Block chain distributed storage resource allocation method and device based on Starburg game

Abstract

The invention relates to a block chain distributed storage resource allocation method based on the Starburg game, and also relates to a block chain distributed storage resource allocation device based on the Starburg game, and belongs to the technical field of network information security, and the method comprises the steps: constructing constraint strategies of a storage resource price and a storage resource application amount; according to the resource price and the storage resource application amount, respectively establishing utility functions of the storage resource provider and the storage resource user; combining the resource price constraint strategy with the corresponding utility function to obtain a high-level optimization problem; combining the constraint strategy of the storage resource application quantity with the corresponding utility function to obtain a low-layer optimization problem, and constructing a storage resource pricing and allocation optimization problem; and solving a storage resource pricing and allocation optimization problem to obtain a resource price for allocation and a corresponding storage resource application quantity. According to the invention, the resource supply capability can be provided in a balanced manner, and the diversity and timeliness of the demand mode can be considered.

Description

Technical Field

[0001] The present invention relates to a blockchain distributed storage resource allocation method based on the Starberg game, and also relates to a blockchain distributed storage resource allocation device based on the Starberg game, belonging to the field of network information security technology. Background Art

[0002] Blockchain is a decentralized, distributed ledger in which every participating node maintains a complete copy of the transaction history. This ledger is maintained consistent through a consensus algorithm, using public key cryptography for authentication and transaction signing, and hash functions to generate unique block identifiers, ensuring data integrity and security.

[0003] The core mechanism of a blockchain system is the consensus mechanism, which ensures that the blockchain system reaches consensus. For public chains, the consensus algorithm determines the system's incentive mechanism. Common consensus algorithms include Proof of Work (PoW), Proof of Stake (PoS), and Proof of Space-Time (PoST).

[0004] The Proof-of-Spacetime consensus mechanism is a blockchain consensus algorithm based on storage resources and time verification. It aims to maintain network security and decentralization by leveraging node storage space and time, while reducing the reliance of traditional Proof-of-Work on high-energy hardware. The core idea of ​​this mechanism is to require consensus participants to provide verifiable storage resources and prove the authenticity of their contributions through continuity over time.

[0005] Blockchain systems such as FileCoin and Chia Network achieve efficient storage resource utilization and incentive allocation by introducing Proof of Space and Time (PoS / T) consensus mechanisms. In these systems, consensus participants compete for recordkeeping rights and earn system rewards by offering their own storage space, while storage demanders pay tokens for storage services. However, when it comes to resource allocation in blockchain storage systems, traditional uniform or static pricing mechanisms struggle to adapt to the dynamic demands of real-world applications due to the complexity of the network structure and heterogeneity of node performance. On the one hand, significant differences in node resources such as storage capacity, bandwidth, and uptime lead to uneven resource supply. On the other hand, storage demanders also have diverse and time-sensitive demand patterns. Currently, due to the heterogeneity of blockchain network structures and nodes, resource pricing techniques such as uniform pricing are unable to meet the dynamic demands of real-world applications and ensure fair allocation. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to overcome the shortcomings of the existing technology and provide a blockchain distributed storage resource allocation method and device based on the Starberg game, which can provide resource supply capabilities in a balanced manner and take into account the diversity and timeliness of demand patterns.

[0007] To achieve the above technical objectives, on the one hand, the present invention provides a blockchain distributed storage resource allocation method based on the Städtberg game, comprising:

[0008] According to the supply and demand relationship of distributed storage resources in the blockchain, based on the Starberg game model, the leader is assumed to be the storage resource provider and the followers are the storage resource users. Constraint strategies for the resource price provided by the storage resource provider and the storage resource application amount of the storage resource user are constructed respectively.

[0009] Based on resource prices and storage resource application amounts, utility functions for storage resource providers and storage resource users are established respectively;

[0010] Combine resource price constraint strategies and corresponding utility functions to obtain a high-level optimization problem. Combine storage resource application quantity constraint strategies and corresponding utility functions to obtain a low-level optimization problem, and construct a storage resource pricing and allocation optimization problem.

[0011] Solve the storage resource pricing and allocation optimization problem, and obtain the resource price used for allocation and the corresponding storage resource application amount.

