Data visualization methods, devices, systems, equipment, and media for distributed databases

Abstract

This application provides a data visualization method, apparatus, system, device, and medium for distributed databases, comprising: extracting data to be displayed from a distributed database based on user business requirement information; extracting corresponding feature data from the data to be displayed according to preset business feature types; finding a business background image based on the business requirement information and extracting several activity trajectories from the business background image, each activity trajectory corresponding to a preset business feature type; adding the feature data to the business background image to obtain a business feature image, and then controlling the visualization of the feature data within the business feature image according to the activity trajectories. This application clarifies business features based on business requirements, selectively extracts various types of data, and dynamically presents them. This not only enhances the visual effect through dynamic display but also presents richer data details by displaying data according to features, further optimizing the display effect.

Description

Technical Field

[0001] This application relates to the field of data visualization, and in particular to a data visualization method, apparatus, system, device, and medium for a distributed database. Background Technology

[0002] With the increasing popularity of online transactions, the volume of online transaction data is growing exponentially, encompassing various types of data such as transaction records, customer information, account details, and risk control data. Traditional centralized databases have inherent bottlenecks and cannot meet the demands of high concurrency, high reliability, and strong compliance in online transactions. Therefore, various types of online transaction data are typically stored in distributed databases, leveraging their elastic scaling, multi-site active-active architecture, and parallel processing capabilities to achieve the storage and processing of massive amounts of concurrent online transaction data.

[0003] Because online transactions involve massive amounts of data, data visualization is necessary to facilitate user access to various data types. Currently, there are two main data visualization methods: one is the indicator dashboard method, which focuses on displaying core statistical data, such as synchronization completion rate, number of partitions, and data transmission rate, presented in numbers, simple bar charts, or line charts; the other is the partition status list method, which lists the synchronization status of each partition in tabular form (such as "normal", "abnormal", "synchronizing"), only noting simple reasons for abnormalities (such as "data missing").

[0004] However, these two methods can only statically display the macroscopic state of the data, and have the problems of limited visualization effects and lack of data details. They are prone to overlooking changes or anomalies in some transaction data, resulting in a discrepancy between the visualized content and the actual state of the data, poor visualization effects, and difficulty in meeting the needs of existing applications. Summary of the Invention

[0005] In view of the aforementioned problems, this application is proposed to provide a data visualization method, apparatus, system, device, and medium for a distributed database that overcomes or at least partially solves the aforementioned problems, comprising:

[0006] A data visualization method for a distributed database, the method comprising:

[0007] Data to be displayed is extracted from a distributed database based on the user's business needs information. The data to be displayed is data that has been verified by data integrity, data consistency and data sequence logic.

[0008] According to preset business feature types, corresponding feature data are extracted from the data to be displayed. The preset business feature types include: transaction scale, transaction conversion, user features, product features, and time dimension.

[0009] Based on the business requirement information, a business background image is located, and several activity trajectories are extracted from the business background image, wherein each activity trajectory corresponds to a preset business feature type;

[0010] After adding the feature data to the business background image to obtain the business feature image, the feature data is then visualized within the business feature image based on the activity trajectory.

[0011] A data visualization device for a distributed database, the device comprising:

[0012] The acquisition module is used to extract data to be displayed from a distributed database based on the user's business needs information. The data to be displayed is data that has been verified by data integrity, data consistency and data sequence logic.

[0013] The extraction module is used to extract corresponding feature data from the data to be displayed according to preset business feature types, including: transaction scale, transaction conversion, user features, product features, and time dimension.

[0014] The trajectory module is used to find a business background image based on the business requirement information and extract several activity trajectories from the business background image, wherein each activity trajectory corresponds to a preset business feature type;

[0015] The visualization module is used to add the feature data to the business background image to obtain a business feature image, and then control the visualization of the feature data within the business feature image according to the activity trajectory.

[0016] A data visualization system for a distributed database, the system comprising: a management platform, a distributed database, and several user terminals;

[0017] The management platform is connected to the distributed database and several user terminals respectively;

[0018] The management platform is applicable to the data visualization method for distributed databases as described above.

[0019] An apparatus includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the steps of the data visualization method for a distributed database as described above.

[0020] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the data visualization method for a distributed database as described above.

[0021] This application has the following advantages:

[0022] In the embodiments of this application, the application can extract data to be displayed from a distributed database based on the user's business requirements information; extract corresponding feature data from the data to be displayed according to preset business feature types; find a business background image based on the business requirements information, and extract several activity trajectories from the business background image, wherein each activity trajectory corresponds to a preset business feature type; add the feature data to the business background image to obtain a business feature image, and then control the visualization of the feature data within the business feature image according to the activity trajectories. This application clarifies business features based on business requirements, extracts various types of data in a targeted manner, and dynamically presents them. This not only enhances the visual effect through dynamic display but also presents richer data details by displaying by features, further optimizing the display effect. Furthermore, the visualized data is validated data, which can reduce data deviation and improve the accuracy of the visualized data. Attached Figure Description

[0023] To more clearly illustrate the technical solution of this application, the drawings used in the description of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating the steps of a data visualization method for a distributed database provided in an embodiment of this application;

[0025] Figure 2 This is a structural block diagram of a data visualization device for a distributed database provided in one embodiment of this application;

[0026] Figure 3 This is a structural block diagram of a data visualization system for a distributed database provided in one embodiment of this application;

[0027] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0028] To make the objectives, features, and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0029] With the increasing popularity of online transactions, the amount of data generated by online transactions is growing exponentially, including transaction records, customer information, account details, and risk control data. Since the inherent bottlenecks of traditional centralized databases cannot meet the demands of high concurrency, high reliability, and strong compliance in online transactions, various types of online transaction data can be stored in distributed databases. Through the elastic scaling, geographically dispersed, and parallel processing capabilities of distributed databases, massive amounts of concurrent online transaction data can be stored and processed.

