Privacy computing task scheduling method and system based on block chain

Abstract

The invention provides a privacy computing task scheduling method and system based on a block chain, and the method comprises the steps: extracting an execution state of a privacy computing task in the block chain from task progress information, and determining the elastic computing power of a scheduling resource in the block chain for privacy protection through the execution state and a task unloading strategy of the block chain in computing power resource scheduling; dividing the computing power resources in the block chain into a plurality of computing power resource nodes, determining a resource sharing relationship among the computing power resource nodes, and determining the privacy protection cost of each computing power resource node for the privacy computing task according to the resource sharing relationship; and evaluating a conflict relationship between computing power allocation and privacy protection in the block chain through each privacy protection cost and the elastic computing power to obtain a balance value of scheduling conflicts in the block chain, and performing balanced allocation of privacy computing tasks on each computing power resource node in the block chain based on the balance value of the scheduling conflicts. On the basis of the scheme, balanced distribution of the privacy computing tasks in the block chain can be realized.

Description

Technical Field

[0001] This application relates to the technical field of task scheduling. More specifically, this application relates to a privacy computing task scheduling method and system based on blockchain. Background Art

[0002] The core features of blockchain include decentralization, data immutability, anonymity, transparency, and support for smart contracts. These features give blockchain significant advantages in solving trust issues and security risks in traditional centralized systems and enable the implementation of decentralized applications.

[0003] Privacy computing tasks involve the processing of sensitive data, usually including encrypting, calculating, and analyzing the privacy information of individuals, enterprises, or organizations. In a decentralized blockchain network, task scheduling needs to consider how to effectively allocate computing tasks among multiple nodes without exposing or leaking sensitive data. Traditional task scheduling methods can lead to uneven task distribution, resulting in some block nodes possibly bearing excessive processing tasks of sensitive data. This not only causes uneven computing loads but also may increase the risk of data leakage. For example, if a block node processes a large amount of privacy data for a long time, it may become a target of attack or be subject to a man-in-the-middle attack during data transmission, thus exposing the user's privacy information. Therefore, how to achieve balanced allocation of privacy computing tasks in blockchain has become a difficult problem faced by the industry. Summary of the Invention

[0004] This application provides a privacy computing task scheduling method and system based on blockchain, which can achieve balanced allocation of privacy computing tasks in blockchain.

[0005] In a first aspect, this application provides a privacy computing task scheduling method based on blockchain, including: When a privacy computing task is executed on the blockchain, real-time monitoring of the task progress information of the privacy computing tasks in the blockchain; Extracting the execution status of the privacy computing tasks in the blockchain from the task progress information, and then determining the elastic computing power for privacy protection of the scheduling resources in the blockchain through the execution status and the task offloading strategy of the computing power resource scheduling in the blockchain; Using a hierarchical structure to divide the computing power resources in the blockchain into multiple computing power resource nodes, then determining the resource sharing relationship between each computing power resource node, and determining the privacy protection cost of each computing power resource node for privacy computing tasks according to the resource sharing relationship and the privacy protection characteristics in the blockchain; Evaluate the conflict relationship between computing power allocation and privacy protection in the blockchain through each privacy protection cost and the elastic computing power, obtain the balance value of the scheduling conflict in the blockchain, and evenly allocate the privacy computing tasks to each computing power resource node in the blockchain based on the balance value of the scheduling conflict.

[0006] In some embodiments, extracting the execution status of the privacy computing tasks in the blockchain from the task progress information specifically includes: Obtain all the computing tasks being executed in the blockchain; For each computing task, extract the task completion progress and task completion speed of the computing task from the task progress information; Determine the execution characteristics of the computing task through the task completion progress and the task completion speed, and thus obtain the execution characteristics of each computing task; Extract the execution status of the privacy computing tasks in the blockchain from all the execution characteristics.

[0007] In some embodiments, determining the elastic computing power for privacy protection of the scheduling resources in the blockchain through the execution status and the task offloading strategy of the computing power resource scheduling in the blockchain specifically includes: Obtain the task offloading strategy of the computing power resource scheduling in the blockchain; For each computing task, extract the execution status value of the computing task from the execution status; Conduct an extensibility evaluation on the execution status value through the task offloading strategy to obtain the scalable value of the computing power in the computing task, and thus obtain the scalable values of the computing power in each computing task; Determine the elastic computing power for privacy protection of the scheduling resources in the blockchain according to all the scalable values.

