Internet of vehicles control method and system based on block chain and smart contract

Abstract

The invention discloses an Internet of Vehicles control method and system based on a block chain and a smart contract, and the method comprises the steps: carrying out the learning of target Internet of Vehicles traffic data on a block chain network through employing an artificial intelligence algorithm, obtaining block chain domain knowledge, and constructing a block chain domain knowledge base according to the block chain domain knowledge; when the current real-time Internet of Vehicles traffic data is uploaded to the block chain network and the target smart contract satisfies a preset execution condition, the target smart contract generates a target traffic processing scheme for the current Internet of Vehicles traffic condition according to the block chain domain knowledge base and the current real-time Internet of Vehicles traffic data, and controlling the target vehicle to execute a corresponding action according to the target traffic processing scheme. The intelligent contract can be dynamically adjusted according to real-time traffic conditions and environmental factors, the most appropriate decision path is automatically selected, more accurate decision support is provided, the resource scheduling and traffic management efficiency in the Internet of Vehicles is improved, and the intelligent contract can be widely applied to the technical field of computers.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to an Internet of Vehicles control method and system based on blockchain and smart contracts. Background Art

[0002] Currently, although the Internet of Vehicles system has begun to use blockchain technology to improve data security and transparency, there are still some technical defects and challenges. First, the scalability of blockchain smart contracts is limited. Traditional smart contracts are mostly based on simple "If-Then" logic and cannot handle complex Internet of Vehicles data types and scenarios. Second, the problem of data islands between different blockchain systems leads to ineffective information sharing, resulting in data redundancy and low efficiency. In addition, current smart contracts lack sufficient intelligence and cannot make dynamic adjustments and provide precise decision support according to real-time traffic conditions.

[0003] In summary, the technical problems existing in the related technologies need to be improved. Summary of the Invention

[0004] Embodiments of this application aim to at least solve one of the technical problems in the related technologies to some extent. For this reason, the main purpose of the embodiments of this application is to propose an Internet of Vehicles control method and system based on blockchain and smart contracts, which can make dynamic adjustments according to real-time traffic conditions and environmental factors, automatically select the most suitable decision-making path, provide more precise decision support, and improve resource scheduling and traffic management efficiency in the Internet of Vehicles.

[0005] To achieve the above object, on the one hand, an embodiment of this application proposes an Internet of Vehicles control method based on blockchain and smart contracts. The method includes the following steps:

[0006] Obtain the original Internet of Vehicles traffic data of the Internet of Vehicles system;

[0007] Preprocess the original Internet of Vehicles traffic data to obtain target Internet of Vehicles traffic data;

[0008] Upload the target Internet of Vehicles traffic data to the blockchain network corresponding to the Internet of Vehicles system; a target smart contract is deployed in the blockchain network;

[0009] Use an artificial intelligence algorithm to learn the target Internet of Vehicles traffic data on the blockchain network to obtain blockchain domain knowledge, and construct a blockchain domain knowledge base according to the blockchain domain knowledge;

[0010] When the current real-time vehicle networking traffic data is uploaded to the blockchain network and the target smart contract meets the preset execution conditions, the target smart contract generates a target traffic processing solution for the current vehicle networking traffic condition according to the blockchain domain knowledge base and the current real-time vehicle networking traffic data, so as to control the target vehicle to perform corresponding actions according to the target traffic processing solution.

[0011] To achieve the above object, another aspect of the embodiments of the present application proposes a vehicle networking control system based on a blockchain and a smart contract. The system includes the following modules:

[0012] An original vehicle networking traffic data acquisition module, configured to acquire the original vehicle networking traffic data of the vehicle networking system;

[0013] An original vehicle networking traffic data preprocessing module, configured to preprocess the original vehicle networking traffic data to obtain target vehicle networking traffic data;

[0014] A target vehicle networking traffic data on-chain module, configured to upload the target vehicle networking traffic data to the blockchain network corresponding to the vehicle networking system; a target smart contract is deployed in the blockchain network;

[0015] A blockchain domain knowledge base construction module, configured to use an artificial intelligence algorithm to learn the target vehicle networking traffic data on the blockchain network to obtain blockchain domain knowledge, and construct a blockchain domain knowledge base according to the blockchain domain knowledge;

[0016] A smart contract execution module, configured to when the current real-time vehicle networking traffic data is uploaded to the blockchain network and the target smart contract meets the preset execution conditions, the target smart contract generates a target traffic processing solution for the current vehicle networking traffic condition according to the blockchain domain knowledge base and the current real-time vehicle networking traffic data, so as to control the target vehicle to perform corresponding actions according to the target traffic processing solution.

[0017] To achieve the above object, another aspect of the embodiments of the present application proposes an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the foregoing method is implemented.

[0018] To achieve the above object, another aspect of the embodiments of the present application proposes a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the foregoing method is implemented.

[0019] The embodiments of the present application at least include the following beneficial effects: The present application provides a vehicle networking control method and system based on blockchain and smart contracts. This solution obtains the original vehicle networking traffic data of the vehicle networking system; preprocesses the original vehicle networking traffic data to obtain the target vehicle networking traffic data; uploads the target vehicle networking traffic data to the corresponding blockchain network of the vehicle networking system; a target smart contract is deployed in the blockchain network; uses an artificial intelligence algorithm to learn the target vehicle networking traffic data on the blockchain network to obtain blockchain domain knowledge, and constructs a blockchain domain knowledge base based on the blockchain domain knowledge; when the current real-time vehicle networking traffic data is uploaded to the blockchain network and the target smart contract meets the preset execution conditions, the target smart contract generates a target traffic processing plan for the current vehicle networking traffic condition according to the blockchain domain knowledge base and the current real-time vehicle networking traffic data, so as to control the target vehicle to execute corresponding actions according to the target traffic processing plan. By introducing artificial intelligence and a blockchain domain knowledge base, the smart contract in the embodiments of the present application can be dynamically adjusted according to the real-time traffic condition and environmental factors. The smart contract can automatically select the most appropriate decision-making path according to complex traffic scenarios, providing more accurate decision-making support and improving the intelligent level of the smart contract; through the optimization and automatic execution of the smart contract, the system can respond to traffic changes in real time, improving the resource scheduling and traffic management efficiency in the vehicle networking. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a flowchart of the vehicle networking control method based on blockchain and smart contracts provided by the embodiments of the present application;

[0021] Figure 2 is a schematic structural diagram of the smart contract design provided by the embodiments of the present application;

[0022] Figure 3 is a schematic structural diagram of the vehicle networking control system based on blockchain and smart contracts provided by the embodiments of the present application;

[0023] Figure 4 is a schematic hardware structure diagram of the electronic device provided by the embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] In order to make the objectives, technical solutions, and advantages of this application clearer and more understandable, the following further details this application in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely used to explain this application and are not intended to limit this application. When the following description involves the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the embodiments of this application. They are merely examples of systems and methods that are consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0025] It can be understood that the terms "first", "second", etc. used in this application may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, the first information may also be referred to as the second information. Similarly, the second information may also be referred to as the first information. Depending on the context, the words "if", "when" as used herein may be interpreted as "when...", "while...", or "in response to determining".

[0026] The terms "at least one", "multiple", "each", "any one", etc. used in this application, at least one includes one, two, or more than two, multiple includes two or more than two, each refers to each of the corresponding multiple, and any one refers to any one of the multiple.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0028] Currently, although the vehicle networking system has begun to use blockchain technology to improve data security and transparency, there are still some technical defects and challenges. First, the scalability of blockchain smart contracts is limited. Traditional smart contracts are mostly based on simple "If-Then" logic and cannot handle complex vehicle networking data types and scenarios. Second, the data silo problem between different blockchain systems results in ineffective information sharing, causing data redundancy and low efficiency. In addition, the current smart contracts lack sufficient intelligence and cannot make dynamic adjustments and provide accurate decision support based on real-time traffic conditions.

[0029] In view of this, the embodiments of the present application provide a vehicle networking control method and system based on blockchain and smart contracts. The solution includes obtaining the original vehicle networking traffic data of the vehicle networking system; preprocessing the original vehicle networking traffic data to obtain target vehicle networking traffic data; uploading the target vehicle networking traffic data to the blockchain network corresponding to the vehicle networking system; a target smart contract is deployed in the blockchain network; using an artificial intelligence algorithm to learn the target vehicle networking traffic data on the blockchain network to obtain blockchain domain knowledge, and constructing a blockchain domain knowledge base according to the blockchain domain knowledge; when the current real-time vehicle networking traffic data is uploaded to the blockchain network and the target smart contract meets the preset execution conditions, the target smart contract generates a target traffic processing plan for the current vehicle networking traffic condition according to the blockchain domain knowledge base and the current real-time vehicle networking traffic data, so as to control the target vehicle to perform corresponding actions according to the target traffic processing plan. By introducing artificial intelligence and blockchain domain knowledge base, the smart contract in the embodiments of the present application can be dynamically adjusted according to the real-time traffic conditions and environmental factors. The smart contract can automatically select the most suitable decision-making path according to complex traffic scenarios, providing more accurate decision-making support and improving the intelligent level of the smart contract; through the optimization and automatic execution of the smart contract, the system can respond to traffic changes in real time, improving the resource scheduling and traffic management efficiency in vehicle networking.

[0030] The vehicle networking control method based on blockchain and smart contracts provided by the embodiments of the present application relates to the field of computer technology. The vehicle networking control method based on blockchain and smart contracts provided by the embodiments of the present application can be applied to a terminal, can also be applied to a server, or can be software running on a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, a vehicle-mounted terminal, etc., but is not limited thereto; the server side can be configured as an independent physical server, can also be configured as a server cluster or a distributed system composed of multiple physical servers, or can be configured as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. The server can also be a node server in the blockchain network; the software can be an application implementing the vehicle networking control method based on blockchain and smart contracts, etc., but is not limited to the above forms.

[0031] This application can be used in numerous general-purpose or special-purpose computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs (Personal Computers), minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This application can also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0032] It should be noted that in each specific implementation manner of this application, when it comes to relevant processing based on data related to the user's identity or characteristics, such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first. Moreover, the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when this application embodiment needs to obtain the user's sensitive personal information, the user's separate permission or separate consent will be obtained through methods such as pop-up windows or redirecting to a confirmation page. After clearly obtaining the user's separate permission or separate consent, the necessary user-related data for the normal operation of this application embodiment will be obtained.

