AI system customization method, system and device based on block chain and medium

Abstract

The invention provides an AI system customization method, system and device based on a block chain and a medium, and belongs to the technical field of computers. The method comprises the following steps: constructing an AI model according to requirements; deploying the AI model on a block chain, and setting a calling rule, a data storage mode and a transaction verification mechanism of the AI model by defining an intelligent contract; performing access control on the AI model and related data by using the key; zero-knowledge proof or anonymization processing is carried out on the sensitive business data, the model training data and the system interaction data; and the AI technology is utilized to monitor the block chain network flow and the system operation state in real time, identify abnormity and optimize the block chain network performance. By fusing the block chain technology, the security deployment of the AI model and the automatic management of the smart contract are realized, the privacy protection of data access is ensured, the AI technology is utilized to monitor and optimize the block chain network performance, and the security, transparency and operation efficiency of the system are significantly improved.

Description

Technical Field

[0001] The present invention belongs to the field of computer technology, and more specifically, relates to a method, system, device and medium for customizing an AI system based on blockchain. Background Art

[0002] In the digital age, artificial intelligence (AI) technology, as a force that cannot be ignored, is profoundly changing the operation models and business processes of enterprises. By deploying AI systems, enterprises can efficiently analyze big data, optimize complex business processes, significantly improve the customer experience, and achieve unprecedented levels of automation and intelligence in multiple fields such as supply chain management, manufacturing, and marketing. The wide application of AI technology has undoubtedly greatly enhanced the market competitiveness and operational efficiency of enterprises.

[0003] However, as enterprises' dependence on AI technology deepens, how to ensure the security and confidentiality of internal enterprise data, especially preventing the leakage of sensitive information, has become an urgent problem to be solved. In the process of AI systems processing big data containing sensitive information such as customer privacy and business secrets, the security and confidentiality of data face severe challenges. Once this data is leaked or misused, it may cause significant economic losses and reputational damage to enterprises.

[0004] In related technologies, although some measures have been taken to protect data security, these measures often have obvious defects. For example, traditional encryption technologies and access control means can prevent data leakage to a certain extent, but in the face of increasingly complex and changeable network attack means, their protection capabilities are stretched. Once the encryption key is cracked, the security guarantee of the data will be completely lost; while access control may fail due to malicious operations by internal personnel or elaborate disguises by external hackers.

[0005] In addition, common data access control and privacy protection mechanisms often lack transparency and traceability. Once data is leaked, it is often difficult for enterprises to track and locate the source of the leak, so they cannot take effective countermeasures in time. This not only increases the risk of data leakage but also brings great troubles to the data security management of enterprises. Summary of the Invention

[0006] Aiming at the above problems, the purpose of the present invention is to provide a method, system, device and medium for customizing an AI system based on blockchain. By deeply integrating blockchain technology, it realizes the secure deployment of AI models and the automated management of smart contracts, ensures strict control of data access and privacy protection, and at the same time uses AI technology to monitor and optimize the performance of the blockchain network, significantly improving the security, transparency and operating efficiency of the system.

[0007] To achieve the above object, the present invention is implemented through the following technical solutions: In a first aspect, an embodiment of the present application provides a method for customizing an AI system based on a blockchain, including: Construct an AI model according to requirements; Deploy the AI model to the blockchain, and set the call rules, data storage method, and transaction verification mechanism of the AI model by defining a smart contract; Use a key to perform access control on the AI model and related data; Perform zero-knowledge proof or anonymization processing on sensitive business data, model training data, and system interaction data; Use AI technology to monitor the blockchain network traffic and system operation status in real time, identify anomalies, and optimize the blockchain network performance.

[0008] In an optional embodiment, the constructing an AI model according to requirements includes: Design the AI model architecture according to the enterprise business characteristics, generate the AI model, and clarify the data processing logic; the AI model architecture includes, but is not limited to, neural networks and decision trees; Customize the training data set of the AI model according to the enterprise scenario; Encrypt the training data set using the AES-256 algorithm, and enable the TLS 1.3 protocol on the transmission channel to perform end-to-end protection of the training data set and interaction data.

[0009] In an optional embodiment, the deploying the AI model to the blockchain and setting the call rules, data storage method, and transaction verification mechanism of the AI model by defining a smart contract includes: Select a blockchain platform according to the enterprise scenario, and deploy the AI model to the blockchain platform; the blockchain platform includes: Ethereum and Hyperledger Fabric; Define the call rules, data storage rules, and transaction verification mechanism of the AI model; Package the AI model as a blockchain interface, and set the AI model to support execution through contract calls; Before each call of the AI model, collect and organize the input data necessary for the execution of the AI model, Upload the input data to the blockchain, and use the hash algorithm to verify the input data; Automatically record the source of the input data and the data processing process through the blockchain; Audit the entire life cycle of the AI model through the blockchain.

