Hierarchical public data visualization retrieval system and method based on crowd-sourcing analysis and data standardization
By building a data platform based on crowdsourced analytics, the problems of public data resource integration and comparability have been solved, enabling seamless data transition and display from macro to micro levels, improving data utilization efficiency and comparability, and supporting expansion into multiple fields.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 杨小莲
- Filing Date
- 2026-03-08
- Publication Date
- 2026-06-30
Smart Images
Figure CN122309602A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to data processing and visualization technology, specifically to a hierarchical visualization retrieval system and method for public data resources based on crowdsourcing analysis. It is particularly suitable for extracting information in specific fields from dispersed public data sources (such as government announcements and listed company reports), constructing comparable development indices through data standardization processing (including unified exchange rate conversion and reporting period alignment), and providing directional navigation from macro trends to micro information through interactive charts. Background Technology
[0002] Governments, listed companies, and social organizations regularly release vast amounts of information to the public through designated platforms, creating a massive public data resource. However, this data is typically scattered across different websites or databases, with varying formats and a lack of a unified access point. Users who want to understand global development trends in a specific field (such as additive manufacturing), or trace macro trends down to micro-level information about specific companies (such as revenue, recruitment, and patents), often need to manually switch between multiple platforms, which is time-consuming, laborious, and makes it difficult to intuitively understand the relationships between the data.
[0003] While existing data visualization tools can generate charts, they typically only offer static displays or simple data filtering, failing to achieve hierarchical interaction and context-aware, precise linking based on the chart. For example, users cannot directly access a detailed list of all companies in a region by clicking on a curve in a "Global Additive Manufacturing Development Index" chart, and then be redirected to a specific company's recruitment page. This technological deficiency leads to inefficient use of public data and difficulty in gaining insights.
[0004] Furthermore, the analysis and structuring of public data requires a great deal of expertise. Traditionally, this work has been undertaken by a few specialized institutions, resulting in slow data updates, limited coverage, and an inability to meet the needs of ordinary users.
[0005] Existing technologies also suffer from two key technical drawbacks: First, the incomparability of multi-currency data: Financial reports from globally listed companies use different currencies (USD, EUR, RMB, JPY, etc.), and direct comparison leads to data distortion, making it impossible to generate a meaningful global index. Second, inconsistent financial reporting cycles: Different companies have different financial reporting cycles (calendar year vs. fiscal year), causing time-series data to be misaligned and unable to accurately reflect annual trends. Summary of the Invention
[0006] This invention aims to solve the aforementioned technical problems by providing a hierarchical visualization retrieval system and method for public data resources based on crowdsourced analysis. Its core lies in constructing a crowdsourced-driven data analysis platform. Through data standardization processing (including unified exchange rate conversion and reporting cycle alignment), it ensures data comparability. Furthermore, through an event-triggered mechanism based on chart elements and context-aware navigation, it achieves seamless transitions and related displays from macro-level indices to micro-level enterprise data.
[0007] Technical Solution Overview. The system first receives analytical contributions from domain expert users through a crowdsourced analytics platform. Expert users analyze raw public data (such as publicly available company reports) for specific domains, extract key characteristic indicators (such as total corporate assets, owner's equity, operating revenue, annual profit, and operating revenue of specific business lines), and submit them to the platform. After reviewing the contributions, the platform transmits the data to the data standardization module.
[0008] The data standardization module consists of two sub-modules:
[0009] Unified exchange rate conversion module: Obtains the real-time exchange rate of each financial data point as of December 31st of the corresponding year, and converts financial data denominated in multiple original currencies into a base currency (such as the US dollar).
[0010] Reporting Period Standardization Module: Identifies the reporting period of each company's financial reports. For companies whose reporting period is not a calendar year, the financial data is converted into a standard calendar year period (January 1 to December 31) based on their annual and quarterly reports.
[0011] The indicator calculation module calculates the individual development indicator for a specific enterprise based on the following formula and standardized data: I = w1×A + w2×E + w3×R + w4×P + w5×S, where:
[0012] A represents total assets (after standardization).