[0033] Please refer to Figure 1 , Figure 1 which is an optional flowchart of the vehicle networking control method based on blockchain and smart contracts provided by an embodiment of this application. Figure 1 The method in

[0034] may include but is not limited to steps S101 to S105.

[0035] In some embodiments, step S101 may include: obtaining original in-vehicle sensor data through in-vehicle sensors of vehicles in the vehicle networking system; wherein the original in-vehicle sensor data includes at least one of the following: the vehicle surrounding environment, traffic conditions, vehicle speed, and vehicle location; obtaining original off-chain traffic data corresponding to the vehicle networking system from outside the blockchain network using oracle technology; wherein the original off-chain traffic data is real-time traffic data outside the blockchain network, and the original off-chain traffic data includes at least one of the following: current real-time traffic conditions, weather information, and road closure conditions; fusing the original in-vehicle sensor data and the original off-chain traffic data to obtain original vehicle networking traffic data.

[0036] Optionally, the original vehicle networking traffic data includes the original in-vehicle sensor data and the original off-chain traffic data. The original vehicle networking traffic data is used to be uploaded to the blockchain network and participate in the construction of the blockchain domain knowledge base.

[0037] Among them, the original in-vehicle sensor data is the in-vehicle sensor data without any processing. The in-vehicle sensor data refers to real-time traffic data collected by sensors on intelligent vehicles in the vehicle networking and other vehicles. The in-vehicle sensor data may include, but is not limited to: road conditions, vehicle location, vehicle speed, traffic events (such as accidents, congestion), weather changes, etc. These data are uploaded to the blockchain in real time, and every time a smart contract is triggered, new data will be recorded in the blockchain.

[0038] For the original off-chain traffic data, it is the off-chain traffic data without any processing. The off-chain traffic data is data obtained from outside the blockchain using oracle technology. The off-chain traffic data is real-time traffic data outside the blockchain network. The off-chain traffic data may include, but is not limited to: such as weather, traffic signal changes, road closures, etc. By introducing the off-chain traffic data into the blockchain, the data source of the blockchain is further enriched. Among them, oracle technology is a mechanism that connects the blockchain with the external world (such as real-time traffic conditions, weather information, road closure conditions, etc.). It obtains external real-time data and introduces it into the blockchain system, thus supplementing the dynamic environmental data that the blockchain itself cannot access.

[0039] Step S102, preprocess the original vehicle networking traffic data to obtain target vehicle networking traffic data;

[0040] In some embodiments, step S102 may include: performing denoising processing on the original vehicle networking traffic data to obtain clean vehicle networking traffic data; performing standardization processing on the clean vehicle networking traffic data to obtain standard vehicle networking traffic data; performing feature extraction processing on the standard vehicle networking traffic data to obtain target vehicle networking traffic data.

[0041] Among them, the target vehicle networking traffic data includes target vehicle-mounted sensor data and target off-chain traffic data. The target vehicle-mounted sensor data refers to the vehicle-mounted sensor data after preprocessing, and the target off-chain traffic data refers to the off-chain traffic data after preprocessing.

[0042] In specific implementation, the specific implementation steps of data collection and preprocessing are as follows:

[0043] (1) Vehicle networking data collection: including vehicle-mounted sensor data collected from vehicle-mounted sensors (such as vehicle position, speed, driving status, surrounding environment, etc.), and external environment data obtained by an oracle (such as traffic signals, weather, etc.).

[0044] Optionally, the vehicle-mounted sensor data is mainly directly collected by sensors in the vehicle networking. In the initial data collection stage, the intervention of an oracle is not required for these vehicle-mounted sensor data. The vehicle-mounted sensor data is collected through the sensor nodes local to the vehicle and is real-time information directly related to the vehicle itself. The external environment data is obtained by the oracle from the off-chain environment. The oracle is a bridge between the blockchain system and the external world, responsible for obtaining data from the outside and introducing it into the blockchain. Therefore, in the initial stage of vehicle networking data collection, if external environment data (such as weather, traffic signals, etc.) is involved, the intervention of the oracle is required to introduce this external data into the blockchain network for subsequent use by smart contracts and decision-making models, while the collection of vehicle-mounted sensor data does not require the direct intervention of the oracle.

[0045] Among them, the blockchain data and oracle data may contain outliers, missing data, or inaccurate information, and data cleaning methods (such as mean filling, interpolation, outlier detection, etc.) are required to clean the data. In order to make the data adapt to different machine learning and deep learning algorithms, it is necessary to standardize the data to make it meet the model input requirements, which is beneficial to improving the training effect and avoiding affecting the accuracy of model training due to different data scales.

[0046] (2) Perform preprocessing operations such as denoising, standardization, and formatting on the vehicle networking data to ensure the quality and consistency of the data. Only after these data are cleaned and transformed can they be used as the basis for further analysis and modeling.

[0047] (3) In addition to preprocessing operations such as noise removal, normalization, and formatting, for the convenience of processing, basic feature extraction can also be classified into the preprocessing steps. That is, machine learning techniques (such as clustering and classification algorithms) are used to extract features from the preprocessed data, and key features that are significantly meaningful for scenarios such as traffic management, vehicle behavior prediction, and accident handling are identified, such as the degree of traffic congestion, the impact of meteorological data on traffic flow, and the running trajectories of vehicles. That is, the target vehicle networking traffic data is obtained, and these features will be used as the input for model training. For example, in traffic flow prediction, historical and real-time traffic data can be processed through deep learning algorithms (such as convolutional neural networks and long short-term memory networks) to predict the probability of congestion occurrence.

[0048] In the embodiment of the present application, the vehicle collects various information such as the surrounding environment, traffic conditions, vehicle speed, and location through in-vehicle sensor nodes, and shares and collaborates with the data of other vehicles in the vehicle networking. The sensor nodes convert these data into digital signals and perform preliminary preprocessing, such as noise removal, normalization, and formatting, to obtain the target in-vehicle sensor data, ensuring the integrity and accuracy of the data. At the same time, real-time traffic data (such as traffic signals, weather conditions, road closure information, etc.) is obtained from outside the blockchain through oracle technology, and the same preprocessing operations are also performed on the traffic data outside the blockchain to obtain the target traffic data outside the blockchain, ensuring the integrity and accuracy of the data.

[0049] It should be noted that in the embodiment of the present application, the data for constructing the knowledge base in the blockchain field includes not only in-vehicle sensor data and traffic data outside the blockchain, but also internal blockchain data on the blockchain network. Among them, the internal blockchain data includes transaction data, block information, node status, and smart contract execution results on the blockchain. The internal blockchain data provides important background information for the state and events of the blockchain network. That is, data collection is the primary step in constructing the knowledge base in the blockchain field, involving obtaining key data (in-vehicle sensor data, traffic data outside the blockchain, and internal blockchain data) from different sources. Before the multi-source data is input into machine learning and deep learning models, preprocessing must be performed to ensure the quality and adaptability of the data. It can be understood that in addition to in-vehicle sensor data and traffic data outside the blockchain, the internal blockchain data on the blockchain network also needs to be collected. Before being input into machine learning and deep learning models, data preprocessing and feature extraction are also required. The data preprocessing and feature extraction processes for the internal blockchain data are similar to those for in-vehicle sensor data and traffic data outside the blockchain, and are not elaborated in the embodiment of the present application.

[0050] Step S103: Upload the target vehicle networking traffic data to the blockchain network corresponding to the vehicle networking system; a target smart contract is deployed in the blockchain network.

[0051] In specific implementation, the preprocessed vehicle data is uploaded to the blockchain network. During the upload process, the data is verified and signed through the smart contract to ensure the credibility and security of the data. Moreover, when the data is uploaded to the blockchain, the smart contract will determine whether to automatically trigger the corresponding execution process according to the vehicle data based on preset rules (such as time, space, traffic rules, etc.). For example, when a vehicle collides within a specific area, the smart contract can automatically trigger the accident handling process, record the event, and notify other relevant parties.

[0052] Among them, when the target vehicle networking traffic data is uploaded to the blockchain network corresponding to the vehicle networking system, in addition to the target vehicle-mounted sensor data and the target external blockchain traffic data on the blockchain network, it also includes the open-source and closed-source data inside the blockchain. The data inside the blockchain includes transaction data, block information, node information, the execution status of smart contracts, etc. These data provide the original records for various events on the blockchain, including content such as the blockchain status, transaction logs, and the execution results of smart contracts.

[0053] When the target vehicle networking traffic data (target vehicle-mounted sensor data and target external blockchain traffic data) obtained after data preprocessing and feature extraction is uploaded to the blockchain network, there will be target vehicle-mounted sensor data, target external blockchain traffic data, and the internal blockchain data after data preprocessing and feature extraction in the blockchain network. Then, the data from different sources (the vehicle-mounted sensor data, external blockchain traffic data, and internal blockchain data after data preprocessing and feature extraction) are fused, integrated into a unified format, and then input into the artificial intelligence algorithm. By using artificial intelligence algorithms (such as machine learning and deep learning) for training to generate blockchain domain knowledge. Through data fusion, the system can identify traffic patterns, trends, and potential events, thereby generating valuable knowledge support. Machine learning and deep learning algorithms can help extract potential associations and rules from different data, promoting the support and optimization of smart contract decisions.

[0054] Step S104: Use artificial intelligence algorithms to learn the target vehicle networking traffic data on the blockchain network to obtain blockchain domain knowledge, and build a blockchain domain knowledge base according to the blockchain domain knowledge.

[0055] In some embodiments, step S104 may include: inputting the target vehicle networking traffic data on the blockchain network into a traffic scenario parsing model; training the traffic scenario parsing model according to the target vehicle networking traffic data through artificial intelligence algorithms to output blockchain domain knowledge; and constructing a blockchain domain knowledge base according to the blockchain domain knowledge.

[0056] Among them, the artificial intelligence algorithms may include machine learning, deep learning, etc.

[0057] Regarding the blockchain domain knowledge, it is obtained by processing, analyzing, and learning through artificial intelligence algorithms (such as machine learning, deep learning, etc.) based on new data. Exemplarily, based on new traffic data, the artificial intelligence model may identify some new traffic patterns or predict changes in vehicle behavior under specific conditions (such as special weather or accidents). The knowledge generated through learning will be transformed into "blockchain domain knowledge" and stored in the blockchain domain knowledge base. The blockchain domain knowledge includes information such as traffic rules, emergency handling solutions, and road traffic optimization strategies, which can provide decision-making support for subsequent smart contracts.