[0010] In an optional embodiment, the using a key to perform access control on the AI model and related data includes: Assign a unique public key and private key to each registered user; When a registered user accesses the AI model and data, use the private key for authentication; Use the public key to encrypt the data to be stored and transmitted; Automatically execute the access control logic through a smart contract, record each data access and the usage of the AI model, and regularly check the data usage records.

[0011] In an optional embodiment, the zero-knowledge proof or anonymization processing of sensitive business data, model training data, and system interaction data includes: Classify and uniformly format the sensitive business data, design a circuit to prove specific attributes of the data, generate relevant keys, encrypt the data, and then generate and verify a zero-knowledge proof to confirm the data attributes without exposing the content; Screen and encode the model training data, construct a circuit to prove its training attributes, encrypt the data, and then generate and verify a zero-knowledge proof to ensure the legality and privacy of the training data; Capture and parse the system interaction data, design and optimize a circuit to prove the interaction attributes, encrypt the data, and then generate and verify a zero-knowledge proof in real time to ensure the legality and security of the interaction.

[0012] In an optional embodiment, the zero-knowledge proof or anonymization processing of sensitive business data, model training data, and system interaction data further includes: Based on the sensitive business data, identify sensitive fields and evaluate the sensitivity level, and select a data desensitization or generalization method to process the sensitive fields; Analyze the data characteristics of the model training data, adopt data synthesis or differential privacy technology to transform the data characteristics, and use the transformed model training data to train the AI model and verify its performance; Classify the system interaction data according to the classification strategy, and use a partial hiding or tokenization method to process the classified system interaction data in real time.

[0013] In an optional embodiment, the use of AI technology to real-time monitor the blockchain network traffic and system operation status, identify anomalies and optimize the blockchain network performance includes: Use AI technology to real-time monitor the blockchain network traffic, identify abnormal data packets or unauthorized transmissions, regard the corresponding transmission nodes as suspicious nodes, and isolate them; Use AI technology to analyze the on-chain data patterns, where the data patterns include transaction frequency, block filling rate, and node response time; Dynamically adjust the block size, the timeout time of the consensus mechanism, or the node election strategy according to the on-chain data patterns to optimize the network throughput and response speed.

[0014] In a second aspect, an embodiment of the present application further provides a blockchain-based AI system customization system, including: A model construction module, configured to construct an AI model according to requirements; A blockchain and smart contract integration module, configured to deploy the AI model onto the blockchain, and set the calling rules, data storage method, and transaction verification mechanism of the AI model by defining a smart contract; A dynamic permission control module, configured to perform access control on the AI model and related data by using a key; A privacy enhancement processing module, configured to perform zero-knowledge proof or anonymization processing on sensitive business data, model training data, and system interaction data; A monitoring and optimization module, configured to use AI technology to monitor the blockchain network traffic and system operation status in real time, identify anomalies, and optimize the blockchain network performance.

[0015] In a third aspect, an embodiment of the present application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the blockchain-based AI system customization method described in any one of the above are implemented.

[0016] In a fourth aspect, an embodiment of the present application further provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the blockchain-based AI system customization method described in any one of the above are implemented.

[0017] It can be seen from the above technical solutions that the present invention has the following advantages: In the blockchain-based AI system customization method provided by the present application, by combining the latest AI technology and the characteristics of the blockchain, such as decentralization, immutability, and transparency, a secure data processing platform is provided for enterprises. The system adopts data encryption technology to ensure the security of enterprise internal data during storage and transmission. Through the distributed ledger technology of the blockchain, data is dispersed and stored on multiple nodes of the network, thereby reducing the risk of single-point failure and improving the anti-attack ability of the data. In addition, the system realizes fine-grained data access control through smart contracts, and only authorized users can access specific data resources, which greatly reduces the risk of unauthorized access. The system utilizes the immutability of the blockchain to ensure that the records of each AI call and data processing are securely stored on the blockchain, forming a transparent audit trail. This not only helps enterprises monitor and audit the usage of internal data, but also provides reliable tracking and tracing capabilities in the event of a security incident.

[0018] This application utilizes the decentralized feature of the blockchain to make data tamper-proof and unauthorized access impossible, thereby enhancing the security and privacy protection level of the data.

[0019] Based on the immutability and transparency features of the blockchain, this application makes the operation process of the AI model more traceable and trustworthy. The records of each AI call are stored on the blockchain, which not only enhances the transparency of the data but also improves the trust level in the decision-making process.

