Leasing platform management system

Abstract

The invention relates to the technical field of rental platforms, and discloses a rental platform management system, which comprises a main system and a mobile storage module, and is characterized in that the main system comprises an information acquisition module, an information processing module, a calculation module, an analysis module and an execution module; the whole process from data acquisition to execution is covered by the main system, and high efficiency, accuracy and flexibility of data processing are ensured. And the mobile storage module is independent of the main system and focuses on storing and managing user information, so that the data processing and storage process is clearer and more orderly, the ability of the platform to cope with different service scenes is also improved, and compared with a traditional centralized management system, the system has the advantages that the management efficiency is improved. The separated architecture is remarkably improved in the aspects of data processing efficiency, storage flexibility and system security, so that the overall operation efficiency and service quality of the platform are comprehensively improved.

Description

Technical Field

[0001] This invention relates to the field of rental platform technology, specifically a rental platform management system. Background Art

[0002] A rental platform is an online or physical service system designed to connect users who need to rent items or services with lessors who provide them. Rental platforms typically use a centralized web platform or application that allows users to browse, book, and pay for rental items or services. The range of rental items or services can be very broad, including but not limited to vehicles, houses, equipment, clothing, digital products, etc.

[0003] Current rental platforms exhibit significant shortcomings in user data management. Traditional systems suffer from limitations in data integration and processing capabilities. Lacking effective multi-source data integration and intelligent analysis functions, traditional systems often fail to adapt promptly to changes in market and user needs. This not only leads to inefficient information processing but can also result in a decline in user experience, customer churn, and missed business opportunities. Ultimately, these deficiencies may negatively impact the platform's market competitiveness and business growth. Therefore, enhancing data processing capabilities and achieving intelligent user information management are crucial to solving these problems.

[0004] Therefore, we propose a rental platform management system to address the aforementioned issues. Summary of the Invention

[0005] The purpose of this invention is to provide a rental platform management system to address the limitations of traditional systems in data integration and processing capabilities, as described in the background section. Due to the lack of effective multi-source data integration and intelligent analysis functions, traditional systems often fail to adapt to changes in market and user needs in a timely manner. This not only leads to inefficient information processing but may also result in a decline in user experience, customer churn, and missed business opportunities. Ultimately, these shortcomings may negatively impact the platform's market competitiveness and business growth.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a rental platform management system, comprising a main system and a mobile storage module, wherein the main system includes an information acquisition module, an information processing module, a calculation module, an analysis module, and an execution module;

[0007] The information collection module is used to extract user information from multiple sources in the database;

[0008] The information processing module is used to preprocess the multi-source data extracted by the information acquisition module, and reorganize the preprocessed data into a first dataset, a second dataset, and a third dataset.

[0009] The calculation module is used to integrate the first dataset, the second dataset, and the third dataset to calculate and obtain the user information utility coefficient YHX;

[0010] The analysis module is used to compare the calculated user information utility coefficient YHX with a preset first threshold Y to generate a first comparison result, and to determine whether the current user information has utility based on the first comparison result.

[0011] If the judgment result is that it has utility, the user information utility coefficient YHX is integrated with the preset first threshold Y to generate the utility level coefficient LJX, and the utility level coefficient LJX is compared with the preset second threshold R to generate the second comparison result. The user information utility level is judged based on the second comparison result.

[0012] The execution module creates multiple storage folders based on the second comparison result and categorizes the hierarchical user files.

[0013] The mobile storage module is used to store categorized user information.

[0014] Preferably, the information acquisition module includes a first extraction unit, a second extraction unit, and a third extraction unit;

[0015] The first extraction unit is used to extract credit data under user information, including compliance records, overdue records, usage frequency, account opening duration, and total debt.

[0016] The second extraction unit is used to extract communication intention data from user information, including communication frequency, response speed, and initiative;

[0017] The third extraction unit is used to extract user behavior data under user information, including the number of views, page dwell time, number of clicks, and order conversion rate.

[0018] Preferably, the information processing module includes an information preprocessing unit and an information classification unit;

[0019] The information preprocessing unit is used to preprocess the multi-source data collected by the information acquisition module;

[0020] The information classification unit is used to make the preprocessed data dimensionless and to reorganize the dimensionless data to generate the first dataset, the second dataset, and the third dataset.