[0030] Because online transactions involve massive amounts of data, data visualization is crucial for users to easily view various data types. Common data visualization methods include: using dashboards to focus on displaying core statistical data, such as synchronization completion rate, number of partitions, and data transfer rate, presented as numbers, simple bar charts, or line charts. Alternatively, a partition status list can be used, listing the synchronization status of each partition in tabular form (e.g., "Normal," "Abnormal," "Synchronizing"), with only simple reasons for abnormalities noted (e.g., "Data Missing").

[0031] While dashboards and partitioned status lists can display the macro-level state of data, the following technical issues remain: statically displaying the macro-level state results in a limited and monotonous visualization, lacking detailed data and thus overlooking changes or anomalies in some transaction data. Consequently, the visualized content deviates from the actual state of the data, leading to poor visualization quality and failing to meet current application needs.

[0032] To address the aforementioned issues, the following detailed description and explanation will be provided through specific embodiments of a distributed database data visualization method, apparatus, system, device, and medium provided in this application.

[0033] Reference Figure 1 The diagram shows a flowchart of a data visualization method for a distributed database provided in an embodiment of this application.

[0034] In one embodiment, the data visualization method for distributed databases is applicable to an enterprise's data management system or management platform, and data visualization operations are performed through the management system or management platform.

[0035] As an example, the data visualization method for the distributed database may include:

[0036] S11. Extract the data to be displayed from the distributed database based on the user's business needs information, wherein the data to be displayed is data that has been verified by data integrity, data consistency and data sequence logic.

[0037] In one embodiment, user business requirement information can be obtained. This business requirement information is the user's need to visualize data, including the business type, data duration, data storage address, data content, data characteristics, and visualization method of the business data to be displayed.

[0038] After determining the user's business requirements, corresponding business data can be extracted from a distributed database. Simultaneously, data integrity, consistency, and sequence logic checks are performed on this business data. Once all three checks pass, the extracted business data can be used as the data to be displayed.

[0039] By performing integrity checks, consistency checks, and sequence logic checks on the extracted business data, the reliability of the data to be displayed is ensured.

[0040] If any verification fails, the verification result can be sent back to the user, and the verification failure information can be reported to the user. At the same time, the data of the items that failed the verification can be recovered accordingly.

[0041] As an example, the step of retrieving the data to be displayed from the distributed database based on the user's business needs information may include the following sub-steps:

[0042] S111. Obtain the user's business requirement information, and extract business data from the distributed database according to the business duration corresponding to the business requirement information.

[0043] S112. Perform data integrity verification, data consistency verification, and data sequence logic verification on the business data in sequence. When the data integrity verification, the data consistency verification, and the data sequence logic verification all pass, the data to be displayed is obtained.

[0044] The data integrity verification is a verification that compares the sequence number of the business data with a preset sequence number.

[0045] The data consistency check is a check to verify whether there is a conflict between the partition ID and sequence number of the Binlog of the business data;

[0046] The data sequence logical verification is a verification process to determine whether there are any anomalies in the business data.

[0047] In one operational example, the user could be an e-commerce operations staff member whose business requirement is to "view the transaction data and user conversion status of the women's clothing category on the xx APP from June 1st to June 30th, 20x4". Based on the business duration corresponding to this requirement (the entire month of June), all transaction data and user behavior data for the "women's clothing category" and "APP" within that period are extracted from a distributed database (sharded by user ID and stored in tables by time). This involves 8 shards and 30 tables, with a raw data volume of 1.2 million records. Next, these 1.2 million records of raw data can be subjected to integrity checks, consistency checks, and sequence logic checks. If each check passes, these 1.2 million records of raw data can be used as the data to be displayed.

[0048] This application performs data integrity verification, data consistency verification, and data sequence logic verification to ensure that the data to be visualized is complete, consistent, and has a logically coherent sequence number, providing a reliable data foundation for subsequent visualization.

[0049] In one embodiment, data integrity verification involves comparing the sequence number of business data with a preset sequence number to determine whether there is any missing data.

[0050] Specifically, the data integrity verification process is as follows: Traverse all partitions on the producer side of the distributed data to obtain the unique sequence number of the data. Using the "partition ID-sequence number range" mapping table, verify whether the applied range [Start, End] of each partition is consistent with the range of the Binlog sequence number (i.e., the unique sequence number) issued by the producer side. If they are consistent, the verification passes; otherwise, it fails. To facilitate notification to users, missing intervals (such as the missing [1001, 1001] in the consumer-side P0 partition range {[0, 1000], [1002, 1050]}) and duplicate sequence numbers can be marked.

[0051] In a specific operational example, each transaction data in the distributed database's sharded table contains a unique sequence number (formatted as "shard ID-table ID-auto-incrementing sequence number", such as "DB01-TB05-000389"). The preset sequence number range is a consecutive sequence number from June 1st 00:00 to June 30th 23:59 for each sharded table (the preset sequence number range for sharded table DB01-TB05 is 000001-004200). Traversing the 1200 records of this sharded table, if the sequence numbers 000156 and 002310 are found to be missing, the integrity check fails and can be marked as "missing data". Conversely, if there are no missing records, the data integrity check passes.

[0052] In one embodiment, data consistency verification can be used to verify whether there are conflicts between the partition ID and sequence number of the Binlog of business data, ensuring that there is no cross-partition duplication and that the data has not been tampered with.

[0053] Specifically, data consistency verification can verify whether there is a conflict in the unique identifier of "partition ID-sequence number" of each Binlog, ensuring that there is no duplicate sequence number across partitions; and verify that the hash value of the Binlog content is consistent with the producer's record, eliminating tampering or damage during data transmission.

