Hierarchical block chain Internet of Things data processing system, method and equipment

Abstract

The invention provides a hierarchical block chain Internet of Things data processing system, method and device, and relates to the technical field of data processing, and the system comprises a permission analysis module which is used for analyzing the identity information and downloading permission of a to-be-accessed user to obtain a first analysis result, the personal data input module is used for uploading user personal data to the data collection module, and the analysis and calculation module is used for analyzing and calculating the model data, the observation data and the user personal data in the data collection module to obtain a second analysis result and process data and recording the second analysis result and the process data; the data integration module is used for integrating the data resources and the computing resources in the resource management module to obtain integrated data, and the block chain module is used for storing the first analysis result and the integrated data. The problems that in the prior art, scientific data cannot be analyzed on line, the data processing efficiency is low, the time cost is too high, and the data safety is low are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of scientific data processing, and particularly to a hierarchical blockchain Internet of Things data processing method, system and device. Background Art

[0002] Currently, the processing and transmission of blockchain Internet of Things data mainly rely on traditional file transfer and data storage methods. Data is usually stored on local servers or cloud storage, and users can access and download the data through a Web browser. The implementation solutions of the prior art generally include the following key components, such as Figure 1 as shown.

[0003] With the rapid growth of space science data, the traditional method of downloading data and then performing local analysis is not only inefficient, but also requires a huge amount of network resources and storage resources. How to improve the convenience of data processing and use has become an urgent problem to be solved. The main defects and problems existing in the existing scientific data sharing and processing technologies include:

[0004] (1) Low data download efficiency: Due to the huge amount of data in the scientific data center and the limited bandwidth of the current public network, the time for users to download data is too long. This not only affects the progress of scientific research work, but also causes a huge pressure on network resources and storage resources.

[0005] (2) Lack of online analysis function: The existing scientific data sharing systems do not integrate data analysis modules, and scientific research personnel need to download data for local analysis. This method not only requires a high demand for computing resources and storage devices, but also requires scientific research personnel to deploy relevant basic software and scientific software by themselves, increasing the time cost.

[0006] (3) Low data security: The existing data sharing systems have low security. As long as the system can be accessed, the data in the system can be simply modified. Summary of the Invention

[0007] In order to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a hierarchical blockchain Internet of Things data processing system and method, which solve the problems of inability to perform online analysis, low data processing efficiency, too high time cost and low data security in the existing scientific data sharing.

[0008] To achieve the above purpose, the present invention provides the following solutions:

[0009] A hierarchical blockchain Internet of Things data processing system, the blockchain Internet of Things data processing system includes: a user management module, a data service module, a data aggregation module and a resource management module, and further includes:

[0010] A data integration module, a personal data input module, an analysis and calculation module, an analysis permission module, and a blockchain module respectively connected to the data integration module and the analysis permission module;

[0011] The data integration module is connected to the resource management module, the personal data input module is connected to the data collection module, the analysis and calculation module is connected to the data service module, and the analysis permission module is connected to the user management module;

[0012] The analysis permission module is used to analyze the identity information and download permission of the user to be accessed to obtain a first analysis result. The personal data input module is used to upload the user's personal data to the data collection module. The analysis and calculation module is used to analyze and calculate the model data, observation data, and user's personal data in the data collection module to obtain a second analysis result and process data and record them. The data integration module is used to integrate the data resources and computing resources in the resource management module to obtain integrated data and perform distributed storage, scheduling, and management of the integrated data using a network file system and a container orchestration system. The blockchain module is used to store the first analysis result and the integrated data respectively.

[0013] Preferably, the analysis permission module includes:

[0014] A user verification sub-module and a download permission verification sub-module;

[0015] The user verification sub-module is used to verify the identity information to be accessed to obtain a first verification result. The download permission verification sub-module is used to verify the download permission of the user to be accessed when the first verification result is passed to obtain a second verification result.

[0016] Preferably, the analysis and calculation module includes:

[0017] A first analysis sub-module, a second analysis sub-module, and a recording sub-module;

[0018] The first analysis sub-module is used to analyze and calculate the model data, observation data, and user's personal data to obtain process data. The second analysis sub-module is used to perform sensitive operation analysis on the process data to obtain a second analysis result. The recording sub-module is used to record the second analysis result and the process data.

