Automobile information protection system, method and equipment based on block chain and artificial intelligence, and medium

Abstract

The invention provides an automobile information protection system, method and device based on a block chain and artificial intelligence, and a medium, and relates to the technical field of information safety protection, the system comprises a data collection module used for collecting target operation data of a target vehicle; the threat identification module is used for inputting an operation characteristic parameter set obtained based on predetermined operation characteristic traversal into a security threat identification machine model to obtain a real-time security threat; the data storage module is used for storing the target data block to a preset block chain; the security monitoring module is used for obtaining target security monitoring information; a monitoring judgment module; and the protection execution module is used for activating an intelligent contract to perform safety protection processing on the real-time safety threat of the target vehicle. The problem that an existing traditional security system is difficult to adapt to constantly changing threat environments can be solved. Through an intelligent contract and a machine learning model, the system can adapt to a constantly changing threat environment, and a flexible protection strategy is realized.

Description

Technical Field

[0001] The present application relates to the field of information security protection technology, and in particular to an automobile information protection system, method, device and medium based on blockchain and artificial intelligence. Background Art

[0002] In modern vehicle systems, with the continuous advancement of intelligent and connected technologies, vehicle safety has become an increasingly important topic. Especially with the development of autonomous driving and connected vehicle technologies, ensuring vehicle safety during operation has become a hot topic of research. Traditional vehicle safety measures have primarily focused on physical protection and passive safety systems. However, with the advancement of technology, cybersecurity threats have become a significant concern. Existing methods typically rely on the real-time collection and analysis of vehicle operating data to promptly detect and respond to potential security threats. However, with the increasing volume of data and the diversification of threat types, traditional security analysis methods are increasingly unable to meet these requirements. Against this backdrop, methods using machine learning and blockchain technologies to enhance vehicle security are gaining attention. Machine learning can identify vehicle operating patterns and potential security threats by analyzing large amounts of data. Blockchain technology, with its decentralized, tamper-proof, and transparent nature, offers new solutions for the storage and security management of vehicle data. In general, existing traditional security systems typically employ fixed protection strategies, making them difficult to adapt to the ever-changing threat landscape.

[0003] In summary, existing traditional vehicle safety systems have difficulty identifying and responding to various complex security threats in real time, and have difficulty adapting to the ever-changing threat environment. Summary of the Invention

[0004] The purpose of this application is to provide a blockchain- and artificial intelligence-based automobile information protection system, method, device, and medium to address the problem that existing traditional vehicle safety systems have difficulty in identifying and responding to various complex security threats in real time and have difficulty adapting to the ever-changing threat environment.

[0005] In view of the above problems, this application provides an automobile information protection system, method, device and medium based on blockchain and artificial intelligence.

[0006] In the first aspect, the present application provides an automobile information protection system based on blockchain and artificial intelligence, wherein the automobile information protection system based on blockchain and artificial intelligence includes: a data collection module for collecting target operation data of a target vehicle; a threat identification module for inputting an operation feature parameter set obtained by traversing the target operation data based on predetermined operation features into a security threat identification machine model, and obtaining real-time security threats through the security threat identification machine model; a data storage module for generating a target data block of the target vehicle based on the real-time security threat, and storing the target data block in a preset blockchain; a security monitoring module for performing security monitoring on the target electronic control unit of the target vehicle to obtain target security monitoring information; a monitoring judgment module for analyzing the target security monitoring information and judging whether the target electronic control unit complies with predetermined security constraints; and a protection execution module for activating a smart contract to perform security protection processing on the real-time security threat of the target vehicle if it complies.

[0007] On the second aspect, the present application also provides a vehicle information protection method based on blockchain and artificial intelligence, wherein the method includes collecting target operating data of a target vehicle; inputting an operating characteristic parameter set obtained by traversing the target operating data based on predetermined operating characteristics into a security threat identification machine model, and obtaining real-time security threats through the security threat identification machine model; generating a target data block of the target vehicle based on the real-time security threat, and storing the target data block in a preset blockchain; performing security monitoring on the target electronic control unit of the target vehicle to obtain target security monitoring information; analyzing the target security monitoring information and determining whether the target electronic control unit complies with predetermined security constraints; if so, activating a smart contract to perform security protection processing on the real-time security threat of the target vehicle.

