Data processing method fusing trusted computing and block chain

Abstract

The invention discloses a data processing method fusing trusted computing and a block chain, and belongs to the technical field of data processing, and the method comprises the steps: obtaining industrial control data, calculating the correlation of the industrial control data according to a trained long-short term memory network, feeding back a calculation result to the industrial control data, and generating marked industrial control data; encrypting the marked industrial control data through a trusted computing model to obtain encrypted industrial control data; sending the encrypted industrial control data to an industrial control data acquisition engine for analysis to obtain analyzed industrial control data; and verifying the analyzed industrial control data, transmitting the verified and analyzed industrial control data to a block chain to construct a knowledge graph, and generating the knowledge graph of the industrial control data. The industrial control data processing method is high in safety, efficiency, reliability and innovativeness, and powerful support is provided for intelligent upgrading of an industrial control system.

Description

Technical Field

[0001] The present invention belongs to the field of data processing technology, and in particular to a data processing method integrating trusted computing and blockchain. Background Art

[0002] In the context of the Industrial Internet, the amount and value of industrial control data are rising rapidly. In addition to the rich value inherent in the data itself, resource mining and analysis of data sources can create greater economic and social value. On the other hand, the industrial control network environment includes more control technology, information technology, network technology, communication technology, and more technologies and frequent data interactions. While improving processing efficiency, because heterogeneous data needs to be integrated for processing, there are risks of data credibility and privacy leakage, and it is also more vulnerable to security attacks.

[0003] In the context of centralized storage, processing, and sharing of data, data opacity during system operation will face more severe security threats and challenges, becoming more complicated compared to the security of traditional data information. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a data processing method that integrates trusted computing and blockchain to solve the problems existing in the above-mentioned prior art.

[0005] To achieve the above objectives, the present invention provides a data processing method integrating trusted computing and blockchain, comprising:

[0006] Acquire industrial control data, calculate the correlation of the industrial control data according to the trained long short-term memory network, feed the calculation result back to the industrial control data, and generate labeled industrial control data;

[0007] Encrypting the marked industrial control data through a trusted computing model to obtain encrypted industrial control data;

[0008] Sending the encrypted industrial control data to an industrial control data acquisition engine for parsing to obtain the parsed industrial control data; wherein the industrial control data acquisition engine is generated by a trusted computing monitoring system;

[0009] The parsed industrial control data is verified, and the parsed industrial control data after verification is transmitted to the blockchain for constructing a knowledge graph to generate a knowledge graph of the industrial control data.

[0010] Preferably, the industrial control data includes but is not limited to equipment data, production process data, control data, maintenance data, energy consumption data, inventory and supply chain data, safety data and network communication data.

[0011] Preferably, the process of calculating the correlation of the industrial control data according to the trained long short-term memory network includes:

[0012] Constructing a long-short time neural network, adding the sum of the absolute values ​​of the weights to the loss function of the long-short time neural network as a penalty term, and randomly discarding a portion of neurons for training to generate a model after the first training;

[0013] Constructing an attention mechanism and a residual module and fusing them with the model after the first training to obtain an optimized model;

[0014] Adding the sum of squares of weights as a penalty term to the loss function of the optimization model and then performing training to obtain a trained long short-term memory network;

[0015] The industrial control data is input into the trained long short-term memory network for calculation to generate a calculation result.

[0016] Preferably, the process of encrypting the marked industrial control data through the trusted computing model to obtain the encrypted industrial control data includes: extracting the verification value of the marked industrial control data, comparing the verification value with the expected verification value, encrypting the verified data according to the trusted computing model, and generating the encrypted industrial control data, wherein the encrypted industrial control data is a block.

[0017] Preferably, the encrypted industrial control data is sent to an industrial control data acquisition engine for parsing, and the process of obtaining the parsed industrial control data includes:

[0018] Build a trusted security management mechanism based on the on-site control equipment layer and monitoring management layer;

[0019] Building an industrial control data acquisition engine based on the trusted security management mechanism;

[0020] The encrypted industrial control data is sent to the industrial control data acquisition engine for parsing to obtain the parsed industrial control data.

[0021] Preferably, before verifying the parsed industrial control data, the method further includes: building multiple blockchain nodes based on the Hyperledger open source blockchain framework and time bank-related smart contracts.