[0021] The first dataset includes records of compliance with agreements, overdue records, usage frequency, number of accounts opened, and total debt.

[0022] The records of keeping the agreement are recorded according to the timestamp as: SYL1, SYL2, SYL3, ..., SYL N ;

[0023] Overdue records are categorized by timestamp as: YQL1, YQL2, YQL3, ..., YQL N ;

[0024] Usage frequency is recorded according to timestamps as: YPL1, YPL2, YPL3, ..., YPL N ;

[0025] The number of accounts opened is recorded according to the timestamp as: KHL1, KHL2, KHL3, ..., KHL N ;

[0026] The total amount of debt is recorded as follows, based on the timestamp: ZWL1, ZWL2, ZWL3, ..., ZWL N ;

[0027] The second dataset includes communication frequency, response speed, and initiative;

[0028] The communication frequencies are recorded according to timestamps as: GTL1, GTL2, GTL3, ..., GTL N ;

[0029] Response speeds are denoted by timestamps as: XYS1, XYS2, XYS3, ..., XYS N ;

[0030] Initiatives are recorded as follows, based on their timestamps: ZDS1, ZDS2, ZDS3, ..., ZDS N ;

[0031] The third dataset includes page views, page dwell time, click count, and order conversion rate;

[0032] The number of views is recorded according to the timestamp as: LLS1, LLS2, LLS3, ..., LLS N ;

[0033] Page dwell time is recorded as follows, based on timestamps: YMS1, YMS2, YMS3, ..., YMS N ;

[0034] Click counts are recorded according to timestamps as: DJS1, DJS2, DJS3, ..., DJS N ;

[0035] Order conversion rates are recorded as follows, based on timestamps: DDS1, DDS2, DDS3, ..., DDS N .

[0036] Preferably, the calculation module calculates and obtains the user information utility coefficient YHX using the following formula;

[0037]

[0038] In the formula: XYX is the credit reference coefficient obtained by integrating and calculating the first dataset; GTX is the communication willingness reference coefficient obtained by integrating and calculating the second dataset; and XWX is the user behavior reference coefficient obtained by integrating and calculating the third dataset.

[0039] a1, a2, and a3 are weight values, and these values ​​are adjusted and set by the user.

[0040] Preferably, the calculation module calculates and obtains the credit reference coefficient XYX, the communication willingness reference coefficient GTX, and the user behavior reference coefficient XWX using the following formulas respectively;

[0041]

[0042] In the formula: b1, b2, b3, b4, b5, c1, c2, c3, d1, d2, and d3 are weight values, and the values ​​of b1, b2, b3, b4, b5, c1, c2, c3, d1, d2, and d3 are adjusted and set by the user, and log is a logarithmic function;

[0043] SYL N YQL N YPL N KHL N ZWL N GTL N XYS N ZDS N ,LLS N YMS N DJS N and DDS N These are, respectively, the compliance records, overdue records, usage frequency, number of accounts opened, total debt, communication frequency, response speed, initiative, number of views, page dwell time, number of clicks, and order conversion rate under N timestamps;

[0044] SYL N-1 YQL N-1 YPL N-1 KHL N-1 ZWL N-1 GTL N-1 XYS N-1 ZDS N-1 ,LLS N-1 YMSN-1 , DJS N-1 and DDS N-1 are respectively the compliance records, overdue records, usage frequency, number of accounts opened, total debt, communication frequency, response speed, initiative, number of page views, page stay time, number of clicks, and order conversion rate at the N-1 time stamp.

[0045] Preferably, the first comparison result generated by the analysis module is as follows;

[0046] When YHX < Y; it represents that the current user information does not have effectiveness;

[0047] When YHX > Y; it represents that the current user information has effectiveness.

[0048] Preferably, the second comparison result generated by the analysis module is as follows;

[0049] LJZ < R

[0050] When this is the case, it represents that the current user information is at the first-level retention level;

[0051] When R ≤ LJZ < R × 110%, it represents that the current user information is at the second-level retention level;

[0052] When LJZ ≥ R × 110%, it represents that the current user information is at the third-level retention level.

[0053] Preferably, the utility level coefficient is obtained through the following formula calculation;

[0054]

[0055] In the formula: YHX is the user information utility coefficient, and Y is the first threshold.