[0012] Preferably, the high-level optimization problem includes resource price conditions; the low-level optimization problem includes storage resource application quantity conditions;

[0013] The storage resource pricing and allocation optimization problem specifically includes:

[0014] Initialize resource prices based on the storage capacity of the storage resource provider and the storage resource application quantity.

[0015] Remove storage resource users whose accepted resource prices do not meet the resource price conditions from the storage resource user set;

[0016] Calculate the resource prices received by the remaining resource users in the removed storage resource user set until the resource prices received by all resource users in the storage resource user set meet the storage resource application quantity conditions;

[0017] The corresponding storage resource application amount is obtained based on the calculated resource price.

[0018] Preferably, the step of removing storage resource users whose accepted resource prices do not meet the resource price condition from the storage resource user set specifically includes:

[0019] Calculate the total storage resource application amount of each storage resource user corresponding to the minimum allowable resource price in the accepted resource price condition;

[0020] The storage capacity is updated after subtracting the total amount of storage resource applications from the storage capacity owned by the storage resource provider.

[0021] Preferably, the calculation of the resource prices received by the remaining resource users in the set of storage resource users after the removal specifically includes:

[0022] Calculate the corresponding resource price based on the updated storage capacity.

[0023] Preferably, the constraint strategy specifically includes:

[0024] Storage Resource Provider (SRP) i Storage Resource User SRU j Price offered p ij Satisfy the constraints:

[0025]

[0026] In formula (1), p min is the lowest price of storage resources, p max is the maximum price of storage resources, S i Storage Resource Provider (SRP) i The collection of storage resource users providing services;

[0027] Storage Resource User (SRU) j From Storage Resource Provider (SRP) i The amount of storage resources requested b ij Satisfy the constraints:

[0028]

[0029] In formula (2), N j For SRU j A collection of storage resource providers for the service.

[0030] Preferably, the utility function of the storage resource user is for:

[0031]

[0032] In formula (4), θ j is a non-negative parameter.

[0033] Preferably, the storage resource provider SRP i The utility function for:

[0034]

[0035] In formula (6), α i and β i are all non-negative parameters, parameter H ij For application with SRP i Corresponding SRUs j The weight of satisfaction.

[0036] Preferably, the low-level optimization problem P1 is:

[0037]

[0038] In formula (7), constraint C1 represents SRU j The amount requested from a given storage resource must be non-negative.

[0039] Preferably, the high-level optimization problem P2 is:

[0040]

[0041] In formula (8), constraint C2 represents the storage resource application quantity constraint, constraint C3 represents the resource price constraint, and C i Storage Resource Provider (SRP) i The storage capacity available.

[0042] On the other hand, the present invention provides a blockchain distributed storage resource allocation device based on the Starberg game, including: a processor and a memory, wherein the processor reads a computer program in the memory and performs the following operations:

[0043] According to the supply and demand relationship of distributed storage resources in the blockchain, based on the Starberg game model, the leader is assumed to be the storage resource provider and the followers are the storage resource users. Constraint strategies for the resource price provided by the storage resource provider and the storage resource application amount of the storage resource user are constructed respectively.

[0044] Based on resource prices and storage resource application amounts, utility functions for storage resource providers and storage resource users are established respectively;

[0045] Combine resource price constraint strategies and corresponding utility functions to obtain a high-level optimization problem. Combine storage resource application quantity constraint strategies and corresponding utility functions to obtain a low-level optimization problem, and construct a storage resource pricing and allocation optimization problem.

[0046] Solve the storage resource pricing and allocation optimization problem, and obtain the resource price used for allocation and the corresponding storage resource application amount.

