Block chain edge computing resource collaborative scheduling system of Internet of Things equipment

Abstract

The invention discloses a block chain edge computing resource collaborative scheduling system of Internet of Things equipment, and relates to the technical field of Internet of Things, comprising an equipment edge networking module used for deploying edge nodes in an Internet of Things equipment dense area and accessing the edge nodes to a block chain network; the resource state initialization module is used for sending task information to the edge node layer after the Internet of Things equipment generates a task; the intelligent task distribution module is used for calculating an optimal task distribution scheme after the edge node receives the task, and distributing the task to the edge node for processing according to the optimal task distribution scheme; and the dynamic resource adjustment module is used for monitoring the resource load condition of the edge node in real time, and reallocating resources according to a dynamic resource self-adaptive adjustment strategy when the resource load is unbalanced, so that the stable operation of the system is realized. And efficient collaborative scheduling and safe and credible management of edge computing resources among the Internet of Things equipment are realized.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things technology, and more specifically, to a blockchain edge computing resource collaborative scheduling system for Internet of Things devices. Background Art

[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 challenges in processing IoT data, including high network latency, bandwidth constraints, and data privacy. Edge computing, by deploying computing resources at the edge of the network, can process data generated by IoT devices locally, effectively reducing latency and bandwidth constraints. 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, and secure nature, combines blockchain with edge computing to offer a new approach to addressing these issues. However, a mature system for collaboratively scheduling IoT device blockchain-edge computing resources is currently lacking.

[0003] Deficiencies in existing technologies:

[0004] In traditional IoT systems, edge computing resource management is decentralized, and edge nodes lack effective coordination, making it difficult to flexibly allocate resources based on actual needs and resulting in low resource utilization. Data transmission and storage processes present security risks, making data susceptible to tampering and falsification, and lacking effective traceability mechanisms. Blockchain technology has been introduced relatively infrequently and its application has been limited. Previous task allocation methods often lack comprehensive consideration of task characteristics and resource status, potentially leading to high task processing latency and low efficiency. Existing systems struggle to monitor resource load in real time, making it difficult to make timely adjustments when resource load imbalances occur, impacting overall system performance and stability.

[0005] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a blockchain edge computing resource collaborative scheduling system for IoT devices, which includes a device edge networking module, a resource status initialization module, an intelligent task allocation module, and a dynamic resource adjustment module. There are connections between the modules:

[0007] The device edge networking module is used to deploy edge nodes in areas with dense IoT devices and connect them to the blockchain network. IoT devices connect to edge nodes via wireless networks for data transmission and task submission.

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

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

[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 reallocated according to the dynamic resource adaptive adjustment strategy to achieve stable system operation; in order to solve the problems raised in the above background technology.

[0011] To achieve the above object, the present invention provides the following technical solutions:

[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. The modules are connected:

[0013] The device edge networking module is used to deploy edge nodes in areas with dense IoT devices and connect them to the blockchain network. IoT devices connect to edge nodes via wireless networks for data transmission and task submission.

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

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

[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 reallocated according to the dynamic resource adaptive adjustment strategy to achieve stable system operation.

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

[0018] Use geographic information system technology to define the boundaries of the IoT device-dense area, abstract the IoT device-dense area into a two-dimensional plane graph, and obtain its area S;

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

[0020]

[0021] Where ρ is the average equipment distribution density in the region, 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 area of ​​IoT devices;

[0022] Compare and analyze the average device distribution density in the IoT device-dense area with the preset threshold. If the average device distribution density in the IoT device-dense area is greater than or equal to the preset distribution density threshold, edge nodes need to be deployed in the IoT device-dense area.

[0023] If the average device distribution density in an IoT device-dense area is less than the preset distribution density threshold, there is no need to deploy edge nodes in the IoT device-dense area.

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

[0025] Randomly deploy m edge nodes to the intersection of grid units, and calculate the comprehensive allocation evaluation coefficient of each allocation situation. By comparing the comprehensive allocation evaluation coefficients of various allocation situations, the allocation situation with the largest comprehensive allocation evaluation coefficient is obtained, and the edge node deployment location is selected based on this allocation situation.

[0026] In a preferred embodiment, the process of obtaining the comprehensive allocation evaluation coefficient 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 is the comprehensive allocation evaluation coefficient, C is the coverage function, T lp is the data transmission delay, α1 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 solution based on the intelligent task allocation algorithm in combination with its own resource status and resource information of other nodes 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, check the current computing resource status, and obtain the computing capacity of the edge node;

[0033] If the own resources cannot meet the task requirements, it enters the resource information interaction stage;

[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 exchange resource information 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 at the same time.

