Data sharing platform system based on data extraction and system modeling

Abstract

The invention discloses a data sharing platform system based on data extraction and system modeling, and the system comprises a first determination module which is used for obtaining a data calling request of a user, and determining a calling data type and a calling data size of the user according to the data calling request; the extraction module is used for determining a data extraction mode according to the data type and the data calling amount, and extracting expected data from the database through the data extraction mode; the second determination module is used for determining an analysis form of expected data according to the data operation requirements of the user and determining modeling parameters according to the analysis form; and the display module is used for constructing a data processing model for the expected data based on the modeling parameters, and displaying the data processing parameters for the expected data through the data processing model. It can be guaranteed that the finally displayed result is the result obtained after data analysis and processing instead of pure data, the use experience of a user is improved, and the service requirements of the user are met.

Description

Technical Field

[0001] The present invention relates to the technical field, and in particular to a data sharing platform system based on data extraction and system modeling. Background Art

[0002] With the rapid development of the Internet and information technology, more and more organizations and institutions have accumulated a large amount of data resources. These data contain valuable knowledge and insights. However, due to the barriers and isolation of data, many data cannot be fully utilized and shared. Data mainly exists within each organization, and the format, standards and permissions are also different, which makes it difficult for data to be interoperable. In addition, data security and privacy protection are also a major problem facing data sharing. In order to solve these problems, data sharing platforms have emerged. Data sharing platforms aim to provide a safe and reliable environment for different organizations and individuals to promote data sharing and communication. By adopting advanced technical means such as encryption, identity authentication, access control, etc., data sharing platforms ensure the security of data during transmission and storage. At the same time, data sharing platforms also provide functions such as data integration, processing and analysis, so that users can better understand and use data. However, it has limitations. It can only provide simple data sharing services and cannot effectively analyze and process data according to users' data needs, which reduces the user experience and data value. Summary of the invention

[0003] In response to the above-mentioned problems, the present invention provides a data sharing platform system based on data extraction and system modeling to solve the problem that the existing knowledge sharing platform mentioned in the background technology can only provide simple data sharing services, cannot effectively analyze and process data according to the user's data needs, and reduces the user experience and data value.

[0004] A data sharing platform system based on data extraction and system modeling, the system comprising:

[0005] A first determination module is used to obtain a data retrieval request from a user and determine the type and amount of data to be retrieved by the user according to the data retrieval request;

[0006] The extraction module is used to determine the data extraction method according to the data type and the data retrieved amount, and extract the expected data from the database through the data extraction method;

[0007] The second determination module is used to determine the analysis form for the expected data according to the data operation requirements of the user, and determine the modeling parameters according to the analysis form;

[0008] The display module is used to construct a data processing model for the expected data based on the modeling parameters, and to display the data processing parameters for the expected data through the data processing model.

[0009] Preferably, before the first determining module obtains the user's data retrieval request and determines the type and amount of data to be retrieved by the user according to the data retrieval request, the system is further configured to:

[0010] Obtain the user's platform login information, and determine the user's identity level and authority level based on the platform login information;

[0011] Determine the user's free operation authority and audit operation authority based on identity level and authority level;

[0012] Obtain the target data levels corresponding to the user's free operation permissions and audit operation permissions respectively, and determine the current data level of the expected data according to the user's data retrieval request;

[0013] The user's access permissions for the desired data are determined based on the current data level and the target data levels corresponding to the user's free operation permissions and audit operation permissions.

[0014] Preferably, if the access permission is a free operation permission, it is determined that the user meets the data access permission requirements;

[0015] If the access permission is the review operation permission, obtain the account status of the reviewer, and filter out the online reviewers based on the account status;

[0016] Generate permission approval request to the online status reviewer account and receive feedback information, and obtain approval results based on the feedback information;

[0017] If the approval result is failed, it is determined that the user does not meet the data retrieval permission requirements. If the approval result is passed, it is determined that the user meets the data retrieval permission requirements.

