Mass data sharing method and open sharing platform system

Abstract

The invention discloses a mass data sharing method and system, and the method comprises the steps: obtaining a data calling demand of each user, determining a data action item of each user according to the data calling demand, and determining a data analysis form according to the data action item; determining a data response state according to the data analysis form, and determining distributed nodes for data calculation based on the data response state; constructing a data sharing channel based on the distributed node and the user client, and calculating and sharing the to-be-shared data in the data sharing channel through the distributed node; and performing identity security verification on each client, determining an access strategy of each client according to a verification result, and configuring a sharing permission of each client according to the access strategy. Data calculation and processing can be rapidly carried out according to the preset node configuration parameters, and the data processing efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data sharing, and in particular, to a method for sharing massive data and an open sharing platform system. Background Art

[0002] At present, the rapid development of Internet and big data technologies has brought fundamental changes to the generation and use of data. From personal behavior data to enterprise operation data, and then to government public data, various types of data are continuously created. With the increasing demand for the analysis and utilization of these data, traditional data sharing methods can no longer meet this demand. Therefore, it is necessary to find more efficient and secure data sharing methods. With the popularization of data sharing, the issue of user privacy has become increasingly prominent. Without effective data privacy protection measures, users' personal information will face a serious risk of leakage. Therefore, how to effectively protect user privacy while ensuring data sharing has become an urgent problem to be solved. Existing data sharing methods all extract and share data through a cloud data platform, which realizes security verification by verifying identities on the platform, and performs data extraction and sharing after the verification passes. However, there are the following problems: Due to the large data scale and the heavy load of the cloud data platform, the processing of users' data sharing requests is slow, and it is impossible to search and calculate data in a timely manner, reducing the data processing efficiency and the user experience. Summary of the Invention

[0003] In view of the problems shown above, the present invention provides a method for sharing massive data and an open sharing platform system to solve the problems mentioned in the background art, that is, due to the large data scale and the heavy load of the cloud data platform, the processing of users' data sharing requests is slow, and it is impossible to search and calculate data in a timely manner, reducing the data processing efficiency and the user experience.

[0004] A method for sharing massive data includes the following steps:

[0005] Obtain the data retrieval requirements of each user, determine the data function items of each user according to the data retrieval requirements, and determine the data analysis form according to the data function items;

[0006] Determine the data response status according to the data analysis form, and determine the distributed nodes for data calculation based on the data response status;

[0007] Construct a data sharing channel between the distributed nodes and the user clients, and calculate and share the data to be shared in the data sharing channel through the distributed nodes;

[0008] Authenticate the identity security of each client, determine the access policy of each client according to the authentication result, and configure the sharing permissions of each client according to the access policy.

[0009] Preferably, the steps of obtaining the data retrieval requirements of each user, determining the data function items of each user according to the data retrieval requirements, and determining the data analysis form according to the data function items include:

[0010] Obtain the data retrieval requirements of users in an online form, and determine the businesses involved in the data of each user based on the data retrieval requirements;

[0011] Obtain the relevant business items of the businesses involved in the data, determine the data function items according to the relevant business items, and determine the project objectives of the data function items;

[0012] Determine the data distribution sequence according to the project objectives, determine the data processing scale based on the data distribution sequence, and determine the optimal data processing method according to the data processing scale;

[0013] Obtain the processing logic of the optimal data processing method, and determine the data analysis form of the optimal data processing method according to the processing logic.

[0014] Preferably, the steps of determining the data response status according to the data analysis form and determining the distributed nodes for data calculation based on the data response status include:

[0015] Determine the data analysis objective according to the data analysis form, determine the data response status based on the data analysis objective, and determine the data behavior and data result according to the data response status;

[0016] Determine the calculation task type according to the data behavior and data result, and determine the types of calculation nodes and the task calculation complexity based on the calculation task type;

[0017] Determine the performance requirements for various types of calculation nodes according to the task calculation complexity, and determine the data calculation microservice architecture according to the performance requirements;

[0018] Match the calculation microservice architecture with the preset data calculation functions of each distributed node in the cloud server, and determine the distributed nodes for data calculation according to the matching result.

