Distributed Internet of Things management method and system based on edge cloud computing

Abstract

The invention relates to the technical field of block chains, 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, a self-adaptive data synchronization module, an access control module, a block chain optimization module and a security authentication module. The intelligent data acquisition module collects equipment data in real time, and the adaptive data synchronization module realizes cross-regional synchronization based on a HyperSync technology. The access control module intelligently distributes user and equipment requests by using a HyperAgent technology and transmits the user and equipment requests to the block chain optimization module, and the block chain optimization module optimizes the data processing performance of block chain nodes through a Hyper I O technology. And the security authentication module is combined with a dynamic key and a zero-trust authentication mechanism to realize multi-level application and equipment security authentication. According to the invention, the real-time performance of Internet of Things management, the stability of cross-regional synchronization, the intelligence of request processing, the efficiency of block chain operation and the overall security of the system are improved.

Description

Technical Field

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

[0002] With the rapid development of IoT technology, the number of connected devices is growing exponentially. Traditional IoT architectures usually adopt a centralized cloud computing model, uploading large amounts of data to the cloud for processing and storage. However, with the increase in the number of IoT devices, the centralized architecture has exposed some significant limitations. The following problems still exist: existing technologies are difficult to collect and process data from a large number of IoT devices in real time and efficiently, affecting the response speed of the system; traditional methods lack dynamic adjustment capabilities, resulting in data synchronization delays, packet loss and other problems, making it difficult to ensure data consistency and accuracy; centralized processing methods occupy a large amount of resources and cannot effectively respond to a large number of concurrent requests, reducing the processing efficiency and response speed of the system. Summary of the invention

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

[0004] To achieve the above object, the technical solution adopted by the present invention is:

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

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

[0007] The adaptive data synchronization module is used to synchronize the device data received from the intelligent data acquisition module across regions based on HyperSync technology and using an adaptive algorithm;

[0008] The access control module is used to implement intelligent proxy distribution of cross-region user and device requests through HyperAgent technology, and pass the processed requests and instructions to the blockchain optimization module;

[0009] The blockchain optimization module is used to optimize the data reading and writing performance of blockchain nodes through HyperIO technology, including connection pool management, data caching, asynchronous writing transitive model and user data preheating mechanism;

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

[0011] Furthermore, the operation process of the adaptive data synchronization module includes the following steps:

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

[0013] Based on adaptive algorithms, it dynamically evaluates network bandwidth, device load, and data importance to select the optimal data synchronization strategy.

[0014] According to the selected synchronization strategy, the device data is synchronized across regions in a distributed manner;

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

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

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

[0018]

[0019] Among them, S opt represents the optimal data synchronization strategy; S i represents the i-th strategy in the currently available synchronization strategy set; α represents the network bandwidth weight coefficient, which determines the influence weight of network bandwidth in strategy selection; B net represents the current network bandwidth; β represents the device load weight coefficient, which determines the influence weight of the device load in the strategy selection; L dev represents the device load, that is, the percentage of the device's current processing capacity occupied; γ represents the data importance weight coefficient, which determines the influence weight of data importance in strategy selection; I data Indicates the importance of the data.

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

[0021] The database access proxy unit is used to filter, distribute and optimize the access requests to the database through predefined security policies and dynamic authorization mechanisms;

[0022] The IoT access proxy unit is used to intelligently distribute and control access requests of devices based on the identity authentication, permission allocation and access history records of IoT devices through HyperAgent technology.

[0023] Furthermore, the operation process of the IoT access proxy unit includes the following steps:

[0024] Dynamically assign access rights by analyzing IoT devices and user identities in real time;

[0025] Use HyperAgent technology to intelligently proxy cross-region user requests and device instructions, optimizing the routing and distribution paths of requests;

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

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

[0028] Use HyperIO technology to manage the connection pool and dynamically allocate the connection resources of blockchain nodes;

[0029] Use data caching mechanism to reduce the I / O operation frequency of blockchain network;

[0030] Asynchronous writing technology is used to improve system response speed and implement a preheating mechanism for object models and user data;

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

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

[0033] Furthermore, it also includes dividing its architecture into infrastructure layer, domain layer, application layer and access layer through the domain-driven design model, and the various layers communicate through standardized interfaces.