[0058] In specific implementations, whether it is the internal data of the blockchain, the sensor data obtained by in-vehicle sensors, or the external data obtained by oracles, ultimately, they all need to be analyzed and learned through artificial intelligence algorithms (such as machine learning, deep learning, etc.) to be transformed into blockchain domain knowledge. These blockchain domain knowledge can be stored in the blockchain domain knowledge base to guide the execution and optimization of smart contracts. However, external data (provided by oracles) usually comes from off-chain environments, such as traffic signals, weather conditions, road closures, etc. These data may have strong timeliness and variability; the internal data of the blockchain is the data generated and maintained by the blockchain network itself, such as transaction records, smart contract execution status, etc. Therefore, when external data enters the blockchain through an oracle, stronger real-time and reliability checks may be required, and additional verification and synchronization mechanisms may be needed during processing.

[0059] It should be noted that in the process of constructing the blockchain domain knowledge base, there are two data construction methods for transforming data into blockchain domain knowledge, and the specific content is as follows:

[0060] (1) Data is jointly transformed into domain knowledge after combination: If the goal is to consider the external data and internal data of the blockchain together to form a comprehensive knowledge system, the internal data of the blockchain can be combined with the external data provided by the oracle, and then they are jointly learned and transformed through artificial intelligence algorithms to generate unified domain knowledge. This method can ensure that all relevant data (whether inside or outside the chain) interact and integrate with each other during knowledge construction, and can provide more comprehensive and intelligent decision-making support.

[0061] (2) Independently update the knowledge base with data: Process the internal data of the blockchain and the external data obtained by the oracle separately. That is, first build an initial knowledge base based on the internal data of the blockchain, and then update or expand the existing knowledge base by introducing external data in subsequent processes. The external data and the internal data of the blockchain are processed independently and are interconnected through the knowledge base, rather than being directly combined. This approach can simplify data processing, but may result in less information flow between external and internal data, and more complex mechanisms are required to ensure effective integration between the two.

[0062] Therefore, whether the two types of data are combined and uniformly transformed into blockchain domain knowledge depends on the actual application scenario. Both can be processed and updated independently in the knowledge base, but in some cases, combining the two may yield more accurate and intelligent results. In the embodiments of this application, the knowledge of the blockchain domain is transformed by combining the internal data and external data of the blockchain to obtain more accurate and intelligent results.

[0063] For the traffic scenario analysis model, it operates on a blockchain network as a whole and consists of two parts: a collaborative structure and a shared structure, including four roles: an organizer, a verifier, a trainer, and an executor. The collaborative structure is responsible for providing outsourcing of learning tasks and collaborative mining of distributed participants, while the shared structure is responsible for collecting the verified and evaluated models generated in the collaborative structure to form a trusted model library and a trusted smart contract library. The role of the traffic scenario analysis model is to input traffic data into the model on the vehicle-mounted cloud side, and then process and analyze it through its collaborative structure and shared structure, and output an analysis solution for the traffic scenario.

[0064] Specifically, the traffic scenario analysis model is used to train based on historical data and real-time data to predict and analyze traffic scenarios, including traffic behaviors, accident occurrence patterns, etc. The traffic scenario analysis model uses supervised learning and unsupervised learning methods (such as classification algorithms, regression algorithms, clustering algorithms, etc.) to analyze and identify key features and patterns in vehicle-to-everything (V2X) traffic. It can be understood that the traffic scenario analysis model can be defined as a machine learning / deep learning model specifically designed to process complex traffic data involved in the V2X system. These data may include vehicle speed, traffic events (such as accidents and congestion), weather, traffic signals, etc. The traffic scenario analysis model is trained with these data to understand and predict traffic conditions, and the prediction results can help smart contracts make dynamic adjustments and decisions in traffic management. That is, the traffic scenario analysis model is used to analyze and process various traffic data in the V2X network, and generate intelligent decision support for traffic through machine learning and deep learning algorithms.

[0065] In specific implementation, based on the internal data of the blockchain and the external data obtained by the oracle, machine learning and deep learning algorithms are used for model training to construct an effective knowledge base in the field of blockchain. Among them, the selection and construction of models based on artificial intelligence algorithms may include the following:

[0066] (1) Supervised learning: Through existing labeled data (such as historical traffic data and event processing results), machine learning algorithms can train a classification or regression model to predict the occurrence and subsequent impacts of traffic events. For example, predicting the probability of traffic jams or accidents, so as to provide decision-making support for smart contracts.

[0067] (2) Unsupervised learning: Unsupervised learning algorithms can cluster data to discover potential patterns in the data. For example, by clustering data such as vehicle locations and speeds, specific traffic patterns or anomalies can be discovered, so as to make predictions or adjustments in future decisions.

[0068] (3) Deep learning: Deep learning algorithms, especially Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), can extract features from complex and time-series traffic data. For example, using RNN to analyze vehicle driving trajectories and traffic signal control, extracting time-series patterns in traffic flow, and constructing scenario-based smart contracts.

[0069] In the process of constructing the knowledge base in the field of blockchain, a knowledge base in the field of blockchain is constructed through the trained models and the blockchain domain knowledge extracted from the data. The knowledge base in the field of blockchain will include the following parts:

[0070] (1) Knowledge graph: The relationship between traffic rules, historical events and future predictions extracted through deep learning can be used to construct a knowledge graph containing various traffic events and their interconnections. For example, the knowledge graph can connect different traffic accidents, vehicle states and environmental factors, thus helping smart contracts make more accurate decisions in complex traffic scenarios.

[0071] (2) Rule and experience summary: Translate the rules and experiences learned from a large amount of data into decision-making logic in smart contracts. For example, specific traffic conditions may trigger specific smart contracts, and summarize and optimize the responses to various scenarios based on historical data.

[0072] In practical applications, as the data of the blockchain network continues to accumulate and the oracle technology obtains more external data, the knowledge in the knowledge base of the blockchain field will be continuously updated and optimized. Whenever new data (e.g., the latest traffic accident report or real-time traffic flow data) is introduced, the machine learning model will be retrained based on this new data, thereby optimizing the execution strategy of the smart contract. Moreover, the result feedback during the execution of the smart contract will also be incorporated into the knowledge base, and this feedback is conducive to correcting and optimizing the knowledge base of the blockchain field to ensure its accuracy and timeliness. Exemplarily, assuming that the processing result of a certain traffic event does not meet the expectation, the system can adjust the model according to the execution result.

[0073] In the embodiments of this application, in order to make the smart contract more intelligent, by introducing artificial intelligence technology and combining the real-time data (such as weather, road conditions, etc.) obtained from the outside by the oracle, the data inside and outside the blockchain is analyzed and learned to construct a knowledge base of the blockchain field. The knowledge base of the blockchain field includes knowledge such as traffic rules, emergency handling plans, and historical traffic data, and provides support for the decision-making of the smart contract. Moreover, the knowledge base of the blockchain field is used to support the learning and optimization of the smart contract, and improve its decision-making ability in complex traffic scenarios.

[0074] In specific implementation, using historical data and real-time data, supervised learning (such as classification algorithms, regression algorithms) or unsupervised learning (such as clustering algorithms) is used to train traffic scene parsing models. These models are trained based on data such as traffic behaviors and accident occurrence patterns, and can "learn" and predict complex traffic conditions; deep learning can be used to process more complex data features, such as image data (traffic monitoring videos collected by cameras) or time series data (such as real-time traffic flow data), and extract traffic patterns. Through the above learning process, knowledge in the blockchain field can be formed. These knowledge in the blockchain field include basic rules in traffic scenarios (such as the traffic flow rules of certain road sections), and how to make intelligent decisions according to traffic states (such as adjusting traffic lights, diverting traffic flows, etc.). Through multiple rounds of training and optimization, the model will become more and more accurate, and finally can "understand" complex traffic rules and behavior patterns to a certain extent. Then, based on the knowledge in the blockchain field obtained by learning, a knowledge base of the blockchain field is constructed. The knowledge base of the blockchain field can be regarded as a structured database, which contains various traffic rules, prediction models, event response strategies, etc.

[0075] Among them, the knowledge base of the blockchain field is dynamic. With the input of new data, the continuous optimization of the model, and the effect of the feedback mechanism, the content of the knowledge base of the blockchain field will be continuously updated, expanded, and improved, and finally form a knowledge system that can support the decision-making of the smart contract.

[0076] In the embodiments of the present application, technologies and methods such as oracle technology, machine learning, and deep learning are adopted to achieve the autonomous learning of the underlying algorithms, mechanisms, and protocols of the blockchain, and form a knowledge base in the blockchain field. Specifically, first, the existing data of the actual blockchain composed of open-source and closed-source data of the blockchain, as well as the off-chain actual data based on the oracle, are expanded to form the blockchain intelligent data that combines the virtual and the real. Further, the blockchain domain knowledge that has been repeatedly verified and the constructed blockchain domain knowledge base can be combined to design smart contracts, including efficient consensus algorithms, secure data distribution protocols, effective incentive mechanisms, reasonable digital asset valuation methods, etc., and convert them into modular and pluggable algorithm forms for subsequent encapsulation, screening, and combination, so as to provide accurate knowledge and decision-making for the actual blockchain system.

[0077] The repeated verification of the blockchain domain knowledge base, that is, through multiple learning and feedback iterations, makes the blockchain domain knowledge base gradually mature and reach the standard capable of accurately guiding the execution of blockchain smart contracts. The blockchain domain knowledge base after repeated verification will be used to support the operation of the actual blockchain system to improve the decision-making ability and execution efficiency of the system. The repeated verification process of the blockchain domain knowledge base can be understood as an iterative optimization process. Specifically, the repeated verification process of the blockchain domain knowledge base can include the following points:

[0078] (1) Construction of the initial knowledge base: In the initial stage of the system, based on the existing traffic data and domain knowledge (such as traffic rules, historical data, traffic models, etc.), a preliminary blockchain domain knowledge base is constructed. This preliminary blockchain domain knowledge base may contain some basic traffic patterns, prediction models, accident handling strategies, etc.

[0079] (2) Continuous learning and optimization: As the blockchain network runs, new traffic data is continuously collected, and the system continuously learns and gives feedback. For example, when a smart contract is executed, the blockchain will record the execution result, and these results will become the input data for model optimization.

[0080] (3) Repeated verification: Through continuous model training and verification, the blockchain domain knowledge base will be continuously optimized according to new data. For example, a certain traffic management strategy may not be as efficient as expected in actual application. After feedback and optimization, the model will adjust the decision-making rules, and these optimized blockchain domain knowledge will be fed back into the blockchain domain knowledge base and used as new decision-making bases in smart contracts.

