A high-efficiency, low-latency distributed consensus method and its application system based on the Internet of Things environment

Abstract

The present invention proposes a high-efficiency, low-latency distributed consensus method and its application system based on the Internet of Things environment. In the process of forming a blockchain consensus in a distributed system, based on the clustering characteristics of Internet of Things nodes, an Internet of Things environment architecture is formed with Internet of Things nodes, node cluster aggregation, and high-efficiency distributed arrangement middleware connected. In combination with the three-level fault-tolerant mechanism configuration of the fault-tolerant node pool, fault-tolerant backup node pool, and fault-tolerant main error correction node cluster pool of the inter-cluster fault-tolerant consensus middleware, a high-efficiency, low-latency distributed consensus is finally formed in the Internet of Things environment. At the same time, the present invention combines the intra-cluster consensus configuration and inter-cluster consensus configuration of the Internet of Things nodes, and uses node performance ranking to set the main error correction node cluster in the consensus stage, so as to realize the security verification and information correction of the consensus information, and improve the high-efficiency, low-latency distributed consensus performance for Internet of Things devices and collected information in the Internet of Things environment.

Description

Technical Field

[0001] The present invention belongs to the field of new generation information technology, and in particular relates to a high-efficiency, low-latency distributed consensus method and its application system based on the Internet of Things environment. Background Art

[0002] With the gradual implementation and expansion of Internet of Things technology in information technology, industrial manufacturing, home life and other fields, related applications are becoming increasingly active, and the requirements for data consistency are also increasing.

[0003] The Internet of Things (IoT) is a technology architecture that connects physical devices via a network, enabling them to exchange data and information. It's not limited to traditional computers and mobile devices, but encompasses a wide range of physical objects, from household appliances to industrial machinery, medical equipment to transportation infrastructure. These devices are intelligent through embedded sensors, software, and other technologies, enabling them to automatically collect and transmit data, enabling remote monitoring, automated control, and data analysis. For example, a smart home system can connect home devices such as lights, thermostats, and security cameras through IoT technology. Users can remotely control these devices through mobile apps, improving convenience and safety. The core of the IoT lies in connectivity and interaction. It breaks down information silos between physical devices, enabling them to work together and provide users with more intelligent services.

[0004] The IoT has a wide range of application scenarios, encompassing smart homes, smart cities, industrial automation, healthcare, and more. In the smart home sector, IoT technology enables intelligent control of home appliances, allowing users to remotely control their appliances via their phones or voice assistants, improving convenience and comfort. In the smart city sector, IoT connects urban infrastructure, such as traffic lights, waste collection stations, and water supply systems, enabling intelligent urban management and optimized resource allocation. In industrial automation, IoT technology enables real-time data transmission from factory equipment, enabling monitoring and optimization of production processes, improving efficiency and product quality. In healthcare, IoT devices such as wearable health monitors and telemedicine systems can collect patient health data in real time, providing doctors with timely diagnostic information and enhancing the quality and efficiency of medical services. The widespread application of the IoT has not only improved operational efficiency in various sectors but also brought significant convenience to people's lives.

[0005] While the Internet of Things (IoT) offers numerous advantages, it also faces several challenges. First, the diversity and complexity of IoT devices make interoperability a challenge. Devices from different manufacturers may use different communication protocols and data formats, making connectivity and data exchange difficult. Second, the security and privacy of IoT systems are significant issues. Because IoT devices are widely distributed and connected to the network, they are vulnerable to cyberattacks. Once attacked, they could leak user privacy information or cause device loss of control. Furthermore, the reliability and stability of IoT systems are also a concern. Given the large number and widespread distribution of devices, ensuring stable system operation and reliable device connectivity is a technical challenge. However, these challenges also present new opportunities. With the continuous advancement of technology, researchers are developing more universal communication protocols and data standards to improve interoperability between devices. At the same time, new security technologies and privacy protection mechanisms are emerging to ensure the security of IoT systems and user privacy. In the future, the IoT is expected to be applied in even more fields, bringing greater convenience and innovation to people's lives and work. With the continuous advancement of technology, the IoT is moving towards greater intelligence and automation. Researchers are developing more advanced sensor technologies and communication protocols to improve the efficiency of device connectivity and data transmission speeds. At the same time, the application of artificial intelligence and machine learning technologies enables IoT devices to process data more intelligently, enabling automated decision-making and control. For example, through machine learning algorithms, smart home systems can learn user habits, automatically adjust device operating status, and improve energy efficiency. In the industrial sector, the combination of IoT and AI can enable intelligent monitoring and predictive maintenance of production processes, improving production efficiency and equipment lifespan. In the future, the IoT is expected to be applied in even more fields, bringing greater convenience and innovation to people's lives and work. Furthermore, as the technology matures, IoT systems will place greater emphasis on user experience and security to meet the needs of diverse users. The development of the IoT will not only drive the advancement of intelligence in various fields but also provide new ideas and approaches for addressing global challenges such as resource management, environmental protection, and healthcare.