[0018] Preferably, the first determining module includes:

[0019] The first determination submodule is used to obtain the user's data retrieval request through the data query window of the data sharing platform, and determine the data source subject according to the data retrieval request;

[0020] The second determination submodule is used to obtain the data attributes and data format of the output data of the data source subject, and determine the data type according to the data attributes and data format;

[0021] The acquisition submodule is used to obtain the time period description parameters and distribution description parameters of the data that the user expects to retrieve;

[0022] The third determination submodule is used to determine the data retrieval amount according to the time period description parameter and distribution description parameter of the expected data retrieval and the volume of the unit memory of the expected data retrieval.

[0023] Preferably, the extraction module comprises:

[0024] A fourth determination submodule is used to determine a data query method according to the data retrieved amount, and determine an arrangement method of the expected data according to the data query method;

[0025] A fifth determination submodule is used to determine the limiting factors of data extraction according to the arrangement of the expected data, and determine the queue mechanism when extracting data according to the limiting factors;

[0026] A sixth determination submodule, configured to select an adapted data extraction method according to the queue mechanism and determine a data format of the expected data according to the data type;

[0027] The extraction submodule is used to locate the target database according to the data format and extract the expected data from the target database through data extraction.

[0028] Preferably, the second determining module includes:

[0029] The seventh determination submodule is used to obtain the user's data operation instruction, analyze the data operation instruction to determine the user's data operation requirements, and the data operation requirements include: data screening, data comparison, data statistics, data summarization and data classification;

[0030] An eighth determination submodule is used to determine an execution mode for the expected data according to the data operation requirements, and determine an analysis form for the expected data according to the execution mode;

[0031] A definition submodule is used to determine data qualitative objectives and data quantitative objectives based on the analysis form, and define a data model according to the data qualitative objectives and data quantitative objectives;

[0032] The ninth determination submodule is used to obtain model variables of the data model and determine modeling parameters according to the model variables.

[0033] Preferably, the display module comprises:

[0034] The tenth determination submodule is used to retrieve the data processing model architecture according to the analysis form and determine the model algorithm and indicator optimization parameters according to the modeling parameters;

[0035] A construction submodule is used to construct a data processing model for expected data based on the model algorithm and indicator optimization parameters;

[0036] The import submodule is used to import the expected data and model output targets into the data processing model to obtain the data processing parameters of the model output;

[0037] The display submodule is used to determine the conclusion form of the data processing parameters, determine the display method according to the conclusion form, and display the data processing parameters through the display method.

[0038] Preferably, the system is also used for:

[0039] Add the user's data retrieval request to the task queue, and filter out the data retrieval requests for the same batch of data in the task queue;

[0040] Activate multiple transmission channels and obtain the IP address of each user according to the data retrieval request, and determine the data transmission distance according to the IP address;

[0041] Generate a user list based on the principle of far to near according to the data transmission distance;

[0042] Synchronize data transmission for users in the user list and detect data transmission status for real-time updates.

[0043] Preferably, the system is also used for:

[0044] Generate a data retrieval statistics log for each user based on the historical retrieval data of the user, and determine the data reserve of each user based on the data retrieval statistics log;

[0045] Generate a data retrieval restriction condition parameter for each user based on the data reserve of each user and the data retrieval frequency of the user;

[0046] Generate data retrieval period restriction parameters and data volume restriction parameters for each user according to the data retrieval restriction condition parameters;

[0047] Control each user's subsequent data retrieval operations based on the data retrieval period limit parameters and data volume limit parameters.