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

[0035] Collect IoT device data in real time and store the data in local or distributed databases as needed;

[0036] Using HyperSync technology, the network and device conditions are evaluated based on adaptive algorithms, and the optimal strategy is selected for cross-region data synchronization;

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

[0038] Through HyperIO technology, we manage connection pools, data caches, and asynchronous writing to improve the read and write performance of blockchain nodes.

[0039] Combined with dynamic key generation and blockchain's zero-trust authentication mechanism, security authentication of applications and devices is performed layer by layer.

[0040] The beneficial effects of the present invention are:

[0041] In the present invention, through the intelligent data acquisition module, the system can collect data of IoT devices in real time and support flexible storage of local and distributed databases. This ensures the efficiency and real-time performance of data acquisition, reduces data delay, and improves the system response speed. The adaptive data synchronization module uses HyperSync technology and adaptive algorithms to achieve cross-regional synchronization of data, and can dynamically adjust the synchronization strategy to ensure the accuracy and stability of data synchronization. Especially 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-regional user and device requests. This method not only improves the efficiency of request processing, but also optimizes the use of system resources and reduces the occupancy of network bandwidth. The blockchain optimization module improves the data reading and writing performance of blockchain nodes through HyperIO technology. Using connection pool management, data caching, asynchronous writing and preheating mechanisms, the module can significantly reduce the delay of blockchain operations and improve the overall processing capacity of the system, making the management of IoT data more efficient. The security authentication module adopts a dynamic key generation mechanism and a zero-trust authentication mechanism based on blockchain to provide strong security protection. This multi-level security authentication mechanism can effectively prevent unauthorized access and data leakage, ensuring the security and credibility of each application and device within the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a module schematic diagram of a distributed Internet of Things management system based on edge cloud computing of the present invention.

[0043] Figure 2 It is a flowchart of the operation process of the adaptive data synchronization module provided by an embodiment of the present invention.

[0044] Figure 3 It is a flow chart of a distributed Internet of Things management method based on edge cloud computing of the present invention. DETAILED DESCRIPTION

[0045] See also Figure 1-3 As shown, the present invention relates to a distributed Internet of Things management method and system based on edge cloud computing.

[0046] Example 1

[0047] 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 which are sequentially connected in communication;

[0048] The intelligent data acquisition module is used to collect device data from IoT devices 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) equipped with a variety of sensor arrays. These sensors may include but are not limited to:

[0050] Environmental sensors: such as temperature sensors, humidity sensors, atmospheric pressure sensors, and gas detection sensors, used to monitor environmental conditions.

[0051] Motion sensors: such as accelerometers and gyroscopes, used to detect the motion state and orientation changes of objects.

[0052] Optical sensors: such as light intensity sensors and infrared sensors, used to sense changes in light and the proximity of objects.

[0053] Current / voltage sensor: used to monitor the working status of electrical equipment and detect key power parameters such as current and voltage.

[0054] The microcontroller exchanges data with these sensors through a variety of communication interfaces (such as I2C, SPI, UART, etc.) to obtain real-time status information of IoT devices. In order to achieve low-power operation, the microcontroller adopts dynamic power management technology to dynamically adjust its operating frequency and power supply 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: Filter the raw data using preset thresholds and conditions to remove noise and abnormal data. For example, if the data of the temperature sensor fluctuates violently in a short period of time, the system will determine that it may be a misreading and filter it out. Use lightweight compression algorithms (such as Delta compression and Huffman coding) to compress the data to reduce the bandwidth occupied by data transmission and storage. This is especially important when the network bandwidth is limited or the data needs to be stored for a long time. Aggregate high-frequency data in a short period of time to generate representative statistics (such as average, maximum, minimum) to reduce data redundancy and storage load. Each set of data is attached with a timestamp and a unique identifier to ensure the traceability and timing consistency of the data in the system. This is particularly critical when synchronizing data in 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 non-volatile memory such as SD card 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 by maintaining a high-speed cache area in RAM. This caching mechanism significantly reduces the frequency of reading and writing non-volatile memory and extends the service life of the memory.

