Blockchain and zero-knowledge proof-based autonomous settlement method and system for intelligent agents

Abstract

The application discloses an agent autonomous settlement method and system based on a blockchain and zero-knowledge proof, and can, after a first agent publishes current to-be-processed task parameters to a cooperative network in a homomorphic commitment vector and current zero-knowledge range proof mode and determines a second agent that satisfies conditions, settlement data between the two agents is settled and confirmed in the homomorphic commitment vector and zero-knowledge proof mode, the amount of shared plaintext data in the whole process is reduced, and data security is improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent agent autonomous collaborative network technology in artificial intelligence, and in particular to an intelligent agent autonomous settlement method and system based on blockchain and zero-knowledge proof. Background Technology

[0002] With the development of autonomous collaborative network technology for AI agents, multiple agents in such networks currently complete tasks such as task publishing, task protocol negotiation, task execution, and settlement processing mostly without a centralized trusted party. In patent CN122472800A, the first agent publishes a task requirement containing computing power specification parameters P and task duration parameters T; the two agents negotiate a revenue distribution coefficient α through an automated game mechanism; an agent protocol containing executable instructions is generated; the second agent executes the task and generates a verifiable execution certificate; when the second agent receives an execution certificate that meets the verification trigger instruction and executes the revenue distribution instruction, it distributes computing power vouchers between the first and second AI agents according to the revenue distribution coefficient. In the above process, the task requirement parameters published by the first agent and the data during revenue settlement are all published in plaintext, which increases the amount of plaintext shared data and reduces data security. Summary of the Invention

[0003] This invention provides a method and system for autonomous settlement of intelligent agents based on blockchain and zero-knowledge proofs. It aims to solve the problem that in the prior art, when the first intelligent agent in an autonomous collaborative network publishes task requirement parameters and processes settlement revenue, the data is published in plaintext, which increases the amount of plaintext shared data and reduces data security.

[0004] In a first aspect, embodiments of the present invention provide an agent-based autonomous settlement method based on blockchain and zero-knowledge proof, which is applied to an agent-based autonomous settlement system based on blockchain and zero-knowledge proof. The agent-based autonomous settlement system includes multiple agents forming a collaborative network, with each agent's corresponding device serving as a blockchain node. The method includes: In the collaborative network, the first intelligent agent acting as the demand side responds to the task release instruction, obtains the corresponding current task to be processed and the current task to be processed parameters, and broadcasts the current homomorphic commitment vector corresponding to the current task to be processed parameters and the corresponding current zero-knowledge range proof to the collaborative network. If the second agent in the collaborative network, acting as the responder, determines that the computing power requirement parameters corresponding to the current homomorphic commitment vector and the current zero-knowledge range proof do not exceed the upper limit of the second agent's computing power requirement parameters, and the response time to the current homomorphic commitment vector does not exceed the preset waiting time, then the task acceptance protocol data is generated between the collaborative network and the first agent and updated to the current homomorphic commitment vector. The second agent obtains the current task execution result of the current task to be processed, and determines that the task duration corresponding to the current task execution result satisfies the task completion judgment condition in the current homomorphic commitment vector. Then, the actual benefit allocation coefficient and settlement parameters of the second agent are added to the current homomorphic commitment vector and a settlement zero-knowledge proof is generated accordingly. If the second agent determines that the settlement zero-knowledge proof has passed the validity verification, it sends the current task execution result to the first agent through an encrypted channel with the first agent.

[0005] Secondly, this invention also provides an intelligent agent autonomous settlement system based on blockchain and zero-knowledge proof, which consists of multiple intelligent agents and a collaborative network composed of multiple intelligent agents, with each intelligent agent's corresponding device serving as a blockchain node in the blockchain. In the collaborative network, the first intelligent agent acting as the demand side responds to the task release instruction, obtains the corresponding current task to be processed and the current task to be processed parameters, and broadcasts the current homomorphic commitment vector corresponding to the current task to be processed parameters and the corresponding current zero-knowledge range proof to the collaborative network. If the second agent in the collaborative network, acting as the responder, determines that the computing power requirement parameters corresponding to the current homomorphic commitment vector and the current zero-knowledge range proof do not exceed the upper limit of the second agent's computing power requirement parameters, and the response time to the current homomorphic commitment vector does not exceed the preset waiting time, then the task acceptance protocol data is generated between the collaborative network and the first agent and updated to the current homomorphic commitment vector. The second agent obtains the current task execution result of the current task to be processed, and determines that the task duration corresponding to the current task execution result satisfies the task completion judgment condition in the current homomorphic commitment vector. Then, the actual benefit allocation coefficient and settlement parameters of the second agent are added to the current homomorphic commitment vector and a settlement zero-knowledge proof is generated accordingly. If the second agent determines that the settlement zero-knowledge proof has passed the validity verification, it sends the current task execution result to the first agent through an encrypted channel with the first agent.

[0006] Thirdly, embodiments of the present invention also provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect above.

[0007] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, the computer program including program instructions that, when executed by a processor, can implement the method described in the first aspect above.

[0008] This invention provides a method and system for autonomous settlement by intelligent agents based on blockchain and zero-knowledge proofs. The method includes: a first intelligent agent, acting as a demand party in a collaborative network, responding to a task release instruction, obtaining the corresponding current task to be processed and its parameters, and broadcasting the current homomorphic commitment vector corresponding to the current task parameters and the corresponding current zero-knowledge range proof to the collaborative network; a second intelligent agent, acting as a response party in the collaborative network, determines that the computing power requirement parameters corresponding to the current homomorphic commitment vector and the current zero-knowledge range proof do not exceed the upper limit of the second intelligent agent's computing power requirement parameters, and that the response time to the current homomorphic commitment vector does not exceed a preset time limit. The waiting time is calculated by generating task acceptance protocol data between the collaborative network and the first agent, and updating it to the current homomorphic commitment vector. The second agent obtains the current task to be processed, obtains the current task execution result, and determines that the task execution time corresponding to the current task execution result meets the task completion judgment condition in the current homomorphic commitment vector. Then, the actual benefit allocation coefficient and settlement parameters of the second agent are added to the current homomorphic commitment vector, and a corresponding settlement zero-knowledge proof is generated. If the second agent determines that the settlement zero-knowledge proof has passed the validity verification, it sends the current task execution result to the first agent through an encrypted channel with the first agent. In this embodiment of the invention, when the first agent publishes the current task parameters to be processed to the collaborative network, it publishes them using the homomorphic commitment vector and the current zero-knowledge range proof, and after determining that the second agent meets the conditions, the settlement data between the two is also settled and confirmed using the homomorphic commitment vector and the zero-knowledge proof, reducing the amount of shared plaintext data in the whole process and improving data security. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1A schematic diagram illustrating an application scenario of the intelligent agent autonomous settlement method based on blockchain and zero-knowledge proof provided in an embodiment of the present invention; Figure 2 A flowchart illustrating the autonomous settlement method for intelligent agents based on blockchain and zero-knowledge proofs provided in an embodiment of the present invention; Figure 3 A schematic diagram of the first sub-process of the intelligent agent autonomous settlement method based on blockchain and zero-knowledge proof provided in an embodiment of the present invention; Figure 4 Another flowchart illustrating the intelligent agent autonomous settlement method based on blockchain and zero-knowledge proof provided in this embodiment of the invention; Figure 5 A schematic diagram of the second sub-process of the intelligent agent autonomous settlement method based on blockchain and zero-knowledge proof provided in an embodiment of the present invention; Figure 6 A schematic block diagram of a computer device provided for an embodiment of the present invention. Detailed Implementation

