A blockchain-based data credibility processing method and system

Abstract

This invention discloses a data credibility processing method and system based on blockchain, comprising the following steps: acquiring device data, verifying the device data, generating an encrypted verification signature through a trusted execution environment device, and merging the verification signature with the original data and uploading it to the blockchain; based on the device data, using smart contracts for data verification rule judgment and filtering, the smart contracts responding to preset standards to judge the data, or using machine learning to predict and analyze the quality of uploaded data to determine the quality of the data on the chain; this invention proposes to encrypt and sign the data source through trusted hardware and store it on the blockchain, providing higher data source credibility and preventing single-point tampering. Multiple verification mechanisms: combining smart contracts, blockchain consensus mechanisms, and off-chain data verification, ensuring multi-dimensional verification of blockchain data and avoiding the limitations of a single verification method.

Description

Technical Field

[0001] This invention relates to the field of blockchain data technology, specifically to a blockchain-based data trust processing method and system. Background Technology

[0002] The decentralized, immutable, and transparent characteristics offered by blockchain technology ensure the credibility of data. This approach is typically used in scenarios where the source, integrity, and security of data need to be guaranteed, such as IoT data, financial data, and medical data.

[0003] Existing technical issues:

[0004] Data write efficiency: Blockchain data write speed is relatively slow, especially on public chains, where each node needs to verify the data, which may lead to high latency and make it unsuitable for large-scale data processing.

[0005] Storage costs: Data storage in blockchain networks is distributed, and as the amount of data surges over time, storage costs may become unaffordable, especially when data is frequently updated.

[0006] The complexity of data quality verification: Blockchain itself does not directly verify the accuracy of data, but only guarantees its immutability. Ensuring the quality and reliability of the data uploaded to the blockchain remains a challenge.

[0007] Privacy Protection: For some sensitive data (such as medical and health data), the openness on the blockchain may pose a risk of privacy breaches. How to protect user privacy while ensuring transparency remains a pressing issue.

[0008] Smart contract vulnerabilities: Although smart contracts can automate data verification and processing, vulnerabilities or improper design of smart contracts may lead to security risks in the system and affect the effectiveness of data trustworthiness processing.

[0009] Based on these issues, we propose a data trust processing method and system based on blockchain. Summary of the Invention

[0010] To address one of the aforementioned technical problems, a data trust processing method and system based on blockchain is provided. While current blockchain technology can ensure the immutability and transparency of data, ensuring the quality, accuracy, and reliability of data sources on the blockchain remains an urgent issue to be resolved.

[0011] To achieve the above objectives, the technical invention adopted in this invention is as follows:

[0012] First aspect: A data trust processing method based on blockchain, including the following steps:

[0013] Acquire device data, verify the device data, generate an encrypted verification signature through a trusted execution environment device, and merge the verification signature with the original data and upload it to the blockchain;

[0014] The acquired device data is collected through IoT sensors or smart meters. In the initial stage of data collection, a trusted execution environment device is used for source encryption and a digital signature is generated. The signature is protected by a symmetric encryption algorithm.

[0015] Based on the device data, the smart contract is used for data verification rules judgment and filtering. The smart contract responds to preset standards to judge the data, or uses machine learning to predict and analyze the quality of uploaded data to determine the quality of the on-chain data. The preset standards include data legality, scope verification, format requirements and logical consistency.

[0016] The prediction and analysis of the quality of uploaded data through machine learning specifically includes:

[0017] The data was initially screened using anomaly detection algorithms in machine learning to identify outliers;

[0018] Based on effective data cleaning and standardization, data preprocessing models are used to standardize the data and determine data quality. The anomaly detection algorithms include Isolation Forest and One-Class SVM. The data preprocessing models include Z-score normalization and Min-Max normalization. The machine learning includes either the Isolation Forest algorithm or One-Class SVM. The specific calculation formula for Z-score normalization is as follows:

[0019] ;

[0020] Where X is the original data, μ is the data mean, and σ is the standard deviation;

[0021] Based on the credibility and accuracy of the data uploaded to the blockchain, each piece of data undergoes multiple independent verifications before being uploaded, through a multi-verification mechanism or blockchain consensus mechanism. These multi-verification mechanisms include:

[0022] Step 301, First-level verification - hardware encryption signature verification: The encryption signature generated by the trusted hardware device is compared with the original data on the blockchain to determine that the uploaded data is consistent with the data signature generated by the trusted hardware.