[0056] Preferably, the specific classification method of the execution module for user files is as follows;

[0057] For the user information at the first-level retention level, the execution module creates a green folder starting with Y, registers and stores the user information at the first-level retention level in this file, and saves the folder in the main system;

[0058] For the user information at the second-level retention level, the execution module creates a yellow folder starting with R, registers and stores the user information at the second-level retention level in this file, and saves the folder in the main system and the mobile storage module;

[0059] For the user information at the third-level retention level, the execution module creates a red folder starting with S, registers and stores the user information at the first-level retention level in this file, and saves the folder in the mobile storage module.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] 1. This system separates the main system and the mobile storage module. The main system covers the entire process from data acquisition to execution, ensuring high efficiency, accuracy, and flexibility in data processing. The mobile storage module, independent of the main system, focuses on storing and managing user information. This separation design not only makes the data processing and storage workflow clearer and more organized but also enhances the platform's ability to handle different business scenarios. Compared to traditional centralized management systems, this separate architecture significantly improves data processing efficiency, storage flexibility, and system security, thereby comprehensively enhancing the platform's overall operational efficiency and service quality.

[0062] 2. This system can manage and analyze user information more accurately and comprehensively, greatly improving the precision and efficiency of data analysis compared to traditional, coarse data processing methods. This improvement not only enhances the platform's capabilities in user credit assessment, communication willingness analysis, and behavioral pattern recognition, but also strengthens the platform's real-time response capability to dynamic user data.

[0063] 3. This system, through multi-dimensional comprehensive calculations, not only improves the accuracy of user information utility assessment but also enhances the system's flexibility and adaptability. This enables the platform to more accurately identify users' true needs and behavioral patterns, thereby providing users with more personalized and precise services. These improvements effectively enhance the platform's overall capabilities in user credit assessment, communication behavior analysis, and user behavior recognition, thus giving the platform a significant advantage in market competition.

[0064] 4. This system seamlessly integrates user information utility evaluation, hierarchical management, and categorized storage, achieving higher accuracy and efficiency in user data processing and management. Compared to traditional single-data processing methods, this multi-dimensional and multi-layered management approach effectively enhances the platform's data processing capabilities and operational efficiency, further optimizes the user experience, and strengthens the platform's competitiveness in information security and data management. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a system flowchart of the present invention.

[0066] In the diagram: 1. Information acquisition module; 11. First extraction unit; 12. Second extraction unit; 13. Third extraction unit; 2. Information processing module; 21. Information preprocessing unit; 22. Information classification unit; 3. Calculation module; 4. Analysis module; 5. Execution module; 6. Mobile storage module. Detailed Implementation

[0067] 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.

[0068] Example 1: Please refer to Figure 1 A rental platform management system includes a main system and a mobile storage module 6. The main system includes an information acquisition module 1, an information processing module 2, a calculation module 3, an analysis module 4, and an execution module 5.

[0069] Information collection module 1 is used to extract user information from multiple sources in the database;

[0070] Information processing module 2 is used to preprocess the multi-source data extracted by information acquisition module 1, and reorganize the preprocessed data into a first dataset, a second dataset, and a third dataset;

[0071] Calculation module 3 is used to integrate the first dataset, the second dataset, and the third dataset to calculate the user information utility coefficient YHX;

[0072] The analysis module 4 is used to compare the calculated user information utility coefficient YHX with the preset first threshold Y to generate a first comparison result, and to determine whether the current user information has utility based on the first comparison result;

[0073] If the judgment result is that it has utility, the user information utility coefficient YHX is integrated with the preset first threshold Y to generate the utility level coefficient LJX, and the utility level coefficient LJX is compared with the preset second threshold R to generate the second comparison result. The user information utility level is judged based on the second comparison result.

[0074] Based on the second comparison result, execution module 5 creates multiple storage folders and categorizes the hierarchical user files;

[0075] Mobile storage module 6 is used to store categorized user information.

[0076] In this embodiment: Information Acquisition Module 1 is primarily responsible for extracting user information from multiple sources within the database. By integrating data from multiple sources, Information Acquisition Module 1 achieves comprehensive acquisition of user information, ensuring data integrity and breadth. This function effectively solves the problems of single data sources and incomplete information in traditional systems, thus laying a solid foundation for subsequent data processing and analysis. Through the operation of this module, the platform can accurately collect user information from different channels, providing strong support for further optimizing the user experience.