[0011] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0012] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0013] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0014] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0015] Please also refer to Figure 1 and Figure 2 ,in Figure 1This is a schematic diagram illustrating an application scenario of the intelligent agent autonomous settlement method based on blockchain and zero-knowledge proof according to an embodiment of the present invention. Figure 2 This is a flowchart illustrating the autonomous settlement method for intelligent agents based on blockchain and zero-knowledge proofs provided in an embodiment of the present invention. Figure 1 As shown, the intelligent agent autonomous settlement method based on blockchain and zero-knowledge proof provided in this embodiment of the invention is applied to an intelligent agent autonomous settlement system based on blockchain and zero-knowledge proof. The intelligent agent autonomous settlement system includes multiple intelligent agents 11, and these agents form a collaborative network 10. Each intelligent agent 11 corresponds to a device 20, which serves as a blockchain node in the blockchain. The device 20 corresponding to each intelligent agent 11 can be a data center, server, etc. Figure 2 As shown, the method includes the following steps S110 to S140.

[0016] S110. In the collaborative network, the first agent acting as the demand side responds to the task release instruction, obtains the corresponding current task to be processed and the parameters of the current task to be processed, and broadcasts the current homomorphic commitment vector corresponding to the parameters of the current task to be processed and the corresponding current zero-knowledge range proof to the collaborative network.

[0017] In this embodiment, to fully utilize the computing resources among multiple intelligent agents in a collaborative network, each intelligent agent's corresponding device (such as a data center, server, etc.) can be used as a blockchain node in the blockchain. Then, when any intelligent agent corresponding to a blockchain node in the collaborative network receives a current task to be processed, for example, if the first intelligent agent (the requesting party) determines locally that its remaining computing resources are insufficient to support the processing of the current task, in order to process the task promptly, it can obtain the current task parameters (such as all parameters including computing power specifications, maximum allowable processing time, and revenue distribution coefficient; more specifically, the current task parameters include computing power specifications of 8 vCPUs, a maximum allowable processing time of 4 hours, and a revenue distribution coefficient ranging from 0.25 to 0.3). To avoid broadcasting the current task parameters in plaintext directly in the collaborative network, we can construct the current homomorphic commitment vector by performing additive homomorphic commitment, generate the current zero-knowledge range proof corresponding to the current homomorphic commitment vector, and then broadcast it to the collaborative network in a non-plaintext manner.

[0018] In one embodiment, such as Figure 3 As shown, step S110 includes: S111. Obtain the computing power specification parameters, the maximum allowable processing time of the task, and the revenue distribution coefficient from the parameters of the current task to be processed, and randomly generate a first random blinding factor, a second random blinding factor, and a third random blinding factor locally for the computing power specification parameters, the maximum allowable processing time of the task, and the revenue distribution coefficient, respectively. S112. Based on the cryptographic group generator and public key parameters corresponding to the preset additive homomorphic commitment construction model, the computing power specification parameters, the maximum allowable processing time of the task, the revenue distribution coefficient, and the corresponding first random blinding factor, second random blinding factor, and third random blinding factor, construct a first commitment value corresponding to the computing power specification parameters, a second commitment value corresponding to the maximum allowable processing time of the task, and a third commitment value corresponding to the revenue distribution coefficient. S113. The current homomorphic commitment vector is formed sequentially from the first commitment value, the second commitment value, and the third commitment value. S114. Generate corresponding zero-knowledge scope proofs from the first commitment value, the second commitment value, and the third commitment value, respectively, to form the current zero-knowledge scope proof.

[0019] In this embodiment, in the first device corresponding to the first intelligent agent, which is one of the blockchain nodes, in order to avoid uploading task parameters in plaintext, it can first specifically obtain the computing power specification parameter A, the maximum allowable processing time B, and the revenue distribution coefficient C in the current task parameters to be processed, and locally generate a first random blinding factor r1, a second random blinding factor r2, and a third random blinding factor r3 for the computing power specification parameter, the maximum allowable processing time B, and the revenue distribution coefficient, respectively, wherein the values ​​of r1, r2, and r3 are all in the range of 0 to 1. Then, on the first device, the cryptographic group generator g and public key parameter h corresponding to the additive homomorphic commitment model are constructed locally. Combined with the previously obtained computing power specification parameter A, the maximum allowable processing time B, the revenue distribution coefficient C, and the corresponding first random blinding factor r1, second random blinding factor r2, and third random blinding factor r3, a first commitment value PA corresponding to the computing power specification parameter A, a second commitment value PB corresponding to the maximum allowable processing time B, and a third commitment value PC corresponding to the revenue distribution coefficient C are constructed. Specifically, PA = g. A ·h r1 PB=g B ·h r2 And PC=g C ·h r3Then, the current homomorphic commitment vector is composed of (PA, PB, PC), and the current zero-knowledge scope proof is generated based on the Bulletproofs zero-knowledge scope proof generation model for the first commitment value PA, the second commitment value PB, and the third commitment value PC. The first zero-knowledge scope proof π corresponding to the first commitment value PA in the current zero-knowledge scope proof is then used. PA (It is used to prove that the computing power specification parameter A bound to the first commitment value belongs to the interval A1~A2), and the second zero-knowledge range proof π corresponding to the second commitment value PB. PB (It is used to prove that the maximum processing time B of the task bound to the second commitment value belongs to the interval B1~B2), and the third zero-knowledge range proof π corresponding to the third commitment value PC. PC (It is used to prove that the benefit distribution coefficient C bound to the third commitment value belongs to the interval C1~C2). Finally, the message broadcast by the first agent to the cooperative network can be represented as M1={(PA, PB, PC), (π PA , π PB , π PC Furthermore, the sender's distributed identity ID1 corresponding to the first agent can be added to the above message. This method ensures that the parameters of the current task to be processed are not broadcast in plaintext within the cooperative network, thus improving data security.

