Data processing method and device based on block chain, equipment and medium

Abstract

The invention provides a data processing method based on a block chain. The method can be applied to the field of block chains and the technical field of financial science and technology. The block chain-based data processing method comprises the steps of determining cloud resource demand information in response to a transaction request of a user, the transaction request being initiated through at least one block chain; and determining cloud resource scheduling information, wherein the cloud resource scheduling information represents scheduling distribution of various heterogeneous cloud resources. And processing the cloud resource scheduling information and the cloud resource demand information by adopting a dynamic bilateral auction algorithm to obtain a cloud resource allocation result, the dynamic bilateral auction algorithm being used for determining the resource demand satisfaction degree of the user and the optimal solution of the cloud resource supply cost. And allocating resources to the block chain based on the cloud resource allocation result. The invention further provides a data processing device and equipment based on the block chain, a storage medium and a program product.

Description

Technical Field

[0001] The present disclosure relates to the fields of blockchain and financial technology, and specifically to a data processing method, device, equipment, medium and program product based on blockchain. Background Art

[0002] Since its introduction, blockchain technology has been regarded as an excellent solution to solve various banking pain points. Blockchain technology can help banks solve information asymmetry and trust asymmetry problems. The decentralized transaction network can help banks optimize the wide-area transaction system, making transactions efficient and secure. Blockchain has provided major innovations to the financial market, improving the efficiency and operational performance of digital payments and settlements.

[0003] However, due to the continuous development of advanced underlying technologies and the increasingly complex international trust situation, the promotion of traditional self-sustaining resource-supported blockchain technology in cross-border financial business has encountered barriers. Summary of the invention

[0004] In view of the above problems, the embodiments of the present disclosure provide a blockchain-based data processing method, apparatus, device, medium and program product that improves the efficiency of cloud resource utilization supporting blockchain operations.

[0005] According to a first aspect of the present disclosure, a data processing method based on blockchain is provided, comprising: determining cloud resource demand information in response to a transaction request of a user, the transaction request being initiated through at least one blockchain. Determining cloud resource scheduling information, the cloud resource scheduling information representing the scheduling allocation of heterogeneous multiple cloud resources. Using a dynamic bilateral auction algorithm to process the cloud resource scheduling information and the cloud resource demand information to obtain a cloud resource allocation result, the dynamic bilateral auction algorithm being used to determine the optimal solution for the user's resource demand satisfaction and the cloud resource supply cost. Based on the cloud resource allocation result, resources are allocated to the blockchain.

[0006] According to an embodiment of the present disclosure, cloud resources include storage resources, and a dynamic bilateral auction algorithm is used to process cloud resource scheduling information and cloud resource demand information to obtain a cloud resource allocation result, including: extracting cloud resource demand information to obtain the user's cloud resource demand. Matching the cloud resource demand information and the cloud resource scheduling information to determine the user's expected purchase amount of storage resources. Based on the cloud resource demand and the expected purchase amount, determine the user's first utility function for storage resources, and the first utility function is a monotonic function. Based on the expected purchase amount and the cost factor, determine the seller's cost function, and the cost factor is a positive number so that the seller's cost increases with the increase in the cloud resource supply. Minimize the difference between the first utility function and the seller's cost function to obtain the optimal solution corresponding to the cloud resource allocation result.

[0007] According to an embodiment of the present disclosure, cloud resources also include computing resources, and a dynamic bilateral auction algorithm is used to process cloud resource scheduling information and cloud resource demand information to obtain a cloud resource allocation result, which further includes: determining a distance factor for users to evaluate the supply quality of computing resources. According to the cloud resource demand, the distance factor, and the expected purchase amount, a second utility function of the user for the computing resources is determined, and the second utility function is a monotonic function. Minimize the difference between the second utility function and the seller's cost function to obtain an optimal solution corresponding to the cloud resource allocation result.

[0008] According to an embodiment of the present disclosure, cloud resource demand information is determined according to auction time slots, and the auction time slots correspond to the operation clock of the blockchain.

[0009] According to an embodiment of the present disclosure, a plurality of cloud resources correspond to at least one cloud resource supplier, and each cloud resource supplier determines cloud resource scheduling information through at least one bid.

[0010] According to an embodiment of the present disclosure, the supply price of cloud resources includes an expected transaction price and a minimum transaction price. When the transaction price is not restricted by a third party, the transaction is conducted at the expected transaction price. When the transaction price is restricted by a third party, the minimum transaction price is used as the minimum value of the transaction price.

[0011] According to an embodiment of the present disclosure, a blockchain-based data processing method is implemented through a cloud resource trading platform, and the cloud resource trading platform is an open source container orchestration platform.

[0012] According to an embodiment of the present disclosure, at least one of the user and the supplier of the cloud resources is a financial institution.

[0013] The second aspect of the present disclosure provides a data processing device based on blockchain, including: a first determination module, which is used to determine cloud resource demand information in response to a user's transaction request, and the transaction request is initiated through at least one blockchain. A second determination module is used to determine cloud resource scheduling information, and the cloud resource scheduling information represents the scheduling allocation of a variety of heterogeneous cloud resources. A processing module is used to use a dynamic bilateral auction algorithm to process cloud resource scheduling information and cloud resource demand information to obtain a cloud resource allocation result, and the dynamic bilateral auction algorithm is used to determine the user's resource demand satisfaction and the optimal solution for the cloud resource supply cost. An allocation module is used to allocate resources to the blockchain based on the cloud resource allocation result.

[0014] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the blockchain-based data processing method.

[0015] The fourth aspect of the present disclosure also provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above-mentioned blockchain-based data processing method when the above-mentioned computer program or instructions are executed by a processor.

