Data drilling analysis method, device and equipment and storage medium

Abstract

The invention discloses a data drilling analysis method and device, equipment and a storage medium. Comprising the following steps: determining a drill-down dimension sequence based on to-be-analyzed original data, and grouping the data layer by layer according to the dimension sequence; counting the number of samples of each group on different dates, and calculating the number of samples in a preset time period and the ranking of the number in the whole historical time period; drawing a sun graph corresponding to the drill-down dimension sequence based on the number of samples in the preset time period and the ranking of the number in the whole historical time period; and visually displaying the sun graph. According to the data analysis method, the life cycle of the whole data down-drilling analysis is completely compressed in one picture, the down-drilling process is displayed layer by layer through the sun graph, related business personnel are helped to efficiently locate significant abnormal items from a large amount of business data under the condition of only depending on a single graph, and the data analysis efficiency is improved. And the analysis conclusion is displayed systematically and intuitively.

Description

Technical Field

[0001] This application relates to the technical field of data analysis. Specifically, it relates to a data drill-down analysis method, apparatus, device, and storage medium. Background Art

[0002] In the field of data analysis, in the face of detailed data of business objects containing multiple dimensions and event dates, when data analysts analyze the corresponding change trends, potential risks, and key issues of the business, they often need to perform a series of different types of statistical analyses, chart drawing, reasoning and verification processes. At the same time, they also need to rely on a deep understanding of this business field to obtain a preliminary analysis conclusion. The entire process requires a large amount of time and computing resources. At the same time, in order to interface with business-related personnel, it often takes additional time and effort to organize an analysis report containing several different types of charts and explanatory texts.

[0003] Therefore, there are many deficiencies in the prior art. First, data analysts need to continuously propose, verify, and adjust conjectures based on a deep understanding of the business. Second, when the number of dimensions is large, such as more than three, the number of pie charts statistically drawn based on each dimension surges, and the core information contained in these pie charts is limited, and manual analysis still needs to rely on business experience. Third, when it comes to cross-dimension combinations, the number of combinations grows exponentially, not only relying on more manual experience, but also the traditional BI tools are complex and abstract to operate when dealing with such problems, and the display effect is not intuitive enough.

[0004] In addition, manually adjusting the filter will waste a lot of time when the data volume is large, and it is easy to make mistakes and miss important information. When there are many dimensions, the corresponding time series inspection workload is huge, which is only suitable for verifying and displaying results and is difficult to be used for analyzing and locating problems. Temporarily adjusting the code to draw charts will bring additional workload and seriously affect the conclusion delivery time. Writing analysis reports, including documents, spreadsheets, presentations, etc., is a time-consuming and laborious task, and it is difficult for untrained personnel to be competent. Even more difficult is to let the counterparts with different technical and business backgrounds understand the data analysis conclusion within a limited time, which is extremely challenging. Summary of the Invention

[0005] Embodiments of this application provide a data drill-down analysis method, apparatus, device, and storage medium to at least solve the technical problem in the related art that it is difficult to quickly perform data analysis and visualization.

[0006] According to one aspect of the embodiments of this application, a data drill-down analysis method is provided, including:

[0007] Determining a drill-down dimension sequence based on the original data to be analyzed, and grouping the data layer by layer according to the dimension sequence;

[0008] Count the number of samples in each group on different dates, and calculate the number of samples within a preset period and its ranking in the entire historical period;

[0009] Based on the number of samples within the preset period and its ranking in the entire historical period, draw a sunburst chart corresponding to the drill-down dimension sequence;

[0010] Visualize and display the sunburst chart, where the color represents the ranking and the size of the central angle represents the number of samples.

[0011] In one implementation, determining the drill-down dimension sequence based on the raw data to be analyzed includes:

[0012] Obtain the raw data, which includes multiple dimension information, the enumerated values corresponding to each dimension, and date information;

[0013] Combine the multiple dimensions corresponding to the raw data to obtain a dimension sequence.

[0014] In one implementation, grouping the data layer by layer according to the dimension sequence includes:

[0015] Group the data included in the first-layer dimension of the dimension sequence;

[0016] Group the combination of the first-layer dimension and the second-layer dimension of the dimension sequence; Based on a progressive manner layer by layer, until the grouping is completed according to the last-layer dimension in the drill-down dimension sequence.