[0020] S120. If the second agent in the cooperative network, acting as the responder, determines that the computing power requirement parameters corresponding to the current homomorphic commitment vector and the current zero-knowledge range proof do not exceed the upper limit of the computing power requirement parameters of the second agent, and the response time to the current homomorphic commitment vector does not exceed the preset waiting time, then the task acceptance protocol data is generated between the cooperative network and the first agent and updated to the current homomorphic commitment vector.

[0021] In this embodiment, since there are multiple blockchain nodes in the collaborative network, the device corresponding to the agent deployed in each blockchain node, after obtaining the current homomorphic commitment vector and the current zero-knowledge range proof, performs a non-plaintext verification based on its own device's remaining computing power specifications, the maximum allowable processing time of the task, and the acceptable revenue distribution coefficient to confirm whether it can accept the current pending task issued by the first agent. Moreover, in order to quickly reach an agreement, it is necessary to select the agents deployed in the devices corresponding to the blockchain nodes whose response time to the current homomorphic commitment vector does not exceed the preset waiting time, and the agents determine that the computing power requirement parameters corresponding to the current homomorphic commitment vector and the current zero-knowledge range proof do not exceed the upper limit of the computing power requirement parameters of the second agent. Only when the response time and computing power requirement meet the corresponding conditions can the task acceptance protocol data be generated with the first agent and updated to the current homomorphic commitment vector. The task acceptance protocol data includes joint signature data between the first intelligent agent and the second intelligent agent, and the joint signature data includes a globally unique protocol ID, the intelligent agent identity identifier of the first intelligent agent in the collaborative network, the intelligent agent identity identifier of the second intelligent agent in the collaborative network, and information such as task completion judgment conditions.

[0022] In one embodiment, such as Figure 4 As shown, the procedure before step S120 also includes: S1201. Call the locally preset zero-knowledge range proof verification model that corresponds to the zero-knowledge range proof generation model. S1202. The cryptographic group generator and public key parameters corresponding to the additive homomorphic commitment construction model, the current homomorphic commitment vector, and the current zero-knowledge range proof are input into the zero-knowledge range proof verification model to obtain the current zero-knowledge range verification result. S1203. If it is determined that the current zero-knowledge scope verification result corresponds to the computing power requirement parameter of the first agent that does not exceed the upper limit of the computing power requirement parameter of the second agent, and the response time to the current homomorphic commitment vector does not exceed the preset waiting time, then the task acceptance protocol data is generated between the cooperative network and the first agent and updated to the current homomorphic commitment vector.

[0023] In this embodiment, before selecting a second agent in the cooperative network, all agents in the cooperative network other than the first agent can first call a locally preset zero-knowledge scope proof verification model corresponding to the zero-knowledge scope proof generation model (such as the Bulletproof verification model corresponding to the Bulletproofs zero-knowledge scope proof generation model). Then, the cryptographic group generator g and public key parameter h corresponding to the additive homomorphic commitment construction model, the current homomorphic commitment vector, and the current zero-knowledge scope proof are input into the zero-knowledge scope proof verification model to obtain the current zero-knowledge scope verification result.

[0024] For example, taking one agent in a collaborative network as an example, after obtaining the current zero-knowledge scope verification result corresponding to the current homomorphic commitment vector and the current zero-knowledge scope proof based on the zero-knowledge scope proof verification model, if it is determined that the first zero-knowledge scope proof π corresponding to the first commitment value in the current zero-knowledge scope proof... PA The upper limit A2 of the bound interval A1~A2 does not exceed the remaining computing power specification parameters of its own device (which can also be regarded as the upper limit of the computing power requirement parameters of the second intelligent agent), and determines the second zero-knowledge range proof π corresponding to the second commitment value in the current zero-knowledge range proof. PB The upper limit B2 of the bound interval B1~B2 does not exceed the maximum allowable processing time of the task on its own device, and the third zero-knowledge range proof π corresponding to the third commitment value in the current zero-knowledge range proof is determined. PC If the upper limit C2 of the bound interval C1~C2 does not exceed the acceptable benefit allocation coefficient of the device itself, and the response time to the current homomorphic commitment vector does not exceed the preset waiting time, then it can be determined that the agent can be selected as the second agent. If multiple agents meet the above conditions, the third zero-knowledge scope proof π in the current zero-knowledge scope proof is used. PC The agent with the smallest corresponding upper limit value is currently selected as the second agent.

[0025] In one embodiment, such as Figure 5 As shown, step S1202 includes: S12021. Obtain the first commitment value in the current homomorphic commitment vector, the first zero-knowledge scope proof in the current zero-knowledge scope proof and the corresponding first zero-knowledge scope, and input the first commitment value, the first zero-knowledge scope proof and the first zero-knowledge scope into the zero-knowledge scope proof verification model to obtain the first zero-knowledge scope verification result. S12022: Obtain the second commitment value in the current homomorphic commitment vector, the second zero-knowledge scope proof in the current zero-knowledge scope proof, and the corresponding second zero-knowledge scope, and input the second commitment value, the second zero-knowledge scope proof, and the second zero-knowledge scope into the zero-knowledge scope proof verification model to obtain the second zero-knowledge scope verification result; S12023. Obtain the third commitment value in the current homomorphic commitment vector, the third zero-knowledge scope proof in the current zero-knowledge scope proof, and the corresponding third zero-knowledge scope, and input the third commitment value, the third zero-knowledge scope proof, and the third zero-knowledge scope into the zero-knowledge scope proof verification model to obtain the third zero-knowledge scope verification result. S12024. If it is determined that the first zero-knowledge range verification result, the second zero-knowledge range verification result, and the third zero-knowledge range verification result are all verification pass results, then the verification pass result is taken as the current zero-knowledge range verification result, and the computing power requirement parameter of the first agent does not exceed the upper limit of the computing power requirement parameter of the second agent.

[0026] In this embodiment, if at least one of the first, second, and third zero-knowledge scope verification results fails, then the result that fails to pass verification is taken as the current zero-knowledge scope verification result. Specifically, when performing the above operation in agents other than the first agent in the cooperative network, the three commitment values ​​in the current homomorphic commitment vector and the three zero-knowledge scope proofs in the current zero-knowledge scope proof can be verified step by step. Only when all three verifications pass can the overall verification be considered successful and the corresponding agent be capable of undertaking the current pending task issued by the first agent; otherwise, the verification is considered unsuccessful and the corresponding agent cannot undertake the current pending task issued by the first agent.