[0048] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0049] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0051] Figure 1A schematic diagram of the structure of a data sharing platform system based on data extraction and system modeling provided by the present invention;

[0052] Figure 2 A schematic diagram of the structure of a first determination module in a data sharing platform system based on data extraction and system modeling provided by the present invention;

[0053] Figure 3 This is a schematic diagram of the structure of the second determination module in the data sharing platform system based on data extraction and system modeling provided by the present invention. DETAILED DESCRIPTION

[0054] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0055] With the rapid development of the Internet and information technology, more and more organizations and institutions have accumulated a large amount of data resources. These data contain valuable knowledge and insights, which can bring great value to corporate decision-making, scientific research, government governance and other fields. However, due to the barriers and isolation of data, many data cannot be fully utilized and shared. Data mainly exists within each organization, and the format, standards and permissions are also different, which makes it difficult for data to be interoperable. In addition, data security and privacy protection are also a major problem facing data sharing. In order to solve these problems, data sharing platforms have emerged. Data sharing platforms are designed to provide a safe and reliable environment for different organizations and individuals to promote data sharing and communication. By adopting advanced technical means, such as encryption, identity authentication, access control, etc., data sharing platforms ensure the security of data during transmission and storage. At the same time, data sharing platforms also provide functions such as data integration, processing and analysis, so that users can better understand and use data. However, it has limitations. It can only provide simple data sharing services, and cannot effectively analyze and process data according to the data needs of users, which reduces the user experience and data value. In order to solve the above problems, this embodiment discloses a data sharing platform system based on data extraction and system modeling.

[0056] A data sharing platform system based on data extraction and system modeling, such as Figure 1 As shown, the system includes:

[0057] The first determination module 101 is used to obtain a data retrieval request from a user, and determine the type and amount of data to be retrieved by the user according to the data retrieval request;

[0058] An extraction module 102 is used to determine a data extraction method according to the data type and the amount of data to be retrieved, and extract the desired data from the database through the data extraction method;

[0059] The second determination module 103 is used to determine the analysis form for the expected data according to the user's data operation requirements, and determine the modeling parameters according to the analysis form;

[0060] The display module 104 is used to construct a data processing model for the expected data based on the modeling parameters, and to display the data processing parameters for the expected data through the data processing model.

[0061] In this embodiment, the data retrieval request is represented by a platform online data retrieval request issued by a user;

[0062] In this embodiment, the amount of data to be retrieved is represented by the memory component of the data to be retrieved by the user;

[0063] In this embodiment, the analysis form is represented by the analysis form corresponding to the execution flow of the data that the user expects;

[0064] In this embodiment, the modeling parameters are represented by parameters required for building a model corresponding to the execution process.

[0065] The working principle of the above technical solution is: first, the user's data retrieval request is obtained through the first determination module, and the user's retrieval data type and retrieval data amount are determined according to the data retrieval request; secondly, the extraction module is used to determine the data extraction method according to the data type and data retrieval amount, and the expected data is extracted from the database through the data extraction method; then, based on the second determination module, the analysis form for the expected data is determined according to the user's data operation requirements, and the modeling parameters are determined according to the analysis form; finally, the display module is used to construct a data processing model for the expected data based on the modeling parameters, and the data processing parameters for the expected data are displayed through the data processing model.

[0066] The beneficial effects of the above technical solution are: by only determining the data extraction method according to the data type and data retrieval amount, the data extraction efficiency can be improved while ensuring the stability and integrity of the data during the extraction process. Furthermore, by obtaining the user's processing requirements for extracted data to build a real-time data processing model for intelligent data processing, it can be ensured that what is finally displayed is the result of data analysis and processing rather than simple data, thereby improving the user's usage experience and adapting to the user's business needs, and solving the problem mentioned in the prior art that the existing data sharing platform can only provide simple data sharing services, and cannot effectively analyze and process data according to the user's data needs, thereby reducing the user's experience and data value.

[0067] In one embodiment, before the first determination module obtains the user's data retrieval request and determines the type and amount of data to be retrieved by the user according to the data retrieval request, the system is further configured to:

[0068] Obtain the user's platform login information, and determine the user's identity level and authority level based on the platform login information;

[0069] Determine the user's free operation authority and audit operation authority based on identity level and authority level;

[0070] Obtain the target data levels corresponding to the user's free operation permissions and audit operation permissions respectively, and determine the current data level of the expected data according to the user's data retrieval request;

[0071] The user's access permissions for the desired data are determined based on the current data level and the target data levels corresponding to the user's free operation permissions and audit operation permissions.