[0016] The fifth aspect of the present disclosure also provides a computer program product, including a computer program or instructions, which, when executed by a processor, implement the steps of the above-mentioned blockchain-based data processing method. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0018] Figure 1 The application scenario diagram of the blockchain-based data processing method, device, equipment, medium and program product according to the embodiment of the present disclosure is schematically shown; Figure 2 The flowchart of the data processing method based on blockchain according to an embodiment of the present disclosure is schematically shown; Figure 3 A resource allocation flow chart based on dynamic bilateral auction according to an embodiment of the present disclosure is schematically shown; Figure 4 The system architecture diagram corresponding to the blockchain-based data processing method according to an embodiment of the present disclosure is schematically shown; Figure 5 The following schematically shows a flowchart of scheduling cloud resources according to an embodiment of the present disclosure; Figure 6 A block diagram schematically shows a structure of a data processing device based on blockchain according to an embodiment of the present disclosure; and Figure 7 A block diagram of an electronic device suitable for implementing a blockchain-based data processing method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0019] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0020] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0021] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0022] The embodiments of the present disclosure provide a data processing method, device, equipment, medium and program product based on blockchain, which can be used in the field of financial technology or other fields. It should be noted that the data processing method, device, equipment, medium and program product based on blockchain disclosed in the present disclosure can be used in the field of financial technology, and can also be used in any field other than the field of financial technology. The application field of the data processing method, device, equipment, medium and program product based on blockchain disclosed in the present disclosure is not limited.

[0023] In the technical solution of the present disclosure, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0024] In the scenario of using personal information for automated decision-making, the methods, devices, and systems provided by the embodiments of the present disclosure provide users with corresponding operation portals for users to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered. The expression "automated decision-making" here refers to the activity of automatically analyzing and evaluating a person's behavioral habits, interests and hobbies, or economic, health, credit status, etc. through computer programs, and making decisions. The expression "expert decision-making" here refers to the activity of making decisions by people who specialize in a certain field, have specialized experience, knowledge and skills, and have reached a certain level of professionalism.

[0025] The embodiment of the present disclosure provides a data processing method based on blockchain, including: determining cloud resource demand information in response to a user's transaction request, and the transaction request is initiated through at least one blockchain. Determine cloud resource scheduling information, and the cloud resource scheduling information represents the scheduling allocation of heterogeneous multiple cloud resources. Use a dynamic bilateral auction algorithm to process cloud resource scheduling information and cloud resource demand information to obtain a cloud resource allocation result, and the dynamic bilateral auction algorithm is used to determine the user's resource demand satisfaction and the optimal solution for the cloud resource supply cost. Based on the cloud resource allocation result, allocate resources to the blockchain. By bringing together the blockchain networks of multiple buyers and the cloud resources of multiple sellers for bilateral auctions, the most suitable cloud resources can be found for buyers, and the most suitable buyers can be found for sellers, so that the benefits of the entire system are maximized and the utilization efficiency of cloud resources is improved.

[0026] Figure 1 The application scenario diagram of the blockchain-based data processing method, device, equipment, medium and program product according to an embodiment of the present disclosure is schematically shown.

[0027] like Figure 1 As shown, the application scenario 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the first terminal device 101, the second terminal device 102, the third terminal device 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0028] The user can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only for example).

[0029] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0030] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0031] It should be noted that the data processing method based on blockchain provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the data processing device based on blockchain provided in the embodiment of the present disclosure can generally be set in the server 105. The data processing method based on blockchain provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the data processing device based on blockchain provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0032] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0033] The following will be based on Figure 1 The scene described by Figure 2~Figure 7 The data processing method based on blockchain in the disclosed embodiment is described in detail.

[0034] Figure 2 The flowchart of the blockchain-based data processing method according to an embodiment of the present disclosure is schematically shown.

[0035] According to the embodiments of the present disclosure, Figure 2 As shown, the blockchain-based data processing method of this embodiment includes, for example, operations S210 to S240. The blockchain-based data processing method can be executed by a computer program on corresponding computer hardware.

[0036] In operation S210, cloud resource demand information is determined in response to a transaction request from a user, and the transaction request is initiated through at least one blockchain.

[0037] First, the method responds to transaction requests from users. These transaction requests are initiated through at least one blockchain network, indicating that the user wants to perform some data processing or transaction operations in the blockchain environment.

[0038] For example, blockchain can be of many types. Based on different dimensions such as application scope, deployment mechanism and docking type, blockchain can be divided into the following main types:

[0039] Public chains, also known as permissionless blockchains, are open to anyone at any node. Everyone can participate in the calculation of the blockchain and download the complete blockchain data. Public chains have a high degree of transparency and participation, promote decentralization through cryptoeconomics, and ensure cooperation and transparency of the system.

[0040] In a consortium chain, the rights of each node are completely equal, and the nodes do not need to fully trust each other to achieve trusted data exchange. Consortium chains usually have corresponding physical organizations, and can only join or exit the network after authorization. It is a model for companies and companies, organizations and organizations to form alliances, with moderate openness and high credibility.

[0041] A private chain is a blockchain where the write permission is only in the hands of one organization, and the read permission or the permission to open to the outside world is restricted. A private chain allows users to verify and record certain information in the block without too much pressure, with higher transaction speed and better privacy protection. It is usually operated by a limited number of users and used by enterprises or organizations.

[0042] It is understandable that the specific type of blockchain to choose depends on factors such as actual application scenarios, security requirements, participation requirements, and cost-effectiveness. For example, if a high degree of transparency and participation is required, a public chain can be selected; if data storage and management is required within an enterprise or organization, a private chain can be selected; if trusted data exchange is required between multiple organizations, a consortium chain can be selected. At the same time, according to actual needs, technologies such as side chains or interconnected chains can also be considered to achieve cross-chain communication and value transfer.