[0047] In this paper, we introduce the multi-leader-multi-follower Stackelberg game theory to establish the utility functions of storage resource providers and storage resource users. By solving the storage resource pricing and allocation optimization problem, we obtain the resource price for allocation and the corresponding storage resource request quantity. Thus, the storage resource provider, as the leader, sets the resource price it charges each storage resource user, while the storage resource user, as the follower, specifies the storage resource demand. This achieves the realistic needs of dynamic resource pricing and fair allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 This is a schematic diagram of the method flow of an embodiment of the present application;

[0050] Figure 2 This is a schematic diagram of the structure of the device in the embodiment of the present application;

[0051] Figure 3 This is a diagram of the storage resource allocation architecture in an embodiment of the present application;

[0052] Figure 4 This is a flow chart of storage resource allocation in an embodiment of the present application. DETAILED DESCRIPTION

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0054] like Figure 1 As shown in the figure, a blockchain distributed storage resource allocation method based on the Starberg game is proposed, including:

[0055] 101. According to the supply and demand relationship of distributed storage resources in the blockchain, based on the Stuttgart game model, the leader is assumed to be the storage resource provider and the followers are assumed to be the storage resource users. Constraint strategies for the resource prices provided by the storage resource providers and the storage resource application quantities of the storage resource users are constructed respectively.

[0056] In this embodiment, if Figure 3As shown in the figure, the storage resource provider SRP in the blockchain distributed storage network obtains the system incentive of the blockchain by providing its own storage resources, and the storage resource user (i.e., storage user) SRU obtains storage resources in the form of payment.

[0057] like Figure 4 As shown, for a blockchain distributed storage resource allocation method based on the Starberg game provided in this embodiment, the first step is to establish the supply and demand relationship of blockchain distributed storage resources:

[0058] In a typical Stackelberg game, the leader solves the high-level optimization problem and selects the optimal strategy first, and then the follower solves the low-level optimization problem and selects the corresponding response strategy.

[0059] The second step is to establish a differentiated storage resource pricing and allocation model based on the Stackelberg game:

[0060] The storage resource provider SRP acts as a leader to determine the price of storage resources, and the storage resource user SRU acts as a follower to determine the demand for storage resources. For SRPi (i∈I) in the network, the set of user autonomous domains it serves is represented by the set S i The policy of SRPi, i.e. the storage resource price it charges to each SRU, is represented by the set {p ij} description, where j∈S i For SRU in the network j (j∈J), the storage resource provider for its service is a commercial set N j Indicates. SRU j The strategy of the storage resource user is to use the set {b ij}(where i∈N j ) to describe. make

[0061] The storage resource pricing and allocation problem described by the multi-leader-multi-follower Stackelberg game model can be described as follows:

[0062] a. Players. The storage resource providers SRP in set I are the leaders of the game, and the storage resource demanders in set J, i.e., the storage resource users SRU, are the followers of the game.

[0063] b. Strategy. For storage resource provider SRPi, its strategy is to provide the price p to storage resource user SRUj ij , and the storage resource price satisfies p ij The constraints are the resource price constraints.

[0064] The constraint strategy specifically includes:

[0065] Storage Resource Provider (SRP) i Storage Resource User SRU j Price offered p ij Satisfy the constraints:

[0066]

[0067] In formula (1), p min is the lowest price of storage resources, p max is the maximum price of storage resources, S i Storage Resource Provider (SRP) i The collection of storage resource users providing services;

[0068] Storage Resource User (SRU) j From Storage Resource Provider (SRP) i The amount of storage resources requested b ij Satisfy the constraints, that is, the constraint strategy for storage resource application amount:

[0069]

[0070] In formula (2), N j For SRU j A collection of storage resource providers for the service.

[0071] c. Utility function. For storage resource provider SRPi, its utility function is Indicates that for storage resource user SRUj, its utility function is express.

[0072] 102. Based on the resource price and the storage resource application amount, utility functions of storage resource providers and storage resource users are established respectively.

[0073] Step 3: Calculate the utility function of the blockchain distributed storage resource provider SRP and the storage resource demander SRU;

[0074] a. Utility function of storage resource user SRU.

[0075] The SRU in the network applies for storage resources from its corresponding SRP to meet its storage resource needs and pays the corresponding price. Therefore, the utility function of the SRU can be given by the following formula:

[0076]

[0077] In formula (3), function f(·) is used to describe the satisfaction of storage resource users, function C(·) is used to describe the cost of purchasing storage resources; parameter θ is non-negative. j Used to characterize the SRU's sensitivity to resource satisfaction, the higher the j The value of means that SRU tends to be less concerned about the economic cost of applying for storage resources.

[0078] In line with the actual situation, the function f(·) should satisfy the law of diminishing marginal returns, that is, when the SRU has already applied for more storage resources, the improvement in its satisfaction brought by the additional storage resources subsequently applied for will be lower than when the SRU has already applied for fewer storage resources.

