Blockchain edge computing resource collaborative scheduling system of internet of things device

Abstract

The application discloses a kind of blockchain edge computing resource collaborative scheduling systems of Internet of Things equipment, it is related to Internet of Things technical field, including equipment edge networking module, for deploying edge node in the dense area of Internet of Things equipment, and edge node is accessed blockchain network;Resource state initialization module, for after Internet of Things equipment generates task, task information is sent to edge node layer;Intelligent task allocation module, for after edge node receives task, root calculates optimal task allocation scheme, and according to optimal task allocation scheme, task is allocated to edge node and is handled;Dynamic resource adjustment module, for real-time monitoring the resource load condition of edge node, when resource load is not balanced, according to dynamic resource self-adapting adjustment strategy, resource is re-allocated, and system stable operation is realized. Realize the efficient collaborative scheduling and safe and reliable management of edge computing resource between Internet of Things equipment.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, and more specifically, to a blockchain edge computing resource collaborative scheduling system for IoT devices. Background Technology

[0002] With the rapid development of IoT technology, the amount of data generated by a large number of IoT devices is exploding. Traditional cloud computing models face problems such as high network latency, high bandwidth pressure, and data privacy and security when processing IoT data. Edge computing, by deploying computing resources at the network edge, can process data generated by IoT devices locally, effectively reducing latency and bandwidth pressure. However, current edge computing resource scheduling suffers from low resource utilization and a lack of secure and reliable mechanisms. Blockchain technology, with its decentralized, tamper-proof, secure, and reliable characteristics, offers a new approach to solving these problems when combined with edge computing. However, a mature blockchain-based edge computing resource collaborative scheduling system for IoT devices is currently lacking.

[0003] Shortcomings of existing technology:

[0004] In traditional IoT systems, edge computing resource management is decentralized, with edge nodes lacking effective collaboration and making it difficult to flexibly allocate resources according to actual needs, resulting in low resource utilization. Data transmission and storage processes pose security risks; data is easily tampered with and forged, and there is a lack of effective traceability mechanisms. The introduction and application of blockchain technology are limited. Previous task allocation methods often lack comprehensive consideration of task characteristics and resource status, potentially leading to high processing latency and low efficiency. Existing systems struggle to monitor resource load in real time, and when resource load imbalances occur, timely adjustments cannot be made, impacting overall system performance and stability.

[0005] To address the above problems, this invention proposes a solution. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a blockchain edge computing resource collaborative scheduling system for IoT devices. This system includes a device edge networking module, a resource status initialization module, an intelligent task allocation module, and a dynamic resource adjustment module, with connections between the modules.

[0007] The device edge networking module is used to deploy edge nodes in areas with dense IoT devices and connect the edge nodes to the blockchain network. IoT devices connect to the edge nodes through a wireless network to transmit data and submit tasks.

[0008] The resource status initialization module is used by each edge node to initialize the local resource scheduling ledger, record the initial resource status, and send the task information to the edge node layer after the IoT device generates a task.

[0009] The intelligent task allocation module is used to calculate the optimal task allocation scheme based on the intelligent task allocation algorithm, combined with its own resource status and the resource information of other nodes, after the edge node receives a task, and then allocate the task to the edge node for processing according to the optimal task allocation scheme.

[0010] The dynamic resource adjustment module is used to monitor the resource load of edge nodes in real time. When resource load imbalance occurs, resources are redistributed according to the dynamic resource adaptive adjustment strategy to achieve stable system operation, thereby solving the problems mentioned in the background technology.

[0011] To achieve the above objectives, the present invention provides the following technical solution:

[0012] A blockchain edge computing resource collaborative scheduling system for IoT devices includes a device edge networking module, a resource status initialization module, an intelligent task allocation module, and a dynamic resource adjustment module, with connections between the modules.

[0013] The device edge networking module is used to deploy edge nodes in areas with dense IoT devices and connect the edge nodes to the blockchain network. IoT devices connect to the edge nodes through a wireless network to transmit data and submit tasks.

[0014] The resource status initialization module is used by each edge node to initialize the local resource scheduling ledger, record the initial resource status, and send the task information to the edge node layer after the IoT device generates a task.