[0013] E represents owner's equity (after standardization).
[0014] R represents operating revenue (after standardization).
[0015] P represents annual profit (after standardization).
[0016] S represents the operating revenue of a specific business line (after standardization).
[0017] w1 to w5 are preset weights that satisfy w1+w2+w3+w4+w5=1
[0018] In a preferred embodiment, for the additive manufacturing field, the weights are set as follows: w1 = 0.1 (total assets weight), w2 = 0.1 (owner's equity weight), w3 = 0.1 (operating revenue weight), w4 = 0.1 (annual profit weight), and w5 = 0.6 (additive manufacturing business revenue weight).
[0019] The index generation module further benchmarks the individual development indicators, using the indicator value of a benchmark company (such as 3DSYSTEMS) in a benchmark year (such as 2011) as the benchmark value (such as 1000), and calculates the individual development index value: I_relative(y, c) = [I_company(y, c) / Ibenchmark] × 1000.
[0020] The regional development index is calculated by aggregating the individual indices of all enterprises within that region, while the global development index is calculated by aggregating the individual indices of all enterprises across all regions.
[0021] The index generation module generates interactive index charts (such as the Global Additive Manufacturing Development Index Chart) that characterize the macro development trends of the field based on these data. The charts contain multiple interactive graphic elements (such as curves representing different economic zones). All indicators, indices, charts and related information are stored in a multi-level index database.
[0022] When a user clicks on a specific graphic element on the chart, the interaction response module detects the click event, retrieves the corresponding secondary data (such as a list of companies in the region or a regional index) from the database, and generates a secondary chart.
[0023] Users can further select function tags (such as "Recruitment Information", "Cooperation Information", "Announcement Information"), and then click on the specific company icon in the secondary chart. The context-aware navigation module determines the target URL from the link data based on the selected function tag and company icon, and automatically navigates to the corresponding preset external webpage (such as recruitment page, announcement page) of the company.
[0024] Beneficial effects:
[0025] Lowering the barrier to public access to data: By distributing professional data analysis work to domain experts through a crowdsourcing analytics model, ordinary users can obtain high-quality structured data without professional knowledge.
[0026] Data comparability: By standardizing exchange rates and reporting cycles, we ensure that data from different countries, currencies, and financial reporting cycles are comparable, enabling global indices to truly reflect development trends.
[0027] Scientific index construction: Accurately measure the development level of a specific industry through specific weighting formulas (especially assigning a weight of 0.6 to the revenue of a specific business line) and benchmarking.
[0028] Enhance macro-level insights: Through the global development index chart, users can intuitively grasp the overall development trend and historical evolution of a particular field.
[0029] Achieve efficient targeted retrieval: By clicking on charts and selecting function tags, users can quickly drill down from macro trends to micro information and directly navigate to the original data source, significantly reducing retrieval time and operation steps.
[0030] Sustainable scalability: The crowdsourcing model can be gradually expanded to more fields (such as artificial intelligence, quantum technology, public administration, etc.) to form a widely covered public data resource network. Attached Figure Description
[0031] Figure 1 This is a schematic diagram of the overall architecture of the system of the present invention.
[0032] Figure 2 The flowchart of the method of the present invention
[0033] Figure 3 An example interactive interface for the Global Additive Manufacturing Development Index (primary data).
[0034] Figure 4 A sample interactive interface for the development index of listed additive manufacturing companies in America (secondary data).
[0035] Figure 5 Revenue scale of publicly traded additive manufacturing companies in America (exemplary interactive interface for level 3 data)
[0036] Figure 6 A diagram illustrating the selection of function tabs and external link navigation. Detailed Implementation
[0037] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. The system architecture is as follows: Figure 1 As shown, the system of the present invention includes:
[0038] Crowdsourced analytics platform 101 is configured to receive analytical contributions from experts in various fields regarding public data in specific areas. These contributions include characteristic indicators extracted from the original public data, such as total assets, owner's equity, operating revenue, annual profit, and additive manufacturing revenue extracted from listed company reports. The platform also includes a review module for manually or automatically reviewing user-submitted analytical contributions, publishing them to the system upon approval. The platform further includes an expert registration module, a contribution record module, and a contribution incentive module to maintain the sustainable operation of the crowdsourcing community.