[0027] In one embodiment, the method further includes the following step before step S1201: Obtain the sender's distributed identity identifier of the first intelligent agent and verify it accordingly. If it is determined that the sender's distributed identity identifier belongs to the registered intelligent agent identity identifier of the first intelligent agent in the cooperative network, then the identity verification of the first intelligent agent is deemed to be successful.

[0028] In this embodiment, before any blockchain node in the collaborative network calls its locally preset zero-knowledge range proof verification model, which corresponds to the zero-knowledge range proof generation model, to verify the first intelligent agent's current homomorphic commitment vector and the current zero-knowledge range proof, it can also verify the first intelligent agent's sender distributed identity identifier. Only when it is determined that the sender distributed identity identifier belongs to the first intelligent agent's registered intelligent agent identity identifier in the collaborative network can the first intelligent agent's identity verification be deemed successful, and other intelligent agents in the collaborative network can then proceed with subsequent operations. If it is determined that the sender distributed identity identifier does not belong to the first intelligent agent's registered intelligent agent identity identifier in the collaborative network, then all intelligent agents in the collaborative network can cease all processing and do not need to respond to the first intelligent agent's currently pending task.

[0029] S130. The second agent obtains the current task execution result of the current task to be processed, and determines that the task duration corresponding to the current task execution result satisfies the task completion judgment condition in the current homomorphic commitment vector. Then, the actual benefit allocation coefficient and settlement parameters of the second agent are added to the current homomorphic commitment vector and a settlement zero-knowledge proof is generated accordingly.

[0030] In this embodiment, after the second agent is selected, it acts as the actual executor of the current task to be processed issued by the first agent. It can receive the current task to be processed in plaintext form sent by the first agent and then execute it accordingly to obtain the current task execution result. Since the current homomorphic commitment vector received by the second agent has been updated with task acceptance protocol data between the first and second agents, including task completion judgment conditions for the current task to be processed (such as setting the task completion time to not exceed the maximum allowable processing time), if the task duration corresponding to the current task execution result satisfies the task completion judgment conditions stored in plaintext form in the current homomorphic commitment vector, it indicates that the second agent has successfully completed the execution of the current task to be processed within the specified time limit and output the current task execution result. In the second agent, a settlement zero-knowledge proof is generated based on the current homomorphic commitment vector. Alternatively, the Bulletproofs zero-knowledge range proof generation model can be used. In this case, the actual revenue allocation coefficient and settlement parameters of the second agent (which can be obtained by multiplying the actual revenue allocation coefficient of the second agent with its computing power specification parameters and the maximum allowable processing time of the task) need to be added to the current homomorphic commitment vector. After updating the current homomorphic commitment vector again, the settlement zero-knowledge proof is generated again by combining the Bulletproofs zero-knowledge range proof generation model.

[0031] S140. If the second intelligent agent determines that the settlement zero-knowledge proof has passed the validity verification, it sends the current task execution result to the first intelligent agent through an encrypted channel with the first intelligent agent.

[0032] Wherein, the second intelligent agent inputs into the zero-knowledge scope proof verification model in the blockchain node that is a verification node in the local area or in the collaborative network to obtain the settlement zero-knowledge verification result, and if the zero-knowledge verification result is confirmed as a verification result, the settlement zero-knowledge proof is determined to have passed the validity verification.

[0033] In this embodiment, if the second intelligent agent determines that the settlement zero-knowledge proof has passed the validity verification, it indicates that the second intelligent agent can send the current task execution result to the first intelligent agent through an encrypted channel with the first intelligent agent. Furthermore, the blockchain node acting as the settlement node in the collaborative network can further obtain encrypted settlement parameters generated based on the settlement parameters using a homomorphic encryption algorithm from the second intelligent agent as the settlement amount, and perform homomorphic blind updates on the balances of both the first and second intelligent agents, making the amount calculation process continuously invisible.

[0034] As can be seen, the implementation of this method enables the first agent to publish the current task parameters to the cooperative network, and after the second agent is published and the conditions are determined by homomorphic commitment vector and current zero-knowledge scope proof, the settlement data between the two is also settled and confirmed by homomorphic commitment vector and zero-knowledge proof, which reduces the amount of shared plaintext data in the whole process and improves data security.

[0035] Corresponding to the above-described agent-based autonomous settlement method using blockchain and zero-knowledge proofs, this invention also provides an agent-based autonomous settlement system using blockchain and zero-knowledge proofs. This agent-based autonomous settlement system includes a unit for executing the aforementioned agent-based autonomous settlement method using blockchain and zero-knowledge proofs. Please refer again. Figure 1 The intelligent agent autonomous settlement system based on blockchain and zero-knowledge proof includes multiple intelligent agents 11 and a collaborative network 10 composed of multiple intelligent agents 11. The device 20 corresponding to each intelligent agent 11 serves as a blockchain node in the blockchain, and the device 20 corresponding to each intelligent agent 11 is a data center, server, etc.

[0036] In the collaborative network, the first intelligent agent acting as the demand side is used to respond to the task release instruction, obtain the corresponding current task to be processed and the parameters of the current task to be processed, and broadcast the current homomorphic commitment vector corresponding to the parameters of the current task to be processed and the corresponding current zero-knowledge range proof to the collaborative network.

[0037] In this embodiment, to fully utilize the computing resources among multiple intelligent agents in a collaborative network, each intelligent agent's corresponding device (such as a data center, server, etc.) can be used as a blockchain node in the blockchain. Then, when any intelligent agent corresponding to a blockchain node in the collaborative network receives a current task to be processed, for example, if the first intelligent agent (the requesting party) determines locally that its remaining computing resources are insufficient to support the processing of the current task, in order to process the task promptly, it can obtain the current task parameters (such as all parameters including computing power specifications, maximum allowable processing time, and revenue distribution coefficient; more specifically, the current task parameters include computing power specifications of 8 vCPUs, a maximum allowable processing time of 4 hours, and a revenue distribution coefficient ranging from 0.25 to 0.3). To avoid broadcasting the current task parameters in plaintext directly in the collaborative network, we can construct the current homomorphic commitment vector by performing additive homomorphic commitment, generate the current zero-knowledge range proof corresponding to the current homomorphic commitment vector, and then broadcast it to the collaborative network in a non-plaintext manner.