[0022] Preferably, the process of building multiple blockchain nodes includes:

[0023] Configure the Hyperledger framework and write network configuration files to define the topology of the blockchain network, the roles of participants, the consensus mechanism, and the identity authentication method information;

[0024] Starting a blockchain node based on the Hyperledger framework, connecting to the blockchain node through the network configuration file, and generating a blockchain network;

[0025] Constructing a time bank-related smart contract and deploying the smart contract to each node of the blockchain network;

[0026] Testing the network of each node of the blockchain network, configuring the communication protocol and data transmission method between the nodes, and generating access nodes;

[0027] Monitor the operating status and performance indicators of the blockchain network, and maintain nodes and smart contracts.

[0028] Preferably, the verified and parsed industrial control data is transmitted to the blockchain for constructing a knowledge graph, and the process of generating a knowledge graph of the industrial control data includes:

[0029] The verified and parsed industrial control data is transmitted to the off-chain industrial control database corresponding to each node in the blockchain;

[0030] Extract key concepts and relevance of data, and establish concept nodes and relationship edges;

[0031] Calculating implicit connections between data based on the correlations;

[0032] Based on the implicit connection, the concept nodes and relationship edges are updated to generate a knowledge graph of the industrial control data;

[0033] The knowledge graph of the industrial control data is fed back to each node in the blockchain for updating.

[0034] On the other hand, the present invention also provides an electronic device, comprising a memory, a processor, and a computing program stored in the memory and executable on the processor, characterized in that the processor implements the data processing method integrating trusted computing and blockchain when executing the computer program.

[0035] On the other hand, the present invention also provides a computer-readable storage medium, which stores a computer program, and is characterized in that when the computer program is executed by a processor, it implements the data processing method that integrates trusted computing and blockchain.

[0036] Compared with the prior art, the present invention has the following advantages and technical effects:

[0037] The trusted computing model of the present invention ensures the security of industrial control data during transmission and storage, and prevents data from being tampered with or stolen. At the same time, the long short-term memory network (LSTM) can effectively process sequence data, capture long-term dependencies, and improve the accuracy and efficiency of data analysis. The combination of the trusted computing model and blockchain technology improves the system's ability to resist malicious attacks and internal errors, and enhances the stability of the system.

[0038] The present invention constructs an industrial control data processing method with high security, efficiency, reliability and innovation through the comprehensive application of technologies such as long short-term memory networks, trusted computing models, blockchains and knowledge graphs, providing strong support for the intelligent upgrade of industrial control systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0040] Figure 1 This is a flow chart of a data processing method that integrates trusted computing and blockchain according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0042] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0043] First, the technical terms involved in the following embodiments are explained below:

[0044] The first step is to obtain energy data, which is collected by a meter implanted with a trusted computing module; the trusted computing module performs trusted computing processing, including the following: trusted measurement and evaluation of the meter implanted with the trusted computing module and the IoT gateway implanted with the trusted computing module through trusted computing; energy data collection and filtering tasks are completed by running energy data measurement and evaluation algorithms in the trusted computing environment of the oracle client at the IoT gateway, thereby realizing trusted collection of IoT energy data; the IoT gateway implanted with the trusted computing module establishes a secure local communication channel with the oracle client through the hardware authentication mechanism in the trusted computing module;

[0045] The second step is to generate a message for the energy data in the first step according to the protocol rules, and the message includes at least device information, time information, and energy data information of the energy sensor;

[0046] The third step is to send the message in the second step to the IoT gateway implanted with the trusted computing module through the communication module;

[0047] In the fourth step, after receiving the message in the third step, the IoT gateway completes the identity authentication of the energy data source and sends the message to the energy data collection platform based on the trusted computing monitoring system;

[0048] Step 5: After the energy data collection platform in step 4 receives the message, the energy data collection platform parses the message to obtain energy data;

[0049] Step 6: Generate energy data fingerprint from the energy data in step 5;

[0050] Step 7: Upload the energy data fingerprint in step 6 to the blockchain;

[0051] Step 8: After receiving the energy data fingerprint in step 7, the blockchain conducts a trusted verification of the energy data on and off the chain and compares it with the previously uploaded energy data fingerprint;

[0052] Step 9: After the energy data fingerprint verification in step 8 is passed, the energy data will be stored in the database to achieve energy data on-chain;

[0053] Step 10. After the energy data in step 9 is uploaded to the chain, the energy data will be sent to the off-chain energy database corresponding to the blockchain node. The blockchain node will read the energy data fingerprint of the on-chain energy data for verification. After the verification is passed, the off-chain energy database of the blockchain node will perform the storage operation to realize the trusted collection and calculation of energy data.