[0039] Data standardization module 102: Configured to standardize the raw financial data submitted by the crowdsourcing analytics platform to ensure data comparability. This module further includes:
[0040] The unified exchange rate conversion module 1021 is configured to obtain real-time exchange rate data for each financial data point as of December 31st of the current year. This data can be obtained via API from external financial services (such as Reuters and Bloomberg) or publicly available data from central banks. Based on this exchange rate data, the original currency value is converted to a unified base currency (such as the US dollar). For financial data denominated in non-US dollar currencies, the conversion formula is: V_USD = V_original × ER date. Where V_original is the original currency value, and ER date is the exchange rate of that currency against the US dollar on December 31st of that year.
[0041] Reporting Period Standardization Module 1022: Configured to identify the reporting period of each company's financial reports (e.g., January 1st - December 31st, April 1st - March 31st of the following year, etc.). For companies whose reporting period is not a calendar year (January 1st - December 31st), obtain their quarterly report data and convert the company's financial data to a standard calendar year period using one of the following methods:
[0042] Weighted average method: The average is calculated based on the proportion of time each quarter's data takes up in the calendar year.
[0043] Interpolation: Using linear interpolation or financial interpolation algorithms to estimate financial data at the end of a calendar year.
[0044] Hybrid approach: Annual report data is used first, and quarterly report data is used to supplement the data for months not covered by the annual report.
[0045] In a preferred embodiment, for a company whose fiscal year runs from April 1st to March 31st of the following year, its estimated operating revenue for the 2023 calendar year is: R_2023=R_Q4_2022+R_Q1_2023+R_Q2_2023+R_Q3_2023
[0046] Indicator Calculation Module 103: Configured to calculate the individual development indicators of a specific enterprise according to the following formula: I = w1 × A + w2 × E + w3 × R + w4 × P + w5 × S. Where: A is total assets (standardized), E is owner's equity (standardized), R is operating revenue (standardized), P is annual profit (standardized), S is operating revenue of a specific business line (standardized), and w1 to w5 are preset weights, satisfying w1 + w2 + w3 + w4 + w5 = 1.
[0047] In a preferred embodiment, for the additive manufacturing field, the weights are set as follows: w1 = 0.1 (total assets weight), w2 = 0.1 (owner's equity weight), w3 = 0.1 (operating revenue weight), w4 = 0.1 (annual profit weight), and w5 = 0.6 (additive manufacturing business revenue weight).
[0048] Index generation module 104: Configured to perform benchmarking processing on the calculated individual development indicators, using the indicator value of a benchmark company (such as 3DSYSTEMS) in the benchmark year (such as 2011) as the benchmark value (such as 1000), and calculate the relative index value: I_relative(y, c) = [I_company(y, c) / Ibenchmark] × 1000.
[0049] The regional development index is calculated by aggregating the relative index values of all enterprises in the region: I_region(y)=∑I_relative(y,c)for all c in region.
[0050] The Global Development Index is calculated by aggregating the relative index values of all companies across all regions.
[0051] The index generation module generates the first interactive chart (e.g., based on the processed data) Figure 3 The chart shown is a global additive manufacturing development index chart. This chart contains several interactive graphical elements, each corresponding to a sub-region or sub-category.
[0052] Multi-level index database 105: Configured to store all indicators, indices, charts, and related information. The multi-level public data includes at least:
[0053] Primary data: Macro trend data, namely global and regional development indices calculated based on aggregated data from enterprises in various regions.
[0054] Secondary data: Regional detailed data, namely, the list of eligible enterprises and their development index in each region.