[0006] Distributed consensus is a mechanism for achieving data consistency in distributed systems. In a distributed system, multiple nodes (or computers) are connected via a network to jointly complete computing tasks or store data. Because these nodes are geographically dispersed and may be affected by network latency, failures, or other factors, a mechanism is needed to ensure data consistency across all nodes. Distributed consensus algorithms enable communication and negotiation between nodes in the network to enable all nodes to reach a consistent consensus on the state of data. For example, in blockchain technology, distributed consensus algorithms such as Proof of Work or Proof of Stake are used to ensure consistent transaction records across all nodes. The core of distributed consensus lies in solving the data consistency problem in distributed systems through algorithms and protocols, thereby ensuring system reliability and stability.

[0007] Distributed consensus has important applications in various fields, particularly in blockchain technology. Blockchain is a distributed ledger technology that uses a distributed consensus algorithm to ensure consistent ledger data across all participating nodes. In the financial sector, this technology is used to achieve decentralization and security in digital currency transactions, ensuring that every transaction is accurately recorded and cannot be tampered with. Beyond finance, distributed consensus also plays a vital role in a variety of fields, including the Internet of Things (IoT), supply chain management, and smart contracts. In the IoT, distributed consensus can be used to ensure data synchronization and consistency between devices, improving system reliability and security. In supply chain management, distributed consensus can be used to track the flow of goods, ensuring transparency and traceability. In smart contracts, distributed consensus can ensure the automatic execution and immutability of contract terms, enhancing the efficiency and trust of commercial transactions. The application of distributed consensus not only improves system reliability and security but also opens up new business models and innovative opportunities across various sectors.

[0008] While distributed consensus offers numerous advantages, it also faces several challenges. First, distributed consensus algorithms require extensive communication and negotiation between nodes in the network, which can result in high communication costs and computational overhead. This overhead can significantly impact system performance, particularly in situations with a large number of nodes or poor network conditions. Second, distributed consensus algorithms must cope with various network failures and attacks, such as node failures, network partitions, and malicious attacks. These situations can lead to interrupted consensus processes or data inconsistencies. Furthermore, the design of distributed consensus algorithms requires a trade-off between security and performance, and different application scenarios may require different consensus algorithms. However, these challenges also present new opportunities. With continuous technological advancements, researchers are developing more efficient consensus algorithms to reduce communication costs and improve system performance. At the same time, new security technologies and mechanisms are emerging to enhance the resilience and reliability of distributed systems. In the future, distributed consensus is expected to be applied in even more fields, bringing greater convenience and innovation to people's lives and work.

[0009] With the continuous advancement of technology, distributed consensus is moving towards greater efficiency and security. Researchers are exploring new consensus algorithms, such as Byzantine Fault Tolerance (BFT) and Practical Byzantine Fault Tolerance (PBFT). These algorithms can significantly improve system performance and scalability while ensuring security. Furthermore, the development of blockchain technology has brought new application scenarios and innovative opportunities to distributed consensus. For example, through smart contracts and decentralized applications (DApps), distributed consensus can be used to achieve more secure and transparent financial transactions and business collaboration. In the Internet of Things (IoT), distributed consensus can be used to build more reliable device networks, enabling autonomous collaboration and data sharing between devices. In the future, distributed consensus is expected to be applied in even more areas, bringing greater convenience and innovation to people's lives and work. Furthermore, as the technology matures, distributed consensus systems will place greater emphasis on user experience and security to meet the needs of diverse users. The development of distributed consensus will not only promote progress in fields such as blockchain and the IoT, but will also provide new ideas and methods for addressing global challenges such as data security, privacy protection, and trust building.

[0010] However, current distributed consensus systems lack a pooled fault-tolerance mechanism for multi-pool coordination, and a multi-layer architecture based on the virtual three-dimensional structure of the Internet of Things (IoT) has not been established for efficiency. This results in a lack of basis for the placement of node logical functions during the distributed consensus process. Furthermore, the lack of relevant applications that reference cluster-based node architecture design during the consensus process has resulted in some nodes being disengaged from the consensus process, lacking participation in the consensus process and, consequently, lacking data consistency.

[0011] The present invention proposes a high-efficiency, low-latency distributed consensus method and its application system based on the Internet of Things environment. In the process of forming a blockchain consensus in a distributed system, based on the clustering characteristics of the Internet of Things nodes, a multi-level, multi-sub-layer Internet of Things environment architecture is formed with Internet of Things nodes, node clusters, and high-efficiency distributed arrangement middleware connected. The high-efficiency distributed arrangement middleware system logical positioning of the Internet of Things nodes is combined with the three-level fault-tolerant mechanism configuration of the fault-tolerant node pool, fault-tolerant backup node pool, and fault-tolerant main error correction node cluster pool of the inter-cluster fault-tolerant consensus middleware, and finally a high-efficiency, low-latency distributed consensus is formed in the Internet of Things environment. At the same time, the present invention combines the intra-cluster consensus configuration and inter-cluster consensus configuration of the Internet of Things nodes, and uses the node performance ranking to set the main error correction node cluster in the consensus stage, so as to realize the security verification and information correction of the consensus information, and improve the high-efficiency, low-latency distributed consensus performance for Internet of Things devices and collected information in the Internet of Things environment. Summary of the Invention

[0012] The present invention aims to provide a high-efficiency, low-latency distributed consensus method and its application system based on the Internet of Things environment that are superior to the existing technology.