[0054] The present application provides a data processing method, apparatus, and computing device, which can improve the credibility of off-chain computing and promote the integrated development of blockchain technology and artificial intelligence technology.

[0055] In a first aspect, a method for data processing is provided, which is applied to a cloud blockchain system, wherein the cloud blockchain system includes multiple blockchain nodes, a cloud computing platform deploys the cloud blockchain system in the cloud computing resources it manages, and the cloud computing platform deploys blockchain services in the cloud computing resources. The method includes: a first blockchain node receives task information, and the first blockchain node is a node in the cloud blockchain system; the first blockchain node sends the task information to a task execution device, which is a server outside the cloud blockchain system, and the task execution device is used to perform inference calculation on a first model according to the task information to obtain an inference result of the first model, and the input of the first model includes the task information; the task execution device sends the inference result to a trusted execution environment TEE for verification to obtain a verified inference result; the first blockchain node obtains the verified inference result from the TEE and stores it on the chain; other nodes in the cloud blockchain system re-verify the verified inference result through their respective corresponding TEEs, and store the re-verified inference result on the chain.

[0056] In the above technical solution, the results of model inference performed on servers outside the blockchain network are verified through TEE, and the verified results are stored on the chain through blockchain nodes. In this way, blockchain technology can be used to protect the reasoning data of artificial intelligence from being tampered with, thereby promoting the integrated development of blockchain technology and artificial intelligence technology.

[0057] Blockchain is a decentralized distributed ledger that is tamper-proof, secure and reliable. It combines distributed storage, peer-to-peer transmission, consensus mechanism, cryptography and other technologies to record transactions and information through a growing data block chain to ensure data security and transparency.

[0058] The characteristics of blockchain include decentralization, immutability, transparency, security and programmability. Each data block is linked to the previous block to form a continuous chain, which guarantees the integrity of transaction history. Smart contract technology makes blockchain programmable and supports a wider range of applications.

[0059] Blockchain technology is a distributed ledger technology that combines data blocks into a chain data structure in a sequential manner according to time sequence, and uses cryptography to ensure that they cannot be tampered with or forged

[33] . In a broad sense, blockchain technology is a new distributed infrastructure and computing paradigm that uses a block chain data structure to verify and store data, a distributed node consensus algorithm to generate and update data, cryptography to ensure the security of data transmission and access, and smart contracts composed of automated script code to program and operate data.

[0060] Trusted computing is an active defense technology based on hardware security mechanisms. It aims to ensure the security of sensitive operations on computing platforms by establishing a trusted computing environment with isolated execution. Its core idea is to ensure the trustworthiness of computing services and enhance the trust of systems and networks from an architectural perspective by protecting trusted code.

[0061] In combination with the first aspect, in certain implementations of the first aspect, the task execution device is a first server, which communicates with the first blockchain node. The first server is used to perform inference calculations on the first model based on the task information to obtain an inference result of the first model, and the first server sends the inference result to the TEE for verification.

[0062] In combination with the first aspect, in certain implementations of the first aspect, the task execution device is the TEE, and the TEE communicates with the first server. The method also includes: the first server receives the task information sent by the first blockchain node; the first server sends the task information to the TEE, and the TEE is used to perform inference calculation on the first model according to the task information to obtain the inference result of the first model, and verify the inference result to obtain the verified inference result.

[0063] In combination with the first aspect, in certain implementations of the first aspect, the task execution device is an edge server, and the method further includes: the first server or the TEE sends the task information to the edge server, and the edge server performs inference calculations on the first model based on the task information to obtain an inference result of the first model; the edge server sends the inference result to the TEE for verification.

[0064] In combination with the first aspect, in certain implementations of the first aspect, the TEE is deployed on the first blockchain node.