[0077] Information processing module 2 preprocesses the extracted multi-source data and reorganizes it into three datasets: Dataset 1, Dataset 2, and Dataset 3. This preprocessing step is crucial for improving data quality and usability. Through cleaning, transformation, and standardization, information processing module 2 eliminates noise and redundant information in the data, ensuring data consistency and accuracy. Ultimately, this module provides a structured and easy-to-use dataset for subsequent calculations and analysis, improving the overall processing efficiency of the system.

[0078] Calculation module 3 is responsible for integrating the preprocessed datasets and calculating the user information utility coefficient YHX. By integrating and calculating multiple datasets, calculation module 3 can comprehensively evaluate the utility of user information, providing a quantitative basis for the classification and management of user information. This module solves the problem of inaccurate evaluation of user information in traditional systems. Through precise calculation, it provides accurate utility coefficients for the subsequent analysis module 4, enhancing the system's insight into user data.

[0079] Analysis module 4 compares the user information utility coefficient YHX with a preset first threshold Y to determine the utility of the user information and further generate a utility magnitude coefficient. This module plays a decision support role in the system. Through precise comparative analysis, it can identify user information with actual utility and quantify and classify its utility level. This function significantly improves the system's ability to evaluate user information, ensuring that the platform can focus on processing and utilizing truly valuable data.

[0080] Based on the comparison results generated by the analysis module 4, execution module 5 establishes multiple storage folders and categorizes and manages the hierarchical user files. This module makes the storage and management of user information more organized. Through hierarchical categorization, the platform can manage user data more efficiently, improving the standardization and operability of data storage. The introduction of execution module 5 solves the problem of chaotic data classification and management in traditional systems and further optimizes the platform's operation process.

[0081] The mobile storage module 6 is used to store categorized user information. The addition of this module makes data storage more flexible and convenient. By storing categorized user information on portable devices, the platform can better respond to changes in data storage needs, while also facilitating secure data backup and migration. The mobile storage module 6 solves the problem of inflexible data storage in traditional systems, improving the resilience and reliability of data management.

[0082] This system separates the main system and the mobile storage module 6. The main system covers the entire process from data acquisition to execution, ensuring high efficiency, accuracy, and flexibility in data processing. The mobile storage module 6, independent of the main system, focuses on storing and managing user information. This separation design not only makes the data processing and storage workflow clearer and more organized but also enhances the platform's ability to handle different business scenarios. Compared to traditional centralized management systems, this separate architecture significantly improves data processing efficiency, storage flexibility, and system security, thereby comprehensively enhancing the platform's overall operational efficiency and service quality.

[0083] Example 2: Please refer to Figure 1 The information acquisition module 1 includes a first extraction unit 11, a second extraction unit 12, and a third extraction unit 13;

[0084] The first extraction unit 11 is used to extract credit data under user information, including compliance records, overdue records, usage frequency, account opening duration, and total debt.

[0085] The second extraction unit 12 is used to extract communication intention data under user information, including communication frequency, response speed and initiative.

[0086] The third extraction unit 13 is used to extract user behavior data under user information, including the number of views, page dwell time, number of clicks, and order conversion rate.

[0087] In this embodiment, the information collection module 1 comprises three independent extraction units, each specifically responsible for extracting a particular type of user data. The first extraction unit 11 focuses on user credit data, including compliance records, overdue records, usage frequency, account opening duration, and total debt, enabling the platform to more accurately assess user creditworthiness. The second extraction unit 12 focuses on user communication willingness data, such as communication frequency, response speed, and initiative, thereby providing the platform with in-depth analysis support regarding user communication behavior. The third extraction unit 13 is responsible for extracting user behavior data, covering page views, page dwell time, click count, and order conversion rate, helping the platform to comprehensively understand user behavior patterns and preferences.

[0088] By clearly defining the data extraction function, the system can more accurately capture and analyze user information from different dimensions. Compared to traditional single data extraction methods, this design not only improves the efficiency of data collection but also enhances the targeting and depth of data analysis. Through refined extraction units, the system demonstrates higher accuracy and flexibility when processing complex multidimensional data, thus providing a more accurate and reliable foundation for user credit assessment, communication willingness analysis, and behavioral pattern recognition.