[0057] In order to reduce latency 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 failure risk of equipment based on historical data and current environmental conditions, and issue early warnings in real time. The module communicates with the upper-level management system through standardized interfaces (such as MQTT, HTTP / REST) ​​to ensure that the data can be seamlessly integrated into a larger-scale IoT management platform. At the same time, the module supports the OTA (Over-The-Air) update mechanism, which can remotely update its firmware and software to ensure that the system is always in the latest security and functional state.

[0058] The adaptive data synchronization module is used to synchronize the device data received from the intelligent data acquisition module across regions based on HyperSync technology and using an adaptive algorithm;

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

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

[0061] Based on adaptive algorithms, it dynamically evaluates network bandwidth, device load, and data importance to select the optimal data synchronization strategy.

[0062] According to the selected synchronization strategy, the device data is synchronized across regions in a distributed manner;

[0063] During cross-region synchronization, monitor and handle abnormal situations such as synchronization failure or delay;

[0064] After the synchronization is completed, the consistency of the data is verified through a verification mechanism, and the verification results are fed back to the intelligent data acquisition module.

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

[0066]

[0067] Among them, S opt represents the optimal data synchronization strategy; S i represents the i-th strategy in the currently available synchronization strategy set; α represents the network bandwidth weight coefficient, which determines the influence weight of network bandwidth in strategy selection; B net represents the current network bandwidth; β represents the device load weight coefficient, which determines the influence weight of the device load in the strategy selection; L dev represents the device load, that is, the percentage of the device's current processing capacity occupied; γ represents the data importance weight coefficient, which determines the influence weight of data importance in strategy selection; I data Indicates the importance of the data.

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

[0069] Automatic data collection: Monitor the PostgreSQL database logs in the edge cluster and automatically collect the required data from the database when device and user modifications are found.

[0070] Generate data:

[0071] Generate basic information of the object model: Basic information of the object model refers to the device asset information used to verify whether the identity of the IoT device is legitimate, 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 basic information of the object model according to the product to which the device belongs and the password generation rules, and generate a hash check of the object model object of the current device.

[0072] Generate user identity information: User information refers to information used to verify whether the user's identity is legitimate, including: user ID, user name, password (encrypted with salt), registration area, permanent area and third-party platform ID (WeChat, email, Facebook, X, Google, Apple, etc., only the first three are synchronized) and hash verification of user data objects. When a user registers or updates data, the required information is automatically picked up and a hash verification of the current user data object is generated.

[0073] Data temporary storage: In order to reduce the number of changes to the data on the chain, these data will be temporarily stored in the local Redis service for a period of time (different data have different temporary storage times). If there are any changes during the temporary storage period, only the local Redis cache will be updated, and the data on the blockchain will not be updated.

[0074] Data release: When the data storage period expires, HyperSync will automatically retrieve the data from the local Redis cache, encrypt the data, and publish it to the chain.

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

[0076] Full data synchronization: In order to reduce the amount of data transmission and considering the actual application scenarios, the synchronization of full data only occurs between different edge clusters in a certain geographical area, and a piece of data is synchronized to at most two edge clusters to achieve three copies of storage. During synchronization, HyperSync automatically determines whether it is necessary to update the full data to the local area for disaster recovery based on the approximate physical distance between the node location where the core data on the chain changes and the region where it is located.

[0077] The access control module is used to implement intelligent proxy distribution of cross-region user and device requests through HyperAgent technology, and pass the processed requests and instructions to the blockchain optimization module;

[0078] Wherein, the access control module includes a database access proxy unit and an Internet of Things access proxy unit;

[0079] The database access proxy 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, the administrator defines a series of security policies based on business needs and security requirements. These policies include user permission levels, data classification, access time windows, etc. For example, some sensitive data is only allowed to be accessed by high-level users during a specific time period, while other users can only access part of the data or data that has been desensitized. When each access request is issued, the database access proxy unit dynamically generates an authorization token based on the current user's authentication information, request type, and data sensitivity. The token contains the user's permission range, validity period, and access control policy, ensuring that access control can be dynamically adjusted even if the user's permissions change during the access period. After receiving the database access request, the database access proxy unit first filters the request through the security policy to exclude access that does not comply with the security policy. For requests that comply with the policy, the unit analyzes the current load of the database and distributes the request to the appropriate database node through an intelligent distribution algorithm. The algorithm takes into account factors such as the complexity of the request, the physical location of the data, and the load of the node, and optimizes the overall efficiency of database access. The database access proxy unit is also responsible for optimizing the request, such as request merging, query caching, and prefetching. Request merging technology is used to merge the same or similar query requests into one database query in high-concurrency scenarios to reduce the database load. Query caching caches frequently accessed data to reduce database I / O operations.