[0065] In a second aspect, a method for data processing is provided, including: a first blockchain node receives task information, the first blockchain node is a node in the blockchain system; the first blockchain node sends the task information to a task execution device, the task execution device is a server outside the blockchain system, the task execution device is used to perform inference calculation on a first model according to the task information to obtain an inference result of the first model, and the input of the first model includes the task information; the task execution device sends the inference result to a trusted execution environment TEE for verification to obtain a verified inference result; the first blockchain node obtains the verified inference result from the TEE and stores it on the chain; other nodes in the blockchain system re-verify the verified inference result through their respective corresponding TEEs, and store the re-verified inference result on the chain.

[0066] In combination with the second aspect, in certain implementations of the second aspect, the task execution device is a first server, which communicates with the first blockchain node. The first server is used to perform inference calculations on the first model based on the task information to obtain an inference result of the first model, and the first server sends the inference result to the TEE for verification.

[0067] In combination with the second aspect, in certain implementations of the second aspect, the task execution device is the TEE, and the TEE communicates with the first server. The method also includes: the first server receives the task information sent by the first blockchain node; the first server sends the task information to the TEE, and the TEE is used to perform inference calculation on the first model based on the task information to obtain the inference result of the first model, and verify the inference result to obtain the verified inference result.

[0068] In combination with the second aspect, in certain implementations of the second aspect, the task execution device is an edge server, and the method also includes: the first server or the TEE sends the task information to the edge server, and the edge server performs inference calculations on the first model based on the task information to obtain an inference result of the first model; the edge server sends the inference result to the TEE for verification.

[0069] In combination with the second aspect, in certain implementations of the second aspect, the TEE is deployed on the first blockchain node.

[0070] Embodiment 1

[0071] like Figure 1 As shown, this embodiment provides a data processing method integrating trusted computing and blockchain, including:

[0072] Acquire industrial control data, calculate the correlation of the industrial control data according to the trained long short-term memory network, feed the calculation result back to the industrial control data, and generate labeled industrial control data;

[0073] Encrypting the marked industrial control data through a trusted computing model to obtain encrypted industrial control data;

[0074] Sending the encrypted industrial control data to an industrial control data acquisition engine for parsing to obtain the parsed industrial control data; wherein the industrial control data acquisition engine is generated by a trusted computing monitoring system;

[0075] The parsed industrial control data is verified, and the parsed industrial control data after verification is transmitted to the blockchain for constructing a knowledge graph to generate a knowledge graph of the industrial control data.

[0076] Furthermore, the industrial control data includes but is not limited to equipment data, production process data, control data, maintenance data, energy consumption data, inventory and supply chain data, security data and network communication data.

[0077] Device data includes:

[0078] Sensor data: including physical parameters such as temperature, pressure, vibration, humidity, flow, etc.

[0079] Status information: equipment operating status, performance indicators, health status, etc.

[0080] Production process data includes:

[0081] Process parameters: such as production speed, raw material usage, and production batch information.

[0082] Quality data: product quality inspection results, defect rate, pass rate, etc.

[0083] Control data includes:

[0084] PLC data: control instructions issued by a programmable logic controller (PLC).

[0085] SCADA system data: Data from the supervisory control and data acquisition system, including real-time monitoring information.

[0086] Maintenance data includes:

[0087] Predictive maintenance data: Predict when maintenance is needed based on equipment operating data.

[0088] Maintenance records: historical maintenance data, fault causes and solutions.

[0089] Energy consumption data includes:

[0090] Power consumption: This includes the amount of electricity used by individual devices or the entire facility.

[0091] Other resource consumption: such as water, natural gas, etc.

[0092] Inventory and supply chain data includes:

[0093] Inventory levels: Data on raw materials and finished goods inventory.

[0094] Logistics information: Shipping, delivery, and receiving data in the supply chain.

[0095] Safety and compliance data includes:

[0096] Safety incident data: data on accidents, hazardous events and their response measures.

[0097] Compliance Data: Data records that ensure operations comply with industry standards and regulations.

[0098] Network and communications data includes:

[0099] Communication log: communication data between devices or between devices and the control center.

[0100] Network performance data: Network performance indicators such as latency, throughput, and error rate.