[0008] In some embodiments, using a hierarchical structure to divide the computing power resources in the blockchain into multiple computing power resource nodes specifically includes: Obtain all the resource blocks of the blockchain, and thus determine the computing power characteristics of each resource block; Perform computing power grading on all the computing power characteristics through the hierarchical structure to obtain multiple computing power levels; Divide all the resource blocks into multiple computing power resource nodes according to each computing power level.

[0009] In some embodiments, determining the resource sharing relationship between each computing power resource node specifically includes: For each computing power resource node, obtain all the shared blocks between the computing power resource node and other computing power resource nodes; Determine the topological association graph of the computing power resource node through all the shared blocks, and thus obtain the topological association graph of each computing power resource node; Generate the resource sharing relationships among various computing power resource nodes based on all the topological association graphs.

[0010] In some embodiments, determining the privacy protection cost of each computing power resource node for the privacy computing task according to the resource sharing relationship and the privacy protection feature in the blockchain specifically includes: For each computing power resource node, extract the resource sharing feature of the computing power resource node from the resource sharing relationship; Extract the trust level of the computing power resource node for the privacy computing task from the privacy protection feature in the blockchain; Associate the trust level through the resource sharing feature to obtain the privacy protection cost of the computing power resource node for the privacy computing task, and further obtain the privacy protection cost of each computing power resource node for the privacy computing task.

[0011] In some embodiments, the privacy computing task is a distributed computing task based on homomorphic privacy encryption.

[0012] In a second aspect, the present application provides a privacy computing task scheduling system based on a blockchain, including: A monitoring module, configured to, when the blockchain executes a privacy computing task, monitor the task progress information of the privacy computing task in the blockchain in real time; A processing module, configured to extract the execution status of the privacy computing task in the blockchain from the task progress information, and then determine the elastic computing power for privacy protection of the scheduling resources in the blockchain through the execution status and the task offloading strategy of the computing power resource scheduling in the blockchain; The processing module is further configured to divide the computing power resources in the blockchain into multiple computing power resource nodes by using a hierarchical structure, and then determine the resource sharing relationships among the respective computing power resource nodes, and determine the privacy protection cost of each computing power resource node for the privacy computing task according to the resource sharing relationship and the privacy protection feature in the blockchain; An execution module, configured to evaluate the conflict relationship between the computing power allocation and the privacy protection in the blockchain through each privacy protection cost and the elastic computing power, obtain the balance value of the scheduling conflict in the blockchain, and perform an equilibrium allocation of the privacy computing task for each computing power resource node in the blockchain based on the balance value of the scheduling conflict.

[0013] In a third aspect, the present application provides a computer device, where the computer device includes a memory and a processor, the memory is used to store a computer program, and the processor is configured to call and run the computer program from the memory, so that the computer device executes the above-mentioned privacy computing task scheduling method based on a blockchain.

[0014] Fourthly, the present application provides a computer-readable storage medium storing instructions or code, which, when running on a computer, cause the computer to execute the above-mentioned blockchain-based privacy computing task scheduling method.

[0015] The technical solutions provided by the disclosed embodiments of the present application have the following beneficial effects: In the blockchain-based privacy computing task scheduling method and system provided by the present application, when the blockchain executes a privacy computing task, the task progress information of the privacy computing task in the blockchain is monitored in real time; the execution status of the privacy computing task in the blockchain is extracted from the task progress information, and then the elastic computing power for privacy protection of the scheduling resources in the blockchain is determined through the execution status and the task offloading strategy for computing power resource scheduling in the blockchain; the computing power resources in the blockchain are divided into multiple computing power resource nodes by using a hierarchical structure, and then the resource sharing relationship between the computing power resource nodes is determined, and the privacy protection cost of each computing power resource node for the privacy computing task is determined according to the resource sharing relationship and the privacy protection characteristics in the blockchain; the conflict relationship between computing power allocation and privacy protection in the blockchain is evaluated through each privacy protection cost and the elastic computing power, and the balance value of the scheduling conflict in the blockchain is obtained, and the privacy computing tasks are evenly allocated to each computing power resource node in the blockchain based on the balance value of the scheduling conflict.