[0081] The IoT access proxy unit is used to intelligently distribute and control access requests of devices based on the identity authentication, permission allocation and access history records of IoT devices through HyperAgent technology.

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

[0083] Dynamically assign access rights by analyzing IoT devices and user identities in real time;

[0084] Use HyperAgent technology to intelligently proxy cross-region user requests and device instructions, optimizing the routing and distribution paths of requests;

[0085] Dynamically adjust request processing strategies based on network status and device load, including local processing, edge computing node distribution, and cloud integrated processing.

[0086] Specifically, the IoT access proxy unit first authenticates the device and user through a security protocol (such as OAuth2.0 or a blockchain-based authentication mechanism). After the authentication is passed, the system dynamically assigns access rights based on the type of device, the user's permission level, access history, etc. For example, when a user accesses a new device, the system assigns temporary or long-term access rights to the user based on factors such as the user's historical behavior record and the importance of the device. Using HyperAgent technology, the IoT access proxy unit intelligently optimizes the routing and distribution path of the request 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 load on the edge node is too high, the system will dynamically adjust and forward the request to another node with lower load or cloud integrated processing. Based on the network status (such as bandwidth, latency) and device load (such as CPU, memory usage), the IoT access proxy unit will dynamically adjust the request processing strategy. For environments with light load and sufficient bandwidth, the system will give priority to local processing to reduce the cost of cross-regional data transmission. When the network is unstable or 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 a complex network environment and guarantees the security and stability of the device.

[0087] The blockchain optimization module is used to optimize the data reading and writing performance of blockchain nodes through HyperIO technology, including connection pool management, data caching, asynchronous writing transitive model and user data preheating mechanism;

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

[0089] Use HyperIO technology to manage the connection pool and dynamically allocate the connection resources of blockchain nodes;

[0090] Use data caching mechanism to reduce the I / O operation frequency of blockchain network;

[0091] Asynchronous writing technology is used to improve system response speed and implement a preheating mechanism for object models and user data;

[0092] Dynamically monitor node load and network latency and adjust operation strategies.

[0093] It should be noted that based on OpenResty and Redis technology, an extension component for high-performance reading of data on the blockchain is implemented: HyperIO, which is used to solve the IO performance problem of the blockchain. HyperIO re-encapsulates the HyperLedger API and maintains compatibility with the read and write interfaces of HyperLedger. The user end can ignore the difference between the two. HyperIO mainly achieves the following key performance improvements:

[0094] Connection pool: Maintain a long connection pool with HyperLedger to reduce the additional overhead caused by frequent connections and disconnections.

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

[0096] Write interface: The write instructions sent by the client are cached in the MQ queue, and consumed and submitted to the chain by another thread.

[0097] Object model preheating: monitor the changes of the object model on the blockchain in real time and update it to the Redis cluster in real time.

[0098] User model preheating: monitor changes in user data on the blockchain in real time and update them to the Redis cluster in real time.

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

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

[0101] In the zero-trust framework, the system does not trust any device or user by default, regardless of its position in the system or past trust history. Each request needs to be re-authenticated, which greatly enhances the security of the system. Blockchain plays the role of trusted storage and verification. The identity information of devices and users will be recorded in the blockchain. For 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 the identity information will not be exposed during the verification process. The module divides the authentication process into multiple levels. The first level is device layer verification, which mainly checks the physical characteristics and network environment of the device. The second level is user layer verification, which is based on the user behavior model. The last level is 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 other levels of protection. The security authentication module supports blockchain-based smart contract technology, which defines and executes security policies through contracts. Smart contracts can dynamically adjust authentication policies. For example, when abnormal behavior or potential attacks are detected, the system will automatically increase the difficulty of authentication or require additional multi-factor verification. Blockchain is also used to manage the life cycle of keys. After a dynamic key is generated, its use and update records will be stored in the blockchain. Through smart contracts, the expiration, update and cancellation of keys can be automatically managed to ensure 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 the domain-driven design model, and the various layers communicate through standardized interfaces.

