Multi-scale data processing method and device, equipment, program product and medium
Through the segmented linear mapping algorithm, multiple sets of time sequence data are mapped into preset intervals and remapping into original data values on the drawing window, solving the problem of multi-scale data being displayed in the same figure, realizing clear data display and Accurate data analysis.
Patent Information
- Application Number
- CN202411578947.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-07
- Publication Date
- 2025-02-07
AI Technical Summary
The prior art is difficult to clearly display multiple time series data of different value ranges and their mutual influence relationships in the same figure, resulting in the problem of insufficient fluctuation trend.
By obtaining multiple sets of timing data, merge into target timing data, and sorting it according to preset sorting rules. The data is mapped into the preset interval using the segmented linear mapping algorithm to generate a mapping relationship table. The mapped timing data is then remapped on the drawing window to the original data value to generate a visual presentation of cross-scale data.
It realizes the clear display of time series data and its fluctuation trends in different value ranges in the same figure, avoiding the problem of information loss caused by data scale differences, and improving the accuracy and work efficiency of data analysis.
Smart Images
Figure CN119396924A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of data processing technology, and in particular to a multi-scale data processing method, device, equipment, program product and medium. Background Art
[0002] In scenarios such as industrial monitoring, equipment status monitoring, and process control, it is crucial to comprehensively analyze and display multiple time series data (such as temperature, current, voltage, liquid level, feed amount, etc.) of a machine or a system. These data usually have different physical units and numerical ranges, making it very difficult to display them simultaneously on the same graph. Currently, there are several common solutions in the industry for this multi-scale data visualization problem:
[0003] 1. Frame drawing: Draw data of different scales on different graphs. Although this method is simple and direct, when there are many types of data to be compared, users must frequently switch views to observe the relationship between different data, which not only reduces work efficiency but also increases the difficulty of understanding.
[0004] 2. Fixed scale combined drawing: All data are drawn in the same graph using a unified scale. In this way, the data curve with a smaller value range may appear flat due to the relatively large data, thus failing to accurately reflect its actual fluctuation, thus affecting the quality of data analysis.
[0005] 3. Manually adjust the scale mapping: Allow users to manually set the scale factor of each data line according to their needs. Although this method can solve the problem of inconsistent scale to a certain extent, it requires users to have certain professional knowledge and needs to be reconfigured every time a new data set is encountered, which is more complicated to operate.
[0006] Therefore, in practical applications, the implementation of this multi-scale data visualization problem is not ideal. Summary of the invention
[0007] In view of this, the purpose of the present invention is to provide a multi-scale data processing method, device, equipment, program product and medium, which can clearly display multiple time series data with different value ranges and their mutual influence relationships in the same graph, and solve the problem of unclear fluctuation trend caused by data scale differences. The specific scheme is as follows:
[0008] In a first aspect, the present application discloses a multi-scale data processing method, comprising:
[0009] Acquire multiple groups of time series data, and merge the multiple groups of time series data into one group of time series data to obtain target time series data; each group of time series data includes multiple original data values;
[0010] Sorting the original data values in the target time series data according to a preset sorting rule, and mapping each of the sorted original data values to a preset interval using a piecewise linear mapping algorithm to generate a mapping relationship table; each of the original data values in the mapping relationship table corresponds to a respective mapping value;
[0011] Determine mapped time series data corresponding to the multiple groups of time series data according to the mapping relationship table, and remap each of the mapped values in the mapped time series data to the original data value on a drawing window to generate a cross-scale data visualization display diagram.
[0012] Optionally, the using of a piecewise linear mapping algorithm to map the sorted original data values into a preset interval to generate a mapping relationship table includes:
[0013] Performing the same statistical characteristic extraction on the sorted original data values and the reference data values in the preset interval respectively, so as to divide the target time series data and the preset interval into a plurality of data interval segments;
[0014] The data interval segments corresponding to the target time series data are matched one by one with the data interval segments corresponding to the preset interval, and then the mapping values corresponding to the original data values in the preset interval are determined by using a pre-constructed mapping formula;
[0015] The mapping relationship table is generated according to the original data value and the mapping value.