[0036] Construct a task allocation model that satisfies the constraints that the task execution time is less than or equal to the set task completion time limit, and the resource usage of each edge node cannot exceed its available resources. Suppose there are k tasks in total, x ij is 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, the task execution time is calculated, and the optimal task allocation solution is solved based on the task execution time.

[0038] In a preferred embodiment, the process of obtaining the execution time of the calculation task 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] Obtain the task computing time based on the CPU computing resources expected to be required for the task and the computing power of the edge node;

[0041] The task execution time is obtained by adding the data transmission time and the task calculation time. The task execution time calculation formula is as follows:

[0042]

[0043] Where t is the task execution time, m is the number of edge nodes, k is the number of tasks, and x ij is the decision variable, C j is the CPU computing resources expected to be required for the task, c i is the computing power of the edge node, b i is the network bandwidth between the edge node and the IoT device, D j is the data size of the task.

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

[0045]

[0046] Where, t ij is the task execution time, x ij is the decision variable, m is the number of edge nodes, k is the number of tasks, T deadline，j It is to set the time limit for task completion, C j is the CPU computing resources expected to be required for the task, c i is the computing power of the edge node, b i is the network bandwidth between the edge node and the IoT device, D j is the data size of the task.

[0047] In a preferred embodiment, the coverage function and data transmission delay acquisition process are as follows:

[0048] Assume that the effective coverage radius of the edge node is r, and 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, the coverage function C is introduced:

[0049]

[0050] Where C is the coverage function, S is the area of ​​the densely populated area of ​​IoT devices, and A covered，i is the area of ​​the ith grid cell covered by the edge nodes, and n is the number of grid cells;

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

[0052]

[0053] Where, T lp 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] Monitor the resource load of edge nodes in real time, obtain the total amount of CPU computing resources and CPU computing resource usage of edge nodes, and calculate the percentage of available CPU resources. The calculation formula is as follows:

[0056]

[0057] Where, P storage is the percentage of available CPU resources, S total is the total amount of CPU computing resources of the edge node, S used It is the CPU computing resource usage.

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

[0059] Compare the available CPU resource percentages of each edge node. If the available CPU resource percentage of a node exceeds 1.5 times the system average load, and at least one node has a load less than 50% of the average load, then the computing resource load is considered unbalanced.

[0060] A priority-based preemptive scheduling algorithm is used to reallocate resources. When a high-priority task requests resources, if the current node resources are insufficient, the low-priority task is temporarily interrupted and the 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. The present invention accurately evaluates the task resource requirements through the intelligent task allocation module, and makes optimal allocation based on the resource status of the edge nodes. At the same time, the dynamic resource adjustment module optimizes the unbalanced resources, so that the system can make full use of edge computing resources, reduce resource waste, and significantly improve resource utilization. The edge nodes are close to the IoT devices and perform data processing and task execution locally, which reduces the process of data transmission to the cloud and greatly reduces data transmission delay. The device-edge networking module ensures the efficiency of data transmission and meets the real-time requirements of IoT applications, such as real-time decision-making needs in scenarios such as industrial control and intelligent transportation. The application of blockchain technology enables data to be recorded in a distributed ledger, which is tamper-proof and traceable. The data recording and consensus modules ensure that the data of each node is consistent, effectively preventing the data from being maliciously tampered with and forged, ensuring the security and credibility of IoT data, and enhancing users' trust in the system.

[0063] 2. The present invention monitors resource loads in real time through a dynamic resource adjustment module, promptly discovers and resolves resource imbalance problems, and avoids system crashes caused by excessive loads on some nodes. At the same time, intelligent task allocation and data security mechanisms also ensure the smooth execution of tasks and the integrity of data, thereby improving the stability and reliability of the system and reducing system failures and service interruptions. The design of each module of the system has good flexibility and can adapt to dense areas of IoT devices of different sizes and types. Whether it is adding new IoT devices or expanding edge nodes, the device-edge networking module and the intelligent task allocation module can quickly adapt to changes, realize resource reconfiguration and reasonable scheduling of tasks, and facilitate system expansion and upgrades. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 This is a schematic diagram of the structure of a blockchain edge computing resource collaborative scheduling system for an Internet of Things device of the present invention. DETAILED DESCRIPTION

[0065] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0066] Example 1, Figure 1 The present invention provides a blockchain edge computing resource collaborative scheduling system for Internet of Things devices.

[0067] The device edge networking module is used to deploy edge nodes in areas with dense IoT devices and connect them to the blockchain network. IoT devices connect to edge nodes via wireless networks for data transmission and task submission.