[0019] Preferably, the data integration module includes:

[0020] A combination sub-module, a distributed storage sub-module, and a resource scheduling sub-module;

[0021] The binding sub-module is used to integrate the data resources and computing resources to obtain integrated data. The distributed storage sub-module uses a network file system to perform distributed storage on the integrated data. The resource scheduling sub-module is used to schedule and manage the integrated data using a container orchestration system.

[0022] Preferably, the resource scheduling sub-module includes:

[0023] A first environment setting unit and a second environment setting unit;

[0024] The first environment setting unit uses a container engine to set up an independent computing environment to achieve the management and scheduling of computing resources. The second environment setting unit uses Jupyter notebook to set up an independent analysis environment to achieve the management and scheduling of data resources.

[0025] Preferably, the blockchain module includes:

[0026] An encryption sub-module, a first sub-blockchain module, and a second sub-blockchain module. Both the first sub-blockchain module and the second sub-blockchain module are multiple distributed blockchain nodes;

[0027] The encryption sub-module is used to encrypt the first analysis result and the integrated data to obtain the first encrypted data and the second encrypted data. The first sub-blockchain module is used to store the first encrypted data. The second sub-blockchain module is used to store the second encrypted data.

[0028] Preferably, the second sub-blockchain module includes:

[0029] A secondary encryption unit and a decryption unit;

[0030] The secondary encryption unit is used to obtain the unique serial number of the second encrypted data to achieve secondary encryption. The decryption unit is used to generate a corresponding decryption key according to the unique serial number of the second encrypted data. A blockchain Internet of Things data processing method integrating big data analysis functions, the method includes:

[0031] A user accesses the data service module and performs data retrieval, and judges whether to download or analyze data according to the retrieval result to obtain a judgment result;

[0032] If the judgment result is yes, the user accesses the user management module to submit a login request and uses the analysis permission module to verify and analyze the login request to obtain a first analysis result;

[0033] If the first analysis result is passed, the Web service reads the Jupyter Notebook service configuration information in the database service, creates the data storage area for the user according to the service configuration, mounts the scientific data storage area, creates a user container. The created container meets the CPU, memory, and storage resource limit amounts in the service configuration information, runs the Jupyter Notebook program, starts the Jupyter Notebook service, and the Web service forwards the URL path of the service to the user terminal through the reverse proxy service. The user terminal logs in to the analysis and calculation module using the obtained URL path to get the second analysis result;

[0034] Store the second analysis result in the resource management module;

[0035] Use the data integration module to integrate all the data in the resource management module and retain the integrated data through NFS distributed storage and MySQL storage services;

[0036] Perform data mapping and data conversion on the integrated data to map the integrated data to a preset sharing standard to obtain shared standard data;

[0037] Encrypt and chain the shared standard data to obtain a blockchain including the shared standard data; each piece of the shared standard data is stored in the form of a block, and the shared standard data contains a timestamp and the hash value of the previous block; the shared standard data under each user's name constitutes the user's digital sharing materials;

[0038] Construct an intelligent contract containing the rules and logic of data sharing and add the intelligent contract to the blockchain;

[0039] Adopt multi-signature and distributed storage technologies to protect the secure storage of the digital sharing materials;

[0040] Perform access control on the digital sharing materials in the blockchain through key management and access control lists; the blockchain is used to provide interfaces for external systems and users to access, share, and query the digital sharing materials, and realize data query and sharing through API call interfaces; the decentralized network is composed of multiple nodes in the blockchain;

[0041] Be responsible for providing the sending and receiving functions of the digital sharing materials in the blockchain and querying the sharing records and retrieval records of the digital sharing materials.

[0042] Preferably, it further includes:

[0043] The user stops the operation of the JupyterNotebook service through the user terminal by using the "Stop Service" menu option in the page provided by the JupyterNotebook service; or

[0044] The JupyterNotebook service automatically stops after being idle for a fixed period of time;

[0045] The container where the JupyterNotebook service is located is terminated and deleted, and the occupied hardware resources are released.