[0081] (4) Maturity and Application of the Knowledge Base in the Blockchain Field: The initial knowledge base may not be completely accurate, and the system may need to go through multiple cycles (i.e., multiple validations and feedback optimizations) to stably provide accurate and reliable decision-making support for the blockchain network. Among them, a threshold is defined in the blockchain system, that is, the knowledge base will be used to guide the actual execution of the blockchain only after reaching a certain "maturity". This threshold may mean that when some optimization models have been verified and optimized multiple times, their accuracy and reliability reach the expected standards, so that they can be officially applied to the decision-making and execution processes of the blockchain system.

[0082] (5) Dynamic Update of the Knowledge Base: Since the traffic conditions and events in the vehicle networking environment are dynamically changing, the knowledge base in the blockchain field must be continuously updated. After new data is added, the knowledge base in the blockchain field will be updated regularly through a predefined learning strategy to adapt to the constantly changing traffic scenarios.

[0083] In practical applications, based on the knowledge base in the blockchain field, intelligent contracts that adapt to different traffic scenarios can be designed. The intelligent contracts will autonomously analyze information such as the current traffic conditions and vehicle positions, and select the optimal contract execution path. For example, in case of traffic congestion, the intelligent contracts may choose to reallocate the traffic flow, adjust the traffic signals, etc.; while in case of an accident, the intelligent contracts will execute tasks such as accident handling and resource allocation.

[0084] Step S105, when the current real-time vehicle networking traffic data is uploaded to the blockchain network and the target intelligent contract meets the preset execution conditions, the target intelligent contract generates a target traffic processing plan for the current vehicle networking traffic conditions according to the knowledge base in the blockchain field and the current real-time vehicle networking traffic data, so as to control the target vehicle to execute corresponding actions according to the target traffic processing plan.

[0085] Optionally, the current real-time vehicle networking traffic data includes current real-time in-vehicle sensor data and current real-time external blockchain traffic data, and the current real-time in-vehicle sensor data includes current user data and current vehicle position information. The current real-time vehicle networking traffic data refers to the data collected in real time, that is, it includes the in-vehicle sensor data and external blockchain traffic data collected in real time. It should be noted that the current real-time vehicle networking traffic data here refers to the data that has been preprocessed and feature-extracted. The steps of preprocessing and feature extraction are omitted, that is, the current real-time vehicle networking traffic data is the data that can be uploaded to the blockchain. The process of preprocessing and feature extraction is similar to the processing flow of the original vehicle networking traffic data, and this is not elaborated in this embodiment of the present application.

[0086] Among them, the smart contract is dynamically configured and executed through a "plug-in" architecture, and can flexibly combine different contract modules according to different traffic task requirements. These modules can include permission management smart contracts, data transmission smart contracts, model verification smart contracts, contribution evaluation smart contracts, incentive quantification smart contracts, etc. Each module is responsible for a specific function and can be adjusted according to real-time traffic data. Among them, modules such as permission management smart contracts, data transmission smart contracts, model verification smart contracts, contribution evaluation smart contracts, and incentive quantification smart contracts are the underlying implementation logics and basic functional modules of the system, ensuring the legality, transparency, and security of blockchain operations, and supporting smart contracts to call the knowledge base in the blockchain field to execute corresponding actions.

[0087] Among them, the permission management smart contract is used to manage the access permissions of different users (such as vehicles, sensors, service providers, etc.), ensuring that only authorized users can participate in certain smart contract operations; the data transmission smart contract is responsible for ensuring the correct transmission and verification of data, ensuring the security and reliability of information transmission; the model verification smart contract is used to verify whether the models constructed through machine learning and deep learning algorithms meet the expected requirements and whether they can correctly process the data in traffic scenarios; the contribution evaluation smart contract is used to evaluate and quantify the contributions made by different nodes (such as vehicles, sensors, etc.) in the blockchain network, and allocate rewards or resources according to the contribution situation; the incentive quantification smart contract is used to quantify the contributions of participants in the system and conduct incentives. Exemplarily, when a vehicle provides effective traffic information, the system gives rewards according to the preset incentive rules.

[0088] In some embodiments, it may further include: encrypting the current user data and the current vehicle location information using a preset encryption technology; uploading the encrypted current user data and the encrypted current vehicle location information to the blockchain network for storage.

[0089] In specific implementation, in order to ensure the privacy and security of vehicle network users, the embodiments of the present application adopt encryption technology to protect user data. The smart contract encrypts the user's personal information and vehicle location, and stores and verifies them on the chain. During the execution of the contract, the smart contract will only expose necessary shared information to ensure that privacy is not leaked.

[0090] In specific implementation, in order to ensure the privacy and security of vehicle network users, the embodiments of the present application adopt encryption technology to protect user data. The specific implementation process is as follows:

[0091] (1) User data collection and encryption;

[0092] 1) Data collection: The vehicle collects information such as the surrounding environment, vehicle speed, and location through in-vehicle sensor nodes. This information includes personal privacy data, such as vehicle location, speed, and driving habits.

[0093] 2) Data Encryption: After data collection, to prevent information leakage, sensitive data (such as vehicle location, personal identity information, etc.) is encrypted. Symmetric encryption or asymmetric encryption technology is used for encryption: For symmetric encryption, encryption algorithms such as AES (Advanced Encryption Standard) are used to encrypt the data collected by sensors, ensuring that only authorized parties with the encryption key can decrypt it; for particularly sensitive information such as user identity, public-key encryption technology such as RSA (Rivest-Shamir-Adleman) is used, and only the private-key holder (such as the user himself or the authorized management party) can decrypt it to ensure the privacy of the data.

[0094] Exemplarily, the encryption process is as follows: After the sensor collects data, the original data (such as vehicle location, vehicle speed, etc.) is first converted into an encrypted format; then a symmetric key (such as an AES key) or the public key in asymmetric encryption is used to encrypt the data; after the data is encrypted, the encrypted data is sent to the blockchain network for storage.

[0095] (2) Data Storage and Blockchain Verification;

[0096] 1) Blockchain Storage: The encrypted data is uploaded to the blockchain network for storage. The data stored on the blockchain has the characteristics of decentralization and immutability, preventing the data from being illegally tampered with or forged.

[0097] 2) Data Verification: On the blockchain, each transaction or data storage will be verified by multiple nodes. The verification process includes checking the integrity and correctness of the data through encryption algorithms. The smart contract verifies the data through a hash function to ensure that the uploaded encrypted data has not been tampered with. In addition, the nodes in the blockchain network verify each data block according to preset rules (such as digital signatures and public-key verification). The smart contract will check when the data is uploaded to confirm whether the data meets the standards and has not been tampered with.

[0098] (3) Execution of Smart Contracts and Privacy Protection;

[0099] 1) Triggering of Smart Contracts: Smart contracts are automatically executed according to events or conditions. To ensure privacy, when designing smart contracts, it is stipulated that only necessary shared information is exposed. Exemplarily, when a traffic accident occurs, the smart contract will only expose the summary of the accident location and relevant vehicle information, without revealing the specific owner's identity or the detailed location of the vehicle. The smart contract determines whether an event meets certain conditions (such as location, vehicle speed, etc.) based on the encrypted information without decrypting the entire dataset.

[0100] 2) Permission Control: The smart contract will set permissions for data visitors to ensure that only authorized parties can decrypt sensitive data. Exemplarily, assume that only authorized entities such as traffic management departments and insurance companies can access specific data, such as the complete information at the time of an accident. When the contract is executed, the user's personal information will remain encrypted, and only authorized verification nodes can access specific information (such as vehicle location or user identity).

[0101] (4) Encryption Verification and Interaction with External Data;

[0102] 1) External Data Verification (through Oracle): Data outside the blockchain (such as weather, traffic signals, etc.) is introduced into the blockchain through an oracle. External data can only be used for the execution of smart contracts after being encrypted and verified with the blockchain system. The oracle guarantees the authenticity and security of external data. The oracle works with the smart contract to ensure that the introduced external data (such as weather information, road closure information) is encrypted for storage and verification, thus ensuring that user privacy will not be leaked.

[0103] 2) Enhancement of Smart Contract: Through externally introduced encrypted data, the smart contract can perform intelligent optimization without exposing sensitive vehicle owner information or vehicle location information.

[0104] (5) Privacy Protection Strategies;

[0105] 1) Principle of Data Minimization: The smart contract will only process data related to task execution to avoid excessive exposure of sensitive information. For example, when a traffic accident occurs, only part of the information related to the accident is exposed, without involving the vehicle owner's personal identity or detailed location.

[0106] 2) Data Encryption and Desensitization: For non-essential sensitive information, data desensitization techniques (such as anonymization) are used to replace it with meaningless data blocks to ensure that the vehicle owner's personal privacy will not be leaked under any circumstances.

[0107] (6) Feedback and Iteration after Smart Contract Execution;

[0108] 1) Contract Execution Feedback: After the smart contract is executed, the system will give feedback. The smart contract will be adjusted according to preset rules, new data will be encrypted and uploaded, and old data will continue to be encrypted and not exposed.

[0109] 2) Knowledge Base Update and Optimization: The smart contract updates the knowledge base through continuous learning, optimization, and the introduction of external data, but always ensures that the privacy and security of user data will not be violated.

[0110] Through this encryption and verification method, the blockchain system protects the privacy of Internet of Vehicles users while ensuring the security, integrity, and legality of information.

[0111] In some embodiments, it may further include: when the current real-time vehicle networking traffic data is uploaded to the blockchain network, accessing the current real-time vehicle networking traffic data through the target smart contract and verifying the current real-time vehicle networking traffic data; when the authenticity verification of the current real-time vehicle networking traffic data passes, judging whether the target smart contract meets the preset execution conditions according to the current real-time vehicle networking traffic data and the preset execution judgment rules through the target smart contract.

[0112] Optionally, the execution of the smart contract depends not only on the real-time vehicle-mounted sensor data being uploaded to the chain, but also on the external real-time traffic data obtained by the oracle being uploaded to the chain. The uploading of both types of data may trigger the execution of the smart contract. Specifically: The uploading of vehicle-mounted sensor data to the chain is a basic condition for triggering the smart contract. When the vehicle-mounted sensor data is uploaded to the blockchain, the smart contract will verify according to these vehicle-mounted sensor data and generate a target traffic processing plan based on the verification results to control the corresponding actions of the vehicle. In addition to the vehicle-mounted sensor data, the real-time traffic data obtained externally through oracle technology (such as weather, traffic signals, etc.) can also be uploaded to the blockchain. These external data can also be utilized by the smart contract and combined with the vehicle-mounted sensor data to generate a target traffic processing plan adapted to the current traffic conditions. Therefore, the uploading of both types of data (vehicle-mounted sensor data and external real-time traffic data obtained by the oracle) may trigger the execution of the smart contract.