[0068] It is necessary to evaluate the densely populated areas of IoT devices to determine the geographical scope of the area, the distribution density of IoT devices, the edge node coverage function and the data transmission delay;

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

[0070] Using geographic information system (GIS) technology, the boundaries of the IoT device-dense area are defined and abstracted into a two-dimensional plane graphic (such as a polygon) to obtain its area S.

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

[0072]

[0073] Where ρ is the average equipment distribution density in the region, N i is the number of IoT devices in each grid unit, n is the number of grid units, and S is the area of ​​the densely populated area of ​​IoT devices.

[0074] Compare and analyze the average device distribution density in the IoT device-dense area with the preset threshold. If the average device distribution density in the IoT device-dense area is greater than or equal to the preset distribution density threshold, edge nodes need to be deployed in the IoT device-dense area.

[0075] If the average device distribution density in an IoT device-dense area is less than the preset distribution density threshold, there is no need to deploy edge nodes in the IoT device-dense area.

[0076] Randomly allocate and deploy m edge nodes to the grid unit intersection, and calculate the comprehensive allocation evaluation coefficient of each allocation situation. Select the deployment edge node location based on the comprehensive allocation evaluation coefficient of each allocation situation. The process of obtaining the comprehensive allocation evaluation coefficient is as follows:

[0077] Assume that the effective coverage radius of the edge node is r, and 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, the coverage function C is introduced:

[0078]

[0079] Where C is the coverage function, S is the area of ​​the densely populated area of ​​IoT devices, and A covered，i is the area of ​​the ith grid unit covered by the edge nodes, and n is the number of grid units.

[0080] Obtain the straight-line distance from each IoT device to each edge node, and select the nearest edge node for each IoT device by comparing the straight-line distances, as well as the straight-line distance between each IoT device and the nearest edge node. Calculate the total number of IoT devices based on the number of IoT devices in each grid cell, and calculate the data transmission delay based on the signal transmission speed. The specific calculation formula is as follows:

[0081]

[0082] Where, T lp 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 should be noted that each IoT device's tasks may require processing by multiple edge nodes. However, these multiple edge nodes include the nearest edge node, so only the nearest edge node is considered here. Considering only the nearest edge node significantly simplifies the calculation of data transmission delay. This eliminates the need to consider the complex relationships between multiple edge nodes and IoT devices, as well as the impact of varying transmission paths. This reduces computational effort and system complexity, while improving computational efficiency and system operability. This minimizes the distance data must travel within the network, reducing signal attenuation and latency during transmission. Because signal transmission speed is limited, shorter transmission distances shorten the time it takes for data to reach 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 is the comprehensive allocation evaluation coefficient, C is the coverage function, T lp is the data transmission delay, α1 is the coverage function weight coefficient, and α2 is the data transmission delay weight coefficient.

[0087] It should be noted that both α1 and α2 are 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 various allocation situations, the allocation situation with the largest comprehensive allocation evaluation coefficient is obtained, and the edge node deployment location is selected based on the allocation situation.

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

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

[0091] When the edge node starts, it calls the hardware detection interface of the underlying system to obtain the initial configuration information of computing resources, storage resources, and network resources;

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

[0093] Calculate the initial available resource status based on the obtained hardware configuration information and system resource usage;

[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 query, update 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. Task information contains several key fields:

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

[0099] Task type: clarify the nature of the task, such as data collection task, data processing task, equipment control task, etc.

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

[0101] Computing requirements: Quantify the computing resource requirements of the task, including the CPU computing resources expected to be required for the task;

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

[0103] C j =αT+βD;

[0104] Among them, α and β are weight coefficients adjusted according to actual conditions, C j Estimate the CPU computing resources required for the task;

[0105] It's important to note that data processing is a critical component of the various tasks generated by IoT devices, and the CPU is the core component for this 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 solely on CPU computing resource requirements, while ignoring other resource requirements, can transform complex multi-resource-constrained problems into single-resource-constrained ones. This significantly reduces the complexity of the task allocation algorithm and facilitates the rapid identification of viable allocation solutions. For example, in some simple IoT monitoring systems, where tasks primarily involve simple calculations and analysis of sensor data, CPU computing resources may be the primary limiting factor. Ignoring other resource requirements can simplify the calculation process and improve task allocation efficiency.