[0008] In a third aspect, the present application further provides an electronic device, comprising:

[0009] at least one processor;

[0010] a memory communicatively coupled to the at least one processor;

[0011] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the steps of the method in the second aspect above.

[0012] In a fourth aspect, a computer-readable storage medium is provided, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed, the steps of the method in the second aspect are implemented.

[0013] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0014] 1. A data collection module collects target operating data from a target vehicle. A threat identification module inputs a set of operating characteristic parameters obtained by traversing the target operating data based on predetermined operating characteristics into a security threat identification machine model, and uses the security threat identification machine model to obtain real-time security threats. A data storage module generates a target data block for the target vehicle based on the real-time security threats and stores the target data block in a pre-set blockchain. A security monitoring module monitors the target electronic control unit of the target vehicle to obtain target security monitoring information. A monitoring judgment module analyzes the target security monitoring information and determines whether the target electronic control unit complies with predetermined security constraints. If so, a protection execution module activates a smart contract to perform security protection on the real-time security threats of the target vehicle. In other words, through a series of modular operations, the target vehicle's operating data and security threats are collected, identified, stored, and processed, ultimately achieving security protection for the target vehicle. This modular design achieves security protection for the target vehicle, improves vehicle safety, ensures data security and integrity, and enhances system flexibility and adaptability. Through real-time monitoring and intelligent protection, the risk of cyberattacks and malicious manipulation is significantly reduced. Using blockchain technology to store data ensures the data’s immutability and integrity, and improves data security.

[0015] 2. Through real-time monitoring and intelligent protection, it can identify and respond to various security threats in real time, effectively reduce vehicle safety risks, and significantly reduce the risk of vehicles being attacked by network attacks and malicious manipulation.

[0016] 3. Use blockchain technology to ensure the secure storage and transmission of vehicle operation data, prevent data leakage or tampering, ensure the immutability and integrity of data, and improve data security.

[0017] 4. Through smart contracts and machine learning models, the system can adapt to the ever-changing threat environment, achieve flexible and intelligent security protection processing, and improve user trust and satisfaction.

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, which can be implemented in accordance with the contents of the description, and to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically listed below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in this application or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and a person of ordinary skill in the art can obtain other drawings based on the provided drawings without creative work.

[0020] Figure 1 This is a schematic diagram of the structure of the automobile information protection system based on blockchain and artificial intelligence in this application;

[0021] Figure 2 This is a flowchart of the automobile information protection method based on blockchain and artificial intelligence in this application;

[0022] Figure 3 This is a schematic diagram of the structure of an exemplary electronic device of the automotive information protection device based on blockchain and artificial intelligence in this application.

[0023] Description of reference numerals:

[0024] Data collection module 11, threat identification module 12, data storage module 13, security monitoring module 14, monitoring judgment module 15, protection execution module 16, bus 300, receiver 301, processor 302, transmitter 303, memory 304, bus interface 305. DETAILED DESCRIPTION

[0025] This application addresses the difficulties faced by existing traditional vehicle safety systems in identifying and responding to various complex security threats in real time, and in adapting to the ever-changing threat landscape, by providing a blockchain- and artificial intelligence-based automotive information protection system, method, device, and media. Through a series of modular designs, the system achieves security protection for target vehicles, improves vehicle safety, ensures data security and integrity, and enhances system flexibility and adaptability. Through real-time monitoring and intelligent protection, the risk of vehicles being subject to cyberattacks and malicious manipulation is significantly reduced. By utilizing blockchain technology to store data, the system ensures its immutability and integrity, thereby improving data security.