[0072] In this embodiment, the identity level is represented by the user's authenticated identity level in the data sharing platform;

[0073] In this embodiment, different identity levels have different data permissions. Only those who have the permission to query and read data have the first-level permission, those who have the permission to query and download data have the second-level permission, and those who have the permission to modify data and approve the access of others have the third-level permission.

[0074] In this embodiment, the data levels that users with different identity levels and authority levels can access are also different. Users with ordinary authority can only access non-encrypted data, while users with high authority can access encrypted data and private data.

[0075] The beneficial effects of the above technical solution are: the user's free operation permissions and audit operation permissions are determined according to the user's platform login information, so as to ensure the security of platform data. Furthermore, the corresponding target data level is obtained, and the current data level of the expected data is determined in combination with the user's data retrieval request, thereby determining the user's retrieval permissions for the expected data, which can avoid data leakage and abuse. At the same time, it makes data retrieval more rigorous and ensures the privacy of platform data.

[0076] In one embodiment, if the access permission is a free operation permission, it is determined that the user meets the data access permission requirement;

[0077] If the access permission is the review operation permission, obtain the account status of the reviewer, and filter out the online reviewers based on the account status;

[0078] Generate permission approval request to the online status reviewer account and receive feedback information, and obtain approval results based on the feedback information;

[0079] If the approval result is failed, it is determined that the user does not meet the data retrieval permission requirements. If the approval result is passed, it is determined that the user meets the data retrieval permission requirements.

[0080] The beneficial effects of the above technical solution are: sending the permission approval request to the online status reviewer account and receiving feedback information, obtaining the approval result based on the feedback information, and thus determining whether the user is eligible for data retrieval, which can improve the reliability and privacy of platform data retrieval and ensure data security.

[0081] In one embodiment, Figure 2 As shown, the first determining module 101 includes:

[0082] The first determination submodule 1011 is used to obtain the user's data retrieval request through the data query window of the data sharing platform, and determine the data source subject according to the data retrieval request;

[0083] The second determination submodule 1012 is used to obtain the data attributes and data format of the output data of the data source subject, and determine the data type according to the data attributes and data format;

[0084] The acquisition submodule 1013 is used to acquire the time period description parameter and distribution description parameter of the data that the user expects to retrieve;

[0085] The third determining submodule 1014 is used to determine the data retrieval amount according to the time period description parameter and distribution description parameter of the expected data retrieval and the volume of the unit memory of the expected data retrieval.

[0086] In this embodiment, the data source subject is represented as the output place of the data source, for example: the production data and processing data of xx company;

[0087] In this embodiment, the data attributes are represented as commercial attributes or business attributes of the data;

[0088] In this embodiment, the data type indicates which department and which aspect of the enterprise's data, for example: daily coal transportation data of the transportation department of a coal mining enterprise;

[0089] In this embodiment, the time period description parameter indicates the statistical data of which time period, for example: the coal transportation data of the transportation department of a coal mining enterprise on the morning of May 28;

[0090] In this embodiment, the distribution description data is represented as statistical parameters of data distribution, for example: the transportation location distribution data of the daily coal transportation data of the transportation department of a coal mining enterprise;

[0091] In this embodiment, the volume of the data unit memory is represented as the volume of data within a preset unit, for example, a 1M video is a 2s volume.

[0092] The beneficial effects of the above technical solution are: determining the data attributes and data format of the output data according to the data source entity, thereby determining the data type, which can narrow the precise scope of data extraction and improve data extraction efficiency. Furthermore, the data retrieval amount is determined according to the time period description parameters and distribution description parameters of the expected data, which can quickly estimate the memory size of the user's data to be extracted and lay the foundation for the subsequent selection of data extraction methods, thereby improving practicality.