[0020] This application ensures the confidentiality of enterprise internal materials. Through the combination of smart contracts and the blockchain, the system can control the access rights of data to ensure that only authorized internal personnel can access sensitive data. Brief Description of the Drawings

[0021] In order to more clearly illustrate the technical solutions of the present invention, the accompanying drawings required for the description will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0022] Figure 1 It is a schematic flowchart of the method for customizing an AI system based on the blockchain provided by this application.

[0023] Figure 2 It is a schematic structural diagram of the system for customizing an AI system based on the blockchain provided by this application.

[0024] Figure 3 It is a schematic structural diagram of the electronic device provided by this application. Detailed Embodiments

[0025] In the following, the specific steps of the method for customizing an AI system based on the blockchain will be described in detail, and various embodiments of the present disclosure will be described more comprehensively. The present disclosure can have various embodiments, and adjustments and changes can be made therein. However, it should be understood that there is no intention to limit the various embodiments of the present disclosure to the specific embodiments disclosed herein, but the present disclosure should be understood to cover all adjustments, equivalents, and / or alternative solutions falling within the spirit and scope of the various embodiments of the present disclosure.

[0026] In the following, the term "comprising" or "may comprise" that can be used in various embodiments of the present disclosure indicates the presence of the disclosed functions, operations or elements, and does not limit the addition of one or more functions, operations or elements. Further, as used in various embodiments of the present disclosure, the terms "comprising", "having" and their cognates are only intended to indicate specific features, numbers, steps, operations, elements, components or combinations of the foregoing items, and should not be construed as first excluding the existence of one or more other features, numbers, steps, operations, elements, components or combinations of the foregoing items or the possibility of adding one or more features, numbers, steps, operations, elements, components or combinations of the foregoing items.

[0027] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0028] Please refer to Figure 1 The following is a method flowchart of a method for customizing an AI system based on blockchain in a specific embodiment. The method includes: S1: Construct an AI model according to requirements.

[0029] In this step, the company needs to design and implement a specific AI model according to its specific requirements to ensure that the AI model can correctly process the internal data of the company. This includes strict control over the quality management, representativeness, accuracy, and integrity of the data to avoid data deviation and errors and ensure that the model can correctly process the internal data of the company. Specifically, the training data set can be customized according to the specific business and data characteristics of different companies. These data sets usually contain sensitive information of the company, such as customer data, financial information, etc., which are the company's privacy and confidential information. During the data transmission and storage process, the data is encrypted using the Advanced Encryption Standard (AES-256) algorithm to ensure the security of the data and prevent the data from being accessed or leaked without authorization.

[0030] For example, first, design the AI model architecture according to the enterprise business characteristics, generate the AI model, and clarify the data processing logic; the AI model architecture includes but is not limited to: neural network and decision tree; the data processing logic includes data cleaning, feature extraction and other processing methods. The fundamental purpose is to avoid data deviation.

[0031] Then, customize the training data set of the AI model according to the enterprise scenario; the training data set can include sensitive data such as customer data and financial information to ensure the representativeness of the data.

[0032] Finally, the training dataset is encrypted using the AES-256 algorithm, and the TLS 1.3 protocol is enabled on the transmission channel to provide end-to-end protection for the training dataset and interactive data, ensuring transmission and storage security.

[0033] S2: Deploy the AI model to the blockchain and define the invocation rules, data storage method, and transaction verification mechanism of the AI model by defining smart contracts.

[0034] Exemplarily, first select a blockchain platform according to the enterprise scenario, such as Ethereum or Hyperledger Fabric, and deploy the AI model to the blockchain platform.

[0035] Then, write a smart contract to achieve the automated execution of the AI model. The smart contract defines the invocation rules, data storage method, and transaction verification mechanism of the AI model, and deploys the trained AI model to the blockchain. Among them, the AI model also needs to be encapsulated as a blockchain interface, and the AI model is set to support execution through contract calls, enabling it to respond to the calls of smart contracts and execute in the blockchain environment.

[0036] It should be noted that the invocation rules include trigger conditions and input / output formats, and the data storage method includes on-chain / off-chain storage strategies.

[0037] Before each call of the AI model, collect and organize the input data necessary for the execution of the AI model. Upload the input data to the blockchain and use the hash algorithm to verify the input data to ensure traceability of the source. Among them, the input data can include task descriptions, input parameters, etc. submitted by employees, which are necessary for the execution of the AI model.

[0038] During each call of the AI model, automatically record the source of the input data and the data processing process through the blockchain. The specific recorded content includes information such as the call time, input / output data, and model parameters. These information will be encrypted and stored in the blockchain block to ensure its immutability and transparency.

[0039] At the same time, conduct a full-process audit of the AI model life cycle through the blockchain. The audit content includes the recording of information such as the source of training data, algorithm usage, and subsequent updates, thus providing a verifiable track for the audit. This transparency and immutability enhance the supervision and accountability of the operation of the AI system. Any improper use or data leakage of the model can be traced and recorded, thereby improving the security and credibility of the system.