[0017] In one implementation, counting the number of samples in each group on different dates, and calculating the number of samples within a preset period and its ranking in the entire historical period includes:

[0018] For each group, count the number of samples in this group on different dates;

[0019] Based on the number of samples in each group on different dates, count the number of samples in this group within the preset period;

[0020] Based on how many dates' cumulative sample quantities within the preset period the sample quantity within the preset period exceeds in history, obtain the ranking.

[0021] In one implementation, drawing a sunburst chart corresponding to the drill-down dimension sequence includes:

[0022] Draw a sunburst chart based on the drill-down dimension sequence, and the number of ring layers of the sunburst chart corresponds to the drill-down dimension sequence;

[0023] Among them, the innermost layer of the sunburst chart corresponds to the first layer of the dimension sequence, and the second innermost layer of the sunburst chart corresponds to the combination of the first layer and the second layer of the dimension sequence.

[0024] In one implementation, based on the number of samples within the preset time period and the ranking of this number in the entire historical time period, drawing a sunburst chart corresponding to the drill-down dimension sequence includes:

[0025] Determining the central angle corresponding to each ring based on the number of samples;

[0026] Among them, for each layer of the sunburst chart, each ring corresponds to an enumerated value of a dimension combination.

[0027] In one implementation, based on the number of samples within the preset time period and the ranking of this number in the entire historical time period, drawing a sunburst chart corresponding to the drill-down dimension sequence includes:

[0028] Determining the ring color corresponding to the number of samples based on the ranking of the number of samples in the entire historical time period;

[0029] Painting each ring in the sunburst chart, with different colors representing different ranking intervals.

[0030] According to another aspect of the embodiments of the present application, there is provided a data drill-down analysis device, including:

[0031] A data grouping module, configured to determine a drill-down dimension sequence based on the raw data to be analyzed, and group the data layer by layer according to the dimension sequence;

[0032] A statistics module, configured to count the number of samples in each group on different dates, and calculate the number of samples within the preset time period and the ranking of this number in the entire historical time period;

[0033] A chart generation module, configured to draw a sunburst chart corresponding to the drill-down dimension sequence based on the number of samples within the preset time period and the ranking of this number in the entire historical time period;

[0034] A visualization module, configured to visually display the sunburst chart, where the color represents the ranking and the size of the central angle represents the number of samples.

[0035] According to another aspect of the embodiments of the present application, there is also provided an electronic device, including a memory and a processor, where a computer program is stored in the memory, and the processor is configured to execute the above data drill-down analysis method through the above computer program.

[0036] According to another aspect of the embodiments of the present application, there is also provided a computer-readable storage medium storing a computer program, wherein the computer program is configured to execute the above data drill-down analysis method when running.

[0037] The technical solutions provided by the embodiments of the present application may include the following beneficial effects:

[0038] The beneficial effects of the present application are significant and multifaceted. First of all, it greatly reduces the threshold of data analysis, and analysts do not need to have a very deep understanding of the business. Secondly, this solution solves all-dimensional problems through a single graph. Analysts no longer need to repeatedly select and observe different graphs of different dimensions, and the processing and analysis of cross-dimensions can also be easily completed within a single graph, avoiding the complex operations and inefficient analysis caused by the exponential increase in the number of dimension combinations in traditional methods. Moreover, this solution does not require the use of filters, eliminating the cumbersome steps of adjusting filters and reducing the risk of errors and missing important information. By calculating the anomaly level (ranking) and combining with the color filling scheme, the analysis process is intuitive and saves a lot of time and effort in repeatedly checking time series graphs, enabling analysts to quickly locate problems. In addition, this solution does not require coding, and the resulting picture itself is of great value. Finally, the operation of this solution is very simple, completely compressing the entire data drill-down analysis life cycle into a single picture, only requiring simple mouse dragging and clicking. Moreover, the technical solution of the present application has strong versatility and can be directly reused in almost all similar business scenarios. As long as the input is detailed data with dimensions and dates, an extended sunburst chart representing data information can be generated, providing an efficient, intuitive, easy-to-use, and general solution for the field of data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings described herein are used to provide a further understanding of the present application, and constitute a part of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings:

[0040] Figure 1 is a flowchart of a data drill-down analysis method according to an embodiment of the present application;

[0041] Figure 2 is a schematic diagram of a generated sunburst chart according to an embodiment of the present application;

[0042] Figure 3 is a schematic diagram of a data drill-down analysis device according to an embodiment of the present application;

[0043] Figure 4 is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] In order to enable those skilled in the art to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0045] It should be noted that the terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances, so that the embodiments of this application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products or devices.