[0016] Optionally, respectively performing the same statistical characteristic extraction on the sorted original data values and the reference data values in the preset interval to divide the target time series data and the preset interval into a plurality of data interval segments includes:
[0017] Determine a first minimum value, a first maximum value, and first preset quantiles among the sorted original data values, and divide the target time series data into a plurality of real data segments based on the first minimum value, the first maximum value, and the first preset quantiles;
[0018] Determine a second minimum value, a second maximum value, and each second preset quantile among the reference data values within the preset interval, and divide the preset interval into a plurality of standard data segments based on the second minimum value, the second maximum value, and each second preset quantile;
[0019] The first preset quantile and the second preset quantile adopt the same quantile condition.
[0020] Optionally, the data interval segments corresponding to the target time series data are matched one-to-one with the data interval segments corresponding to the preset interval, and then the mapping values corresponding to the original data values in the preset interval are determined using a pre-constructed mapping formula, including:
[0021] The real data segments are matched one-to-one with the standard data segments, and then the mapping values corresponding to the original data values within the preset interval are determined by using a pre-constructed mapping formula;
[0022] The mapping formula is: ;in, is the mapping value, is the original data value, for The minimum value in the real data segment, for The maximum value in the real data segment, for Corresponding to the maximum value in the standard data segment, for Corresponds to the minimum value in the standard data segment.
[0023] Optionally, the multiple groups of time series data are time series data with different value ranges obtained from a data source, wherein the raw data values in the target time series data are sorted according to a preset sorting rule, and each of the sorted raw data values is mapped to a preset interval using a piecewise linear mapping algorithm, including:
[0024] The original data values in the target time series data are sorted in positive order according to their sizes, and each of the original data values is mapped into a preset interval in ascending order using a piecewise linear mapping algorithm.
[0025] Optionally, remapping each of the mapped values in the mapped time series data to the original data value on the drawing window to generate a cross-scale data visualization display diagram includes:
[0026] Each of the mapped values in the mapped time series data is plotted in the corresponding preset interval on the data axis on the drawing window, and the mapped values are remapped to the original data values to generate a cross-scale data visualization display diagram.
[0027] In a second aspect, the present application discloses a multi-scale data processing device, comprising:
[0028] A data processing module, used to obtain multiple groups of time series data, and merge the multiple groups of time series data into one group of time series data to obtain target time series data; each group of time series data contains multiple original data values;
[0029] A data mapping module, used to sort the original data values in the target time series data according to a preset sorting rule, and map each of the sorted original data values to a preset interval using a piecewise linear mapping algorithm to generate a mapping relationship table; each of the original data values in the mapping relationship table corresponds to a respective mapping value;
[0030] A data display module is used to determine the mapped time series data corresponding to the multiple groups of time series data according to the mapping relationship table, and remap each of the mapping values in the mapped time series data to the original data value on the drawing window to generate a cross-scale data visualization display diagram.
[0031] In a third aspect, the present application discloses an electronic device, comprising a processor and a memory; wherein the memory is used to store a computer program, and the computer program is loaded and executed by the processor to implement the multi-scale data processing method as described above.
[0032] In a fourth aspect, the present application discloses a computer program product, including a computer program / instruction, which implements the steps of the aforementioned multi-scale data processing method when executed by a processor.
[0033] In a fifth aspect, the present application discloses a computer-readable storage medium for storing a computer program; wherein the computer program, when executed by a processor, implements the multi-scale data processing method as described above.
[0034] The present application provides a multi-scale data processing method, which obtains multiple groups of time series data and merges the multiple groups of time series data into one group of time series data to obtain target time series data; each group of time series data contains multiple original data values; the original data values in the target time series data are sorted according to a preset sorting rule, and each of the sorted original data values is mapped to a preset interval using a piecewise linear mapping algorithm to generate a mapping relationship table; each of the original data values in the mapping relationship table corresponds to a respective mapping value; the mapped time series data corresponding to the multiple groups of time series data is determined according to the mapping relationship table, and each of the mapping values in the mapped time series data is remapped to the original data value on a drawing window to generate a cross-scale data visualization display diagram.