[0054] In a specific operational example, the Binlog log corresponding to each piece of data can be extracted to verify whether the unique identifier of "partition ID-sequence number" (such as "P08-000389") is unique in all database shards and tables, and no duplicates are found; the MD5 hash value of each piece of data content (such as the hash value of transaction data "order number: ORD20240615089, amount: 399 yuan" is "7a3f2d1e5b7c9d0f") is consistent with the hash value recorded on the producer side, confirming that the data has not been tampered with, and confirming that the data passes the consistency check.

[0055] In one embodiment, the sequence number logic verification can be used to determine whether there are any anomalies such as sequence number overlap or rollback in the business data, so as to ensure the logical continuity of the sequence.

[0056] Specifically, the serial number logical check can check whether the set of serial number ranges for each partition satisfies the characteristics of "monotonically increasing and non-overlapping". For example, it can check whether there are overlapping intervals such as [100,200] and [150,300], or abnormal situations such as serial number rollback (e.g., serial number 180 appears after the existing range [200,300]).

[0057] In a specific operational example, the sequence number range of a DB02-TB08 partition table can be checked. If an overlapping interval of [001200-001500] and [001450-001800] is found, the overlapping sequence numbers correspond to 5 duplicate transaction data, which are marked as "abnormal data".

[0058] The system can examine all data tables and, if no sequence number rollback is found (e.g., subsequent data sequence numbers are less than previous data), then 5 abnormal data entries can be removed from these 1.2 million entries, resulting in 1,199,995 data entries to be displayed.

[0059] In an optional embodiment, the data used to record the verification process can be marked as "valid", missing data as "to be completed", and duplicate / abnormal data as "invalid", and a verification report can be generated to clarify the data quality status of each partition.

[0060] S12. Extract corresponding feature data from the data to be displayed according to the preset business feature types. The preset business feature types include: transaction scale, transaction conversion, user features, product features, and time dimension.

[0061] Since there are multiple data entries to be extracted and displayed, in order to highlight the characteristics of the data while preserving the data content, in one embodiment, several preset business feature types can be pre-defined, and corresponding feature data can be extracted from the data to be displayed according to the preset business feature types.

[0062] Specifically, the preset business characteristic types include: transaction size, transaction conversion, user characteristics, product characteristics, and time dimension.

[0063] Among them, transaction volume data can include: number of transactions (successful / failed / pending payment), transaction amount (GMV / actual payment / refund), number of orders (single product / multiple products / group purchase), average order value (average actual payment per person), and number of payments / amount.

[0064] Transaction conversion data can include: order payment conversion rate, add-to-cart order conversion rate, visitor order conversion rate, new user conversion rate, and repeat purchase rate of existing users.

[0065] User characteristic data can include: number of new / old users, user region (province / city / district), user terminal (APP / mini-program / H5 / PC), user consumption level (high / medium / low), and user payment method (WeChat / Alipay / bank card / digital RMB).

[0066] Product characteristic data may include: number of best-selling / slow-moving SKUs, transaction percentage of product categories, number / amount of product transactions, product conversion rate, and product refund rate.

[0067] Time-based data can include: hourly (per hour), daily, weekly, and monthly transaction data, peak transaction volume during peak hours, and transaction data during holidays / major promotions.

[0068] In one operational example, assuming we have 1,199,995 data entries to be displayed, we can extract data from these 1,199,995 entries based on feature type:

[0069] For data related to transaction volume, examples could be: 1,199,995 transactions on the women's clothing app in June, transaction amount of 47,899,727 yuan, number of orders of 1,199,995, average order value of 39.9 yuan, and number of payments / amount of 1,250,502 yuan.

[0070] For transaction conversion data, examples could be: order payment conversion rate 92.3% (1,105,994 / 1,199,993), new user conversion rate 35.6% (427,197 / 1,199,993), and repeat purchase rate of existing users 28.7%.

[0071] For user characteristic data, it could include: 427,197 new users and 772,796 returning users; user geographic distribution (Guangdong 21.3%, Zhejiang 18.7%, Jiangsu 15.2%); and user terminals all being apps (iOS 58.3%, Android 41.7%).

[0072] For product feature data, it could be the top 3 best-selling SKUs (Women's dress SKU2024056: 89,321 transactions; Women's T-shirt SKU2024089: 76,542 transactions; Women's jeans SKU2024103: 68,915 transactions); or the percentage of product categories (dresses 32.5%, T-shirts 28.3%, jeans 21.7%, others 17.5%).

[0073] For time-based data, it could be daily transaction data in June (peak of 128,935 transactions on June 18), intraday transaction data (23.5% from 10:00 to 12:00 and 28.7% from 20:00 to 22:00), and 156,892 transactions during holidays (3 days of Dragon Boat Festival).

[0074] S13. Find a business background image based on the business requirement information, and extract several activity trajectories from the business background image, wherein each activity trajectory corresponds to a preset business feature type.

[0075] After extracting the corresponding feature data, due to the large amount of data, simply displaying the data is not effective. In order to meet the user's display needs and improve the display effect, a business background image can be found based on the user's business requirements. This business background image can be the visualization background required by the user.

[0076] Next, several activity trajectories can be determined in the business background image, where each activity trajectory corresponds to a preset business feature type. Based on the previous example, there are 5 preset business feature types, corresponding to 5 activity trajectories that can be extracted. An activity trajectory refers to the movement path of each data point when the feature data corresponding to a preset business feature type is displayed on the business background image.

[0077] Subsequently, the feature data corresponding to each preset business feature type can be controlled to move or move on the business background image. By dynamically displaying the feature data corresponding to the preset business feature type on the business background image, the display effect can be improved while retaining the data features.

