Data management system

Abstract

The invention discloses a database data acquisition and management system, and relates to the technical field of data management, a data management unit is used for acquiring and managing multi-dimensional information data of all credit enterprises, and a behavior analysis unit is used for analyzing behaviors of authorized users; the data management unit generates a reading strategy of each dimension, and the enterprise portrait unit generates portrait analysis of each credit enterprise based on different dimensions. In the process of comparing and generating the reading strategy of each dimension, a plurality of character data are intercepted from the dimension data table corresponding to the dimension with relatively high click frequency and relatively large data capacity; according to the method, the high-frequency access data is stored in the independent table to generate the derivative data table of the corresponding dimension, in this way, the database cache can be more effectively utilized, and more common data can be ensured to be reserved in the cache by separating the high-frequency data, so that the performance is improved, the contention for a large table can be reduced, and the possibility of lock contention is reduced.

Description

Technical Field

[0001] This invention relates to the field of data management technology, and more specifically to a data management system. Background Technology

[0002] Enterprise profiling is a tagging system that takes an enterprise as the object and collects and analyzes various data of the enterprise, including but not limited to the enterprise's basic information, behavioral characteristics, products and services, and supply chain relationships, to label the enterprise and form a tagging system. The generation of corporate profiles is inseparable from the multi-dimensional information data of the enterprise, and the multi-dimensional information data used to generate corporate profiles is stored in the corresponding dimension data tables. In existing technologies, the process of generating enterprise profiles utilizes pre-made enterprise profile templates, inserting enterprise information data of different dimensions into corresponding positions within the templates. This allows for the instant generation of the corresponding enterprise profile when a user requests access to it. Furthermore, the process of generating enterprise profiles involves setting up a semi-display function for different dimensions of data to prevent irrelevant data from interfering with the user's browsing experience. This instant generation method uses the same approach to read and display both high-frequency and low-frequency access data. However, if the high-frequency access data is large in volume, it will significantly slow down the enterprise profile generation rate, reduce the user's browsing experience, and also affect the database access efficiency. To address the above problems, this invention proposes a solution. Summary of the Invention

[0003] The purpose of this invention is to provide a data management system that addresses the problem in existing technologies where real-time reading during the generation of enterprise profiles can significantly slow down the generation rate and reduce the user's browsing experience if the data volume is large and accessed frequently. The objective of this invention can be achieved through the following technical solutions: A data management system, comprising: The data management unit collects multi-dimensional information data from all creditworthy enterprises; The behavior analysis unit analyzes the behavior of authorized users browsing the corporate profiles of credit companies. By analyzing the trigger frequency of authorized users based on each dimension and the data volume of each dimension within t behavior cycles, the intention frequency and data volume of all dimensions are obtained. For each dimension, the data management unit compares its corresponding intention frequency and dimension data volume with the preset intention frequency and dimension data volume of the corresponding dimension, and generates a reading strategy for each dimension based on the comparison results. For a credit enterprise, based on its multi-dimensional information data, the enterprise portrait unit generates portrait branches for each dimension of the credit enterprise according to the reading strategies for each dimension, and draws the enterprise portrait of the credit enterprise based on them.

[0004] Furthermore, the multi-dimensional information data of a credit enterprise includes basic information, market, intellectual property, legal litigation records, credit records, investors, human resources, customer and partner relationships, products and services.