[0093] In one embodiment, the extraction module includes:

[0094] A fourth determination submodule is used to determine a data query method according to the data retrieved amount, and determine an arrangement method of the expected data according to the data query method;

[0095] A fifth determination submodule is used to determine the limiting factors of data extraction according to the arrangement of the expected data, and determine the queue mechanism when extracting data according to the limiting factors;

[0096] A sixth determination submodule, configured to select an adapted data extraction method according to the queue mechanism and determine a data format of the expected data according to the data type;

[0097] The extraction submodule is used to locate the target database according to the data format and extract the expected data from the target database through data extraction.

[0098] The beneficial effects of the above technical solution are: determining the arrangement of the expected data according to the amount of data retrieved, thereby determining the limiting factors of data extraction, which can ensure the quality and accuracy of the data. Further, selecting an adaptive data extraction method, determining the data format of the data, locating the target database, improving the extraction efficiency of the expected data, and at the same time, ensuring the reliability and integrity of the expected data.

[0099] In this embodiment, the limiting factors of data extraction are determined according to the arrangement of the expected data, and the queue mechanism for extracting data is determined according to the limiting factors, including:

[0100] Determine the regional structure diagram of the expected data display content changing over time during the extraction process according to the arrangement of the expected data;

[0101] Extracting the boundary data features and the center data features of the expected data according to the regional structure diagram, and determining the characteristic attributes of the boundary data features and the center data features respectively;

[0102] Determine the data loss of the regional structure diagram during the extraction process of the expected data according to the characteristic attributes, and determine the data aggregation and data density based on the data loss;

[0103] Determine the limiting factors of data extraction based on data aggregation and data density. The limiting factors related to data aggregation are: data scale and data complexity, and the limiting factors related to data density are: data value and data extraction time cost budget;

[0104] Determine resource control parameters and network delay control parameters when extracting expected data based on limiting factors;

[0105] Determine a data buffer structure when extracting expected data according to a resource control parameter and a network delay control parameter;

[0106] Obtain data distribution parameters of expected data, and determine high-value data distribution and common data distribution according to the data distribution parameters;

[0107] Determine a buffer data queue for expected data based on high-value data distribution and common data distribution and a data buffer inter-architecture;

[0108] The queue data arrangement characteristics of the buffer data queue are obtained, and the queue mechanism when extracting data is determined according to the queue data arrangement characteristics.

[0109] The beneficial effects of the above technical solution are: by determining the limiting factors of data extraction based on data aggregation and data density, the external factors that limit the complete extraction of the expected data can be intuitively determined based on the changes in the expected data during the extraction process, thereby ensuring the comprehensiveness and accuracy of the acquisition factors. Furthermore, by determining the queue mechanism based on the data distribution of the buffer data queue of the expected data under the limiting factors, the data queue arrangement mechanism of the expected data during extraction can be quickly determined, thereby improving accuracy and reliability.

[0110] In one embodiment, Figure 3 As shown, the second determining module 103 includes:

[0111] The seventh determination submodule 1031 is used to obtain the user's data operation instruction, analyze the data operation instruction to determine the user's data operation requirements, and the data operation requirements include: data screening, data comparison, data statistics, data summarization and data classification;

[0112] An eighth determination submodule 1032 is used to determine an execution mode for the expected data according to the data operation requirement, and determine an analysis form for the expected data according to the execution mode;

[0113] A definition submodule 1033 is used to determine the data qualitative target and the data quantitative target based on the analysis form, and define the data model according to the data qualitative target and the data quantitative target;

[0114] The ninth determination submodule 1034 is used to obtain model variables of the data model and determine modeling parameters according to the model variables.

[0115] The beneficial effects of the above technical solution are: determining the execution method for the expected data according to the user's data operation requirements, being able to more accurately determine the user's processing operations on the expected data, and further determining the data qualitative goals and data quantitative goals according to the analysis form, determining the modeling parameters, improving the interpretability and reliability of the model, and improving the prediction ability of the model.

