A distributed internet of things management method and system based on edge cloud computing

Abstract

The application relates to the technical field of blockchains, in particular to a distributed Internet of Things management method and system based on edge cloud computing. The system comprises an intelligent data acquisition module, an adaptive data synchronization module, an access control module, a blockchain optimization module and a security authentication module. The intelligent data acquisition module collects device data in real time, and the adaptive data synchronization module realizes cross-region synchronization based on HyperSync technology. The access control module intelligently distributes user and device requests by using HyperAgent technology, and transmits the requests to the blockchain optimization module, which optimizes the data processing performance of the blockchain node by using Hyper I / O technology. The security authentication module combines dynamic keys and a zero-trust authentication mechanism to realize multi-level application and device security authentication. The application improves the real-time performance of Internet of Things management, the stability of cross-region synchronization, the intelligence of request processing, the efficiency of blockchain operation and the overall security of the system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of blockchain, and in particular to a distributed Internet of Things management method and system based on edge cloud computing. BACKGROUND

[0002] With the rapid development of Internet of Things technology, the number of connected devices is growing exponentially. Traditional Internet of Things architecture usually adopts a centralized cloud computing mode, uploading a large amount of data to the cloud for processing and storage. However, with the increase in the number of Internet of Things devices, the centralized architecture exposes some significant limitations. The existing technology has the following problems: it is difficult to collect and process a large amount of Internet of Things device data in real time and efficiently, affecting the response speed of the system; the traditional method lacks dynamic adjustment capability, resulting in data synchronization delay, packet loss and other problems, making it difficult to ensure data consistency and accuracy; the centralized processing mode occupies a large amount of resources and cannot effectively handle a large number of concurrent requests, reducing the processing efficiency and response speed of the system. SUMMARY

[0003] To solve the above problems, the present application provides a distributed Internet of Things management method and system based on edge cloud computing.

[0004] To achieve the above purpose, the technical solution adopted by the present application is:

[0005] On the one hand, a distributed Internet of Things management system based on edge cloud computing, comprising an intelligent data acquisition module, an adaptive data synchronization module, an access control module, a blockchain optimization module and a security authentication module connected in sequence in communication;

[0006] The intelligent data acquisition module is used to acquire device data from Internet of Things devices in real time and store the data in a local or distributed database;

[0007] The adaptive data synchronization module is used to receive device data from the intelligent data acquisition module for cross-regional synchronization based on HyperSync technology and using an adaptive algorithm;

[0008] The access control module is used to realize intelligent agent distribution for cross-regional user and device requests through HyperAgent technology, and to transmit the processed requests and instructions to the blockchain optimization module;

[0009] The blockchain optimization module is used to optimize the data read-write performance of the blockchain node through HyperIO technology, including connection pool management, data caching, asynchronous writing and preheating mechanism for object models and user data;

[0010] The security authentication module is used to realize multi-level security authentication of applications and devices through a dynamic key generation mechanism and a zero-trust authentication mechanism based on blockchain.

[0011] Further, the running process of the adaptive data synchronization module includes the following steps:

[0012] Receiving real-time collected device data from the intelligent data collection module through HyperSync technology;

[0013] Based on the adaptive algorithm, dynamically evaluate network bandwidth, device load and data importance, and select the optimal data synchronization strategy;

[0014] According to the selected synchronization strategy, the device data is subjected to distributed synchronization operation across regions;

[0015] During the cross-regional synchronization process, monitor and handle abnormal situations such as synchronization failure or delay;

[0016] After synchronization is completed, the consistency of the data is verified through a verification mechanism, and the verification result is fed back to the intelligent data collection module.

[0017] Further, the formula of the adaptive algorithm is as follows:

[0018]

[0019] Where S opt represents the optimal data synchronization strategy; S i represents the i-th strategy in the current available synchronization strategy set; a represents the network bandwidth weight coefficient, which determines the influence weight of network bandwidth in strategy selection; B net represents the current network bandwidth; b represents the device load weight coefficient, which determines the influence weight of device load in strategy selection; L dev represents the device load, i.e. the percentage of the current processing capacity of the device; g represents the data importance weight coefficient, which determines the influence weight of data importance in strategy selection; I data represents the importance of the data.