[0026] Below, the technical solutions in this application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited to the example embodiments described herein. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should also be noted that, for the convenience of description, only the parts related to this application, rather than all of them, are shown in the accompanying drawings.

[0027] For example, see the attached Figure 1 This application provides a blockchain-based and artificial intelligence-based automobile information protection system, wherein the blockchain-based and artificial intelligence-based automobile information protection system includes:

[0028] A data collection module 11 is used to collect target operation data of a target vehicle;

[0029] a threat identification module 12, configured to input an operation characteristic parameter set obtained by traversing the target operation data based on predetermined operation characteristics into a security threat identification machine model, and obtain real-time security threats through the security threat identification machine model;

[0030] A data storage module 13 is configured to generate a target data block of the target vehicle based on the real-time security threat, and store the target data block in a preset blockchain;

[0031] A safety monitoring module 14 is used to perform safety monitoring on a target electronic control unit of the target vehicle to obtain target safety monitoring information;

[0032] a monitoring and judging module 15 for analyzing the target safety monitoring information and judging whether the target electronic control unit complies with predetermined safety constraints;

[0033] The protection execution module 16 is used to activate the smart contract to perform security protection processing on the real-time security threat of the target vehicle if it meets the requirements.

[0034] Specifically, the blockchain- and AI-based automotive information protection system comprises six main modules: a data collection module 11, a threat identification module 12, a data storage module 13, a security monitoring module 14, a monitoring and judgment module 15, and a protection execution module 16. These modules work together to provide security protection for the target vehicle. The data collection module 11 is responsible for collecting the target vehicle's operating data. The threat identification module 12 then traverses this data to obtain a set of operating characteristic parameters, which it then inputs into a security threat identification machine model to identify real-time security threats. Next, the data storage module 13 generates target data blocks based on these real-time security threats and stores them on a pre-defined blockchain. Furthermore, the security monitoring module 14 monitors the target vehicle's electronic control unit (ECU) and collects target security monitoring information. The monitoring and judgment module 15 then analyzes this security monitoring information and determines whether the ECU complies with predefined security constraints. Finally, if the security constraints are met, the protection execution module 16 activates the smart contract to implement security protection measures for the target vehicle's real-time security threats. In general, through a series of modular operations, the target vehicle's operating data and security threats are collected, identified, stored and processed, ultimately achieving security protection for the target vehicle.

[0035] Furthermore, the threat identification module 12 is further configured to:

[0036] Obtain a vehicle operation database; extract a first operation record of a first vehicle from the vehicle operation database, wherein the first vehicle has an identifier of a first security threat; extract a first operation feature parameter corresponding to a first operation feature in the first operation record; perform a correlation analysis between the first operation feature parameter and the first security threat to obtain a first correlation degree; when the first correlation degree reaches a predetermined correlation threshold, add the first operation feature to the predetermined operation feature.

[0037] Specifically, by acquiring and analyzing the vehicle operation database, operating characteristics related to security threats are identified, and these characteristics are added to a predetermined operating characteristic list when certain conditions are met. This process can achieve early identification and prevention of specific security threats, thereby improving the safety performance of the vehicle. Specifically, first, the vehicle operation database is acquired. Next, the first operation record of the first vehicle is extracted from the database, and the record corresponds to a vehicle with a first security threat identifier. Then, the first operating characteristic parameter related to the first operating characteristic is extracted from this operation record. Next, a correlation analysis is performed between these parameters and the first security threat to obtain a first correlation degree. In addition, when this correlation degree reaches a predetermined correlation threshold, this operating characteristic is added to a predetermined operating characteristic list. By analyzing the vehicle operation data, operating characteristics related to security threats are identified, and when certain conditions are met, these characteristics are added to a predetermined operating characteristic list, thereby achieving early warning and prevention of security threats, thereby improving the safety performance of the vehicle.