[0035] The beneficial technical effect of the present application is: by performing relevant processing on multiple groups of time series data, using the piecewise linear mapping algorithm to determine the corresponding proportional mapping relationship, automatically calculating the mapping value corresponding to the original data value, and generating a mapping relationship table. Therefore, when drawing on the drawing window, the mapping value is remapped to the original data value according to the mapping relationship table. Multiple groups of time series data with different value ranges can be clearly displayed in the same figure, and even data within a large range of amplitudes can accurately reflect their subtle fluctuation trends, avoiding the problem of information loss caused by differences in data scales. At the same time, users can intuitively observe the changes in all data without frequently switching views, which is convenient for users to intuitively compare and analyze the mutual influence relationship between different data. This not only improves work efficiency, but also enhances the accuracy of data analysis.
[0036] In addition, the present application provides a multi-scale data processing device, equipment, program product and medium, which correspond to the above-mentioned multi-scale data processing method and have the same effect as above. BRIEF DESCRIPTION OF THE DRAWINGS
[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.
[0038] Figure 1 A flow chart of a multi-scale data processing method disclosed in this application;
[0039] Figure 2 A schematic diagram of a specific multi-scale data processing flow disclosed in this application;
[0040] Figure 3 This is a schematic diagram of conventional processing of multi-scale data disclosed in this application;
[0041] Figure 4 A schematic diagram of a multi-scale data processing method disclosed in this application after processing;
[0042] Figure 5 This is a schematic diagram of the structure of a multi-scale data processing device disclosed in this application;
[0043] Figure 6 This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION
[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0045] At present, several common solutions in the industry for multi-scale data visualization have information loss problems caused by single or static scale settings, which cannot accurately reflect the actual fluctuations, thus affecting the quality of data analysis. To this end, this application provides a multi-scale data processing solution that can ensure that even data that varies within a large range of amplitudes can clearly express its subtle fluctuation characteristics, while maintaining a good contrast with other data series, thereby improving the effect and practicality of cross-scale data visualization.
[0046] The embodiment of the present invention discloses a multi-scale data processing method, see Figure 1 As shown, the method includes:
[0047] Step S11: Acquire multiple groups of time series data, and merge the multiple groups of time series data into one group of time series data to obtain target time series data.
[0048] In scenarios such as industrial monitoring, equipment status monitoring, and process control, a machine or a system often generates a variety of time series data with different physical units and value ranges, such as temperature, current, voltage, liquid level, feed amount, etc. In the embodiment of the present application, time series data with different value ranges are processed, and multi-scale data are clearly displayed in the same figure. First, multiple groups of time series data are obtained from data sources (such as sensors, databases, etc.), and the multiple groups of time series data include data with different value ranges, and can be data with a large range of amplitude changes. Among them, each group of time series data contains multiple original data values.
[0049] In addition, it is understandable that the present invention is not only applicable to industrial monitoring and equipment status monitoring, but also to financial data analysis, environmental monitoring, medical health and other fields. Regardless of the source and type of data, it can provide a consistent and efficient visualization solution with wide applicability. For example, in the financial field, stock prices, trading volumes, market indices and other data can be displayed in the same graph to help investors better understand market dynamics.
[0050] Furthermore, after acquiring multiple sets of time series data, the multiple sets of time series data are merged into one set of data to obtain target time series data.
[0051] Step S12: sorting the original data values in the target time series data according to a preset sorting rule, and mapping each of the sorted original data values into a preset interval using a piecewise linear mapping algorithm to generate a mapping relationship table.
[0052] In the embodiment of the present application, relevant sorting rules for sorting data values are pre-set, and each original data value in the target time series data is sorted according to the preset sorting rules. It should be pointed out that the sorting rules refer to a series of criteria formulated in a specific scenario in order to arrange data in a certain order. These rules can be based on different attributes of the data, such as numerical value, alphabetical order, date order, etc.
[0053] In a specific implementation, the original data values in the target time series data are sorted in positive order according to their sizes, ensuring that the data is arranged from small to large. Then, the need to manually adjust the scale factor is eliminated, and a set of sorted data is mapped to a preset interval using a piecewise linear mapping algorithm. That is, each of the original data values is mapped to a preset interval in order from small to large using a piecewise linear mapping algorithm. After the mapping is completed, a mapping relationship table is generated, wherein each original data value in the mapping relationship table corresponds to a respective mapping value.