[0016] It can be seen that in this application, by evaluating the conflict relationship between computing power allocation and privacy protection in the blockchain through each privacy protection cost and the elastic computing power, a balance value of the scheduling conflict in the blockchain is obtained, and based on the balance value of the scheduling conflict, the privacy computing tasks are evenly allocated to each computing power resource node in the blockchain; First, determining the elastic computing power can obtain the ability of the computing power resources in the blockchain to be flexibly adjusted during task scheduling. In privacy computing tasks, the complexity and data sensitivity of tasks may vary over time and computing stages. The dynamic adjustment of the elastic computing power ensures the efficient utilization of computing power resources. When the computing power of a computing power resource node is insufficient, the elastic computing power can be redistributed in the network to ensure the smooth execution of tasks and avoid performance bottlenecks. At the same time, the determination of the elastic computing power can also help to preferentially allocate resources to tasks with high privacy protection requirements, reducing the risk of privacy leakage caused by uneven computing power allocation, thereby achieving a balance between computing performance and privacy protection and effectively promoting the even allocation of tasks; Then, determining the representation of the privacy protection cost can obtain the cost required for the computing power resource node to support the privacy computing task under the premise of meeting the privacy protection requirements, which helps to identify the adaptability of each node to privacy tasks, thereby avoiding nodes with low privacy protection capabilities from undertaking highly sensitive tasks and reducing the risk of privacy leakage. At the same time, during the task allocation process, by weighing the privacy protection cost and the computing power requirements, tasks can be effectively allocated to nodes with matching privacy protection capabilities and computing power capabilities, thereby maximizing the utilization of network resources and achieving overall load balancing, and further improving the resource utilization efficiency and privacy protection level; In summary, based on the above solutions, the even allocation of privacy computing tasks in the blockchain can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 is an exemplary flowchart of a privacy computing task scheduling method based on a blockchain shown in some embodiments of the present application; Figure 2 is a control logic diagram of a blockchain shown in some embodiments of the present application; Figure 3 is a schematic flowchart of determining the balance value of the scheduling conflict shown in some embodiments of the present application; Figure 4 is a schematic structural diagram of a privacy computing task scheduling system based on a blockchain shown in some embodiments of the present application; Figure 5It is a schematic structural diagram of a computer device for implementing a blockchain-based privacy computing task scheduling method as shown in some embodiments of the present application. Detailed implementation manners

[0019] To better understand the technical solutions of the present application, the technical solutions of the present application will be described in detail below in conjunction with the specification drawings and specific implementation manners.

[0020] Refer to Figure 1 , which is an exemplary flowchart of a blockchain-based privacy computing task scheduling method shown in some embodiments of the present application. The blockchain-based privacy computing task scheduling method mainly includes the following steps: In step 101, when a privacy computing task is executed on the blockchain, the task progress information of the privacy computing task in the blockchain is monitored in real time.

[0021] It should be noted that in the present application, the task progress information uses the information describing the completion status of the privacy computing task in the blockchain; the privacy computing task is a distributed computing task based on homomorphic privacy encryption; specifically, when the privacy computing task is executed on the blockchain, all the computing tasks being executed in the blockchain are obtained, and the task completion progress and task completion speed of each computing task are monitored in real time. The task completion progress is the percentage of the completed tasks, and the task completion speed is the progress of the tasks completed per second. The set of the task completion progress and task completion speed of all computing tasks can be used as the task progress information of the privacy computing task in the blockchain.

[0022] In some embodiments, refer to Figure 2 as described, this figure is the control logic diagram of the blockchain shown in some embodiments of the present application. This figure shows the control logic structure of a blockchain network, which includes a control center and multiple node groups; the control center is responsible for managing the main chain and sub-chains of the entire network, communicating and synchronizing data with each node group through the main chain. Each node group consists of multiple nodes, such as node group A and node group N. Each node group is connected internally through the sub-chain, so that data sharing and communication between nodes within the group can be realized.

[0023] In this control logic structure, node group A and node group N each contain four nodes. Each node is connected to other nodes within the group through the sub-chain and connected to the control center through the main chain. The control center sends instructions and data to each node group through the main chain and simultaneously receives information from each node group, which can ensure the decentralized characteristics of the blockchain network and at the same time realize the effective management and coordination of the entire network through the control center. Through this structure, the blockchain network can achieve efficient data transmission and processing, while ensuring the security and reliability of the blockchain network.

[0024] In step 102, the execution status of the privacy computing task in the blockchain is extracted from the task progress information, and then the elastic computing power for privacy protection of the scheduling resources in the blockchain is determined through the execution status and the task offloading strategy of the computing power resource scheduling in the blockchain.

[0025] In some embodiments, the extraction of the execution status of the privacy computing task in the blockchain from the task progress information can be implemented by the following steps: Obtain all the computing tasks being executed in the blockchain; For each computing task, extract the task completion progress and the task completion speed of the computing task from the task progress information; Determine the execution characteristics of the computing task through the task completion progress and the task completion speed, and then obtain the execution characteristics of each computing task; Extract the execution status of the privacy computing task in the blockchain from all the execution characteristics.