[0019] Preferably, the steps of constructing a data sharing channel between the distributed nodes and the user clients, and calculating and sharing the data to be shared through the distributed nodes in the data sharing channel include:

[0020] Obtain the IP address of each client, determine the preset data sharing target and scope according to the IP address, define the data sharing model according to the preset sharing target and scope, and determine the sharing characteristics of the data sharing model;

[0021] Write a data sharing interface according to the sharing characteristics, and deploy the data interface to distributed nodes to build a data sharing channel;

[0022] Determine the data to be retrieved, perform logical calculations on the data to be retrieved through distributed nodes, obtain the calculation results, and share the calculation results through the data sharing channel.

[0023] Preferably, perform identity security verification on each client, determine the access policy of each client according to the verification result, and configure the sharing permissions of each client, including:

[0024] Obtain the device identifier of each client, confirm whether each client is a trusted device according to the device representation, and if so, obtain the identity information of the accessing client through each client;

[0025] Determine the identity level of each accessing client according to the identity information, and determine the access policy of each accessing client based on the identity level;

[0026] Determine the data operation permission and data control permission of each accessing client according to the access policy, and determine the data sharing condition of each client according to the data operation permission and data control permission;

[0027] Configure the sharing permission parameters of each client according to the data sharing condition of each client.

[0028] A massive data open sharing platform system, which includes:

[0029] The first determination module is used to obtain the data retrieval requirements of each user, determine the data action item of each user according to the data retrieval requirements, and determine the data analysis form according to the data action item;

[0030] The second determination module is used to determine the data response status according to the data analysis form, and determine the distributed nodes for data calculation based on the data response status;

[0031] The calculation and sharing module is used to build a data sharing channel between the distributed nodes and the user clients, and calculate and share the data to be shared through the distributed nodes in the data sharing channel;

[0032] The configuration module is used to perform identity security verification on each client, determine the access policy of each client according to the verification result, and configure the sharing permissions of each client according to the access policy.

[0033] Preferably, the first determination module includes:

[0034] The first determination sub-module is used to obtain the user's data retrieval requirements in an online form and determine the business involved in each user's data based on the data retrieval requirements.

[0035] The second determination sub-module is used to obtain the relevant business items involved in the business, determine the data action items according to the relevant business items, and determine the project objectives of the data action items.

[0036] The third determination sub-module is used to determine the data distribution sequence according to the project objectives, determine the data processing scale based on the data distribution sequence, and determine the optimal data processing method according to the data processing scale.

[0037] The fourth determination sub-module is used to obtain the processing logic of the optimal data processing method and determine the data analysis form of the optimal data processing method according to the processing logic.

[0038] Preferably, the second determination module includes:

[0039] The fifth determination sub-module is used to determine the data analysis objective according to the data analysis form, determine the data response status based on the data analysis objective, and determine the data behavior and data result according to the data response status.

[0040] The sixth determination sub-module is used to determine the calculation task type according to the data behavior and data result, and determine the calculation node type and task calculation complexity based on the calculation task type.

[0041] The seventh determination sub-module is used to determine the performance requirements for various types of calculation nodes according to the task calculation complexity, and determine the calculation microservice architecture for the data according to the performance requirements.

[0042] The eighth determination sub-module is used to match the calculation microservice architecture with the preset data calculation functions of each distributed node in the cloud server, and determine the distributed nodes for data calculation according to the matching results.

[0043] Preferably, the calculation and sharing module includes:

[0044] The ninth determination sub-module is used to obtain the IP address of each client, determine the preset data sharing target and scope according to the IP address, define the data sharing model according to the preset sharing target and scope, and determine the sharing characteristics of the data sharing model.

[0045] The deployment sub-module is used to write the data sharing interface according to the sharing characteristics and deploy the data interface to the distributed nodes to build a data sharing channel.

[0046] The calculation and sharing sub-module is used to determine the data to be retrieved, perform logical calculations on the data to be retrieved through the distributed nodes, obtain the calculation results, and share the calculation results through the data sharing channel.