[0043] After receiving a transaction request, the system will determine the required cloud resource demand information based on the content of the transaction request. This information describes in detail the specific types and quantities of various cloud resources (such as computing power, storage space, network bandwidth, etc.) required by the user to complete the transaction request.

[0044] In operation S220 , cloud resource scheduling information is determined, where the cloud resource scheduling information represents scheduling allocation of a variety of heterogeneous cloud resources.

[0045] Next, the system will determine the cloud resource scheduling information based on the current cloud resource status (including the availability and performance of various heterogeneous cloud resources, etc.). This information represents the scheduling allocation of various heterogeneous cloud resources, that is, the information of cloud resources that can be obtained and allocated at present.

[0046] Cloud resources can include many types, and these resources are usually provided by a cloud service provider (CSP). Examples include:

[0047] Computing resources: such as virtual machines (VMs), virtualized computing environments that can run operating systems and applications. Container services, such as Docker containers, provide lightweight, portable, self-contained software packaging solutions. Bare metal servers, physical servers that have not been virtualized, provide higher performance and flexibility.

[0048] Storage resources: such as object storage, which is used to store large amounts of unstructured data, such as files, images, and videos. Block storage, which provides block-level storage services and is commonly used in databases and file systems. File storage, which provides file-level storage services and supports file sharing and access control.

[0049] Network resources: such as virtual private cloud (VPC), an isolated virtual network environment created on the public cloud. Load balancer, used to distribute network traffic to multiple servers to improve system availability and performance. Virtual network, a virtual network created in a cloud environment, used to connect virtual machines and other cloud resources. VPN connection, used to establish a secure network connection between the cloud environment and the local network. Content delivery network (CDN), used to accelerate content distribution and improve user access speed.

[0050] Database resources: such as relational databases, such as MySQL, which support complex data queries and operations. In-memory databases, such as Redis, which provide high-speed data read and write services.

[0051] These cloud resources are scalable and flexible, and users can quickly expand or reduce resources according to actual needs without making large upfront investments and maintenance. Cloud service providers can provide a management console and API that enables users to easily manage and automate the use of cloud resources.

[0052] In operation S230, a dynamic bilateral auction algorithm is used to process the cloud resource scheduling information and the cloud resource demand information to obtain a cloud resource allocation result. The dynamic bilateral auction algorithm is used to determine the optimal solution of the user's resource demand satisfaction and the cloud resource supply cost.

[0053] In order to obtain the optimal cloud resource allocation result, this method adopts a dynamic bilateral auction algorithm, which comprehensively considers the user's resource demand satisfaction and cloud resource supply cost, and finds a balance point through the auction mechanism, so as to meet user needs while minimizing the cloud resource supply cost.

[0054] In operation S240, resources are allocated to the blockchain based on the cloud resource allocation result.

[0055] Finally, according to the cloud resource allocation results obtained by the dynamic bilateral auction algorithm, the system allocates corresponding resources to the blockchain. These resources will be used to support users' transactions or data processing operations on the blockchain.

[0056] This embodiment responds to transaction requests, determines cloud resource demand information and cloud resource scheduling information, and uses a dynamic bilateral auction algorithm to obtain the optimal cloud resource allocation result, and finally allocates the required resources for the blockchain. This method can effectively utilize heterogeneous cloud resources, meet the diverse needs of users, and reduce the supply cost of cloud resources.

[0057] According to an embodiment of the present disclosure, cloud resources include storage resources. For example, a dynamic bilateral auction algorithm is used to process cloud resource scheduling information and cloud resource demand information through operations S331 to S335 to obtain a cloud resource allocation result.

[0058] In operation S331, cloud resource demand information is extracted to obtain the user's cloud resource demand.

[0059] In operation S332, the cloud resource demand information and the cloud resource scheduling information are matched to determine the user's expected purchase amount of storage resources.

[0060] In operation S333, a first utility function of the user for storage resources is determined according to the cloud resource demand and the expected purchase amount, and the first utility function is a monotonic function.

[0061] In operation S334, a seller cost function is determined according to the expected purchase amount and the cost factor, where the cost factor is a positive number so that the seller cost increases with the increase in the cloud resource supply.

[0062] In operation S335 , the difference between the first utility function and the seller cost function is minimized to obtain an optimal solution corresponding to the cloud resource allocation result.

[0063] In a cloud computing environment, the allocation of cloud resources (including storage resources, computing resources, etc.) is a key issue. In order to allocate cloud resources efficiently and fairly, this embodiment uses a dynamic bilateral auction algorithm to process cloud resource scheduling information and cloud resource demand information to obtain cloud resource allocation results.

[0064] In some embodiments, cloud resource demand information is first extracted from a resource request submitted by a user, including the resource type (eg, storage resource), demand quantity, expected price, etc. required by the user.

[0065] Then, according to the cloud resource demand information and cloud resource scheduling information (such as the currently available resource type, quantity, price, etc.), matching is performed to determine the user's expected purchase of storage resources. The matching process may involve multiple factors, such as price, availability, service quality, etc.

[0066] Then, based on the user's cloud resource demand and expected purchase volume, the user's first utility function for storage resources is determined. The first utility function is a monotonic function, which means that the user's satisfaction or utility for resources increases (or decreases, but usually increases in this scenario) as the amount of resources increases.

[0067] For example, in order to quantify the utility of buyers purchasing cloud resources, the embodiment of the present disclosure uses the Stone-Geary Function in economics to construct a buyer utility function to reflect the buyer's resource demand satisfaction.