[0055] Level 3 data: Enterprise-level micro data, namely, the revenue scale, employee scale, R&D investment, etc. of each enterprise.
[0056] Link data: URLs of external web pages corresponding to each company, including official websites, recruitment information pages, cooperation information pages, and announcement information pages.
[0057] Interactive response module 106: configured to receive a user's first click on a first graphic element on the first interactive chart, and in response to the first click, retrieve secondary data associated with the first graphic element from a multi-level index database.
[0058] Data display module 107: configured to generate a second interactive chart (e.g., based on the secondary data) Figure 4 (See the chart showing the development index of listed additive manufacturing companies in the Americas region).
[0059] Context-aware navigation module 108: Configured to receive a second click by the user on a second graphic element in the second interactive chart, and to receive a pre-selected function label by the user. Based on the function label and the enterprise corresponding to the second graphic element, it generates a third interactive chart or determines a target URL from link data and navigates to that target URL.
[0060] Multilingual interface module 109: Configured to support users switching between Chinese and English interfaces to meet the needs of global users.
[0061] Data organization examples
[0062] Table 1: Global Additive Manufacturing Development Index (Level 1 Data, Standardized)
[0063]
[0064] Table 2: Development Index of Listed Additive Manufacturing Companies in America (Secondary Data, Partial)
[0065]
[0066] Table 3: External Links Data for Enterprises (Example)
[0067]
[0068]
[0069] Crowdsourcing Analysis Process Example
[0070] Step 1: Expert Registration and Certification. Domain expert users register through the crowdsourcing analytics platform 101, submitting their professional background information. After verifying the user's qualifications, the platform grants them data contribution permissions.
[0071] Step 2: Data Contribution. Certified expert users access the data contribution interface provided by the platform and select the public data source to be analyzed (such as a company's latest annual report). Users use the analysis tools provided by the platform to extract data such as total assets, owner's equity, operating revenue, annual profit, and additive manufacturing business operating revenue from the report, and fill in relevant auxiliary information (such as data source and extraction method). Users submit the analysis results to the platform.
[0072] Step 3: Contribution Review. The platform's review module reviews the analysis results submitted by users, checking the accuracy, completeness, and consistency of the data. After the review is passed, the data is transferred to the data standardization module 102.
[0073] Step 4: Data standardization processing.
[0074] Unified exchange rate conversion: The unified exchange rate conversion module 1021 obtains the exchange rate data for each financial data point as of December 31st of the current year, and converts the financial data denominated in multiple original currencies into US dollar values.
[0075] Reporting Period Standardization: The Reporting Period Standardization module 1022 identifies the reporting period of each company's financial reports. For companies whose reporting period is not a calendar year, the financial data is converted into a standard calendar year period (January 1 to December 31) based on their annual and quarterly reports.
[0076] Step 5: Index Calculation and Storage. The index calculation module 103 calculates the individual development indicators for each enterprise according to the formula. The index generation module 104 performs benchmarking processing, calculating regional and global indices. The standardized data, calculated indices, charts, and related information are stored in a multi-level index database 105.
[0077] Step 6: Chart Update. The index generation module 104 periodically updates the interactive chart based on the latest data in the database.
[0078] Example of interactive process.
[0079] Step 1: Macro-level overview. When a user visits the website homepage, the index generation module generates a line chart of the "Global Additive Manufacturing Development Index" based on primary data (e.g., ...). Figure 3 (As shown). The graph contains three curves, representing the index changes in the Americas, APAC, and EMEA regions from 2011 to 2024.
[0080] Step 2: Drill down the region. The user clicks the "Americas" curve. The interaction response module detects this click event and requests the secondary data (i.e., Table 2) of the Americas region from the multi-level index database. The data display module generates a "Americas Additive Manufacturing Listed Companies Development Index" chart based on the returned data (e.g., ...). Figure 4 As shown in the figure, the historical index of each company in the region is displayed.