[0005] Furthermore, the steps for the behavior analysis unit to analyze and obtain the intention frequencies and dimension data volumes for all dimensions are specifically as follows: S21: First, select a dimension of the credit enterprise as the dimension to be analyzed, and select an enterprise portrait of a credit enterprise as the portrait to be analyzed; S22: Obtain the trigger frequencies of the dimension to be analyzed in t behavior cycles, respectively marked as A1, A2,..., Aa, a≥1. The trigger frequency of the dimension to be analyzed refers to the number of clicks on the details display button corresponding to the dimension to be analyzed in the portrait to be analyzed within the behavior cycle. The t behavior cycles are t behavior cycles traced back from the current behavior cycle to the past; S23: Use the formula , 1≤b≤a to calculate and obtain the discrete value B1 of the trigger frequencies of the dimension to be analyzed in t behavior cycles, compare the sizes of B1 and B. At this time, A is the average value of Ab, and B is the discrete threshold of the trigger frequencies of the dimension to be analyzed; If B1≥B, delete the corresponding Ab in descending order of |Ab - A|, and then calculate and obtain the discrete value B1 of the remaining Ab. At this time, compare the sizes of B1 and B again until B1 < B, and re-calibrate the average value of the Ab participating in the calculation of the remaining discrete value B1 as the intention frequency of the dimension to be analyzed; S24: Obtain the data volume sizes of the information data corresponding to all credit enterprises in the dimension to be analyzed, and calculate and obtain their average value using the sum and average formula, and re-calibrate it as the dimension data volume of the dimension to be analyzed; S25: Select all dimensions of the credit enterprise as the dimensions to be analyzed in sequence according to S21, and calculate and obtain the intention frequencies and dimension data volumes for all dimensions in sequence according to S22 to S24; The behavior analysis module transmits the intention frequencies and dimension data volumes for all dimensions analyzed to the data management unit for storage.

[0006] Advantages of the present invention: (1) This invention collects and manages multi-dimensional information data of all credit enterprises by setting up a data management unit, and analyzes the behavior of authorized users browsing the corporate profiles of credit enterprises to obtain the intention frequency and dimension data volume of all dimensions. The data management unit compares the intention frequency and dimension data volume of each dimension with the intention frequency and dimension data volume of the preset corresponding dimension. Based on the comparison results, a reading strategy for each dimension is generated. The corporate profile unit generates a profile analysis of each credit enterprise based on different dimensions based on the reading strategies of different dimensions. In the process of comparing and generating the reading strategy for each dimension, this invention extracts a number of character data from the dimension data table corresponding to the dimension with high click frequency and large data volume to generate a derivative data table for the corresponding dimension. In this way, storing the frequently accessed data in a separate table can make more effective use of the database cache. Database systems usually keep the recently accessed data in the cache. By separating the high-frequency data, it can ensure that more frequently used data stays in the cache, thereby improving performance and reducing contention for large tables, thereby reducing the possibility of lock contention. (2) In this invention, for dimensions corresponding to the enterprise profile drawing, where there are derived data tables, data is preferentially read from these derived data tables. Furthermore, during the display of the enterprise profile, specific details of this data are simultaneously retrieved. When an authorized user clicks the details display button, the detailed information is displayed instantly. This method of storing and prioritizing a small amount of high-frequency data significantly reduces I / O operations, improves data retrieval speed, and quickly returns the data most relevant to the authorized user, enhancing the user experience. Users do not need to wait for all data to load before they can begin viewing and operating the most critical information. Attached Figure Description

[0007] The invention will now be further described with reference to the accompanying drawings.