[0020] Further, the access control module includes a database access agent unit and an Internet of Things access agent unit;

[0021] The database access agent unit is used to filter, distribute and optimize the access request to the database through the predefined security policy and dynamic authorization mechanism;

[0022] The Internet of Things access agent unit is used to intelligently distribute and control the access request of the device through HyperAgent technology based on the identity verification, permission allocation and access history record of the Internet of Things device.

[0023] Further, the running process of the Internet of Things access agent unit includes the following steps:

[0024] Dynamically allocate access permissions by real-time analyzing IoT devices and user identities;

[0025] Optimize request routing and distribution paths using HyperAgent technology for cross-region user requests and device instructions;

[0026] Adjust request processing strategies dynamically based on network status and device load, including local processing, edge computing node distribution, and cloud integrated processing.

[0027] Further, the operation process of the blockchain optimization module includes the following steps:

[0028] Implement connection pool management through HyperIO technology to dynamically allocate connection resources for blockchain nodes;

[0029] Reduce I / O operation frequency of the blockchain network using data caching mechanisms;

[0030] Improve system response speed using asynchronous write technology and implement pre-warming mechanisms for physical models and user data;

[0031] Dynamically monitor node load and network latency to adjust operation strategies.

[0032] Further, the security authentication module is based on a dynamic key generation mechanism, which generates multi-level dynamic keys based on device physical characteristics, network environment, and user behavior characteristics, and performs layer-by-layer verification through a zero-trust authentication mechanism based on blockchain.

[0033] Further, it also includes dividing its architecture into infrastructure layer, domain layer, application layer, and access layer through domain-driven design model, and communicating between layers through standardized interfaces.

[0034] On the other hand, a distributed IoT management method based on edge cloud computing includes the following steps:

[0035] Collect IoT device data in real time and store data in local or distributed databases according to requirements;

[0036] Use HyperSync technology to evaluate network and device status based on adaptive algorithms, and select the optimal strategy for cross-region data synchronization;

[0037] Use HyperAgent technology to perform intelligent agent and permission management for cross-region user requests and device instructions;

[0038] Through HyperIO technology, manage connection pools, data caching, and asynchronous writing to improve read-write performance of blockchain nodes;

[0039] The zero-trust authentication mechanism combines dynamic key generation and blockchain, and performs security authentication of applications and devices layer by layer.

[0040] The present application has the following advantages:

[0041] In the present application, the intelligent data acquisition module enables the system to collect data from Internet of Things devices in real time and supports flexible storage in local and distributed databases. This ensures the efficiency and real-time performance of data collection, reduces data delay, and improves system response speed. The adaptive data synchronization module uses HyperSync technology and adaptive algorithms to achieve cross-region data synchronization, dynamically adjusts synchronization strategies, and ensures the accuracy and stability of data synchronization. In particular, in complex network environments, this technology can effectively reduce data loss and synchronization delay. Through the HyperAgent technology in the access control module, the system can intelligently proxy and distribute cross-region user and device requests. This approach not only improves the efficiency of request processing, but also optimizes the use of system resources and reduces network bandwidth usage. The blockchain optimization module improves the data read-write performance of blockchain nodes using HyperIO technology. By using connection pool management, data caching, asynchronous writing, and preheating mechanisms, this module can significantly reduce blockchain operation delays and improve overall system processing capacity, making Internet of Things data management more efficient. The security authentication module uses a dynamic key generation mechanism and a zero-trust authentication mechanism based on blockchain to provide strong security guarantees. This multi-level security authentication mechanism can effectively prevent unauthorized access and data leakage, ensuring the security and trustworthiness of applications and devices within the system. BRIEF DESCRIPTION OF DRAWINGS

[0042] Fig. 1 is a module schematic diagram of a distributed Internet of Things management system based on edge cloud computing according to the present application.

[0043] Fig. 2 is a flowchart of the running process of the adaptive data synchronization module provided in an embodiment of the present application.