[0068] In this system, blockchain nodes, as rational people, generate consumption needs according to their own roles and generate purchasing preferences according to different service qualities. Therefore, the system utility function is defined as:

[0069] (1)

[0070] Among them, w i represents the willingness of buyer i to purchase cloud resources. After taking logarithmic monotonic transformation, the utility function of buyer i purchasing storage resources at time slot t is represented as:

[0071] (2)

[0072] Consider a ij =a i min In the case of , the utility function is meaningless, then adjust formula (2) to:

[0073] (3)

[0074] The seller cost function is determined based on the expected purchase volume and cost factors (such as resource production cost, maintenance cost, etc.). The cost factor is a positive number to ensure that the seller cost increases with the increase in cloud resource supply.

[0075] For example, the seller cost function is used to describe the cost paid by cloud resource providers when providing and maintaining cloud resources to blockchain nodes. Here, we consider introducing a cost factor to represent it as a strictly convex function that increases with the amount of resources provided:

[0076] (4)

[0077] Among them, l1 and l2 are cost factors greater than 0.

[0078] Minimize the difference between the first utility function and the seller's cost function to obtain the optimal solution corresponding to the cloud resource allocation result. This can be achieved through mathematical optimization methods (such as gradient descent, simulated annealing, etc.) or heuristic algorithms (such as genetic algorithms, ant colony algorithms, etc.).

[0079] For example, according to the above analysis, the social welfare maximization objective function of storage resources and computing power resources in a period can be expressed as:

[0080]

[0081] (5)

[0082] Among them, a it min represents the minimum demand for resources by buyer i in time slot t, d j max represents the maximum resource supply of seller j. The objective function is a strictly concave function, while the constraint condition is a strictly convex function. Obviously, there is a unique optimal solution to the problem. Based on formulas (1) to (5), the Lagrange multiplier function L1 is constructed:

[0083] (6)

[0084] According to the KKT (Karush-Kuhn-Tucker) condition, the optimal solution of the storage resource allocation model should meet the following conditions:

[0085] (7)

[0086] The optimal allocation scheme for heterogeneous storage resources that maximizes social welfare can be obtained through formulas (6) to (7).

[0087] Finally, cloud resource allocation is performed according to the optimal solution to allocate storage resources to corresponding users. The allocation process may involve resource scheduling, resource allocation strategies (such as polling, weighted polling, minimum number of connections, etc.), etc.

[0088] By using a dynamic bilateral auction algorithm to process cloud resource scheduling information and cloud resource demand information, this embodiment can efficiently allocate cloud resources to meet user needs while ensuring that sellers' costs are reasonably controlled. In addition, the method also has certain flexibility and scalability and can adapt to cloud computing environments of different sizes and types.

[0089] It should be noted that in actual applications, the algorithm needs to be adjusted and optimized according to the specific cloud computing environment and user needs. Attention should be paid to the stability and reliability of the algorithm to ensure the accuracy and efficiency of cloud resource allocation. The security and privacy protection of the algorithm need to be considered to ensure that user data and privacy are properly protected.

[0090] According to an embodiment of the present disclosure, cloud resources also include computing resources. For example, a dynamic bilateral auction algorithm may be used to process cloud resource scheduling information and cloud resource demand information through operations S431 to S433 to obtain a cloud resource allocation result.

[0091] In operation S431, a distance factor for users to evaluate the quality of computing resource provision is determined.

[0092] In operation S432, a second utility function of the user for computing resources is determined according to the cloud resource demand, the distance factor, and the expected purchase amount, where the second utility function is a monotonic function.

[0093] In operation S433, the difference between the second utility function and the seller cost function is minimized to obtain an optimal solution corresponding to the cloud resource allocation result.

[0094] In some embodiments, for specific types of cloud resources, such as computing resources, users' evaluation of the quality of computing resource supply also needs to be considered. This embodiment, for example, uses a further improved dynamic bilateral auction algorithm to process cloud resource scheduling information and cloud resource demand information to obtain cloud resource allocation results.

[0095] Among them, considering the user's evaluation of the quality of computing resource supply, a distance factor is introduced to quantify the relative distance or quality difference between the user and the computing resource supply point. The distance factor can be determined based on a variety of factors, such as network latency, geographical location, service quality, etc.

[0096] Then, according to the cloud resource demand, distance factor and expected purchase amount, the user's first utility function for storage resources and the second utility function for computing resources are determined.

[0097] For example, computing power resources place higher demands on service quality, especially in speed competition involving proof of work, where objective factors such as transmission efficiency and latency need to be considered. Therefore, the distance factor z is included in the willingness, and its value is derived from the service quality parameters provided in the buyer's bid. The utility function of purchasing computing power resources is represented as follows:

[0098] (8)

[0099] By comparing with formula (3), we can see that the utility function of purchasing storage resources can be regarded as a special case of the computing power resource utility function where the distance factor is 0. Therefore, it is substituted into the objective function for subsequent specific analysis.

[0100] The optimal solution of the computing resource allocation model should meet the following conditions:

[0101] (9)

[0102] Formulas (6) and (9) can be used to obtain the optimal allocation plan for heterogeneous computing resources that maximizes social welfare.

[0103] By adopting a dynamic bilateral auction algorithm to process cloud resource scheduling information and cloud resource demand information, and introducing a distance factor to quantify the user's evaluation of the quality of computing resource supply, this embodiment can efficiently allocate cloud resources to meet user needs while ensuring that the seller's costs are reasonably controlled.

[0104] According to an embodiment of the present disclosure, cloud resource demand information is determined according to auction time slots, and the auction time slots correspond to the operation clock of the blockchain.

