Data visualization decision-making system and method

Abstract

The invention discloses the technical field of data processing and information visualization, and particularly relates to a data visualization decision-making system and method, which comprises a big data analysis engine, a multi-dimensional data analysis module, an intelligent decision-making module, a visualization display module and an interactive interface module, a built-in efficient big data analysis engine of the system is used for carrying out comprehensive processing and multi-dimensional analysis on mass data, data quality is ensured, then multi-dimensional data analysis is carried out on the data, the data is sliced and perspective according to different angles and dimensions selected by a user, potential information of the data is deeply mined, and an intelligent algorithm is combined, so that the data quality is ensured. The system analyzes historical data and trends, provides real-time decision-making suggestions for users, and enhances the accuracy of decision-making.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing and information visualization, and particularly relates to a data visualization decision-making system and method. Background Art

[0002] In today's information age, the generation of massive data poses a huge challenge to traditional data processing methods. Business and social decisions increasingly rely on data rather than intuition or experience. Enterprises need to extract insights from data to optimize operations, improve efficiency, predict trends, and formulate strategies. However, existing data processing and visualization systems often struggle to meet the needs of in-depth analysis, especially in terms of data processing speed, analysis dimensions, and visual representation. Simple data reports and tables are often difficult for non-professionals to understand and digest, which is not conducive to helping decision-makers quickly capture key information and make correct decisions.

[0003] The patent document with the patent number 2015109878177 discloses a data visualization method and a data visualization device for plotting data in a two-dimensional space based on the similarity of corresponding section samples. The device captures a click data sequence, and the click data sequence includes multiple click data. Each click data is compared with a section sample corresponding to a first sequence section to generate a similarity corresponding to each click data. Multiple click data with the maximum similarity are captured, and a second sequence section corresponding to each of the multiple click data with the maximum similarity is captured. Finally, each second sequence section is visualized in a two-dimensional space. This method usually involves visualizing data in a two-dimensional space, especially emphasizing the layout and display of data points by calculating the similarity between different data points or data sections, and focuses more on the visualization of the similarity between data points and the internal structure of data. It is more suitable for data exploration, but lacks decision support from intelligent algorithms and advanced customization options, and is not suitable for business decisions.

[0004] The patent document with the patent number 2018113722428 discloses a big data center monitoring data visualization system and method. This solution collects the status information of monitored devices in real time through a data collection module, processes and stores it, and finally visualizes it through a data visualization module according to the actual operation and maintenance business scenario. However, this patent mainly focuses on how to effectively collect, process, and visualize the monitoring data of the data center. In terms of visual display, this solution lacks some advanced or specific field charts, such as Sankey diagrams, heat maps, tree diagrams, parallel coordinate diagrams, etc. It does not support customized chart display, which may lead to a reduction in the flexibility and efficiency of the analysis process. When facing the growing needs of the data center, it may be difficult to adapt and upgrade, increasing the cost and complexity of long-term maintenance. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a data visualization decision-making system and method, which uses an efficient built-in big data analysis engine in the system to comprehensively process and multi-dimensionally analyze massive data to ensure data quality, then conduct multi-dimensional data analysis on the data, slice and perspective the data according to different angles and dimensions selected by the user, deeply explore the potential information of the data, and then combine intelligent algorithms. The system analyzes historical data and trends to provide real-time decision-making suggestions for users and enhance the accuracy of decision-making.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The present invention provides a data visualization decision-making system, including a big data analysis engine, a multi-dimensional data analysis module, an intelligent decision-making module, a visualization display module and an interactive interface module. The big data analysis engine collects data from various data sources, performs data cleaning and preprocessing, and then stores the cleaned data in a database and establishes an index to optimize data retrieval efficiency; the multi-dimensional analysis module imports the data into an SQL analysis tool to preliminarily explore each data indicator, understand the basic statistical characteristics of the data, and analyze the relationship between data and data; the intelligent decision-making module analyzes data trends through intelligent algorithms to provide decision-making suggestions for users; the visualization display module and the interactive interface module are used for the user to select the type of chart to be displayed and freely adjust the analysis dimension and display method.

[0008] A method based on the data visualization decision-making system described in claim 1, including the following steps:

[0009] S1: Determine the analysis target;

[0010] S2: Data storage and management;

[0011] S3: Data analysis and calculation;

[0012] S4: Data visualization display;

[0013] S5: Intelligent decision-making support;

[0014] S6: Interface response and security protection.