[0038] Furthermore, the threat identification module 12 is further configured to:

[0039] Based on the predetermined operating characteristics, the first operating record is traversed to obtain a first operating characteristic parameter set; a first training data group is formed based on the first operating characteristic parameter set and the first security threat; machine learning training is performed on the first training data group based on the principle of neural network to obtain an initial security threat identification machine model; a model loss feedback correction function is introduced to correct and adjust the initial security threat identification machine model to obtain the security threat identification machine model; wherein, the expression of the model loss feedback correction function is: L(y,y')=-[y*log(y')+(1-y)*log(1-y')]+μ|y-y'|; y refers to the real security threat, and y' refers to the model prediction probability of the initial security threat identification machine model.

[0040] Specifically, a training data set is constructed by traversing and analyzing vehicle operation records. Machine learning training is then performed using neural network principles to ultimately generate a security threat identification machine model. This process enables automated and intelligent identification of security threats, improving vehicle safety performance. First, based on predetermined operation characteristics, a first operation record is traversed to obtain a first operation characteristic parameter set. Next, a first training data set is constructed based on this parameter set and a first security threat. Machine learning training is then performed on this training data set using neural network principles to ultimately generate a security threat identification machine model. By traversing and analyzing vehicle operation records to construct a training data set and using neural network principles to perform machine learning training, an initial security threat identification machine model is generated. Furthermore, a model loss feedback correction function is introduced to correct and adjust the initial security threat identification machine model to obtain the security threat identification machine model. The first term in the model loss feedback correction function is a standard binary cross-entropy loss, which measures the difference between the model's predicted probability and the true label. The second term is an additional absolute difference loss, which increases the penalty for false positives and false negatives. The parameter μ is a weight used to adjust the strength of the absolute difference loss. The purpose of this loss function is to increase the penalty when there is a significant discrepancy between the model's predicted probability and the actual security threat, thereby encouraging the model to more accurately identify security threats. By adjusting the value of μ, the penalty for false positives and false negatives can be balanced based on the actual situation. This enables automated and intelligent identification of security threats, improving vehicle safety performance.

[0041] Furthermore, the data storage module 13 is further configured to:

[0042] The target driving operation log of the target user is analyzed, where the target user is the driver of the target vehicle; the target driving operation log is hashed to generate a target hash value; the target identity information of the target user is obtained and combined with the target hash value and the real-time security threat structure to obtain the target data block.

[0043] Specifically, the system analyzes the target vehicle's operating characteristic parameter set, obtains the target user's driving operation log, and hashes these logs to generate a target hash value. This is then combined with the target user's identity information and real-time security threats to construct a target data block. This process enables tracking and analysis of the target user's driving behavior, as well as recording and monitoring of real-time security threats, improving vehicle safety.

[0044] First, the target user's driving operation logs are analyzed. These logs are hashed to generate a target hash value. The target user's target identity information is then obtained and combined with the target hash value and real-time security threats to form a target data block. This process enables tracking and analysis of the target user's driving behavior, as well as recording and monitoring of real-time security threats, to improve vehicle safety. By analyzing the target vehicle's operating characteristic parameter set, the target user's driving operation logs are obtained and hashed to generate a target hash value. This data block is then combined with the target user's identity information and real-time security threats to form a target data block. This enables tracking and analysis of the target user's driving behavior, as well as recording and monitoring of real-time security threats, to improve vehicle safety.

[0045] Furthermore, the data storage module 13 is further configured to:

[0046] The target data block is verified through the preset network of the preset blockchain to obtain a verification result; when the verification result meets the constraints of the preset consensus mechanism, the target data block is stored in the preset blockchain in an unalterable manner.

[0047] Specifically, the target data block is verified through a preset blockchain network. If the verification result complies with the constraints of the preset consensus mechanism, the target data block is stored in an unalterable manner on the preset blockchain. First, the target data block is verified through the preset blockchain network to obtain a verification result. Next, if this verification result complies with the constraints of the preset consensus mechanism, the target data block is stored in an unalterable manner on the preset blockchain. By verifying the target data block through the preset blockchain network and if the verification result complies with the constraints of the preset consensus mechanism, the target data block is stored in an unalterable manner on the preset blockchain. This ensures data security and integrity, improves system transparency and traceability, and enhances vehicle safety.