[0103] Specifically, the system is designed using the domain-driven design (DDD) model, splitting the system into four layers:

[0104] 1. Infrastructure layer: mainly provides common platform-level capabilities, including various data middleware and platform middleware. Among them:

[0105] Data middleware: mainly used to provide storage support for device and business data within a single cluster, including a PostgreSQL cluster for storing node device data, a Redis cluster for caching storage object models and business data models, 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 common platform middleware such as rule engine, low-code engine, object model engine, protocol access engine, application access engine and data synchronization engine.

[0107] 2. Domain layer: It mainly builds basic services in various fields to provide basic business capability support for the application layer. Application services, IoT services, and blockchain services provide encapsulated microservice interfaces for different applications, isolating the operations of the data middleware from the upper-layer applications.

[0108] 3. Application layer: various microservices that carry business layer codes and process business logic.

[0109] 4. Access layer: mainly provides authentication plug-ins, Web gateways, RESTAPI gateways and IoT gateways. Among them, the authentication plug-in is divided into two parts:

[0110] Application authentication: Application authentication is a service used to verify user client access rights, which is further divided into two parts: 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 connect to the platform and communicate normally.

[0112] Example 2

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

[0114] Collect IoT device data in real time and store the data in local or distributed databases as needed;

[0115] Using HyperSync technology, the network and device conditions are evaluated based on adaptive algorithms, and the optimal strategy is selected for cross-region data synchronization;

[0116] Use HyperAgent technology to perform intelligent proxy and permission management for cross-region user requests and device instructions;

[0117] Through HyperIO technology, we manage connection pools, data caches, and asynchronous writing to improve the read and write performance of blockchain nodes.

[0118] Combined with dynamic key generation and blockchain's zero-trust authentication mechanism, security authentication of applications and devices is performed layer by layer.

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

[0120] In summary, the intelligent data acquisition module in the present invention can not only collect diverse device data in real time, but also has powerful data preprocessing capabilities, including noise filtering, data compression and aggregation. 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 during cross-regional data synchronization, thereby selecting 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 invention uses HyperAgent technology and dynamic authorization mechanism to intelligently distribute and manage access requests of users and devices. Through the database and the Internet of Things access proxy unit, the system can effectively filter illegal requests, and dynamically adjust the request processing strategy according to the current network and device status, thereby improving the security and access efficiency of the system. The blockchain optimization module uses HyperIO technology to perform connection pool management, data caching, asynchronous writing and other operations, which greatly improves the read and write performance of blockchain nodes. Especially when processing a large number of object models and user data, the preheating mechanism ensures the real-time nature of the data and the efficient response of the system, and adapts to the needs of large-scale Internet of Things device management.

[0122] The security authentication module in the present invention realizes multi-level security authentication of applications and devices through a dynamic key generation mechanism and a zero-trust authentication mechanism based on blockchain. The system can not only generate dynamic keys based on the physical characteristics of the device, the network environment, and the user behavior characteristics, but also ensure the credibility and non-tamperability of the authentication process through blockchain 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 the domain-driven design model, and each layer communicates through a standardized interface. This layered design not only makes the system more modular, easy to maintain and expand, but also can be flexibly adjusted according to different application scenarios to meet diverse business needs.

[0123] The above implementation modes are merely descriptions of the preferred implementation modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary engineering and technical personnel in the field shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A distributed Internet of Things management system based on edge cloud computing, characterized in that: It includes an intelligent data acquisition module, an adaptive data synchronization module, an access control module, a blockchain optimization module and a security authentication module which are sequentially connected in communication; The intelligent data acquisition module is used to collect device data from IoT devices in real time and store the data in a local or distributed database; The adaptive data synchronization module is used to synchronize the device data received from the intelligent data acquisition module across regions based on HyperSync technology and using an adaptive algorithm; The access control module is used to implement intelligent proxy distribution of cross-region user and device requests through HyperAgent technology, and pass the processed requests and instructions to the blockchain optimization module; The blockchain optimization module is used to optimize the data reading and writing performance of blockchain nodes through HyperIO technology, including connection pool management, data caching, asynchronous writing transitive model and user data preheating mechanism; The security authentication module is used to achieve multi-level security authentication of applications and devices through a dynamic key generation mechanism and a zero-trust authentication mechanism based on blockchain.