[0054] In a specific implementation, the preset interval is set to 0 to 1. Then, a piecewise linear mapping algorithm is used to map the sorted set of data to between 0 and 1 to generate a mapping relationship table. Each original data value corresponds to a mapping value between 0 and 1. It can be seen that the traditional manual adjustment of the proportional mapping method requires the user to have certain professional knowledge, and each time a new data set is faced, it needs to be reconfigured, and the operation complexity is relatively high. In the embodiment of the present application, the user only needs to provide the original data, and the data processing and mapping can be automatically completed through the automated proportional mapping algorithm, and the optimal proportional mapping relationship can be calculated, which greatly simplifies the user's operation process and improves user experience and work efficiency.
[0055] Step S13: determining mapped time series data corresponding to the multiple groups of time series data according to the mapping relationship table, and remapping each of the mapping values in the mapped time series data to the original data value on the drawing window to generate a cross-scale data visualization display diagram.
[0056] Since the mapping relationship table records the original data values and their corresponding mapping values, the mapped time series data corresponding to the data values in each set of original time series data are converted into mapping values can be determined according to the mapping relationship table. Use appropriate drawing tools to draw multiple sets of mapped time series data. In the drawing window, remap the mapping values to the true values of the original data to generate the final cross-scale data visualization display diagram, ensuring that data with different value ranges can clearly show their fluctuation trends in the same diagram and maintain good contrast.
[0057] Specifically, each of the mapped values in the mapped time series data is plotted within the corresponding preset interval on the data axis on the drawing window, and the mapped values are remapped to the original data values to generate a cross-scale data visualization display diagram.
[0058] Exemplarily, the mapping value is plotted on the Y-axis of the graph as an example. Since the mapping value corresponding to the original data value can be determined according to the mapping relationship table, the mapping value between 0 and 1 is plotted on the Y-axis of the graph when drawing, and the original data value corresponding to each mapping value can be determined according to the mapping relationship table. Therefore, during the drawing process, each mapping value in the mapped time series data can be remapped to the original data value, thereby generating the final cross-scale data visualization display diagram. In this way, through adaptive proportional mapping, the status data of multiple sensors or devices (such as temperature, current, voltage, liquid level, etc.) are displayed in the same graph, and the user can intuitively observe the changes of all data without frequently switching views. In addition, data with different value ranges can clearly show their fluctuation trends in the same graph, realizing efficient visualization of multi-scale data while ensuring that information loss is avoided.
[0059] The present application provides a multi-scale data processing method, which obtains multiple groups of time series data and merges the multiple groups of time series data into one group of time series data to obtain target time series data; each group of time series data contains multiple original data values; the original data values in the target time series data are sorted according to a preset sorting rule, and each of the sorted original data values is mapped to a preset interval using a piecewise linear mapping algorithm to generate a mapping relationship table; each of the original data values in the mapping relationship table corresponds to a respective mapping value; the mapped time series data corresponding to the multiple groups of time series data is determined according to the mapping relationship table, and each of the mapping values in the mapped time series data is remapped to the original data value on a drawing window to generate a cross-scale data visualization display diagram.
[0060] The beneficial technical effect of the present application is: by performing relevant processing on multiple groups of time series data, using the piecewise linear mapping algorithm to determine the corresponding proportional mapping relationship, automatically calculating the mapping value corresponding to the original data value, and generating a mapping relationship table. Therefore, when drawing on the drawing window, the mapping value is remapped to the original data value according to the mapping relationship table. Multiple groups of time series data with different value ranges can be clearly displayed in the same figure, and even data within a large range of amplitudes can accurately reflect their subtle fluctuation trends, avoiding the problem of information loss caused by differences in data scales. At the same time, users can intuitively observe the changes in all data without frequently switching views, which is convenient for users to intuitively compare and analyze the mutual influence relationship between different data. This not only improves work efficiency, but also enhances the accuracy of data analysis.
[0061] Based on the above embodiment, this embodiment will specifically explain S12 in the above embodiment. In order to ensure that data with different value ranges clearly show their fluctuation trends in the same graph and avoid information loss, the process of mapping the sorted original data values into a preset interval using a piecewise linear mapping algorithm to generate a mapping relationship table may include the following steps:
[0062] Step 1: Perform the same statistical characteristic extraction on the sorted original data values and the reference data values in the preset interval respectively, so as to divide the target time series data and the preset interval into a plurality of data interval segments.