[0105] In a cloud computing environment, the allocation and scheduling of cloud resources is a complex and critical issue. In order to allocate cloud resources more efficiently and fairly while ensuring data traceability and security, this embodiment uses a dynamic bilateral auction algorithm and combines blockchain technology to process cloud resource scheduling information and cloud resource demand information. Cloud resource allocation is achieved by determining cloud resource demand information according to auction time slots and corresponding to the operation clock of the blockchain.

[0106] Figure 3 A flow chart of resource allocation based on dynamic double auction according to an embodiment of the present disclosure is schematically shown.

[0107] like Figure 3 As shown, the client buyer's tender is related to the blockchain node status, which is calibrated by the dynamic auction time slot. In the auction process of this article, the time slot is linked to the underlying chain operation clock, so that each round of blockchain nodes completes the formulation of the tender expression while completing the block update height and entering the next round of consensus.

[0108] If a bidding information from a buyer is not received in a certain round, a clock for evaluating the block time will be waited for. If no response is received, the node is considered to be down and will not participate in this round of auction.

[0109] At the end of each round of auction, BaaS reclaims the excess computing resources of the miner nodes in this round, updates the resource information, and enters the next round of auction.

[0110] For example, when building a blockchain platform, the blockchain's operating clock is set and auction time slots are defined. The auction time slots correspond to the block generation time or consensus time of the blockchain, ensuring that each auction time slot has a unique blockchain timestamp.

[0111] At the beginning of each auction time slot, cloud resource demand information is collected from the user side. This information includes the type of resources required by the user (such as storage resources, computing resources), demand, expected price, etc. And according to the current state of the cloud resource pool, cloud resource scheduling information is prepared. This information includes the type, quantity, price, etc. of currently available resources.

[0112] In each auction time slot, cloud resource demand information is matched with cloud resource scheduling information according to the dynamic bilateral auction algorithm. The user's expected purchase amount of cloud resources is determined, and the user's utility function and the seller's cost function are calculated based on factors such as purchase amount, demand amount, and cost factor.

[0113] Through the optimization algorithm, the difference between the utility function and the cost function is minimized to obtain the optimal solution for cloud resource allocation.

[0114] In addition, the cloud resource allocation results of each auction time slot can be recorded on the blockchain. This includes information such as the specific resource type, quantity, price, and allocation timestamp allocated to the user. The transparency and security of the cloud resource allocation process are ensured through the immutability and traceability of the blockchain.

[0115] The allocation and scheduling of cloud resources are performed according to the cloud resource allocation results recorded on the blockchain.

[0116] Through the auction mechanism, cloud resource demand and supply can be efficiently matched to achieve optimal resource allocation. According to the cloud resource demand information of each auction time slot, the resource allocation strategy can be dynamically adjusted to adapt to the ever-changing cloud computing environment.

[0117] According to an embodiment of the present disclosure, a plurality of cloud resources correspond to at least one cloud resource supplier, and each cloud resource supplier may determine cloud resource scheduling information through at least one bid.

[0118] In some embodiments, in order to efficiently and accurately process cloud resource allocation data while ensuring optimal resource configuration, this embodiment adopts a dynamic bilateral auction algorithm and combines a bidding mechanism to process cloud resource scheduling information and cloud resource demand information.

[0119] For example, each cloud resource supplier prepares a bid based on its own capabilities and resource conditions. The bid details the type, quantity, price, service quality and other scheduling information of the cloud resources provided. The bid should have a certain degree of flexibility and scalability to adapt to the ever-changing demand for cloud resources.

[0120] Collect cloud resource demand information from the user side, including the required resource type, demand volume, expected price, service quality requirements, etc. The demand information should be clear and specific so as to match it with the supplier's bid.

[0121] Matching is performed based on user needs and supplier bids. The matching process takes into account multiple factors such as price, service quality, and resource availability. A dynamic bilateral auction algorithm is used to determine the optimal solution for cloud resource allocation based on matching results, user utility function, seller cost function, etc.

[0122] For another example, based on the auction results and changes in market demand, the supplier can also update and adjust the bid. The updated and adjusted bid should be resubmitted to the system for the next round of matching and auction.

[0123] According to the auction results, cloud resources are allocated and scheduled. The allocation process should ensure the effective utilization of resources and the continuity of services.

[0124] The bidding mechanism can efficiently match user needs and supplier resources to achieve optimal resource allocation. Suppliers can flexibly update and adjust bidding documents based on market demand and their own capabilities to adapt to the ever-changing market environment.

[0125] According to an embodiment of the present disclosure, the supply price of cloud resources includes an expected transaction price and a minimum transaction price. When the transaction price is not restricted by a third party, the transaction is conducted at the expected transaction price. When the transaction price is restricted by a third party, the minimum transaction price is used as the minimum value of the transaction price.

[0126] In some embodiments, in order to process cloud resource allocation data more flexibly and ensure the fairness and rationality of transactions, this embodiment introduces a more detailed cloud resource supply price mechanism. The supply price of cloud resources includes the expected transaction price and the minimum transaction price, and the final transaction price is determined based on whether the transaction price is restricted by a third party.

[0127] Each cloud resource supplier prepares a bid based on its own capabilities and resource conditions. In addition to listing the scheduling information such as the type, quantity, and service quality of the cloud resources provided, the bid must also clearly state the expected transaction price and the minimum transaction price. The expected transaction price is the transaction price expected by the supplier, while the minimum transaction price is the lowest transaction price that the supplier is willing to accept.

[0128] Collect cloud resource demand information from the user side, including the required resource type, demand volume, expected price, service quality requirements, etc. The user's demand information can also include the acceptable price range in order to match it with the supplier's bid.

[0129] Matching is done based on user needs and supplier bids. The matching process takes into account multiple factors such as price, service quality, and resource availability.