[0047] Preferably, the configuration module includes:

[0048] An obtaining sub-module, configured to obtain the device identifier of each client, confirm whether each client is a trusted device according to the device representation, and if so, obtain the identity information of the accessing customer through each client;

[0049] A tenth determining sub-module, configured to determine the identity level of each accessing customer according to the identity information, and determine the access policy of each accessing customer based on the identity level;

[0050] An eleventh determining sub-module, configured to determine the data operation permission and data control permission of each accessing customer according to the access policy, and determine the data sharing condition of each client according to the data operation permission and data control permission;

[0051] A configuration sub-module, configured to configure the sharing permission parameter of each client according to the data sharing condition of each client.

[0052] Other features and advantages of the present invention will be described in the subsequent specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be realized and obtained by the structure specifically pointed out in the written specification and the drawings.

[0053] The technical solution of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0054] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, and do not constitute a limitation to the present invention.

[0055] Figure 1 It is a working flowchart of a method for sharing massive data provided by the present invention;

[0056] Figure 2 It is another working flowchart of a method for sharing massive data provided by the present invention;

[0057] Figure 3 It is a schematic structural diagram of a massive data open sharing platform system provided by the present invention;

[0058] Figure 4 It is a schematic structural diagram of the first determining module of a massive data open sharing platform system provided by the present invention. Detailed Embodiments

[0059] Exemplary embodiments will be described in detail herein, and examples thereof are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0060] Currently, the rapid development of Internet and big data technologies has brought about fundamental changes in the way data is generated and used. From personal behavior data to enterprise operation data, and then to government public data, various types of data are continuously being created. With the increasing demand for the analysis and utilization of this data, traditional data sharing methods can no longer meet this demand. Therefore, it is necessary to find more efficient and secure data sharing methods. With the popularization of data sharing, the issue of user privacy has become increasingly prominent. Without effective data privacy protection measures, users' personal information will face a serious risk of leakage. Therefore, how to effectively protect user privacy while ensuring data sharing has become an urgent problem to be solved. Existing data sharing methods all extract and share data through a cloud data platform, which achieves security verification by verifying identities through the platform, and performs data extraction and sharing after the verification passes. However, it has the following problems: Due to the large scale of data and the heavy load of the cloud data platform, the processing of users' data sharing requests is slow, and it is unable to search for and calculate data in a timely manner, reducing the data processing efficiency and the user experience. To solve the above problems, this embodiment discloses a data sharing method based on data processing and data transmission of distributed nodes.

[0061] A method for sharing massive data, as Figure 1 shown, includes the following steps:

[0062] Step S101: Obtain the data retrieval requirements of each user, determine the data function items of each user according to the data retrieval requirements, and determine the data analysis form according to the data function items;

[0063] Step S102: Determine the data response status according to the data analysis form, and determine the distributed nodes for data calculation based on the data response status;

[0064] Step S103: Build a data sharing channel between the distributed nodes and the user clients, and calculate and share the data to be shared in the data sharing channel through the distributed nodes;

[0065] Step S104: Perform identity security verification on each client, determine the access policy for each client according to the verification result, and configure the sharing permissions for each client according to the access policy.

[0066] In this embodiment, the data retrieval requirement is represented as the data type requirement to be retrieved by each user;

[0067] In this embodiment, the data function item is represented as the function item of the user for the data to be retrieved;

[0068] The working principle of the above technical solution is as follows: Obtain the data retrieval requirements of each user, determine the data function item of each user according to the data retrieval requirements, and determine the data analysis form according to the data function item; Determine the data response status according to the data analysis form, and determine the distributed nodes for data calculation based on the data response status; Construct a data sharing channel between the distributed nodes and the user clients, and calculate and share the data to be shared through the distributed nodes in the data sharing channel; Perform identity security verification on each client, determine the access policy of each client according to the verification result, and configure the sharing permission of each client according to the access policy.