[0023] Step 302, Second-level verification - data validity check, based on smart contract verification data to determine whether it conforms to the rules;

[0024] Step 304, Third-layer verification - Blockchain network consensus verification, which performs secondary confirmation of data based on the blockchain consensus mechanism, wherein the blockchain consensus mechanism includes PoW or PoS;

[0025] An off-chain data quality monitoring model is constructed. Based on this model, on-chain data is periodically backtracked and verified. Regular interaction with external systems is also performed to check and verify the consistency between on-chain data and the original data. These external systems include databases and IoT platforms. The consistency verification is achieved by comparing encrypted hash values, with the specific expression being:

[0026] Hash Blockchain (Data) = Hash External Source (Data);

[0027] The off-chain data quality monitoring model, combined with the event listener and AI anomaly detection algorithm on the blockchain, continuously monitors the on-chain data and verifies the results; if data quality anomalies or the risk of tampering are detected, an early warning mechanism is automatically triggered.

[0028] Based on the verification results, a reliability score for the generated data is determined.

[0029] Preferably, determining the data credibility score based on the verification results specifically includes:

[0030] Based on AI models, several quality indicators of the data will be transformed into specific score values;

[0031] It provides a real-time data monitoring panel that displays the source, verification results, and quality score information for each data point.

[0032] The AI ​​models include regression analysis and classification models.

[0033] The second aspect: a blockchain-based data trust processing system, comprising:

[0034] The data acquisition module is used to acquire device data, verify the device data, generate an encrypted verification signature through a trusted execution environment device, and merge the verification signature with the original data and upload it to the blockchain.

[0035] The data verification quality module is used to judge and filter data based on the device data through smart contracts. The smart contracts respond to preset standards to judge the data, or use machine learning to predict and analyze the quality of uploaded data to determine the quality of the data on the chain.

[0036] An independent verification module is used to verify the credibility and accuracy of on-chain data. Each piece of data undergoes multiple independent verifications before being uploaded to the blockchain through a multi-verification mechanism or blockchain consensus mechanism.

[0037] The off-chain data quality monitoring module is used to build an off-chain data quality monitoring model. Based on the off-chain data quality monitoring model, it periodically performs backtracking verification on the on-chain data and interacts with external systems regularly to verify the consistency between the on-chain data and the original data.

[0038] The early warning module is used to continuously monitor on-chain data based on the off-chain data quality monitoring model, combined with the event listener and AI anomaly detection algorithm on the blockchain, and verify the results; if data quality anomalies or the risk of tampering are detected, an early warning mechanism is automatically triggered.

[0039] The credibility scoring module is used to determine the credibility score of the generated data based on the verification results.

[0040] Third aspect: A computer device comprising:

[0041] processor;

[0042] Memory, used to store executable instructions;

[0043] The processor is used to read the executable instructions from the memory and execute the executable instructions to implement the blockchain-based data trust processing method.

[0044] Fourth aspect: A computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement the blockchain-based data trust processing method.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0046] 1. This invention proposes to encrypt and sign the data source using trusted hardware (such as TEE) and store it on the blockchain, thereby providing higher credibility of the data source and preventing single point of tampering.

[0047] 2. Multi-dimensional verification mechanism: Combining smart contracts, blockchain consensus mechanisms, and off-chain data verification, it ensures multi-dimensional verification of blockchain data and avoids the limitations of a single verification method.

[0048] 3. AI-driven quality assessment: Using artificial intelligence (especially machine learning algorithms) to dynamically analyze and predict data quality can intelligently identify potential problems and anomalies, thereby improving the automation level of data credibility monitoring.

[0049] 4. Data quality prediction through machine learning can screen data before it is uploaded and make predictive decisions based on historical data patterns, thereby reducing human intervention and operational errors and significantly improving the accuracy of data processing. Attached Figure Description

[0050] Figure 1 This is a flowchart of the blockchain-based data trust processing method of the present invention;

[0051] Figure 2 This is a block diagram of the blockchain-based data trust processing system of the present invention.