2. According to claim 1, a distributed Internet of Things management system based on edge cloud computing is characterized in that: The operation process of the adaptive data synchronization module includes the following steps: Receiving the real-time collected device data from the intelligent data collection module through HyperSync technology; Based on adaptive algorithms, it dynamically evaluates network bandwidth, device load, and data importance to select the optimal data synchronization strategy. According to the selected synchronization strategy, the device data is synchronized across regions in a distributed manner; During cross-region synchronization, monitor and handle abnormal situations such as synchronization failure or delay; After the synchronization is completed, the consistency of the data is verified through a verification mechanism, and the verification results are fed back to the intelligent data acquisition module.

3. A distributed Internet of Things management system based on edge cloud computing according to claim 2, characterized in that: The formula of the adaptive algorithm is as follows: Among them, S opt represents the optimal data synchronization strategy; S i represents the i-th strategy in the currently available synchronization strategy set; α represents the network bandwidth weight coefficient, which determines the influence weight of network bandwidth in strategy selection; B net represents the current network bandwidth; β represents the device load weight coefficient, which determines the influence weight of the device load in the strategy selection; L dev represents the device load, that is, the percentage of the device's current processing capacity; γ represents the data importance weight coefficient, which determines the influence weight of data importance in strategy selection; I data Indicates the importance of the data.

4. According to the distributed Internet of Things management system based on edge cloud computing according to claim 1, it is characterized in that: The access control module includes a database access proxy unit and an Internet of Things access proxy unit; The database access proxy unit is used to filter, distribute and optimize the access requests to the database through predefined security policies and dynamic authorization mechanisms; The IoT access proxy unit is used to intelligently distribute and control access requests of devices based on the identity authentication, permission allocation and access history records of IoT devices through HyperAgent technology.

5. A distributed Internet of Things management system based on edge cloud computing according to claim 4, characterized in that: The operation process of the Internet of Things access proxy unit includes the following steps: Dynamically assign access rights by analyzing IoT devices and user identities in real time; Use HyperAgent technology to intelligently proxy cross-region user requests and device instructions, optimizing the routing and distribution paths of requests; Dynamically adjust request processing strategies based on network status and device load, including local processing, edge computing node distribution, and cloud integrated processing.

6. A distributed Internet of Things management system based on edge cloud computing according to claim 1, characterized in that: The operation process of the blockchain optimization module includes the following steps: Use HyperIO technology to manage the connection pool and dynamically allocate the connection resources of blockchain nodes; Use data caching mechanism to reduce the I / O operation frequency of blockchain network; Asynchronous writing technology is used to improve system response speed and implement a preheating mechanism for object models and user data; Dynamically monitor node load and network latency and adjust operation strategies.

7. The distributed Internet of Things management system based on edge cloud computing according to claim 1 is characterized in that: The security authentication module is based on a dynamic key generation mechanism, specifically combining the physical characteristics of the device, the network environment, and the user behavior characteristics to generate multi-level dynamic keys, and performs layer-by-layer verification through a zero-trust authentication mechanism based on blockchain.

8. The distributed Internet of Things management system based on edge cloud computing according to claim 1 is characterized in that: It also includes dividing its architecture into infrastructure layer, domain layer, application layer and access layer through the domain-driven design model, and the various layers communicate through standardized interfaces.

9. A distributed Internet of Things management method 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 described in any one of claims 1 to 8, comprising the following steps: Collect IoT device data in real time and store the data in local or distributed databases as needed; Using HyperSync technology, the network and device conditions are evaluated based on adaptive algorithms, and the optimal strategy is selected for cross-region data synchronization; Use HyperAgent technology to perform intelligent proxy and permission management for cross-region user requests and device instructions; Through HyperIO technology, we manage connection pools, data caches, and asynchronous writing to improve the read and write performance of blockchain nodes. Combined with dynamic key generation and blockchain's zero-trust authentication mechanism, security authentication of applications and devices is performed layer by layer.

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