key process nodes at any zoom level, effectively supporting fault tracing and process analysis.

[0122] In some embodiments, to prevent abnormal signals from being smoothed or missed due to high downsampling ratio, the method further includes: determining an identification threshold for abnormal data points based on a preset mapping relationship and the downsampling ratio, wherein the identification threshold is negatively correlated with the downsampling ratio.

[0123] Specifically, the preset mapping relationship refers to the pre-established rule or function relationship used to associate the downsampling ratio with the threshold for identifying abnormal data points, which can be flexibly set based on experience, simulation or historical data analysis.

[0124] The higher the downsampling ratio (i.e., the stronger the data compression and the more blurred the details), the lower the corresponding recognition threshold, thereby improving the sensitivity of anomaly detection and compensating for the loss of details caused by data aggregation.

[0125] For example, process concerns include the chamber pressure value at the initial moment of RF power application in dry etching processes; the data anomalies are detected using existing algorithms such as Z-score or Isolation Forest; for instance, when the downsampling ratio is 1:100, the anomaly detection threshold is set to 3σ, while when the downsampling ratio is 1:1000, because the larger downsampling ratio may mask transient changes, the threshold is adjusted to 2.5σ, allowing more potential anomalies to be identified and forcibly retained in the final target data point set. Thus, by making the detection threshold negatively correlated with the downsampling ratio, it ensures that even under highly compressed views, key anomalies can still be effectively captured and retained, avoiding sacrificing analytical integrity for visualization optimization.

[0126] In some embodiments, a preset coordinate axis scale is intelligently configured for each key data point based on its distribution characteristics or time span along the time axis. The method further includes: determining a preset coordinate axis scale for each key data point based on its distribution characteristics or time span; generating at least one key chart attribute based on the preset coordinate axis scale of the key data points to ensure the chart fully covers or displays the key data points; and, in response to a user's selection of a key data point in the currently displayed chart, invoking the key chart attribute corresponding to the selected key data point to render and display the data to be charted.

[0127] Among them, time span refers to the duration of the event corresponding to the key point, such as instantaneous point or duration; distribution characteristics refer to its spatial relationship with other key points, such as isolated, densely clustered, or periodically appearing.

[0128] Specifically, if the key data points are single points or sparsely distributed, the time range of the coordinate axis is determined by taking their timestamps as the center and setting a preset time offset; if multiple key data points are continuous or densely distributed in time, the earliest and latest timestamps are used as the boundaries, and a certain buffer is extended to preserve the context, thereby forming a coordinate axis range that covers all relevant events.

[0129] Based on this, and considering the length of the time span, the corresponding data granularity is matched (e.g., a short window corresponds to fine granularity, and a long window corresponds to coarse granularity) to generate structured key chart attributes. When a user selects a key data point, its associated key chart attributes are invoked, and the data to be charted is re-rendered, ensuring that the key data point is fully presented in the optimal view, thus improving the efficiency of anomaly tracing and process analysis.

[0130] For example, when the process is dry etching, if an abnormal data point occurs at a moment of abnormal fluctuation in chamber pressure, key chart attributes are generated with that point as the center, the data range being one hour before and after, and the data granularity being one data point per second.

[0131] In some embodiments, chart attributes include axis scales (such as data granularity and data range), dimension combinations, aggregation methods, and display modes. Once the target candidate chart attribute is determined, two types of current display views can be generated, which users can choose between based on their analysis objectives. The first view retains the initial chart attribute's aggregation method and display mode, only updating its axis scale to match the scale in the candidate chart attribute. This is suitable for scenarios where users only want to adjust the observation granularity or time range without changing the analytical perspective, ensuring consistency and stability of the interaction. The second view fully utilizes all elements of the candidate chart attribute, including axis scales, aggregation methods, and display modes, generating a completely new view suitable for guiding users to discover potential analytical paths. It should be noted that the dimension combination is fixed by the process context or the user's initial selection, remaining consistent between the initial and candidate chart attributes and not changing with switching, ensuring consistency of the analysis object.