[0116] In one embodiment, the display module includes:

[0117] The tenth determination submodule is used to retrieve the data processing model architecture according to the analysis form and determine the model algorithm and indicator optimization parameters according to the modeling parameters;

[0118] A construction submodule is used to construct a data processing model for expected data based on the model algorithm and indicator optimization parameters;

[0119] The import submodule is used to import the expected data and model output targets into the data processing model to obtain the data processing parameters of the model output;

[0120] The display submodule is used to determine the conclusion form of the data processing parameters, determine the display method according to the conclusion form, and display the data processing parameters through the display method.

[0121] The beneficial effects of the above technical solution are: determining the model algorithm and indicator optimization parameters according to the modeling parameters, building a data processing model for the expected data, obtaining the data processing parameters of the model output, determining the display method according to the conclusion form and displaying it, thereby improving the transparency of data processing and facilitating the modification and optimization of data processing parameters.

[0122] In one embodiment, the system is further configured to:

[0123] Add the user's data retrieval request to the task queue, and filter out the data retrieval requests for the same batch of data in the task queue;

[0124] Activate multiple transmission channels and obtain the IP address of each user according to the data retrieval request, and determine the data transmission distance according to the IP address;

[0125] Generate a user list based on the principle of far to near according to the data transmission distance;

[0126] Synchronize data transmission for users in the user list and detect data transmission status for real-time updates.

[0127] The beneficial effects of the above technical solution are: obtaining the IP address of each user based on the retrieval request of the same batch of data from multiple users, determining the transmission distance, generating a user list for synchronous transmission, and improving the transmission efficiency of the system data. At the same time, synchronous data transmission can ensure the consistency of data between different nodes, avoiding the problem of data inconsistency.

[0128] In one embodiment, the system is further configured to:

[0129] Generate a data retrieval statistics log for each user based on the historical retrieval data of the user, and determine the data reserve of each user based on the data retrieval statistics log;

[0130] Generate a data retrieval restriction condition parameter for each user based on the data reserve of each user and the data retrieval frequency of the user;

[0131] Generate data retrieval period restriction parameters and data volume restriction parameters for each user according to the data retrieval restriction condition parameters;

[0132] Control each user's subsequent data retrieval operations based on the data retrieval period limit parameters and data volume limit parameters.

[0133] The beneficial effects of the above technical solution are: generating data retrieval restriction condition parameters for each user based on the user's data reserves and data retrieval frequency, which can protect the security and privacy of platform data. Furthermore, obtaining the user's data retrieval period restriction parameters and data volume restriction parameters, thereby controlling the user's retrieval operations, which can improve the security of the system and reduce the system load, making the system more secure and reliable.

[0134] Those skilled in the art should understand that the first and second in the present invention merely refer to different application stages.

[0135] Other embodiments of the present disclosure will be readily apparent to those skilled in the art after considering the specification and practicing the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0136] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A data sharing platform system based on data extraction and system modeling, characterized in that: The system includes: A first determination module is used to obtain a data retrieval request from a user and determine the type and amount of data to be retrieved by the user according to the data retrieval request; The extraction module is used to determine the data extraction method according to the data type and the data retrieved amount, and extract the expected data from the database through the data extraction method; The second determination module is used to determine the analysis form for the expected data according to the data operation requirements of the user, and determine the modeling parameters according to the analysis form; The display module is used to construct a data processing model for the expected data based on the modeling parameters, and to display the data processing parameters for the expected data through the data processing model.

2. The data sharing platform system based on data extraction and system modeling according to claim 1 is characterized in that: Before the first determination module obtains the user's data retrieval request and determines the type and amount of data to be retrieved by the user according to the data retrieval request, the system is further configured to: Obtain the user's platform login information, and determine the user's identity level and authority level based on the platform login information; Determine the user's free operation authority and audit operation authority based on identity level and authority level; Obtain the target data levels corresponding to the user's free operation permissions and audit operation permissions respectively, and determine the current data level of the expected data according to the user's data retrieval request; The user's access permissions for the desired data are determined based on the current data level and the target data levels corresponding to the user's free operation permissions and audit operation permissions.