[0040] S3: Use keys to perform access control on the AI model and related data.

[0041] Exemplarily, a unique public key and private key are assigned to each registered user. This process is completed when the user registers or first accesses the system, ensuring that each user has a unique key pair. The public key is public and is used to encrypt data; the private key is used to decrypt data and must be kept strictly confidential and only known to the user himself.

[0042] When a registered user accesses the AI model and data, the private key is used for authentication. That is, when the user accesses data or the model, the private key signature is required, and the system authorizes the operation after verifying the permissions. For example, when employees attempt to access the AI model / company data, they must use their private keys for authentication. This step ensures that only users with the correct private key can request data access, and the system confirms the identity and permissions of the user by verifying the validity of the private key. If the permission check is correct, the operation can continue.

[0043] At the same time, the public key is used to encrypt the data to be stored and transmitted. For example, company data is encrypted with the public key before storage and transmission to ensure that the data cannot be read under unauthorized access. This step prevents the data from being intercepted and misused during transmission. Authorized users can decrypt the data with their private keys for access and use.

[0044] Finally, the access control logic is automatically executed through smart contracts, the usage of each data access and AI model is recorded, and the data usage records are regularly checked. Smart contracts automatically execute the access control logic, including permission allocation, request approval, and permission revocation. Smart contracts record the usage of each data access and AI model, including the access time, the identity of the visitor, and the accessed content. These records provide the necessary information for auditing and monitoring. Smart contracts regularly check the data usage records to ensure that all data access and usage comply with the preset compliance standards. This helps to promptly detect and correct non-compliant data access behaviors.

[0045] S4: Perform zero-knowledge proof or anonymization on sensitive business data, model training data, and system interaction data.

[0046] The purpose of this step is to perform data encryption and privacy processing on critical data. When processing, a zero-knowledge proof scheme (zk-SNARKs) or anonymization can be selected.

[0047] If the zero-knowledge proof scheme (zk-SNARKs) is selected, it is possible to prove specific attributes of the data without revealing the specific information, and the data to be verified is encrypted so that data verification and model training can be carried out without exposing the specific content.

[0048] If the anonymization of sensitive data is selected, such as data desensitization, data generalization, or data synthesis, the parts of the data that identify individuals or sensitive information are hidden or modified while preserving the usability of the data. Through anonymization, even if the data is accessed improperly, it is impossible to trace back to specific individuals or disclose sensitive information, thus effectively preventing data leakage and abuse.

[0049] For example, for sensitive business data, model training data, and system interaction data, the execution processes of the two processing methods are specifically as follows: 1. Zero - knowledge proof: 1.1. Classify and uniformly format sensitive business data, design a circuit to prove specific attributes of the data, generate relevant keys, encrypt the data, and then generate and verify a zero - knowledge proof to confirm the data attributes without exposing the content.

[0050] For sensitive business data, comprehensive and meticulous data pre - processing is required first. Business personnel and data experts need to collaborate. According to business rules and security requirements, the data is accurately classified, such as financial data, customer privacy data, etc. Different types of data may be stored in different databases or file systems, and they need to be collected and integrated, and the data format is unified. For example, convert date data in different formats to the standard YYYY - MM - DD format to lay a foundation for subsequent processing.

[0051] Next is the crucial circuit design link. Professional cryptographers and developers define the specific attributes of the data to be proved, which may involve the range, logical relationship, etc. of the data. Taking financial data as an example, to prove that a certain transaction amount is within the specified budget range. Use professional tools such as ZoKrates to construct a circuit that can accurately describe the data attributes and verification logic. After the circuit is constructed, public parameters are generated through a trusted setup process. These parameters contain encrypted information, and then proof keys and verification keys are generated based on this.

[0052] During data processing, the AES - 256 algorithm is used to encrypt sensitive business data to ensure the security of the data during transmission and processing. Use the proof key to perform complex operations on the encrypted data to generate a zero - knowledge proof. The verifier uses the verification key to verify the proof to confirm whether the data meets specific attributes, and the entire process strictly protects data privacy.

[0053] 1.2. Screen and encode model training data, construct a circuit to prove its training attributes, encrypt the data, and then generate and verify a zero - knowledge proof to ensure the legality and privacy of the training data.

[0054] When processing model training data, first screen out sensitive data for training from a vast amount of raw datasets. Data scientists need to use data mining and machine learning algorithms to clean and preprocess the data, removing noisy data and outliers. Encode the screened data so that it is suitable for processing in a zero-knowledge proof system.