[0101] Furthermore, the process of calculating the correlation of the industrial control data according to the trained long short-term memory network includes:

[0102] Constructing a long-short time neural network, adding the sum of the absolute values ​​of the weights to the loss function of the long-short time neural network as a penalty term, and randomly discarding a portion of neurons for training to generate a model after the first training;

[0103] Constructing an attention mechanism and a residual module and fusing them with the model after the first training to obtain an optimized model;

[0104] Adding the sum of squares of weights as a penalty term to the loss function of the optimization model and then performing training to obtain a trained long short-term memory network;

[0105] The industrial control data is input into the trained long short-term memory network for calculation to generate a calculation result.

[0106] In deep learning, the capacity and complexity of the model are directly affected by the number of hidden layers and the number of neurons in each layer. Increasing the number of hidden layers or neurons can enable the model to learn more complex features and is suitable for processing complex time series data. However, increasing complexity may lead to overfitting of the model, especially when the amount of data is insufficient or the noise is large.

[0107] By adding the sum of the absolute values ​​of the weights as a penalty term in the loss function, some weights in the network are forced to zero, thereby achieving the purpose of feature selection and dimensionality reduction. This helps reduce the complexity of the model and prevent overfitting.

[0108] By adding the sum of squares of weights as a penalty term in the loss function, the size of the weights is limited, making the model more inclined to learn smaller weights. This can also reduce the complexity of the model and improve generalization ability.

[0109] Dropout is a technique that randomly discards a portion of neurons during training. By introducing noise, Dropout can effectively prevent neural network overfitting and improve the generalization ability of the model.

[0110] In addition, PyTorch provides a variety of optimizers, such as SGD, Adam, and RMSprop. For different tasks and data, choosing the right optimizer can improve training efficiency and model performance.

[0111] In deep LSTM networks, due to the existence of the back-propagation algorithm, there will be problems with gradient vanishing or gradient exploding. Gradient clipping is a commonly used method to alleviate this problem. Its basic principle is to limit the gradient during the back-propagation process and clip it when the norm of the gradient exceeds the set threshold. This can avoid the impact of excessive or too small gradients on model training.

[0112] Furthermore, the process of encrypting the marked industrial control data through the trusted computing model to obtain the encrypted industrial control data includes: extracting the verification value of the marked industrial control data, comparing the verification value with the expected verification value, encrypting the verified data according to the trusted computing model, and generating the encrypted industrial control data, wherein the encrypted industrial control data is a block.

[0113] Select an appropriate verification method according to the specific requirements of the industrial control protocol, such as CRC verification, hash value calculation, etc.

[0114] The check digit is extracted from the parsed data or the check value is calculated using a check algorithm.

[0115] Compare the calculated checksum with the expected checksum to ensure the integrity and accuracy of the data. Use encryption technology, such as SSL / TLS protocol, to ensure the security of data during transmission. Select appropriate data transmission protocols, such as HTTP, WebSocket, etc., based on real-time and reliability requirements. Encapsulate the verified data into blocks, each of which contains a certain number of transaction records or data entries. Ensure the consistency and security of data among multiple nodes through consensus mechanisms such as PoW (proof of work) and PoS (proof of stake). Use smart contracts to automatically execute data on-chain operations to ensure that data cannot be tampered with once on-chain.

[0116] Furthermore, the encrypted industrial control data is sent to the industrial control data acquisition engine for parsing, and the process of obtaining the parsed industrial control data includes:

[0117] Build a trusted security management mechanism based on the on-site control equipment layer and monitoring management layer;

[0118] Building an industrial control data acquisition engine based on the trusted security management mechanism;

[0119] The encrypted industrial control data is sent to the industrial control data acquisition engine for parsing to obtain the parsed industrial control data.

[0120] A trusted security management mechanism is built between the field control device layer and the monitoring management layer in the industrial control system. This mechanism is used to supervise the system startup instruction process of the engineer station in the monitoring management layer, and complete the integrity measurement and trust chain transmission in the startup phase.

[0121] The internal data of the industrial control system is encrypted and stored through a trusted security management mechanism. This ensures the security of the data during storage and prevents unauthorized access and tampering.

[0122] Furthermore, before verifying the parsed industrial control data, it also includes: building multiple blockchain nodes based on the Hyperledger open source blockchain framework and time bank related smart contracts.

[0123] Install and configure a Hyperledger framework, such as Hyperledger Fabric or Hyperledger Sawtooth, on each computer where you want to create a node. Make sure all nodes have the same development environment and configuration.