3. The data sharing platform system based on data extraction and system modeling according to claim 2 is characterized in that: If the access permission is a free operation permission, it is determined that the user meets the data access permission requirements; If the access permission is the review operation permission, obtain the account status of the reviewer, and filter out the online reviewers based on the account status; Generate permission approval request to the online status reviewer account and receive feedback information, and obtain approval results based on the feedback information; If the approval result is failed, it is determined that the user does not meet the data retrieval permission requirements. If the approval result is passed, it is determined that the user meets the data retrieval permission requirements.

4. The data sharing platform system based on data extraction and system modeling according to claim 1 is characterized in that: The first determining module includes: The first determination submodule is used to obtain the user's data retrieval request through the data query window of the data sharing platform, and determine the data source subject according to the data retrieval request; The second determination submodule is used to obtain the data attributes and data format of the output data of the data source subject, and determine the data type according to the data attributes and data format; The acquisition submodule is used to obtain the time period description parameters and distribution description parameters of the data that the user expects to retrieve; The third determination submodule is used to determine the data retrieval amount according to the time period description parameter and distribution description parameter of the expected data retrieval and the volume of the unit memory of the expected data retrieval.

5. The data sharing platform system based on data extraction and system modeling according to claim 1 is characterized in that: The extraction module comprises: A fourth determination submodule is used to determine a data query method according to the data retrieved amount, and determine an arrangement method of the expected data according to the data query method; A fifth determination submodule is used to determine the limiting factors of data extraction according to the arrangement of the expected data, and determine the queue mechanism when extracting data according to the limiting factors; A sixth determination submodule, configured to select an adapted data extraction method according to the queue mechanism and determine a data format of the expected data according to the data type; The extraction submodule is used to locate the target database according to the data format and extract the expected data from the target database through data extraction.

6. The data sharing platform system based on data extraction and system modeling according to claim 1 is characterized in that: The second determining module includes: The seventh determination submodule is used to obtain the user's data operation instruction, analyze the data operation instruction to determine the user's data operation requirements, and the data operation requirements include: data screening, data comparison, data statistics, data summarization and data classification; An eighth determination submodule is used to determine an execution mode for the expected data according to the data operation requirements, and determine an analysis form for the expected data according to the execution mode; A definition submodule is used to determine data qualitative objectives and data quantitative objectives based on the analysis form, and define a data model according to the data qualitative objectives and data quantitative objectives; The ninth determination submodule is used to obtain model variables of the data model and determine modeling parameters according to the model variables.

7. The data sharing platform system based on data extraction and system modeling according to claim 1 is characterized in that: The display module comprises: The tenth determination submodule is used to retrieve the data processing model architecture according to the analysis form and determine the model algorithm and indicator optimization parameters according to the modeling parameters; A construction submodule is used to construct a data processing model for expected data based on the model algorithm and indicator optimization parameters; The import submodule is used to import the expected data and model output targets into the data processing model to obtain the data processing parameters of the model output; The display submodule is used to determine the conclusion form of the data processing parameters, determine the display method according to the conclusion form, and display the data processing parameters through the display method.

8. The data sharing platform system based on data extraction and system modeling according to claim 1 is characterized in that: The system is also used to: Add the user's data retrieval request to the task queue, and filter out the data retrieval requests for the same batch of data in the task queue; Activate multiple transmission channels and obtain the IP address of each user according to the data retrieval request, and determine the data transmission distance according to the IP address; Generate a user list based on the principle of far to near according to the data transmission distance; Synchronize data transmission for users in the user list and detect data transmission status for real-time updates.

9. The data sharing platform system based on data extraction and system modeling according to claim 1 is characterized in that: The system is also used to: Generate a data retrieval statistics log for each user based on the historical retrieval data of the user, and determine the data reserve of each user based on the data retrieval statistics log; Generate a data retrieval restriction condition parameter for each user based on the data reserve of each user and the data retrieval frequency of the user; Generate data retrieval period restriction parameters and data volume restriction parameters for each user according to the data retrieval restriction condition parameters; Control each user's subsequent data retrieval operations based on the data retrieval period limit parameters and data volume limit parameters.

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