[0113] Among them, the uploading of data to the chain is a prerequisite for triggering the smart contract. The triggering of the smart contract is based on this uploaded data (vehicle-mounted sensor data or external traffic data obtained by the oracle) and is executed when the triggering conditions are met. The smart contract will monitor the data on the blockchain in real time and execute immediately when the conditions are satisfied. Therefore, the triggering logic of the smart contract is closely related to the uploading of data to the chain. The data must be uploaded to the blockchain first, and then it is judged whether to trigger the execution of the corresponding contract according to the uploaded data. Specifically, the relationship between the triggering of the smart contract and the uploading of data to the chain is as follows:

[0114] (1) Prerequisite of data uploading to the chain: The execution of the smart contract requires data as a condition. Only after the data is uploaded to the blockchain can the smart contract judge whether the execution conditions are met by accessing this data. Therefore, the uploading of data to the chain is a prerequisite for triggering the smart contract. Without the uploading of data to the chain, the smart contract cannot know whether it needs to be executed.

[0115] (2) Judgment of triggering conditions: Once the data is uploaded to the blockchain, the smart contract will make judgments according to preset rules (such as time, location, occurrence of events, etc.). Exemplarily, when vehicle data is uploaded to the blockchain and the time and location of an accident are recorded, the smart contract will decide whether to trigger subsequent processing procedures (such as accident handling, emergency rescue, etc.) based on these conditions. The timing of judgment and triggering is carried out in real time after the data is uploaded to the blockchain, that is, the smart contract will immediately conduct condition verification after the data is uploaded. If the preset rules are met (for example, the time or location meets the triggering conditions), the smart contract will immediately trigger the corresponding execution process.

[0116] (3) Execution timing of the smart contract: The triggering of the smart contract is based on real-time judgment of the uploaded data. Whether an accident occurs or the traffic signal changes, the contract can be triggered at any time when the data meets the preset rules. Its triggering conditions are judged according to the data (such as vehicle status, event time, etc.), and when the data meets the conditions, the smart contract can be executed immediately.

[0117] Among them, the blockchain system or the smart contract itself will select and execute certain decisions according to the data in the blockchain domain knowledge base. Specifically, the design of the smart contract is not static, but dynamically interacts with the blockchain domain knowledge base and external environment data in the blockchain, and optimizes and makes decisions according to the current traffic scenario. The blockchain domain knowledge base plays a role in supporting the decision-making of the smart contract. It contains traffic rules, historical data, and external data (such as weather, road conditions, etc.), and is continuously updated and optimized through artificial intelligence algorithms. When the smart contract is triggered, based on real-time data such as the current traffic conditions and vehicle location, the smart contract will select and execute according to the optimal decision-making path provided by the knowledge base. This execution path can be the most suitable path for traffic congestion, accident handling, or other tasks. Therefore, the automatic selection and execution process of the smart contract provided in the embodiments of the present application depends on the blockchain domain knowledge base, and continuously self-optimizes through technologies such as machine learning and deep learning to achieve better decision-making effects. That is, the smart contract can dynamically select the most suitable path according to the blockchain domain knowledge base and real-time data, rather than a fixed process set artificially in advance.

[0118] The process of the smart contract automatically selecting the optimal decision depends on the blockchain domain knowledge and information stored in the blockchain domain knowledge base (such as historical data, traffic rules, real-time data, etc.). These knowledge will be used to build models and guide the execution of the smart contract. Among them, the blockchain domain knowledge base contains traffic flow, historical accidents, traffic signal rules, road construction information, etc. Combining machine learning algorithms, these data help the system understand and predict the possible best coping strategies in different traffic scenarios.

[0119] Exemplarily, assume that the information uploaded to the blockchain in real time indicates traffic congestion. Then, the smart contract will automatically select an appropriate action strategy (such as adjusting the traffic signal cycle, reallocating the traffic flow, or initiating an accident handling process) based on the data in the blockchain domain knowledge base. This process is dynamic and adjusted according to the real-time data in the blockchain domain knowledge base. Therefore, the smart contract does not merely execute fixed rules, but continuously optimizes and adjusts decisions through interaction with the blockchain domain knowledge base during the execution process. Exemplarily, in some cases, the smart contract may update the rules according to the newly learned traffic patterns and generate new decision-making schemes. In the traffic scenario, as the sensor data is continuously updated (e.g., vehicle speed, vehicle location, weather changes, etc.), the smart contract will dynamically reselect the optimal strategy and execute it. Exemplarily, assume an unexpected situation occurs, the smart contract will automatically call the appropriate module (such as traffic accident handling or emergency rescue) and execute it immediately.

[0120] In some embodiments, after step S105, it may further include: after the target vehicle executes corresponding actions according to the target traffic handling plan, obtaining the system execution result; feeding back the system execution result to the traffic scenario analysis model and the blockchain domain knowledge base to update the traffic scenario analysis model and the blockchain domain knowledge base; optimizing the target smart contract based on the updated traffic scenario analysis model and the blockchain domain knowledge base.

[0121] In practical applications, the knowledge in the blockchain domain knowledge base in the blockchain field is used to guide the operation process of the actual blockchain, thereby continuously generating new data. In this continuous iterative process, data and knowledge interact continuously, new data and new knowledge are continuously generated, and finally convergence will be achieved. To avoid the convergence of data and knowledge within the blockchain, with the help of the oracle technology in the blockchain, external data is continuously introduced into the blockchain, and the knowledge generated from these data is continuously added to the blockchain domain knowledge base, thereby continuously enriching the data and domain knowledge in the blockchain, with the intention of realizing a "smart" smart contract. Among them, the new data refers to the real-time traffic data collected by sensors and other vehicles in the vehicle-to-everything network, such as: road conditions, vehicle positions, vehicle speeds, traffic events (such as accidents, congestion), weather changes, etc. These data will be uploaded to the blockchain in real time, and every time a smart contract is triggered, the new data will be recorded in the blockchain; in addition, external data (such as weather, traffic signal changes, road closures, etc.) will also be introduced into the blockchain through oracle technology to further enrich the data sources. The new knowledge refers to the knowledge obtained through artificial intelligence algorithms (such as machine learning, deep learning, etc.) based on new data. For example, based on the new traffic data, the artificial intelligence model may identify some new traffic patterns or predict changes in vehicle behavior under specific conditions (such as special weather or accidents). The knowledge generated through learning will be transformed into "blockchain domain knowledge" and stored in the blockchain domain knowledge base. This blockchain domain knowledge includes information such as traffic rules, emergency handling plans, and road traffic optimization strategies, which can provide decision-making support for subsequent smart contracts. In this iterative process of using the blockchain domain knowledge in the blockchain domain knowledge base to guide the operation of the actual blockchain and continuously generate new data, new data and old data interact continuously. Every time a smart contract is executed, the system will learn and optimize based on the latest blockchain data (including all the data accumulated before). The old data refers to the data stored in the blockchain before. These data, together with the new data, are used to train the artificial intelligence model and continuously iterate and update the knowledge base. It can be understood that new data is not only a supplement and update to old data, but also the basis for promoting the continuous improvement of smart contracts.

[0122] In specific implementation, each time the smart contract is triggered, new data is used to train the artificial intelligence model. As more data accumulates, the model will become more accurate and intelligent. Moreover, through machine learning algorithms, new data and the generated knowledge are continuously fed back into the blockchain to update the knowledge base in the blockchain field. Among them, the knowledge in the knowledge base guides the operation process of the blockchain, thereby optimizing the decision-making of the smart contract. As the iteration progresses, the smart contract becomes more and more intelligent and can make decisions autonomously in complex traffic scenarios. In addition, each time the smart contract is executed and new data is generated, the system will feedback these data back to the blockchain and use the feedback data to update the execution rules of the smart contract. This is a dynamic and continuously self-optimizing process. In this way, a feedback loop is formed among data, knowledge, and the smart contract, making the entire system more accurate and efficient in processing traffic scenarios and ultimately achieving the "convergence" effect, that is, the system can adapt and efficiently handle different traffic scenarios. It can be understood that each execution of the smart contract will generate new data. After these data are analyzed by artificial intelligence algorithms, new knowledge is refined and updated to the knowledge base in the blockchain field. As this process continues, the system's response and decision-making capabilities in the face of complex traffic scenarios will become more intelligent and accurate.

[0123] In the blockchain network, the smart contract is optimized as data accumulates and learning progresses. After each execution of the contract, the smart contract provides feedback based on the execution result and updates the model through machine learning algorithms. This iterative process enables the smart contract to gradually adapt to increasingly complex traffic scenarios and provide more accurate decision-making support. Specifically, this process actually refers to the feedback mechanism of the smart contract and the model update process. The feedback of the smart contract execution result affects the optimization of the algorithm model, and the result after the algorithm model is optimized is fed back to the knowledge base in the blockchain field, thereby enhancing the overall performance and decision-making ability of the smart contract. Therefore, the smart contract execution result directly affects the model update, and the model itself does not update the knowledge base in the blockchain field, but feeds back the optimized decision result to the knowledge base. Specifically, feeding back to the algorithm model means that the smart contract will collect results during the execution process and transfer these execution results as "feedback" to a learning algorithm model (such as a machine learning or deep learning model). This learning algorithm model will adjust its internal parameters or strategies according to the execution result to optimize the decision-making ability of the smart contract. Updating the knowledge base means that the system will update the knowledge base in the blockchain field based on the feedback result and incorporate the new learning achievements (i.e., the experience obtained from the execution result) into the knowledge base in the blockchain field. These new data or updated information can improve the decision-making process of subsequent smart contracts. The knowledge base in the blockchain field is a place to store "domain-specific knowledge" (such as traffic rules, historical events, etc.), and these knowledge provide references for the execution of subsequent smart contracts.