[0124] Write a network configuration file to define the topology of the blockchain network, the roles of participants, the consensus mechanism, the identity authentication method, etc. Ensure that the network configuration file can support the joining and communication of multiple nodes.

[0125] Start a blockchain node on each computer and connect to the blockchain network using the same network configuration file. Make sure each node can start normally and join the network.

[0126] Develop and deploy smart contracts related to time banks, ensure that the contract logic meets the needs of time banks and can run in the blockchain network. Deploy contracts to each node and ensure the consistency and synchronization of contracts.

[0127] Configure the identity and permissions of each node to ensure that only authorized participants can access and execute smart contracts. Use identity management tools provided by Hyperledger, such as Fabric CA or Sawtooth Identity, to manage the identity and permissions of nodes.

[0128] Furthermore, the process of constructing multiple blockchain nodes includes:

[0129] Configure the Hyperledger framework and write network configuration files to define the topology of the blockchain network, the roles of participants, the consensus mechanism, and the identity authentication method information;

[0130] Starting a blockchain node based on the Hyperledger framework, connecting to the blockchain node through the network configuration file, and generating a blockchain network;

[0131] Constructing a time bank-related smart contract and deploying the smart contract to each node of the blockchain network;

[0132] Testing the network of each node of the blockchain network, configuring the communication protocol and data transmission method between the nodes, and generating access nodes;

[0133] Monitor the operating status and performance indicators of the blockchain network, and maintain nodes and smart contracts.

[0134] The data model uses the tamper-proof feature of blockchain to ensure data transmission and data authenticity. For example, various data transmission records of the time bank (such as time recharge, exchange, etc.) are stored in the blockchain in the form of data transmission. Each data transmission needs to be digitally signed, and can only be added to the blockchain after the data transmission initiator signs. Each new data transmission block needs to be verified by other nodes through consensus algorithms (such as PoW, PoS, etc.) before it is permanently recorded on the blockchain. Once the data transmission is recorded in the blockchain, its hash value is irreversible and tamper-proof, ensuring the atomicity and tamper-proof nature of the data transmission record. Nodes continuously synchronize and verify new data transmission blocks to form a distributed ledger to record all data transmission history. Users and systems verify specific data transmission or time balance information in the account by querying the blockchain data transmission record. At the same time, encryption algorithms are used to protect personal privacy data from being leaked during data transmission.

[0135] Furthermore, the verified and parsed industrial control data is transmitted to the blockchain for constructing a knowledge graph, and the process of generating a knowledge graph of the industrial control data includes:

[0136] The verified and parsed industrial control data is transmitted to the off-chain industrial control database corresponding to each node in the blockchain;

[0137] Extract key concepts and relevance of data, and establish concept nodes and relationship edges;

[0138] Calculating implicit connections between data based on the correlations;

[0139] Based on the implicit connection, the concept nodes and relationship edges are updated to generate a knowledge graph of the industrial control data;

[0140] The knowledge graph of the industrial control data is fed back to each node in the blockchain for updating.

[0141] This embodiment also develops an application system based on the blockchain network, including an APP front-end and a back-end management system. Configure permission management and encryption algorithms, use a decentralized multi-node deployment method to run the network, and deploy and test smart contracts and application system functions on the test network. Test network connectivity and performance, and check the implementation of App and management system functions. When building blockchain infrastructure, contracts and application development, an open blockchain network is established, allowing relevant institutions / individuals to participate as nodes and jointly maintain network operations. Design standardized data models and interfaces to support data exchange and sharing between different nodes. At the same time, use smart contracts to implement resource calls and service binding, facilitate multi-party resource integration applications, develop open platform APIs, and support third-party application access and resource integration into the system. Add incentive mechanisms to the system to encourage data sharing designs, such as issuing token rewards based on contribution, and through user feedback, incentivize optimization of service content and service satisfaction, and finally form a consensus mechanism.

[0142] On the other hand, this embodiment also provides an electronic device, including a memory, a processor, and a computing program stored in the memory and executable on the processor, characterized in that the data processing method integrating trusted computing and blockchain is implemented when the processor executes the computer program.