[0052] Figure 3 This is a schematic diagram of the computer device structure of the present invention;

[0053] In the diagram, 10 is computer equipment; 1002 is processor; 1004 is memory; and 1006 is transmission device. Detailed Implementation

[0054] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0055] Example 1:

[0056] To effectively address the complexity of data quality verification, this invention proposes the following technical invention based on trusted hardware and multi-factor authentication mechanisms. This invention fully leverages the advantages of blockchain and combines the latest hardware trust technologies and multi-factor authentication mechanisms to enhance the ability to verify data quality and trustworthiness.

[0057] Reference Figure 1 As shown, a data trust processing method based on blockchain includes the following steps:

[0058] Step 10: Obtain device data, verify the device data, generate an encrypted verification signature through a trusted execution environment device, and merge the verification signature with the original data and upload it to the blockchain;

[0059] In the data generation or acquisition phase, this application uses Trusted Execution Environment (TEE) technology to verify external data sources. Trusted hardware (such as Intel's SGX) ensures that the data generation process cannot be tampered with by guaranteeing the confidentiality and integrity of the computing process at the hardware level.

[0060] The verification process includes:

[0061] Verification Signature = HMAC SHA-256 (Data, Secret Key);

[0062] Here, Data represents the collected data, and Secret Key is the security key provided by the hardware device. The generated Verification Signature is stored in the blockchain to ensure the trustworthiness of the data source and data content;

[0063] Step 20: Based on the device data, the smart contract is used for data verification rules judgment and filtering. The smart contract responds to preset standards to judge the data, or uses machine learning to predict and analyze the quality of the uploaded data to determine the quality of the on-chain data.

[0064] Preset verification rules: In a smart contract, the first step is to set the rules for data quality verification, such as the legal scope of the data, format requirements, and logical consistency.

[0065] For example, assuming the collected data should be in the range [0, 100], the smart contract will verify whether the incoming data conforms to this rule.

[0066] Verification logic:

[0067] if(Data≥MinandData≤Max)thenValid,elseInvalid;

[0068] Data is the incoming collected data, and Min and Max are the minimum and maximum values ​​of the data, respectively;

[0069] Step 30: Based on the credibility and accuracy of the data uploaded to the blockchain, each piece of data undergoes multiple independent verifications before being uploaded, using a multi-verification mechanism or blockchain consensus mechanism. The multi-verification mechanism includes:

[0070] Step 301, First-level verification - hardware encryption signature verification: The encryption signature generated by the trusted hardware device is compared with the original data on the blockchain to determine that the uploaded data is consistent with the data signature generated by the trusted hardware.

[0071] Step 302, Second-level verification - data validity check, based on smart contract verification data to determine whether it conforms to the rules;

[0072] Step 304, Third-layer verification - Blockchain network consensus verification, which performs secondary confirmation of data based on the blockchain consensus mechanism, wherein the blockchain consensus mechanism includes PoW or PoS;

[0073] To enhance the credibility and accuracy of the data, a multi-verification mechanism is introduced to ensure that each piece of data undergoes multiple independent verification steps before being uploaded to the blockchain.

[0074] First-level verification - hardware cryptographic signature verification: compares the cryptographic signature generated by a trusted hardware device with the original data on the blockchain.

[0075] Verification formula:

[0076] HMAC SHA-256 (Data, Secret Key)=Signature from TEE;

[0077] Ensure that the uploaded data is consistent with the data signature generated by the trusted hardware.

[0078] Second-level verification - data validity check: Use smart contracts to verify whether the data conforms to the rules, such as data format and range.

[0079] Third-layer verification - Blockchain network consensus verification: The blockchain consensus mechanism (such as PoW or PoS) is used to confirm the data a second time, ensuring that multiple nodes in the network reach a consensus and avoiding single point of failure or tampering.

[0080] Step 40: Construct an off-chain data quality monitoring model, periodically backtrack and verify the on-chain data according to the off-chain data quality monitoring model, and periodically interact with external systems (such as databases or IoT platforms) to check and verify the consistency between the on-chain data and the original data.

[0081] To ensure the continued credibility of on-chain data, an off-chain data quality monitoring model is set up to periodically backtrack and verify the on-chain data, ensuring data consistency.