[0044] Fig. 3 is a flowchart of a distributed Internet of Things management method based on edge cloud computing according to the present application. DETAILED DESCRIPTION

[0045] Please refer to Figs. 1-3 The present application relates to a distributed Internet of Things management method and system based on edge cloud computing.

[0046] Embodiment 1

[0047] The application discloses a kind of distributed Internet of Things management systems based on edge cloud computing, including the communication connection of intelligent data acquisition module, adaptive data synchronization module, access control module, block chain optimization module and security authentication module in turn;

[0048] The intelligent data acquisition module is used to collect device data from the Internet of Things in real time, and store the data in a local or distributed database.

[0049] Specifically, the intelligent data acquisition module is usually composed of a high-performance microcontroller (such as ARM Cortex-M4 or higher version), which carries a variety of sensor arrays. These sensors can include but are not limited to:

[0050] Environmental sensors: such as temperature sensors, humidity sensors, barometric pressure sensors, gas detection sensors, for monitoring environmental conditions.

[0051] Motion sensors: such as accelerometers, gyroscopes, for detecting the motion state and orientation change of objects.

[0052] Optical sensors: such as light intensity sensors, infrared sensors, for sensing light changes and object proximity.

[0053] Current / voltage sensors: for monitoring the working state of electrical equipment, detecting current, voltage and other key power parameters.

[0054] The microcontroller exchanges data with these sensors through various communication interfaces (such as I2C, SPI, UART, etc.), and obtains the state information of the Internet of Things in real time. In order to realize low-power operation, the microcontroller uses dynamic power management technology to dynamically adjust its working frequency and power voltage according to the workload.

[0055] It should be noted that the collected data is often massive and diverse, in order to improve the efficiency of subsequent processing and the effectiveness of data, the intelligent data acquisition module is equipped with a data preprocessing unit. Preprocessing includes the following steps: using preset thresholds and conditions to filter raw data, eliminating noise and abnormal data. For example, if the temperature sensor data fluctuates sharply within a short period of time, the system will judge that it may be a misreading and filter it out. Use lightweight compression algorithms (such as Delta compression, Huffman coding) to compress data, reduce data transmission and storage bandwidth occupancy. This is particularly important when network bandwidth is limited or data needs to be stored for a long time. Aggregate high-frequency data within a short period of time to generate representative statistical data (such as average, maximum, minimum), to reduce data redundancy and storage load. Each group of data is attached with a timestamp and a unique identifier to ensure the traceability and time sequence consistency of the data in the system. This is particularly important when synchronizing data from multiple regions and multiple devices.

[0056] The intelligent data acquisition module has a local storage function, usually using embedded flash memory (such as NOR flash or NAND flash) or SD card and other non-volatile memory to store pre-processed data. In the case of unstable or disconnected network connection, local storage can ensure the integrity and persistence of data. Once the network is restored, the module will automatically synchronize the locally stored data to the distributed database. In addition, the module also has a data caching mechanism, which temporarily stores high-frequency access or high-priority data in a cache area in RAM. This caching mechanism significantly reduces the read and write frequency of non-volatile memory, prolonging the service life of the memory.

[0057] In order to reduce delay and improve the overall efficiency of the system, the intelligent data acquisition module has certain edge computing capabilities. By deploying lightweight machine learning models (such as decision trees, k-nearest neighbor algorithms) in the module, the system can make simple decisions and predictions while collecting data. For example, the module can predict the risk of device failure based on historical data and current environmental conditions and issue real-time warnings. The module communicates with the upper management system through standardized interfaces (such as MQTT, HTTP / REST) to ensure that data can be seamlessly integrated into larger-scale Internet of Things management platforms. At the same time, the module supports OTA (Over-The-Air) update mechanism, which can remotely update its firmware and software, ensuring that the system is always in the latest safe and functional state.