[0008] Figure 1 This is a system block diagram of the present invention; Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0009] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0010] like Figure 1 , 2 As shown, a data management system includes an enterprise service platform and a browser module; The enterprise service platform is used to manage and display enterprise profiles of creditworthy enterprises to authorized users. The enterprise service platform includes a data management unit, a behavior analysis unit, and an enterprise profile unit. The data management unit periodically collects and stores multi-dimensional information data from several credit enterprises. In one embodiment of the present invention, a credit enterprise refers to an enterprise that has been authorized by the enterprise service platform and is allowed to connect with the enterprise service platform; In one embodiment of the present invention, the multi-dimensional information data of a credit enterprise includes basic information, market, intellectual property, legal litigation records, credit records, investors, human resources, customer and partner relationships, products and services; In one embodiment of the present invention, the information data in the basic information dimension includes enterprise name, registered address, legal representative, registered capital, registration time, enterprise type, industry category, business scope, etc.; the information data in the market dimension includes macro data such as market size, market share, competitive situation, and industry development trends of the industry in which the enterprise operates; the information data in the intellectual property information dimension includes the ownership and application of intellectual property rights such as trademarks, patents, and copyrights; the information data in the legal litigation record dimension includes the enterprise's historical legal litigation and arbitration cases, etc.; the information data in the credit record dimension includes credit rating, loan records, repayment status, and bad credit records, etc.; the information data in the investor dimension includes information such as shareholder structure, major investors, and investment rounds; the information data in the human resources dimension includes employee size, structure, talent turnover rate, and training system, etc.; the information data in the customer and partner relationship dimension includes information on major customers, suppliers, and partners; and the information data in the product and service dimension includes the types of products and services provided by the enterprise, market share, customer feedback, etc. The data management unit includes several dimension data tables and several preset number of characters to be read for each dimension. One dimension data table corresponds to one dimension of a credit enterprise. One dimension data table stores information data of all credit enterprises corresponding to that dimension. One dimension corresponds to one preset number of characters to be read. The enterprise profiling unit is used to draw and display enterprise profiles of all creditworthy enterprises. The enterprise profiling unit pre-stores several label box images and label description box images. In one embodiment of the present invention, the label frame image includes circles, rectangles, rhombuses, and triangles, and the label description box image includes circles, rectangles, rhombuses, and triangles; The enterprise profiling unit generates corresponding enterprise profile data for each creditworthy enterprise based on multi-dimensional information data of all creditworthy enterprises. The steps for drawing an enterprise profile for a creditworthy enterprise are as follows: S11: Select one dimension of the credit enterprise as the dimension to be drawn, and obtain the reading strategy of the dimension to be drawn stored in the enterprise profile unit; S12: If the reading strategy for the dimension to be drawn carries the reading type as direct reading, then the image branch for the dimension to be drawn is generated according to the preset first generation rule, as follows: S121: Obtain the preset number of characters E1 to be read and the storage address of the corresponding dimension data table from the reading strategy of the dimension to be drawn. S122: Based on the storage address of the obtained dimension data table, retrieve the information data of the credit enterprise in the dimension to be drawn from the corresponding dimension data table, and according to the preset number of characters to be read E1, extract the first E1 characters of the information data of the credit enterprise in the dimension to be drawn in the order from left to right, and generate the initial display information data of the dimension to be drawn based on the extracted first E1 characters. The information data of the credit enterprise in the dimension to be drawn will be used as the detailed display data of the dimension to be drawn; S123: Select a label box image and a label description box image respectively, fill the label box image and the initial display information data of the dimension to be drawn into the label box image and the label description box image respectively, and connect the filled label box image and the label description box image with a straight line to obtain the portrait branch of the dimension to be drawn. When filling the initial display information data of the dimension to be drawn into the label description box image, a details display button is set at the last character of the initial display information data in the label description box image. The details display button is bound to a trigger event, which stores the details display data of the dimension to be drawn. When an authorized user clicks the details display button, its bound trigger event will be triggered, and the trigger event will display the details display data of the dimension to be drawn held within it to the authorized user for viewing; It should be noted that unless otherwise specified by the creditworthy enterprise, the selected label box image is a circle by default, and the selected label description box image is a rectangle by default. S13: If the reading strategy for the dimension to be drawn carries the reading type of indirect reading, then the image branch for the dimension to be drawn is generated according to the preset second generation rule, as follows: S131: In the reading strategy of the dimension to be drawn, obtain the preset number of characters E1 corresponding to the dimension to be drawn and the storage address of the corresponding dimension derivative table and dimension data table. S132: Based on the storage address of the obtained dimension derived table, find the corresponding dimension derived table, and retrieve the data of the credit enterprise from it, using it as the initial display information data of the dimension to be drawn; S133: Select a label box image and a label description box image respectively, fill the label box image and the initial display information data of the dimension to be drawn into the label box image and the label description box image respectively, and connect the filled label box image and the label description box image with a straight line to obtain the portrait branch of the dimension to be drawn. When the initial display information data of the dimension to be drawn is filled into the label description box image, a details display button is set at the last character of the initial display information data in the label description box image. The details display button is bound to a trigger event, which stores the storage address of the dimension data table corresponding to the dimension to be drawn. S14: Select all dimensions of the credit enterprise as the dimensions to be drawn in sequence according to S11, and generate the profile branches of all dimensions of the credit enterprise in sequence according to S12 to S13. S15: Select a circular label box image, fill the credit company's name into it, and use straight lines to connect all the profile branches of the credit