[0124] In the embodiments of the present application, a smart contract combined with traffic scenario analysis is proposed. Knowledge for traffic scenarios is formed through blockchain data analysis, and the knowledge and rules are solidified into the smart contract, intending to be executed independently and autonomously without relying on a third party. Furthermore, through flexible configuration and computational experiments on such smart contracts for complex traffic scenarios, agile, focused, and convergent intelligent decisions are adaptively generated and executed. Specifically, vehicle-connected traffic visual data is put into the blockchain system, and the blockchain is used to manage these data. During this process, artificial intelligence algorithms are used to continuously learn these data using data such as block information, node information, and transaction information obtained from the blockchain, generating blockchain domain knowledge, and a blockchain domain knowledge base is constructed based on the generated domain knowledge, thus realizing the process from blockchain data to blockchain domain knowledge.

[0125] Steps S101 to S105 illustrated in the embodiments of the present application include obtaining the original vehicle-connected traffic data of the vehicle-connected network system; preprocessing the original vehicle-connected traffic data to obtain target vehicle-connected traffic data; uploading the target vehicle-connected traffic data to the blockchain network corresponding to the vehicle-connected network system; a target smart contract is deployed in the blockchain network; artificial intelligence algorithms are used to learn the target vehicle-connected traffic data on the blockchain network to obtain blockchain domain knowledge, and a blockchain domain knowledge base is constructed based on the blockchain domain knowledge; when the current real-time vehicle-connected traffic data is uploaded to the blockchain network and the target smart contract meets the preset execution conditions, the target smart contract generates a target traffic processing plan for the current vehicle-connected traffic situation according to the blockchain domain knowledge base and the current real-time vehicle-connected traffic data, so as to control the target vehicle to execute corresponding actions according to the target traffic processing plan. By introducing artificial intelligence and a blockchain domain knowledge base, the smart contract in the embodiments of the present application can be dynamically adjusted according to the real-time traffic situation and environmental factors. The smart contract can automatically select the most suitable decision path according to complex traffic scenarios, providing more accurate decision support and improving the intelligent level of the smart contract. Through the optimization and automatic execution of the smart contract, the system can respond to traffic changes in real time, improving the resource scheduling and traffic management efficiency in the vehicle-connected network.

[0126] To explain the principle of the technical solution of the present invention in detail, the overall process of the present invention will be described below in conjunction with some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and should not be regarded as a limitation of the present invention.

[0127] Please refer to Figure 2 , Figure 2 which is a schematic diagram of the smart contract design structure provided by the embodiments of the present application; Figure 2 which is the smart contract framework designed by the embodiments of the present application, as Figure 2As shown in the figure, the implementation of smart contracts relies on a decentralized blockchain system. The structure of the decentralized blockchain system can be divided into three parts: a collaborative structure, a shared structure, and a smart contract module. Among them, the traffic scenario analysis model runs on a blockchain network as a whole and consists of a collaborative structure and a shared structure. Specifically, the collaborative structure includes roles such as organizers, verifiers, trainers, and executors, which are responsible for task allocation and execution; the shared structure is responsible for collecting and storing various models and smart contracts, constituting a trusted model library and a smart contract library; the smart contract module includes five major modules: permission management, data transmission, model verification, contribution evaluation, and incentive quantification, supporting customized requirements for different scenarios.

[0128] Among them, the collaborative structure is responsible for task allocation and execution in the vehicle networking smart contract system, covering roles such as organizers, verifiers, trainers, and executors. The specific work content is as follows: The organizer is responsible for the planning and scheduling of the entire task process, determining the execution order of smart contract tasks and task allocation rules. Exemplarily, the organizer will decide that the target smart contract is executed in a specific traffic scenario or trigger the target smart contract for processing when a traffic accident occurs. The verifier is responsible for verifying the authenticity and validity of the data uploaded to the blockchain, including vehicle data and traffic data, etc. These data will be verified through smart contracts to ensure the credibility of the data in the system. The trainer is responsible for training the data in the blockchain through machine learning and deep learning algorithms to update and optimize the smart contract. The trainer is mainly associated with the knowledge base in the blockchain field and continuously improves the execution efficiency and accuracy of the smart contract by analyzing the accumulated traffic data. The executor is responsible for performing actual blockchain verification and blockchain computing work. The executor ensures that data is stored, updated, and confirmed on the blockchain by processing transactions and executing smart contracts.

[0129] For the shared structure, its main function is to collect and store various models and smart contracts, constituting a trusted model library and a smart contract library. The trusted model library is used to store machine learning and deep learning models that have been verified and evaluated. These models are used to guide the decision-making and execution of smart contracts in traffic scenarios. The trusted model library works closely with the knowledge base in the blockchain field and uses continuously updated data and models (i.e., Figure 2 the local model and the traffic scenario analysis model in it) to adjust and optimize the execution of smart contracts. The smart contract library stores smart contract modules that have been designed, verified, and optimized. These contract modules include permission management contracts, data transmission contracts, model verification contracts, contribution evaluation smart contracts, incentive quantification smart contracts, etc. The system will select and configure the most suitable smart contract from the library according to the current traffic scenario and task requirements. This smart contract library works closely with the verifiers and trainers in the collaborative structure to ensure the effectiveness of the smart contract through continuous update and optimization.

[0130] Among them, the permission management smart contract is used to manage the access permissions of different users (such as vehicles, sensors, service providers, etc.), ensuring that only authorized users can participate in certain smart contract operations; the data transmission smart contract is responsible for ensuring the correct transmission and verification of data, guaranteeing the security and reliability of information transmission; the model verification smart contract is used to verify whether the models constructed through machine learning and deep learning algorithms meet the expected requirements and can correctly process the data in traffic scenarios; the contribution evaluation smart contract is used to evaluate and quantify the contributions made by different nodes (such as vehicles, sensors, etc.) in the blockchain network and allocate rewards or resources according to the contribution situation; the incentive quantification smart contract is used to quantify the contributions of participants in the system and conduct incentives. Exemplarily, when a vehicle provides effective traffic information, the system gives rewards according to the preset incentive rules.

[0131] The smart contract framework proposed in the embodiments of this application is mainly implemented by five smart contracts: the permission management smart contract, the data transmission smart contract, the model verification smart contract, the contribution evaluation smart contract, and the incentive quantification smart contract. Since these smart contracts can be flexibly coded to encapsulate different distributed collaboration processes and relationships, the proposed structural framework can support customized workflows and collaboration modes for different scenarios to combine traffic scenario tasks to protect the privacy and security of intelligent vehicle network users.

[0132] In specific implementation, the organizers, verifiers, and trainers in the collaboration structure are responsible for triggering and verifying the execution of smart contracts based on the data collected by vehicle network sensors, ensuring the authenticity and intelligence of the data during the contract execution process. The smart contract library and the trusted model library stored in the shared structure provide verified and optimized contract and model support, ensuring that smart contracts can handle complex traffic scenarios. The roles in the collaboration structure will obtain appropriate contracts and models from the library according to actual needs and optimize and make decisions in combination with the latest data.

[0133] As Figure 2 shown, user private data, local models, and local solutions are key components involved in data privacy protection, machine learning, and smart contract decision-making in vehicle networks. Figure 2 The description of the smart contract framework in

[0134] (1) The meaning of user private data and training local models;

[0135] Among them, user private data refers to sensitive information generated and controlled by each vehicle or user in the Internet of Vehicles, such as vehicle location data, driving behavior data, vehicle sensor data, and in-vehicle device and environment data. Among them, vehicle location data includes GPS location, driving route, vehicle speed, etc.; driving behavior data includes behaviors such as driver acceleration, braking, and turning; vehicle sensor data includes vehicle health-related information such as temperature, humidity, pressure, and tire status; in-vehicle device and environment data includes surrounding traffic environment information (such as traffic light status, road conditions, weather, etc.). These data are private data of users and vehicles and are used to train local models.

[0136] Local models are machine learning models trained locally on the vehicle side, aiming to perform personalized learning and optimize decisions based on private data. These models include driving behavior prediction models and traffic condition recognition models. Among them, the driving behavior prediction model predicts possible driver behaviors and decisions, such as speeding and braking, by learning information such as vehicle location and speed; the traffic condition recognition model combines traffic environment data around the vehicle (such as traffic lights, road conditions, other vehicles, etc.) to predict traffic flow on the road and possible events.

[0137] In specific implementation, the training of local models can be carried out using edge computing, that is, data processing and model update are performed on the vehicle side, thereby avoiding uploading private data to the blockchain or the cloud and ensuring data privacy.

[0138] (2) The meaning and model fusion of the local solution;

[0139] Among them, the local solution refers to a personalized decision-making solution generated based on user private data and local models, and the local solution involves how the vehicle responds to specific traffic scenarios. Exemplarily, assuming a traffic jam situation, the local solution may recommend automatically adjusting the vehicle speed or route; in the event of an accident, the local solution will trigger emergency braking or other safety measures.

[0140] In the embodiments of this application, the fusion of local models and local solutions refers to how to combine multiple learned models and strategies to generate the most appropriate response locally on the vehicle. This can be accomplished through model fusion algorithms, which synthesize the prediction results of multiple local models according to weights to obtain a unified decision.

[0141] (3) Analyzing, monitoring, and feeding back to private data is a continuous iterative process, aiming to continuously optimize the decision-making and execution efficiency of local models. The specific implementation process is as follows:

[0142] Data Monitoring: The vehicle continuously collects real-time data through sensors and uses this data to monitor changes in driving behavior and traffic environment. The monitored data includes the vehicle's speed, location, surrounding traffic conditions, driver behavior, etc.

[0143] Model Feedback: When the local model makes a decision and executes it, the smart contract monitors these execution results. Exemplarily, if the vehicle makes an optimized decision in a specific traffic scenario (such as avoiding traffic congestion or preventing an accident), the result is fed back to the local model for further optimization. Whenever the model is executed in a new environment or task, the model automatically updates through machine learning algorithms, learning new behavior patterns and decision-making strategies.

[0144] (4) The private data is directly related to the aforementioned vehicle networking sensor data (such as the vehicle's GPS, speed, road conditions, etc.). The user's private data is collected by in-vehicle devices during actual driving and belongs to the sensitive data of individual users, mainly used to train the local model. The local model is a personalized model generated based on this private data, and the aforementioned smart contract uses these models to make decisions. For example, when the traffic scenario changes, the smart contract executes the contract based on the traffic information obtained from the blockchain and the results predicted by the local model, optimizing traffic management or control strategies.

[0145] In specific implementation, the knowledge base in the blockchain field aggregates data collected from the entire network and external sources (such as oracle technology), and these data are used to support and expand the local model to make it more intelligent. External traffic signals, weather, road closure information, etc. are obtained through oracle technology, combined with the decision-making ability of the local model, thereby enhancing the intelligence of the overall traffic system.