[0082] Periodic backtracking verification: The system will periodically and automatically backtrack historical data through the off-chain monitoring system and perform secondary verification through trusted hardware.

[0083] Data consistency check: Regularly interact with external systems (such as databases or IoT platforms) to verify whether the on-chain data is consistent with the original data and ensure that it has not been tampered with.

[0084] For example, use cryptographic hash (SHA-256) values ​​to check the consistency of the data.

[0085] Hash Blockchain (Data) = Hash External Source (Data);

[0086] Step 50: Based on the off-chain data quality monitoring model, combined with the event listener and AI anomaly detection algorithm on the blockchain, continuously monitor the on-chain data and verify the results; if data quality anomalies or tampering risks are detected, an early warning mechanism is automatically triggered.

[0087] Smart contracts automatically repair data according to preset rules (such as deleting invalid data or replacing abnormal data). The rules for repairing data are executed through smart contracts, ensuring that the repair process is transparent and traceable.

[0088] Historical backtracking and verification: Every so often, the system will use historical data from the blockchain and external verification mechanisms to perform a full backtracking to ensure that the data always meets quality requirements.

[0089] Step 60: Based on the verification results, determine the credibility score of the generated data.

[0090] Specifically: The determination of the generated data credibility score based on the verification results includes:

[0091] Based on AI models, several quality indicators of the data will be transformed into specific score values;

[0092] It provides a real-time data monitoring panel that displays the source, verification results, and quality score information for each data point.

[0093] The AI ​​models include regression analysis and classification models.

[0094] Based on the verification results at each stage, a data credibility score is automatically generated. AI models (such as regression analysis and classification models) are used to convert various data quality indicators into specific score values, which are then used to demonstrate the data's credibility.

[0095] It provides a real-time data monitoring panel that displays information such as the source, verification results, and quality score of each data point, helping users understand the credibility status of the data in real time.

[0096] Specifically, this invention ensures the integrity, accuracy, and credibility of data sources through hardware, smart contracts, multiple verifications, and risk assessments.

[0097] Traditional blockchain data verification typically focuses on preventing data from being tampered with, while this invention combines hardware and smart contract mechanisms to comprehensively enhance the integrity, validity, and transparency of data.

[0098] This invention enables off-chain monitoring and verification, ensuring that historical data is not distorted by external factors and avoiding the risk of data losing credibility over time.

[0099] Example 2

[0100] To address the aforementioned technical problems, based on Embodiment 1, this application adopts another technical solution: a data trust processing method based on blockchain, comprising the following steps:

[0101] Step 1: Data is collected through various sensors or devices (IoT, smart meters, etc.). In the initial stage of data collection, a Trusted Execution Environment (TEE) (such as Intel SGX or AMDSEV) is used for source encryption.

[0102] A digital signature is generated through a TEE, and the signature is protected using a symmetric encryption algorithm (such as HMAC).

[0103] The signature format is:

[0104] Signature Data =HMAC-SHA 256 (Data, Secret Key);

[0105] Here, Data refers to the raw data collected, and Secret Key is the security key of the hardware device.

[0106] The data and digital signature are submitted to the blockchain together, and the signature is stored together with the data through a smart contract.

[0107] Examples of data and signature storage structures in a blockchain include:

[0108] TransactionData = {Data, SignatureData, Timestamp, HashData} where Data refers to the actual data involved in the transaction, such as the amount and information of the two parties involved.

[0109] SignatureData: This is typically used to verify the authenticity and integrity of a transaction, ensuring that the transaction has not been tampered with.

[0110] Timestamp: This is a marker that records when a transaction occurred, used to determine the order and timing of transactions.

[0111] HashData: Hash data is typically used to ensure data integrity. It uses a hash function to convert data into a fixed-length hash value, making it easier to verify whether the data has been tampered with.

[0112] Verification: Blockchain nodes can verify the matching of data with hardware signatures through smart contracts, ensuring that the data has not been tampered with.

[0113] HMAC-SHA256(Data, SecretKey)=SignatureData

[0114] Data:

[0115] This is the data to be processed. In practical applications, this can be any data that requires integrity verification and authentication, such as user login information, transaction data, etc.

[0116] Secret Key:

[0117] This is a key, and only the party possessing this key can generate and verify signatures. The confidentiality of this key is crucial, because if the key is compromised, the security of the data will be compromised.