[0038] In one embodiment, the first intelligent agent is specifically used for: Obtain the computing power specification parameters, the maximum allowable processing time of the task, and the revenue distribution coefficient from the parameters of the current task to be processed, and randomly generate a first random blinding factor, a second random blinding factor, and a third random blinding factor locally for the computing power specification parameters, the maximum allowable processing time of the task, and the revenue distribution coefficient, respectively. Based on the cryptographic group generator and public key parameters corresponding to the preset additive homomorphic commitment construction model, the computing power specification parameters, the maximum allowable processing time of the task, the revenue distribution coefficient, and the corresponding first random blinding factor, second random blinding factor, and third random blinding factor, a first commitment value corresponding to the computing power specification parameters, a second commitment value corresponding to the maximum allowable processing time of the task, and a third commitment value corresponding to the revenue distribution coefficient are constructed. The current homomorphic commitment vector is formed sequentially by the first commitment value, the second commitment value, and the third commitment value; The first commitment value, the second commitment value, and the third commitment value are used to generate corresponding zero-knowledge scope proofs, which together form the current zero-knowledge scope proof.

[0039] In this embodiment, in the first device corresponding to the first intelligent agent, which is one of the blockchain nodes, in order to avoid uploading task parameters in plaintext, it can first specifically obtain the computing power specification parameter A, the maximum allowable processing time B, and the revenue distribution coefficient C in the current task parameters to be processed, and locally generate a first random blinding factor r1, a second random blinding factor r2, and a third random blinding factor r3 for the computing power specification parameter, the maximum allowable processing time B, and the revenue distribution coefficient, respectively, wherein the values ​​of r1, r2, and r3 are all in the range of 0 to 1. Then, on the first device, the cryptographic group generator g and public key parameter h corresponding to the additive homomorphic commitment model are constructed locally. Combined with the previously obtained computing power specification parameter A, the maximum allowable processing time B, the revenue distribution coefficient C, and the corresponding first random blinding factor r1, second random blinding factor r2, and third random blinding factor r3, a first commitment value PA corresponding to the computing power specification parameter A, a second commitment value PB corresponding to the maximum allowable processing time B, and a third commitment value PC corresponding to the revenue distribution coefficient C are constructed. Specifically, PA = g. A ·h r1 PB=g B ·h r2 And PC=g C ·h r3 Then, the current homomorphic commitment vector is composed of (PA, PB, PC), and the current zero-knowledge scope proof is generated based on the Bulletproofs zero-knowledge scope proof generation model for the first commitment value PA, the second commitment value PB, and the third commitment value PC. The first zero-knowledge scope proof π corresponding to the first commitment value PA in the current zero-knowledge scope proof is then used. PA (It is used to prove that the computing power specification parameter A bound to the first commitment value belongs to the interval A1~A2), and the second zero-knowledge range proof π corresponding to the second commitment value PB. PB (It is used to prove that the maximum processing time B of the task bound to the second commitment value belongs to the interval B1~B2), and the third zero-knowledge range proof π corresponding to the third commitment value PC. PC (It is used to prove that the benefit distribution coefficient C bound to the third commitment value belongs to the interval C1~C2). Finally, the message broadcast by the first agent to the cooperative network can be represented as M1={(PA, PB, PC), (π PA , π PB , π PC Furthermore, the sender's distributed identity ID1 corresponding to the first agent can be added to the above message. This method ensures that the parameters of the current task to be processed are not broadcast in plaintext within the cooperative network, thus improving data security.

[0040] In the collaborative network, the second agent, acting as the responder, is configured to generate task acceptance protocol data between the collaborative network and the first agent and update the current homomorphic commitment vector if it is determined that the computing power requirement parameters corresponding to the current homomorphic commitment vector and the current zero-knowledge range proof do not exceed the upper limit of the computing power requirement parameters of the second agent, and the response time to the current homomorphic commitment vector does not exceed the preset waiting time.

[0041] In this embodiment, since there are multiple blockchain nodes in the collaborative network, the device corresponding to the agent deployed in each blockchain node, after obtaining the current homomorphic commitment vector and the current zero-knowledge range proof, performs a non-plaintext verification based on its own device's remaining computing power specifications, the maximum allowable processing time of the task, and the acceptable revenue distribution coefficient to confirm whether it can accept the current pending task issued by the first agent. Moreover, in order to quickly reach an agreement, it is necessary to select the agents deployed in the devices corresponding to the blockchain nodes whose response time to the current homomorphic commitment vector does not exceed the preset waiting time, and the agents determine that the computing power requirement parameters corresponding to the current homomorphic commitment vector and the current zero-knowledge range proof do not exceed the upper limit of the computing power requirement parameters of the second agent. Only when the response time and computing power requirement meet the corresponding conditions can the task acceptance protocol data be generated with the first agent and updated to the current homomorphic commitment vector. The task acceptance protocol data includes joint signature data between the first intelligent agent and the second intelligent agent, and the joint signature data includes a globally unique protocol ID, the intelligent agent identity identifier of the first intelligent agent in the collaborative network, the intelligent agent identity identifier of the second intelligent agent in the collaborative network, and information such as task completion judgment conditions.

[0042] In one embodiment, the agents in the cooperative network other than the first agent are further used for: Call the locally preset zero-knowledge scope proof verification model that corresponds to the zero-knowledge scope proof generation model; The cryptographic group generator and public key parameters corresponding to the additive homomorphic commitment construction model, the current homomorphic commitment vector, and the current zero-knowledge range proof are input into the zero-knowledge range proof verification model to obtain the current zero-knowledge range verification result. If it is determined that the current zero-knowledge scope verification result corresponds to the computing power requirement parameter of the first agent not exceeding the upper limit of the computing power requirement parameter of the second agent, and the response time to the current homomorphic commitment vector does not exceed the preset waiting time, then the task acceptance protocol data is generated between the cooperative network and the first agent and updated to the current homomorphic commitment vector.

[0043] In this embodiment, before selecting a second agent in the cooperative network, all agents in the cooperative network other than the first agent can first call a locally preset zero-knowledge scope proof verification model corresponding to the zero-knowledge scope proof generation model (such as the Bulletproof verification model corresponding to the Bulletproofs zero-knowledge scope proof generation model). Then, the cryptographic group generator g and public key parameter h corresponding to the additive homomorphic commitment construction model, the current homomorphic commitment vector, and the current zero-knowledge scope proof are input into the zero-knowledge scope proof verification model to obtain the current zero-knowledge scope verification result.

[0044] For example, taking one agent in a collaborative network as an example, after obtaining the current zero-knowledge scope verification result corresponding to the current homomorphic commitment vector and the current zero-knowledge scope proof based on the zero-knowledge scope proof verification model, if it is determined that the first zero-knowledge scope proof π corresponding to the first commitment value in the current zero-knowledge scope proof... PA The upper limit A2 of the bound interval A1~A2 does not exceed the remaining computing power specification parameters of its own device (which can also be regarded as the upper limit of the computing power requirement parameters of the second intelligent agent), and determines the second zero-knowledge range proof π corresponding to the second commitment value in the current zero-knowledge range proof. PB The upper limit B2 of the bound interval B1~B2 does not exceed the maximum allowable processing time of the task on its own device, and the third zero-knowledge range proof π corresponding to the third commitment value in the current zero-knowledge range proof is determined. PC If the upper limit C2 of the bound interval C1~C2 does not exceed the acceptable benefit allocation coefficient of the device itself, and the response time to the current homomorphic commitment vector does not exceed the preset waiting time, then it can be determined that the agent can be selected as the second agent. If multiple agents meet the above conditions, the third zero-knowledge scope proof π in the current zero-knowledge scope proof is used. PC The agent with the smallest corresponding upper limit value is currently selected as the second agent.