[0055] Professionals define the attributes that the model training data needs to prove, such as the distribution characteristics and correlations of the data. Use zero-knowledge proof tools to construct circuits that can accurately prove these attributes. Circuit construction should fully consider the specific requirements and algorithms of model training to ensure the accuracy and effectiveness of the proof.

[0056] Adopt advanced encryption algorithms to encrypt the screened and encoded training data to protect data privacy. Use the proof key to perform a series of operations on the encrypted training data to generate zero-knowledge proofs. During or after the model training process, use the verification key to verify the proofs to ensure the legality and privacy of the training data and provide a reliable data basis for model training.

[0057] 1.3. Capture and parse system interaction data, design and optimize circuits for proving interaction attributes, generate and verify zero-knowledge proofs in real time after encrypting the data, and ensure the legality and security of the interaction.

[0058] During system interaction, use network monitoring tools and data capture technologies to capture interaction data involving sensitive information in real time, such as users' login information, transaction requests, etc. Use data parsing tools to parse the captured interaction data in detail, understand the structure and content of the data, and determine the attributes that need to be proved.

[0059] Professional cryptography engineers and developers design circuits for proving the attributes of interaction data according to the security policies and business requirements of the system. Use optimization algorithms to optimize the performance of the circuits, reduce the time for proof generation and verification, and improve the response speed of the system.

[0060] After capturing the interaction data, immediately encrypt it using a high-strength encryption algorithm to prevent data leakage during transmission and processing. Use the proof key to process the encrypted interaction data to generate zero-knowledge proofs. During system interaction, verify the generated proofs in real time to ensure the legality and security of the interaction and provide guarantee for the stable operation of the system.

[0061] 2. Anonymization processing: 2.1. Based on sensitive business data, identify sensitive fields and evaluate their sensitivity levels, and select data desensitization or generalization methods to process sensitive fields.

[0062] First, comprehensively identify sensitive business data to determine sensitive fields therein, such as customer names, ID numbers, bank card numbers, etc. Evaluate the sensitivity levels of these sensitive fields through professional data analysis tools and algorithms. Based on the evaluation results, select appropriate anonymization methods.

[0063] For critical sensitive fields, adopt data desensitization techniques, such as using replacement, masking, etc. Replace some digits of the ID number with asterisks, which not only protects privacy but also retains the data's relevance to a certain extent. For data that needs to retain statistical characteristics, data generalization methods can be used to generalize specific dates of birth to the year of birth. Use scripts or professional tools to batch process a large amount of data for anonymization to improve processing efficiency. After processing, strictly verify the anonymized data to ensure that the data meets the anonymity requirements and maintains business usability.

[0064] 2.2. Analyze the data characteristics of the model training data, adopt data synthesis or differential privacy techniques to transform the data characteristics, and use the transformed model training data to train the AI model and verify its performance.

[0065] Conduct in-depth feature analysis on the model training data. Data scientists use statistical analysis and machine learning algorithms to understand which parts of the data belong to sensitive information and the impact of this information on model training. Based on the analysis results, determine the goals of anonymization processing, such as retaining the statistical characteristics of the data and not affecting the performance of the model.

[0066] Select appropriate anonymization techniques according to the goals. For cases where the overall characteristics of the data need to be retained, data synthesis techniques can be used, such as using generative adversarial networks (GANs) to generate new data with similar statistical characteristics to the original training data. To meet certain privacy protection requirements, noise can be added to the data using differential privacy techniques. Convert the original model training data into anonymized data. During the conversion process, continuously adjust parameters and algorithms to ensure the best balance between data usability and anonymity. Use the anonymized training data for model training, and verify whether the performance of the model is affected through methods such as cross-validation. If there is an impact, adjust the anonymization strategy in a timely manner.

[0067] 2.3. Classify the system interaction data according to the classification strategy and process the classified system interaction data in real time using partial hiding or tokenization methods.

[0068] Classify the system interaction data. Classify it into different categories according to the sensitivity of the data. For example, classify users' personal information as highly sensitive data and the system's operation logs as low-sensitive data. Develop corresponding anonymization processing strategies for different categories of interaction data.

[0069] For highly sensitive data, adopt a partial hiding method. When displaying the user's mobile phone number, only display part of the digits. For some data that needs to be exchanged between different systems, tokenization technology can be used to replace sensitive information with tokens, and the tokens are associated with the original information through a secure mapping relationship. During the system interaction process, anonymize the interaction data in real time by writing code and integrating relevant tools. Regularly review the anonymized interaction data, use data analysis and security audit technologies to evaluate the effect of anonymization processing, and adjust the processing strategy in a timely manner according to the evaluation results to ensure the effective protection of data privacy.

[0070] S5: Use AI technology to monitor the blockchain network traffic and system running status in real time, identify anomalies and optimize the blockchain network performance.