[0132] The methods and apparatus of this application can be used in a wide variety of general-purpose or special-purpose computing system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer terminal devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

[0133] Please see Figure 6 , Figure 6 This is a schematic block diagram illustrating the structure of a computer device according to an embodiment of this application. The computer device may be a terminal device or a server.

[0134] For example, the above method can be implemented as a computer program, which can be used in, for example... Figure 6 It runs on the computer device shown.

[0135] like Figure 6 As shown, the computer device includes a processor, memory, and network interface connected via a system bus, wherein the memory may include non-volatile storage media and internal memory.

[0136] Non-volatile storage media can store operating systems and computer programs. The computer program includes program instructions that, when executed, cause the processor to perform any method for graphically displaying machine data and the specific implementation steps of that method.

[0137] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0138] The internal memory provides an environment for the execution of computer programs stored in non-volatile storage media. When the computer program is executed by the processor, it enables the processor to execute any method for graphical display of machine data and the specific implementation steps of the method.

[0139] This network interface is used for network communication, such as sending assigned tasks.

[0140] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0141] In one embodiment, the processor is configured to run a computer program stored in memory to perform the following steps:

[0142] S201, Obtain the data to be visualized from the target machine, as well as the historical browsing records corresponding to the target machine and the preset browsing suggestions corresponding to the process performed by the target machine;

[0143] S202, determine initial chart attributes based on the preset browsing suggestions; determine multiple candidate chart attributes based on the historical browsing records; wherein, the chart attributes include coordinate axis scale, and the coordinate axis scale includes data granularity and / or data range;

[0144] S203, Render and display the data to be charted based on the initial chart attributes to obtain the currently displayed chart;

[0145] S204, responding to the user's scaling operation on the currently displayed chart, updates the currently displayed chart in real time.

[0146] In one embodiment, the processor is configured to run a computer program stored in memory to perform the following steps:

[0147] S2041, Obtain the user's input adjustment intention, the adjustment intention including one or both of the following: adjustment object, adjustment trend, and adjustment range; the adjustment trend includes a refinement trend or a coarsening trend;

[0148] S2042, based on the adjustment intention and the current chart attribute of the currently displayed chart, find the target chart attribute with the highest matching degree with the adjustment intention among the multiple candidate chart attributes;

[0149] S2043, Based on the target chart attributes, render and display the data to be charted, so as to update the currently displayed chart.

[0150] In one embodiment, the processor is configured to run a computer program stored in memory to perform the following steps:

[0151] S101, Obtain the data to be charted from the target machine, wherein the data to be charted includes several data points associated with timestamps;

[0152] S102, Identify key data points from a number of data points;

[0153] S103, determine the downsampling ratio based on the current window's browsing memory capacity, and perform downsampling processing on the data to be charted based on the downsampling ratio to obtain the downsampled dataset;

[0154] S104, the downsampled dataset is merged with the key data points to form a target data point set, which is used to generate a chart.

[0155] For example, the processor is used to run a computer program stored in the memory, and is also used to implement the steps of the graphical display method for machine data provided in any embodiment of this application and the specific implementation steps of the method, which will not be repeated here.

[0156] The embodiments of this application also provide a computer-readable storage medium storing a computer program, the computer program including program instructions, and the processor executing the program instructions to implement the steps of the graphical display method for machine data provided in any of the embodiments of this application, and the specific implementation steps of the method.

[0157] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device.