[0081] Step 3: Function Selection and Micro-links. Users select "Job Postings" (e.g., ...) from the function tab bar at the top of the page. Figure 6 (As shown). Then click Figure 4 The data row corresponding to "3D SYSTEMS" is displayed. The context-aware navigation module recognizes that the currently active function is "Job Postings," searches for the URL of 3D SYSTEMS's job postings (www.3dsystems.com / careers) in the linked data, and opens the page in a new window in the browser.
[0082] Step 4: Other Functionality Extensions. If the user selects the "Revenue Scale" function and then clicks the "Americas" curve, the system will retrieve the revenue data from the Americas region of the three-level data set and generate a graph like this. Figure 5 The line chart shown. If a user selects the "Public Information" feature and clicks on a company, they are redirected to that company's SEC filings page.
[0083] Variant Implementation. The system can be extended to other technological fields (such as artificial intelligence, quantum technology) or public administration. Data levels can be increased as needed (e.g., four levels of data: subdivided by product area). Chart types may include pie charts, scatter plots, heatmaps, etc. The crowdsourced analytics platform can incorporate automated auxiliary tools, such as machine learning-based financial statement information extraction assistants, to improve expert analysis efficiency.
[0084] The weights in the index calculation formula can be adjusted according to the characteristics of different fields. For example, in the field of artificial intelligence, R&D investment may receive a higher weight.
Claims
1. A public data resource visualized retrieval system based on crowd-sourcing analysis and data standardization, characterized in that, include: The crowdsourced analytics platform is configured to receive analytical contributions from multiple domain expert users on public data in a specific domain. The analytical contributions include feature indicator data extracted from the original public data, and the platform publishes the analytical contributions after review. The data standardization module is configured to standardize the financial data in the analysis contribution. The data standardization module further includes: a unified exchange rate conversion module, configured to obtain the exchange rate data as of December 31 of the corresponding year of the financial data, and uniformly convert the financial data denominated in multiple original currencies into a base currency value; and a reporting period standardization module, configured to identify the reporting period of each company's financial report, and for companies whose reporting period is not a calendar year, convert the financial data into a standard calendar year period based on their annual and quarterly report data. The indicator calculation module is configured to calculate the individual development indicators of a specific enterprise according to the following formula: I=w1×A+w2×E+w3×R+w4×P+w5×S; where A is the total assets, E is the owner's equity, R is the operating revenue, P is the annual profit, S is the operating revenue of a specific business line, and w1 to w5 are preset weights that satisfy w1+w2+w3+w4+w5=1; The index generation module is configured to generate an individual development index based on the individual development indicators and benchmark values: I_relative(y, c) = [I_company(y, c) / Ibenchmark] × 1000; and generate a regional development index and a global development index: I_region(y) = ∑I_relative(y, c) for all c in region; and then generate a first interactive chart representing the macro development trend of the specific field, wherein the first interactive chart contains multiple interactive graphic elements; A multi-level index database is configured to store the standardized feature indicator data, multi-level indices and charts. The multi-level index database includes at least first-level macro trend data, second-level regional detailed data, and link data pointing to external information sources. The interactive response module is configured to receive a user's first click on a first graphic element on the first interactive chart, and in response to the first click, retrieve detailed data of the secondary region associated with the first graphic element from the multi-level index database. The data display module is configured to generate a second interactive chart based on the detailed data of the secondary region; The context-aware navigation module is configured to receive a second click by the user on a second graphic element on the second interactive chart, and to receive a pre-selected function label by the user; based on the function label and the entity corresponding to the second graphic element, it generates a third interactive chart or determines a target URL from the link data, and navigates to the target URL.
2. The system of claim 1, wherein, The functional tags include at least one of "Recruitment Information", "Cooperation Information", "Public Announcement Information", "Revenue Scale", "Employee Scale", and "R&D Investment Scale".
3. The system of claim 1, wherein, The specific field is additive manufacturing, the public data includes publicly disclosed data from listed companies, and the revenue of the specific business line is the revenue from additive manufacturing business.