[0046] The present invention discloses the following technical effects:

[0047] The present invention provides a hierarchical blockchain Internet of Things data processing system. The blockchain Internet of Things data processing system includes: a user management module, a data service module, a data aggregation module, and a resource management module, and further includes: a data integration module, a personal data input module, an analysis and calculation module, an analysis permission module, and a blockchain module respectively connected to the data integration module and the analysis permission module; the data integration module is connected to the resource management module, the personal data input module is connected to the data aggregation module, the analysis and calculation module is connected to the data service module, and the analysis permission module is connected to the user management module; the analysis permission module is used to analyze the identity information and download permission of the user to be accessed to obtain a first analysis result, the personal data input module is used to upload the user's personal data to the data aggregation module, the analysis and calculation module is used to analyze and calculate the model data, observation data, and user's personal data in the data aggregation module to obtain a second analysis result and process data and record them, the data integration module is used to integrate the data resources and computing resources in the resource management module to obtain integrated data and perform distributed storage, scheduling, and management of the integrated data by using a network file system and a container orchestration system, and the blockchain module is used to store the first analysis result and the integrated data respectively.. By using the data integration module to integrate data resources and computing resources, the present invention avoids the cumbersome process of data downloading; adding analysis modules to each module enables users to quickly obtain and use data, improves work efficiency, provides strong support for the research and application in the field of space science, and uses the blockchain module to store and double-encrypt the integrated data, and even if the access permission is passed, it is necessary to decrypt and verify again when modifying the data, which improves the security of the data in the sharing system. Description of the Drawings

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required in the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0049] Figure 1 Schematic diagram of a hierarchical blockchain Internet of Things data processing system structure provided by an embodiment of the present invention;

[0050] Figure 2 Schematic diagram of a hierarchical blockchain Internet of Things data processing system function provided by an embodiment of the present invention;

[0051] Figure 3 Schematic diagram of a hierarchical blockchain Internet of Things data processing method flow provided by an embodiment of the present invention.

[0052] Explanation of reference numerals:

[0053] 1 - User management module, 2 - Data service module, 3 - Data aggregation module, 4 - Resource management module, 5 - Data integration module, 6 - Personal data input module, 7 - Analysis and calculation module, 8 - Analysis permission module, 9 - Blockchain module. Detailed implementation manners

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0055] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific implementation manners.

[0056] As Figure 1 shown, the present invention provides a hierarchical blockchain Internet of Things data processing system and method. The blockchain Internet of Things data processing system includes: user management module 1, data service module 2, data aggregation module 3, and resource management module 4. The system further includes:

[0057] Data integration module 5, personal data input module 6, analysis and calculation module 7, analysis permission module 8, and blockchain module 9 respectively connected to data integration module 5 and analysis permission module 8;

[0058] The data integration module 5 is connected to the resource management module 4, the personal data input module 6 is connected to the data aggregation module 3, the analysis and calculation module 7 is connected to the data service module 2, and the analysis permission module 8 is connected to the user management module 1;

[0059] The analysis permission module 8 is used to analyze the identity information and download permissions of the user to be accessed to obtain a first analysis result. The personal data input module 6 is used to upload the user's personal data to the data aggregation module 3. The analysis and calculation module 7 is used to analyze and calculate the model data, observation data, and user personal data in the data aggregation module 3 to obtain a second analysis result and process data and record them. The data integration module 5 is used to integrate the data resources and computing resources in the resource management module 4 to obtain integrated data and perform distributed storage, scheduling, and management of the integrated data using a network file system and a container orchestration system. The blockchain module 9 is used to store the first analysis result and the integrated data respectively.

[0060] Among them, the resource management module 4 mainly includes the management of computing resources and storage resources.

[0061] The data aggregation module 3 collects scientific data from multiple sources, mainly including model data, observation data, etc. These data have different formats and standards, so operations such as data cleaning, format conversion, and standardization are required to ensure the accuracy and consistency of the data.

[0062] The data service module 2 needs to provide data sharing and distribution functions, mainly including modules such as classification navigation, data retrieval, and visual display. Users can select data for download according to their needs. To improve the data access speed and efficiency, a data distribution network and a caching mechanism also need to be established.

[0063] The user management module 1 includes functions such as user registration approval, unified login, and download permission management.

[0064] Furthermore, as Figure 2 shown, analysis functions are added on the basis of the current mainstream functional modules, specifically including: (1) adding analysis permission management to user management; (2) adding analysis and calculation functions to data services; (3) allowing users to submit personal data in their personal space for comprehensive analysis in data aggregation; (4) adding the management of container resources to resource management.