[0078] In order to find a corresponding background image based on the user's actual business needs and extract trajectories for data activity from the image, in one embodiment, finding a business background image based on the business needs information and extracting several activity trajectories from the business background image may include the following sub-steps:

[0079] S131. Extract business requirement text from the business requirement information, and match one of the multiple business images from the preset material library as the business background image based on the business requirement text.

[0080] S132. Determine the number of coordinates based on the number of data for each feature data, and randomly select several image coordinate points in the business background image according to the number of coordinates.

[0081] S133. Connect the image coordinate points corresponding to each feature data into a closed curve to obtain several activity trajectories.

[0082] In one operation mode, text containing business requirement information can be read to obtain multiple business requirement texts. Then, one of the business background images can be matched from multiple business images in a preset material library based on these multiple business requirement texts. Specifically, the preset material library stores multiple different business images, each with corresponding image description information. Multiple business requirement texts can be combined into a text set, and then the matching degree between the text set and the image description information can be calculated. Finally, the business image with the highest matching degree is selected from the multiple matching degrees and chosen as the business background image.

[0083] In one operational example, business requirement text can be extracted from user demand information, such as "National APP-based women's clothing category transaction and user conversion in June 20x4". The preset material library stores multiple different business images, such as "e-commerce transaction global topology map", "category sales regional distribution map", and "user behavior trajectory map", each with corresponding image description information. The matching degree of each business image is determined, and based on the matching degree, a business background image is selected as the "Women's Clothing Category APP-based Transaction Analysis Background Image" (which may include elements such as a national regional map, timeline, and category classification bar).

[0084] Next, the number of coordinates can be determined based on the amount of data for each feature. As illustrated in the previous example, there may be 199,995 data points to be displayed. Among these, the transaction scale data includes: 1,199,995 transactions on the women's clothing app in June, transaction amount of 47,899,727 yuan, number of orders of 1,199,995, average order value of 39.9 yuan, and number of payments / amount of 1,250,502 yuan. This transaction scale data includes 5 indicators, and the number of these indicators can be used as the number of coordinates, resulting in 5 coordinates. Similarly, for the transaction conversion data, which includes: order payment conversion rate of 92.3% (1,105,994 / 1,199,993), new user conversion rate of 35.6% (427,197 / 1,199,993), and repeat purchase rate of existing users of 28.7%, the number of these indicators can be used as the number of coordinates, resulting in 3 coordinates.

[0085] Next, several image coordinate points can be randomly selected from the business background image based on the number of coordinates for each feature data. For example, the transaction scale category corresponds to 5 image coordinate points, and the transaction conversion category corresponds to 3 image coordinate points. Then, the several image coordinate points corresponding to each feature data are connected to form a closed curve, resulting in several activity trajectories, such that each activity trajectory corresponds to a preset business feature type.

[0086] In one operation mode, the business background image has an image coordinate system, which can take the top left corner of the image as the origin (0,0), with the X-axis positive to the right and the Y-axis positive downward. Each pixel corresponds to a unique (x,y) integer coordinate. The coordinate value range is first determined by the image width and height (X∈[0,width) and Y∈[0,height)).

[0087] Next, within the effective pixel range of the image, a specified number of discrete coordinate points are randomly generated to obtain several image coordinate points corresponding to the feature data, which can avoid the point set being too dense or sparse.

[0088] We can sort the image coordinates corresponding to each feature data by polar angle (using the geometric center of the point set as the pole, and sorting the points according to the angle between each point and the pole from smallest to largest), making the point set "ring-shaped and ordered," laying the foundation for a closed curve (if we directly connect disordered random points, it will form messy intersecting lines). Finally, we can connect the last sorted point to the first point to achieve a closure, and use an interpolation algorithm to add transition points between discrete points, turning the trajectory line from a "broken line" into a smooth curve; and we can draw the processed smooth closed curve on the image of the business requirements, set the line color, width, and other styles, and output the final activity trajectory.

[0089] For example, there are 5 indicators in the feature data of the transaction scale category. Randomly select 5 coordinate points in the "core indicator area" of the business background image, which are: (100, 200), (150, 250), (200, 230), (180, 180), (130, 160); sort the coordinate points according to the polar angle (with (150, 200) as the pole), and after sorting, they are (100, 200), (130, 160), (180, 180), (200, 230), (150, 250). Connect the last point and the first point to form a closed curve, and supplement the transition points through the interpolation algorithm to obtain a smooth "ring track", thus obtaining the activity track of the transaction scale category.

[0090] For another example, there are 3 indicators in the feature data of the transaction conversion category. Select 3 coordinate points in the "conversion analysis area" of the business background image: (300, 200), (350, 240), (320, 180), and form a "triangle track" according to the above method; similarly, the user feature category, commodity feature category, and time dimension category respectively generate a "sector track", a "bar track", and a "linear track" in the corresponding areas, with a total of 5 activity tracks.

[0091] In the specific operation, use the cv2.imread() 3-5 tool to read the image and return a three-dimensional pixel matrix (height h, width w, number of channels) (color Figure 3 channel, grayscale Figure 1 channel); the coordinate range of the image is determined by the width and height of the image: on the X-axis, 0 ≤ x < w, and on the Y-axis, 0 ≤ y < h, ensuring that the random coordinates are inside the image and do not exceed the boundary. When randomly screening coordinates, np.random.randint(low, high, size) can be used to generate a specified number of random integers, which are used as the X and Y coordinates respectively; then use np.column_stack() to splice the X and Y coordinates into a matrix of (N, 2), and each row corresponds to a (x, y) coordinate point, which is convenient for subsequent processing;

[0092] Optionally, the number of coordinates can be 3-5. Preferably, the number of coordinates is set to 10-50. If the number of data is insufficient, it can be adjusted to 10, because if the number is too small, the track line is too simple, and if the number is too large, the track line is prone to be messy.