[0063] In the embodiment of the present application, the same statistical characteristics are extracted from the sorted original data values in the target time series data and the reference data values in the preset interval. These statistical characteristics are used to define the segmentation of data in the data interval, and the data can be divided into multiple interval segments by the values of these statistical characteristics.
[0064] Specifically, the process of extracting statistical characteristics may include the following steps:
[0065] Determine a first minimum value, a first maximum value, and first preset quantiles among the sorted original data values, and divide the target time series data into a plurality of real data segments based on the first minimum value, the first maximum value, and the first preset quantiles;
[0066] Determine a second minimum value, a second maximum value, and each second preset quantile among the reference data values within the preset interval, and divide the preset interval into a plurality of standard data segments based on the second minimum value, the second maximum value, and each second preset quantile;
[0067] The first preset quantile and the second preset quantile adopt the same quantile condition, such as quartile, decile, etc.
[0068] Step 2: Make a one-to-one correspondence between the data interval segments corresponding to the target time series data and the data interval segments corresponding to the preset interval, and then use a pre-constructed mapping formula to determine each mapping value corresponding to each original data value within the preset interval;
[0069] In this embodiment of the present application, the real data segments extracted in step one and the standard data segments extracted in a preset interval (such as between 0 and 1) are matched one-to-one according to the same statistical characteristics (i.e., minimum value, maximum value and each quantile), thereby obtaining a mapping segment between 0 and 1 corresponding to each real data segment and the standard data segment.
[0070] Furthermore, a linear mapping formula is established to map the value in each real data segment to a corresponding standard data segment between 0 and 1 in the mapping segment. The mapping formula is:
[0071] ;in, is the mapping value, is the original data value, for The minimum value in the real data segment, for The maximum value in the real data segment, for Corresponding to the maximum value in the standard data segment, for Corresponds to the minimum value in the standard data segment.
[0072] Step three: Generate the mapping relationship table according to the original data value and the mapping value.
[0073] In an embodiment of the present application, the above-mentioned linear mapping formula is applied to each original data value contained in the real data segment to calculate the mapping value between 0 and 1 corresponding to each original data value, and generate a mapping relationship table containing each original data value and its corresponding mapping value between 0 and 1.
[0074] like Figure 2The figure shows a schematic diagram of the overall process of processing multi-scale data by the present invention. The optimal proportional mapping relationship is automatically identified and calculated through piecewise linear mapping technology, and multiple time series data are displayed in one figure, clearly showing the fluctuation trend of data in different value ranges. Users can observe the changes of multiple key indicators in one figure at the same time. By maintaining a good contrast, users can more accurately identify the correlation and anomalies between data. In industrial monitoring and process control, users can intuitively compare and analyze the mutual influence relationship between different data, significantly improve the visualization effect of cross-scale data, and improve the accuracy and efficiency of data analysis. Help engineers quickly discover potential problems in equipment operation and take timely measures to improve the stability and reliability of the system.
[0075] The content disclosed in the above embodiments is described below with a specific example.
[0076] Step 1: Obtain multiple sets of time series data from data sources (such as sensors, databases, etc.). Each set of time series data contains multiple data values. The following are three sets of data with different value ranges:
[0077] A=[-0.1, 0.6, 0.5, 0.7, 0.4, 0.8];
[0078] B=[3, 45, 67, 2, 8, 7];
[0079] C=[1000, 2334, 345, 266, 2998, 4444];
[0080] Step 2: Merge the three groups of data A, B, and C to obtain the target time series data D; sort the data in D in positive order to obtain the time series data D after the data values are sorted = [-0.1, 0.4, 0.5, 0.6, 0.7, 0.8, 2, 3, 7, 8, 45, 67, 266, 345, 1000, 2334, 2998, 4444];
[0081] Step 3: Map the set of data sorted in step 2 to between 0 and 1 according to the piecewise linear mapping algorithm to generate a mapping relationship table; each original data value corresponds to a mapping value between 0 and 1.