4. The system of claim 3, wherein, For the additive manufacturing field, the preset weights are set as follows: w1 = 0.1, w2 = 0.1, w3 = 0.1, w4 = 0.1, w5 = 0.
6.
5. The system of claim 1, wherein, The index generation module is also configured to use the individual development indicators of the benchmark enterprise in the benchmark year as the benchmark value, perform benchmarking processing on the individual development indicators of other enterprises, generate individual development indices, and further generate regional development indices and global development indices, and then generate charts at all levels.
6. The system of claim 5, wherein, The benchmark company is 3D SYSTEMS, the benchmark year is 2011, and the benchmark value is 1000.
7. The system of claim 1, wherein, The base currency is the US dollar.
8. The system of claim 1, wherein, The reporting period standardization module uses at least one of the weighted average method, interpolation method, or a hybrid method to convert financial data.
9. The system of claim 1, wherein, The crowdsourcing analysis platform also includes an expert registration module, a contribution record module, and a contribution review module.
10. The system of claim 1, wherein, It also includes a multilingual interface module, configured to allow users to switch between Chinese and English interfaces.
11. A method for visually retrieving public data resources based on crowdsourcing analysis and data standardization, comprising the following steps: Receive analytical contributions from multiple domain expert users to public data in a specific domain, the analytical contributions including feature index data extracted from the original public data; The analytical contributions are reviewed, and after approval, data standardization is performed. The data standardization includes: obtaining the exchange rate data as of December 31 of the year corresponding to the financial data, and uniformly converting the financial data denominated in multiple original currencies into a base currency value; and identifying the reporting period of each company's financial report. For companies whose reporting period is not a calendar year, the financial data is converted into a standard calendar year based on their annual and quarterly reports. The individual development index of a specific enterprise is calculated according to the following formula: I = w1×A + w2×E + w3×R + w4×P + w5×S; where A is the total assets, E is the owner's equity, R is the operating revenue, P is the annual profit, S is the operating revenue of a specific business line, and w1 to w5 are preset weights and their sum is 1. Based on the individual development indicators and benchmark values, generate an individual development index: I_relative(y, c) = [I_company(y, c) / I_benchmark] × 1000; and generate a regional development index and a global development index: I_region(y) = ∑I_relative(y, c) for all c in region; then generate a first interactive chart representing the macro development trend of the specific field, the first interactive chart containing multiple interactive graphic elements; The data at all levels is stored in a multi-level index database, which includes at least first-level macro trend data and second-level regional detailed data, as well as link data pointing to external information sources; Receive the user's first click on the first graphic element on the first interactive chart; In response to the first click, detailed data of the secondary region associated with the first graphic element is retrieved from the multi-level index database; A second interactive chart is generated and displayed based on the detailed data of the secondary region. Receives a second click by the user on a second graphic element on the second interactive chart, and receives a pre-selected function label by the user; Based on the functional label and the entity corresponding to the second graphic element, the target URL is determined from the link data, and navigation is performed to the target URL.
12. The method of claim 11, wherein, The functional tags include at least one of "Recruitment Information", "Cooperation Information", "Public Announcement Information", "Revenue Scale", "Employee Scale", and "R&D Investment Scale".
13. The method of claim 11, wherein, The specific field is additive manufacturing, the public data includes publicly disclosed data from listed companies, and the revenue of the specific business line is the revenue from additive manufacturing business.
14. The method according to claim 13, characterized in that, For the additive manufacturing field, the preset weights are set as follows: w1 = 0.1, w2 = 0.1, w3 = 0.1, w4 = 0.1, w5 = 0.
6.
15. The method according to claim 11, characterized in that, It also includes the step of using the individual development indicators of the benchmark enterprise in the benchmark year as the benchmark value, and then benchmarking the individual development indicators of other enterprises to form a development index.
16. The method according to claim 11, characterized in that, The base currency is the US dollar.
17. A computer-readable storage medium comprising executable instructions that, when executed by one or more processors, cause the processors to perform the method of any one of claims 11-16.