[0013] In order to achieve the above object, the technical solution of the present invention is as follows:

[0014] A high-performance, low-latency distributed consensus method and its application system based on the Internet of Things environment, the system includes at least multiple Internet of Things terminal nodes, as well as high-performance distributed permutation middleware, fault-tolerant consensus middleware, and consensus termination state balancing module, wherein:

[0015] Specific IoT terminal nodes form an IoT terminal node cluster. The cluster head node based on the system configuration aggregates the information data packets collected by the IoT terminals in the cluster and submits them to the high-performance distributed arrangement middleware for distributed processing.

[0016] High-performance distributed permutation middleware, which connects any two IoT terminal node clusters in series and determines the distributed permutation algorithm for each IoT terminal node in the system based on non-actual geographic location information;

[0017] The fault-tolerant consensus middleware sets a dynamic fault-tolerant mechanism based on the fault-tolerant threshold of the distributed consensus, including a fault-tolerant node pool, a fault-tolerant backup node pool, and a fault-tolerant primary error-correcting node cluster pool; wherein the fault-tolerant node pool includes a first type of fault-tolerant nodes; the fault-tolerant backup node pool includes a second type of fault-tolerant nodes; and the fault-tolerant primary error-correcting node cluster pool includes a primary error-correcting node cluster;

[0018] A consensus termination state balancing module, which confirms that at least some correct nodes have reached a specific consensus and establishes an intra-cluster termination parameter table and an inter-cluster termination parameter table;

[0019] Based on the distributed permutation algorithm, dynamic fault tolerance mechanism, intra-cluster termination parameter table and inter-cluster termination parameter table determined by the high-performance distributed permutation middleware, high-performance and low-latency distributed consensus is executed.

[0020] Preferably, the IoT terminal node is used to connect to a terminal IoT device, collect device status information, and form an IoT terminal collection information data message after associating the device ID.

[0021] Preferably, the high-performance distributed arrangement middleware determines the distributed arrangement algorithm of each IoT terminal node in the system based on the type ratio of IoT terminal nodes in the series cluster and the system logical location information of all IoT terminal nodes in the cluster rather than the actual geographical location information;

[0022] The high-performance distributed arrangement middleware ID is formed by sequentially connecting two hexadecimal cluster IDs of a series cluster, with the cluster ID with a smaller value being located at a lower position.

[0023] Preferably, the fault tolerance threshold of the distributed consensus is the ratio of the preset fault tolerance nodes to the total number of nodes in this consensus;

[0024] The dynamic fault-tolerant mechanism of the fault-tolerant consensus middleware specifically includes: the fault-tolerant node pool includes a first type of fault-tolerant node, which is manually set to be fault-free when a consensus-unable error occurs in the consensus stage, and information assignment is enforced to form a consensus based on the consensus node information; the fault-tolerant backup node pool includes a second type of fault-tolerant node, which is manually set to be fault-free when a consensus-unable error occurs in the consensus stage, and is placed from the fault-tolerant backup node pool into the fault-tolerant node pool; the fault-tolerant master error correction node cluster pool includes a master error correction node cluster, which includes at least one specific node in each node cluster, and which is used to enforce overwriting of a preset consensus value to all nodes in the cluster when a consensus-unable error occurs in the consensus stage.

[0025] Preferably, the intra-cluster termination parameter table is used to regulate the intra-cluster termination parameters, that is, to determine the flow direction and consensus information transmission parameters between the consensus flooding sublayers of the intra-cluster consensus, and to determine the maximum number of consensus nodes within the cluster; the inter-cluster termination parameter table is used to regulate the inter-cluster termination parameters, that is, to determine the inter-cluster information flow direction and consensus information transmission parameters of the inter-cluster consensus, and to determine the maximum number of consensus clusters between clusters.

[0026] Preferably, each IoT terminal node uses its own IoT terminal node ID to identify the IoT terminal collection information data message and submits it to the cluster head node;

[0027] The cluster head node constructs a summary table of the IoT terminal collection information data messages of its cluster based on the aggregated IoT terminal collection information data messages, wherein the summary table of the IoT terminal collection information data messages at least includes the IoT terminal node ID and its corresponding IoT terminal collection information data message, and the cluster ID;

[0028] The cluster ID is the hexadecimal identifier of the cluster to which the IoT terminal node belongs; and the IoT terminal node ID is the hexadecimal identifier of the IoT terminal node.

[0029] Preferably, the preset consensus value is system preset consensus information, or,

[0030] The preset consensus value is a system instruction identifier, which is used to notify the system to clear the consensus information and restart the consensus.

[0031] Preferably, the master error correction node in the master error correction node cluster is elected by each node cluster, and at least the node with the second best processing capability is used as the master error correction node, and the node with the best processing capability is not used as the master error correction node, so that the node with the best processing capability is reserved for cluster information aggregation processing.