[0082] Furthermore, the specific steps of step 3 are as follows:
[0083] Step 3.1: Extract statistical characteristics of the data sorted in step 2, including minimum value, maximum value and various quantiles (such as quartiles, deciles, etc.). Assume that quartiles are used, so that the maximum value in array D is 4444, the minimum value is -0.1, the quarter quantile is 0.775, the median is 7.5, and the quarter quantile is 508.75. Through the values of these statistical characteristics, the data is divided into 4 intervals ([-0.1, 0.775], [0.776, 7.5], [7.6, 508.75], [508.76, 4444]);
[0084] Step 3.2: Perform similar statistical feature extraction on the data between 0 and 1, including the minimum value 0, the maximum value 1, and the quartiles (the first quartile is 0.25, the median is 0.5, and the third quartile is 0.75). These statistical features are used to define the standard segmentation of the data between 0 and 1 ([0, 0.25], [0.26, 0.5], [0.51, 0.75], [0.76, 1]);
[0085] Step 3.3: Make a one-to-one correspondence between the real data segments extracted in step 3.1 and the standard data segments between 0 and 1 extracted in step 3.2 according to the same statistical characteristics (i.e., minimum value, maximum value, and each quantile), so as to obtain the mapping segment between 0 and 1 corresponding to each real data segment. That is, [−0.1,0.775] corresponds to [0,0.25], [0.776,7.5] corresponds to [0.26,0.5], [7.6,508.75] corresponds to [0.51,0.75], and [508.76,4444] corresponds to [0.76,1];
[0086] Step 3.4: Establish a mapping formula to map the value in each real data segment to the corresponding mapping segment between 0 and 1. The formula is as follows:
[0087] ;
[0088] In the formula, , is the maximum and minimum value of a real data segment for which the mapping value needs to be calculated in step 3.3, is an original data value in the real data segment, , is the maximum and minimum value of the standard data segment corresponding to the real data segment in the mapping end, is the mapping value of the real value;
[0089] Step 3.5: Apply the linear mapping formula in step 3.4 to the numerical value in each real data segment in step 3.3, calculate the mapping value between 0 and 1 corresponding to each original data value, and generate a mapping relationship table, as shown in Table 1, which contains each original data value and its corresponding mapping value between 0 and 1.
[0090] Table 1 Mapping relationship table
[0091]
[0092] Step 4: According to the mapping relationship table in step 3, convert each data value of each group of original time series data in step 1 into the corresponding mapping value to generate multiple groups of mapped time series data:
[0093] A_mapped=[0.000, 0.057, 0.050, 0.064, 0.043, 0.064];
[0094] B_mapped=[0.339, 0.528, 0.538, 0.304, 0.510, 0.509];
[0095] C_mapped=[0.790, 0.871, 0.750, 0.745, 0.912, 1.000];
[0096] Step 5: Use appropriate drawing tools to draw multiple groups of time series data after mapping in step 4. On the Y axis of the graph, remap the mapped values (values between 0 and 1) back to the true values of the original data to generate the final cross-scale data visualization display graph, ensuring that data with different value ranges can clearly show their fluctuation trends in the same graph and maintain good contrast for comparison. Figure 3 and Figure 4 The difference before and after applying the present invention.
[0097] It can be seen that in Figure 3 In the example above, the inappropriate proportion setting causes subtle changes in some data series to be obscured. Figure 4 In the process of intelligently adjusting the display ratio, the subtle differences and overall trends between data can be effectively displayed. Even in the case of large numerical differences, the detailed changes of each set of data can be clearly and accurately presented. This method greatly enhances the comparability and visibility of data across scales, allowing users to more effectively identify trends and anomalies, thereby improving the accuracy and response speed of decision-making. By optimizing the data display method, the quality and efficiency of data analysis have been significantly improved, providing strong support for applications in multiple industries.
[0098] Correspondingly, the present application also discloses a multi-scale data processing device, see Figure 5As shown, the device comprises:
[0099] The data processing module 11 is used to obtain multiple groups of time series data and merge the multiple groups of time series data into one group of time series data to obtain target time series data; each group of time series data contains multiple original data values;
[0100] The data mapping module 12 is used to sort the original data values in the target time series data according to a preset sorting rule, and map each of the sorted original data values to a preset interval using a piecewise linear mapping algorithm to generate a mapping relationship table; each of the original data values in the mapping relationship table corresponds to a respective mapping value;
[0101] The data display module 13 is used to determine the mapped time series data corresponding to the multiple groups of time series data according to the mapping relationship table, and remap each of the mapping values in the mapped time series data to the original data value on the drawing window to generate a cross-scale data visualization display diagram.