[0079] It can be seen that the function f(·) should be strictly monotonically increasing and is a concave function. In this embodiment, the logarithmic function is used to describe the satisfaction of the SRU, thereby finally obtaining the storage resource user SRU j The utility function expression is as follows:

[0080] The utility function of the storage resource user for:

[0081]

[0082] In formula (4), θ j is a non-negative parameter.

[0083] b. Utility function of storage resource provider SRP.

[0084] The SRP in the network provides the required storage resources to its corresponding SRUs and charges corresponding storage resource usage fees and user satisfaction. Therefore, the storage resource provider SRP i The profit function expression is as follows:

[0085]

[0086] In formula (5), the parameter α is non-negative. i With parameter β i It is used to adjust the relative proportion of economic benefits and satisfaction benefits. The function R(·) is used to represent the economic benefit function, and the function f(·) is used to represent the SRU satisfaction that constitutes the reputation benefit.

[0087] Parameter H ij It is applied to SRP i Corresponding SRUs j The weight of satisfaction, used to describe SRP i SRUs from different perspectives j The heterogeneous storage stability reflects the SRPi To SRU j The preference for providing storage resources is to provide resources to users who use storage resources stably.

[0088] In this embodiment, the parameter α i With parameter β i Should satisfy α i +β i = 1. Therefore, the final SRP i The utility function expression is as follows:

[0089] The Storage Resource Provider (SRP) i The utility function for:

[0090]

[0091] In formula (6), α i and β i are all non-negative parameters, parameter H ij For application with SRP i Corresponding SRUs j The weight of satisfaction.

[0092] 103. Combine the resource price constraint strategy and the corresponding utility function to obtain the high-level optimization problem; combine the storage resource application quantity constraint strategy and the corresponding utility function to obtain the low-level optimization problem, and construct the storage resource pricing and allocation optimization problem.

[0093] The fourth step is to establish the optimization constraints for pricing and allocation of blockchain distributed storage resources, that is, the optimization problem of storage resource pricing and allocation.

[0094] In this step, we describe the two-stage optimization problem of the multi-leader-multi-follower Stackelberg game model in the storage resource pricing and allocation process. First, we explain the second-stage optimization problem, i.e., the low-level optimization problem. For the storage resource user SRU j , after receiving storage resource pricing information After SRU j The decision is made on the amount of storage resources required (the amount of storage resources required must be non-negative) to maximize its utility function. Therefore, the low-level optimization problem can be expressed symbolically as follows:

[0095] The low-level optimization problem P1 is:

[0096]

[0097] In formula (7), constraint C1 represents SRU j The amount requested from a given storage resource must be non-negative.

[0098] When SRU j When it does not want to apply for storage resources, it can set the parameter θ j Set to 0, the corresponding storage resource application amount b ij Its value will also be 0.

[0099] For Storage Resource Providers (SRPs) i , whose goal is to price storage resources ij ∈[p min ,p max ] Make decisions to maximize your own utility function.

[0100] In addition, SRP i It should ensure that the total storage resource application volume under its jurisdiction does not exceed its storage capacity C i In summary, the high-level optimization problem can be formulated as follows:

[0101] The high-level optimization problem P2 is:

[0102]

[0103] In formula (8), constraint C2 represents the storage resource application quantity constraint, constraint C3 represents the resource price constraint, and C i Storage Resource Provider (SRP) i The storage capacity available.

[0104] 104. Solve the storage resource pricing and allocation optimization problem to obtain the resource price for allocation and the corresponding storage resource application amount.

[0105] Step 5: Solve the optimization problem of differentiated storage resource pricing and allocation based on the Stackelberg game.

[0106] In this embodiment, the high-level optimization problem includes a resource price condition C3; the low-level optimization problem includes a storage resource application amount condition C1;

[0107] The storage resource providers and storage resource users in the blockchain distributed storage network make decisions on their own optimal strategies by solving their own optimization problems. The optimal strategies obtained by solving the above high-level and low-level optimization problems are as well as It forms a Stackelberg equilibrium point SE.