[0045] In one embodiment, the cryptographic group generator and public key parameters corresponding to the additive homomorphic commitment construction model, the current homomorphic commitment vector, and the current zero-knowledge range proof are input into the zero-knowledge range proof verification model to obtain the current zero-knowledge range verification result, including: Obtain the first commitment value in the current homomorphic commitment vector, the first zero-knowledge scope proof in the current zero-knowledge scope proof, and the corresponding first zero-knowledge scope; and input the first commitment value, the first zero-knowledge scope proof, and the first zero-knowledge scope into the zero-knowledge scope proof verification model to obtain the first zero-knowledge scope verification result. Obtain the second commitment value in the current homomorphic commitment vector, the second zero-knowledge scope proof in the current zero-knowledge scope proof, and the corresponding second zero-knowledge scope. Input the second commitment value, the second zero-knowledge scope proof, and the second zero-knowledge scope into the zero-knowledge scope proof verification model to obtain the second zero-knowledge scope verification result. Obtain the third commitment value in the current homomorphic commitment vector, the third zero-knowledge scope proof in the current zero-knowledge scope proof, and the corresponding third zero-knowledge scope. Input the third commitment value, the third zero-knowledge scope proof, and the third zero-knowledge scope into the zero-knowledge scope proof verification model to obtain the third zero-knowledge scope verification result. If the first zero-knowledge range verification result, the second zero-knowledge range verification result, and the third zero-knowledge range verification result are all verified as passed, then the verified as passed is taken as the current zero-knowledge range verification result, and the computing power requirement parameter of the first agent does not exceed the upper limit of the computing power requirement parameter of the second agent.

[0046] In this embodiment, if at least one of the first, second, and third zero-knowledge scope verification results fails, then the result that fails to pass verification is taken as the current zero-knowledge scope verification result. Specifically, when performing the above operation in agents other than the first agent in the cooperative network, the three commitment values ​​in the current homomorphic commitment vector and the three zero-knowledge scope proofs in the current zero-knowledge scope proof can be verified step by step. Only when all three verifications pass can the overall verification be considered successful and the corresponding agent be capable of undertaking the current pending task issued by the first agent; otherwise, the verification is considered unsuccessful and the corresponding agent cannot undertake the current pending task issued by the first agent.

[0047] In one embodiment, the collaborative network is further used for: Obtain the sender's distributed identity identifier of the first intelligent agent and verify it accordingly. If it is determined that the sender's distributed identity identifier belongs to the registered intelligent agent identity identifier of the first intelligent agent in the cooperative network, then the identity verification of the first intelligent agent is deemed to be successful.

[0048] In this embodiment, before any blockchain node in the collaborative network calls its locally preset zero-knowledge range proof verification model, which corresponds to the zero-knowledge range proof generation model, to verify the first intelligent agent's current homomorphic commitment vector and the current zero-knowledge range proof, it can also verify the first intelligent agent's sender distributed identity identifier. Only when it is determined that the sender distributed identity identifier belongs to the first intelligent agent's registered intelligent agent identity identifier in the collaborative network can the first intelligent agent's identity verification be deemed successful, and other intelligent agents in the collaborative network can then proceed with subsequent operations. If it is determined that the sender distributed identity identifier does not belong to the first intelligent agent's registered intelligent agent identity identifier in the collaborative network, then all intelligent agents in the collaborative network can cease all processing and do not need to respond to the first intelligent agent's currently pending task.

[0049] The second agent is used to obtain the current task execution result of the current task to be processed, and to determine that the task duration corresponding to the current task execution result meets the task completion judgment condition in the current homomorphic commitment vector. Then, the actual revenue allocation coefficient and settlement parameters of the second agent are added to the current homomorphic commitment vector and a settlement zero-knowledge proof is generated accordingly.

[0050] In this embodiment, after the second agent is selected, it acts as the actual executor of the current task to be processed issued by the first agent. It can receive the current task to be processed in plaintext form sent by the first agent and then execute it accordingly to obtain the current task execution result. Since the current homomorphic commitment vector received by the second agent has been updated with task acceptance protocol data between the first and second agents, including task completion judgment conditions for the current task to be processed (such as setting the task completion time to not exceed the maximum allowable processing time), if the task duration corresponding to the current task execution result satisfies the task completion judgment conditions stored in plaintext form in the current homomorphic commitment vector, it indicates that the second agent has successfully completed the execution of the current task to be processed within the specified time limit and output the current task execution result. In the second agent, a settlement zero-knowledge proof is generated based on the current homomorphic commitment vector. Alternatively, the Bulletproofs zero-knowledge range proof generation model can be used. In this case, the actual revenue allocation coefficient and settlement parameters of the second agent (which can be obtained by multiplying the actual revenue allocation coefficient of the second agent with its computing power specification parameters and the maximum allowable processing time of the task) need to be added to the current homomorphic commitment vector. After updating the current homomorphic commitment vector again, the settlement zero-knowledge proof is generated again by combining the Bulletproofs zero-knowledge range proof generation model.

[0051] The second intelligent agent is further configured to, if it is determined that the settlement zero-knowledge proof has passed the validity verification, send the current task execution result to the first intelligent agent through an encrypted channel with the first intelligent agent.

[0052] Wherein, the second intelligent agent inputs into the zero-knowledge scope proof verification model in the blockchain node that is a verification node in the local area or in the collaborative network to obtain the settlement zero-knowledge verification result, and if the zero-knowledge verification result is confirmed as a verification result, the settlement zero-knowledge proof is determined to have passed the validity verification.

[0053] In this embodiment, if the second intelligent agent determines that the settlement zero-knowledge proof has passed the validity verification, it indicates that the second intelligent agent can send the current task execution result to the first intelligent agent through an encrypted channel with the first intelligent agent. Furthermore, the blockchain node acting as the settlement node in the collaborative network can further obtain encrypted settlement parameters generated based on the settlement parameters using a homomorphic encryption algorithm from the second intelligent agent as the settlement amount, and perform homomorphic blind updates on the balances of both the first and second intelligent agents, making the amount calculation process continuously invisible.