[0015] The intelligent task allocation module is used to calculate the optimal task allocation scheme based on the intelligent task allocation algorithm, combined with its own resource status and the resource information of other nodes, after the edge node receives a task, and then allocate the task to the edge node for processing according to the optimal task allocation scheme.

[0016] The dynamic resource adjustment module is used to monitor the resource load of edge nodes in real time. When resource load imbalance occurs, resources are redistributed according to the dynamic resource adaptive adjustment strategy to achieve stable system operation.

[0017] In a preferred embodiment, the process of obtaining the dense area of ​​IoT devices is as follows:

[0018] Geographic Information System (GIS) technology is used to define the boundaries of densely populated areas of IoT devices, and the densely populated areas of IoT devices are abstracted into two-dimensional planar graphics to obtain their area S.

[0019] Divide the area into n grid cells and count the number of IoT devices N in each grid cell. i The formula for calculating the regional average equipment distribution density ρ is:

[0020]

[0021] In the formula, ρ is the average equipment distribution density in the region, and N i is the number of IoT devices in each grid cell, n is the number of grid cells, and S is the area of ​​the densely populated IoT device region;

[0022] The average device distribution density in densely populated IoT device areas is compared with a preset threshold. If the average device distribution density in densely populated IoT device areas is greater than or equal to the preset distribution density threshold, then edge nodes need to be deployed in densely populated IoT device areas.

[0023] If the average device density in a densely populated area of ​​IoT devices is less than a preset density threshold, then it is not necessary to deploy edge nodes in such areas.

[0024] In a preferred embodiment, the process of deploying edge nodes is as follows:

[0025] Randomly assign m edge nodes to the grid cell boundaries and calculate the comprehensive allocation evaluation coefficient for each assignment. By comparing the comprehensive allocation evaluation coefficients of each assignment, the assignment with the largest comprehensive allocation evaluation coefficient is obtained, and the deployment location of the edge nodes is selected based on this assignment.

[0026] In a preferred embodiment, the process for obtaining the comprehensive allocation evaluation coefficients is as follows:

[0027] An evaluation index system is constructed based on the coverage function and data transmission delay to obtain the comprehensive allocation evaluation coefficient. The specific calculation formula is as follows:

[0028]

[0029] In the formula, Score q It is the comprehensive allocation evaluation coefficient, C is the coverage function, and T is the comprehensive allocation evaluation coefficient. lp α1 is the data transmission delay, α2 is the coverage function weight coefficient, and α2 is the data transmission delay weight coefficient.

[0030] In a preferred embodiment, after receiving a task, the edge node calculates the optimal task allocation scheme based on its own resource status and the resource information of other nodes using an intelligent task allocation algorithm, as follows:

[0031] After receiving the task data packet sent by the IoT device, the edge node parses the task information according to the encapsulation protocol and extracts key information.

[0032] Query the local resource scheduling ledger to check the current computing resource status of the edge node and obtain its computing capabilities.

[0033] If the resources available are insufficient to meet the task requirements, then the process enters the resource information exchange phase.

[0034] If its own resources meet the task requirements, it will participate in the subsequent optimal solution calculation as a task execution node;

[0035] Edge nodes interact with other edge nodes through the blockchain network. Each edge node broadcasts its current resource status information to other nodes and receives resource status information from other nodes.

[0036] Construct a task allocation model that satisfies the constraints that task execution time is less than or equal to the set task completion deadline, and that resource usage of each edge node cannot exceed its available resources. Assume there are k tasks in total, x... ij As a decision variable, when task j is assigned to edge node i, x ij =1, otherwise x ij =0;

[0037] For each task j assigned to edge node i, calculate the task execution time and solve for the optimal task allocation scheme based on the task execution time.

[0038] In a preferred embodiment, the process of obtaining the task execution time is as follows:

[0039] Calculate the data transmission time based on the data volume of the task and the network bandwidth between the edge node and the IoT device;

[0040] The task computation time is determined based on the estimated CPU computing resources required for the task and the computing capabilities of the edge nodes.