[0058] The adaptive data synchronization module is used for receiving device data from the intelligent data acquisition module based on HyperSync technology and adaptive algorithm for cross-regional synchronization;

[0059] The operation process of the adaptive data synchronization module includes the following steps:

[0060] Receiving real-time collected device data from the intelligent data acquisition module through HyperSync technology;

[0061] Based on the adaptive algorithm, dynamically evaluate network bandwidth, device load and data importance, and select the optimal data synchronization strategy;

[0062] According to the selected synchronization strategy, perform distributed synchronization operation of device data across regions;

[0063] In the cross-regional synchronization process, monitor and handle abnormal situations such as synchronization failure or delay;

[0064] After synchronization is completed, verify the consistency of the data through a verification mechanism, and feed back the verification result to the intelligent data acquisition module.

[0065] Further, the formula of the adaptive algorithm is as follows:

[0066]

[0067] wherein S opt represents the optimal data synchronization strategy; S i represents the ith strategy in the current available set of synchronization strategies; a represents a network bandwidth weight coefficient, which determines the influence weight of network bandwidth in strategy selection; B net represents the current network bandwidth; b represents a device load weight coefficient, which determines the influence weight of device load in strategy selection; L dev represents the device load, i.e., the percentage of the current processing capacity of the device; g represents a data importance weight coefficient, which determines the influence weight of data importance in strategy selection; I data represents the importance of data.

[0068] It should be noted that the main functions of HyperSync are as follows:

[0069] Automatic data pickup: Listen to the PostgreSQL database log in the edge cluster, and when a device and user modification is found, automatically pick up the required data from the database.

[0070] Data generation:

[0071] Generation of basic information of the object model: The basic information of the object model refers to the device asset information used to verify whether the identity of the IoT device is legal, including: product primary key, product key, device ID, login password, and object model object hash check. When a user creates an IoT device in the system, the system will automatically generate the basic information of the object model according to the product to which the device belongs and the password generation rule, and generate the hash check of the current device object model object.

[0072] Generation of user identity information: User information refers to information used to verify whether the identity of the user is legal, including: user ID, user name, password (salted encryption), registered region, permanent region, and third-party platform ID (WeChat, email, Facebook, X, Google, Apple, etc., only the first three are synchronized), and hash check of user data object. When a user registers or updates data, the required information is automatically picked up, and the hash check of the current user data object is generated.

[0073] Data staging: In order to reduce the number of changes on the chain, these data will be temporarily stored in the local Redis service for a period of time (different data staging times are different), and any changes during the staging period will only update the local Redis cache, without updating the data on the blockchain.

[0074] Data publishing: After the data storage period expires, HyperSync will automatically take the data out of the local Redis cache, encrypt the data, and publish it on the chain.

[0075] Data update: When the data is published on the chain, HyperSync on other edge clusters will receive the corresponding update after a certain delay. At this time, the local cluster's HyperSync automatically takes out the data and updates it to the local cluster's Redis cluster, achieving global synchronization.

[0076] Full data synchronization: To reduce data transmission, considering the actual application scenario, full data synchronization only occurs between different edge clusters in a certain geographical area, and a piece of data is synchronized to at most two edge clusters, achieving three-copy storage. When synchronizing, HyperSync automatically determines whether to update the full data to the local area for disaster recovery based on the node location of the core data change on the chain and the approximate physical distance from the local area.

[0077] The access control module is used to realize intelligent agent distribution of cross-regional user and device requests through HyperAgent technology, and to deliver the processed requests and instructions to the blockchain optimization module.

[0078] The access control module includes a database access agent unit and an Internet of Things access agent unit.

[0079] The database access agent unit is used to filter, distribute, and optimize the access requests to the database through predefined security policies and dynamic authorization mechanisms.

[0080] Specifically, during the system initialization phase, administrators define a series of security policies based on business needs and security requirements. These policies include user permission levels, data classification, access time windows, and more. For example, certain sensitive data is only allowed to be accessed by high-level users during specific time periods, while other users can only access partial data or data that has been desensitized. Upon each access request, the database access agent unit dynamically generates an authorization token based on the current user's authentication information, request type, and data sensitivity. This token contains the user's permission scope, validity period, and access control policy, ensuring that even if the user's permissions change during access, access control can be dynamically adjusted. Upon receiving a database access request, the database access agent unit first filters the request through security policies, excluding access that does not meet security policies. For requests that meet the policies, the unit analyzes the current load of the database and assigns the request to the appropriate database node through an intelligent distribution algorithm. This algorithm takes into account factors such as the complexity of the request, the physical location of the data, and the load of the node, optimizing the overall efficiency of database access. The database access agent unit is also responsible for optimizing requests, such as request merging, query caching, and prefetching. Request merging technology is used to combine identical or similar query requests into a single database query in high-concurrency scenarios, reducing the load on the database. Query caching caches frequently accessed data, reducing I / O operations on the database.