company in all dimensions with the label box image filled with the credit company's name to obtain the credit company's profile. Specifically, connect the label description box image in each profile branch in all dimensions with the label box image filled with the credit company's name. S16: Generate and store enterprise profiles for all creditworthy enterprises according to S11 to S15; The browsing module is used to authorize users to browse the corporate profiles of creditworthy companies; After the currently authorized user enters the search information for the credit enterprise to be viewed, the browsing module retrieves the information and transmits it to the enterprise profile unit. After receiving the transmitted search information, the enterprise profiling unit obtains the name of the credit enterprise carried within it, finds the corresponding credit enterprise's enterprise profile in the enterprise profiling unit, and displays it to the current authorized user for viewing. During the display process, if the displayed enterprises contain a certain dimension's details display button whose trigger event carries the storage address of the dimension data table, the trigger event is immediately triggered. The trigger event will automatically carry the credit enterprise's name to the corresponding dimension data table to retrieve the corresponding dimension's information data. After the information is fully retrieved, when the current authorized user clicks the details display button, the retrieved information data will be displayed to the current authorized user for viewing. In this embodiment, the search information refers to the name of the credit enterprise; The behavior analysis unit periodically analyzes the behavior of authorized users, with the specific analysis as follows: S21: First, select one dimension of a credit enterprise as the dimension to be analyzed, and select a corporate profile of a credit enterprise as the profile to be analyzed. S22: Obtain the trigger frequencies of the dimensions to be analyzed in t behavior cycles, which are respectively labeled as A1, A2, ..., Aa, where a≥1. The trigger frequency of the dimension to be analyzed refers to the number of clicks on the details display button in the label description box corresponding to the dimension to be analyzed in the portrait to be analyzed within this behavior cycle. The t behavior cycles are t behavior cycles traced back from the current behavior cycle to the past; S23: Use the formula , 1≤b≤a to calculate and obtain the discrete value B1 of the trigger frequencies of the dimensions to be analyzed in t behavior cycles. Compare the magnitudes of B1 and B. At this time, A is the average value of Ab, and B is the discrete threshold of the trigger frequencies of the dimension to be analyzed; If B1≥B, delete the corresponding Ab in descending order of |Ab - A|, then calculate and obtain the discrete value B1 of the remaining Ab. At this time, compare the magnitudes of B1 and B again until B1 < B. Re-calibrate the average value of the Ab participating in the calculation of the remaining discrete value B1 at this time as the intention frequency of the dimension to be analyzed; S24: Obtain the data volume size of the information data corresponding to all credit enterprises in the dimension to be analyzed, and calculate and obtain its average value using the sum and average formula, and re-calibrate it as the dimension data volume of the dimension to be analyzed; S25: Select all dimensions of the credit enterprise as the dimensions to be analyzed in sequence according to S21, and calculate and obtain the intention frequencies and dimension data volumes of all dimensions in sequence according to S22 to S25; The behavior analysis module transmits the intention frequencies and dimension data volumes of all dimensions obtained from the analysis to the data management unit for storage; After receiving the intention frequencies and dimension data volumes of all dimensions transmitted, the data management unit generates reading strategies for all dimensions according to the preset determination generation rules, specifically as follows: S31: First, select a dimension of the credit enterprise as the dimension to be evaluated, and obtain the intention frequency C1 and dimension data volume D1 of the dimension to be evaluated; S32: Compare the magnitudes of C1 and PC1, and D1 and PD1 in sequence. PD1 and PD2 are respectively the preset comparison thresholds for the intention frequencies and dimension data volumes of the dimension to be evaluated: S321: If C1 < PC1, directly determine that the reading type of the dimension to be evaluated is direct reading, and generate a reading strategy for the dimension to be evaluated based on the determination result, the preset number of reading characters corresponding to the dimension to be evaluated, and the storage address of its corresponding dimension data table. At this time, do not compare the magnitudes of D1 and PD1 anymore; S322: If C1≥PC1 and D1≥PD1, determine that the reading type of the dimension to be evaluated is indirect reading, and generate a dimension derivative table for the dimension to be evaluated based on the dimension data table corresponding to the dimension to be evaluated according to the preset derivative steps. The derivative steps are as follows: Obtain the preset number of read characters G1 corresponding to the dimension to be evaluated, and based on it, extract the first G1 characters in the information data of each credit enterprise in the dimension to be evaluated from left to right according to the information data of each credit enterprise in the dimension to be evaluated, and generate a dimension derivative table for the dimension to be evaluated and store it; Generate a reading strategy for the dimension to be evaluated based on the determination result, the generated dimension derivative table of the dimension to be evaluated, and the storage address of its corresponding dimension data table; S323: If C1≥PC1 and D1<PD1, it is determined that the reading type of the dimension to be evaluated is direct reading, and a reading strategy for the dimension to be evaluated is generated based on the determination result, the preset number of read characters corresponding to the dimension to be evaluated, and the storage address of its corresponding dimension data table; S33: Select all dimensions of the credit enterprise as the dimensions to be evaluated in sequence according to S31, and generate reading strategies for all dimensions according to S32; The data management unit transmits the generated reading strategies for all dimensions to the enterprise portrait unit for updated storage; In the description of the specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0011] The above has described an embodiment of the present invention in detail, but the content described is only a preferred embodiment of the present invention and cannot be considered as used to limit the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. A data management system, characterized in that, It includes: A data management unit that collects multi-dimensional information data of all credit enterprises; A behavior analysis unit that analyzes the behavior of authorized users browsing the enterprise portraits of credit enterprises. By analyzing the trigger frequencies of authorized users based on each dimension and the data volume sizes of information data in each dimension within t behavior cycles, the intention frequencies and dimension data volumes of all dimensions are obtained; For each dimension, the data management unit compares the corresponding intention frequency and dimension data volume with the preset intention frequency and dimension data volume of the corresponding dimension respectively, and generates a reading strategy for each dimension based on the comparison result; For a credit enterprise, the enterprise portrait unit generates portrait branches for each dimension of the credit enterprise based on the multi-dimensional information data thereof, and draws the enterprise portrait of the credit enterprise based on these portrait branches.