[0071] For example, on the one hand, use AI technology to monitor the blockchain network traffic in real time, identify abnormal data packets or unauthorized transmissions, regard the corresponding transmission nodes as suspicious nodes, and isolate them. On the other hand, use AI technology to analyze the data patterns on the chain, and the data patterns include transaction frequency, block filling rate, and node response time.

[0072] By the above means, continuously monitor the system running status and data flow, detect and respond to potential security threats in real time. Continuously optimize the detection strategy and response speed, reduce the false alarm rate, ensure the accuracy of security alerts, and avoid unnecessary interference and resource waste. Among them, AI technology can be used to monitor the data packets sent and received in the blockchain network to identify any abnormal or unauthorized data transmissions. In addition, AI technology can perform real-time intelligent analysis on the network traffic, discover abnormal behaviors in a timely manner and take measures such as request interruption and other corresponding protection measures, automatically isolate suspicious nodes or restrict suspicious transactions.

[0073] At the same time, dynamically adjust the block size, the timeout of the consensus mechanism, or the node election strategy according to the data patterns on the chain to optimize the network throughput and response speed. For example, adjust the parameters and strategies of the AI model according to the user's usage habits and feedback; optimize the node configuration and consensus algorithm according to the performance changes of the blockchain network.

[0074] By optimizing the blockchain network performance and the data uploading process of the blockchain, improve the overall system response speed and processing capacity, and reduce energy consumption. AI algorithms can analyze a large amount of data stored on the blockchain to identify patterns, anomalies, and potential fraud behaviors, which helps to strengthen the security of the blockchain network, maintain the integrity of transactions, and at the same time improve the system's self-learning and self-optimization capabilities.

[0075] In this embodiment, by directly deploying the AI model on the blockchain platform and using smart contracts to finely set the invocation rules, data storage strategies, and transaction verification processes of the AI model, the high transparency and immutability of model operations are ensured. At the same time, with the help of the distributed ledger feature of the blockchain, a comprehensive audit of the AI model's life cycle is realized, effectively preventing data tampering and model piracy.

[0076] In terms of data access control, this method uses the key management mechanism of the blockchain to assign a unique public-private key pair to each user, achieving strict access control over the AI model and related data. This mechanism not only enhances data security but also ensures that only authorized users can access and use the AI model, effectively preventing data leakage.

[0077] In addition, this method also introduces zero-knowledge proof and anonymization processing technologies to deeply protect the privacy of sensitive business data, model training data, and system interaction data. These technologies can verify the specific attributes and legality of data without exposing the data content, thus ensuring the secure transmission and storage of data on the blockchain.

[0078] More importantly, this method also uses AI technology to monitor the blockchain network traffic and system operation status in real time. By identifying abnormal data packets, unauthorized transmissions, and analyzing on-chain data patterns, etc., suspicious nodes are timely discovered and isolated, and the block size, the timeout time of the consensus mechanism, and the node election strategy are dynamically adjusted, thereby significantly improving the throughput, response speed, and overall performance of the blockchain network.

[0079] In summary, the method for customizing an AI system based on blockchain disclosed in this embodiment not only realizes the secure deployment and efficient management of the AI model through deep integration of blockchain technology but also ensures data privacy protection and system performance optimization, providing strong support for the wide application of AI technology.

[0080] As Figure 2 shown, the following is an embodiment of a system for customizing an AI system based on blockchain provided by this disclosure. This system and the method for customizing an AI system based on blockchain in the above embodiments belong to the same inventive concept. For the details not described in detail in the embodiment of the system for customizing an AI system based on blockchain, reference can be made to the embodiments of the method for customizing an AI system based on blockchain.

[0081] A system for customizing an AI system based on blockchain includes: a model construction module, a blockchain and smart contract integration module, a dynamic permission control module, a privacy enhancement processing module, and a monitoring and optimization module.

[0082] The model construction module is used to construct an AI model according to requirements.

[0083] A blockchain and smart contract integration module for deploying an AI model onto a blockchain, and setting the invocation rules, data storage methods, and transaction verification mechanisms of the AI model by defining smart contracts.

[0084] A dynamic permission control module for performing access control on the AI model and related data using keys.

[0085] A privacy enhancement processing module for performing zero-knowledge proofs or anonymization processing on sensitive business data, model training data, and system interaction data.

[0086] A monitoring and optimization module for using AI technology to monitor the blockchain network traffic and system operating status in real time, identify anomalies, and optimize the blockchain network performance.

[0087] The blockchain-based AI system customization system provided in this embodiment realizes the secure, transparent, and traceable deployment of the AI model by deeply integrating blockchain technology, ensures the automation and immutability of data access and transaction verification using smart contracts, and at the same time strengthens data privacy protection and access control by combining the distributed characteristics of the blockchain. Finally, the performance and security of the blockchain network are optimized through AI monitoring technology, and the reliability and efficiency of the system are overall improved.