[0158] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for graphically displaying machine data, characterized in that, The method includes: S201, Obtain the data to be visualized from the target machine, as well as the historical browsing records corresponding to the target machine and the preset browsing suggestions corresponding to the process performed by the target machine; S202, determine initial chart attributes based on the preset browsing suggestions; determine multiple candidate chart attributes based on the historical browsing records; wherein, the chart attributes include coordinate axis scale, and the coordinate axis scale includes data granularity and / or data range; S203, Render and display the data to be charted based on the initial chart attributes to obtain the currently displayed chart; S204, in response to the user's scaling operation on the currently displayed chart, update the currently displayed chart in real time, including: S2041, Obtain the user's input adjustment intention, the adjustment intention including one or both of the following: adjustment object, adjustment trend, and adjustment range; the adjustment trend includes a refinement trend or a coarsening trend; S2042, based on the adjustment intention and the current chart attribute of the currently displayed chart, find the target chart attribute with the highest matching degree with the adjustment intention among the multiple candidate chart attributes; S2043, Based on the target chart attributes, render and display the data to be charted, so as to update the currently displayed chart; The data to be charted includes several data points associated with timestamps; correspondingly, the method includes: S102, determine key data points from a number of data points, wherein the key data points are process concern points or data anomaly points; wherein the process concern points are data points under important moments or events defined by process specifications or operating procedures; S103, determine the downsampling ratio based on the current window's browsing memory capacity, and perform downsampling processing on the data to be charted based on the downsampling ratio to obtain the downsampled dataset; S104, the downsampled dataset is merged with the key data points to form a target data point set for generating a chart; based on a preset mapping relationship, the identification threshold of data anomalies is determined according to the downsampling ratio, and the identification threshold is negatively correlated with the downsampling ratio.

2. The method according to claim 1, characterized in that, S204 includes: Based on the increasing or decreasing trend of the coordinate axis scale, the initial chart attributes and multiple candidate chart attributes are arranged to obtain the chart call sequence; Locate the current chart attribute in the chart call sequence, and select the chart attribute that precedes or follows the current chart attribute as the target chart attribute according to the adjustment trend.

3. The method according to claim 2, characterized in that, The chart call sequence is arranged in order of data granularity from fine to coarse and / or data range from short to long; the refinement trend corresponds to the data granularity becoming finer or the data range becoming shorter; The coarsening trend corresponds to either coarsening of the data granularity or an increase in the data range. The method further includes: When the adjustment trend is a refinement trend, select the chart attribute in the chart call sequence that precedes the current chart attribute; When the adjustment trend is a coarsening trend, select the chart attribute that follows the current chart attribute in the chart call sequence.

4. The method according to claim 1, characterized in that, The method further includes: Obtain the first historical browsing record of the target machine; Statistical analysis is performed on the first historical browsing records of the target machine to extract several high-frequency chart attributes whose usage popularity is higher than a first preset threshold; The high-frequency chart attributes are used as candidate chart attributes for the target machine.

5. The method according to claim 1 or 4, characterized in that, The method further includes: Construct a machine profile for the machine, wherein the machine profile includes at least one of the following: machine type and process; Based on the machine profile, multiple machines are clustered into one or more machine groups; The second historical browsing records of each machine in the machine group to which the target machine belongs are obtained, and the second historical browsing records are statistically analyzed to extract several high-frequency chart attributes whose usage popularity is higher than a second preset threshold; wherein, the second preset threshold is greater than or equal to the first preset threshold. The high-frequency chart attributes are used as candidate chart attributes for the target machine.

6. The method according to claim 1, characterized in that, The method further includes: Based on the distribution characteristics or time span of the key data points, determine the preset coordinate axis scale for each key data point; Based on the preset coordinate axis scale of the key data points, at least one key chart attribute is generated so that the chart can fully cover or display the key data points; In response to the user's selection of key data points in the currently displayed chart, the key chart attributes corresponding to the selected key data points are invoked to render and display the data to be charted.

7. A computer device, characterized in that, The device includes: Memory, used to store computer programs; A processor is configured to execute the computer program and, in executing the computer program, implement the graphical display method of machine data as described in 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 that, when executed by a processor, causes the processor to implement the graphical display method of machine data as described in any one of claims 1 to 6.

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