[0026] It should be noted that in this application, the execution status represents the real-time execution progress information of the computing task on the computing power resource node, including states such as task startup, processing, suspension, and completion; in specific implementation, first, obtain all the computing tasks being executed in the blockchain; second, for each computing task, extract the task completion progress and the task completion speed of the computing task from the task progress information; then, determine the execution characteristics of the computing task through the task completion progress and the task completion speed, and then the execution characteristics of each computing task can be implemented in the following way, that is, the result of (1 - task completion progress) / task completion speed can be used as the execution characteristic of the computing task, and the execution characteristics of each computing task can be obtained through the above method, and this execution characteristic represents the time required for the uncompleted part of the computing task; finally, a clustering algorithm can be used to classify all the execution characteristics into multiple state clusters, and for each state cluster, the mean value of all the execution characteristics in the state cluster can be used as the execution status value of the state cluster, and the execution status value of each state cluster can be obtained through the above method, so that the set of all the execution status values can be used as the execution status of the privacy computing task in the blockchain.

[0027] In some embodiments, the determination of the elastic computing power for privacy protection of the scheduling resources in the blockchain through the execution status and the task offloading strategy of the computing power resource scheduling in the blockchain can be implemented by the following steps: Obtain the task offloading strategy of the computing power resource scheduling in the blockchain; For each computing task, extract the execution status value of the computing task from the execution status; Conduct an extensibility evaluation on the execution status value through the task offloading strategy to obtain the scalable value of the computing power in the computing task, and then obtain the scalable value of the computing power in each computing task; Determine the elastic computing power of scheduling resources in the blockchain for privacy protection based on all expandable values.

[0028] In specific implementation, first, the task offloading strategy for computing power resource scheduling in the blockchain can be obtained in the following way: the task offloading strategy for computing power resource scheduling in the blockchain can be obtained from the control center of the blockchain; second, for each computing task, the execution status value of the computing task can be extracted from the execution status in the following way: for each computing task, the execution status value of the status cluster to which the computing task belongs in the execution status can be used as the execution status value of the computing task; then, the expandable value of the computing power in the computing task can be obtained through the expandability evaluation of the execution status value by the task offloading strategy, and further the expandable value of the computing power in each computing task can be obtained in the following way: initialize a computing power evaluation model based on a neural network, use the task offloading strategy as the computing power release strategy of the computing task in this computing power evaluation model, use the execution status value as the output label in this computing power evaluation model, and use this computing power evaluation model to evaluate the expandable computing power in the computing task, so that the evaluation result of this computing power evaluation model can be used as the expandable value of the computing power in the computing task, and the expandable value of the computing power in each computing task can be obtained through the above method; finally, the set of all expandable values can be used as the elastic computing power of the scheduling resources in the blockchain for privacy protection.

[0029] It should be noted that in this application, the elastic computing power refers to the task computing ability that the computing power resource node can schedule without reducing performance when processing computing tasks; the task offloading strategy refers to the decision rule for removing computing tasks from the computing power resource node in the blockchain; the execution status value refers to the current execution progress of the privacy computing task on the computing power resource node; the expandable value refers to the expandable ability metric of the computing power resource node when processing computing tasks; the computing power evaluation model is a model based on a neural network, aiming to evaluate the expandability of computing tasks on different computing power resource nodes. In the computing power evaluation model, the task offloading strategy is used as the input feature of the model, representing how to allocate computing tasks to different nodes according to task requirements and node capabilities, and the execution status value is used as the output label, reflecting the actual execution progress of the task. By training this computing power evaluation model, the relationship between the task offloading strategy and the task execution characteristics can be learned, so as to predict the computing power expandability of different computing tasks on different nodes, and finally output the expandable value to guide the reasonable scheduling of tasks and resource allocation.

[0030] In step 103, the computing power resources in the blockchain are divided into multiple computing power resource nodes using a hierarchical structure, and then the resource sharing relationship between each computing power resource node is determined. The privacy protection cost of each computing power resource node for privacy computing tasks is determined according to the resource sharing relationship and the privacy protection characteristics in the blockchain.

[0031] In some embodiments, the following steps may be adopted to divide the computing power resources in the blockchain into multiple computing power resource nodes by using a hierarchical structure: Obtain all resource blocks of the blockchain, and then determine the computing power characteristics of each resource block; Perform computing power grading on all computing power characteristics through a hierarchical structure to obtain multiple computing power levels; Divide all resource blocks into multiple computing power resource nodes according to each computing power level.