[0041] The task execution time is obtained by adding the data transmission time to the task computation time. The formula for calculating the task execution time is as follows:

[0042]

[0043] In the formula, t is the task execution time, m is the number of edge nodes, k is the number of tasks, and x is the number of tasks. ij It is a decision variable, C j This refers to the estimated CPU computing resources required for the task, c i It refers to the computing power of the edge nodes, b i It is the network bandwidth between edge nodes and IoT devices, D j It refers to the amount of data in the task.

[0044] In a preferred embodiment, the constraint condition expression is as follows:

[0045]

[0046] In the formula, t ij It is the task execution time, x ij These are decision variables, where m is the number of edge nodes, k is the number of tasks, and T is the number of tasks. deadline，j It sets the time limit for task completion, C j This refers to the estimated CPU computing resources required for the task, c i It refers to the computing power of the edge nodes, b i It is the network bandwidth between edge nodes and IoT devices, D j It refers to the amount of data in the task.

[0047] In a preferred embodiment, the coverage function and data transmission latency are obtained as follows:

[0048] Let r be the effective coverage radius of the edge node. Draw a circle with the candidate location as the center and r as the radius to construct the coverage area. To ensure that all IoT devices are effectively covered, a coverage function C is introduced:

[0049]

[0050] In the formula, C is the coverage function, S is the area of ​​the densely populated area of ​​IoT devices, and A covered，i Let n be the area covered by the edge nodes of the i-th grid cell, and n be the number of grid cells.

[0051] The total number of IoT devices is calculated based on the number of IoT devices in each grid cell, and the data transmission latency is calculated in conjunction with the signal transmission speed. The specific calculation formula is as follows:

[0052]

[0053] In the formula, T lp It is the data transmission delay, d lpj is the straight-line distance between each IoT device and the nearest edge node, the signal transmission speed is v, and N is the total number of IoT devices.

[0054] In a preferred embodiment, the process of real-time monitoring of the resource load of edge nodes is as follows:

[0055] Real-time monitoring of edge node resource load, obtaining the total CPU computing resources and CPU computing resource usage of edge nodes, and calculating the percentage of available CPU resources. The calculation formula is as follows:

[0056]

[0057] In the formula, P storage It represents the percentage of available CPU resources, S. total S represents the total CPU computing resources of the edge nodes. used This refers to the amount of CPU computing resources used.

[0058] In a preferred embodiment, when resource load imbalance occurs, the resource reallocation process according to the dynamic resource adaptive adjustment strategy is as follows:

[0059] By comparing the percentage of available CPU resources of each edge node, if the percentage of available CPU resources of a node exceeds 1.5 times the average system load, and at least one node has a load below 50% of the average load, then the computing resource load is determined to be unbalanced.

[0060] Resource reallocation is achieved using a priority-based preemptive scheduling algorithm. When a high-priority task requests resources, if the current node does not have sufficient resources, the low-priority task is temporarily interrupted, and CPU core computing resources are reallocated to ensure that the high-priority task is executed first.

[0061] The technical effects and advantages of the blockchain edge computing resource collaborative scheduling system for IoT devices of the present invention are as follows:

[0062] 1. This invention uses an intelligent task allocation module to accurately assess task resource requirements and optimize allocation based on edge node resource status. Simultaneously, a dynamic resource adjustment module optimizes unbalanced resources, enabling the system to fully utilize edge computing resources, reducing waste and significantly improving resource utilization. Edge nodes are located close to IoT devices, performing data processing and task execution locally, reducing data transmission to the cloud and greatly lowering latency. The device-edge networking module ensures efficient data transmission, meeting the real-time requirements of IoT applications, such as real-time decision-making in industrial control and intelligent transportation scenarios. The application of blockchain technology ensures data is recorded in a distributed ledger, making it immutable and traceable. The data recording and consensus module ensures data consistency across nodes, effectively preventing malicious tampering and forgery, guaranteeing the security and trustworthiness of IoT data, and enhancing user trust in the system.