[0032] At the same time, the present invention also proposes a high-efficiency, low-latency distributed consensus method based on an Internet of Things environment, which is applied to the system as described above. The method at least includes:

[0033] Step 1: Group specific IoT terminal nodes into an IoT terminal node cluster. Based on the system configuration, the cluster head node aggregates the information data packets collected by the IoT terminals in the cluster and submits them to the high-performance distributed arrangement middleware for distributed processing.

[0034] Step 2: Use high-performance distributed permutation middleware to connect any two IoT terminal node clusters in series. Based on non-actual geographic location information, determine the distributed permutation algorithm for each IoT terminal node in the system.

[0035] Step 3: Using the fault-tolerant consensus middleware based on the fault-tolerant threshold of distributed consensus, a dynamic fault-tolerant mechanism is set up, including a fault-tolerant node pool, a fault-tolerant backup node pool, and a fault-tolerant primary error-correcting node cluster pool; wherein, the fault-tolerant node pool includes the first type of fault-tolerant nodes; the fault-tolerant backup node pool includes the second type of fault-tolerant nodes; and the fault-tolerant primary error-correcting node cluster pool includes the primary error-correcting node cluster;

[0036] Step 4: Use the consensus termination state balance module to confirm that at least some correct nodes have reached a specific consensus, and establish an intra-cluster termination parameter table and an inter-cluster termination parameter table;

[0037] Step 5: Based on the distributed permutation algorithm, dynamic fault tolerance mechanism, intra-cluster termination parameter table and inter-cluster termination parameter table determined by the high-performance distributed permutation middleware, perform high-performance and low-latency distributed consensus.

[0038] At the same time, the present invention also proposes a computer program product, which includes computer instructions, and when the computer instructions are run by a processor, they execute the corresponding functions of the high-performance, low-latency distributed consensus application system based on the Internet of Things environment as described above.

[0039] The present invention proposes a high-efficiency, low-latency distributed consensus method and its application system based on the Internet of Things environment. In the process of forming a blockchain consensus in a distributed system, based on the clustering characteristics of the Internet of Things nodes, a multi-level, multi-sub-layer Internet of Things environment architecture is formed with Internet of Things nodes, node clusters, and high-efficiency distributed arrangement middleware connected. The high-efficiency distributed arrangement middleware system logical positioning of the Internet of Things nodes is combined with the three-level fault-tolerant mechanism configuration of the fault-tolerant node pool, fault-tolerant backup node pool, and fault-tolerant main error correction node cluster pool of the inter-cluster fault-tolerant consensus middleware, and finally a high-efficiency, low-latency distributed consensus is formed in the Internet of Things environment. At the same time, the present invention combines the intra-cluster consensus configuration and inter-cluster consensus configuration of the Internet of Things nodes, and uses the node performance ranking to set the main error correction node cluster in the consensus stage, so as to realize the security verification and information correction of the consensus information, and improve the high-efficiency, low-latency distributed consensus performance for Internet of Things devices and collected information in the Internet of Things environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a basic example diagram of a high-performance, low-latency distributed consensus application system based on the Internet of Things environment shown in the present invention;

[0041] Figure 2 This is a basic example diagram of an IoT terminal node connecting to a terminal IoT device in a high-performance, low-latency distributed consensus application system based on an IoT environment shown in the present invention;

[0042] Figure 3This is an example diagram of a dynamic fault-tolerant mechanism including a fault-tolerant node pool, a fault-tolerant backup node pool, and a fault-tolerant main error correction node cluster pool in a high-performance, low-latency distributed consensus application system based on an Internet of Things environment for which protection is sought by the present invention;

[0043] Figure 4 This is one of the relevant module embodiments in the high-performance, low-latency distributed consensus application system based on the Internet of Things environment claimed by the present invention;

[0044] Figure 5 It is one of the specific embodiments of the high-efficiency, low-latency distributed consensus method based on the Internet of Things environment requested for protection by the present invention. DETAILED DESCRIPTION

[0045] The following describes in detail several embodiments and beneficial effects of the high-efficiency, low-latency distributed consensus method based on the Internet of Things environment and its application system and method, which are claimed in the present invention, to facilitate a more detailed review and decomposition of the present invention.

[0046] In order to better understand the technical solution of the present invention, the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0047] It should be understood that the embodiments described are only a portion of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative work are within the scope of protection of the present invention.

[0048] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "an", "the" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise.

[0049] It should be understood that the term "and / or" as used herein is merely a description of the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0050] It should be understood that although the terms "first," "second," etc. may be used to describe the methods and corresponding apparatuses in embodiments of the present invention, these keywords should not be limited to these terms. These terms are merely used to distinguish keywords from one another. For example, a first-type fault-tolerant node, etc., may also be referred to as a second-type fault-tolerant node, and a second-type fault-tolerant node, etc., may also be referred to as a first-type fault-tolerant node without departing from the scope of embodiments of the present invention.