[0102] Among them, for more specific working processes of the above-mentioned modules, please refer to the corresponding contents disclosed in the aforementioned embodiments, which will not be repeated here.
[0103] It can be seen that through the above-mentioned scheme of this embodiment, multiple groups of time series data are obtained, and the multiple groups of time series data are merged into one group of time series data to obtain target time series data; each group of time series data contains multiple original data values; the original data values in the target time series data are sorted according to the preset sorting rules, and the sorted original data values are mapped to the preset interval using a piecewise linear mapping algorithm to generate a mapping relationship table; each of the original data values in the mapping relationship table corresponds to its own mapping value; the mapped time series data corresponding to the multiple groups of time series data is determined according to the mapping relationship table, and each of the mapping values in the mapped time series data is remapped to the original data value on the drawing window to generate a cross-scale data visualization display diagram.
[0104] The beneficial technical effect of the present application is: by performing relevant processing on multiple groups of time series data, using the piecewise linear mapping algorithm to determine the corresponding proportional mapping relationship, automatically calculating the mapping value corresponding to the original data value, and generating a mapping relationship table. Therefore, when drawing on the drawing window, the mapping value is remapped to the original data value according to the mapping relationship table. Multiple groups of time series data with different value ranges can be clearly displayed in the same figure, and even data within a large range of amplitudes can accurately reflect their subtle fluctuation trends, avoiding the problem of information loss caused by differences in data scales. At the same time, users can intuitively observe the changes in all data without frequently switching views, which is convenient for users to intuitively compare and analyze the mutual influence relationship between different data. This not only improves work efficiency, but also enhances the accuracy of data analysis.
[0105] Furthermore, the present application also discloses an electronic device. Figure 6 This is a structural diagram of an electronic device 20 according to an exemplary embodiment, and the content in the diagram cannot be considered as any limitation on the scope of use of the present application.
[0106] Figure 6 The present invention provides a schematic diagram of the structure of an electronic device 20 provided in an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the multi-scale data processing method disclosed in any of the above embodiments. In addition, the electronic device 20 in this embodiment may specifically be a computer.
[0107] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.
[0108] In addition, the memory 22 as a carrier for resource storage may be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon may include an operating system 221, a computer program 222 and data 223, etc. The data 223 may include various data. The storage method may be temporary storage or permanent storage.
[0109] The operating system 221 is used to manage and control the hardware devices and computer programs 222 on the electronic device 20, and may be Windows Server, Netware, Unix, Linux, etc. In addition to computer programs that can be used to complete the multi-scale data processing method performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 may further include computer programs that can be used to complete other specific tasks.
[0110] Furthermore, the embodiment of the present application also discloses a computer-readable storage medium, wherein the computer-readable storage medium mentioned here includes a random access memory (Random Access Memory, RAM), a memory, a read-only memory (Read-Only Memory, ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a magnetic disk or an optical disk, or any other form of storage medium known in the technical field. Wherein, when the computer program is executed by the processor, the aforementioned multi-scale data processing method is implemented. For the specific steps of the method, reference can be made to the corresponding contents disclosed in the aforementioned embodiment, and no further description will be given here.
[0111] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.
[0112] The steps of the multi-scale data processing method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
[0113] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.
[0114] The above is a detailed introduction to a multi-scale data processing method, device, equipment, program product and medium provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the idea of the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting the present invention.
Claims
1. A multi-scale data processing method, characterized in that: include: Acquire multiple groups of time series data, and merge the multiple groups of time series data into one group of time series data to obtain target time series data; Each set of time series data contains multiple original data values; Sorting the original data values in the target time series data according to a preset sorting rule, and mapping each of the sorted original data values into a preset interval using a piecewise linear mapping algorithm to generate a mapping relationship table; Each of the original data values in the mapping relationship table corresponds to a respective mapping value; Determine mapped time series data corresponding to the multiple groups of time series data according to the mapping relationship table, and remap each of the mapped values in the mapped time series data to the original data value on a drawing window to generate a cross-scale data visualization display diagram.