[0130] For example, in the matching process, the expected transaction price is first used as a reference for the transaction price. It is then determined whether the transaction price is subject to third-party constraints. Third-party constraints may include policy restrictions, market rules, contractual constraints, etc.

[0131] If the transaction price is not restricted by a third party, the transaction will be conducted at the expected transaction price.

[0132] If the transaction price is restricted by a third party, check whether the lowest transaction price meets the restriction. If so, the transaction is carried out with the lowest transaction price as the minimum transaction price; if not, further price negotiation is required or the transaction is abandoned.

[0133] According to the finalized transaction price, cloud resources are allocated and scheduled. The allocation process should ensure the effective utilization of resources and the continuity of services.

[0134] Suppliers can flexibly set the expected transaction price and the minimum transaction price based on their own circumstances and market demand to adapt to the ever-changing market environment.

[0135] According to an embodiment of the present disclosure, a blockchain-based data processing method is implemented through a cloud resource trading platform, and the cloud resource trading platform is an open source container orchestration platform.

[0136] In some embodiments, in order to efficiently process cloud resource allocation data while ensuring optimal resource configuration and service continuity, this embodiment adopts a blockchain-based data processing method and implements it through an open source container orchestration platform (such as Kubernetes in the cloud native field) as a cloud resource trading platform.

[0137] Figure 4 The following schematically shows a system architecture diagram corresponding to the blockchain-based data processing method according to an embodiment of the present disclosure.

[0138] For example, Figure 4 As shown in the figure, select and build an open source container orchestration platform, such as Kubernetes, as a cloud resource trading platform.

[0139] Configure blockchain nodes on the platform to ensure the traceability and security of transaction data.

[0140] Suppliers register accounts on the cloud resource trading platform and publish scheduling information such as the types, quantities, and prices of cloud resources they can provide. The platform uses blockchain technology to ensure the authenticity and immutability of this information.

[0141] The demander registers an account on the platform and submits his or her cloud resource demand information, including the required resource type, quantity, price range, etc. The platform matches the demander with the most suitable supplier based on the demander's information.

[0142] For example, the cloud resource scheduling module can monitor the auction controller to obtain the cloud resource scheduling information of each round, and the k8s cluster schedules and allocates resources according to the scheduling information.

[0143] The blockchain node uniformly pulls the underlying chain image file, including the blockchain running binary executable file and configuration file, etc. The underlying chain interacts with the cloud resource scheduling module through the RESTful API, and the k8s cluster processes the routing requests of the underlying chain through the API server.

[0144] After the match is successful, the platform automatically generates a smart contract to clarify the rights and obligations of both parties to the transaction, including transaction price, transaction time, service quality, etc. Both parties to the transaction confirm the smart contract through blockchain technology to ensure the fairness and immutability of the transaction.

[0145] According to the agreement of the smart contract, the platform automatically allocates and schedules cloud resources. The allocation process takes into account multiple factors such as resource availability, performance, and service quality to ensure the optimal configuration of resources.

[0146] Figure 5 A flowchart for scheduling cloud resources according to an embodiment of the present disclosure is schematically shown.

[0147] For example, Figure 5 As shown in the figure, the specific workflow of resource scheduling mainly includes eight stages.

[0148] Phase 1: After the node client and node replica join the blockchain network, they actively register information with the master node Monitor. The registration information includes, for example, node information and node task information. The node information includes the IP and port number of the node and node replica, the node ID, netURL, blockchain network chain ID, bootNode information, health status and load status, etc.; the node task information is mainly the node role and task information contained in the initial bid configuration file, etc. After that, Monitor will actively collect node messages through the API server and kublete.

[0149] Phase 2: Monitor writes the collected messages to the Etcd cache through the API server.

[0150] Phase 3: The Scheduler packages resource pool bid information and node bid information, and calls the auction controller to make resource allocation decisions and schedule.

[0151] Phase 4: The auction controller completes the resource allocation decision based on the collected bilateral bidding information and issues charging instructions to the nodes through the API. The API server updates the heterogeneous resource allocation plan to the Etcd cache.

[0152] Phase 5: The cloud disk storage volume plug-in Ceph RBD monitors changes in the storage resource allocation plan in Etcd and completes the scheduling of storage resources.

[0153] The sixth and seventh stages: pod allocation is completed according to computing resource scheduling, and cloud storage resource scheduling is completed through the cloud disk storage volume plug-in Ceph RBD. Each pod mounts Ceph RBD through StorageClass to access the OSD node in the RBD storage pool.

[0154] Phase 8: Kublete interacts with the API server and Ceph RBD in each round to obtain the updated pod configuration information and RBD image information in each round to ensure the operation of the blockchain node. Among them, the auction controller plug-in is implemented through dependency injection, defining a unified auction controller interface to select a specific decision algorithm to encapsulate different implementation classes. The system can add different resource allocation algorithm plug-ins according to the different underlying chains, thereby achieving a certain degree of scalability.

[0155] The platform can monitor the transaction process in real time to ensure the smooth progress of the transaction and the effective use of resources. After the transaction is completed, the platform can also collect feedback from both parties to optimize future resource allocation strategies and service quality.

[0156] Through the intelligent matching algorithm of the open source container orchestration platform and the traceability of blockchain technology, it is possible to efficiently match suppliers and demanders and achieve optimal resource allocation. The open source container orchestration platform has good scalability and flexibility and can adapt to changing market demands and resource conditions.

[0157] According to an embodiment of the present disclosure, at least one of the user and the supplier of the cloud resources is a financial institution.