[0108] In a Stackelberg equilibrium, no player can improve their utility simply by changing their strategy. Followers will choose the optimal response strategy based on the leader's strategy.

[0109] In this embodiment, Algorithm 1 is proposed to find the optimal solution for differentiated storage resource pricing. Algorithm 1 consists of two main parts: initialization and iteration. During each iteration, the algorithm searches for all storage resource prices that do not satisfy constraint C3.

[0110] The 103 specifically includes:

[0111] 1031. Initialize the resource price based on the storage capacity owned by the storage resource provider and subject to the storage resource application quantity being met. Specifically:

[0112] 10311, according to the storage resource provider SRP i Owned storage capacity, obtain Lagrange multiplier;

[0113]

[0114] In formula (9), λ i is the Lagrange multiplier.

[0115] 10312. Calculate the resource price under initialization based on the obtained Lagrange multiplier.

[0116]

[0117] In formula (10), is the Lagrange multiplier that satisfies the conditions.

[0118] 1032. Remove the storage resource users whose accepted resource prices do not meet the resource price conditions from the storage resource user set; specifically:

[0119] 10321. Calculate the total storage resource application amount of each storage resource user corresponding to the minimum allowable resource price in the accepted resource price condition.

[0120] In this embodiment, during each iteration of the algorithm, the algorithm will search for all storage resource prices that do not meet the constraint C3. Note that we assume that p max The value of is large enough so that the right half of the constraint condition C3 p ij ≤p max It can always be satisfied. Therefore, in our iterative algorithm we only consider p min ≤p ij That is, the storage resource price is less than the minimum allowed storage resource price.

[0121] For storage resource users who receive a storage resource price that is less than the minimum allowed price, the storage resource price they ultimately use will be set to the minimum allowed storage resource price p min ,Right now Calculate the corresponding storage resource application amount according to the following formula:

[0122]

[0123] In formula (11), p ij Set to p min .

[0124] 10322. Update the storage capacity by subtracting the total amount of storage resource applications from the storage capacity owned by the storage resource provider.

[0125] At the same time, the algorithm updates these storage resource prices to the minimum allowed storage resource price p min Storage resource users from the set S i Removed.

[0126] Update SRP i Storage resource capacity:

[0127]

[0128] In formula (12), the set Express satisfaction The collection of j.

[0129] 1033. Calculate the resource prices received by the remaining resource users in the removed storage resource user set until the resource prices received by all resource users in the storage resource user set meet the storage resource application quantity conditions; specifically:

[0130] 10331. Calculate the corresponding resource price based on the updated storage capacity.

[0131] The storage resource prices received by other storage resource users are then recalculated. The algorithm terminates when the storage resource prices received by all storage resource users meet constraint C3.

[0132] In this embodiment, the calculations of formulas (9) and (10) are repeated to obtain the corresponding resource prices.

[0133] 1034. Obtain the corresponding storage resource application amount based on the calculated resource price.

[0134] Repeat formula (11) to calculate the corresponding storage resource application amount.

[0135] In Algorithm 1 of this embodiment, the resource price is directly obtained using formulas (10) and (11) to obtain the corresponding storage resource application amount. The derivation process of formulas (10) and (11) is as follows:

[0136] In order to transform the utility function The optimization problem of have We can transform the utility function It is expressed as follows:

[0137]

[0138] Thus the high-level optimization problem P2 becomes:

[0139]

[0140] The Lagrangian function corresponding to the optimization problem P3 is:

[0141]

[0142] In formula (15), and is the Lagrange multiplier.

[0143] Then the Carlo-Kuhn-Tucker condition (KKT) of the above optimization problem can be given by the following formula:

[0144]

[0145] Assume that the parameter p max The value is large enough to expand the feasible domain of the above optimization problem, which is in line with the actual situation. First, consider the case where the constraint C9 is not at the boundary. In order to maximize its own utility function Set its price to Under such storage resource price setting, constraint C9 can be satisfied.

[0146] Then, this embodiment considers the case where the constraint condition C9 takes a value at the boundary and the constraint condition C10 does not take a value at the boundary. i , as well as satisfy:

[0147] Using KKT conditions We can get the following about the variable x ij The quadratic equation:

[0148]

[0149] By solving the quadratic equation expressed in Equation (17), we can obtain the variable x ij The solution is:

[0150]

[0151] In formula (18), the discriminant Δij The expression is variable x ij The value of must be positive, so the negative roots of the quadratic equation need to be discarded.