[0063] 2. This invention uses a dynamic resource adjustment module to monitor resource load in real time, promptly identifying and resolving resource imbalances to prevent system crashes caused by excessive load on some nodes. Simultaneously, intelligent task allocation and data security mechanisms ensure smooth task execution and data integrity, thereby improving system stability and reliability and reducing system failures and service interruptions. The design of each module in the system offers good flexibility, adapting to densely populated areas with varying sizes and types of IoT devices. Whether adding new IoT devices or expanding edge nodes, the device-edge networking module and intelligent task allocation module can quickly adapt to changes, enabling resource reconfiguration and rational task scheduling, facilitating system expansion and upgrades. Attached Figure Description

[0064] Figure 1 This is a schematic diagram of the structure of a blockchain edge computing resource collaborative scheduling system for IoT devices according to the present invention. Detailed Implementation

[0065] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0066] Example 1, Figure 1 This invention presents a blockchain edge computing resource collaborative scheduling system for Internet of Things (IoT) devices.

[0067] The device edge networking module is used to deploy edge nodes in areas with dense IoT devices and connect the edge nodes to the blockchain network. IoT devices connect to the edge nodes through a wireless network to transmit data and submit tasks.

[0068] It is necessary to assess areas with high density of IoT devices to determine the geographical extent of the area, the distribution density of IoT devices, the edge node coverage function, and the data transmission latency.

[0069] Based on the regional assessment results, the process of selecting suitable locations to deploy edge nodes is as follows:

[0070] Using Geographic Information System (GIS) technology, the boundaries of densely populated areas of IoT devices are defined, and the densely populated areas of IoT devices are abstracted into two-dimensional planar graphics (such as polygons) to obtain their area S.

[0071] Divide the area into n grid cells and count the number of IoT devices N in each grid cell. i The formula for calculating the regional average equipment distribution density ρ is:

[0072]

[0073] In the formula, ρ is the average equipment distribution density in the region, and N i is the number of IoT devices in each grid cell, n is the number of grid cells, and S is the area of ​​the densely populated IoT device region.

[0074] The average device distribution density in densely populated IoT device areas is compared with a preset threshold. If the average device distribution density in densely populated IoT device areas is greater than or equal to the preset distribution density threshold, then edge nodes need to be deployed in densely populated IoT device areas.

[0075] If the average device density in a densely populated area of ​​IoT devices is less than a preset density threshold, then it is not necessary to deploy edge nodes in such areas.

[0076] Randomly assign m edge nodes to the grid cell boundaries and calculate the comprehensive allocation evaluation coefficient for each assignment. Select the deployment location of the edge nodes based on the comprehensive allocation evaluation coefficient for each assignment. The process of obtaining the comprehensive allocation evaluation coefficient is as follows:

[0077] Let r be the effective coverage radius of the edge node. Draw a circle with the candidate location as the center and r as the radius to construct the coverage area. To ensure that all IoT devices are effectively covered, a coverage function C is introduced:

[0078]

[0079] In the formula, C is the coverage function, S is the area of ​​the densely populated area of ​​IoT devices, and A covered，i Let be the area covered by the edge nodes of the i-th grid cell, and n be the number of grid cells.

[0080] The system obtains the straight-line distance from each IoT device to each edge node, selects the nearest edge node for each IoT device by comparing the straight-line distances, and calculates the straight-line distance between each IoT device and its nearest edge node. It also calculates the total number of IoT devices based on the number of IoT devices in each grid cell, and calculates the data transmission delay by combining this with the signal transmission speed. The specific calculation formula is as follows:

[0081]

[0082] In the formula, T lp It is the data transmission delay, d lpj is the straight-line distance between each IoT device and the nearest edge node, the signal transmission speed is v, and N is the total number of IoT devices.

[0083] It's important to note that while tasks for various IoT devices may require processing by multiple edge nodes, this includes the nearest edge node; here, we only consider the nearest edge node. Focusing solely on the nearest edge node significantly simplifies the calculation of data transmission latency. It eliminates the need to consider the complex relationships between multiple edge nodes and IoT devices, as well as the impact of different transmission paths, reducing computational load and system complexity while improving computational efficiency and system operability. It also minimizes the distance data travels within the network, thereby reducing signal attenuation and latency during transmission. Since signal transmission speed is finite, shorter transmission distances mean shorter data arrival times at edge nodes, better meeting the real-time requirements of IoT applications.