2. The data management system according to claim 1, characterized in that, The multi-dimensional information data of a credit enterprise includes basic information, market, intellectual property, legal litigation records, credit records, investors, human resources, customer and partner relationships, products, and services.

3. A data management system according to claim 1, characterized in that, The steps for the behavior analysis unit to analyze and obtain the intention frequencies and dimension data volumes of all dimensions are as follows: S21: First, select a dimension of a credit enterprise as the dimension to be analyzed, and select an enterprise portrait of a credit enterprise as the portrait to be analyzed; S22: Obtain the trigger frequencies of the dimension to be analyzed in t behavior cycles, and mark them as A1, A2,..., Aa, where a≥1. The trigger frequency of the dimension to be analyzed refers to the number of clicks on the details display button in the label description box corresponding to the dimension to be analyzed in the portrait to be analyzed within this behavior cycle. The t behavior cycles are traced back t behavior cycles from the current behavior cycle to the past; S23: Utilize the formula ,1≤b≤a Calculate and obtain the discrete value B1 of the expected trigger frequency of the dimension to be analyzed in t behavior weeks, compare the size of B1 and B, where A is the average value of Ab, and B is the discrete threshold of the trigger frequency of the dimension to be analyzed. If B1≥B, delete the corresponding Ab in descending order of |Ab - A|, and then calculate and obtain the discrete value B1 of the remaining Ab. At this time, compare B1 and B again until B1 < B, and re-calibrate the average value of the Ab participating in the calculation of the remaining discrete value B1 as the intention frequency of the dimension to be analyzed; S24: Obtain the data volume sizes of the information data corresponding to all credit enterprises in the dimension to be analyzed, and calculate and obtain its average value using the sum and average formula, and re-calibrate it as the dimension data volume of the dimension to be analyzed; S25: Select all dimensions of the credit enterprise as the dimension to be analyzed in sequence according to S21, and calculate and obtain the intention frequencies and dimension data volumes of all dimensions in sequence according to S22 to S24; The behavior analysis module transmits the intention frequencies and dimension data volumes of all dimensions obtained from the analysis to the data management unit for storage.