[0051] The word "if," as used herein, may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0052] As the instruction manual Figure 1 -Attached Figure 4 , which is a basic example diagram of a high-performance, low-latency distributed consensus method and its application system based on the Internet of Things environment according to the present invention. As a superimposed preferred embodiment, preferably, each node or module can interconnect data and transmit instructions with other nodes or modules. Of course, as another superimposed preferred embodiment, some nodes may not have interconnection with some other nodes or may be allowed to turn off or on interconnection with other nodes.

[0053] The present invention claims a high-performance, low-latency distributed consensus method based on an Internet of Things environment and its application system, wherein the system comprises at least a plurality of Internet of Things terminal nodes, a high-performance distributed arrangement middleware, a fault-tolerant consensus middleware, and a consensus termination state balancing module, wherein:

[0054] Specific IoT terminal nodes form an IoT terminal node cluster. The cluster head node based on the system configuration aggregates the information data packets collected by the IoT terminals in the cluster and submits them to the high-performance distributed arrangement middleware for distributed processing.

[0055] High-performance distributed permutation middleware, which connects any two IoT terminal node clusters in series and determines the distributed permutation algorithm for each IoT terminal node in the system based on non-actual geographic location information;

[0056] The fault-tolerant consensus middleware sets a dynamic fault-tolerant mechanism based on the fault-tolerant threshold of the distributed consensus, including a fault-tolerant node pool, a fault-tolerant backup node pool, and a fault-tolerant primary error-correcting node cluster pool; wherein the fault-tolerant node pool includes a first type of fault-tolerant nodes; the fault-tolerant backup node pool includes a second type of fault-tolerant nodes; and the fault-tolerant primary error-correcting node cluster pool includes a primary error-correcting node cluster;

[0057] A consensus termination state balancing module, which confirms that at least some correct nodes have reached a specific consensus and establishes an intra-cluster termination parameter table and an inter-cluster termination parameter table;

[0058] Based on the distributed permutation algorithm, dynamic fault tolerance mechanism, intra-cluster termination parameter table and inter-cluster termination parameter table determined by the high-performance distributed permutation middleware, high-performance and low-latency distributed consensus is executed.

[0059] As a superimposable preferred embodiment, the distributed permutation algorithm, dynamic fault tolerance mechanism, and intra-cluster and inter-cluster termination parameter tables determined by the high-performance distributed permutation middleware are used to implement high-performance, low-latency distributed consensus. Specifically, the following steps are used: Determine the system hierarchical architecture based on the distributed permutation algorithm determined by the high-performance distributed permutation middleware; Determine the node pool to which each node belongs based on the dynamic fault tolerance mechanism; and, when each node belongs to a first-type fault-tolerant node, enforce information assignment to form a consensus based on the node consensus rules within the corresponding node pool; Manually set each node to be fault-free and transferred from the fault-tolerant backup node pool to the fault-tolerant node pool when it belongs to the second-type fault-tolerant node; The fault-tolerant primary error-correcting node cluster pool includes a primary error-correcting node cluster. When each node belongs to the primary error-correcting node cluster, it enforces overwriting of the preset consensus value for all nodes in the cluster. At other times, information updates are performed according to the conventional blockchain consensus algorithm. The nodes within the fault-tolerant node pool, fault-tolerant backup node pool, and fault-tolerant primary error-correcting node cluster pool are preset by the system based on the processing capacity or load capacity of the nodes. According to the intra-cluster termination parameter table and the inter-cluster termination parameter table, the consensus flooding flow direction and consensus information transmission parameters within and between clusters are determined, and high-efficiency and low-latency distributed consensus is executed accordingly.

[0060] As a superimposable preferred embodiment, the Internet of Things terminal node is used to connect to the terminal Internet of Things device, collect device status information, and form an Internet of Things terminal collection information data message after associating the device ID.

[0061] As another superimposable preferred embodiment, the high-performance distributed permutation middleware determines the distributed permutation algorithm for each IoT terminal node in the system based on the type ratio of IoT terminal nodes in the series cluster and the system logical location information of all IoT terminal nodes in the cluster rather than the actual geographical location information;