[0084] An evaluation index system is constructed based on the coverage function and data transmission delay to obtain the comprehensive allocation evaluation coefficient. The specific calculation formula is as follows:

[0085]

[0086] In the formula, Score q It is the comprehensive allocation evaluation coefficient, C is the coverage function, and T is the comprehensive allocation evaluation coefficient. lp α1 is the data transmission delay, α2 is the coverage function weight coefficient, and α2 is the data transmission delay weight coefficient.

[0087] It should be noted that α1 and α2 are both greater than 0, and are obtained by analyzing the coverage function and the importance of data transmission delay based on historical data.

[0088] By comparing the comprehensive allocation evaluation coefficients of each allocation scenario, the allocation scenario with the highest comprehensive allocation evaluation coefficient is obtained, and the deployment location of the edge node is selected based on this allocation scenario.

[0089] The resource status initialization module is used by each edge node to initialize the local resource scheduling ledger, record the initial resource status, and send the task information to the edge node layer after the IoT device generates a task.

[0090] Each edge node initializes its local resource scheduling ledger and records the initial resource status as follows:

[0091] When an edge node starts up, it obtains the initial configuration information of computing resources, storage resources, and network resources by calling the underlying hardware detection interface of the system.

[0092] Read the configuration parameters of the edge node's operating system and related services to determine the amount of resources allocated to the system's own operation;

[0093] Based on the obtained hardware configuration information and system resource usage, the initial available resource status is calculated.

[0094] The calculated initial resource status information is written into the local resource scheduling ledger using a data structure that combines key-value pairs and lists.

[0095] At the same time, a unique identifier (UUID) is assigned to each resource record to facilitate subsequent querying, updating and management of resource status.

[0096] After an IoT device generates a task, the process of sending the task information to the edge node layer is as follows:

[0097] During operation, IoT devices generate tasks based on their own functions and user instructions. The task information includes several key fields:

[0098] Task Identifier: Generate a unique identifier for each task to distinguish different tasks in the system;

[0099] Task type: Define the nature of the task, such as data acquisition task, data processing task, equipment control task, etc.;

[0100] Data requirements: Specify the data source, data format, and data volume required for the task; for example, a data acquisition task needs to specify the type of sensor and sampling frequency; a data processing task needs to provide the storage location and format requirements of the data to be processed.

[0101] Computational requirements: Quantify the computational resource requirements of the task, which include the CPU computational resources that the task is expected to require;

[0102] The estimated CPU computing resources required for a task are calculated using a task complexity assessment model, as follows: Assuming the task complexity is T and the data volume is D, the formula for calculating the estimated CPU computing resources required for the task is as follows:

[0103] C j =αT + βD;

[0104] Where α and β are weighting coefficients adjusted according to the actual situation, and C j Estimate the CPU computing resources required for the task;

[0105] It's important to note that data processing is a crucial step in the various tasks generated by IoT devices, and the CPU is the core component for data processing. In the initial stages of task allocation or in certain specific scenarios, simplifying the model is necessary to make the problem easier to handle and analyze. Focusing only on CPU computing resource requirements, without considering other resource requirements, can transform complex multi-resource-constrained problems into single-resource-constrained problems, significantly reducing the complexity of task allocation algorithms and facilitating the rapid finding of feasible allocation solutions. For example, in some simple IoT monitoring systems, the main task is to perform simple calculations and analyses on sensor data. In this case, CPU computing resources may be the primary limiting factor; ignoring other resource requirements can simplify the calculation process and improve the efficiency of task allocation.