[0089] Example 3: Please refer to Figure 1 The information processing module 2 includes an information preprocessing unit 21 and an information classification unit 22;

[0090] Information preprocessing unit 21 is used to preprocess the multi-source data collected by information acquisition module 1;

[0091] The information classification unit 22 is used to make the preprocessed data dimensionless and to reorganize the dimensionless data to generate the first dataset, the second dataset, and the third dataset.

[0092] The first dataset includes records of compliance with agreements, overdue records, usage frequency, number of accounts opened, and total debt.

[0093] The records of keeping the agreement are recorded according to the timestamp as: SYL1, SYL2, SYL3, ..., SYL N ;

[0094] Overdue records are categorized by timestamp as: YQL1, YQL2, YQL3, ..., YQL N ;

[0095] Usage frequency is recorded according to timestamps as: YPL1, YPL2, YPL3, ..., YPL N ;

[0096] The number of accounts opened is recorded according to the timestamp as: KHL1, KHL2, KHL3, ..., KHL N ;

[0097] The total amount of debt is recorded as follows, based on the timestamp: ZWL1, ZWL2, ZWL3, ..., ZWL N ;

[0098] The second dataset includes communication frequency, response speed, and initiative;

[0099] The communication frequencies are recorded according to timestamps as: GTL1, GTL2, GTL3, ..., GTL N ;

[0100] Response speeds are denoted by timestamps as: XYS1, XYS2, XYS3, ..., XYS N ;

[0101] Initiatives are recorded as follows, based on their timestamps: ZDS1, ZDS2, ZDS3, ..., ZDS N ;

[0102] The third dataset includes page views, page dwell time, click count, and order conversion rate;

[0103] The number of views is recorded according to the timestamp as: LLS1, LLS2, LLS3, ..., LLS N ;

[0104] Page dwell time is recorded as follows, based on timestamps: YMS1, YMS2, YMS3, ..., YMS N ;

[0105] Click counts are recorded according to timestamps as: DJS1, DJS2, DJS3, ..., DJS N ;

[0106] Order conversion rates are recorded as follows, based on timestamps: DDS1, DDS2, DDS3, ..., DDS N .

[0107] In this embodiment, the information processing module 2 consists of an information preprocessing unit 21 and an information classification unit 22, each optimized for data preprocessing and classification respectively. The information preprocessing unit 21 is responsible for preprocessing the multi-source data from the information acquisition module 1. Through cleaning and normalization operations, noise and redundancy in the data are eliminated, ensuring data consistency and accuracy. The information classification unit 22 further performs dimensionless processing on the preprocessed data and reorganizes it according to the characteristics of the data, generating three independent datasets: a first dataset, a second dataset, and a third dataset.

[0108] By introducing dimensionless processing and timestamps, data standardization was achieved, greatly enhancing data comparability and the system's computational capabilities. The first dataset encompasses user credit data and is finely labeled with timestamps, enabling the system to dynamically track and analyze user credit trends. The second dataset focuses on communication intention data, also labeled with timestamps, improving the ability to observe and analyze user communication behavior in detail. The third dataset processes user behavior data, tracking user behavior patterns on the platform through timestamps, laying the foundation for further user behavior analysis.

[0109] Through this refined data processing approach, the system can manage and analyze user information more accurately and comprehensively, significantly improving the precision and efficiency of data analysis compared to traditional, coarse data processing methods. This improvement not only enhances the platform's capabilities in user credit assessment, communication willingness analysis, and behavioral pattern recognition, but also strengthens its real-time responsiveness to dynamic user data.

[0110] Example 4: Please refer to Figure 1 The calculation module 3 calculates and obtains the user information utility coefficient YHX using the following formula;

[0111]

[0112] In the formula: XYX is the credit reference coefficient obtained by integrating and calculating the first dataset; GTX is the communication willingness reference coefficient obtained by integrating and calculating the second dataset; and XWX is the user behavior reference coefficient obtained by integrating and calculating the third dataset.

[0113] a1, a2, and a3 are weight values, and these values ​​are adjusted and set by the user.