[0081] The Internet of Things access agent unit is used to intelligently distribute and control access to devices based on the authentication, permission allocation, and access history of Internet of Things devices through HyperAgent technology.

[0082] The operation process of the Internet of Things access agent unit includes the following steps:

[0083] By analyzing the identities of Internet of Things devices and users in real time, access permissions are dynamically allocated;

[0084] Using HyperAgent technology to intelligently proxy user requests and device instructions across regions, optimizing request routing and distribution paths;

[0085] According to the network state and device load, dynamically adjust the request processing strategy, including local processing, edge computing node distribution, and cloud integrated processing.

[0086] Specifically, the IoT access agent unit first authenticates the device and user through a secure protocol such as OAuth2.0 or a blockchain-based identity verification mechanism. After authentication, the system dynamically allocates access permissions based on the type of device, the user's permission level, access history, and other factors. For example, when a user accesses a new device, the system assigns temporary or long-term access permissions to the user based on the user's historical behavior records, the importance of the device, and other factors. Using HyperAgent technology, the IoT access agent unit intelligently optimizes the routing and distribution path of requests based on factors such as the source location of the request, the geographic location of the target device, and the current network status. For example, the system can choose to send the request directly to the nearest edge computing node for processing to reduce latency. If the edge node is overloaded, the system will dynamically adjust and forward the request to another node with lower load or to the cloud for integrated processing. Based on network status (such as bandwidth, latency) and device load (such as CPU, memory usage), the IoT access agent unit dynamically adjusts the processing strategy of the request. For environments with light load and sufficient bandwidth, the system will prefer local processing to reduce the cost of cross-regional data transmission. In unstable network environments or when the device load is high, the system will distribute some requests to other edge computing nodes or the cloud for processing. This dynamic adjustment mechanism ensures the efficient operation of the system in complex network environments and guarantees the security and stability of the device.

[0087] The blockchain optimization module is used to optimize the data read-write performance of the blockchain node through HyperIO technology, including connection pool management, data caching, asynchronous writing, and preheating mechanisms for physical models and user data.

[0088] The operation process of the blockchain optimization module includes the following steps:

[0089] Connection pool management is achieved through HyperIO technology, dynamically allocating connection resources for the blockchain node.

[0090] Data caching mechanism is used to reduce the I / O operation frequency of the blockchain network.

[0091] Asynchronous writing technology is used to improve system response speed and implement preheating mechanisms for physical models and user data.

[0092] Dynamic monitoring of node load and network latency to adjust operation strategy.

[0093] It should be noted that based on the OpenResty and Redis technologies, an extension component HyperIO for high-performance reading of data on the blockchain is realized to solve the IO performance problem of the blockchain. HyperIO performs secondary encapsulation on the API of HyperLedger and maintains compatibility with the read-write interface of HyperLedger, and the user end can ignore the difference between the two. HyperIO mainly realizes the following key performance improvement work:

[0094] Connection pool: maintain a long connection pool with HyperLedger, reduce the additional overhead caused by frequent connection and disconnection.

[0095] Read interface: cache the data read from HyperLedger in the Redis cluster, and the cache time can be set by the business layer according to the actual needs.

[0096] Write interface: cache the write instruction sent by the client to the MQ queue, and consume and submit to the chain by another thread.

[0097] Preheat the physical model: real-time monitoring of changes in the physical model on the blockchain and real-time updating to the Redis cluster.

[0098] User model preheating: real-time monitoring of changes in user data on the blockchain and real-time updating to the Redis cluster.