2. The multi-scale data processing method according to claim 1, characterized in that: The step of mapping the sorted original data values into a preset interval by using a piecewise linear mapping algorithm to generate a mapping relationship table includes: Performing the same statistical characteristic extraction on the sorted original data values and the reference data values in the preset interval respectively, so as to divide the target time series data and the preset interval into a plurality of data interval segments; The data interval segments corresponding to the target time series data are matched one by one with the data interval segments corresponding to the preset interval, and then the mapping values corresponding to the original data values in the preset interval are determined by using a pre-constructed mapping formula; The mapping relationship table is generated according to the original data value and the mapping value.
3. The multi-scale data processing method according to claim 2, characterized in that: The extracting the same statistical characteristics of the sorted original data values and the reference data values in the preset interval respectively to divide the target time series data and the preset interval into a plurality of data interval segments includes: Determine a first minimum value, a first maximum value, and first preset quantiles among the sorted original data values, and divide the target time series data into a plurality of real data segments based on the first minimum value, the first maximum value, and the first preset quantiles; Determine a second minimum value, a second maximum value, and each second preset quantile among the reference data values within the preset interval, and divide the preset interval into a plurality of standard data segments based on the second minimum value, the second maximum value, and each second preset quantile; The first preset quantile and the second preset quantile adopt the same quantile condition.
4. The multi-scale data processing method according to claim 3, characterized in that: The data interval segments corresponding to the target time series data are matched one by one with the data interval segments corresponding to the preset interval, and then the mapping values corresponding to the original data values in the preset interval are determined by using a pre-constructed mapping formula, including: The real data segments are matched one-to-one with the standard data segments, and then the mapping values corresponding to the original data values within the preset interval are determined by using a pre-constructed mapping formula; The mapping formula is: ;in, is the mapping value, is the original data value, for The minimum value in the real data segment, for The maximum value in the real data segment, for Corresponding to the maximum value in the standard data segment, for Corresponds to the minimum value in the standard data segment.
5. The multi-scale data processing method according to claim 1, characterized in that: The multiple groups of time series data are time series data with different value ranges obtained from a data source, wherein the original data values in the target time series data are sorted according to a preset sorting rule, and each of the sorted original data values is mapped to a preset interval using a piecewise linear mapping algorithm, including: The original data values in the target time series data are sorted in positive order according to their sizes, and each of the original data values is mapped into a preset interval in ascending order using a piecewise linear mapping algorithm.
6. The multi-scale data processing method according to any one of claims 1 to 5, characterized in that: The step of remapping each of the mapped values in the mapped time series data to the original data value on the drawing window to generate a cross-scale data visualization display diagram includes: Each of the mapped values in the mapped time series data is plotted in the corresponding preset interval on the data axis on the drawing window, and the mapped values are remapped to the original data values to generate a cross-scale data visualization display diagram.
7. A multi-scale data processing device, characterized in that: include: A data processing module, used to acquire multiple groups of time series data, and merge the multiple groups of time series data into one group of time series data to obtain target time series data; Each set of time series data contains multiple original data values; A data mapping module, used to sort the original data values in the target time series data according to a preset sorting rule, and map each of the sorted original data values into a preset interval using a piecewise linear mapping algorithm to generate a mapping relationship table; Each of the original data values in the mapping relationship table corresponds to a respective mapping value; A data display module is used to determine the mapped time series data corresponding to the multiple groups of time series data according to the mapping relationship table, and remap each of the mapping values in the mapped time series data to the original data value on the drawing window to generate a cross-scale data visualization display diagram.
8. An electronic device, characterized in that: The electronic device comprises a processor and a memory; wherein the memory is used to store a computer program, and the computer program is loaded and executed by the processor to implement the multi-scale data processing method according to any one of claims 1 to 6.
9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the multi-scale data processing method according to any one of claims 1 to 6 are implemented.
10. A computer-readable storage medium, characterized in that: Used to store a computer program; wherein when the computer program is executed by a processor, the multi-scale data processing method according to any one of claims 1 to 6 is implemented.