[0062] As an overlayable preferred embodiment, the high-performance distributed permutation middleware connects any two IoT terminal node clusters in series and determines the distributed permutation algorithm for each IoT terminal node in the system based on the IoT terminal node type ratio of the connected clusters and the system logical location information of all IoT terminal nodes within the cluster, rather than their actual geographic location information. Specifically, the high-performance, low-latency distributed consensus application system preconfigures IoT terminal node types based on the IoT environment. As another overlayable preferred embodiment, the preconfigured IoT terminal node types may include: Type Weight 1, IoT nodes with central control processing chips but not image storage; Type Weight 2, IoT nodes for communication without central control but not image storage; Type Weight 3, image storage nodes, used to distinguish IoT information processing and aggregation center nodes, communication relays or terminals, image and video processing nodes, etc. As another overlayable preferred embodiment, the high-performance distributed permutation middleware, based on the IoT terminal node type ratio of the connected clusters, can, for example, calculate the sum of the weights based on the product of the weights of IoT terminal nodes with type weights 1-3 within the cluster and determine the system hierarchy of the cluster. As another superimposable preferred embodiment, the sum of the weights is calculated based on the product of the weights of the IoT terminal nodes with type weights 1-3 within the cluster, and the system hierarchy of the cluster is determined. Specifically, for example, a specific cluster includes 3 nodes with type weight 1; 10 nodes with type weight 2; and 2 nodes with type weight 3. Then the sum of the number of IoT terminal nodes with type weights 1-3 within the cluster and the product of the weights is 1*3+2*10+3*2=29. After calculating the sum of the weights of all IoT terminal node clusters in the system, they are sequentially set to distributed system levels I / II / III according to a specific sorting ratio (for example, the first 1 / 3, the middle 1 / 3, the last 1 / 3, etc.) from high to low, and based on the type weight of each node, the sublevel of the node in the current distributed level is set, and the logical position of the system is determined in combination with the cluster ID. For example, in a node cluster (ID 0xC51) at system level II with a type weight of 3, the system logical position of an IoT terminal node is II-0xC51-3, that is, the third sub-level of the 0xC51 cluster at the second logical layer of the system. Each node determines the system logical position in sequence, thus forming a complete distributed system node distribution.

[0063] The high-performance distributed arrangement middleware ID is formed by sequentially connecting two hexadecimal cluster IDs of a series cluster, with the cluster ID with a smaller value being located at a lower position.

[0064] As another superimposable preferred embodiment, the fault tolerance threshold of the distributed consensus is the ratio of the preset fault tolerance nodes to the total number of nodes in this consensus;

[0065] The dynamic fault-tolerant mechanism of the fault-tolerant consensus middleware specifically includes: the fault-tolerant node pool includes a first type of fault-tolerant node, which is manually set to be fault-free when a consensus-unable error occurs in the consensus stage, and information assignment is enforced to form a consensus based on the consensus node information; the fault-tolerant backup node pool includes a second type of fault-tolerant node, which is manually set to be fault-free when a consensus-unable error occurs in the consensus stage, and is placed from the fault-tolerant backup node pool into the fault-tolerant node pool; the fault-tolerant master error correction node cluster pool includes a master error correction node cluster, which includes at least one specific node in each node cluster, and which is used to enforce overwriting of a preset consensus value to all nodes in the cluster when a consensus-unable error occurs in the consensus stage.

[0066] As another superimposable preferred embodiment, the intra-cluster termination parameter table is used to regulate the intra-cluster termination parameters. The intra-cluster termination parameters are a set of system parameters required for executing intra-cluster consensus, that is, determining the flow direction and consensus information transmission parameters between the consensus flooding sublayers of the intra-cluster consensus, and determining the maximum number of consensus nodes within the cluster; the inter-cluster termination parameter table is used to regulate the inter-cluster termination parameters. The inter-cluster termination parameters are a set of system parameters required for executing inter-cluster consensus, that is, determining the flow direction and consensus information transmission parameters between clusters of the inter-cluster consensus, and determining the maximum number of consensus clusters between clusters.

[0067] As another superimposable preferred embodiment, each IoT terminal node identifies the IoT terminal with its own IoT terminal node ID to form an IoT terminal collection information data message and submits it to the cluster head node;

[0068] The cluster head node constructs a summary table of the IoT terminal collection information data messages of its cluster based on the aggregated IoT terminal collection information data messages, wherein the summary table of the IoT terminal collection information data messages at least includes the IoT terminal node ID and its corresponding IoT terminal collection information data message, and the cluster ID;

[0069] The cluster ID is the hexadecimal identifier of the cluster to which the IoT terminal node belongs; and the IoT terminal node ID is the hexadecimal identifier of the IoT terminal node.

[0070] As another superimposable preferred embodiment, the preset consensus value is the system preset consensus information, or,

[0071] The preset consensus value is a system instruction identifier, which is used to notify the system to clear the consensus information and restart the consensus.

[0072] As another superimposable preferred embodiment, the master error correction node in the master error correction node cluster is elected by each node cluster, and at least the node with the second best processing capability is used as the master error correction node, and the node with the best processing capability is not used as the master error correction node, so that the node with the best processing capability is reserved for cluster information aggregation processing.

[0073] At the same time, the present invention also proposes a high-efficiency and low-latency distributed consensus method based on the Internet of Things environment, which is applied to the system as described above. Figure 5 This is one of the specific embodiments of the high-performance, low-latency distributed consensus method based on the Internet of Things environment claimed by the present invention, such as Figure 5 As shown, the method at least includes:

[0074] S102: Grouping specific IoT terminal nodes into an IoT terminal node cluster, aggregating information data messages collected by IoT terminals in the cluster based on the system configuration, and submitting them to a high-performance distributed arranging middleware for distributed processing;

[0075] S104: Using high-performance distributed permutation middleware to connect any two IoT terminal node clusters in series, and determining a distributed permutation algorithm for each IoT terminal node in the system based on non-actual geographic location information;