[0046] The following will introduce the data drilling-down analysis method in the embodiments of this application in detail with reference to the attached Figure 1-2 It should be noted that the following application scenarios are only shown for the convenience of understanding the spirit and principle of this application, and the embodiments of this application are not limited in this regard. On the contrary, the embodiments of this application can be applied to any applicable scenario. As Figure 1 shown, the method mainly includes the following steps:

[0047] S101 Determine the drilling-down dimension sequence based on the original data to be analyzed, and group the data layer by layer according to the dimension sequence.

[0048] In one implementation, determining the drilling-down dimension sequence based on the original data to be analyzed includes: obtaining the original data. The original data in the embodiments of this application includes multiple dimension information, the enumerated values corresponding to each dimension, and date information. For example, the repair records in the repair record form are the original data.

[0049] In an exemplary scenario, there are repair records of several devices sold by the company, which are stored in the "repair record form", and the entire form can also be referred to as the sample space.

[0050] For each repair record, the table stores its dimensional (attribute) information. For example, device model, device part number, factory number, assembly line number, sales region, device usage, model of a certain core component, etc. In the embodiments of the present application, capital letters such as A, B, and C are used to represent a dimension.

[0051] For each dimension, in the entire sample space, there are often a finite number of enumerated values. For example, for the dimension of device model, the device model corresponding to a repair record may be a finite number of enumerated values (strings) such as A_1, A_2, etc. A combination of capital letters and numbers such as A_1, A_2, A_3 is used to represent different enumerated values corresponding to dimension A.

[0052] For each repair record, it also has an attribute, which is the repair date. The entire repair record table covers all repair records for a long period of time (such as the past five years).

[0053] In one embodiment, the data analysis objective of the present application is to discover hidden abnormal problems, change trends, abnormal degrees, performance characteristics of abnormalities under different dimensions (combinations), etc. in the recent data (such as the past 30 days). In order to detect abnormalities, notify the corresponding business department, and take countermeasures or improvement measures to continuously improve product quality.

[0054] Specifically, the data analysis method includes first combining multiple dimensions corresponding to the original data to obtain a dimension sequence.

[0055] In an alternative embodiment, first confirm a "drill-down dimension sequence", which can have certain business meanings. For example, combine multiple dimensions corresponding to the original data to determine a dimension sequence. It can also be just an arbitrary combination of several seemingly important dimensions. For example, we select 3 dimensions and denote this drill-down sequence as A→B→C.

[0056] Furthermore, group the data layer by layer according to the dimension sequence, including: grouping the data included in the first-layer dimension of the dimension sequence; grouping the combination of the first-layer dimension and the second-layer dimension of the dimension sequence; based on a progressive manner layer by layer, until the grouping is completed according to the last-layer dimension in the drill-down dimension sequence.

[0057] Specifically, the data is grouped layer by layer to obtain corresponding sample subsets. For example, first, group only based on A to get several groups such as A[A_1], A[A_2], A[A_3]; then group based on the combination of A - B to get more detailed groups such as A[A_1]-B[B_1], A[A_1]-B[B_2], A[A_2]-B[B_1], A[A_2]-B[B_2], A[A_3]-B[B_1], A[A_3]-B[B_2], etc. The values in the square brackets are enumerated values. In this way, no matter how many dimensions the drill - down sequence contains, we will obtain several groups.

[0058] S102: Count the sample numbers of each group on different dates, and calculate the sample number within a preset period and its ranking in the entire historical period.

[0059] In one implementation, counting the sample numbers of each group on different dates, and calculating the sample number within a preset period and its ranking in the entire historical period includes:

[0060] For each group, count the sample numbers of this group on different dates. Specifically, for each group, count the date sequence of obtaining the sample numbers. For example, for all samples in the group A[A_1]-B[B_1], the sample number on January 1, 2021 is 12, and the sample number on January 2, 2021 is 15, etc.

[0061] Further, based on the sample numbers of the group on different dates, count the sample number of the group within a preset period. For the date sequence of the sample numbers that have been counted for each group, calculate the time period we are concerned about, such as the sample number within the recent 30 days, to obtain the sample number of each group within the recent period.

[0062] Further, based on how many dates in the history the cumulative sample number of the preset period within the preset period exceeds, obtain the ranking.

[0063] For example, in A[A_1]-B[B_1], the sample number within the recent 30 days on January 2, 2021 is 15, and in the entire history (five years), the cumulative sample number of 30 days corresponding to 90.4% of the dates is less than 15. Then it is considered that the ranking level of the group A[A_1]-B[B_1] on January 2, 2021 is 90.4%.