[0152] Substituting Equation (18) into the constraint condition C9, we can obtain Equation (9). Equation (9) is about the Lagrange multiplier λ i The equation, using The solution of equation (9) is represented by , and the optimal price solution is equation (10).

[0153] Constraint C10 does not take values ​​at the boundary here, which means that constraint C3 is i The storage resource price given by formula (10) is also not taken at the boundary. Its value must be in the closed interval [p min ,p max ] is within the range indicated by .

[0154] For the low-level optimization problem P1, we can first prove that the optimization problem P1 is a convex problem. The Hessian matrix corresponding to the utility function is a negative definite matrix, and the utility function is a storage resource vector b j In addition, the constraint C1 is about the bandwidth application vector b j Therefore, the optimization problem P1 is a convex problem. When the first-order derivative of the utility function is 0, the vector b of the resource demander is j satisfy Considering that the storage resource application amount is non-negative, the optimal solution for the application amount can be given by the following formula: That is formula (11).

[0155] The process of Algorithm 1 is shown in the following table:

[0156]

[0157]

[0158] The method described in this embodiment builds upon the supply and demand model of distributed storage resources on a blockchain, leveraging the multi-leader-multi-follower Stackelberg game theory to establish a differentiated storage resource pricing and allocation system based on the Stackelberg game to guide storage resource allocation. This embodiment also provides an algorithm for finding the optimal solution to the differentiated storage resource pricing strategy, ensuring that blockchain distributed storage usage does not exceed capacity.

[0159] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below in conjunction with specific embodiments. The specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0160] like Figure 3 As shown, the introduction of entities in the blockchain:

[0161] (1) Storage Resource User (SRU): The initiator of the storage request uploads the file to be stored to the blockchain network and pays a certain fee based on the size of the stored file and the storage time.

[0162] (2) Storage Resource Provider (SRP): The provider of storage resources stores the files of Storage Resource Users (SRUs) and charges a certain fee based on the size of the stored files and the storage time. The fee is adjusted through the resource allocation process.

[0163] (3) Blockchain: Building a distributed storage network consensus with storage as the incentive.

[0164] like Figure 4 As shown, in this embodiment, the specific implementation steps experienced by the above entity include:

[0165] Step 1: Establish a supply and demand relationship between storage resource users and storage resource providers in the blockchain distributed storage network;

[0166] Step 2: Construct a differentiated storage resource pricing and allocation model based on the Stackelberg game. Describe the multi-leader-multi-follower Stackelberg game model, including its participant definitions, strategies, and utility functions.

[0167] Step 3. Calculate the utility function of the blockchain distributed storage resource provider SRP and the storage resource user SRU;

[0168] Step 4. Establish the optimization constraints for pricing and allocation of distributed storage resources in the blockchain, and give a two-stage optimization problem description of the multi-leader-multi-follower Stackelberg game model in the storage resource pricing and allocation process;

[0169] Step 5. Solve the optimization problem of differentiated storage resource pricing and allocation based on the Stackelberg game.

[0170] like Figure 2 As shown, the embodiment of the present application also provides a blockchain distributed storage resource allocation device based on the Starberg game, including: a memory 10 and a processor 20, wherein the processor 20 reads the computer program in the memory 10 and is configured to perform the following operations:

[0171] According to the supply and demand relationship of distributed storage resources in the blockchain, based on the Starberg game model, the leader is assumed to be the storage resource provider and the followers are the storage resource users. Constraint strategies for the resource price provided by the storage resource provider and the storage resource application amount of the storage resource user are constructed respectively.

[0172] Based on resource prices and storage resource application amounts, utility functions for storage resource providers and storage resource users are established respectively;

[0173] Combine resource price constraint strategies and corresponding utility functions to obtain a high-level optimization problem. Combine storage resource application quantity constraint strategies and corresponding utility functions to obtain a low-level optimization problem, and construct a storage resource pricing and allocation optimization problem.

[0174] Solve the storage resource pricing and allocation optimization problem, and obtain the resource price used for allocation and the corresponding storage resource application amount.