[0065] Specifically, the present invention realizes the big data analysis function through technologies such as Kubernetes (K8s, an open-source container orchestration engine), Docker (containerization technology), JupyterHub, and NFS (Network File System). It combines data resources with computing resources to provide users with a service that directly analyzes and obtains results in the system, avoiding the cumbersome process of data downloading. The NFS (Network File System) is used to provide file sharing services for users to achieve distributed storage of data. K8s is used for scheduling and management of computing resources. Through Docker containerization of applications, each analysis task has an independent computing environment without mutual interference. Jupyternotebook is used to provide an interactive programming analysis environment for users. Users can view and analyze all data resources with access permissions, and can also upload their own data to the system for joint analysis. The user analysis results are saved in the storage system, and users can view the analysis results in real time or export and save them.

[0066] Further, the analysis permission module 8 includes:

[0067] A user verification sub-module and a download permission verification sub-module;

[0068] The user verification sub-module is used to verify the identity information to be accessed to obtain a first verification result, and the download permission verification sub-module is used to verify the download permission of the user to be accessed when the first verification result is passed to obtain a second verification result.

[0069] Further, the analysis and calculation module 7 includes:

[0070] A first analysis sub-module, a second analysis sub-module, and a recording sub-module;

[0071] The first analysis sub-module is used to perform analysis and calculation on model data, observation data, and user personal data to obtain process data. The second analysis sub-module is used to perform sensitive operation analysis on the process data to obtain a second analysis result. The recording sub-module is used to record the second analysis result and the process data.

[0072] Further, the data integration module 5 includes:

[0073] A combination sub-module, a distributed storage sub-module, and a resource scheduling sub-module;

[0074] The combination sub-module is used to integrate the data resources and computing resources to obtain integrated data. The distributed storage sub-module uses the network file system to perform distributed storage on the integrated data. The resource scheduling sub-module is used to use the container orchestration system to schedule and manage the integrated data.

[0075] Furthermore, the resource scheduling sub-module includes:

[0076] A first environment setting unit and a second environment setting unit;

[0077] The first environment setting unit uses a container engine to set up an independent computing environment to achieve the management and scheduling of computing resources, and the second environment setting unit uses Jupyter notebook to set up an independent analysis environment to achieve the management and scheduling of data resources.

[0078] Specifically, the system provides multiple operating environments and can be customized according to user needs, supports multiple programming languages such as Python and R, supports deep learning frameworks such as Pytorch and TensorFlow. It can also integrate machine learning algorithm libraries such as XGBoost and SVM. The system supports multi-user simultaneous online analysis. Through container technology and dynamic resource scheduling, it ensures that each user can obtain sufficient computing resources. The system adopts a data access control mechanism to ensure the security and privacy of data. At the same time, sensitive operations during the user analysis process are audited and recorded to ensure that data is not misused.

[0079] In terms of system expansion and maintenance, through the container orchestration function of K8s, the horizontal expansion of the system can be achieved to cope with large-scale data processing requirements. In terms of system maintenance, using the image management function of Docker, system components can be quickly deployed and updated to improve the maintainability of the system.

[0080] Furthermore, the blockchain module 9 includes:

[0081] An encryption sub-module, a first sub-blockchain module, and a second sub-blockchain module. Both the first sub-blockchain module and the second sub-blockchain module are multiple distributed blockchain nodes;

[0082] The encryption sub-module is used to encrypt the first analysis result and the integrated data to obtain the first encrypted data and the second encrypted data. The first sub-blockchain module is used to store the first encrypted data, and the second sub-blockchain module is used to store the second encrypted data.

[0083] Specifically, multiple blockchain nodes are established in the data sharing system, and each node can store data and verify the validity of the data. These nodes can be distributed in different geographical locations to achieve distributed storage of data; during data transmission and storage, data needs to be encrypted and verified to ensure data security. Each node can encrypt and verify data to ensure data integrity and credibility; data replication and backup are achieved through blockchain technology to ensure data reliability and persistence. In the blockchain network, data is replicated and backed up by multiple nodes, and data loss will not occur even if a certain node fails; record the transmission and operation history of data to achieve data traceability. Use blockchain technology to record the transaction and operation records of data to ensure data authenticity and credibility.

[0084] Further, the second sub-blockchain module includes:

[0085] A secondary encryption unit and a decryption unit;

[0086] The secondary encryption unit is used to obtain the unique serial number of the second encrypted data for secondary encryption, and the decryption unit is used to generate the corresponding decryption key according to the unique serial number of the second encrypted data.