[0064] For each group, calculate its corresponding recent sample number and recent ranking level simultaneously.

[0065] S103: Based on the sample number within the preset period and its ranking in the entire historical period, draw a sunburst chart corresponding to the drill - down dimension sequence.

[0066] In one embodiment, a sunburst chart is drawn based on the drill-down dimension sequence, and the number of ring layers of the sunburst chart corresponds to the drill-down dimension sequence; wherein, the innermost layer of the sunburst chart corresponds to the first layer of the dimension sequence, and the second innermost layer of the sunburst chart corresponds to the combination of the first and second layers of the dimension sequence.

[0067] For example, the innermost layer corresponds to dimension A, the second innermost layer corresponds to dimension A-B, and the outermost layer corresponds to dimension A-B-C. It should be noted that from the inside out, the combination of dimensions increases, rather than each layer corresponding to a single dimension.

[0068] Further, the central angle corresponding to each ring is determined based on the sample quantity; wherein, for each layer of the sunburst chart, each ring corresponds to an enumeration value of a dimension combination.

[0069] For example, in the innermost layer, there are three rings: A[A_1], A[A_2], and A[A-3]; in the second innermost layer, adjacent to A[A_1], there are two rings: A[A_1]-B[B_1] and A[A_1]-B[B_2], and so on.

[0070] The central angle (which can also be understood as the area) corresponding to each ring is consistent with the number of recent samples it contains. That is, the more recent samples a ring corresponds to, the larger its central angle.

[0071] Further, based on the ranking of the sample quantity in the entire historical period, the color of the ring corresponding to the sample quantity is determined; each ring in the sunburst chart is colored, and different colors represent different ranking intervals.

[0072] In an alternative embodiment, a color palette is designed to color each ring. Based on the calculated ranking level, from 0 to 50 to 100, the color transitions from green to yellow and then to red. That is, when the color of a ring is green, it symbolizes that the number of recent samples in the corresponding group is significantly less than the historical average level; while when the color of a ring is red, it symbolizes that the number of recent samples in the corresponding group is significantly more than the historical average level. The redder the ring, the more attention it should be given.

[0073] Specifically, the present application does not make specific limitations on the color setting method, which can be set according to the actual situation.

[0074] According to this step, the generated sunburst chart can be obtained. The present application completely compresses the entire life cycle of data drill-down analysis into a single picture, and through the sunburst chart, the drill-down process is displayed layer by layer. In the case of only relying on a single chart, it helps relevant business personnel efficiently locate significant abnormal items from a large amount of business data, and the system intuitively displays the analysis conclusion.

[0075] Visualize and display a sunburst chart, where the color represents the ranking and the size of the central angle represents the number of samples.

[0076] After generating the sunburst chart, it can be displayed on the terminal page for users to view. Among them, users can obtain the ranking information of the number of samples based on the color, and obtain the number of sample information based on the size of the central angle.

[0077] Based on the visualized sunburst chart, it can be found that notable abnormal items appear as a red band from the inside out, and the width of the band corresponds to the number of samples it corresponds to. From a visualization perspective, the "redder and thicker" the item, the greater the problem. By defining a simple legend, based on just this one chart, the abnormal trends in the entire sample space can be displayed, and at the same time, the relationships of relevant dimensions, that is, the evidence chain, can be fully displayed.

[0078] As Figure 2 shown, it is a sunburst chart generated according to the method of the embodiment of the present application. It can be seen from the figure that during a specific period, among the related products produced by a certain equipment manufacturer, the abnormal level of agricultural equipment is significantly higher than that of heavy trucks, etc. Agricultural equipment is mainly divided into vending machines and tractors. Among them, in harvesters, both corn harvesters and peanut harvesters have relatively high abnormal levels, and the abnormalities of tractors are mainly concentrated in medium tractors. Dimensions can be further added to make the drilling depth reach equipment models, equipment part numbers, and even finer granularity.

[0079] Through the supporting dimension selector and mouse pointing event, the technical solution of the present application realizes the originally very complex data drilling analysis work, which is time-consuming, resource-consuming, and labor-intensive, in the form of a single chart. At the same time, the display of analysis results, evidence chains, etc. is also completed through the same chart, achieving the efficient and intuitive goals planned at the beginning of the design of the technical solution of the present application.