[0069] The beneficial effects of the above technical solution are as follows: By determining the data response status, the calculation form and calculation logic of the data can be accurately determined, and then the final state of the data can be determined. By selecting distributed nodes for data processing, data calculation and processing can be quickly performed according to the preset node configuration parameters, improving the data processing efficiency. It solves the problem mentioned in the prior art that due to the large data scale and the large load of the cloud data platform, the processing of the user's data sharing request is slow, the data cannot be searched and calculated in time, and the data processing efficiency and the user experience are reduced.

[0070] In one embodiment, as Figure 2 shown, the obtaining of the data retrieval requirements of each user, determining the data function item of each user according to the data retrieval requirements, and determining the data analysis form according to the data function item include:

[0071] Step S201: Obtain the data retrieval requirements of the user in an online form, and determine the business involved in the data of each user based on the data retrieval requirements;

[0072] Step S202: Obtain the relevant business items of the business involved in the data, determine the data function item according to the relevant business items, and determine the project objective of the data function item;

[0073] Step S203: Determine the data distribution sequence according to the project objective, determine the data processing scale based on the data distribution sequence, and determine the best data processing method according to the data processing scale;

[0074] Step S204: Obtain the processing logic of the best data processing method, and determine the data analysis form of the best data processing method according to the processing logic.

[0075] The beneficial effects of the above technical solution are as follows: By determining the data distribution sequence and then determining the data processing logic and data analysis form, the data analysis form can be accurately determined according to the distribution characteristics of the data, ensuring the accuracy and consistency of form determination and improving the practicability and reliability.

[0076] In this embodiment, determining the project objective of the data action item includes:

[0077] Obtain the matter process of the data action item, and construct a matter model of the data action item according to the matter process;

[0078] Determine the process service parameters of the data action item according to the matter model, and obtain the matter process characteristics and matter result characteristics based on the process service parameters;

[0079] Determine the matter situation characteristics according to the matter process characteristics and matter result characteristics, and determine the response parameters according to the matter situation characteristics;

[0080] Determine multiple functions according to the response parameters and the action attributes between the functions, and determine the data intermediate state and data end state according to the action attributes and the data processing characteristics of each function;

[0081] Determine the project objective of the data action item according to the data intermediate state and data end state.

[0082] The beneficial effects of the above technical solution are as follows: By determining the matter intermediate process and result and then determining the project objective of the data action item, the project objective can be accurately determined based on the project process parameters and project result parameters of the data action item, ensuring the rationality and accuracy of project objective determination.

[0083] In one embodiment, determining the data response state according to the data analysis form and determining the distributed nodes for data calculation based on the data response state includes:

[0084] Determine the data analysis objective according to the data analysis form, determine the data response state based on the data analysis objective, and determine the data behavior and data result according to the data response state;

[0085] Determine the calculation task type according to the data behavior and data result, and determine the calculation node type and task calculation complexity based on the calculation task type;

[0086] Determine the performance requirements for various types of calculation nodes according to the task calculation complexity, and determine the data calculation microservice architecture for the data according to the performance requirements;

[0087] Match the calculation microservice architecture with the preset data calculation functions of each distributed node in the cloud server, and determine the distributed nodes for data calculation according to the matching result.

[0088] The beneficial effects of the above technical solution are as follows: By determining the computing microservice architecture for data, it can ensure the comprehensiveness of data computing while also ensuring computing performance, further improving the data processing efficiency. Further, by performing distributed node matching according to the architecture, the reliability, stability, and compatibility of the matching nodes can be ensured, improving the overall work efficiency.

[0089] In one embodiment, the construction of a data sharing channel between distributed nodes and user clients, where the distributed nodes calculate and share the data to be shared in the data sharing channel, includes:

[0090] Obtain the IP address of each client, determine the preset data sharing target and scope according to the IP address, define a data sharing model according to the preset sharing target and scope, and determine the sharing characteristics of the data sharing model;

[0091] Write a data sharing interface according to the sharing characteristics, and deploy the data interface to the distributed nodes to build a data sharing channel;

[0092] Determine the data to be retrieved, perform logical calculations on the data to be retrieved through the distributed nodes, obtain the calculation results, and share the calculation results through the data sharing channel.