[0146] In a specific implementation, the vehicle obtains the data collected by the sensor nodes, combines it with the information obtained from other nodes in the vehicular ad-hoc network of the intelligent vehicle networking to achieve safe driving. In the intelligent vehicle networking, messages of the announcement type are sent, collected by the moving vehicles, transmitted to the cloud server for storage, and provided for other vehicles to reference. However, in an open cloud environment, the plaintext data is vulnerable to unauthorized access and even malicious tampering. Currently, many storage solutions for vehicle data are processed based on traditional solutions such as cloud storage and attribute encryption, and these solutions all face the problem of centralized data storage or centralized auditing members in the later stage. The emerging blockchain technology not only has high security but also has relatively efficient performance. The core components (such as smart contracts, etc.) in the current mainstream blockchain systems are all set artificially at the beginning of the system establishment and cannot be changed once they are put on the chain and run. Therefore, the embodiments of this application are directed to the complex and changeable computing tasks and application scenarios of a specific intelligent vehicle networking, and design "intelligent" smart contracts to automatically select and configure the optimal contract combination for the complex traffic task requirements. In the embodiments of this application, the vehicle networking control method based on blockchain and smart contracts is directed to the complex application scenarios in the intelligent vehicle networking, utilizes the characteristics of blockchain such as decentralization, immutability, and traceability, combines with artificial intelligence algorithms to improve the flexibility and "intelligence" degree of the blockchain smart contracts, and further optimizes the automatic execution and decision-making efficiency of various tasks in the vehicle networking. The overall implementation process is as follows:

[0147] Step 1: Data collection and preprocessing;

[0148] (1) The vehicle collects information such as the surrounding environment, traffic conditions, vehicle speed, and location through in-vehicle sensor nodes, and converts it into digital signals for preprocessing. In this process, preprocessing processes such as denoising, standardization, and formatting can ensure the accuracy and integrity of the data. Through the cooperation of the sensor nodes, the vehicle can obtain traffic information in real time, thus providing a reliable data basis for subsequent intelligent decision-making.

[0149] (2) Obtain real-time traffic data from outside the blockchain through oracle technology, such as traffic signals, weather conditions, road closure information, etc., and also perform data preprocessing on the data outside the blockchain.

[0150] (3) Obtain the data inside the blockchain. The data inside the blockchain includes transaction data, block information, node status, and smart contract execution results on the blockchain. The data inside the blockchain provides important background information for the state and events of the blockchain network. Similarly, it is necessary to perform data preprocessing on the data inside the blockchain.

[0151] Step 2: Data uploading and smart contract triggering;

[0152] The pre - processed vehicle sensors and blockchain external data are uploaded to the blockchain network. The smart contract automatically verifies the authenticity of the data and triggers an execution process according to set rules (such as time, location and other conditions). Exemplarily, when a vehicle collision occurs, the smart contract will automatically trigger an accident handling procedure and record relevant information. This process ensures the efficient execution of the smart contract and the reliability of the data. It can be understood that the data must be uploaded to the blockchain, verified before being used, and trigger the corresponding smart contract.

[0153] Step 3: Construction of the blockchain domain knowledge base;

[0154] By introducing artificial intelligence technology, combining the vehicle sensor data, blockchain external data and blockchain internal data obtained after data pre - processing, the data inside and outside the blockchain are analyzed and learned to construct a blockchain domain knowledge base. This knowledge base contains knowledge such as traffic rules, emergency handling plans, historical traffic data, etc., and provides support for the decision - making of smart contracts. The execution of smart contracts needs to be based on the blockchain domain knowledge base. Especially in complex traffic scenarios, a blockchain domain knowledge base must be available to support the learning and optimization of smart contracts.

[0155] Step 4: Autonomous optimization and selection of smart contracts;

[0156] With the support of the blockchain domain knowledge base, the smart contract will automatically select and execute the most appropriate contract path according to real - time information such as traffic conditions and accident types. Exemplarily, during traffic congestion, the contract may re - allocate the traffic flow and optimize the signal light cycle; when an accident occurs, the contract will automatically dispatch first - aid resources to ensure rapid handling.

[0157] Step 5: Dynamic configuration and execution of smart contracts

[0158] The smart contract in the present invention adopts a "plug - in" architecture and has high flexibility. According to specific task requirements, the system can dynamically configure and combine different smart contract modules. This modular design supports personalized configuration, can adjust the task execution strategy according to traffic conditions, and further improves the response ability of the system.

[0159] Step 6: Continuous iteration and optimization of smart contracts

[0160] After each contract execution, the system will conduct feedback learning based on the execution results and continuously optimize the smart contract. During the learning process, the system will update the blockchain domain knowledge base, improve the contract decision - making and execution efficiency, so as to realize an evolving smart contract system.

[0161] In the embodiments of the present application, encryption technology is also used to protect the user's personal data, ensuring that the data stored in the blockchain can only be accessed by authorized parties. The execution process of the smart contract only exposes necessary shared information, avoiding the problem of user privacy leakage. Privacy protection and data security measures run through the entire system. The protection of user data by encryption technology is to be executed at all stages such as data collection, upload, storage, and processing. In addition, the system introduces external environmental data in real time through oracle technology, which can improve the adaptability of the smart contract in complex traffic scenarios. Combining real-time traffic data with historical knowledge, the smart contract can handle emergencies more accurately. After the external data is introduced through the oracle, the execution of the smart contract can be enhanced.

[0162] In the vehicle networking control method based on blockchain and smart contract provided in the embodiments of the present application, in addition to the blockchain smart contract-based solution, another feasible solution is to optimize through a centralized data storage and decision-making system. However, the centralized solution is vulnerable to single-point failures and it is difficult to achieve decentralization and information security. In contrast, the blockchain solution provided in the embodiments of the present application can better ensure the security, transparency, and traceability of the system through distributed ledger, smart contract, and encryption technology.

[0163] Compared with the traditional centralized decision-making system, the embodiments of the present application can achieve real-time and automatic decision-making and response in the vehicle networking environment. Through the optimization and automatic execution of the smart contract, the system can respond to emergencies such as traffic accidents or sudden weather more quickly, reducing the need for manual intervention. Moreover, by adopting the decentralized mechanism of the blockchain, the risk of single-point failure is reduced, ensuring the reliability of the data and the security of the system. Compared with the traditional centralized storage system, blockchain technology significantly reduces the possibility of data loss and tampering. The smart contract can ensure the protection of user privacy, and only exposes necessary information during data transmission and storage. In terms of privacy protection, the embodiments of the present application can effectively avoid the privacy leakage risk in the centralized system. Exemplarily, in some cases, the system can ensure the security of user location and personal information through encryption technology, avoiding the privacy leakage problem in the traditional vehicle data storage solution. Additionally, the smart contract can be flexibly configured and optimized according to the increasing data volume and complex traffic scenarios. Through modular design and artificial intelligence algorithms, the system can adapt to more future application scenarios and complex situations, has strong scalability, and also enhances the security and efficiency of the vehicle networking, with significant technical advantages.

[0164] It should be noted that this embodiment only briefly illustrates the general process of the vehicle networking control method based on blockchain and smart contract. For the detailed description of each step, reference can be made to the relevant content in the foregoing embodiments, and details are not elaborated here. It can be understood that the present invention places no restrictions thereon.

[0165] In the embodiment of the present application, the original vehicle networking traffic data of the vehicle networking system is obtained; the original vehicle networking traffic data is preprocessed to obtain the target vehicle networking traffic data; the target vehicle networking traffic data is uploaded to the blockchain network corresponding to the vehicle networking system; a target smart contract is deployed in the blockchain network; an artificial intelligence algorithm is used to learn the target vehicle networking traffic data on the blockchain network to obtain blockchain domain knowledge, and a blockchain domain knowledge base is constructed according to the blockchain domain knowledge; when the current real-time vehicle networking traffic data is uploaded to the blockchain network and the target smart contract meets the preset execution conditions, the target smart contract generates a target traffic processing plan for the current vehicle networking traffic condition according to the blockchain domain knowledge base and the current real-time vehicle networking traffic data, so as to control the target vehicle to execute corresponding actions according to the target traffic processing plan. In the embodiment of the present application, by introducing artificial intelligence and the blockchain domain knowledge base, the smart contract can be dynamically adjusted according to the real-time traffic condition and environmental factors. The smart contract can automatically select the most suitable decision-making path according to complex traffic scenarios, providing more accurate decision-making support and improving the intelligent level of the smart contract; through the optimization and automatic execution of the smart contract, the system can respond to traffic changes in real time, improving the resource scheduling and traffic management efficiency in the vehicle networking.

[0166] In summary, the key technical points of the vehicle networking control method based on blockchain and smart contract provided by the embodiment of the present application are as follows:

[0167] (1) "Intelligentization" of the smart contract: By introducing an artificial intelligence algorithm, the smart contract can automatically select, configure, and execute the optimal decision in complex traffic scenarios without relying on manual intervention.

[0168] (2) Blockchain domain knowledge base: The blockchain domain knowledge base constructed by using machine learning and deep learning enables the smart contract to self-optimize according to the continuously accumulated data.

[0169] (3) Decentralization and privacy protection: The combination of the decentralization feature of blockchain technology and encryption algorithms effectively protects the privacy information of vehicle networking users while ensuring the security and authenticity of data.

[0170] The vehicle networking control method based on blockchain and smart contract provided by the embodiment of the present application combines blockchain technology and artificial intelligence algorithms to solve the problems of low execution efficiency, poor flexibility, and insufficient security of smart contracts in the current vehicle networking field. Its technical effects include the following:

[0171] (1)Enhance the flexibility and intelligence of smart contracts: By introducing artificial intelligence algorithms, especially machine learning and deep learning methods, smart contracts can automatically select the most appropriate decision-making path according to complex traffic scenarios, improving the "intelligence" level of smart contracts. Exemplarily, in a changing traffic environment, the system can quickly adapt and automatically adjust the contract execution strategy to optimize traffic management. Moreover, by introducing knowledge bases in the fields of artificial intelligence and blockchain, smart contracts can make dynamic adjustments based on real-time traffic conditions and environmental factors, providing more accurate decision-making support.

[0172] (2)Ensure the security and privacy protection of vehicle network data: Through the combination of the decentralized characteristics of blockchain technology and encryption algorithms, the immutability, non-forgeability of vehicle network data and the protection of user privacy are ensured. It avoids the risks of data leakage and tampering faced by centralized systems, enhancing the credibility and security of data.