[0093] The core of polar angle sorting is to order the coordinates. First, calculate the geometric center of the random point set: `np.mean(random_points, axis=0)`, which gives the center coordinates (x_mean, y_mean). Then, calculate the polar angle of each point relative to the center: `np.arctan2(Δy, Δx) = np.arctan2(y-y_mean, x-x_mean)`. The polar angle reflects the direction of the point relative to the center. Finally, sort the points by polar angle from smallest to largest: `np.argsort(angles)` gives the sorting index. Then, rearrange the coordinates according to the index, making the point set circularly ordered (from 0 degrees to 360 degrees around the center), avoiding intersecting lines.

[0094] To balance the curve of the activity trajectory later, interpolation smoothing can be performed to transform the polyline into a smooth curve. Specifically, each sorted point can be traversed, and linear interpolation can be performed between the current point and the next point (the next point after the last point is the first point, implementing closed-loop logic). np.linspace() can be used to generate a specified number of transition points between two points; the more interpolations, the smoother the curve (5-20 is recommended). Finally, all interpolated points are concatenated to form a new set of points, which serves as the coordinate basis for the smooth trajectory line.

[0095] To draw a closed trajectory line, you can use cv2.polylines() to draw the curve. The core parameter isClosed=True forces the curve to be closed, and it will automatically connect even if the last point does not coincide with the first point.

[0096] You can also use the lineType=cv2.LINE_AA tool to enable anti-aliasing, making the edges of the trajectory line smoother and avoiding jagged edges.

[0097] You can also set the line color of the activity trajectory, with the color in BGR format (OpenCV default), such as red (0,0,255), green (0,255,0), and blue (255,0,0).

[0098] S14. After adding the feature data to the business background image to obtain the business feature image, the feature data is visualized and displayed in the business feature image according to the activity trajectory.

[0099] Next, the feature data can be added to the business background image to obtain the business feature image, and then the feature data can be visualized within the business feature image according to the activity trajectory.

[0100] Since the various feature data may be tabular or simply numerical, having a large amount of data moving around in the image can make the image very cluttered, with many lines, making it difficult for users to view.

[0101] To this end, feature data can be converted into different dashboards and topology maps, which can then be combined with a business background image. The dashboards and topology maps can then be controlled to move within the image, with the movement frequency adjustable according to user needs, thus creating a dynamic data display effect.

[0102] In an optional embodiment, after adding the feature data to the business background image to obtain a business feature image, controlling the visualization of the feature data within the business feature image based on the activity trajectory may include the following sub-steps:

[0103] S141. Use visualization components to convert each of the feature data into a visualization chart.

[0104] S142. Add each of the visualization charts to the business background image according to the image coordinate points to obtain the business feature image.

[0105] S143. Obtain the coordinate set of the activity trajectory in the business background image, visualize the business feature image, and control each of the visualized charts to move within the coordinate set.

[0106] In one operation mode, visualization components can be used to convert each feature data into a visual chart.

[0107] You can add a visual chart to the business background image based on the image coordinates to obtain a business feature image; then obtain the coordinate set of the activity trajectory and control the visual chart to move and display within the coordinate set.

[0108] In one operation method, five visualization charts of transaction size are added to the initial image coordinate points of their trajectory filtering to generate business feature images;

[0109] Next, you can control the pie chart (transaction amount) to move along the "circular trajectory" at a speed of 2 seconds per circle, displaying the amount value in real time during the movement; the bar chart (number of transactions) moves synchronously along the trajectory, maintaining a distance from the pie chart;

[0110] For progress bar charts related to transaction conversion, the progress bar moves slowly along a "triangle trajectory" (3 seconds per circle), and the conversion rate value flashes in real time as it moves.

[0111] The remaining feature-based visualizations move along their corresponding trajectories, with no overlap between trajectories. The movement speed is adjusted according to business importance (core indicators move slowly, while auxiliary indicators move quickly). Specific adjustments can be made based on actual needs, and this application does not impose any limitations on this.

[0112] In an optional embodiment, the step of converting each of the feature data into a visual chart using a visualization component may include the following sub-steps:

[0113] S1411. Using visualization components, each of the aforementioned feature data is converted into a chart to obtain several data display charts.

[0114] S1412. Adjust the size of each data display chart based on the numerical value of the feature data to obtain a visual chart.

[0115] In one operation mode, you can select any one of multiple visualization components (such as ECharts, Highcharts, Tableau) as the target visualization component, and then use the target visualization component to convert each feature data into a chart, resulting in several data display charts.

[0116] Optionally, the optimal visualization component can be matched from the visualization components as the target visualization component. Specifically, the specific type of feature data can be specified, which can include: numerical data (such as amount, number of transactions), categorical data (such as region, category), time-series data (such as daily transactions), and percentage data (such as conversion rate); the target visualization component can then be matched according to the data type.

[0117] For example, "new user conversion rate 35.6%" is a percentage-based data, while "daily transaction count in June" is a time-series data.

[0118] Refer to the following "Data Characteristics - Chart Type" mapping rules to determine the core chart type;

[0119] Example: Time series data → line chart / bar chart, percentage data → pie chart / donut chart, ranking data → bar chart.

[0120] For developers, time-series line charts can be created using ECharts' line component, while for non-developers, Tableau can be used to drag and drop to generate percentage pie charts.

[0121] In an optional embodiment, since the feature data belongs to internal enterprise data, in order to facilitate viewing by users who are away or on business trips, as an example, after the step of adding the feature data to the business background image to obtain a business feature image, and then controlling the visualization of the feature data within the business feature image according to the activity trajectory, the method may further include the following sub-steps:

[0122] S15. Encrypt and package multiple feature data into an encrypted data packet, and transmit the encrypted data packet to the user terminal so that the user terminal can decrypt it and display it to the user.