[0143] On the other hand, this embodiment also provides a computer-readable storage medium, which stores a computer program, and is characterized in that when the computer program is executed by a processor, it implements the data processing method that integrates trusted computing and blockchain.

[0144] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A data processing method integrating trusted computing and blockchain, characterized in that: The following steps are involved: Acquire industrial control data, calculate the correlation of the industrial control data according to the trained long short-term memory network, feed the calculation result back to the industrial control data, and generate labeled industrial control data; Encrypting the marked industrial control data through a trusted computing model to obtain encrypted industrial control data; Sending the encrypted industrial control data to an industrial control data acquisition engine for parsing to obtain the parsed industrial control data; wherein the industrial control data acquisition engine is generated by a trusted computing monitoring system; The parsed industrial control data is verified, and the parsed industrial control data after verification is transmitted to the blockchain for constructing a knowledge graph to generate a knowledge graph of the industrial control data.

2. The data processing method integrating trusted computing and blockchain according to claim 1 is characterized in that: The industrial control data includes but is not limited to equipment data, production process data, control data, maintenance data, energy consumption data, inventory and supply chain data, safety data and network communication data.

3. The data processing method integrating trusted computing and blockchain according to claim 1 is characterized in that: The process of calculating the correlation of the industrial control data according to the trained long short-term memory network includes: Constructing a long-short time neural network, adding the sum of the absolute values ​​of the weights to the loss function of the long-short time neural network as a penalty term, and randomly discarding a portion of neurons for training to generate a model after the first training; Constructing an attention mechanism and a residual module and fusing them with the model after the first training to obtain an optimized model; Adding the sum of squares of weights as a penalty term to the loss function of the optimization model and then performing training to obtain a trained long short-term memory network; The industrial control data is input into the trained long short-term memory network for calculation to generate calculation results.

4. The data processing method integrating trusted computing and blockchain according to claim 1 is characterized in that: The process of encrypting the marked industrial control data through the trusted computing model to obtain the encrypted industrial control data includes: extracting the verification value of the marked industrial control data, comparing the verification value with the expected verification value, encrypting the verified data according to the trusted computing model, and generating the encrypted industrial control data, wherein the encrypted industrial control data is a block.

5. The data processing method integrating trusted computing and blockchain according to claim 1 is characterized in that: The encrypted industrial control data is sent to the industrial control data acquisition engine for parsing. The process of obtaining the parsed industrial control data includes: Build a trusted security management mechanism based on the on-site control equipment layer and monitoring management layer; Building an industrial control data acquisition engine based on the trusted security management mechanism; The encrypted industrial control data is sent to the industrial control data acquisition engine for parsing to obtain the parsed industrial control data.

6. The data processing method integrating trusted computing and blockchain according to claim 1 is characterized in that: Before verifying the parsed industrial control data, it also includes: building multiple blockchain nodes based on the Hyperledger open source blockchain framework and time bank related smart contracts.

7. The data processing method integrating trusted computing and blockchain according to claim 6 is characterized in that: The process of building multiple blockchain nodes includes: Configure the Hyperledger framework and write network configuration files to define the topology of the blockchain network, the roles of participants, the consensus mechanism, and the identity authentication method information; Starting a blockchain node based on the Hyperledger framework, connecting to the blockchain node through the network configuration file, and generating a blockchain network; Constructing a time bank-related smart contract and deploying the smart contract to each node of the blockchain network; Testing the network of each node of the blockchain network, configuring the communication protocol and data transmission method between the nodes, and generating access nodes; Monitor the operating status and performance indicators of the blockchain network, and maintain nodes and smart contracts.

8. The data processing method integrating trusted computing and blockchain according to claim 1 is characterized in that: The process of transmitting the verified and parsed industrial control data to the blockchain for constructing a knowledge graph and generating a knowledge graph of the industrial control data includes: The verified and parsed industrial control data is transmitted to the off-chain industrial control database corresponding to each node in the blockchain; Extract key concepts and relevance of data, and establish concept nodes and relationship edges; Calculating implicit connections between data based on the correlations; Based on the implicit connection, the concept nodes and relationship edges are updated to generate a knowledge graph of the industrial control data; The knowledge graph of the industrial control data is fed back to each node in the blockchain for updating.

9. An electronic device comprising a memory, a processor, and a computing program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method described in any one of claims 1 to 8 is implemented.

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