[0087] Specifically, generate a unique serial number: In order to assign a unique serial number to the data, some unique identifier generation algorithms can be used, such as UUID (Universally Unique Identifier) or an auto-incrementing sequence, etc. This can ensure that each data has a unique serial number; only authorized users can access the data. Users can obtain the decryption permission of the data by providing the correct key or authorization certificate. The security of the data is ensured through two-time encryption.

[0088] As Figure 3 shown, this embodiment also provides a hierarchical blockchain Internet of Things data processing system, and the method includes:

[0089] The user accesses the data service module and performs data retrieval, and judges whether it is necessary to download or analyze the data according to the retrieval result to obtain a judgment result;

[0090] If the judgment result is yes, the user accesses the user management module to submit a login request and uses the analysis permission module to verify and analyze the login request to obtain a first analysis result;

[0091] If the first analysis result is passed, the Web service reads the Jupyter Notebook service configuration information in the database service, creates a data storage area for the user according to the service configuration, mounts the scientific data storage area, creates a user container. The created container conforms to the CPU, memory, and storage resource limit amounts in the service configuration information, runs the Jupyter Notebook program, starts the Jupyter Notebook service, and the Web service forwards the URL path of the service to the user terminal through the reverse proxy service. The user terminal logs in to the analysis and calculation module using the obtained URL path to get the second analysis result;

[0092] Store the second analysis result in the resource management module;

[0093] Use the data integration module to integrate all the data in the resource management module, retain it through NFS distributed storage and MySQL storage services, and immediately delete the container resources where the Jupyter Notebook service stops;

[0094] Perform data mapping and data conversion on the integrated data to map the integrated data to a preset sharing standard to obtain shared standard data;

[0095] Encrypt and upload the shared standard data to the blockchain to obtain a blockchain including the shared standard data; each piece of the shared standard data is stored in the form of a block, and the shared standard data contains a timestamp and the hash value of the previous block; the shared standard data under each user's name constitutes the user's digital shared materials;

[0096] Build an intelligent contract containing the rules and logic of data sharing and add the intelligent contract to the blockchain;

[0097] Adopt multi-signature and distributed storage technologies to protect the secure storage of the digital shared materials;

[0098] Perform access control on the digital shared materials in the blockchain through key management and access control lists; the blockchain is used to provide interfaces for external systems and users to access, share, and query the digital shared materials, and realize data query and sharing through API call interfaces; the blockchain is a decentralized network composed of multiple nodes;

[0099] Be responsible for providing the sending and receiving functions of the digital shared materials in the blockchain, and query the sharing records and retrieval records of the digital shared materials.

[0100] Specifically, the system provides services for users through the portal website. First, users can retrieve data. If users wish to download or analyze data, they need to visit the login page and submit a login request. This request is passed to the Web service through the reverse proxy service. The Web service reads the user data in the database and performs a comparison to ensure that the verification is correct. For download tasks, the system guides users to the corresponding download page, and when users click the download link, the file download is initiated. For analysis tasks, the system calls the JupyterHub service, creates a storage area for user data files, and views the user configuration information from the database. Then, the system controls K8s to create containers according to the user configuration and mounts the user data file storage area into the containers. Finally, the system starts the JupyterNotebook service and forwards the page of the Jupyter Notebook service to users through the reverse proxy service.

[0101] Specifically, when users use this system through the user terminal, they first visit the portal website and can retrieve data. If users wish to download or analyze data, they need to log in. After successful authentication, they will be automatically redirected to the download page or the JupyterNotebook programming page.

[0102] Among them, when users use the system for the first time, they need to register an account. After successful registration, the account information and the initialized JupyterNotebook service configuration information will be stored in the MySQL database to complete the registration work. Default data access permissions and service resource configurations are granted during the initial registration. If special permissions are required, a separate application needs to be submitted. The download function process provided by the system is similar to the traditional method. The process of the analysis function is introduced in detail below.

[0103] When users log in for the first time and access the analysis function, the Web service reads the JupyterNotebook service configuration information in the database service, creates a data storage area for this user according to the service configuration, mounts the scientific data storage area, creates user containers. The created containers meet the resource limit amounts for CPU, memory, storage, etc. in the service configuration information, and run the JupyterNotebook program to start the JupyterNotebook service.

[0104] After the JupyterNotebook service is started, the Web service forwards the URL path of this service to the user terminal through the reverse proxy service. The user terminal uses the obtained URL path to log in to the page provided by the Jupyter Notebook service for subsequent programming work.