[0123] Specifically, multiple feature data are encrypted and packaged using the AES-256 encryption algorithm to obtain an encrypted data packet (ciphertext: a7f3d9b2e4c6...), which is transmitted to the user's operation and management terminal via the HTTPS protocol. After decryption, the feature data is displayed in a red pop-up window on the user's terminal.

[0124] In one application example, it could be the visualization of transaction data for women's clothing categories on an e-commerce platform.

[0125] As shown in the operational examples above, e-commerce operators need to view the transaction data and user conversion rates of the women's clothing category on the national mobile app in June 20x4, using the method described in this application:

[0126] 1. Extract 1.2 million raw data entries from the June women's clothing app from a distributed database, and after triple verification, obtain 1,199,995 data entries to be displayed;

[0127] 2. Extract five types of feature data, covering transaction volume, conversion rate, users, products, and time dimensions;

[0128] 3. Match the "Women's Clothing Category APP Transaction Analysis Background Image" to generate 5 activity trajectories with corresponding feature types;

[0129] 4. Convert feature data into visual charts such as pie charts, progress bars, and map markers, and dynamically display them along the trajectory;

[0130] 5. Identify abnormal data with a new user conversion rate of 35.6% (below the threshold of 40%) and encrypt and transmit it to the operation terminal.

[0131] Through a dynamic visualization interface, operators can intuitively see the peak time of women's clothing transactions in June, best-selling products, and user geographic distribution. They can also quickly identify abnormal new user conversion rates and adjust promotion strategies in a timely manner.

[0132] In another application embodiment, it could be the visualization of transaction risk control data on a financial payment platform.

[0133] The user's (risk control personnel's) business requirement is to "view the risk control data of online credit card transactions in July 20x4, focusing on the number of abnormal transactions and user characteristics":

[0134] 1. Extract 800,000 raw data entries from online credit card transactions in July. After verification and removal of 3 missing data entries and 12 abnormal transaction data entries, 799,985 data entries were obtained to be displayed.

[0135] 2. Determine the preset business characteristic types: transaction scale (number of transactions, amount), transaction conversion (normal transaction conversion rate), user characteristics (abnormal transaction user terminals, regions), product characteristics (abnormal transaction product categories), and time dimension (abnormal transaction time-sharing distribution).

[0136] 3. Match the "Financial Transaction Risk Control Analysis Background Image" to generate 5 activity tracks;

[0137] 4. Dynamically display visual charts (such as a line chart showing the time-sharing distribution of abnormal transactions moving along a linear trajectory, and a map marker showing the geographical distribution of abnormal users moving along a fan-shaped trajectory);

[0138] 5. Filter out abnormal data with "abnormal transaction count ratio of 0.0015% (12 / 799985)" (above the threshold of 0.001%), and encrypt and transmit it to the risk control terminal.

[0139] Risk control personnel can quickly locate peak periods (22:00-23:00) and key regions (areas with a high incidence of abnormal transactions) of abnormal transactions through a visual interface, and take timely risk control measures.

[0140] This application can be directly applied to industries such as e-commerce, finance, payment, and logistics that rely on distributed databases to store massive amounts of data. Through dynamic and detailed visualization, it helps business and operations personnel quickly grasp the data status, discover anomalies, and improve decision-making efficiency, thus having significant industrial application value.

[0141] In this embodiment, the present application provides a database data synchronization method, the advantages of which are as follows: This application can extract data to be displayed from a distributed database based on user business requirement information; extract corresponding feature data from the data to be displayed according to preset business feature types; find a business background image based on business requirement information, and extract several activity trajectories from the business background image, wherein each activity trajectory corresponds to a preset business feature type; after adding the feature data to the business background image to obtain a business feature image, the feature data is visualized within the business feature image according to the activity trajectories. This application determines business features based on business requirements, extracts various required data based on the business features, and dynamically displays them. Not only does dynamic display improve the display effect, but displaying based on features can also show more data details, further improving the display effect. In addition, the visualized data is verified data, which can reduce data deviation and improve the accuracy of the visualized data.

[0142] This application also provides a database data synchronization device, see [link to documentation]. Figure 2 The diagram shows a schematic representation of a data synchronization device for a database according to an embodiment of this application.

[0143] As an example, the data synchronization device for the database may include:

[0144] The acquisition module 201 is used to extract data to be displayed from a distributed database based on the user's business needs information, wherein the data to be displayed is data that has been verified by data integrity, data consistency and data sequence logic.

[0145] The extraction module 202 is used to extract corresponding feature data from the data to be displayed according to preset business feature types. The preset business feature types include: transaction scale, transaction conversion, user features, product features, and time dimension.

[0146] The trajectory module 203 is used to find a business background image based on the business requirement information and extract several activity trajectories from the business background image, wherein each activity trajectory corresponds to a preset business feature type;

[0147] The visualization module 204 is used to add the feature data to the business background image to obtain a business feature image, and then control the visualization of the feature data within the business feature image according to the activity trajectory.

[0148] Optionally, the step of retrieving the data to be displayed from the distributed database based on the user's business needs information includes:

[0149] Obtain user business requirement information, and extract business data from the distributed database based on the business duration corresponding to the business requirement information;

[0150] The business data is sequentially checked for data integrity, data consistency, and data sequence logic. When all three checks pass, the data to be displayed is obtained.

[0151] The data integrity verification is a verification that compares the sequence number of the business data with a preset sequence number.

[0152] The data consistency check is a check to verify whether there is a conflict between the partition ID and sequence number of the Binlog of the business data;

[0153] The data sequence logical verification is a verification process to determine whether there are any anomalies in the business data.