[0114] Calculation module 3 calculates and obtains the credit reference coefficient XYX, the communication willingness reference coefficient GTX, and the user behavior reference coefficient XWX using the following formulas respectively;

[0115]

[0116] In the formula: b1, b2, b3, b4, b5, c1, c2, c3, d1, d2, and d3 are weight values, and the values ​​of b1, b2, b3, b4, b5, c1, c2, c3, d1, d2, and d3 are adjusted and set by the user, and log is a logarithmic function;

[0117] SYL N YQL N YPL N KHL N ZWL N GTL N XYS N ZDS N ,LLS N YMS N DJS N and DDS N These are, respectively, the compliance records, overdue records, usage frequency, number of accounts opened, total debt, communication frequency, response speed, initiative, number of views, page dwell time, number of clicks, and order conversion rate under N timestamps;

[0118] SYL N-1 YQLN-1 , YPL N-1 , KHL N-1 , ZWL N-1 , GTL N-1 , XYS N-1 , ZDS N-1 , LLS N-1 , YMS N-1 , DJS N-1 and DDS N-1 are respectively the compliance records, overdue records, usage frequency, number of accounts opened, total debt, communication frequency, response speed, initiative, number of page views, page stay time, number of clicks, and order conversion rate at the N-1 time stamp.

[0119] In this embodiment: The calculation module 3 performs comprehensive calculations on user information through multiple sets of complex formulas to obtain the user information utility coefficient YHX. In this process, the module calculates the credit reference coefficient XYX, the communication willingness reference coefficient GTX, and the user behavior reference coefficient XWX respectively, and allows the weight values of each parameter to be customized according to the actual needs of the user. The design of this function enables the system to adjust the weights according to different business scenarios or strategies, so as to flexibly respond to different user needs and market changes.

[0120] In the calculation of the credit reference coefficient XYX, the system integrates factors such as compliance records, overdue records, usage frequency, number of accounts opened, and total debt in the first data set, and introduces the logarithmic function log for dynamic adjustment, so that the calculation results can more accurately reflect the user's credit history and its changing trend. The calculation of the communication willingness reference coefficient GTX focuses on three dimensions: communication frequency, response speed, and initiative. By adjusting the weights, the system can accurately reflect the user's communication tendency. The user behavior reference coefficient XWX is calculated based on the user's number of page views, page stay time, number of clicks, and order conversion rate, further enhancing the insight into the user's online behavior.

[0121] Through multi-dimensional comprehensive calculations, this system not only improves the accuracy of user information utility evaluation, but also enhances the flexibility and adaptability of the system, enabling the platform to more accurately identify the true needs and behavior patterns of users, and then providing more personalized and accurate services for users. These improvements effectively enhance the overall capabilities of the platform in user credit assessment, communication behavior analysis, and user behavior recognition, thus enabling the platform to gain a significant advantage in the market competition.

[0122] Embodiment Five: Please refer to Figure 1 , the first comparison result generated by the analysis module 4 is as follows;

[0123] When YHX < Y; it means that the current user information does not have utility;

[0124] When YHX > Y; it represents that the current user information has effectiveness.

[0125] The second comparison result generated by the analysis module 4 is as follows;

[0126] LJZ < R

[0127] When [condition not specified], it represents that the current user information is at the first-level retention level;

[0128] When R ≤ ≤ LJZ < R × 110%, it represents that the current user information is at the second-level retention level;

[0129] When LJZ ≥ R × 110%, it represents that the current user information is at the third-level retention level.

[0130] The utility level coefficient is obtained through the following formula;

[0131]

[0132] In the formula: YHX is the user information utility coefficient, and Y is the first threshold.

[0133] The execution module 5 classifies the user files in the following specific ways;

[0134] For the user information at the first-level retention level, the execution module 5 creates a green folder starting with Y, registers and stores the user information at the first-level retention level in this file, and saves the folder in the main system;

[0135] For the user information at the second-level retention level, the execution module 5 creates a yellow folder starting with R, registers and stores the user information at the second-level retention level in this file, and saves the folder in the main system and the mobile storage module 6;

[0136] For the user information at the third-level retention level, the execution module 5 creates a red folder starting with S, registers and stores the user information at the first-level retention level in this file, and saves the folder in the mobile storage module 6.