[0099] The security authentication module is used to realize multi-level security authentication of applications and devices through a dynamic key generation mechanism and a zero-trust authentication mechanism based on a blockchain; the security authentication module generates multi-level dynamic keys based on the dynamic key generation mechanism, in combination with the physical characteristics of the device, the network environment and the user behavior characteristics, and performs layer-by-layer verification through the zero-trust authentication mechanism based on the blockchain.

[0100] It is necessary to note that in the process of generating dynamic keys, the network environment in which the real-time analysis device is located is analyzed in real time. Network features such as IP address, network topology, traffic pattern, connection time, etc. will be used to enhance the dynamicity of the key. By analyzing these network data, it is possible to effectively prevent man-in-the-middle attacks and replay attacks. Through machine learning algorithms, the user's behavior is modeled. Behavioral characteristics include user login time, operation habits, access frequency and pattern, etc. Changes in these characteristics will dynamically affect the generation of the key, ensuring that even if the same device is operated in different environments or by different users, the key will change. Based on the above-mentioned multi-dimensional features collected, an encryption algorithm (such as HMAC-SHA256 or AES-GCM) is used to generate a dynamic key. The process of key generation is continuous, and as the device usage, network environment and user behavior change, the key will be updated continuously. This mechanism ensures that even if the key is leaked at a certain moment, it will quickly become invalid.

[0101] Under the zero trust framework, the system does not trust any device or user by default, regardless of their position in the system or past trust history. Each request requires re-authentication, which greatly enhances the security of the system. Blockchain plays a role in trusted storage and verification. Device and user identity information is recorded in the blockchain. Each authentication request, the system verifies the identity of the requester through the blockchain node. The immutability of the blockchain ensures the integrity and credibility of the identity data. During the verification process, zero-knowledge proof technology is used to ensure that identity information is not exposed during the verification process. The module divides the authentication process into multiple levels. The first layer is the device layer verification, which mainly checks the physical characteristics and network environment of the device. The second layer is the user layer verification, which is based on the user behavior model for verification. The last layer is the operation layer verification, which checks the legality and security of the current operation. This multi-level authentication ensures that even if the device is compromised, the attacker cannot easily break through the protection of other levels. The security authentication module supports blockchain-based smart contract technology, which defines and executes security policies through contracts. Smart contracts can dynamically adjust authentication strategies, such as when abnormal behavior or potential attacks are detected, the system will automatically increase the authentication difficulty, or require additional multi-factor authentication. Blockchain is also used to manage the life cycle of the key. After the dynamic key is generated, its use and update records will be stored in the blockchain. Through smart contracts, the expiration, update and cancellation of the key can be automatically managed, ensuring the automation and security of key management.

[0102] It also includes dividing its architecture into infrastructure layer, domain layer, application layer and access layer through domain-driven design model, and each layer communicates through standardized interface.

[0103] Specifically, the system uses the domain-driven design (DDD) model for design, which divides the system into four layers:

[0104] 1. Infrastructure layer: mainly provides platform-level general capabilities, including two kinds of data middleware and platform middleware. Among them:

[0105] Data middleware: mainly used to provide storage support for device and service data within a single cluster, including a PostgreSQL cluster for storing node device data, a Redis cluster for caching storage model and service data model, an MQ cluster for caching IoT instructions, and a HyperLedger blockchain node for verifying device assets, user data, and data assets.

[0106] Platform middleware: mainly provides the most basic microservices and DDD drivers, in addition, it also provides rule engine, low-code engine, object model engine, protocol access engine, application access engine, and data synchronization engine, etc. Platform machine general capability middleware.

[0107] 2. Domain layer: mainly builds the basic services of each domain, providing basic business capability support for the application layer. Application services, IoT services, and blockchain services provide encapsulated microservice interfaces for different applications, isolating the operation of data middleware from the upper layer application.

[0108] 3. Application layer: various microservices that carry business layer code, handling business logic.

[0109] 4. Access layer: mainly provides authentication plugins, web gateways, REST API gateways, and IoT gateways. Among them, the authentication plugins are divided into two parts:

[0110] Application authentication: application authentication is a service used to verify user client access permissions, which is further divided into C-end user authentication module and B-end user authentication module.