[0154] Optionally, the step of finding a business background image based on the business requirement information and extracting several activity trajectories from the business background image includes:

[0155] Extract business requirement text from the business requirement information, and match one of the multiple business images from a preset material library as the business background image based on the business requirement text;

[0156] The number of coordinates is determined based on the number of data points for each feature data, and several image coordinate points are randomly selected from the business background image according to the number of coordinates.

[0157] Connecting the image coordinate points corresponding to each feature data into a closed curve yields several activity trajectories.

[0158] Optionally, after adding the feature data to the business background image to obtain a business feature image, controlling the visualization of the feature data within the business feature image based on the activity trajectory includes:

[0159] Each of the aforementioned feature data is converted into a visual chart using visualization components;

[0160] Each of the visual charts is added to the business background image according to the image coordinate points to obtain a business feature image;

[0161] Obtain the coordinate set of the activity trajectory in the business background image, visualize the business feature image, and control each of the visualized charts to move within the coordinate set.

[0162] Optionally, the step of converting each feature data into a visual chart using a visualization component includes:

[0163] Each of the aforementioned feature data is converted into a chart using visualization components, resulting in several data display charts;

[0164] The size of each data display chart is adjusted based on the numerical value of the feature data to obtain a visual chart.

[0165] Optionally, the device further includes:

[0166] An encrypted transmission module is used to encrypt and package multiple feature data into an encrypted data packet after the step of adding the feature data to the business background image to obtain a business feature image and controlling the visualization of the feature data in the business feature image according to the activity trajectory, and then transmitting the encrypted data packet to the user terminal for the user terminal to decrypt and display to the user.

[0167] As the system implementation is basically similar to the method implementation, it is described in a relatively simple way. For relevant details, please refer to the description of the method implementation.

[0168] Reference Figure 3The diagram shows a structural block diagram of a data visualization system for a distributed database provided in an embodiment of this application.

[0169] Specifically, the system includes: a management platform, a distributed database, and several user terminals;

[0170] The management platform is connected to the distributed database and several user terminals respectively;

[0171] The management platform is applicable to the data visualization method for distributed databases as described in the above embodiments.

[0172] Reference Figure 4 The computer device illustrating a data visualization method for a distributed database according to this application may specifically include the following:

[0173] The computer device 12 described above is in the form of a general-purpose computing device. The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0174] Bus 18 refers to one or more of several types of bus 18 architectures, including memory bus 18 or memory controller, peripheral bus 18, graphics acceleration port, processor, or local bus 18 using any of the various bus 18 architectures. For example, these architectures include, but are not limited to, Industry Standard Architecture (ISA) bus 18, Micro Channel Architecture (MAC) bus 18, Enhanced ISA bus 18, Audio / Video Electronics Standards Association (VESA) local bus 18, and Peripheral Component Interconnect (PCI) bus 18.

[0175] Computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0176] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (commonly referred to as a "hard disk drive"). Figure 4As not shown, a disk drive for reading and writing to a removable non-volatile disk (such as a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (such as a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules 42 configured to perform the functions of the embodiments of this application.

[0177] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory. Such program modules 42 include—but are not limited to—an operating system, one or more application programs, other program modules 42, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of this application.

[0178] Computer device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, camera, etc.), and with one or more devices that enable a user to interact with the computer device 12, and / or with any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed through input / output (I / O) interface 22. Furthermore, computer device 12 can also communicate with one or more networks (e.g., local area network (LAN)), wide area network (WAN), and / or public networks (e.g., the Internet) via network adapter 20. As shown, network adapter 20 communicates with other modules of computer device 12 via bus 18. It should be understood that, although... Figure 4 Not shown, it can be combined with computer device 12 to use other hardware and / or software modules, including but not limited to: microcode, device drivers, redundant processing unit 16, external disk drive array, RAID system, tape drive and data backup storage system 34, etc.

[0179] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the data visualization method for distributed databases provided in the embodiments of this application.

[0180] That is, when the processing unit 16 executes the above program, it achieves the following:

[0181] Data to be displayed is extracted from a distributed database based on the user's business needs information. The data to be displayed is data that has been verified by data integrity, data consistency and data sequence logic.

[0182] According to preset business feature types, corresponding feature data are extracted from the data to be displayed. The preset business feature types include: transaction scale, transaction conversion, user features, product features, and time dimension.

[0183] Based on the business requirement information, a business background image is located, and several activity trajectories are extracted from the business background image, wherein each activity trajectory corresponds to a preset business feature type;

[0184] After adding the feature data to the business background image to obtain the business feature image, the feature data is then visualized within the business feature image based on the activity trajectory.

[0185] In this application embodiment, this application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the data visualization method for a distributed database as provided in all embodiments of this application.

[0186] That is, to implement the following when the program is executed by the processor:

[0187] Data to be displayed is extracted from a distributed database based on the user's business needs information. The data to be displayed is data that has been verified by data integrity, data consistency and data sequence logic.

[0188] According to preset business feature types, corresponding feature data are extracted from the data to be displayed. The preset business feature types include: transaction scale, transaction conversion, user features, product features, and time dimension.

[0189] Based on the business requirement information, a business background image is located, and several activity trajectories are extracted from the business background image, wherein each activity trajectory corresponds to a preset business feature type;

[0190] After adding the feature data to the business background image to obtain the business feature image, the feature data is then visualized within the business feature image based on the activity trajectory.

[0191] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0192] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0193] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider). The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably.