[0093] The beneficial effects of the above technical solution are as follows: By defining a data sharing model, precise sharing requirements can be defined according to the data sharing object, and then a data sharing interface dedicated to the client can be generated, which can achieve a stable and secure connection between the distributed node and the client, avoiding the risk of data leakage while ensuring the stability of data transmission with the client, and further improving the data processing efficiency.

[0094] In one embodiment, perform identity security verification on each client, determine the access policy of each client according to the verification result, and configure the sharing permissions of each client, including:

[0095] Obtain the device identifier of each client, confirm whether each user client is a trusted device according to the device identifier. If so, obtain the identity information of the accessing client through each client;

[0096] Determine the identity level of each accessing client according to the identity information, and determine the access policy of each accessing client based on the identity level;

[0097] Determine the data operation permission and data control permission of each accessing client according to the access policy, and determine the data sharing condition of each client according to the data operation permission and data control permission;

[0098] Configure the sharing permission parameters of each client according to the data sharing condition of each client.

[0099] The beneficial effects of the above technical solution are as follows: By determining the data sharing conditions of each client according to the access policy, the data sharing permissions within the controllable range of the user can be accurately determined based on the user's identity level, avoiding illegal processing of shared data by the user, and improving data security and reliability.

[0100] In one embodiment, this embodiment also discloses a massive data open sharing platform system, as Figure 3 shown, the system includes:

[0101] A first determination module 301, configured to obtain the data retrieval requirements of each user, determine the data usage items of each user according to the data retrieval requirements, and determine the data analysis form according to the data usage items;

[0102] A second determination module 302, configured to determine the data response status according to the data analysis form, and determine the distributed nodes for data calculation based on the data response status;

[0103] A calculation and sharing module 303, configured to build a data sharing channel between the distributed nodes and the user clients, and calculate and share the data to be shared through the distributed nodes in the data sharing channel;

[0104] A configuration module 304, configured to perform identity security verification on each client, determine the access policy of each client according to the verification result, and configure the sharing permissions of each client according to the access policy.

[0105] The working principle and beneficial effects of the above technical solution have been described in the method embodiment, and will not be elaborated here.

[0106] In one embodiment, as Figure 4 shown, the first determination module 301 includes:

[0107] A first determination sub-module 3011, configured to obtain the data retrieval requirements of the user in an online form, and determine the business involved in the data of each user based on the data retrieval requirements;

[0108] A second determination sub-module 3012, configured to obtain the relevant business items of the business involved in the data, determine the data usage items according to the relevant business items, and determine the project objectives of the data usage items;

[0109] A third determination sub-module 3013, configured to determine the data distribution sequence according to the project objectives, determine the data processing scale based on the data distribution sequence, and determine the optimal data processing method according to the data processing scale;

[0110] The fourth determination sub-module 3014 is configured to obtain the processing logic of the optimal data processing method, and determine the data analysis form of the optimal data processing method according to the processing logic.

[0111] In one embodiment, the second determination module includes:

[0112] The fifth determination sub-module is configured to determine the data analysis target according to the data analysis form, determine the data response status based on the data analysis target, and determine the data behavior and data result according to the data response status;

[0113] The sixth determination sub-module is configured to determine the calculation task type according to the data behavior and data result, and determine the calculation node type and task calculation complexity based on the calculation task type;

[0114] The seventh determination sub-module is configured to determine the performance requirements for various types of calculation nodes according to the task calculation complexity, and determine the calculation microservice architecture for the data according to the performance requirements;

[0115] The eighth determination sub-module is configured to match the calculation microservice architecture with the preset data calculation functions of each distributed node in the cloud server, and determine the distributed node for data calculation according to the matching result.

[0116] In one embodiment, the calculation and sharing module includes:

[0117] The ninth determination sub-module is configured to obtain the IP address of each client, determine the preset data sharing target and scope according to the IP address, define a data sharing model according to the preset sharing target and scope, and determine the sharing characteristics of the data sharing model;

[0118] The deployment sub-module is configured to write a data sharing interface according to the sharing characteristics, and deploy the data interface to the distributed node to build a data sharing channel;

[0119] The calculation and sharing sub-module is configured to determine the data to be retrieved, perform logical calculation on the data to be retrieved through the distributed node, obtain the calculation result, and share the calculation result through the data sharing channel.