[0080] The main technical concept of the present application focuses on innovatively improving the efficiency and intuitiveness of data analysis, which is specifically reflected in the following key points:

[0081] First, an algorithm for layer-by-layer drilling statistics and calculation of rankings based on the drilling dimension sequence is proposed. This algorithm can systematically analyze data in layers, and accurately mine key information in the data in a layer-by-layer in-depth manner, laying a foundation for subsequent visual display and in-depth analysis.

[0082] Secondly, a visualization scheme for displaying the drilling process layer by layer through a sunburst chart is designed. With its unique hierarchical structure, the sunburst chart intuitively presents the distribution and changes of data in different dimensions, making complex multi-dimensional data analysis clear at a glance and greatly improving the efficiency of data interpretation.

[0083] Furthermore, a mechanism for representing the anomaly level with colors is innovatively introduced into the sunburst chart. The intuitiveness of colors enables analysts to quickly identify anomaly points in the data, and make a rapid judgment on the anomalies in the data without complex calculations or in-depth statistical analysis. This innovation greatly improves the real-time performance and response speed of data analysis.

[0084] In addition, the present application completely compresses the entire data drill-down analysis life cycle of learning, analyzing, validating, presenting, and demonstrating into a single picture. This highly integrated analysis method not only saves time and resources, but also makes the analysis process more coherent and efficient, avoiding the problems of information loss and low efficiency caused by multi-step switching in traditional analysis methods.

[0085] Finally, the technical solution of the present invention has wide applicability and can be directly reused in almost all similar business scenarios. As long as the input data contains dimension and date information, this solution can play a huge role, providing a general solution for data analysis in different fields, and having extremely high practical value and promotion potential. Generally speaking, the present application provides an efficient, intuitive, and general new method for multi-dimensional data analysis through algorithm innovation, visualization innovation, and optimization of the analysis process.

[0086] According to another aspect of the embodiments of the present application, there is also provided a data drill-down analysis device for implementing the above data drill-down analysis method. As Figure 3 shown, the device includes:

[0087] A data grouping module 301, configured to determine a drill-down dimension sequence based on the original data to be analyzed, and group the data layer by layer according to the dimension sequence;

[0088] A statistics module 302, configured to count the number of samples in each group on different dates, and calculate the number of samples within a preset period and the ranking of this number in the entire historical period;

[0089] A chart generation module 303, configured to draw a sunburst chart corresponding to the drill-down dimension sequence based on the number of samples within a preset period and the ranking of this number in the entire historical period;

[0090] A visualization module 304, configured to visually display the sunburst chart, where colors represent the ranking and the size of the central angle represents the number of samples.

[0091] It should be noted that when the data drilling analysis device provided in the above embodiments executes the data drilling analysis method, only the division of the above functional modules is used for illustration. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the data drilling analysis device provided in the above embodiments and the embodiments of the data drilling analysis method belong to the same concept. For the implementation process, please refer to the method embodiments and will not be elaborated here.

[0092] According to another aspect of the embodiments of the present application, an electronic device corresponding to the data drilling analysis method provided in the foregoing embodiments is further provided to execute the above data drilling analysis method.

[0093] Please refer to Figure 4 , which shows a schematic diagram of an electronic device provided in some embodiments of the present application. As Figure 4 shown, the electronic device includes: a processor 400, a memory 401, a bus 402, and a communication interface 403. The processor 400, the communication interface 403, and the memory 401 are connected through the bus 402; a computer program that can run on the processor 400 is stored in the memory 401, and when the processor 400 runs the computer program, it executes the data drilling analysis method provided in any of the foregoing embodiments of the present application.

[0094] Among them, the memory 401 may include a high-speed random access memory (RAM: Random Access Memory), and may also include a non-volatile memory, such as at least one disk memory. Through at least one communication interface 403 (which can be wired or wireless), a communication connection is realized between the system network element and at least one other network element, and the Internet, wide area network, local area network, metropolitan area network, etc. can be used.

[0095] The bus 402 may be an ISA bus, a PCI bus, an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. Among them, the memory 401 is used to store the program. After receiving the execution instruction, the processor 400 executes the program. Any of the data drilling analysis methods disclosed in any of the foregoing embodiments of the present application can be applied to the processor 400 or implemented by the processor 400.

[0096] The processor 400 may be an integrated circuit chip with the ability to process signals. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor 400 or the instructions in the form of software. The above-mentioned processor 400 may be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed and completed by the hardware decoding processor, or executed and completed by the combination of the hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory 401, and the processor 400 reads the information in the memory 401 and combines its hardware to complete the steps of the above method.