[0105] Users can stop the operation of the JupyterNotebook service through the user terminal by using the "Stop Service" menu option in the page provided by the JupyterNotebook service. Or the JupyterNotebook service will automatically stop when it has been idle for a fixed period. The container where the JupyterNotebook service is located is terminated and deleted, and the hardware resources it occupies are released. However, the data storage area in the file sharing service and the JupyterNotebook service configuration file are permanently retained through the NFS distributed storage and the MySQL storage service respectively; the container resources where the JupyterNotebook service is located are immediately deleted after the service stops.

[0106] When the user logs in and accesses the analysis function again, the Web service reads the Jupyter Notebook service configuration data in the database and starts the JupyterNotebook service according to the configuration. The data storage area of this user created previously in the file sharing service remains unchanged.

[0107] The beneficial effects of this embodiment are as follows:

[0108] (1) Efficient data sharing: By integrating the analysis function, the data sharing efficiency is significantly improved, thus accelerating the process of scientific research and innovation work. Users can quickly obtain and use the required data, reducing waiting and download times and improving work efficiency.

[0109] (2) Powerful data analysis capabilities: It supports multiple programming languages, providing users with rich choices. At the same time, it integrates a variety of machine learning frameworks and algorithm libraries, enabling users to select appropriate methods for data processing and analysis according to actual needs. This design meets the diverse needs of different users and makes the data processing process more intuitive and convenient.

[0110] (3) Reducing computing and storage costs: The system provides users with an integrated solution for data resources and computing resources. Users do not need to purchase a large number of storage devices or rent computing resources separately. This greatly reduces the costs of users and alleviates the economic burden.

[0111] (4) Strong stability and scalability: With the help of containerization technology, the stability and consistency of the analysis environment are ensured. Combining K8s and NFS technologies, the system can not only meet the current needs but also has excellent scalability and can flexibly expand as the number of users and data volume grow.

[0112] (5) Flexible permission management mechanism: Permissions for the data that users can download and the data that can be analyzed can be assigned separately or managed uniformly, and the computing resources and storage resources that users can use can be flexibly configured. Through Docker technology, in the case of multi-user operation, the requirements for an operating environment where independent computing resources, data storage areas, program execution permissions, etc. are isolated from each other are met.

[0113] (6) Using blockchain technology to store data within the sharing system enhances the security of the data within the sharing system.

[0114] In this specification, each embodiment is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. For the same or similar parts among the embodiments, reference can be made to each other.

[0115] In this article, specific examples are used to elaborate on the principles and implementation methods of the present invention. The descriptions of the above embodiments are only used to help understand the method of the present invention and its core idea; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A hierarchical blockchain Internet of Things data processing system, the blockchain Internet of Things data processing system comprising: A user management module, a data service module, a data aggregation module, and a resource management module, characterized in that it further includes: A data integration module, a personal data input module, an analysis and calculation module, an analysis permission module, and a blockchain module respectively connected to the data integration module and the analysis permission module; The data integration module is connected to the resource management module, the personal data input module is connected to the data aggregation module, the analysis and calculation module is connected to the data service module, and the analysis permission module is connected to the user management module; The analysis permission module is used to analyze the identity information and download permission of the user to be accessed to obtain a first analysis result. The personal data input module is used to upload the user's personal data to the data aggregation module. The analysis and calculation module is used to analyze and calculate the model data, observation data, and user's personal data in the data aggregation module to obtain a second analysis result and process data and record them. The data integration module is used to integrate the data resources and computing resources in the resource management module to obtain integrated data and use a network file system and a container orchestration system to perform distributed storage, scheduling, and management of the integrated data. The blockchain module is used to store the first analysis result and the integrated data respectively.

2. The hierarchical blockchain Internet of Things data processing system according to claim 1, characterized in that, The analysis permission module includes: A user verification sub-module and a download permission verification sub-module; The user verification sub-module is used to verify the identity information to be accessed to obtain a first verification result. The download permission verification sub-module is used to verify the download permission of the user to be accessed when the first verification result is passed to obtain a second verification result.

3. A hierarchical blockchain Internet of Things data processing system according to claim 1, characterized in that, The analysis and calculation module includes: A first analysis sub-module, a second analysis sub-module, and a recording sub-module; The first analysis sub-module is used to analyze and calculate the model data, observation data, and user's personal data to obtain process data. The second analysis sub-module is used to perform sensitive operation analysis on the process data to obtain a second analysis result. The recording sub-module is used to record the second analysis result and the process data.