[0118] Signature Data:

[0119] This is the signature data obtained after processing the Data and Secret Key using the HMAC-SHA256 algorithm. This signature data can be used to verify the integrity and origin of the data.

[0120] If a match is found, it indicates that the data comes from a trusted source.

[0121] Step 2: Use machine learning (ML) to predict and analyze the quality of the uploaded data.

[0122] Design a quality prediction model based on supervised learning, train it on historical data, and learn various data patterns (such as the volatility of sensor-collected data, data noise, etc.).

[0123] Data Processing: Anomaly Detection: Data is initially screened using machine learning anomaly detection algorithms (such as IsolationForest and One-ClassSVM) to identify outliers. The algorithms analyze predictions based on specific thresholds, biases, or other algorithms (such as regression models).

[0124] Data standardization and cleaning: Cleaning and standardizing valid data. Data is standardized using data preprocessing models (such as Z-score standardization or Min-Max normalization) to ensure data quality.

[0125] Suppose we use Z-score for standardization:

[0126]

[0127] Where X represents the original data, μ represents the data mean, and σ represents the standard deviation.

[0128] Quality assessment:

[0129] The accuracy of the data is assessed by comparing it with external data sources (such as the National Weather Service, known standard datasets, etc.).

[0130] Time-series-based algorithms are used to verify the validity of data collection and submission times, ensuring that the data is not outdated or lost.

[0131] Smart contracts automatically execute data quality verification processes within the blockchain. When data quality is determined to be abnormal, the smart contract will either refuse to let the data enter the blockchain or execute an alert.

[0132] Automated data quality verification can be achieved through smart contracts. The conditional logic within the smart contract will determine whether the data meets quality requirements (such as accuracy, timeliness, etc.).

[0133] Smart contract code (Solidity) example:

[0134] functionvalidateData(uint256timestamp, uint256accuracy) publicreturns(bool){

[0135] if(timestamp>now-1hours&&accuracy>90){

[0136] return true;

[0137] }else{

[0138] revert("Data is not valid.");

[0139] }

[0140] }

[0141] This code checks whether the timeliness (whether it is within the past hour) and accuracy (whether it exceeds 90%) of the data meet the requirements. If they do not meet the requirements, the data is rejected from entering the blockchain.

[0142] Step 3: Blockchain data verification: In addition to hardware-level signatures and basic smart contract verification, multiple verification mechanisms will periodically use consensus mechanisms to further confirm the credibility of the data.

[0143] When data is uploaded to the blockchain, in addition to automatic verification by smart contracts, nodes perform secondary confirmation based on blockchain consensus mechanisms (such as PoW or PoS). Each node calculates a weighted average of the data's verification value to ensure consistency across multiple node verifications.

[0144] ;

[0145] Among them, V i w is the verification value of the i-th node. i V represents the node weights (such as reputation, computing power, etc.).final This is the final verification result.

[0146] Cross-chain technology or oracles can be introduced to connect other blockchains and traditional databases to verify the authenticity of off-chain data. For example, oracles can be used to verify external environmental data (such as weather data, currency exchange rates, etc.) to ensure that the data uploaded to the blockchain matches the actual situation.

[0147] Step 4: All requests to access blockchain data require multi-layered authentication and authorization, including passwords, hardware authentication, and fingerprint recognition. Combining the "read-only" nature of blockchain data with multi-factor authentication ensures that every data access request must pass authentication and permission review, preventing malicious data tampering.

[0148] Using multi-factor authentication, including hardware keys (such as USB keys), biometrics (such as fingerprints and facial recognition), and dynamic tokens, further ensures that the access and update process of data is protected from unauthorized attacks.

[0149] Step 5: When data is found to be non-compliant with quality standards or at risk of tampering, the repair process is automatically triggered to ensure that the data's credibility is not affected.

[0150] Data update operations are triggered by smart contracts to replace invalid or problematic data records.

[0151] The system will issue alerts to all nodes, notifying them of potential problems and prompting intervention.