[0048] Furthermore, the data storage module 13 is also used to: when the verification result does not meet the constraints of the preset consensus mechanism, analyze the historical data block information of the target vehicle on the preset blockchain to obtain the monitored security threat, and activate the smart contract to freeze the transaction processing of the monitored security threat.

[0049] Specifically, when the verification result does not meet the pre-set consensus mechanism constraints, the target vehicle's historical data blocks on the pre-set blockchain are analyzed to determine the monitored security threat. A smart contract is then activated to freeze transactions for the monitored security threat. This process enables timely detection and response to potential security threats, preventing their spread and impact, and improving vehicle safety. First, when the verification result does not meet the pre-set consensus mechanism constraints, the target vehicle's historical data blocks on the pre-set blockchain are analyzed to determine the monitored security threat. Next, a smart contract is activated to freeze transactions for the monitored security threat. Finally, this process enables timely detection and response to potential security threats, preventing their spread and impact, and improving vehicle safety. By analyzing the target vehicle's historical data blocks on the pre-set blockchain, a monitored security threat is determined, and a smart contract is activated to freeze transactions for the monitored security threat. This process enables timely detection and response to potential security threats, preventing their spread and impact, and improving vehicle safety.

[0050] Furthermore, the security monitoring module 14 is further configured to:

[0051] Obtain firmware monitoring information of the target electronic control unit, wherein the firmware monitoring information includes firmware integrity data; obtain software monitoring information of the target electronic control unit, wherein the software monitoring information includes software update data and software security data; and compose the target security monitoring information based on the firmware integrity data, the software update data, and the software security data.

[0052] Specifically, by obtaining the firmware monitoring information and software monitoring information of the target electronic control unit and combining this information into target safety monitoring information. This process can achieve comprehensive monitoring of the target electronic control unit, including the integrity and security of the firmware and software, thereby improving the safety performance of the vehicle. First, the firmware monitoring information of the target electronic control unit is obtained, where the firmware monitoring information includes firmware integrity data. Next, the software monitoring information of the target electronic control unit is obtained, where the software monitoring information includes software update data and software security data. Then, based on the firmware integrity data, software update data and software security data, the target safety monitoring information is composed. By obtaining the firmware monitoring information and software monitoring information of the target electronic control unit and combining this information into target safety monitoring information, comprehensive monitoring of the target electronic control unit is achieved, including the integrity and security of the firmware and software, thereby improving the safety performance of the vehicle.

[0053] In summary, the blockchain and artificial intelligence-based automobile information protection system provided by this application has the following technical effects:

[0054] 1. A data collection module collects target operating data from a target vehicle. A threat identification module inputs a set of operating characteristic parameters obtained by traversing the target operating data based on predetermined operating characteristics into a security threat identification machine model, and uses the security threat identification machine model to obtain real-time security threats. A data storage module generates a target data block for the target vehicle based on the real-time security threats and stores the target data block in a pre-set blockchain. A security monitoring module monitors the target electronic control unit of the target vehicle to obtain target security monitoring information. A monitoring judgment module analyzes the target security monitoring information and determines whether the target electronic control unit complies with predetermined security constraints. If so, a protection execution module activates a smart contract to perform security protection on the real-time security threats of the target vehicle. In other words, through a series of modular operations, the target vehicle's operating data and security threats are collected, identified, stored, and processed, ultimately achieving security protection for the target vehicle. This modular design achieves security protection for the target vehicle, improves vehicle safety, ensures data security and integrity, and enhances system flexibility and adaptability. Through real-time monitoring and intelligent protection, the risk of cyberattacks and malicious manipulation is significantly reduced. Using blockchain technology to store data ensures the data’s immutability and integrity, and improves data security.

[0055] 2. Through real-time monitoring and intelligent protection, it can identify and respond to various security threats in real time, effectively reduce vehicle safety risks, and significantly reduce the risk of vehicles being attacked by network attacks and malicious manipulation.