[0120] In one embodiment, the configuration module includes:

[0121] The acquisition sub-module is configured to obtain the device identifier of each client, confirm whether each user end is a trusted device according to the device representation, and if so, obtain the identity information of the accessing client through each client;

[0122] The tenth determination sub-module is configured to determine the identity level of each accessing client according to the identity information, and determine the access policy of each accessing client based on the identity level;

[0123] The eleventh determination sub-module is configured to determine the data operation permissions and data control permissions of each accessing client according to the access policy, and determine the data sharing conditions of each client according to the data operation permissions and data control permissions;

[0124] The configuration sub-module is configured to configure the sharing permission parameters of each client according to the data sharing conditions of each client.

[0125] Those skilled in the art should understand that the first and second in the present invention refer to different application stages.

[0126] After considering the specification and practicing the disclosure herein, those skilled in the art will readily conceive of other embodiments of the present disclosure. This application is intended to cover any variations, uses, or adaptations of the present disclosure, which follow the general principles of the present disclosure and include known common knowledge or conventional technical means in the technical field not disclosed in the present disclosure. The specification and examples are only illustrative, and the true scope and spirit of the present disclosure are pointed out by the following claims.

[0127] It should be understood that the present disclosure is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. A method for sharing massive data, characterized in that, It includes the following steps: Obtain the data retrieval requirements of each user, determine the data function items of each user according to the data retrieval requirements, and determine the data analysis form according to the data function items; Determine the data response status according to the data analysis form, and determine the distributed nodes for data calculation based on the data response status; Construct a data sharing channel between the distributed nodes and the user clients, and calculate and share the data to be shared through the distributed nodes in the data sharing channel; Conduct identity security verification for each client, determine the access policy for each client according to the verification result, and configure the sharing permissions for each client according to the access policy.

2. The method for sharing massive data according to claim 1, wherein The obtaining of the data retrieval requirements of each user, determining the data function items of each user according to the data retrieval requirements, and determining the data analysis form according to the data function items includes: Obtain the data retrieval requirements of the user in an online form, and determine the business involved in the data of each user based on the data retrieval requirements; Obtain the relevant business items of the business involved in the data, determine the data function items according to the relevant business items, and determine the project objectives of the data function items; Determine the data distribution sequence according to the project objectives, determine the data processing scale based on the data distribution sequence, and determine the optimal data processing method according to the data processing scale; Obtain the processing logic of the optimal data processing method, and determine the data analysis form of the optimal data processing method according to the processing logic.

3. The method for sharing massive data according to claim 1, wherein The determining of the data response status according to the data analysis form and determining the distributed nodes for data calculation based on the data response status includes: Determine the data analysis objective according to the data analysis form, determine the data response status based on the data analysis objective, and determine the data behavior and data result according to the data response status; Determine the calculation task type according to the data behavior and data result, and determine the type of calculation nodes and the task calculation complexity based on the calculation task type; Determine the performance requirements for each type of calculation node according to the task calculation complexity, and determine the data calculation microservice architecture according to the performance requirements; Match according to the calculation microservice architecture and the preset data calculation functions of each distributed node in the cloud server, and determine the distributed nodes for data calculation according to the matching result.

4. The method for sharing massive data according to claim 1, wherein The constructing of the data sharing channel between the distributed nodes and the user clients, and calculating and sharing the data to be shared through the distributed nodes in the data sharing channel includes: Obtain the IP address of each client, determine the preset data sharing target and scope according to the IP address, define the data sharing model according to the preset sharing target and scope, and determine the sharing characteristics of the data sharing model; Write the data sharing interface according to the sharing characteristics, and deploy the data interface to the distributed nodes to construct a data sharing channel; Determine the data to be retrieved, perform logical calculation on the data to be retrieved through the distributed nodes, obtain the calculation result, and share the calculation result through the data sharing channel.