[0152] Innovation and breakthroughs of the solution:

[0153] The introduction of trusted hardware: By combining a hardware-level Trusted Execution Environment (TEE), hardware-based cryptographic signatures provide source-level trustworthiness verification for data. This not only ensures the immutability of data but also guarantees the reliability of the data source, overcoming the limitations of current verification mechanisms that rely on the blockchain itself.

[0154] Multiple verification mechanisms: Through multiple verification methods such as data generation, smart contracts, and consensus mechanisms, data can undergo strict multi-layer verification before being uploaded to the blockchain, which improves the guarantee of data credibility and reduces the potential vulnerabilities of a single verification method.

[0155] Off-chain monitoring and continuous verification: By regularly backtracking and verifying off-chain data, the consistency of historical data is ensured, avoiding potential changes and distortions in data over time.

[0156] Smart Contracts and Risk Assessment: By combining automated verification and risk assessment mechanisms for smart contracts, the automation level of data management is improved, the risk of human operation is reduced, and the transparency of the entire system is enhanced.

[0157] Step 6: Further enhance the transparency and credibility of data processing by introducing data visualization and risk assessment modules.

[0158] Data visualization: By analyzing and displaying each data source, verification status, and the flow of data in the blockchain, it helps users understand the credibility of the data.

[0159] Risk assessment: Based on historical verification results and retrospective inspection results, generate a risk report on data credibility and provide an early warning mechanism to promptly identify potential risks.

[0160] Example 3:

[0161] To address the aforementioned technical problems, based on Embodiment 2, this application adopts another technical solution: a data trust processing method based on blockchain, comprising the following steps:

[0162] Step 4: Zero Trust Architecture and Multi-Factor Authentication

[0163] Step 4.1 Zero Trust Architecture:

[0164] Objective: To ensure that every request for data access is subject to strict authentication, without relying on traditional trust models, to prevent unauthorized access and data tampering.

[0165] The core principle of a zero-trust architecture is that no request (whether internal or external) is trusted by default, but rather requires multi-layered authentication.

[0166] All access requests require multi-factor authentication, such as hardware authentication (e.g., USB key, physical authentication device) and biometric authentication (e.g., fingerprint, facial recognition).

[0167] Step 4.2:

[0168] Objective: To ensure the security of data access by adding layers of authentication.

[0169] Use multiple authentication methods such as hardware keys (e.g., USB keys), biometrics (fingerprints, facial recognition), and dynamic tokens (e.g., Google Authenticator, SMS verification codes, etc.) to ensure that every access request is verified.

[0170] The user enters their username and password.

[0171] The system sends a verification code to the user's registered mobile phone or email address.

[0172] Users use hardware keys or biometrics for secondary authentication.

[0173] Once the system verifies the data, the user is authorized to access the data or perform operations.

[0174] Specifically:

[0175] Intelligent data quality screening automates data quality processing through machine learning and achieves automated quality control through smart contracts.

[0176] Employing a multi-factor verification mechanism (combining internal and external blockchain verification), the credibility of data and the transparency of the system are enhanced through consensus mechanisms and external data source verification.

[0177] This invention strengthens data access security through a zero-trust architecture and multi-factor authentication, ensuring that data is not maliciously tampered with or illegally accessed. Compared with existing technologies, this invention can handle data trust issues more efficiently, intelligently, and securely, and possesses greater practicality and innovation.

[0178] Example 4:

[0179] like Figure 2 As shown, a blockchain-based data trust processing system includes:

[0180] The data acquisition module is used to acquire device data, verify the device data, generate an encrypted verification signature through a trusted execution environment device, and merge the verification signature with the original data and upload it to the blockchain.

[0181] The data verification quality module is used to judge and filter data based on the device data through smart contracts. The smart contracts respond to preset standards to judge the data, or use machine learning to predict and analyze the quality of uploaded data to determine the quality of the data on the chain.

[0182] An independent verification module is used to verify the credibility and accuracy of on-chain data. Each piece of data undergoes multiple independent verifications before being uploaded to the blockchain through a multi-verification mechanism or blockchain consensus mechanism.

[0183] The off-chain data quality monitoring module is used to build an off-chain data quality monitoring model. Based on the off-chain data quality monitoring model, it periodically performs backtracking verification on the on-chain data and interacts with external systems regularly to verify the consistency between the on-chain data and the original data.