[0106] Real-time requirement: Set the time limit for task completion, using T. deadline,j express;

[0107] The generated task information is encapsulated according to a specific communication protocol;

[0108] During the encapsulation process, communication control information such as the source device address (the network address of the IoT device itself) and the target edge node address (determined according to the pairing relationship between the device and the edge node or the load balancing strategy) are added to form a complete task data packet;

[0109] IoT devices send encapsulated task data packets to the edge node layer via wireless network. During the transmission process, a retransmission mechanism is used to ensure reliable transmission of task information. If no acknowledgment response is received from the edge node within a specified time, the task data packet is retransmitted. The number of retransmissions can be dynamically adjusted according to network conditions. At the same time, the transmission time of the task information is recorded for subsequent transmission delay calculation and task timeliness judgment.

[0110] The intelligent task allocation module is used to calculate the optimal task allocation scheme based on the intelligent task allocation algorithm, combined with its own resource status and the resource information of other nodes, after the edge node receives a task, and then allocate the task to the edge node for processing according to the optimal task allocation scheme.

[0111] After receiving the task data packet sent by the IoT device, the edge node first parses the task information according to the encapsulation protocol and extracts key information;

[0112] Then, query the local resource scheduling ledger to check its current computing resource status and obtain the computing capabilities of the edge node;

[0113] If its own resources cannot meet the task requirements, it will enter the resource information exchange stage; if its own resources meet the task requirements, it will participate in the subsequent optimal solution calculation as a potential task execution node.

[0114] Edge nodes interact with other edge nodes through the blockchain network. Each edge node broadcasts its current resource status information (obtained from the local resource scheduling ledger) to other nodes, while also receiving resource status information from other nodes.

[0115] It should be noted that, in order to ensure the timeliness and accuracy of information, an information update cycle can be set to periodically exchange and update resource information;

[0116] To minimize task completion time, meet real-time requirements, and satisfy CPU computing resource requirements, a task allocation model is constructed. Let there be a total of k tasks, x ij As a decision variable, when task j is assigned to edge node i, x ij =1, otherwise x ij =0;

[0117] For each task j assigned to edge node i, the task execution time is calculated, and the optimal task allocation scheme is determined based on the task execution time. The process of calculating the task execution time is as follows:

[0118] Calculate the data transmission time based on the data volume of the task and the network bandwidth between the edge node and the IoT device;

[0119] The task computation time is determined based on the estimated CPU computing resources required for the task and the computing capabilities of the edge nodes.

[0120] The task execution time is obtained by adding the data transmission time to the task computation time. The formula for calculating the task execution time is as follows:

[0121]

[0122] In the formula, t is the task execution time, m is the number of edge nodes, k is the number of tasks, and x is the number of tasks. ij It is a decision variable, C j This refers to the estimated CPU computing resources required for the task, c i It refers to the computing power of the edge nodes, b i It is the network bandwidth between edge nodes and IoT devices, D j It refers to the amount of data in the task;

[0123] Furthermore, the following constraints must be met: the task execution time must be less than or equal to the set task completion time limit, and the resource usage of each edge node cannot exceed its available resources.

[0124] The constraint expressions are as follows:

[0125] In the formula, t ij It is the task execution time, xij These are decision variables, where m is the number of edge nodes, k is the number of tasks, and T is the number of tasks. deadline，j It sets the time limit for task completion, C j This refers to the estimated CPU computing resources required for the task, c i It refers to the computing power of the edge nodes, b i It is the network bandwidth between edge nodes and IoT devices, D j It refers to the amount of data in the task.

[0126] Under the conditions of satisfying time and resource constraints, the optimal task allocation scheme is solved by minimizing the task execution time;

[0127] Based on the calculated optimal task allocation scheme, the edge nodes will assign tasks to the corresponding edge nodes for processing.

[0128] It should be noted that the edge node that assigns the task sends a task assignment instruction to the edge node to which the task is assigned, which includes detailed information about the task. After receiving the instruction, the edge node to which the task is assigned obtains the necessary data from the IoT device (if needed) and begins to execute the task. At the same time, each edge node updates its local resource scheduling ledger in real time to record the resource usage during the task execution process.

[0129] The dynamic resource adjustment module is used to monitor the resource load of edge nodes in real time. When resource load imbalance occurs, resources are redistributed according to the dynamic resource adaptive adjustment strategy to ensure stable system operation.