[0088] Figure 3 A schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention.

[0089] The blockchain-based AI system customization method provided in the embodiments of the present application can be applied to an electronic device. Those skilled in the art can understand that the electronic device structure involved in the embodiments of the present invention does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements. In the embodiments of the present invention, the electronic device includes, but is not limited to, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of the present application described herein and / or claimed.

[0090] The electronic device may include a processor, an external memory interface, an internal memory, a Universal Serial Bus (USB) interface, a charging management module, a power management module, a battery, a wireless communication module, an audio module, a speaker, a microphone, a sensor module, keys, a camera, a display screen, and a Subscriber Identity Module (SIM) card interface, etc.

[0091] The processor may include one or more processing units. For example, the processor may include a Central Processing Unit (CPU), etc., an Application Processor (AP), a modem processor, a Graphics Processing Unit (GPU), an Image Signal Processor (ISP), a controller, a memory, a video codec, a Digital Signal Processor (DSP), a baseband processor, and / or a Neural-Network Processing Unit (NPU), etc. Among them, different processing units may be independent devices or integrated in one or more processors.

[0092] Among them, the processor may be the nerve center and command center of the electronic device. The controller may generate operation control signals according to the instruction operation code and timing signals to complete the control of fetching and executing instructions.

[0093] A memory may also be provided in the processor for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory may save the instructions or data that the processor has just used or recycled. If the processor needs to use the instruction or data again, it can directly call it from this memory. This avoids repeated accesses, reduces the waiting time of the processor, and thus improves the system efficiency.

[0094] The external memory interface may be used to connect an external memory card, such as a MicroSD card, to implement the storage capacity expansion of the electronic device. The external memory card communicates with the processor through the external memory interface to achieve the data storage function. For example, files such as music and videos are saved in the external memory card.

[0095] The internal memory can be used to store computer-executable program code, which includes instructions. The processor executes various functional applications and data processing of the electronic device by running the instructions stored in the internal memory. The internal memory can include a program storage area and a data storage area. The internal memory can include a high-speed random access memory, and can also include non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), etc.

[0096] The wireless communication function of the electronic device can be implemented by an antenna, a wireless communication module, a modem processor, a baseband processor, etc.

[0097] The wireless communication module can provide wireless communication solutions applied to the electronic device, including wireless local area networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite systems (GNSS), frequency modulation (FM), near field communication (NFC), infrared technology (IR), etc.

[0098] The electronic device can implement audio functions, etc., through an audio module, a speaker, a receiver, a microphone, a headphone jack, an application processor, etc.

[0099] The electronic device can implement a shooting function through an ISP, a camera, a video codec, a GPU, a display screen, an application processor, etc.

[0100] The electronic device can implement a display function through a GPU, a display screen, an application processor, etc.

[0101] The GPU is a microprocessor for image processing, connecting the display screen and the application processor. The GPU is used to execute mathematical and geometric calculations for graphics rendering. The processor can include one or more GPUs, which execute program instructions to generate or change display information.

[0102] The display screen is used to display images, videos, etc. The display screen includes a display panel.

[0103] The above-mentioned electronic device implements the method for customizing an AI system based on blockchain in the present application. By deeply integrating blockchain technology, it realizes the secure and trustworthy deployment of AI models, refined data access control, strong data privacy protection, and intelligent system monitoring and performance optimization. It fully utilizes the decentralized, immutable, and transparent characteristics of blockchain, achieving the beneficial effect of providing a solid security guarantee and an efficient operating environment for the operation of the AI system.

[0104] In the storage medium provided by the present application, there is a program product capable of implementing the method for customizing an AI system based on blockchain.

[0105] The method for customizing an AI system based on blockchain includes: constructing an AI model according to requirements; deploying the AI model onto the blockchain, and setting the call rules, data storage methods, and transaction verification mechanisms of the AI model by defining smart contracts; performing access control on the AI model and related data using keys; performing zero-knowledge proof or anonymization processing on sensitive business data, model training data, and system interaction data; and using AI technology to monitor the blockchain network traffic and system operating status in real time, identify anomalies, and optimize the performance of the blockchain network.

[0106] In some possible implementation manners, the method for customizing an AI system based on blockchain in the present disclosure can be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary embodiments of the present disclosure described in the "Exemplary Method" section above in this specification.