[0184] The early warning module is used to continuously monitor on-chain data based on the off-chain data quality monitoring model, combined with the event listener and AI anomaly detection algorithm on the blockchain, and verify the results; if data quality anomalies or the risk of tampering are detected, an early warning mechanism is automatically triggered.

[0185] A credibility scoring module, used to determine the credibility score of generated data based on verification results, is part of a blockchain-based data credibility processing system, comprising:

[0186] The task acquisition module is used to acquire historical data during the vehicle's journey, perform edge computing and data preprocessing on the historical data, and obtain target data.

[0187] The feature extraction module is used to extract features from the target data to obtain the input features of the target data. The feature extraction includes temporal feature extraction, environmental feature extraction, and behavioral pattern extraction.

[0188] The dynamic risk assessment module is used to construct a dynamic risk assessment model. The dynamic risk assessment model is trained based on the input features of the target data. The dynamic risk assessment model uses an ARIMA model and an LSTM network model to predict dynamic risks.

[0189] The risk scoring module is used to generate a risk score based on the risk value output by the dynamic risk assessment model, set a threshold based on the risk score, trigger a real-time warning when the threshold is exceeded, and assess the safety level of the current driving status.

[0190] The optimized driving strategy module is used to adjust the model's warning parameters based on real-time data and risk assessment results, and optimizes the driver's behavior patterns through the Q-learning algorithm.

[0191] The foregoing Figure 1 The various variations and specific examples of the blockchain-based data credibility processing method in Embodiment 1 are also applicable to the blockchain-based data credibility processing system in this embodiment. Through the foregoing detailed description of the blockchain-based data credibility processing method, those skilled in the art can clearly understand the implementation method of the blockchain-based data credibility processing system in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0192] Example 5

[0193] This application provides a computer device including a processor and a memory. The memory stores at least one instruction or at least one program, which is loaded and executed by the processor to implement a blockchain-based data trust processing method as provided in the above method embodiments.

[0194] Figure 3 A schematic diagram of the hardware structure of a device for implementing a blockchain-based data trust processing method provided in the embodiments of this application is shown. The device may participate in or include the apparatus or system provided in the embodiments of this application. Figure 3As shown, the computer device 10 may include one or more processors 1002 (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 1004 for storing data, and a transmission device 1006 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 3 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer device 10 may also include... Figure 3 The more or fewer components shown, or having the same Figure 3 The different configurations shown.

[0195] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuit may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer device 10 (or mobile device). As involved in the embodiments of this application, the data processing circuit serves as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0196] The memory 1004 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to a blockchain-based data trust processing method in this embodiment of the application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 1004, thereby implementing the aforementioned method. The memory 1004 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 1004 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer device 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0197] The transmission device 1006 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer device 10. In one example, the transmission device 1006 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 1006 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0198] The display may be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer device 10 (or mobile device).

[0199] Example 6

[0200] This application embodiment also provides a computer-readable storage medium, which can be disposed in a server to store at least one instruction or at least one program related to implementing a blockchain-based data trust processing method in the method embodiment. The at least one instruction or the at least one program is loaded and executed by the processor to implement the blockchain-based data trust processing method provided in the above method embodiment.

[0201] Optionally, in this embodiment, the storage medium may be located at at least one of the multiple network servers in a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0202] Example 7

[0203] This invention also provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform a blockchain-based data trust processing method provided in the various optional embodiments described above.

[0204] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are also possible or may be advantageous.

[0205] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device, equipment, and storage medium embodiments are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0206] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0207] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

[0208] 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 variations or substitutions within the technical scope disclosed in the present invention, and these should all be included 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 data trust processing method based on blockchain, characterized in that, Includes the following steps: Acquire device data, verify the device data, generate an encrypted verification signature through a trusted execution environment device, and merge the verification signature with the original data and upload it to the blockchain; Based on the device data, the smart contract is used for data verification rules judgment and filtering. The smart contract responds to preset standards to judge the data, or uses machine learning to predict and analyze the quality of the uploaded data to determine the quality of the on-chain data. Based on the credibility and accuracy of the data uploaded to the blockchain, each piece of data undergoes multiple independent verifications before being uploaded, through a multi-verification mechanism or blockchain consensus mechanism. These multi-verification mechanisms include: Step 301, First-level verification - Hardware encryption signature verification; Step 302, Second-level validation - Data validity check; Step 303, Third-layer verification - Blockchain network consensus verification; Construct an off-chain data quality monitoring model, periodically backtrack and verify the on-chain data based on the off-chain data quality monitoring model, and periodically interact with external systems to check and verify the consistency between the on-chain data and the original data. The off-chain data quality monitoring model, combined with the event listener and AI anomaly detection algorithm on the blockchain, continuously monitors the on-chain data and verifies the results of steps 301, 302 and 303; if data quality anomalies or tampering risks are detected, an early warning mechanism is automatically triggered. Based on the verification results, a reliability score for the generated data is determined.