[0056] 3. Use blockchain technology to ensure the secure storage and transmission of vehicle operation data, prevent data leakage or tampering, ensure the immutability and integrity of data, and improve data security.

[0057] 4. Through smart contracts and machine learning models, the system can adapt to the ever-changing threat environment, achieve flexible and intelligent security protection processing, and improve user trust and satisfaction.

[0058] Example 2, as Figure 2As shown, based on the same inventive concept as the automobile information protection system based on blockchain and artificial intelligence in the aforementioned embodiment, the present application also provides an automobile information protection method based on blockchain and artificial intelligence, which includes collecting target operation data of a target vehicle; inputting an operation feature parameter set obtained by traversing the target operation data based on predetermined operation features into a security threat identification machine model, and obtaining a real-time security threat through the security threat identification machine model; generating a target data block of the target vehicle based on the real-time security threat, and storing the target data block in a preset blockchain; performing security monitoring on the target electronic control unit of the target vehicle to obtain target security monitoring information; analyzing the target security monitoring information and determining whether the target electronic control unit complies with predetermined security constraints; if so, activating a smart contract to perform security protection processing on the real-time security threat of the target vehicle.

[0059] Furthermore, the method comprises:

[0060] Obtain a vehicle operation database; extract a first operation record of a first vehicle from the vehicle operation database, wherein the first vehicle has an identifier of a first security threat; extract a first operation feature parameter corresponding to a first operation feature in the first operation record; perform a correlation analysis between the first operation feature parameter and the first security threat to obtain a first correlation degree; when the first correlation degree reaches a predetermined correlation threshold, add the first operation feature to the predetermined operation feature.

[0061] Furthermore, the method comprises:

[0062] Based on the predetermined operating characteristics, the first operating record is traversed to obtain a first operating characteristic parameter set; a first training data group is formed based on the first operating characteristic parameter set and the first security threat; machine learning training is performed on the first training data group based on the principle of neural network to obtain an initial security threat identification machine model; a model loss feedback correction function is introduced to correct and adjust the initial security threat identification machine model to obtain the security threat identification machine model; wherein, the expression of the model loss feedback correction function is: L(y,y')=-[y*log(y')+(1-y)*log(1-y')]+μ|y-y'|; y refers to the real security threat, and y' refers to the model prediction probability of the initial security threat identification machine model.

[0063] Furthermore, the method comprises:

[0064] The target driving operation log of the target user is analyzed, where the target user is the driver of the target vehicle; the target driving operation log is hashed to generate a target hash value; the target identity information of the target user is obtained and combined with the target hash value and the real-time security threat structure to obtain the target data block.

[0065] Furthermore, the method comprises:

[0066] The target data block is verified through the preset network of the preset blockchain to obtain a verification result; when the verification result meets the constraints of the preset consensus mechanism, the target data block is stored in the preset blockchain in an unalterable manner.

[0067] Furthermore, the method comprises:

[0068] When the verification result does not comply with the preset consensus mechanism constraint, the historical data block information of the target vehicle on the preset blockchain is analyzed to obtain a monitored security threat, and the smart contract is activated to freeze the transaction for the monitored security threat.

[0069] Furthermore, the method comprises:

[0070] Obtain firmware monitoring information of the target electronic control unit, wherein the firmware monitoring information includes firmware integrity data; obtain software monitoring information of the target electronic control unit, wherein the software monitoring information includes software update data and software security data; and compose the target security monitoring information based on the firmware integrity data, the software update data, and the software security data.

[0071] In the third embodiment, based on the inventive concept of the automobile information protection system based on blockchain and artificial intelligence in the aforementioned embodiment, the present application also provides an electronic device for automobile information protection equipment based on blockchain and artificial intelligence, comprising: at least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the steps of the automobile information protection method based on blockchain and artificial intelligence described in the above-mentioned second embodiment.