[0130] Real-time monitoring of edge node resource load, obtaining the total CPU computing resources and CPU computing resource usage of edge nodes, and calculating the percentage of available CPU resources. The calculation formula is as follows:

[0131]

[0132] In the formula, P storage It represents the percentage of available CPU resources, S. total S represents the total CPU computing resources of the edge nodes. used This refers to the amount of CPU computing resources used.

[0133] By comparing the percentage of available CPU resources of each edge node, if the percentage of available CPU resources of a node exceeds 1.5 times the average system load, and at least one node has a load below 50% of the average load, then the computing resource load is determined to be unbalanced.

[0134] Resource reallocation is achieved using a priority-based preemptive scheduling algorithm. When a high-priority task requests resources, if the current node does not have sufficient resources, the low-priority task is temporarily interrupted, and CPU core computing resources are reallocated to ensure that the high-priority task is executed first.

[0135] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0136] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0137] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0138] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0139] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0140] In conclusion, 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 within the protection scope of the present invention.

Claims

1. A blockchain edge computing resource collaborative scheduling system for Internet of Things (IoT) devices, characterized in that, It includes a device edge networking module, a resource status initialization module, an intelligent task allocation module, and a dynamic resource adjustment module, and the modules are interconnected: The device edge networking module is used to deploy edge nodes in areas with dense IoT devices. The process of deploying edge nodes is as follows: Randomly assign m edge nodes to the grid cell boundaries and calculate the comprehensive allocation evaluation coefficient for each assignment. By comparing the comprehensive allocation evaluation coefficients of each assignment, the assignment with the largest comprehensive allocation evaluation coefficient is obtained, and the deployment location of the edge nodes is selected based on this assignment. The process for obtaining the comprehensive allocation evaluation coefficient is as follows: An evaluation index system is constructed based on the coverage function and data transmission delay to obtain the comprehensive allocation evaluation coefficient. The specific calculation formula is as follows: In the formula, This is the comprehensive allocation evaluation coefficient, and C is the coverage function. It's data transmission delay. These are the weighting coefficients of the coverage function. It is a data transmission delay weighting coefficient; The coverage function and data transmission latency acquisition process are as follows: Let the effective coverage radius of the edge node be r. Draw a circle with the candidate location as the center and r as the radius to construct the coverage area. To ensure that all IoT devices can be effectively covered, a coverage function C is introduced: In the formula, C is the coverage function, and S is the area of ​​the densely populated IoT device region. Let n be the area covered by the edge nodes in the i-th grid cell, and n be the number of grid cells. The total number of IoT devices is calculated based on the number of IoT devices in each grid cell, and the data transmission delay is calculated in conjunction with the signal transmission speed. The specific calculation formula is as follows: In the formula, It's data transmission delay. is the straight-line distance between each IoT device and the nearest edge node, the signal transmission speed is v, and N is the total number of IoT devices; The edge nodes are connected to the blockchain network, and IoT devices connect to the edge nodes via wireless networks to transmit data and submit tasks. The resource status initialization module is used by each edge node to initialize the local resource scheduling ledger, record the initial resource status, and send the task information to the edge node layer after the IoT device generates a task. The intelligent task allocation module is used to calculate the optimal task allocation scheme based on the intelligent task allocation algorithm, combined with its own resource status and the resource information of other nodes, after the edge node receives a task, and then allocate the task to the edge node for processing according to the optimal task allocation scheme. The dynamic resource adjustment module is used to monitor the resource load of edge nodes in real time. When resource load imbalance occurs, resources are redistributed according to the dynamic resource adaptive adjustment strategy to achieve stable system operation.