[0106] Real-time requirements: set a time limit for task completion, using T deadline,j express;

[0107] Encapsulate the generated task information 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 based on the pairing relationship between the device and the edge node or the load balancing strategy) is added to form a complete task data packet;

[0109] The IoT device sends the encapsulated task data packet to the edge node layer via the wireless network. During the sending process, a retransmission mechanism is used to ensure the reliable transmission of task information. If no confirmation response is received from the edge node within the specified time, the task data packet is resent. The number of retransmissions can be dynamically adjusted according to the network conditions. At the same time, the sending 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 plan based on the intelligent task allocation algorithm after the edge node receives the task, combining its own resource status and the resource information of other nodes, and then allocate the task to the edge node for processing according to the optimal task allocation plan;

[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 the key information;

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

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

[0114] Edge nodes exchange resource information 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 and receives resource status information from other nodes at the same time.

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

[0116] With the goal of minimizing task completion time and meeting task real-time and CPU computing resource requirements, a task allocation model is constructed. Suppose there are k tasks, x ij is 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 solution is solved based on the task execution time. The process of obtaining the calculated 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] Obtain the task computing time based on the CPU computing resources expected to be required for the task and the computing power of the edge node;

[0120] The task execution time is obtained by adding the data transmission time and the task calculation time. The task execution time calculation formula is as follows:

[0121]

[0122] Where t is the task execution time, m is the number of edge nodes, k is the number of tasks, and x ij is the decision variable, C j is the CPU computing resources expected to be required for the task, c i is the computing power of the edge node, b i is the network bandwidth between the edge node and the IoT device, D j is the data volume of the task;

[0123] The constraints that need to be met are: 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] Where, t ij is the task execution time, xij is the decision variable, m is the number of edge nodes, k is the number of tasks, T deadline，j It is to set the time limit for task completion, C j is the CPU computing resources expected to be required for the task, c i is the computing power of the edge node, b i is the network bandwidth between the edge node and the IoT device, D j is the data size of the task.

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

[0127] According to the calculated optimal task allocation scheme, the edge node allocates the task to the corresponding edge node 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 contains detailed information about the task. After receiving the instruction, the edge node to which the task is assigned obtains the required data from the IoT device (if necessary) and starts executing the task. At the same time, each edge node updates the local resource scheduling ledger in real time to record the resource usage during task execution.

[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 reallocated according to the dynamic resource adaptive adjustment strategy to ensure stable operation of the system.

[0130] Monitor the resource load of edge nodes in real time, obtain the total amount of CPU computing resources and CPU computing resource usage of edge nodes, and calculate the percentage of available CPU resources. The calculation formula is as follows:

[0131]

[0132] Where, P storage is the percentage of available CPU resources, S total is the total amount of CPU computing resources of the edge node, S used It is the CPU computing resource usage.

[0133] Compare the available CPU resource percentages of each edge node. If the available CPU resource percentage of a node exceeds 1.5 times the system average load, and at least one node has a load less than 50% of the average load, then the computing resource load is considered unbalanced.

[0134] A priority-based preemptive scheduling algorithm is used to reallocate resources. When a high-priority task requests resources, if the current node resources are insufficient, the low-priority task is temporarily interrupted and the CPU core computing resources are reallocated to ensure that the high-priority task is executed first.

[0135] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

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

[0137] Those skilled in the art will appreciate that the modules and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel 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, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

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

[0140] Finally: 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.

Claims

1. A blockchain edge computing resource collaborative scheduling system for IoT devices, characterized in that: It includes device edge networking module, resource status initialization module, intelligent task allocation module, and dynamic resource adjustment module. There are connections between the modules: The device edge networking module is used to deploy edge nodes in areas with dense IoT devices and connect them to the blockchain network. IoT devices connect to edge nodes via wireless networks for data transmission and task submission. The resource status initialization module is used by each edge node to initialize the local resource scheduling ledger and record the initial resource status. After the IoT device generates a task, the task information is sent to the edge node layer. The intelligent task allocation module is used to calculate the optimal task allocation plan based on the intelligent task allocation algorithm after the edge node receives the task, combining its own resource status and the resource information of other nodes, and then allocate the task to the edge node for processing according to the optimal task allocation plan; 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 reallocated 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 is characterized in that: The process of obtaining the dense area of ​​IoT devices is as follows: Use geographic information system technology to define the boundaries of the IoT device-dense area, abstract the IoT device-dense area into a two-dimensional plane graph, and obtain its area S; Divide the area into n grid cells and count the number of IoT devices N in each grid cell i , then the calculation formula for the regional average equipment distribution density ρ is: Where ρ is the average equipment distribution density in the region, 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 area of ​​IoT devices; Compare and analyze the average device distribution density in the IoT device-dense area with the preset threshold. If the average device distribution density in the IoT device-dense area is greater than or equal to the preset distribution density threshold, edge nodes need to be deployed in the IoT device-dense area. If the average device distribution density in an IoT device-dense area is less than the preset distribution density threshold, there is no need to deploy edge nodes in the IoT device-dense area.