[0107] The storage medium of the present disclosure can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0108] The foregoing description of the disclosed embodiments enables those skilled in the art to practice 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 implemented in other embodiments without departing from the spirit or scope of the present invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A blockchain-based AI system customization method, characterized in that: include: Build AI models based on demand; Deploy the AI ​​model to the blockchain and define smart contracts to set the AI ​​model's calling rules, data storage methods, and transaction verification mechanisms; Use keys to control access to AI models and related data; Perform zero-knowledge proof or anonymization on sensitive business data, model training data, and system interaction data; Use AI technology to monitor blockchain network traffic and system operation status in real time, identify anomalies and optimize blockchain network performance.

2. The blockchain-based AI system customization method according to claim 1, characterized in that: The AI ​​model is constructed according to the requirements, including: Design AI model architecture according to the business characteristics of the enterprise, generate AI models, and clarify data processing logic; AI model architecture includes but is not limited to: neural networks and decision trees; Customize AI model training data sets according to enterprise scenarios; The AES-256 algorithm is used to encrypt the training data set, and the TLS 1.3 protocol is enabled in the transmission channel to provide end-to-end protection for the training data set and interaction data.

3. The blockchain-based AI system customization method according to claim 1, characterized in that: The AI ​​model is deployed on the blockchain, and the calling rules, data storage method and transaction verification mechanism of the AI ​​model are set by defining smart contracts, including: Select a blockchain platform based on the enterprise scenario and deploy the AI ​​model on the blockchain platform; the blockchain platform includes Ethereum and Hyperledger Fabric; Define AI model calling rules, data storage rules, and transaction verification mechanisms; Encapsulate the AI ​​model as a blockchain interface and set the AI ​​model to support execution through contract calls; Before each AI model call, collect and organize the input data necessary for AI model execution. Upload the input data to the blockchain and verify the input data using a hash algorithm; Automatically record the source of input data and the data processing process through blockchain; Conduct full audit of the AI ​​model lifecycle through blockchain.

4. The blockchain-based AI system customization method according to claim 1, characterized in that: The use of keys to control access to AI models and related data includes: Assign a unique public key and private key to each registered user; When registered users access AI models and data, they use private keys for authentication; Use the public key to encrypt the data to be stored and transmitted; Access control logic is automatically executed through smart contracts, each data access and AI model usage is recorded, and data usage records are regularly checked.

5. The blockchain-based AI system customization method according to claim 1, characterized in that: The zero-knowledge proof or anonymization of sensitive business data, model training data, and system interaction data includes: Classify sensitive business data, process it in a unified format, design circuits to prove specific attributes of the data, generate relevant keys, encrypt the data, generate and verify zero-knowledge proofs to prove data attributes without exposing the content; Filter and encode model training data, build circuits to prove its training properties, encrypt data, generate and verify zero-knowledge proofs, and ensure the legitimacy and privacy of training data; Capture and parse system interaction data, design and optimize circuits to prove interaction properties, encrypt data, generate and verify zero-knowledge proofs in real time, and ensure the legitimacy and security of the interaction.

6. The blockchain-based AI system customization method according to claim 5, characterized in that: The zero-knowledge proof or anonymization of sensitive business data, model training data, and system interaction data also includes: Based on sensitive business data, identify sensitive fields and assess their sensitivity, and use data desensitization or generalization methods to process sensitive fields; Analyze the data features of the model training data, transform the data features using data synthesis or differential privacy technology, and use the transformed model training data to train the AI ​​model and verify the performance; The system interaction data is classified according to the classification strategy, and the classified system interaction data is processed in real time using a partial hiding or tokenization method.

7. The blockchain-based AI system customization method according to claim 1, characterized in that: The use of AI technology to monitor blockchain network traffic and system operation status in real time, identify anomalies and optimize blockchain network performance includes: Use AI technology to monitor blockchain network traffic in real time, identify abnormal data packets or unauthorized transmissions, treat corresponding transmission nodes as suspicious nodes, and isolate them; Use AI technology to analyze on-chain data patterns, including transaction frequency, block fill rate, and node response time; Dynamically adjust block size, consensus mechanism timeout or node election strategy based on on-chain data patterns to optimize network throughput and responsiveness.

8. A blockchain-based AI system customization system, characterized in that: The system adopts the blockchain-based AI system customization method as described in any one of claims 1 to 7; The system comprises: Model building module, used to build AI models according to requirements; The blockchain and smart contract integration module is used to deploy AI models on the blockchain and set the calling rules, data storage method and transaction verification mechanism of AI models by defining smart contracts; Dynamic permission control module, used to use keys to control access to AI models and related data; Privacy enhancement processing module, used to perform zero-knowledge proof or anonymization on sensitive business data, model training data, and system interaction data; The monitoring and optimization module is used to use AI technology to monitor blockchain network traffic and system operation status in real time, identify anomalies and optimize blockchain network performance.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the blockchain-based AI system customization method as described in any one of claims 1 to 7 are implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the blockchain-based AI system customization method as described in any one of claims 1 to 7 are implemented.

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