[0032] In specific implementation, first, obtaining all resource blocks of the blockchain and then determining the computing power characteristics of each resource block can be achieved in the following manner, that is: all resource blocks and the computing power range of each resource block can be obtained from the control center of the blockchain, and each computing power range can be used as the computing power characteristic of the corresponding resource block, so that the computing power characteristics of each resource block can be obtained; then, performing computing power grading on all computing power characteristics through a hierarchical structure to obtain multiple computing power levels can be achieved in the following manner, that is: obtaining the hierarchical structure from the control center of the blockchain, which contains multiple computing power intervals and their levels, and each computing power characteristic can be used as the computing power level according to the level of the computing power interval to which the computing power range belongs, so that multiple computing power levels can be obtained; finally, dividing all resource blocks into multiple computing power resource nodes according to each computing power level can be achieved in the following manner, that is: for each computing power level, obtaining the set of all resource blocks within the computing power interval of the corresponding level of the computing power level as the computing power resource node of the computing power level, so that multiple computing power resource nodes can be obtained.

[0033] It should be noted that in this application, the computing power resource node represents a node that provides computing power in the blockchain network; the resource block represents the computing power block used to complete computing tasks in the blockchain: represents the node responsible for storing, verifying, and propagating block data in the blockchain network; the computing power characteristic represents the ability characteristic of the computing power resource node when executing computing tasks; the computing power level represents the level division of the computing power resource node in providing computing power.

[0034] In some embodiments, the following steps may be adopted to determine the resource sharing relationship between each computing power resource node: For each computing power resource node, obtain all shared blocks between the computing power resource node and other computing power resource nodes; Determine the topological association graph of the computing power resource node through all shared blocks, and then obtain the topological association graph of each computing power resource node; Generate the resource sharing relationship between each computing power resource node according to all the topological association graphs.

[0035] It should be noted that in this application, the resource sharing relationship is a relationship graph that measures the collaboration ability and resource transfer efficiency between nodes; specifically, in implementation, first, for each computing power resource node, all shared blocks between the computing power resource node and other computing power resource nodes can be obtained in the following manner, that is: for each computing power resource node, the part that duplicates the resource blocks in the computing power resource node is selected from all the resource blocks in other computing power resource nodes as all the shared blocks; then, the topological association graph of the computing power resource node is determined through all the shared blocks, and further the topological association graphs of each computing power resource node are obtained. For example, it can be implemented in the following manner, that is: all the shared blocks can be arranged and connected according to the communication coordinates in the blockchain as the topological association graph of the computing power resource node. Through the above method, the topological association graphs of each computing power resource node can be obtained. Among them, the topological association graph is a network layout graph that reflects the dependency relationship of nodes in the process of resource sharing and task offloading; in the topological association graph, the number of resource blocks in the duplicate part between every two computing power resource nodes can be used as the association value of the corresponding connection in the topological association graph; finally, the resource sharing relationship between each computing power resource node can be generated according to all the topological association graphs in the following manner, that is: all the topological association graphs are fused with the same coordinates according to the communication coordinates in the blockchain, so that the fused topological association graph can be used as the resource sharing relationship between each computing power resource node.

[0036] In some embodiments, determining the privacy protection cost of each computing power resource node for the privacy computing task according to the resource sharing relationship and the privacy protection feature in the blockchain can be implemented by the following steps: For each computing power resource node, extract the resource sharing feature of the computing power resource node from the resource sharing relationship; Extract the trust degree of the computing power resource node for the privacy computing task from the privacy protection feature in the blockchain; Correlate the trust degree through the resource sharing feature to obtain the privacy protection cost of the computing power resource node for the privacy computing task, and further obtain the privacy protection cost of each computing power resource node for the privacy computing task.