4. A data management system according to claim 1, characterized in that, The preset determination generation rule for the data management unit to generate a reading strategy for each dimension is as follows: S31: First, select a dimension of a credit enterprise as the dimension to be evaluated, and obtain the intention frequency C1 and dimension data volume D1 of the dimension to be evaluated; S32: Compare C1 with PC1 and D1 with PD1 in sequence. PD1 and PD2 are respectively the intention frequency comparison threshold and dimension data volume comparison threshold preset for the dimension to be evaluated: S321: If C1 < PC1, directly determine that the reading type of the dimension to be evaluated is direct reading, and generate a reading strategy for the dimension to be evaluated based on the determination result, the preset number of reading characters corresponding to the dimension to be evaluated, and the storage address of the corresponding dimension data table. At this time, the comparison of the sizes of D1 and PD1 is no longer performed; S322: If C1 ≥ PC1 and D1 ≥ PD1, determine that the reading type of the dimension to be evaluated is indirect reading, and generate a dimension derivative table for the dimension to be evaluated based on the corresponding dimension data table of the dimension to be evaluated according to the preset derivative steps. The derivative steps are as follows: Obtain the preset number of reading characters G1 corresponding to the dimension to be evaluated, and based on it, extract the first G1 characters in the information data of each credit enterprise in the dimension to be evaluated in the order from left to right according to the information data of each credit enterprise in the dimension to be evaluated, and generate a dimension derivative table for the dimension to be evaluated based on it and store it; Generate a reading strategy for the dimension to be evaluated based on the determination result, the generated dimension derivative table of the dimension to be evaluated, and the storage address of its corresponding dimension data table; S323: If C1 ≥ PC1 and D1 < PD1, determine that the reading type of the dimension to be evaluated is direct reading, and generate a reading strategy for the dimension to be evaluated based on the determination result, the preset number of reading characters corresponding to the dimension to be evaluated, and the storage address of its corresponding dimension data table; S33: Select all dimensions of the credit enterprise as the dimensions to be evaluated in sequence according to S31, and generate reading strategies for all dimensions according to S32.

5. A data management system according to claim 4, characterized in that, The steps for the enterprise portrait unit to draw the enterprise portrait of a credit enterprise are as follows: S11: Select a dimension of the credit enterprise as the dimension to be drawn, and obtain the reading strategy of the dimension to be drawn stored in the enterprise portrait unit; S12: If the reading type carried in the reading strategy of the dimension to be drawn is direct reading, generate a portrait branch of the dimension to be drawn according to the preset first generation rule; S13: If the reading type carried in the reading strategy of the dimension to be drawn is indirect reading, generate a portrait branch of the dimension to be drawn according to the preset second generation rule; S14: Select all dimensions of the credit enterprise as the dimensions to be drawn in sequence according to S11, and generate portrait branches of all dimensions of the credit enterprise in sequence according to S12 to S13; S15: Select a circular label box image, fill the enterprise name of the credit enterprise into it, and use straight lines to connect the portrait branches of all dimensions of the credit enterprise and the label box image filled with the enterprise name of the credit enterprise to obtain the enterprise portrait of the credit enterprise. Specifically, connect the label description box image in each dimension portrait branch and the label box image filled with the enterprise name of the credit enterprise; S16: Generate enterprise portraits of all credit enterprises and store them according to S11 to S15.