[0175] The specific implementation has been described in detail in the above embodiments and will not be repeated here.

[0176] It should be understood that the specific order or hierarchy of steps in the disclosed processes is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process can be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy described.

[0177] While the above descriptions of embodiments and examples of the present invention are provided for the purpose of providing a more detailed and complete description of the present disclosure, they are not intended to be the only ways to implement or use the embodiments of the present invention. The embodiments cover features of various embodiments, as well as the method steps and sequences for constructing and operating these embodiments. However, other embodiments may be used to achieve the same or equivalent functionality and sequence of steps.

[0178] In the foregoing detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that embodiments of the claimed subject matter require more features than are expressly recited in each claim. On the contrary, as reflected in the appended claims, the invention comprises less than all the features of any individual disclosed embodiment. The appended claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0179] The above description of the disclosed embodiments is intended to enable any person skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the spirit and scope of the present disclosure. Therefore, the present disclosure is not limited to the embodiments presented herein but is intended to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0180] The foregoing description includes examples of one or more embodiments. Of course, it is not possible to describe all possible combinations of components or methods for the purposes of describing the above embodiments, but one of ordinary skill in the art will recognize that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to encompass all such changes, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent the term "comprising" is used in the specification or claims, the term is intended to be encompassed in a manner similar to the term "including," as explained in terms of "including," used as a transitional word in the claims. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or."

[0181] Those skilled in the art will also appreciate that the various illustrative logical blocks, units, and steps listed in the embodiments of the present invention can be implemented by electronic hardware, computer software, or a combination of the two. To clearly demonstrate the interchangeability of hardware and software, the various illustrative components, units, and steps described above have generally described their functions. Whether such functions are implemented by hardware or software depends on the specific application and the design requirements of the entire system. Those skilled in the art may use various methods to implement the described functions for each specific application, but such implementation should not be understood as exceeding the scope of protection of the embodiments of the present invention.

[0182] The various illustrative logic blocks or units described in the embodiments of the present invention can be implemented or operated by a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof. The general-purpose processor can be a microprocessor, and optionally, the general-purpose processor can also be any conventional processor, controller, microcontroller or state machine. The processor can also be implemented by a combination of computing devices, such as a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors combined with a digital signal processor core, or any other similar configuration.

[0183] The steps of the methods or algorithms described in the embodiments of the present invention may be directly embedded in hardware, a software module executed by a processor, or a combination of the two. The software module may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. For example, the storage medium may be connected to the processor so that the processor can read information from the storage medium and write information to the storage medium. Alternatively, the storage medium may also be integrated into the processor. The processor and storage medium may be provided in an ASIC, which may be provided in a user terminal. Alternatively, the processor and storage medium may also be provided in different components in the user terminal.

[0184] In one or more exemplary designs, the above-mentioned functions described in the embodiments of the present invention can be implemented in hardware, software, firmware, or any combination of the three. If implemented in software, these functions can be stored on a computer-readable medium or transmitted in the form of one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media that facilitate the transfer of computer programs from one location to another. Storage media can be any available medium that can be accessed by a general or special computer. For example, such computer-readable media can include but are not limited to RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store program code in the form of instructions or data structures and other forms that can be read by a general or special computer, or a general or special processor. In addition, any connection can be appropriately defined as a computer-readable medium. For example, if the software is transmitted from a website, server or other remote resource via a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless methods such as infrared, wireless, and microwave, it is also included in the definition of computer-readable media. The disks and discs mentioned above include compact disks, laser disks, optical disks, DVDs, floppy disks, and Blu-ray discs. Disks typically reproduce data magnetically, while discs typically reproduce data optically with lasers. Combinations of the above may also be included in computer-readable media.

[0185] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A blockchain distributed storage resource allocation method based on the Städberg game, characterized in that: include: According to the supply and demand relationship of distributed storage resources in the blockchain, based on the Starberg game model, the leader is assumed to be the storage resource provider and the followers are the storage resource users. Constraint strategies for the resource price provided by the storage resource provider and the storage resource application amount of the storage resource user are constructed respectively. Based on resource prices and storage resource application amounts, utility functions for storage resource providers and storage resource users are established respectively; Combine resource price constraint strategies and corresponding utility functions to obtain a high-level optimization problem. Combine storage resource application quantity constraint strategies and corresponding utility functions to obtain a low-level optimization problem, and construct a storage resource pricing and allocation optimization problem. Solve the storage resource pricing and allocation optimization problem, and obtain the resource price used for allocation and the corresponding storage resource application amount.