[0037] In specific implementation, first, for each computing power resource node, the resource sharing characteristics of the computing power resource node can be extracted from the resource sharing relationship in the following way, that is: for each computing power resource node, the set of all associated values corresponding to the computing power resource node in the topological association graph in the resource sharing relationship can be used as the resource sharing characteristics of the computing power resource node; then, the trust degree of the computing power resource node for the privacy computing task can be extracted from the privacy protection characteristics in the blockchain in the following way, that is: the set of privacy protection means of each computing power resource node in the blockchain can be obtained from the control center of the blockchain as the privacy protection characteristics in the blockchain. The privacy protection means include privacy protection technologies such as homomorphic encryption, multi-party computing, and zero-knowledge proof. Thus, the protection degree of the computing power resource node using the privacy protection technology in the privacy protection characteristics can be quantified, and then the quantified protection degree can be used as the trust degree of the computing power resource node for the privacy computing task. In other embodiments, in order to improve the environmental adaptability of the trust degree, the trust degree can also be adjusted in combination with historical experience, which is not limited here; finally, the trust degree is associated with the resource sharing characteristics to obtain the privacy protection cost of the computing power resource node for the privacy computing task, and then the privacy protection cost of each computing power resource node for the privacy computing task can be obtained in the following way, that is: a multi-variable regression model is constructed, the resource sharing characteristics and the trust degree are combined and associated, and the privacy protection cost of the computing power resource node for the privacy computing task is calculated, that is, the privacy protection cost of the computing power resource node for the privacy computing task = a * resource sharing characteristics + b * trust degree + c, where a, b, and c are the parameters of the multi-variable regression model and are obtained by training with historical operation data. Through the above method, the privacy protection cost of each computing power resource node for the privacy computing task can be obtained.

[0038] It should be noted that in this application, the privacy protection cost represents the computing power required for the computing power resource node to perform privacy protection on the privacy computing task; the resource sharing characteristics represent the sharing degree of resources between computing power resource nodes; the trust degree represents the trust degree of the privacy computing task for the computing power resource node.

[0039] In step 104, the conflict relationship between computing power allocation and privacy protection in the blockchain is evaluated through each privacy protection cost and the elastic computing power, and the balance value of the scheduling conflict in the blockchain is obtained. Based on the balance value of the scheduling conflict, the privacy computing tasks of each computing power resource node in the blockchain are evenly allocated.

[0040] In some embodiments, the conflict relationship between computing power allocation and privacy protection in the blockchain is evaluated through each privacy protection cost and the elastic computing power, and the balance value of the scheduling conflict in the blockchain is obtained. Refer to Figure 3As described above, the figure is a schematic flowchart for determining the balance value of scheduling conflicts in some embodiments of the present application. In this embodiment, the balance value of scheduling conflicts can be determined by the following steps: In step 1041, for each computing power resource node, extract the scalable mean of the computing tasks in the computing power resource node from the elastic computing power; In step 1042, determine the conflict value between computing power allocation and privacy protection in the computing power resource node through the computing power resource node for the privacy protection cost of the privacy computing task and the scalable mean, and then obtain the conflict value between computing power allocation and privacy protection in each computing power resource node; In step 1043, determine the balance value of scheduling conflicts in the blockchain according to all the conflict values.

[0041] In specific implementation, first, for each computing power resource node, the scalable value of the computing tasks in the computing power resource node can be extracted from the elastic computing power in the following way, that is: obtain all the computing tasks being executed in the computing power resource node, and the mean value of the scalable values of all the computing tasks can be used as the scalable mean of the computing tasks in the computing power resource node; then, determine the conflict value between computing power allocation and privacy protection in the computing power resource node through the computing power resource node for the privacy protection cost of the privacy computing task and the scalable mean, and then obtain the conflict value between computing power allocation and privacy protection in each computing power resource node in the following way, that is: the reciprocal of the product of the scalable mean and the privacy protection cost can be used as the conflict value between computing power allocation and privacy protection in the computing power resource node, and the conflict value between computing power allocation and privacy protection in each computing power resource node can be obtained through the above method; finally, determine the balance value of scheduling conflicts in the blockchain according to all the conflict values in the following way, that is: the variance of all the conflict values can be used as the balance value of scheduling conflicts in the blockchain.

[0042] It should be noted that in the present application, the balance value represents the balance degree of the conflict between task privacy protection and computing power resource allocation; the scalable mean represents the average scalable ability of the computing power resource node when processing tasks; the conflict value represents the mismatch degree between the task privacy protection requirement and the computing power resource allocation.

[0043] In some embodiments, the balanced allocation of privacy computing tasks to each computing power resource node in the blockchain based on the balance value of the scheduling conflict can be implemented in the following way, that is: obtain a preset balance threshold from the control center of the blockchain. When the balance value of the scheduling conflict is lower than the preset balance threshold, unload the tasks in the resource block with the highest computing power usage in each computing power resource node and allocate them to the resource block with the lowest computing power usage in each computing power resource node until the balance value of the scheduling conflict is greater than or equal to the preset balance threshold.