[0111] Device authentication: device authentication is a module used to authenticate device assets. Only assets that exist in the asset library can normally connect to the platform and communicate.

[0112] Embodiment 2

[0113] A distributed Internet of Things management method based on edge cloud computing, comprising the following steps:

[0114] Real-time collection of Internet of Things device data, and storage of data in local or distributed databases according to demand;

[0115] Using HyperSync technology, based on adaptive algorithm to evaluate network and device status, select the optimal strategy for cross-regional data synchronization;

[0116] HyperAgent technology is used to intelligently manage user requests and device instructions across regions and permissions.

[0117] HyperIO technology is used to manage connection pools, data caching, and asynchronous writing to improve the read-write performance of blockchain nodes.

[0118] Dynamic key generation and zero-trust authentication mechanisms are combined with blockchain to perform security authentication for applications and devices layer by layer.

[0119] In this embodiment, a distributed Internet of Things management method based on edge cloud computing is applied to the distributed Internet of Things management system based on edge cloud computing described in Embodiment 1, which will not be repeated here.

[0120] In summary, the intelligent data acquisition module in the present application can not only collect a variety of device data in real time, but also has powerful data preprocessing capabilities, including noise filtering, data compression and aggregation, etc. These functions greatly reduce the complexity of subsequent data processing and transmission bandwidth requirements, while improving the accuracy and effectiveness of the data, especially suitable for scenarios with limited network bandwidth. Through HyperSync technology and adaptive algorithms, the system can dynamically evaluate network bandwidth, device load and data importance when synchronizing data across regions, and select the optimal synchronization strategy. This mechanism not only optimizes the efficiency of data synchronization, but also ensures data consistency and integrity in complex network environments.

[0121] The access control module in the present application uses HyperAgent technology and dynamic authorization mechanisms to intelligently distribute and manage access requests from users and devices. Through the database and Internet of Things access agent unit, the system can effectively filter illegal requests and dynamically adjust the request processing strategy according to the current network and device status, improving the security and access efficiency of the system. The blockchain optimization module uses HyperIO technology for connection pool management, data caching, asynchronous writing, etc., greatly improving the read-write performance of blockchain nodes. Especially when dealing with a large number of physical models and user data, the preheating mechanism ensures the real-time performance of the data and the efficient response of the system, adapting to the needs of large-scale Internet of Things device management.

[0122] The security authentication module in the application realizes multi-level security authentication of applications and devices through a dynamic key generation mechanism and a zero-trust authentication mechanism based on a block chain. The system can not only generate dynamic keys according to device physical characteristics, network environment and user behavior characteristics, but also ensure the authenticity and non-tamperability of the authentication process through block chain technology, greatly enhancing the overall security of the system. The system architecture is divided into infrastructure layer, domain layer, application layer and access layer through a domain-driven design model, and the layers communicate through standardized interfaces. This layered design not only makes the system more modular, facilitating maintenance and expansion, but also allows for flexible adjustments according to different application scenarios, meeting diverse business needs.

[0123] The above embodiments only describe the preferred embodiments of the application, and do not limit the scope of the application. Without departing from the design spirit of the application, various modifications and improvements to the technical solutions of the application made by ordinary engineering technicians in the art shall fall within the protection scope determined by the claims of the application.