4. A hierarchical blockchain Internet of Things data processing system according to claim 1, characterized in that, The data integration module includes: A combination sub-module, a distributed storage sub-module, and a resource scheduling sub-module; The combination sub-module is used to integrate the data resources and computing resources to obtain integrated data. The distributed storage sub-module uses a network file system to perform distributed storage of the integrated data. The resource scheduling sub-module is used to use a container orchestration system to perform scheduling and management of the integrated data.

5. A hierarchical blockchain Internet of Things data processing system according to claim 1, characterized in that, The resource scheduling sub-module includes: A first environment setting unit and a second environment setting unit; The first environment setting unit uses a container engine to set up an independent computing environment to realize the management and scheduling of computing resources. The second environment setting unit uses Jupyternotebook to set up an independent analysis environment to realize the management and scheduling of data resources.

6. The hierarchical blockchain Internet of Things data processing system according to claim 1, characterized in that, The blockchain module includes: An encryption sub-module, a first sub-blockchain module, and a second sub-blockchain module. The first sub-blockchain module and the second sub-blockchain module are both multiple distributed blockchain nodes; The encryption sub-module is used to encrypt the first analysis result and the integrated data to obtain the first encrypted data and the second encrypted data. The first sub-blockchain module is used to store the first encrypted data, and the second sub-blockchain module is used to store the second encrypted data.

7. A hierarchical blockchain Internet of Things data processing system according to claim 6, characterized in that, The second sub-blockchain module includes: A secondary encryption unit and a decryption unit; The secondary encryption unit is used to obtain the unique serial number of the second encrypted data to implement secondary encryption, and the decryption unit is used to generate the corresponding decryption key according to the unique serial number of the second encrypted data.

8. A hierarchical blockchain Internet of Things data processing method, applied to the system described in any one of claims 1-7, characterized in that, The method includes: The user accesses the data service module and performs data retrieval, and determines whether to download or analyze data according to the retrieval result to obtain a judgment result; If the judgment result is yes, the user accesses the user management module to submit a login request and uses the analysis permission module to verify and analyze the login request to obtain a first analysis result; If the first analysis result is passed, the Web service reads the Jupyter Notebook service configuration information in the database service, creates a corresponding data storage area, mounts the scientific data storage area and the user container according to the service configuration, and runs the Jupyter Notebook program to start the Jupyter Notebook service. And the Web service forwards the corresponding URL path to the user terminal through the reverse proxy service. The user terminal logs in to the analysis and calculation module using the obtained URL path to obtain a second analysis result; Store the second analysis result in the resource management module; Use the data integration module to integrate all the data in the resource management module and retain the integrated data through the NFS distributed storage and the MySQL storage service; Perform data mapping and data conversion on the integrated data to map the integrated data to a preset sharing standard to obtain shared standard data; Encrypt and chain the shared standard data to obtain a blockchain including the shared standard data; each piece of the shared standard data is stored in the form of a block, and the shared standard data includes a time stamp and the hash value of the previous block; the shared standard data under each user's name constitutes the user's digital shared materials; Construct an intelligent contract including the rules and logics of data sharing, and add the intelligent contract to the blockchain; Adopt multi-signature and distributed storage technologies to protect the secure storage of the digital shared materials; Perform access control on the digital shared materials in the blockchain through key management and access control lists; the blockchain is used to provide interfaces for external systems and users to access, share and query the digital shared materials, and realize data query and sharing through API call interfaces; the decentralized network composed of multiple nodes in the blockchain; Responsible for providing the sending and receiving functions of the digital shared materials in the blockchain, and querying the sharing records and retrieval records of the digital shared materials.

9. A hierarchical blockchain Internet of Things data processing method according to claim 1, characterized in that It also includes: The user stops the operation of the JupyterNotebook service through the user terminal by using the "Stop Service" menu option in the page provided by the JupyterNotebook service; or The JupyterNotebook service is automatically stopped after being idle for a fixed duration; The container where the JupyterNotebook service is located is terminated and deleted, and the occupied hardware resources are released.

10. A device, characterized in that, Including: A processor; And a memory for storing the hierarchical blockchain Internet of Things data processing program of the processor; wherein, the processor is configured to execute the system according to any one of claims 1-7 by executing the hierarchical blockchain Internet of Things data processing program.

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