3. The blockchain edge computing resource collaborative scheduling system for IoT devices according to claim 2 is characterized in that: The process of deploying edge nodes is as follows: Randomly deploy m edge nodes to the intersection of grid units, and calculate the comprehensive allocation evaluation coefficient of each allocation situation. By comparing the comprehensive allocation evaluation coefficients of various allocation situations, the allocation situation with the largest comprehensive allocation evaluation coefficient is obtained, and the edge node deployment location is selected based on this allocation situation.

4. The blockchain edge computing resource collaborative scheduling system for IoT devices according to claim 3 is characterized in that: The process of 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, Score q is the comprehensive allocation evaluation coefficient, C is the coverage function, T lp is the data transmission delay, α1 is the coverage function weight coefficient, and α2 is the data transmission delay weight coefficient.

5. The blockchain edge computing resource collaborative scheduling system for IoT devices according to claim 4 is characterized in that: After receiving the task, the edge node calculates the optimal task allocation solution based on the intelligent task allocation algorithm, combining its own resource status and the resource information of other nodes. 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, check the current computing resource status, and obtain the computing capacity of the edge node; If the own resources cannot meet the task requirements, it enters the resource information interaction stage; If its own resources meet the task requirements, it will participate in the subsequent optimal solution calculation as a task execution node; Edge nodes exchange resource information 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 at the same time. Construct a task allocation model that satisfies the constraints that the task execution time is less than or equal to the set task completion time limit, and the resource usage of each edge node cannot exceed its available resources. Suppose there are k tasks in total, x ij is a decision variable. When task j is assigned to edge node i, x ij =1, otherwise x ij =0; For each task j assigned to edge node i, the task execution time is calculated, and the optimal task allocation solution is solved based on the task execution time.

6. The blockchain edge computing resource collaborative scheduling system for IoT devices according to claim 5 is characterized in that: The process of obtaining the execution time of the calculation task 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; Obtain the task computing time based on the CPU computing resources expected to be required for the task and the computing power of the edge node; The task execution time is obtained by adding the data transmission time and the task calculation time. The task execution time calculation formula is as follows: Where t is the task execution time, m is the number of edge nodes, k is the number of tasks, and x ij is the decision variable, C j is the CPU computing resources expected to be required for the task, c i is the computing power of the edge node, b i is the network bandwidth between the edge node and the IoT device, D j is the data size of the task.

7. The blockchain edge computing resource collaborative scheduling system for IoT devices according to claim 5 is characterized in that: The constraint expressions are as follows: Where, t ij is the task execution time, x ij is the decision variable, m is the number of edge nodes, k is the number of tasks, T deadline，j It is to set the time limit for task completion, C j is the CPU computing resources expected to be required for the task, c i is the computing power of the edge node, b i is the network bandwidth between the edge node and the IoT device, D j is the data size of the task.

8. The blockchain edge computing resource collaborative scheduling system for IoT devices according to claim 4 is characterized in that: The process of obtaining the coverage function and data transmission delay is as follows: Assume that the effective coverage radius of the edge node is r, and 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, the coverage function C is introduced: Where C is the coverage function, S is the area of ​​the densely populated area of ​​IoT devices, and A covered，i is the area of ​​the ith grid cell covered by the edge nodes, and n is the number of grid cells; The total number of IoT devices is calculated based on the number of IoT devices in each grid unit, and the data transmission delay is calculated based on the signal transmission speed. The specific calculation formula is as follows: Where, T lp 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.

9. The blockchain edge computing resource collaborative scheduling system for IoT devices according to claim 8 is characterized in that: The process of real-time monitoring of the resource load of edge nodes is as follows: Monitor the resource load of edge nodes in real time, obtain the total amount of CPU computing resources and CPU computing resource usage of edge nodes, and calculate the percentage of available CPU resources. The calculation formula is as follows: Where, P storage is the percentage of available CPU resources, S total is the total amount of CPU computing resources of the edge node, S used It is the CPU computing resource usage.

10. The blockchain edge computing resource collaborative scheduling system for IoT devices according to claim 9 is characterized in that: When resource load imbalance occurs, the resource reallocation process is as follows according to the dynamic resource adaptive adjustment strategy: Compare the available CPU resource percentages of each edge node. If the available CPU resource percentage of a node exceeds 1.5 times the system average load, and at least one node has a load less than 50% of the average load, then the computing resource load is considered unbalanced. A priority-based preemptive scheduling algorithm is used to reallocate resources. When a high-priority task requests resources, if the current node resources are insufficient, the low-priority task is temporarily interrupted and the CPU core computing resources are reallocated to ensure that the high-priority task is executed first.

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