[0137] In this embodiment: Through the first comparison result and the second comparison result generated by the analysis module 4, the effectiveness and retention level of the user information can be effectively evaluated, thus providing a solid foundation for the subsequent classification management of the user information. This multi-level evaluation method enables the system to have higher accuracy and flexibility when processing user data.

[0138] The first comparison result generated by analysis module 4 quickly determines whether the current user information is useful by comparing the user information utility coefficient YHX with a preset first threshold Y. This improved function allows the system to automatically filter out user information that is valuable to the platform, avoiding the accumulation of invalid data and improving the overall operating efficiency of the system. Next, through the second comparison result, the system can further subdivide the useful user information into first-level, second-level, and third-level retention levels. This detailed hierarchical management not only improves the quality of user information management but also enhances the system's ability to provide precise services to different user groups.

[0139] Based on the retention level results generated by the analysis module 4, execution module 5 adopts different classification and storage methods for user information at different retention levels. For user information at the first retention level, execution module 5 creates a green folder and stores it in the main system to ensure efficient use of this core user information. For user information at the second retention level, execution module 5 creates a yellow folder and stores it in both the main system and the mobile storage module 6, achieving redundant backup of information and increasing data security and availability. For user information at the third retention level, execution module 5 creates a red folder and stores it in the mobile storage module 6 for efficient retrieval when needed. This color-coded classification management method not only improves the visual management of user information but also simplifies the archiving and retrieval process for user information at different retention levels.

[0140] This system seamlessly integrates user information utility assessment, hierarchical management, and categorized storage, achieving higher accuracy and efficiency in user data processing and management. Compared to traditional single-data processing methods, this multi-dimensional and multi-layered management approach effectively enhances the platform's data processing capabilities and operational efficiency, further optimizes the user experience, and strengthens the platform's competitiveness in information security and data management.

[0141] The contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0142] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A rental platform management system, characterized by: It comprises a main system and a mobile storage module (6), wherein the main system comprises an information acquisition module (1), an information processing module (2), a calculation module (3), an analysis module (4) and an execution module (5); The information collection module (1) is used to extract multi-source data from user information in the database; The information processing module (2) is used to pre-process the multi-source data extracted by the information acquisition module (1), and reorganize the pre-processed data into a first data set, a second data set, and a third data set; The calculation module (3) is used to integrate the first data set, the second data set and the third data set to calculate and obtain the user information utility coefficient YHX; The analysis module (4) is used to compare the calculated user information utility coefficient YHX with a preset first threshold value Y, thereby generating a first comparison result, and judging whether the current user information is useful according to the first comparison result; If the judgment result is that the information is useful, the user information utility coefficient YHX is integrated with the preset first threshold value Y to generate a utility magnitude coefficient LJX, and the utility magnitude coefficient LJX is compared with the preset second threshold value R to generate a second comparison result, and the user information utility magnitude is judged according to the second comparison result; The execution module (5) establishes multiple storage folders according to the second comparison result, and classifies the classified user files; The mobile storage module (6) is used to store classified user information.

2. A rental platform management system according to claim 1, characterized in that: The information collection module (1) comprises a first extraction unit (11), a second extraction unit (12) and a third extraction unit (13); The first extraction unit (11) is used to extract credit data under the user information, including compliance records, overdue records, usage frequency, account opening time and total debt; The second extraction unit (12) is used to extract communication intention data under the user information, including communication frequency, response speed and initiative; The third extraction unit (13) is used to extract user behavior data under the user information, including the number of views, page dwell time, number of clicks and order conversion rate.