[0015] The beneficial effects of the present invention are:

[0016] 1. The present invention utilizes an efficient built-in big data analysis engine in the system to comprehensively process and multi-dimensionally analyze massive data to ensure data quality. Then, it conducts multi-dimensional data analysis on the data, slices and perspectives the data according to different angles and dimensions selected by the user, deeply mines the potential information of the data, and combines intelligent algorithms (the intelligent algorithms in the present invention mainly involve time series analysis, linear regression, clustering analysis, etc.). The system analyzes historical data and trends, provides real-time decision-making suggestions for users, and enhances the accuracy of decision-making. At the same time, this system has high flexibility and interactivity, provides users with various chart types, such as line charts, pie charts, heat maps, etc. At the same time, the system can adapt to different devices and support users to customize colors and styles and perform interactive operations. Users can select appropriate charts according to their needs and freely adjust the data analysis dimensions and display methods at any time and place.

[0017] 2. The present invention supports customized chart display (the present invention provides various chart types for users to choose from, such as bar charts, line charts, pie charts, scatter plots, heat maps, maps, etc. Users can select the most suitable chart type according to the data characteristics and analysis purposes; users can also customize the color scheme of the chart, including background color, line color, fill color, etc.; the charts provided by the present invention also support dynamic interactive functions, such as mouse hovering to display detailed information, clicking to select data points, zooming, and panning). At the same time, it integrates intelligent algorithms (the intelligent algorithms in the present invention mainly involve time series analysis, linear regression, price elasticity analysis, clustering analysis, anomaly detection, and linear programming, etc.) to provide decision support and provide users with comprehensive and accurate information.

[0018] Other advantages, objectives, and features of the present invention will be described to some extent in the subsequent specification, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be learned from the practice of the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the following specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be described in detail preferably with reference to the accompanying drawings, where:

[0020] Figure 1 is a module diagram of the data visualization decision-making system of the present invention;

[0021] Figure 2 is a flowchart of the data visualization decision-making method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The following describes the embodiments of the present invention through specific examples. Those skilled in the art can easily understand the other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the drawings provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0023] Among them, the drawings are only for illustrative purposes, showing only schematic diagrams, not physical diagrams, and should not be construed as a limitation to this patent; to better illustrate the embodiments of the present invention, some components in the drawings will be omitted, enlarged or reduced, which does not represent the size of the actual product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted. In addition, the present invention may repeat reference numerals and / or letters in different examples. This repetition is for the purpose of simplicity and clarity, and does not itself indicate the relationship between the various embodiments and / or settings discussed.

[0024] As Figure 1 shown, the data visualization decision-making system of the present invention includes a big data analysis engine 1, a multi-dimensional data analysis module 2, an intelligent decision-making module 3, a visualization display module 4, and an interactive interface module 5. Among them, the big data analysis engine 1 collects data from various data sources, performs data cleaning and preprocessing, and then stores the cleaned data in a database and establishes an index to optimize the data retrieval efficiency; the multi-dimensional analysis module 2 imports the data into an SQL analysis tool, conducts a preliminary exploration of each data indicator, understands the basic statistical characteristics of the data, and analyzes the relationship between data and data; the intelligent decision-making module 3 analyzes the data trend through intelligent algorithms and provides decision-making suggestions for users; the visualization display module 4 and the interactive interface module 5 are used to allow users to select the type of chart to be displayed and freely adjust the analysis dimension and display method.

[0025] As Figure 2 shown, a data visualization decision-making method includes the following steps:

[0026] Step S1: Determine the analysis objective. In a specific embodiment of this application, the analysis objective is the ranking of the operating cities of a reservation-based door-to-door massage platform and how the number of followers of the platform's official account changes over time. After determining the analysis objective, data collection and cleaning are carried out to handle issues such as missing values and outliers to ensure the accuracy and integrity of the data. In this embodiment, all operating data of the operating cities is obtained from the platform's back-end management system, and the number of new followers and the cumulative number of followers are obtained from the back-end of the platform's WeChat official account. Code is used to check for null or NULL values in the dataset, and these null values are filled with 0.

[0027] Step S2: Data storage and management. In the previous embodiment, the collected data is stored in a MySQL database. A full backup is performed once a week and backup recovery tests are regularly conducted. The HTTPS protocol is used to encrypt data transmission to ensure data security.