[0054] As can be seen, the implementation of this system enables the first agent to publish the parameters of the current task to be processed to the cooperative network, and after the second agent who meets the conditions is published and determined by homomorphic commitment vector and current zero-knowledge scope proof, the settlement data between the two is also settled and confirmed by homomorphic commitment vector and zero-knowledge proof, which reduces the amount of shared plaintext data in the whole process and improves data security.

[0055] The aforementioned intelligent agent autonomous settlement system based on blockchain and zero-knowledge proofs can be implemented as a computer program, which can, for example... Figure 6 It runs on the computer device shown.

[0056] Please see Figure 6 , Figure 6 This is a schematic block diagram of a computer device provided in an embodiment of the present invention. The computer device integrates any of the intelligent agent autonomous settlement systems based on blockchain and zero-knowledge proofs provided in the embodiments of the present invention.

[0057] See Figure 6 The computer device 400 includes a processor 402, a memory, and a network interface 405 connected via a system bus 401. The memory may include a storage medium 403 and internal memory 404.

[0058] The storage medium 403 may store an operating system 4031 and a computer program 4032. The computer program 4032 includes program instructions that, when executed, cause the processor 402 to execute an agent-based autonomous settlement method based on blockchain and zero-knowledge proofs.

[0059] The processor 402 provides computing and control capabilities to support the operation of the entire computer device.

[0060] The internal memory 404 provides an environment for the computer program 4032 in the storage medium 403 to run. When the computer program 4032 is executed by the processor 402, the processor 402 can execute the above-mentioned intelligent agent autonomous settlement method based on blockchain and zero-knowledge proof.

[0061] This network interface 405 is used for network communication with other devices. Those skilled in the art will understand that... Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0062] The processor 402 is used to run the computer program 4032 stored in the memory to implement the intelligent agent autonomous settlement method based on blockchain and zero-knowledge proof as described above.

[0063] It should be understood that, in this embodiment of the invention, the processor 402 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0064] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program includes program instructions and can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0065] Therefore, the present invention also provides a computer-readable storage medium. This computer-readable storage medium stores a computer program, wherein the computer program includes program instructions. When executed by a processor, the program instructions cause the processor to perform the blockchain-based autonomous settlement method for intelligent agents as described above.

[0066] The storage medium can be any computer-readable storage medium that can store program code, such as a USB flash drive, external hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0067] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0068] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of each unit is merely a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0069] The steps in the method of this invention can be adjusted, merged, or reduced in order according to actual needs. The units in the device of this invention can be merged, divided, or reduced according to actual needs. Furthermore, the functional units in the various embodiments of this invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0070] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0071] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for autonomous settlement of intelligent agents based on blockchain and zero-knowledge proofs, applied to an autonomous settlement system for intelligent agents based on blockchain and zero-knowledge proofs, characterized in that, The intelligent agent autonomous settlement system based on blockchain and zero-knowledge proof includes multiple intelligent agents forming a collaborative network, with each intelligent agent's corresponding device serving as a blockchain node; the method includes: In the collaborative network, the first intelligent agent acting as the demand side responds to the task release instruction, obtains the corresponding current task to be processed and the current task to be processed parameters, and broadcasts the current homomorphic commitment vector corresponding to the current task to be processed parameters and the corresponding current zero-knowledge range proof to the collaborative network. If the second agent in the collaborative network, acting as the responder, determines that the computing power requirement parameters corresponding to the current homomorphic commitment vector and the current zero-knowledge range proof do not exceed the upper limit of the second agent's computing power requirement parameters, and the response time to the current homomorphic commitment vector does not exceed the preset waiting time, then the task acceptance protocol data is generated between the collaborative network and the first agent and updated to the current homomorphic commitment vector. The second agent obtains the current task execution result of the current task to be processed, and determines that the task duration corresponding to the current task execution result satisfies the task completion judgment condition in the current homomorphic commitment vector. Then, the actual benefit allocation coefficient and settlement parameters of the second agent are added to the current homomorphic commitment vector and a settlement zero-knowledge proof is generated accordingly. If the second agent determines that the settlement zero-knowledge proof has passed the validity verification, it sends the current task execution result to the first agent through an encrypted channel with the first agent.

2. The method according to claim 1, characterized in that, The step of broadcasting the current homomorphic commitment vector corresponding to the parameters of the current task to be processed and the corresponding current zero-knowledge range proof to the cooperative network includes: Obtain the computing power specification parameters, the maximum allowable processing time of the task, and the revenue distribution coefficient from the parameters of the current task to be processed, and randomly generate a first random blinding factor, a second random blinding factor, and a third random blinding factor locally for the computing power specification parameters, the maximum allowable processing time of the task, and the revenue distribution coefficient, respectively. Based on the cryptographic group generator and public key parameters corresponding to the preset additive homomorphic commitment construction model, the computing power specification parameters, the maximum allowable processing time of the task, the revenue distribution coefficient, and the corresponding first random blinding factor, second random blinding factor, and third random blinding factor, a first commitment value corresponding to the computing power specification parameters, a second commitment value corresponding to the maximum allowable processing time of the task, and a third commitment value corresponding to the revenue distribution coefficient are constructed. The current homomorphic commitment vector is formed sequentially by the first commitment value, the second commitment value, and the third commitment value; The first commitment value, the second commitment value, and the third commitment value are used to generate corresponding zero-knowledge scope proofs, which together form the current zero-knowledge scope proof.

3. The method according to claim 1, characterized in that, If the second agent, acting as the responder in the collaborative network, determines that the computational power requirement parameters corresponding to the current homomorphic commitment vector and the current zero-knowledge range proof do not exceed the upper limit of the second agent's computational power requirement parameters, and the response time to the current homomorphic commitment vector does not exceed a preset waiting time, then before the step of generating task acceptance protocol data between the collaborative network and the first agent and updating it to the current homomorphic commitment vector, the method further includes: Call the locally preset zero-knowledge scope proof verification model that corresponds to the zero-knowledge scope proof generation model; The cryptographic group generator and public key parameters corresponding to the additive homomorphic commitment construction model, the current homomorphic commitment vector, and the current zero-knowledge range proof are input into the zero-knowledge range proof verification model to obtain the current zero-knowledge range verification result. If it is determined that the current zero-knowledge scope verification result corresponds to the computing power requirement parameter of the first agent not exceeding the upper limit of the computing power requirement parameter of the second agent, and the response time to the current homomorphic commitment vector does not exceed the preset waiting time, then the task acceptance protocol data is generated between the cooperative network and the first agent and updated to the current homomorphic commitment vector.