[0044] In addition, on the other hand of the present application, in some embodiments, the present application provides a blockchain-based privacy computing task scheduling system. Refer to Figure 4 , which is a schematic structural diagram of a blockchain-based privacy computing task scheduling system shown according to some embodiments of the present application. The blockchain-based privacy computing task scheduling system includes: a monitoring module 201, a processing module 202, and an execution module 203, which are described as follows: Monitoring module 201: In the present application, the monitoring module 201 is mainly used to monitor the task progress information of the privacy computing task in the blockchain in real time when the blockchain executes the privacy computing task; Processing module 202: In the present application, the processing module 202 is used to extract the execution status of the privacy computing task in the blockchain from the task progress information, and then determine the elastic computing power for privacy protection of the scheduling resources in the blockchain through the execution status and the task offloading strategy of the computing power resource scheduling in the blockchain; It should be noted that the processing module 202 is further used to divide the computing power resources in the blockchain into multiple computing power resource nodes by using a hierarchical structure, and then determine the resource sharing relationship between the computing power resource nodes. According to the resource sharing relationship and the privacy protection characteristics in the blockchain, determine the privacy protection cost of each computing power resource node for the privacy computing task; Execution module 203: In the present application, the execution module 203 is mainly used to evaluate the conflict relationship between computing power allocation and privacy protection in the blockchain through each privacy protection cost and the elastic computing power, obtain the balance value of the scheduling conflict in the blockchain, and perform an equal distribution of the privacy computing task for each computing power resource node in the blockchain based on the balance value of the scheduling conflict.

[0045] The above text details the examples of the blockchain-based privacy computing task scheduling method and system provided by the embodiments of the present application. It can be understood that, correspondingly, in order to implement the above functions, the device includes the corresponding hardware structure and / or software module for executing each function. Those skilled in the art should easily realize that, combining the units and algorithm steps of each example described in the embodiments disclosed in this article, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the way of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described function for each specific application, but this implementation should not be considered to exceed the scope of the present application.

[0046] In some embodiments, the present application further provides a computer device, which includes a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned blockchain-based privacy computing task scheduling method.

[0047] In some embodiments, referring to Figure 5 , the dashed line in this figure indicates that the unit or module is optional. This figure is a schematic structural diagram of a computer device for implementing the blockchain-based privacy computing task scheduling method according to an embodiment of the present application. The above-mentioned blockchain-based privacy computing task scheduling method can be implemented by Figure 5 the computer device shown. The computer device includes at least one processor 301, a memory 302, and at least one communication unit 305. The computer device can be a terminal device, a server, or a chip.

[0048] The processor 301 can be a general-purpose processor or a special-purpose processor. For example, the processor 301 can be a central processing unit (CPU). The CPU can be used to control the computer device, execute software programs, and process the data of software programs. The computer device can also include a communication unit 305 for implementing signal input (reception) and output (transmission).

[0049] For example, the computer device can be a chip, and the communication unit 305 can be the input and / or output circuit of the chip. Alternatively, the communication unit 305 can be the communication interface of the chip. The chip can be a component of a terminal device, a network device, or other devices.

[0050] Again, for example, the computer device can be a terminal device or a server, and the communication unit 305 can be the transceiver of the terminal device or the server. Alternatively, the communication unit 305 can be the transceiver circuit of the terminal device or the server.

[0051] The computer device may include one or more memories 302, on which there is a program 304. The program 304 can be run by the processor 301 to generate instructions 303, so that the processor 301 executes the method described in the above method embodiments according to the instructions 303. Optionally, data (such as a target audit model) can also be stored in the memory 302. Optionally, the processor 301 can also read the data stored in the memory 302. The data can be stored at the same storage address as the program 304, or the data can be stored at a different storage address from the program 304.

[0052] The processor 301 and the memory 302 can be set separately or integrated together. For example, they can be integrated on a system on chip (SOC) of a terminal device.

[0053] It should be understood that the steps of the above method embodiments can be completed by a logic circuit in hardware form or an instruction in software form in the processor 301. The processor 301 can be a CPU, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices. For example, discrete gate, transistor logic devices, or discrete hardware components.

[0054] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0055] For example, in some embodiments, the present application further provides a computer-readable storage medium, in which instructions or code are stored. When the instructions or code run on a computer, the computer is caused to execute the above-mentioned blockchain-based privacy computing task scheduling method.

[0056] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present application.

[0057] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these changes and modifications.