2. The method according to claim 1, characterized in that: The acquired device data is collected through IoT sensors or smart meters. In the initial stage of data acquisition, a trusted execution environment device is used for source encryption and a digital signature is generated. The signature is protected by a symmetric encryption algorithm.

3. The method according to claim 1, characterized in that: The preset standards include data legality, scope verification, format requirements, and logical consistency; The prediction and analysis of the quality of uploaded data through machine learning specifically includes: The data is initially screened using anomaly detection algorithms in machine learning to identify outliers; Based on effective data cleaning and standardization, the data is standardized through a data preprocessing model to determine data quality. The anomaly detection algorithms include Isolation Forest and One-Class SVM, and the data preprocessing model includes Z-score normalization and Min-Max normalization.

4. The method according to claim 1, characterized in that: The multi-factor authentication mechanism includes: Step 301, First-level verification - hardware encryption signature verification: The encryption signature generated by the trusted hardware device is compared with the original data on the blockchain to determine that the uploaded data is consistent with the data signature generated by the trusted hardware. Step 302, Second-level validation - Data validity check; Step 303, Third-layer verification - Blockchain network consensus verification, which performs secondary confirmation of data based on the blockchain consensus mechanism, wherein the blockchain consensus mechanism includes PoW or PoS.

5. The method according to claim 1, characterized in that: The external system includes a database and an IoT platform. The system checks and verifies the consistency between the on-chain data and the original data by comparing encrypted hash values.

6. The method according to claim 3, characterized in that: The machine learning includes the Isolation Forest algorithm or One-Class SVM, and the Z-score is standardized, with the specific calculation formula as follows: ； in, The original data, The mean of the data. The standard deviation is denoted as .

7. The method according to claim 1, characterized in that: The determination of the generated data credibility score based on the verification results specifically includes: Based on AI models, several quality indicators of the data are transformed into specific score values; It provides a real-time data monitoring panel that displays the source, verification results, and quality score information for each data point. The AI ​​models include regression analysis and classification models.

8. A blockchain-based data trust processing system, applied to the blockchain-based data trust processing method as described in any one of claims 1-7, characterized in that, include: The data acquisition module is used to acquire device data, verify the device data, generate an encrypted verification signature through a trusted execution environment device, and merge the verification signature with the original data and upload it to the blockchain; The data verification quality module is used to judge and filter data based on the device data through smart contracts using data verification rules. The smart contracts respond to preset standards to judge the data, or use machine learning to predict and analyze the quality of uploaded data to determine the quality of the data on the chain. An independent verification module is used to ensure the credibility and accuracy of on-chain data. Each piece of data undergoes multiple independent verifications before being uploaded to the blockchain, using a multi-factor verification mechanism or blockchain consensus mechanism. The off-chain data quality monitoring module is used to build an off-chain data quality monitoring model. Based on this model, it periodically performs backtracking verification on the on-chain data and interacts with external systems regularly to check and verify the consistency between the on-chain data and the original data. The early warning module is used to continuously monitor the on-chain data based on the off-chain data quality monitoring model, combined with the event listener on the blockchain and the AI ​​anomaly detection algorithm, and to verify the results of steps 301, 302 and 303; if data quality anomalies or the risk of tampering are detected, an early warning mechanism is automatically triggered. The credibility scoring module is used to determine the credibility score of the generated data based on the verification results.

9. A computer device, characterized in that, include: processor; Memory, used to store executable instructions; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the blockchain-based data trust processing method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, causes the processor to implement the blockchain-based data trust processing method as described in any one of claims 1 to 7.

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