2. The blockchain distributed storage resource allocation method based on the Starberg game according to claim 1 is characterized in that: The high-level optimization problem includes resource price conditions; The low-level optimization problem includes storage resource application amount conditions; The storage resource pricing and allocation optimization problem specifically includes: Initialize resource prices based on the storage capacity of the storage resource provider and the storage resource application quantity. Remove storage resource users whose accepted resource prices do not meet the resource price conditions from the storage resource user set; Calculate the resource prices received by the remaining resource users in the removed storage resource user set until the resource prices received by all resource users in the storage resource user set meet the storage resource application quantity conditions; The corresponding storage resource application amount is obtained based on the calculated resource price.

3. The blockchain distributed storage resource allocation method based on the Starberg game according to claim 2 is characterized in that: The step of removing storage resource users whose accepted resource prices do not meet the resource price condition from the storage resource user set specifically includes: Calculate the total storage resource application amount of each storage resource user corresponding to the minimum allowable resource price in the accepted resource price condition; The storage capacity is updated after subtracting the total amount of storage resource applications from the storage capacity owned by the storage resource provider.

4. The blockchain distributed storage resource allocation method based on the Starberg game according to claim 3 is characterized in that: The calculation of the resource prices received by the remaining resource users in the removed storage resource user set specifically includes: Calculate the corresponding resource price based on the updated storage capacity.

5. The blockchain distributed storage resource allocation method based on the Starberg game according to claim 1 is characterized in that: The constraint strategy specifically includes: Storage Resource Provider (SRP) i Storage Resource User SRU j Price offered Satisfy the constraints: （1） In formula (1), is the lowest price for storage resources, is the maximum price of storage resources, Storage Resource Provider (SRP) i The collection of storage resource users providing services; Storage Resource User (SRU) j From Storage Resource Provider (SRP) i The amount of storage resources requested Satisfy the constraints: （2） In formula (2), For SRU j A collection of storage resource providers for the service.

6. The blockchain distributed storage resource allocation method based on the Starberg game according to claim 1 is characterized in that: The utility function of the storage resource user for: （4） In formula (4), is a non-negative parameter.

7. The blockchain distributed storage resource allocation method based on the Starberg game according to claim 1 is characterized in that: The Storage Resource Provider (SRP) i The utility function for: （6） In formula (6), and are all non-negative parameters, For application with SRP i Corresponding SRUs j The weight of satisfaction.

8. The blockchain distributed storage resource allocation method based on the Starberg game according to claim 1 is characterized in that: The low-level optimization problem P1 is: （7） In formula (7), the constraint condition Indicates SRU j The amount requested from a given storage resource must be non-negative.

9. The blockchain distributed storage resource allocation method based on the Starberg game according to claim 1 is characterized in that: The high-level optimization problem P2 is: （8） In formula (8), the constraint condition Indicates the storage resource application amount constraint, constraint condition represents the resource price constraint, Storage Resource Provider (SRP) i The storage capacity available.

10. A blockchain distributed storage resource allocation device based on the Starberg game, characterized in that: include: A processor and a memory, wherein the processor reads a computer program in the memory and is configured to perform the following operations: According to the supply and demand relationship of distributed storage resources in the blockchain, based on the Starberg game model, the leader is assumed to be the storage resource provider and the followers are the storage resource users. Constraint strategies for the resource price provided by the storage resource provider and the storage resource application amount of the storage resource user are constructed respectively. Based on resource prices and storage resource application amounts, utility functions for storage resource providers and storage resource users are established respectively; Combine resource price constraint strategies and corresponding utility functions to obtain a high-level optimization problem. Combine storage resource application quantity constraint strategies and corresponding utility functions to obtain a low-level optimization problem, and construct a storage resource pricing and allocation optimization problem. Solve the storage resource pricing and allocation optimization problem, and obtain the resource price used for allocation and the corresponding storage resource application amount.

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