Claims

1. A privacy computing task scheduling method based on blockchain, characterized in that The steps include: When the blockchain executes a privacy computing task, real-time monitor the task progress information of the privacy computing task in the blockchain; Extract the execution status of the privacy computing task in the blockchain from the task progress information, and then determine the elastic computing power for privacy protection of the scheduling resources in the blockchain through the execution status and the task offloading strategy of computing power resource scheduling in the blockchain; Use a hierarchical structure to divide the computing power resources in the blockchain into multiple computing power resource nodes, and then determine the resource sharing relationship between each computing power resource node. Determine the privacy protection cost of each computing power resource node for the privacy computing task according to the resource sharing relationship and the privacy protection characteristics in the blockchain; Evaluate the conflict relationship between computing power allocation and privacy protection in the blockchain through each privacy protection cost and the elastic computing power, obtain the balance value of the scheduling conflict in the blockchain, and perform an equilibrium allocation of the privacy computing task for each computing power resource node in the blockchain based on the balance value of the scheduling conflict.

2. The method according to claim 1, characterized in that Extracting the execution status of the privacy computing task in the blockchain from the task progress information specifically includes: Obtain all the computing tasks being executed in the blockchain; For each computing task, extract the task completion progress and task completion speed of the computing task from the task progress information; Determine the execution characteristics of the computing task through the task completion progress and the task completion speed, and then obtain the execution characteristics of each computing task; Extract the execution status of the privacy computing task in the blockchain from all the execution characteristics.

3. The method according to claim 1, characterized in that, Determining the elastic computing power for privacy protection of the scheduling resources in the blockchain through the execution status and the task offloading strategy of computing power resource scheduling in the blockchain specifically includes: Obtain the task offloading strategy of computing power resource scheduling in the blockchain; For each computing task, extract the execution status value of the computing task from the execution status; Conduct an extensibility evaluation of the execution status value through the task offloading strategy to obtain the scalable value of the computing power in the computing task, and then obtain the scalable values of the computing power in each computing task; Determine the elastic computing power for privacy protection of the scheduling resources in the blockchain according to all the scalable values.

4. The method according to claim 1, wherein Using a hierarchical structure to divide the computing power resources in the blockchain into multiple computing power resource nodes specifically includes: Obtain all the resource blocks of the blockchain, and then determine the computing power characteristics of each resource block; Perform computing power grading on all the computing power characteristics through a hierarchical structure to obtain multiple computing power levels; Divide all the resource blocks into multiple computing power resource nodes according to each computing power level.

5. The method according to claim 1, characterized in that, Determining the resource sharing relationship between each computing power resource node specifically includes: For each computing power resource node, obtain all the shared blocks between the computing power resource node and other computing power resource nodes; Determine the topological association graph of the computing power resource node through all the shared blocks, and then obtain the topological association graphs of each computing power resource node; Generate the resource sharing relationship between each computing power resource node according to all the topological association graphs.

6. The method according to claim 1, wherein Determining the privacy protection cost of each computing power resource node for the privacy computing task according to the resource sharing relationship and the privacy protection characteristics in the blockchain specifically includes: For each computing power resource node, extract the resource sharing characteristics of the computing power resource node from the resource sharing relationship; Extract the trust level of the computing power resource node for the privacy computing task from the privacy protection characteristics in the blockchain; Associate the trust level through the resource sharing characteristics to obtain the privacy protection cost of the computing power resource node for the privacy computing task, and then obtain the privacy protection costs of each computing power resource node for the privacy computing task.

7. The method according to claim 1, characterized in that The privacy computing task is a distributed computing task based on homomorphic privacy encryption.

8. A blockchain-based privacy computing task scheduling system, characterized in that It includes: A monitoring module, configured to, when the blockchain executes a privacy computing task, monitor the task progress information of the privacy computing task in the blockchain in real time; A processing module, configured to extract the execution status of the privacy computing task in the blockchain from the task progress information, and then determine the elastic computing power for privacy protection of the scheduled resources in the blockchain through the execution status and the task offloading strategy of computing power resource scheduling in the blockchain; The processing module is further configured to divide the computing power resources in the blockchain into multiple computing power resource nodes using a hierarchical structure, and then determine the resource sharing relationship between each computing power resource node, and determine the privacy protection cost of each computing power resource node for the privacy computing task according to the resource sharing relationship and the privacy protection characteristics in the blockchain; An execution module, configured to evaluate the conflict relationship between computing power allocation and privacy protection in the blockchain through each privacy protection cost and the elastic computing power, obtain the balance value of the scheduling conflict in the blockchain, and perform an equilibrium allocation of the privacy computing task for each computing power resource node in the blockchain based on the balance value of the scheduling conflict.

9. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the blockchain-based privacy computing task scheduling method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Instructions or code are stored in the computer-readable storage medium. When the instructions or code run on a computer, the computer is caused to execute the blockchain-based privacy computing task scheduling method according to any one of claims 1 to 7.

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