[0173] (3)Optimize the resource management and decision-making efficiency of vehicle networks: Smart contracts can perform task scheduling based on real-time data to optimize resource management in vehicle networks. Through the transparency and immutability of blockchain, the fairness and traceability in the process of resource allocation are ensured. Exemplarily, smart contracts can adjust vehicle flows according to traffic conditions, optimize the release of traffic condition information, improving the overall efficiency and operation stability of vehicle networks.

[0174] Please refer to Figure 3 , the embodiment of the present application also provides a vehicle network control system 300 based on blockchain and smart contracts, which can implement the vehicle network control method based on blockchain and smart contracts. The system 300 includes the following modules:

[0175] The original vehicle network traffic data acquisition module 301 is used to acquire the original vehicle network traffic data of the vehicle network system;

[0176] The original vehicle network traffic data preprocessing module 302 is used to preprocess the original vehicle network traffic data to obtain target vehicle network traffic data;

[0177] The target vehicle network traffic data uploading module 303 is used to upload the target vehicle network traffic data to the blockchain network corresponding to the vehicle network system; a target smart contract is deployed in the blockchain network;

[0178] The blockchain domain knowledge base construction module 304 is used to use artificial intelligence algorithms to learn the target vehicle network traffic data on the blockchain network to obtain blockchain domain knowledge, and construct a blockchain domain knowledge base according to the blockchain domain knowledge;

[0179] The intelligent contract execution module 305 is configured to, when the current real-time vehicle networking traffic data is uploaded to the blockchain network and the target intelligent contract meets the preset execution conditions, generate a target traffic processing solution for the current vehicle networking traffic condition according to the blockchain domain knowledge base and the current real-time vehicle networking traffic data, so as to control the target vehicle to perform corresponding actions according to the target traffic processing solution.

[0180] It can be understood that the content in the above method embodiments is applicable to the system embodiments. The functions specifically implemented in the system embodiments are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those in the above method embodiments.

[0181] An embodiment of the present application further provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the above vehicle networking control method based on the blockchain and intelligent contracts. The electronic device can be any intelligent terminal including a tablet computer, an in-vehicle computer, etc.

[0182] It can be understood that the content in the above method embodiments is applicable to the device embodiments. The functions specifically implemented in the device embodiments are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those in the above method embodiments.

[0183] Please refer to Figure 4 , Figure 4 which schematically shows the hardware structure of an electronic device in another embodiment. The electronic device includes:

[0184] A processor 401, which can be implemented in ways such as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0185] A memory 402, which can be implemented in forms such as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 402 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 402 and are called by the processor 401 to execute the vehicle networking control method based on the blockchain and intelligent contracts in the embodiments of the present application;

[0186] An input / output interface 403 for implementing information input and output;

[0187] A communication interface 404 for implementing communication interaction between this device and other devices, which can implement communication through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.);

[0188] A bus 405 for transmitting information between various components of the device (such as a processor 401, a memory 402, an input / output interface 403, and a communication interface 404);

[0189] Among them, the processor 401, the memory 402, the input / output interface 403, and the communication interface 404 achieve communication connections with each other inside the device through the bus 405.

[0190] The embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned vehicle networking control method based on blockchain and smart contracts.

[0191] It can be understood that the content in the above method embodiments is applicable to this storage medium embodiment. The functions specifically implemented by this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those in the above method embodiments.

[0192] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory can include high-speed random access memory, and can also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory optionally includes a memory remotely set relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above networks include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0193] The vehicle networking control method and the vehicle networking control system based on blockchain and smart contract provided by the embodiments of the present application obtain the original vehicle networking traffic data of the vehicle networking system; preprocess the original vehicle networking traffic data to obtain target vehicle networking traffic data; upload the target vehicle networking traffic data to the blockchain network corresponding to the vehicle networking system; a target smart contract is deployed in the blockchain network; an artificial intelligence algorithm is used to learn the target vehicle networking traffic data on the blockchain network to obtain blockchain domain knowledge, and a blockchain domain knowledge base is constructed according to the blockchain domain knowledge; when the current real-time vehicle networking traffic data is uploaded to the blockchain network and the target smart contract meets the preset execution conditions, the target smart contract generates a target traffic processing plan for the current vehicle networking traffic condition according to the blockchain domain knowledge base and the current real-time vehicle networking traffic data, so as to control the target vehicle to execute corresponding actions according to the target traffic processing plan. By introducing artificial intelligence and the blockchain domain knowledge base, the smart contract can be dynamically adjusted according to the real-time traffic condition and environmental factors, and the smart contract can automatically select the most suitable decision-making path according to complex traffic scenarios, providing more accurate decision-making support and improving the intelligent level of the smart contract; through the optimization and automatic execution of the smart contract, the system can respond to traffic changes in real time, improving the resource scheduling and traffic management efficiency in the vehicle networking.

[0194] The preferred embodiments of the embodiments of the present application have been described above with reference to the accompanying drawings, which does not limit the scope of rights of the embodiments of the present application. Any modification, equivalent replacement, and improvement made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of rights of the embodiments of the present application.

Claims

1. A vehicle networking control method based on blockchain and smart contracts, characterized in that, The method includes the following steps: Obtain the original vehicle networking traffic data of the vehicle networking system; Preprocess the original vehicle networking traffic data to obtain target vehicle networking traffic data; Upload the target vehicle networking traffic data to the blockchain network corresponding to the vehicle networking system; a target smart contract is deployed in the blockchain network; Use an artificial intelligence algorithm to learn the target vehicle networking traffic data on the blockchain network to obtain blockchain domain knowledge, and construct a blockchain domain knowledge base according to the blockchain domain knowledge; When the current real-time vehicle networking traffic data is uploaded to the blockchain network and the target smart contract meets the preset execution conditions, the target smart contract generates a target traffic processing plan for the current vehicle networking traffic condition according to the blockchain domain knowledge base and the current real-time vehicle networking traffic data, so as to control the target vehicle to execute corresponding actions according to the target traffic processing plan.

2. The method according to claim 1, wherein The original vehicle networking traffic data includes original vehicle-mounted sensor data and original blockchain external traffic data. Obtaining the original vehicle networking traffic data of the vehicle networking system includes: Obtain the original vehicle-mounted sensor data through the vehicle-mounted sensors of the vehicles in the vehicle networking system; wherein, the original vehicle-mounted sensor data includes at least one of the following: the vehicle surrounding environment, traffic condition, vehicle speed, and vehicle position; Use oracle technology to obtain the original blockchain external traffic data corresponding to the vehicle networking system from outside the blockchain network; wherein, the original blockchain external traffic data is real-time traffic data outside the blockchain network, and the original blockchain external traffic data includes at least one of the following: the current real-time traffic condition, weather information, and road closure situation; Fuse the original vehicle-mounted sensor data and the original blockchain external traffic data to obtain the original vehicle networking traffic data.

3. The method according to claim 1, wherein Preprocessing the original vehicle networking traffic data to obtain target vehicle networking traffic data includes: Perform denoising processing on the original vehicle networking traffic data to obtain clean vehicle networking traffic data; Perform standardization processing on the clean vehicle networking traffic data to obtain standard vehicle networking traffic data; Perform feature extraction processing on the standard vehicle networking traffic data to obtain the target vehicle networking traffic data.

4. The method according to claim 1, wherein Using an artificial intelligence algorithm to learn the target vehicle networking traffic data on the blockchain network to obtain blockchain domain knowledge, and constructing a blockchain domain knowledge base according to the blockchain domain knowledge includes: Input the target vehicle networking traffic data on the blockchain network into a traffic scenario parsing model; Train the traffic scenario parsing model according to the target vehicle networking traffic data through the artificial intelligence algorithm, and output the blockchain domain knowledge; Construct the blockchain domain knowledge base according to the blockchain domain knowledge.

5. The method according to claim 1, characterized in that The current real-time vehicle networking traffic data includes current real-time vehicle-mounted sensor data, and the current real-time vehicle-mounted sensor data includes current user data and current vehicle position information. The method further includes: Encrypt the current user data and the current vehicle location information using a preset encryption technique; Upload the encrypted current user data and the encrypted current vehicle location information to the blockchain network for storage.

6. The method according to claim 1, wherein The method further includes: When the current real-time vehicle networking traffic data is uploaded to the blockchain network, access the current real-time vehicle networking traffic data through the target smart contract and verify the current real-time vehicle networking traffic data; When the authenticity verification of the current real-time vehicle networking traffic data passes, determine whether the target smart contract meets the preset execution conditions according to the current real-time vehicle networking traffic data and a preset execution judgment rule through the target smart contract.

7. The method according to claim 1, characterized in that When the current real-time vehicle networking traffic data is uploaded to the blockchain network and the target smart contract meets the preset execution conditions, the target smart contract generates a target traffic processing plan for the current vehicle networking traffic condition according to the blockchain domain knowledge base and the current real-time vehicle networking traffic data, so as to control the target vehicle to perform corresponding actions according to the target traffic processing plan. After that, the method further includes: After the target vehicle performs corresponding actions according to the target traffic processing plan, obtain the system execution result; Feedback the system execution result to the traffic scenario analysis model and the blockchain domain knowledge base to update the traffic scenario analysis model and the blockchain domain knowledge base; Optimize the target smart contract based on the updated traffic scenario analysis model and the blockchain domain knowledge base.

8. The vehicle networking control system based on blockchain and smart contract is characterized in that, The system includes the following modules: An original vehicle networking traffic data acquisition module, configured to acquire the original vehicle networking traffic data of the vehicle networking system; An original vehicle networking traffic data preprocessing module, configured to preprocess the original vehicle networking traffic data to obtain target vehicle networking traffic data; A target vehicle networking traffic data on-chain module, configured to upload the target vehicle networking traffic data to the blockchain network corresponding to the vehicle networking system; a target smart contract is deployed in the blockchain network; A blockchain domain knowledge base construction module, configured to use an artificial intelligence algorithm to learn the target vehicle networking traffic data on the blockchain network to obtain blockchain domain knowledge, and construct a blockchain domain knowledge base according to the blockchain domain knowledge; A smart contract execution module, configured to when the current real-time vehicle networking traffic data is uploaded to the blockchain network and the target smart contract meets the preset execution conditions, the target smart contract generates a target traffic processing plan for the current vehicle networking traffic condition according to the blockchain domain knowledge base and the current real-time vehicle networking traffic data, so as to control the target vehicle to perform corresponding actions according to the target traffic processing plan.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the method according to any one of claims 1 to 7 is implemented.

Images