[0076] S106: Using the fault-tolerant consensus middleware based on the fault-tolerant threshold of distributed consensus, a dynamic fault-tolerant mechanism is set up, including a fault-tolerant node pool, a fault-tolerant backup node pool, and a fault-tolerant primary error-correcting node cluster pool; wherein the fault-tolerant node pool includes a first type of fault-tolerant nodes; the fault-tolerant backup node pool includes a second type of fault-tolerant nodes; and the fault-tolerant primary error-correcting node cluster pool includes a primary error-correcting node cluster;

[0077] S108: Using the consensus termination state balancing module to confirm that at least some correct nodes have reached a specific consensus, and to establish an intra-cluster termination parameter table and an inter-cluster termination parameter table;

[0078] S110: Execute high-performance, low-latency distributed consensus based on the distributed permutation algorithm, dynamic fault-tolerance mechanism, intra-cluster termination parameter table, and inter-cluster termination parameter table determined by the high-performance distributed permutation middleware.

[0079] At the same time, the present invention also proposes a computer program product, which includes computer instructions, and when the computer instructions are run by a processor, they execute the corresponding functions of the high-efficiency, low-latency distributed consensus method based on the Internet of Things environment and its application system as described above.

[0080] The present invention proposes a high-efficiency, low-latency distributed consensus method and its application system based on the Internet of Things environment. In the process of forming a blockchain consensus in a distributed system, based on the clustering characteristics of the Internet of Things nodes, a multi-level, multi-sub-layer Internet of Things environment architecture is formed with Internet of Things nodes, node clusters, and high-efficiency distributed arrangement middleware connected. The high-efficiency distributed arrangement middleware system logical positioning of the Internet of Things nodes is combined with the three-level fault-tolerant mechanism configuration of the fault-tolerant node pool, fault-tolerant backup node pool, and fault-tolerant main error correction node cluster pool of the inter-cluster fault-tolerant consensus middleware, and finally a high-efficiency, low-latency distributed consensus is formed in the Internet of Things environment. At the same time, the present invention combines the intra-cluster consensus configuration and inter-cluster consensus configuration of the Internet of Things nodes, and uses the node performance ranking to set the main error correction node cluster in the consensus stage, so as to realize the security verification and information correction of the consensus information, and improve the high-efficiency, low-latency distributed consensus performance for Internet of Things devices and collected information in the Internet of Things environment.

[0081] In all the above embodiments, in order to achieve some special data transmission and read / write function requirements, the above method operation process and its corresponding device can add devices, modules, components, hardware, pin connections or memory, processor differences to expand functions.

[0082] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the methods, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0083] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the method steps is merely a logical or functional division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, which may be electrical, mechanical or other forms.

[0084] The units described as the various steps of the method and the separate components of the device may or may not be logically or physically separated, and may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0085] In addition, the various method steps and their implementations, as well as the functional units in the various embodiments of the present invention, may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional units.

[0086] The above-mentioned methods and apparatuses can be implemented as integrated units in the form of software functional units, which can be stored in a computer-readable storage medium. The above-mentioned software functional units are stored in a storage medium and include instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to perform some of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), NVRAM, a magnetic disk, or an optical disk.

[0087] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0088] It should be noted that the above embodiments are only used to more clearly explain and illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A high-performance, low-latency distributed consensus application system based on the Internet of Things environment, characterized by: The system includes at least a plurality of IoT terminal nodes, as well as a high-performance distributed arrangement middleware, a fault-tolerant consensus middleware, and a consensus termination state balancing module, wherein: Specific IoT terminal nodes form an IoT terminal node cluster. The cluster head node based on the system configuration aggregates the information data packets collected by the IoT terminals in the cluster and submits them to the high-performance distributed arrangement middleware for distributed processing. High-performance distributed permutation middleware, which connects any two IoT terminal node clusters in series and determines the distributed permutation algorithm for each IoT terminal node in the system based on non-actual geographic location information; The fault-tolerant consensus middleware sets a dynamic fault-tolerant mechanism based on the fault-tolerant threshold of the distributed consensus, including a fault-tolerant node pool, a fault-tolerant backup node pool, and a fault-tolerant primary error-correcting node cluster pool; wherein the fault-tolerant node pool includes a first type of fault-tolerant nodes; the fault-tolerant backup node pool includes a second type of fault-tolerant nodes; and the fault-tolerant primary error-correcting node cluster pool includes a primary error-correcting node cluster; A consensus termination state balancing module, which confirms that at least some correct nodes have reached a specific consensus and establishes an intra-cluster termination parameter table and an inter-cluster termination parameter table; Based on the distributed permutation algorithm, dynamic fault tolerance mechanism, intra-cluster termination parameter table and inter-cluster termination parameter table determined by the high-performance distributed permutation middleware, high-performance and low-latency distributed consensus is executed.

2. The high-performance, low-latency distributed consensus application system based on the Internet of Things environment as claimed in claim 1, characterized in that: The IoT terminal node is used to connect to the terminal IoT device, collect device status information, and form an IoT terminal collection information data message after associating the device ID.