[0158] In some embodiments, in order to efficiently process the cloud resource allocation data of financial institutions while ensuring the security, compliance and efficiency of cross-border transactions, this embodiment proposes a method for processing cloud resource allocation data of financial institutions that supports cross-border transactions.

[0159] As a user or supplier of cloud resources, financial institutions register on the cloud resource trading platform and submit identity verification materials. The platform conducts strict identity verification and credit assessment on financial institutions to ensure their legitimacy and reliability.

[0160] Financial institutions submit cloud resource demand or supply information related to cross-border transactions based on their own business needs, including resource type, quantity, price, transaction location, etc. The platform conducts intelligent matching based on the demand and supply information of financial institutions to find the most suitable counterparty for them.

[0161] The platform conducts compliance reviews on cross-border transactions to ensure that the transactions comply with local and international laws and regulations. At the same time, the platform conducts risk assessments on transactions, including market risk, credit risk, exchange rate risk, etc., and provides risk warnings and prevention suggestions for financial institutions.

[0162] After confirming the transaction details and risk assessment results, the financial institution generates a smart contract through the platform. The smart contract specifies the rights and obligations of both parties to the transaction, including transaction price, transaction time, service quality, and liability for breach of contract.

[0163] According to the terms of the smart contract, the platform automatically allocates and schedules cloud resources.

[0164] For cross-border transactions, the platform can use edge computing technology, data encryption technology and content distribution network (CDN) to ensure fast, secure and compliant data transmission.

[0165] In addition, the platform can also monitor cross-border transactions in real time to ensure the smooth progress of transactions and the effective use of resources. After the transaction is completed, the platform provides financial institutions with follow-up services, including resource optimization, performance monitoring, technical support, etc.

[0166] The disclosed embodiments propose a blockchain-as-a-service method for cross-border financial services. By designing an architecture that separates resources from control, hybrid multi-cloud support is achieved. Cross-border resource providers provide cloud resources through wide-area access, and the service model is transformed from "one-to-one" and "one-to-many" to "many-to-many", which fully guarantees the performance of the cross-border blockchain system and solves the problem of cross-border resource trust.

[0167] Based on the above-mentioned blockchain-based data processing method, the present disclosure also provides a blockchain-based data processing device. Figure 6 The blockchain-based data processing device is described in detail.

[0168] Figure 6 The structural block diagram of a blockchain-based data processing device according to an embodiment of the present disclosure is schematically shown.

[0169] like Figure 6 As shown, the blockchain-based data processing device 600 of this embodiment includes, for example: a first determination module 610, a second determination module 620, a processing module 630 and an allocation module 840.

[0170] The first determination module 610 is used to determine cloud resource demand information in response to a transaction request of a user, and the transaction request is initiated through at least one blockchain. In one embodiment, the first determination module 610 can be used to perform the operation S210 described above, which will not be repeated here.

[0171] The second determination module 620 is used to determine cloud resource scheduling information, where the cloud resource scheduling information represents the scheduling allocation of heterogeneous multiple cloud resources. In one embodiment, the second determination module 620 can be used to perform the operation S220 described above, which will not be described in detail here.

[0172] The processing module 630 is used to process the cloud resource scheduling information and the cloud resource demand information using a dynamic bilateral auction algorithm to obtain a cloud resource allocation result. The dynamic bilateral auction algorithm is used to determine the optimal solution for the user's resource demand satisfaction and the cloud resource supply cost. In one embodiment, the processing module 630 can be used to perform the operation S230 described above, which will not be repeated here.

[0173] The allocation module 840 is used to allocate resources to the blockchain based on the cloud resource allocation result. In one embodiment, the allocation module 840 can be used to perform the operation S240 described above, which will not be repeated here.

[0174] According to an embodiment of the present disclosure, any multiple modules of the first determination module 610, the second determination module 620, the processing module 630, and the allocation module 840 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the first determination module 610, the second determination module 620, the processing module 630, and the allocation module 840 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, at least one of the first determination module 610, the second determination module 620, the processing module 630 and the allocation module 840 may be at least partially implemented as a computer program module, and when the computer program module is executed, a corresponding function may be performed.

[0175] According to an embodiment of the present disclosure, the cloud resources include storage resources, and the processing module 630 includes, for example, an extraction submodule, a matching submodule, a first determination submodule, a second determination submodule, and a first minimization submodule.

[0176] The extraction submodule is used to extract cloud resource demand information to obtain the user's cloud resource demand.

[0177] The matching submodule is used to match the cloud resource demand information with the cloud resource scheduling information to determine the user's expected purchase amount of storage resources.

[0178] The first determination submodule is used to determine the user's first utility function for storage resources according to the cloud resource demand and expected purchase quantity, and the first utility function is a monotonic function.

[0179] The second determination submodule is used to determine the seller cost function according to the expected purchase amount and the cost factor, where the cost factor is a positive number so that the seller cost increases with the increase in the cloud resource supply.

[0180] The first minimization submodule is used to minimize the difference between the first utility function and the seller's cost function to obtain an optimal solution corresponding to the cloud resource allocation result.

[0181] According to an embodiment of the present disclosure, cloud resources also include computing resources, and the processing module 630, for example, also includes a third determining submodule, a fourth determining submodule, and a second minimization submodule.

[0182] The third determination submodule is used to determine the distance factor for users to evaluate the quality of computing resource supply.

[0183] The fourth determination submodule is used to determine the user's second utility function for computing resources according to the cloud resource demand, the distance factor and the expected purchase amount, where the second utility function is a monotonic function.

[0184] The second minimization submodule is used to minimize the difference between the second utility function and the seller's cost function to obtain an optimal solution corresponding to the cloud resource allocation result.

[0185] Figure 7 A block diagram of an electronic device suitable for implementing a blockchain-based data processing method according to an embodiment of the present disclosure is schematically shown.