[0028] Step S3: Data analysis and calculation. In a specific embodiment of this application, daily key indicators are calculated based on the processed data. For example: the cumulative number of users on the current day = the number of newly registered users on the current day + the cumulative number of users on the previous day; calculate the monthly ranking. For example: calculate the monthly ranking of partners based on factors such as partner contribution and the number of cooperation projects.

[0029] Step S4: Data visualization display. In a specific embodiment of this application, the order volume of each operating city in the current month is obtained through data analysis and calculation. In order to more accurately understand which regions have higher demand, users can choose a heat map to display the order reservation situation of the operating cities in the current month. The depth of color represents the level of reservation demand. Users can also modify the color and style of the heat map according to their preferences.

[0030] Step S5: Intelligent decision support. In a specific embodiment of this application, a reservation-based door-to-door massage platform predicts the expected reservation volume and the supply status of technicians in the city during a certain period based on historical data of that period. The person in charge of the city's operation can raise the price during peak demand periods in advance and lower the price during off-peak demand periods to encourage users to make reservations during off-peak hours.

[0031] Step S6: Interface response and security protection. In a specific embodiment of this application, users can choose to view business data in real time on a computer or on a mobile device according to the situation, and adjust the operation plan more flexibly. All business data acquisition interfaces use security protocols such as HTTPS to ensure the security of data during transmission.

[0032] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the present technical solution, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A data visualization decision system, characterized in that: It includes a big data analysis engine, a multi-dimensional data analysis module, an intelligent decision-making module, a visualization display module and an interactive interface module. The big data analysis engine collects data from various data sources, performs data cleaning and preprocessing, and then stores the cleaned data in a database, and establishes an index to optimize data retrieval efficiency; The multi-dimensional analysis module imports the data into the SQL analysis tool, conducts preliminary exploration of each data indicator, understands the basic statistical characteristics of the data, and analyzes the relationship between the data; The intelligent decision-making module analyzes data trends through intelligent algorithms and provides decision-making suggestions to users; the visual display module and interactive interface module are used to allow users to choose the type of chart to be displayed and freely adjust the analysis dimension and display method.

2. A method based on the data visualization decision system according to claim 1, characterized in that: The following steps are involved: S1: Determine the analysis objectives; S2: Data storage and management; S3: Data analysis and calculation; S4: Data visualization display; S5: Intelligent decision support; S6: Interface responsiveness and security protection.

3. The data visualization decision-making method according to claim 2, characterized in that: In step S1, the analysis target is the ranking of cities where a home massage booking platform operates and how the number of followers of the platform's official account changes over time. After determining the analysis target, data collection and cleaning are performed. Specifically, the operating data of all operating cities are obtained from the platform's backend management system, and the number of new followers and the cumulative number of followers are obtained from the backend of the platform's WeChat official account. The code is used to check the empty values ​​or NULL values ​​in the data set and fill these empty values ​​with 0.

4. The data visualization decision-making method according to claim 2, characterized in that: In step S2, the collected data is stored in a MySQL database, a full backup is performed once a week and backup and recovery tests are performed regularly, and data transmission is encrypted using the HTTPS protocol.

5. The data visualization decision-making method according to claim 2, characterized in that: In step S3, daily key indicators are calculated based on the processed data, and the key indicators include the cumulative number of users for the day and the ranking for the month, where the cumulative number of users for the day = the number of new registered users for the day + the cumulative number of users yesterday; the ranking for the month is calculated based on the partner contribution and the number of cooperation projects.

6. The data visualization decision-making method according to claim 2, characterized in that: In step S4, the user can select a heat map to display the order reservation status of the operating city for the current month. The depth of color represents the level of reservation demand. The user can also modify the color and style of the heat map according to his or her preferences.

7. The data visualization decision-making method according to claim 2, characterized in that: In step S5, the home massage reservation platform predicts the expected number of reservations for a period of time and the supply of technicians in the city based on the historical data of the period. The person in charge of the city operation can raise the price in advance during the period of high demand and lower the price during the period of low demand to encourage users to make reservations during off-peak hours.

8. The data visualization decision-making method according to claim 2, characterized in that: In step S6, the user can choose to view the business data in real time on a computer or a mobile phone according to the situation.

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