[0097] The electronic device provided by the embodiment of the present application and the data drill-down analysis method provided by the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by it.

[0098] According to another aspect of the embodiments of the present application, there is also provided a computer-readable storage medium corresponding to the data drill-down analysis method provided in the foregoing embodiments, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it will execute the data drill-down analysis method provided in any of the foregoing embodiments.

[0099] It should be noted that examples of computer-readable storage media may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here one by one.

[0100] The computer-readable storage medium provided by the above embodiments of the present application and the data drill-down analysis method provided by the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0101] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0102] The above embodiments only express several implementation manners of the present invention, and the description is relatively specific and detailed. However, it should not be understood as a limitation to the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the appended claims.

Claims

1. A data drill-down analysis method, characterized in that: include: Determine a drill-down dimension sequence based on the original data to be analyzed, and group the data layer by layer according to the dimension sequence; Count the number of samples in each group on different dates, and calculate the number of samples in a preset period and the ranking of the number in the entire historical period; Based on the number of samples in the preset period and the ranking of the number in the entire historical period, a sunburst chart corresponding to the drill-down dimension sequence is drawn; The sunburst chart is displayed visually, wherein the color represents the ranking and the size of the central angle represents the number of samples.

2. The method according to claim 1, characterized in that Determine the drill-down dimension sequence based on the original data to be analyzed, including: Acquire the original data, where the original data includes multiple dimension information, an enumeration value corresponding to each dimension, and date information; The multiple dimensions corresponding to the original data are combined to obtain a dimension sequence.

3. The method according to claim 1, characterized in that The data is grouped layer by layer according to the dimension sequence, including: Grouping the data contained in the first dimension of the dimension sequence; Grouping the combination of the first-level dimension and the second-level dimension of the dimension sequence; Based on a layer-by-layer progressive approach, grouping is completed according to the last dimension in the drill-down dimension sequence.

4. The method according to claim 1, characterized in that: Count the number of samples for each group on different dates, and calculate the number of samples within a preset period and the ranking of the number in the entire historical period, including: For each group, count the number of samples in the group on different dates; Based on the number of samples of the group on different dates, counting the number of samples of the group within a preset time period; The ranking is determined based on how many dates in history the number of samples within the preset period exceeds the cumulative number of samples within the preset period.

5. The method according to claim 1, characterized in that Draw a sunburst chart corresponding to the drill-down dimension sequence, including: Draw a sunburst chart based on the drill-down dimension sequence, where the number of ring layers of the sunburst chart corresponds to the drill-down dimension sequence; The innermost layer of the sunburst graph corresponds to the first layer of the dimension sequence, and the second innermost layer of the sunburst graph corresponds to the union of the first layer and the second layer of the dimension sequence.

6. The method according to claim 1, characterized in that Based on the number of samples in the preset period and the ranking of the number in the entire historical period, a sunburst chart corresponding to the drill-down dimension sequence is drawn, including: Determine the center angle of each circular ring based on the number of samples; Each layer and each circle of the sunburst graph corresponds to an enumeration value of a dimension combination.

7. The method according to claim 1, characterized in that Based on the number of samples in the preset period and the ranking of the number in the entire historical period, a sunburst chart corresponding to the drill-down dimension sequence is drawn, including: Based on the ranking of the sample quantity in the entire historical period, determine the color of the ring corresponding to the sample quantity; Each circle in the sunburst diagram is colored, and different colors represent different ranking intervals.

8. A data drill-down analysis device, characterized in that: include: A data grouping module, used to determine a drill-down dimension sequence based on the original data to be analyzed, and group the data layer by layer according to the dimension sequence; The statistics module is used to count the number of samples in each group on different dates, and calculate the number of samples in a preset period and the ranking of the number in the entire historical period; A chart generation module, used for drawing a sunburst chart corresponding to the drill-down dimension sequence based on the number of samples in the preset time period and the ranking of the number in the entire historical period; A visualization module is used to visualize the sunburst chart, wherein the color represents the ranking and the size of the central angle represents the number of samples.

9. An electronic device, characterized in that: The system comprises a processor and a memory storing program instructions, wherein the processor is configured to execute the data drill-down analysis method according to any one of claims 1 to 7 when executing the program instructions.

10. A computer-readable medium, characterized in that Computer-readable instructions are stored thereon, and the computer-readable instructions are executed by a processor to implement a data drill-down analysis method as described in any one of claims 1 to 7.

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