Claims

1.A distributed Internet of Things management system based on edge cloud computing, characterized in that, The intelligent data acquisition module, the adaptive data synchronization module, the access control module, the blockchain optimization module and the security authentication module are sequentially communicatively connected. The intelligent data acquisition module is configured to acquire device data from Internet of Things devices in real time and store the data in a local or distributed database. The adaptive data synchronization module is configured to receive the device data from the intelligent data acquisition module based on a HyperSync technology and using an adaptive algorithm to perform cross-region synchronization. The access control module is configured to implement intelligent agent distribution for user and device requests across regions by a HyperAgent technology and deliver processed requests and instructions to the blockchain optimization module. The blockchain optimization module is configured to optimize data read-write performance of blockchain nodes by a HyperIO technology, including connection pool management, data caching, asynchronous writing and preheating mechanisms for physical models and user data. The security authentication module is configured to implement multi-level security authentication for applications and devices by a dynamic key generation mechanism and a zero-trust authentication mechanism based on a blockchain. 2.The distributed Internet of Things management system based on edge cloud computing according to claim 1, wherein, The operation process of the adaptive data synchronization module includes the following steps: receiving the real-time acquired device data from the intelligent data acquisition module by the HyperSync technology; dynamically evaluating network bandwidth, device load and data importance based on an adaptive algorithm to select an optimal data synchronization strategy; performing distributed synchronization of device data across regions according to the selected synchronization strategy; monitoring and processing abnormal situations of synchronization failure or delay during the cross-region synchronization process; verifying consistency of the data after synchronization is completed and feeding back the verification result to the intelligent data acquisition module. 3.The distributed Internet of Things management system based on edge cloud computing according to claim 2, wherein, The adaptive algorithm has the following formula: where S opt represents the optimal data synchronization strategy; S i represents the ith strategy in the current available set of synchronization strategies; a represents a network bandwidth weight coefficient, which determines the influence weight of network bandwidth in strategy selection; B net represents the current network bandwidth; b represents a device load weight coefficient, which determines the influence weight of device load in strategy selection; L dev represents the device load, i.e., the percentage of occupation of the current processing capacity of the device; g represents a data importance weight coefficient, which determines the influence weight of data importance in strategy selection; I data represents the importance of data. 4.The distributed Internet of Things management system based on edge cloud computing according to claim 1, wherein, The access control module includes a database access agent unit and an Internet of Things access agent unit. The database access agent unit is configured to filter, distribute and optimize processing of access requests to the database by a predefined security strategy and a dynamic authorization mechanism. The Internet of Things access agent unit is configured to intelligently distribute and control access requests of devices by the HyperAgent technology based on identity verification, permission allocation and access history records of Internet of Things devices. 5.The distributed Internet of Things management system based on edge cloud computing according to claim 4, wherein, The operation process of the Internet of Things access agent unit includes the following steps: dynamically allocating access permissions by real-time analysis of Internet of Things device and user identities; optimizing routing and distribution paths of requests by intelligently agenting user requests and device instructions across regions using the HyperAgent technology; dynamically adjusting request processing strategies, including local processing, edge computing node distribution and cloud integration processing, according to network status and device load. 6.The distributed Internet of Things management system based on edge cloud computing according to claim 1, wherein, The operation process of the blockchain optimization module includes the following steps: implementing connection pool management by the HyperIO technology to dynamically allocate connection resources of blockchain nodes; reducing I / O operation frequency of the blockchain network by using a data caching mechanism; adopting an asynchronous writing technology to improve system response speed and implement preheating mechanisms for physical models and user data; dynamically monitoring node load and network delay to adjust operation strategies. 7.The distributed Internet of Things management system based on edge cloud computing of claim 1, wherein, The security authentication module is based on a dynamic key generation mechanism, specifically combining the physical characteristics of the device, network environment and user behavior characteristics to generate multi-level dynamic keys, and through a zero-trust authentication mechanism based on blockchain to verify layer by layer. 8.The distributed Internet of Things management system based on edge cloud computing of claim 1, wherein, It also includes dividing its architecture into infrastructure layer, domain layer, application layer and access layer through domain-driven design model, and communicating between layers through standardized interfaces. 9.A method for managing a distributed Internet of Things based on edge cloud computing, characterized in that, The system is applied to a distributed Internet of Things management system based on edge cloud computing as claimed in any one of claims 1-8, comprising the following steps: Real-time collection of Internet of Things device data, and storage of data in local or distributed databases according to requirements; Using HyperSync technology, based on adaptive algorithm to evaluate network and device status, select the optimal strategy for cross-region data synchronization; Using HyperAgent technology, intelligent agent and permission management for cross-region user requests and device instructions; Through HyperIO technology, manage connection pool, data cache and asynchronous writing, improve the read-write performance of blockchain nodes; Combined with dynamic key generation and zero-trust authentication mechanism based on blockchain, layer-by-layer security authentication of applications and devices.

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