3. The high-performance, low-latency distributed consensus application system based on the Internet of Things environment as claimed in claim 1, characterized in that: The high-performance distributed permutation middleware determines the distributed permutation algorithm for each IoT terminal node in the system based on the IoT terminal node type ratio of the series cluster and the system logical location information of all IoT terminal nodes in the cluster rather than the actual geographical location information; The high-performance distributed arrangement middleware ID is formed by sequentially connecting two hexadecimal cluster IDs of a series cluster, with the cluster ID with a smaller value being located at a lower position.

4. The high-performance, low-latency distributed consensus application system based on the Internet of Things environment as claimed in claim 2, characterized in that: The fault tolerance threshold of the distributed consensus is the ratio of the pre-set fault tolerance nodes to the total number of nodes in this consensus; The dynamic fault-tolerant mechanism of the fault-tolerant consensus middleware specifically includes: the fault-tolerant node pool includes a first type of fault-tolerant node, which is manually set to be fault-free when a consensus-unable error occurs in the consensus stage, and information assignment is enforced to form a consensus based on the consensus node information; the fault-tolerant backup node pool includes a second type of fault-tolerant node, which is manually set to be fault-free when a consensus-unable error occurs in the consensus stage, and is placed from the fault-tolerant backup node pool into the fault-tolerant node pool; the fault-tolerant master error correction node cluster pool includes a master error correction node cluster, which includes at least one specific node in each node cluster, and which is used to enforce overwriting of a preset consensus value to all nodes in the cluster when a consensus-unable error occurs in the consensus stage.

5. The high-performance, low-latency distributed consensus application system based on the Internet of Things environment as claimed in claim 1, characterized in that: The intra-cluster termination parameter table is used to regulate intra-cluster termination parameters, that is, to determine the flow direction and consensus information transmission parameters between the consensus flooding sublayers of the intra-cluster consensus, and to determine the maximum number of consensus nodes within the cluster; the inter-cluster termination parameter table is used to regulate inter-cluster termination parameters, that is, to determine the flow direction and consensus information transmission parameters between clusters of the inter-cluster consensus, and to determine the maximum number of consensus clusters between clusters.

6. The high-performance, low-latency distributed consensus application system based on the Internet of Things environment as claimed in claim 1, characterized in that: Each IoT terminal node uses its own IoT terminal node ID to identify the IoT terminal collection information data message and submits it to the cluster head node; The cluster head node constructs a summary table of the IoT terminal collection information data messages of its cluster based on the aggregated IoT terminal collection information data messages, wherein the summary table of the IoT terminal collection information data messages at least includes the IoT terminal node ID and its corresponding IoT terminal collection information data message, and the cluster ID; The cluster ID is the hexadecimal identifier of the cluster to which the IoT terminal node belongs; The IoT terminal node ID is the hexadecimal identifier of the IoT terminal node.

7. The high-performance, low-latency distributed consensus application system based on the Internet of Things environment as claimed in claim 4, characterized in that: The preset consensus value is the system preset consensus information, or, The preset consensus value is a system instruction identifier, which is used to notify the system to clear the consensus information and restart the consensus.

8. The high-performance, low-latency distributed consensus application system based on the Internet of Things environment as claimed in claim 2, characterized in that: The master error correction node in the master error correction node cluster is elected by each node cluster, and at least the node with the second best processing capability is used as the master error correction node, and the node with the best processing capability is not used as the master error correction node, so that the node with the best processing capability is reserved for cluster information aggregation processing.

9. A high-performance, low-latency distributed consensus method based on an Internet of Things environment, applied to the system according to any one of claims 1 to 7, characterized in that: The method at least comprises: Step 1: Group specific IoT terminal nodes into an IoT terminal node cluster. Based on the system configuration, the cluster head node aggregates the information data packets collected by the IoT terminals in the cluster and submits them to the high-performance distributed arrangement middleware for distributed processing. Step 2: Use high-performance distributed permutation middleware to connect any two IoT terminal node clusters in series. Based on non-actual geographic location information, determine the distributed permutation algorithm for each IoT terminal node in the system. Step 3: Using the fault-tolerant consensus middleware based on the fault-tolerant threshold of distributed consensus, a dynamic fault-tolerant mechanism is set up, including a fault-tolerant node pool, a fault-tolerant backup node pool, and a fault-tolerant primary error-correcting node cluster pool; wherein, the fault-tolerant node pool includes the first type of fault-tolerant nodes; the fault-tolerant backup node pool includes the second type of fault-tolerant nodes; and the fault-tolerant primary error-correcting node cluster pool includes the primary error-correcting node cluster; Step 4: Use the consensus termination state balance module to confirm that at least some correct nodes have reached a specific consensus, and establish an intra-cluster termination parameter table and an inter-cluster termination parameter table; Step 5: Based on the distributed permutation algorithm, dynamic fault tolerance mechanism, intra-cluster termination parameter table and inter-cluster termination parameter table determined by the high-performance distributed permutation middleware, perform high-performance and low-latency distributed consensus.

10. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by the processor, the corresponding functions of the high-performance, low-latency distributed consensus application system based on the Internet of Things environment as described in any one of claims 1 to 8 are executed.

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