5. The method for sharing massive data according to claim 1, wherein The conducting of identity security verification for each client, determining the access policy for each client according to the verification result, and configuring the sharing permissions for each client according to the access policy includes: Obtain the device identifier of each client, and confirm whether each client is a trusted device according to the device representation. If so, obtain the identity information of the accessing customer through each client; Determine the identity level of each accessing customer according to the identity information, and determine the access policy of each accessing customer based on the identity level; Determine the data operation permission and data control permission of each accessing customer according to the access policy, and determine the data sharing condition of each client according to the data operation permission and data control permission; Configure the sharing permission parameters of each client according to the data sharing condition of each client.

6. A massive data open sharing platform system, characterized in that, The system includes: The first determination module is used to obtain the data retrieval requirements of each user, determine the data application items of each user according to the data retrieval requirements, and determine the data analysis form according to the data application items; The second determination module is used to determine the data response status according to the data analysis form, and determine the distributed nodes for data calculation based on the data response status; The calculation and sharing module is used to build a data sharing channel between the distributed nodes and the user clients, and calculate and share the data to be shared in the data sharing channel through the distributed nodes; The configuration module is used to perform identity security verification on each client, determine the access policy of each client according to the verification result, and configure the sharing permission of each client according to the access policy.

7. The massive data open sharing platform system according to claim 6, wherein The first determination module includes: The first determination sub-module is used to obtain the data retrieval requirements of the user in an online form, and determine the business involved in the data of each user based on the data retrieval requirements; The second determination sub-module is used to obtain the relevant business items of the business involved in the data, determine the data application items according to the relevant business items, and determine the project objectives of the data application items; The third determination sub-module is used to determine the data distribution sequence according to the project objectives, determine the data processing scale based on the data distribution sequence, and determine the best data processing method according to the data processing scale; The fourth determination sub-module is used to obtain the processing logic of the best data processing method, and determine the data analysis form of the best data processing method according to the processing logic.

8. The massive data open sharing platform system according to claim 6, wherein The second determination module includes: The fifth determination sub-module is used to determine the data analysis objective according to the data analysis form, determine the data response status based on the data analysis objective, and determine the data behavior and data result according to the data response status; The sixth determination sub-module is used to determine the calculation task type according to the data behavior and data result, and determine the type of calculation nodes and the task calculation complexity based on the calculation task type; The seventh determination sub-module is used to determine the performance requirements for various types of calculation nodes according to the task calculation complexity, and determine the data calculation microservice architecture for the data according to the performance requirements; The eighth determination sub-module is used to match according to the calculation microservice architecture and the preset data calculation functions of each distributed node in the cloud server, and determine the distributed nodes for data calculation according to the matching result.

9. The massive data open sharing platform system according to claim 6, wherein The calculation and sharing module includes: The ninth determination sub-module is used to obtain the IP address of each client, determine the preset data sharing target and scope according to the IP address, define the data sharing model according to the preset sharing target and scope, and determine the sharing characteristics of the data sharing model; A deployment sub-module, which is used to write a data sharing interface according to shared features and deploy the data interface to distributed nodes to build a data sharing channel; A calculation and sharing sub-module, which is used to determine the data to be retrieved, perform logical calculations on the data to be retrieved through distributed nodes, obtain calculation results, and share the calculation results through the data sharing channel.

10. The massive data open sharing platform system according to claim 6, characterized in that, The configuration module includes: An acquisition sub-module, which is used to acquire the device identifier of each client, confirm whether each client is a trusted device according to the device representation, and if so, acquire the identity information of the accessing client through each client; A tenth determination sub-module, which is used to determine the identity level of each accessing client according to the identity information and determine the access policy of each accessing client based on the identity level; An eleventh determination sub-module, which is used to determine the data operation permission and data control permission of each accessing client according to the access policy, and determine the data sharing condition of each client according to the data operation permission and data control permission; A configuration sub-module, which is used to configure the shared permission parameters of each client according to the data sharing condition of each client.

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