2. The blockchain edge computing resource collaborative scheduling system for IoT devices according to claim 1, characterized in that, The process of obtaining densely populated areas of IoT devices is as follows: Geographic Information System (GIS) technology is used to define the boundaries of densely populated areas of IoT devices, and the densely populated areas of IoT devices are abstracted into two-dimensional planar graphics to obtain their area S. Divide the area into n grid cells and count the number of IoT devices in each grid cell. The average equipment distribution density in the region The calculation formula is: ； In the formula, The average equipment distribution density in the region. is the number of IoT devices in each grid cell, n is the number of grid cells, and S is the area of ​​the densely populated IoT device region; The average device distribution density in densely populated IoT device areas is compared with a preset threshold. If the average device distribution density in densely populated IoT device areas is greater than or equal to the preset distribution density threshold, then edge nodes need to be deployed in densely populated IoT device areas. If the average device density in a densely populated area of ​​IoT devices is less than a preset density threshold, then it is not necessary to deploy edge nodes in such areas.

3. The blockchain edge computing resource collaborative scheduling system for IoT devices according to claim 2, characterized in that, After receiving a task, the edge node calculates the optimal task allocation scheme based on its own resource status and the resource information of other nodes using the intelligent task allocation algorithm. The process is as follows: After receiving the task data packet sent by the IoT device, the edge node parses the task information according to the encapsulation protocol and extracts key information. Query the local resource scheduling ledger to check the current computing resource status of the edge node and obtain its computing capabilities. If the resources available are insufficient to meet the task requirements, then the resource information exchange phase begins. If its own resources meet the task requirements, it will participate in the subsequent optimal solution calculation as a task execution node; Edge nodes interact with other edge nodes through the blockchain network. Each edge node broadcasts its current resource status information to other nodes and receives resource status information from other nodes. Construct a task allocation model that satisfies the constraints that task execution time is less than or equal to the set task completion deadline, and that the resource usage of each edge node cannot exceed its available resources. Assume there are a total of k tasks. As a decision variable, when task j is assigned to edge node i, ,otherwise The constraint expressions are as follows: ; In the formula, It is the task execution time. These are the decision variables, where m is the number of edge nodes and k is the number of tasks. It sets a time limit for completing the task. This refers to the estimated CPU computing resources required for the task. It refers to the computing power of the edge nodes. It refers to the network bandwidth between edge nodes and IoT devices. It refers to the amount of data in the task. It is the upper limit of the total CPU computing resources of the i-th edge node; Under the conditions of satisfying the time limit and resource constraints, for each task j assigned to edge node i, the task execution time is calculated, and the optimal task allocation scheme is solved based on the task execution time.

4. The blockchain edge computing resource collaborative scheduling system for IoT devices according to claim 3, characterized in that, The process for obtaining the task execution time is as follows: Calculate the data transmission time based on the data volume of the task and the network bandwidth between the edge node and the IoT device; The task computation time is determined based on the estimated CPU computing resources required for the task and the computing capabilities of the edge nodes. The task execution time is obtained by adding the data transmission time to the task computation time. The formula for calculating the task execution time is as follows: ； In the formula, t is the task execution time, m is the number of edge nodes, and k is the number of tasks. It is a decision variable. This refers to the estimated CPU computing resources required for the task. It refers to the computing power of the edge nodes. It refers to the network bandwidth between edge nodes and IoT devices. It refers to the amount of data in the task.

5. A blockchain edge computing resource collaborative scheduling system for IoT devices according to claim 4, characterized in that, The process of real-time monitoring of the resource load of edge nodes is as follows: Real-time monitoring of edge node resource load, obtaining the total CPU computing resources and CPU computing resource usage of edge nodes, and calculating the percentage of available CPU resources. The calculation formula is as follows: ； In the formula, It represents the percentage of available CPU resources. It is the total CPU computing resources of the edge nodes. This refers to the usage of CPU computing resources.

6. The blockchain edge computing resource collaborative scheduling system for IoT devices according to claim 5, characterized in that, When resource load imbalance occurs, the resource reallocation process according to the dynamic resource adaptive adjustment strategy is as follows: By comparing the percentage of available CPU resources of each edge node, if the percentage of available CPU resources of a node exceeds 1.5 times the average system load, and at least one node has a load below 50% of the average load, then the computing resource load is determined to be unbalanced. A priority-based preemptive scheduling algorithm is used to reallocate resources. When a high-priority task requests resources, if the current node does not have enough resources, the low-priority task is temporarily interrupted and CPU core computing resources are reallocated to ensure that the high-priority task is executed first.

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