[0194] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0195] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0196] The above provides a detailed description of a data visualization method, apparatus, system, device, and medium for a distributed database provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A data visualization method for a distributed database, characterized in that, The method includes: Data to be displayed is extracted from a distributed database based on the user's business needs information. The data to be displayed is data that has been verified by data integrity, data consistency and data sequence logic. According to preset business feature types, corresponding feature data are extracted from the data to be displayed. The preset business feature types include: transaction scale, transaction conversion, user features, product features, and time dimension. Based on the business requirement information, a business background image is located, and several activity trajectories are extracted from the business background image, wherein each activity trajectory corresponds to a preset business feature type; After adding the feature data to the business background image to obtain the business feature image, the feature data is then visualized within the business feature image based on the activity trajectory. The step of finding a business background image based on the business requirement information and extracting several activity trajectories from the business background image includes: Extract business requirement text from the business requirement information, and match one of the multiple business images from a preset material library as the business background image based on the business requirement text; The number of coordinates is determined based on the number of data points for each feature data, and several image coordinate points are randomly selected from the business background image according to the number of coordinates. Connect the image coordinate points corresponding to each feature data into a closed curve to obtain several activity trajectories; The step of connecting several image coordinate points corresponding to each feature data into a closed curve to obtain several activity trajectories includes: The image coordinate points corresponding to each type of feature data are sorted by polar angle so that the image coordinate points corresponding to each type of feature data are arranged in a circular order; Connect the last image coordinate point of the polar angle sorting with the first image coordinate point to form a closed trajectory line. After supplementing transition points between the discrete points of the trajectory line through an interpolation algorithm, set the color and width of the trajectory line to obtain the activity trajectory corresponding to each feature data. The polar angle sorting operation includes: calculating the geometric center of several image coordinate points to obtain the center coordinates; Calculate the polar angle of each image coordinate point relative to the geometric center, and sort them in ascending order of polar angle to obtain the sort index; Rearrange several image coordinate points according to their sorting index, so that the image coordinate points are arranged in a circular order.

2. The data visualization method for a distributed database according to claim 1, characterized in that, The process of extracting data to be displayed from a distributed database based on user business needs information includes: Obtain user business requirement information, and extract business data from the distributed database based on the business duration corresponding to the business requirement information; The business data is sequentially checked for data integrity, data consistency, and data sequence logic. When all three checks pass, the data to be displayed is obtained. The data integrity verification is a verification that compares the sequence number of the business data with a preset sequence number. The data consistency check is a check to verify whether there is a conflict between the partition ID and sequence number of the Binlog of the business data; The data sequence logical verification is a verification process to determine whether there are any anomalies in the business data.

3. The data visualization method for a distributed database according to claim 1, characterized in that, After adding the feature data to the business background image to obtain the business feature image, the step of visualizing the feature data within the business feature image based on the activity trajectory includes: Each of the aforementioned feature data is converted into a visual chart using visualization components; Each of the visual charts is added to the business background image according to the image coordinate points to obtain a business feature image; Obtain the coordinate set of the activity trajectory in the business background image, visualize the business feature image, and control each of the visualized charts to move within the coordinate set.

4. The data visualization method for a distributed database according to claim 3, characterized in that, The step of converting each feature data into a visual chart using a visualization component includes: Each of the aforementioned feature data is converted into a chart using visualization components, resulting in several data display charts; The size of each data display chart is adjusted based on the numerical value of the feature data to obtain a visual chart.

5. The data visualization method for a distributed database according to any one of claims 1-4, characterized in that, After adding the feature data to the business background image to obtain a business feature image, and controlling the visualization of the feature data within the business feature image based on the activity trajectory, the method further includes: Multiple feature data are encrypted and packaged into an encrypted data packet, which is then transmitted to the user terminal for decryption and display to the user.

6. A data visualization device for a distributed database, characterized in that, The device includes: The acquisition module is used to extract data to be displayed from a distributed database based on the user's business needs information. The data to be displayed is data that has been verified by data integrity, data consistency and data sequence logic. The extraction module is used to extract corresponding feature data from the data to be displayed according to preset business feature types, including: transaction scale, transaction conversion, user features, product features, and time dimension. The trajectory module is used to find a business background image based on the business requirement information and extract several activity trajectories from the business background image, wherein each activity trajectory corresponds to a preset business feature type; The visualization module is used to add the feature data to the business background image to obtain a business feature image, and then control the visualization of the feature data within the business feature image according to the activity trajectory. The step of finding a business background image based on the business requirement information and extracting several activity trajectories from the business background image includes: Extract business requirement text from the business requirement information, and match one of the multiple business images from a preset material library as the business background image based on the business requirement text; The number of coordinates is determined based on the number of data points for each feature data, and several image coordinate points are randomly selected from the business background image according to the number of coordinates. Connect the image coordinate points corresponding to each feature data into a closed curve to obtain several activity trajectories; The step of connecting several image coordinate points corresponding to each feature data into a closed curve to obtain several activity trajectories includes: The image coordinate points corresponding to each type of feature data are sorted by polar angle so that the image coordinate points corresponding to each type of feature data are arranged in a circular order; Connect the last image coordinate point of the polar angle sorting with the first image coordinate point to form a closed trajectory line. After supplementing transition points between the discrete points of the trajectory line through an interpolation algorithm, set the color and width of the trajectory line to obtain the activity trajectory corresponding to each feature data. The polar angle sorting operation includes: calculating the geometric center of several image coordinate points to obtain the center coordinates; Calculate the polar angle of each image coordinate point relative to the geometric center, and sort them in ascending order of polar angle to obtain the sort index; Rearrange several image coordinate points according to their sorting index, so that the image coordinate points are arranged in a circular order.

7. A data visualization system for a distributed database, characterized in that, The system includes: a management platform, a distributed database, and several user terminals; The management platform is connected to the distributed database and several user terminals respectively; The management platform is applicable to the data visualization method for distributed databases as described in any one of claims 1-5.

8. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements the data visualization method for a distributed database as described in any one of claims 1-5.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer-executable program for causing a computer to perform the data visualization method for a distributed database as described in any one of claims 1-5.

Images