3. A rental platform management system according to claim 2, characterized in that: The information processing module (2) comprises an information preprocessing unit (21) and an information classification unit (22); The information preprocessing unit (21) is used to preprocess the multi-source data collected by the information collection module (1); The information classification unit (22) is used to perform dimensionless conversion on the preprocessed data and to reorganize the dimensionless data, thereby generating a first data set, a second data set and a third data set; The first data set includes compliance records, overdue records, usage frequency, number of accounts opened, and total debt; The compliance records are recorded according to the timestamps as follows: SYL1, SYL2, SYL3, ..., SYL N ; Overdue records are recorded according to timestamps as: YQL1, YQL2, YQL3, ..., YQL N ; The usage frequencies are recorded according to the timestamps as: YPL1, YPL2, YPL3, ..., YPL N ; The number of accounts opened is recorded according to the timestamp: KHL1, KHL2, KHL3, ..., KHL N ; The total debt is recorded according to the timestamp as: ZWL1, ZWL2, ZWL3, ..., ZWL N ; The second data set included communication frequency, responsiveness, and proactivity; The communication frequencies are recorded according to the timestamps as: GTL1, GTL2, GTL3, ..., GTL N ; The response speed is recorded according to the timestamp as: XYS1, XYS2, XYS3, ..., XYS N ; The initiative is recorded according to the timestamp: ZDS1, ZDS2, ZDS3, ..., ZDS N ; The third data set includes the number of views, page dwell time, number of clicks, and order conversion rate; The number of views is recorded according to the timestamp: LLS1, LLS2, LLS3, ..., LLS N ; The page dwell time is recorded according to the timestamp as: YMS1, YMS2, YMS3, ..., YMS N ; The number of clicks is recorded according to the timestamp: DJS1, DJS2, DJS3, ..., DJS N ; The order conversion rates are recorded according to the timestamps as: DDS1, DDS2, DDS3, ..., DDS N .

4. A rental platform management system according to claim 3, characterized in that: The calculation module (3) calculates and obtains the user information utility YHX coefficient through the following formula; Where: XYX is the credit reference coefficient obtained by integrating and calculating from the first data set, GTX is the communication willingness reference coefficient obtained by integrating and calculating from the second data set, and XWX is the user behavior reference coefficient obtained by integrating and calculating from the third data set; a1, a2, and a3 are weight values, and the values of a1, a2, and a3 are adjusted and set by the user.

5. A rental platform management system according to claim 4, characterized in that: The calculation module (3) calculates and obtains the credit reference coefficient XYX, the communication willingness reference coefficient GTX, and the user behavior reference coefficient XWX respectively through the following formulas; Where: b1, b2, b3, b4, b5, c1, c2, c3, d1, d2, and d3 are weight values, and the values of b1, b2, b3, b4, b5, c1, c2, c3, d1, d2, and d3 are adjusted and set by the user, and log is the logarithmic function; SYL N , YQL N , YPL N , KHL N , ZWL N 、GTL N 、XYS N 、ZDS N ,LLS N 、YMS N 、DJS N and DDS N They are the compliance records, overdue records, usage frequency, number of accounts opened, total debt, communication frequency, response speed, initiative, number of views, page dwell time, number of clicks, and order conversion rate at N timestamps; SYL N-1 , YQL N-1 , YPL N-1 , KHL N-1 , ZWL N-1 、GTL N-1 、XYS N-1 、ZDS N-1 ,LLS N-1 、YMS N-1 、DJS N-1 and DDS N-1 They are compliance records, overdue records, usage frequency, number of accounts opened, total debt, communication frequency, response speed, initiative, number of views, page dwell time, number of clicks and order conversion rate under the N-1 timestamp.

6. A rental platform management system according to claim 5, characterized in that: The first comparison result generated by the analysis module (4) is as follows; When YHX < Y; it represents that the current user information does not have utility; When YHX > Y; it represents that the current user information has utility.

7. A rental platform management system according to claim 6, characterized in that: The second comparison result generated by the analysis module (4) is as follows; When LJZ < R, it represents that the current user information is at the first-level retention level; When R ≤ LJZ < R × 110%, it represents that the current user information is at the second-level retention level; When LJZ ≥ R × 110%, it represents that the current user information is at the third-level retention level.

8. A rental platform management system according to claim 7, characterized in that: The utility level coefficient is obtained through the following formula calculation; Where: YHX is the user information utility coefficient, and Y is the first threshold.

9. A rental platform management system according to claim 8, characterized in that: The execution module (5) classifies the user files in the following specific ways; For the user information at the first-level retention level, the execution module (5) creates a green folder starting with Y, registers and stores the user information at the first-level retention level in this file, and saves the folder in the main system; For the user information at the second-level retention level, the execution module (5) creates a yellow folder starting with R, registers and stores the user information at the second-level retention level in this file, and saves the folder in the main system and the mobile storage module (6); For the user information at the third-level retention level, the execution module (5) creates a red folder starting with S, registers and stores the user information at the first-level retention level in this file, and saves the folder in the mobile storage module (6).

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