4. The method according to claim 3, characterized in that, The cryptographic group generator and public key parameters corresponding to the additive homomorphic commitment construction model, the current homomorphic commitment vector, and the current zero-knowledge range proof are input into the zero-knowledge range proof verification model to obtain the current zero-knowledge range verification result, including: Obtain the first commitment value in the current homomorphic commitment vector, the first zero-knowledge scope proof in the current zero-knowledge scope proof, and the corresponding first zero-knowledge scope; and input the first commitment value, the first zero-knowledge scope proof, and the first zero-knowledge scope into the zero-knowledge scope proof verification model to obtain the first zero-knowledge scope verification result. Obtain the second commitment value in the current homomorphic commitment vector, the second zero-knowledge scope proof in the current zero-knowledge scope proof, and the corresponding second zero-knowledge scope. Input the second commitment value, the second zero-knowledge scope proof, and the second zero-knowledge scope into the zero-knowledge scope proof verification model to obtain the second zero-knowledge scope verification result. Obtain the third commitment value in the current homomorphic commitment vector, the third zero-knowledge scope proof in the current zero-knowledge scope proof, and the corresponding third zero-knowledge scope. Input the third commitment value, the third zero-knowledge scope proof, and the third zero-knowledge scope into the zero-knowledge scope proof verification model to obtain the third zero-knowledge scope verification result. If the first zero-knowledge range verification result, the second zero-knowledge range verification result, and the third zero-knowledge range verification result are all verified as passed, then the verified as passed is taken as the current zero-knowledge range verification result, and the computing power requirement parameter of the first agent does not exceed the upper limit of the computing power requirement parameter of the second agent.

5. The method according to claim 3, characterized in that, Before the step of invoking the locally preset zero-knowledge range proof verification model corresponding to the zero-knowledge range proof generation model, the method further includes: Obtain the sender's distributed identity identifier of the first intelligent agent and verify it accordingly. If it is determined that the sender's distributed identity identifier belongs to the registered intelligent agent identity identifier of the first intelligent agent in the cooperative network, then the identity verification of the first intelligent agent is deemed to be successful.

6. The method according to claim 2, characterized in that, The task acceptance protocol data includes joint signature data between the first intelligent agent and the second intelligent agent. The joint signature data includes a globally unique protocol ID, the intelligent agent identity identifier of the first intelligent agent in the collaborative network, the intelligent agent identity identifier of the second intelligent agent in the collaborative network, and the task completion judgment conditions.

7. The method according to claim 1, characterized in that, The second intelligent agent inputs the zero-knowledge scope proof verification model into the blockchain node that acts as a verification node in the local area or the collaborative network to obtain the settlement zero-knowledge verification result. If the zero-knowledge verification result is confirmed as a verification pass result, the settlement zero-knowledge proof is determined to have passed the validity verification.

8. An intelligent agent autonomous settlement system based on blockchain and zero-knowledge proof, characterized in that, include: A collaborative network of multiple intelligent agents, with each intelligent agent's corresponding device serving as a blockchain node in the blockchain; In the collaborative network, the first intelligent agent acting as the demand side responds to the task release instruction, obtains the corresponding current task to be processed and the current task to be processed parameters, and broadcasts the current homomorphic commitment vector corresponding to the current task to be processed parameters and the corresponding current zero-knowledge range proof to the collaborative network. If the second agent in the collaborative network, acting as the responder, determines that the computing power requirement parameters corresponding to the current homomorphic commitment vector and the current zero-knowledge range proof do not exceed the upper limit of the second agent's computing power requirement parameters, and the response time to the current homomorphic commitment vector does not exceed the preset waiting time, then the task acceptance protocol data is generated between the collaborative network and the first agent and updated to the current homomorphic commitment vector. The second agent obtains the current task execution result of the current task to be processed, and determines that the task duration corresponding to the current task execution result satisfies the task completion judgment condition in the current homomorphic commitment vector. Then, the actual benefit allocation coefficient and settlement parameters of the second agent are added to the current homomorphic commitment vector and a settlement zero-knowledge proof is generated accordingly. If the second agent determines that the settlement zero-knowledge proof has passed the validity verification, it sends the current task execution result to the first agent through an encrypted channel with the first agent.

9. The intelligent agent autonomous settlement system based on blockchain and zero-knowledge proof according to claim 8, characterized in that, The step of broadcasting the current homomorphic commitment vector corresponding to the parameters of the current task to be processed and the corresponding current zero-knowledge range proof to the cooperative network includes: Obtain the computing power specification parameters, the maximum allowable processing time of the task, and the revenue distribution coefficient from the parameters of the current task to be processed, and randomly generate a first random blinding factor, a second random blinding factor, and a third random blinding factor locally for the computing power specification parameters, the maximum allowable processing time of the task, and the revenue distribution coefficient, respectively. Based on the cryptographic group generator and public key parameters corresponding to the preset additive homomorphic commitment construction model, the computing power specification parameters, the maximum allowable processing time of the task, the revenue distribution coefficient, and the corresponding first random blinding factor, second random blinding factor, and third random blinding factor, a first commitment value corresponding to the computing power specification parameters, a second commitment value corresponding to the maximum allowable processing time of the task, and a third commitment value corresponding to the revenue distribution coefficient are constructed. The current homomorphic commitment vector is formed sequentially by the first commitment value, the second commitment value, and the third commitment value; The first commitment value, the second commitment value, and the third commitment value are used to generate corresponding zero-knowledge scope proofs, which together form the current zero-knowledge scope proof.

10. The intelligent agent autonomous settlement system based on blockchain and zero-knowledge proof according to claim 8, characterized in that, If the second agent, acting as the responder in the collaborative network, determines that the computational power requirement parameters corresponding to the current homomorphic commitment vector and the current zero-knowledge range proof do not exceed the upper limit of the second agent's computational power requirement parameters, and the response time to the current homomorphic commitment vector does not exceed a preset waiting time, then before generating task acceptance protocol data between the collaborative network and the first agent and updating the current homomorphic commitment vector, the process further includes: Call the locally preset zero-knowledge scope proof verification model that corresponds to the zero-knowledge scope proof generation model; The cryptographic group generator and public key parameters corresponding to the additive homomorphic commitment construction model, the current homomorphic commitment vector, and the current zero-knowledge range proof are input into the zero-knowledge range proof verification model to obtain the current zero-knowledge range verification result. If it is determined that the current zero-knowledge scope verification result corresponds to the computing power requirement parameter of the first agent not exceeding the upper limit of the computing power requirement parameter of the second agent, and the response time to the current homomorphic commitment vector does not exceed the preset waiting time, then the task acceptance protocol data is generated between the cooperative network and the first agent and updated to the current homomorphic commitment vector.

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