[0186] like Figure 7 As shown, the electronic device 700 according to an embodiment of the present disclosure includes a processor 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage part 708 to a random access memory (RAM) 703. The processor 701 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 701 may also include an onboard memory for caching purposes. The processor 701 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0187] In RAM 703, various programs and data required for the operation of electronic device 700 are stored. Processor 701, ROM 702 and RAM 703 are connected to each other via bus 704. Processor 701 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 702 and / or RAM 703. It should be noted that the program can also be stored in one or more memories other than ROM 702 and RAM 703. Processor 701 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0188] According to an embodiment of the present disclosure, the electronic device 700 may further include an input / output (I / O) interface 705, which is also connected to the bus 704. The electronic device 700 may further include one or more of the following components connected to the input / output (I / O) interface 705: an input portion 706 including a keyboard, a mouse, etc.; an output portion 707 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 708 including a hard disk, etc.; and a communication portion 709 including a network interface card such as a LAN card, a modem, etc. The communication portion 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the input / output (I / O) interface 705 as needed. A removable medium 711, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 710 as needed, so that a computer program read therefrom is installed into the storage portion 708 as needed.

[0189] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the data processing method based on blockchain according to the embodiment of the present disclosure is implemented.

[0190] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 702 and / or RAM 703 described above and / or one or more memories other than ROM 702 and RAM 703.

[0191] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program includes a program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the blockchain-based data processing method provided by the embodiment of the present disclosure.

[0192] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 701. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0193] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 709, and / or installed from the removable medium 711. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0194] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 709, and / or installed from the removable medium 711. When the computer program is executed by the processor 701, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0195] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0196] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0197] It will be appreciated by those skilled in the art that the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations or combinations are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0198] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A data processing method based on blockchain, characterized in that: The method comprises: Determining cloud resource demand information in response to a transaction request from a user, wherein the transaction request is initiated via at least one blockchain; Determining cloud resource scheduling information, where the cloud resource scheduling information represents scheduling allocation of heterogeneous multiple cloud resources; A dynamic bilateral auction algorithm is used to process the cloud resource scheduling information and the cloud resource demand information to obtain a cloud resource allocation result, wherein the dynamic bilateral auction algorithm is used to determine the optimal solution of the user's resource demand satisfaction and the cloud resource supply cost; Based on the cloud resource allocation result, resources are allocated to the blockchain.

2. The method according to claim 1, characterized in that The cloud resources include storage resources, and the cloud resource scheduling information and the cloud resource demand information are processed by a dynamic bilateral auction algorithm to obtain the cloud resource allocation result, including: Extracting the cloud resource demand information to obtain the cloud resource demand of the user; Matching the cloud resource demand information with the cloud resource scheduling information to determine the expected purchase amount of the storage resource by the user; Determining a first utility function of the user for the storage resource according to the cloud resource demand and the expected purchase amount, wherein the first utility function is a monotonic function; Determining a seller cost function according to the expected purchase amount and a cost factor, wherein the cost factor is a positive number so that the seller cost increases with an increase in the supply of cloud resources; The difference between the first utility function and the seller cost function is minimized to obtain an optimal solution corresponding to the cloud resource allocation result.

3. The method according to claim 2, characterized in that The cloud resources also include computing resources, and the adopting of a dynamic bilateral auction algorithm to process the cloud resource scheduling information and the cloud resource demand information to obtain the cloud resource allocation result also includes: Determining a distance factor for the user to evaluate the quality of the computing resource supply; Determining a second utility function of the user for the computing resources according to the cloud resource demand, the distance factor, and the expected purchase amount, wherein the second utility function is a monotonic function; The difference between the second utility function and the seller cost function is minimized to obtain an optimal solution corresponding to the cloud resource allocation result.

4. The method according to claim 1, characterized in that The cloud resource demand information is determined according to an auction time slot, and the auction time slot corresponds to an operation clock of the blockchain.

5. The method according to claim 1, characterized in that: The multiple cloud resources correspond to at least one cloud resource supplier, and each of the cloud resource suppliers determines the cloud resource scheduling information through at least one bid.

6. The method according to claim 5, characterized in that The supply price of the cloud resources includes the expected transaction price and the minimum transaction price; When the transaction price is not restricted by a third party, the transaction is conducted at the expected transaction price; When the transaction price is restricted by a third party, the lowest transaction price shall be taken as the minimum value of the transaction price.

7. The method according to claim 1, characterized in that The blockchain-based data processing method is implemented through a cloud resource trading platform, which is an open source container orchestration platform.

8. The method according to claim 1, characterized in that At least one of the user and the supplier of the cloud resources is a financial institution.

9. A data processing device based on blockchain, characterized in that: The device comprises: A first determination module, configured to determine cloud resource demand information in response to a transaction request of a user, wherein the transaction request is initiated through at least one blockchain; A second determination module is used to determine cloud resource scheduling information, where the cloud resource scheduling information represents the scheduling allocation of a plurality of heterogeneous cloud resources; a processing module, configured to process the cloud resource scheduling information and the cloud resource demand information using a dynamic bilateral auction algorithm to obtain a cloud resource allocation result, wherein the dynamic bilateral auction algorithm is used to determine an optimal solution for the user's resource demand satisfaction and the cloud resource supply cost; An allocation module is used to allocate resources to the blockchain based on the cloud resource allocation result.

10. An electronic device, comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the blockchain-based data processing method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by the processor, the steps of the blockchain-based data processing method according to any one of claims 1 to 8 are implemented.

12. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by the processor, the steps of the blockchain-based data processing method according to any one of claims 1 to 8 are implemented.

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