6. A data management system according to claim 5, characterized in that, The preset first generation rule for generating the portrait branch of the dimension to be drawn in S12 is as follows: S121: Obtain the preset number of reading characters E1 corresponding to the dimension to be drawn carried in the reading strategy of the dimension to be drawn and the storage address of its corresponding dimension data table; S122: Based on the storage address of the obtained dimension data table, retrieve the information data of the credit enterprise in the dimension to be drawn from the corresponding dimension data table, and according to the preset number of characters to be read E1, extract the first E1 characters of the information data of the credit enterprise in the dimension to be drawn in the order from left to right, and generate the initial display information data of the dimension to be drawn based on the extracted first E1 characters. The information data of the credit enterprise in the dimension to be drawn will be used as the detailed display data of the dimension to be drawn; S123: Select a label box image and a label description box image respectively, fill the label box image and the initial display information data of the dimension to be drawn into the label box image and the label description box image respectively, and connect the filled label box image and the label description box image with a straight line to obtain the portrait branch of the dimension to be drawn. When filling the initial display information data of the dimension to be drawn into the label description box image, a details display button is set at the last character of the initial display information data in the label description box image. The details display button is bound to a trigger event, which stores the details display data of the dimension to be drawn. When an authorized user clicks the details display button, its bound trigger event will be triggered, and the trigger event will display the details of the dimension to be drawn held within it to the authorized user for viewing.

7. A data management system according to claim 5, characterized in that, In step S13, the preset second generation rule for generating the dimensional portrait branch to be drawn is as follows: S131: In the reading strategy of the dimension to be drawn, obtain the preset number of characters E1 corresponding to the dimension to be drawn and the storage address of the corresponding dimension derivative table and dimension data table. S132: Based on the storage address of the obtained dimension derived table, find the corresponding dimension derived table, and retrieve the data of the credit enterprise from it, using it as the initial display information data of the dimension to be drawn; S133: Select a label box image and a label description box image respectively, fill the label box image and the initial display information data of the dimension to be drawn into the label box image and the label description box image respectively, and connect the filled label box image and the label description box image with a straight line to obtain the portrait branch of the dimension to be drawn. When the initial display information data of the dimension to be drawn is filled into the label description box image, a details display button is set at the last character of the initial display information data in the label description box image. The details display button is bound to a trigger event, which stores the storage address of the dimension data table corresponding to the dimension to be drawn. When this event is triggered, it will automatically retrieve the information data of the dimension to be drawn from the corresponding dimension data table based on the storage address of the dimension data table corresponding to the dimension to be drawn, carrying the company name of the credit enterprise. After the information is fully retrieved, it will be displayed to the authorized user for viewing when the authorized user clicks the details display button.

8. A data management system according to claim 7, characterized in that, It also includes a browsing module, which is used to authorize users to search and browse the corporate profiles of creditworthy companies; After the currently authorized user enters the name of the credit enterprise to be viewed, the browsing module retrieves the information and transmits it to the enterprise profile unit. After receiving the transmitted name of the credit enterprise to be viewed, the enterprise profile unit finds the corresponding credit enterprise profile and displays it to the authorized user for viewing. During the display process, if there is a trigger event bound to the details display button of a certain dimension in the corresponding displayed enterprise profile, which carries the storage address of the dimension data table, the trigger event will be automatically triggered.

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