[0072] Attachment Figure 3 This is a schematic diagram of the structure of an exemplary electronic device of this application. Figure 3In the figure, the bus architecture is represented by bus 300, which can include any number of interconnected buses and bridges. Bus 300 connects various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are all well known in the art and therefore will not be described further herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 can be the same component, namely a transceiver, which provides a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 can be used to store data used by processor 302 when performing operations.

[0073] In the fourth embodiment, based on the same inventive concept as the automobile information protection system based on blockchain and artificial intelligence in the aforementioned embodiment, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed, the steps of the automobile information protection method based on blockchain and artificial intelligence described in the aforementioned embodiment two are implemented.

[0074] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

[0075] Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. The automobile information protection system based on blockchain and artificial intelligence is characterized by: The system comprises: A data collection module, used to collect target operation data of a target vehicle; a threat identification module, configured to input an operation characteristic parameter set obtained by traversing the target operation data based on predetermined operation characteristics into a security threat identification machine model, and obtain real-time security threats through the security threat identification machine model; A data storage module, configured to generate a target data block of the target vehicle based on the real-time security threat, and store the target data block in a preset blockchain; A safety monitoring module, configured to perform safety monitoring on a target electronic control unit of the target vehicle and obtain target safety monitoring information; a monitoring and judging module, configured to analyze the target safety monitoring information and judge whether the target electronic control unit complies with predetermined safety constraints; The protection execution module is used to activate the smart contract to perform security protection processing on the real-time security threat of the target vehicle if it meets the requirements.

2. The system according to claim 1, wherein: The threat identification module is further configured to: Obtain vehicle operation database; Retrieving a first operation record of a first vehicle from the vehicle operation database, wherein the first vehicle has an identifier of a first security threat; extracting a first operation feature parameter corresponding to the first operation feature in the first operation record; performing a correlation analysis between the first operating characteristic parameter and the first security threat to obtain a first correlation degree; When the first correlation degree reaches a predetermined correlation threshold, the first operating characteristic is added to the predetermined operating characteristics.

3. The system according to claim 2, characterized in that The threat identification module is further configured to: Based on the predetermined operation characteristic, traversing the first operation record to obtain a first operation characteristic parameter set; forming a first training data set based on the first operating characteristic parameter set and the first security threat; Performing machine learning training on the first training data set based on neural network principles to obtain an initial security threat identification machine model; Introducing a model loss feedback correction function to correct and adjust the initial security threat identification machine model to obtain the security threat identification machine model; Among them, the expression of the model loss feedback correction function is: L(y,y')=-[y*log(y')+(1-y)*log(1-y')]+μ|y-y'|; y refers to the actual security threat, and y' refers to the model prediction probability of the initial security threat identification machine model.

4. The system according to claim 1, wherein: The data storage module is further used for: Analyzing the operating characteristic parameter set to obtain a target driving operation log of a target user, wherein the target user is a driver of the target vehicle; Performing hash processing on the target driving operation log to generate a target hash value; The target identity information of the target user is obtained, and combined with the target hash value and the real-time security threat structure to obtain the target data block.

5. The system according to claim 4, characterized in that The data storage module is further used for: Verifying the target data block through a preset network of the preset blockchain to obtain a verification result; When the verification result meets the constraints of the preset consensus mechanism, the target data block is stored in the preset blockchain in an unalterable manner.

6. The system according to claim 5, characterized in that The data storage module is further configured to: when the verification result does not comply with the preset consensus mechanism constraints, analyze the historical data block information of the target vehicle on the preset blockchain to obtain a monitored security threat, and activate the smart contract to freeze the transaction for the monitored security threat.

7. The system according to claim 1, wherein: The security monitoring module is further used to: Acquiring firmware monitoring information of the target electronic control unit, wherein the firmware monitoring information includes firmware integrity data; Acquiring software monitoring information of the target electronic control unit, wherein the software monitoring information includes software update data and software security data; The target security monitoring information is composed based on the firmware integrity data, the software update data and the software security data.

8. The automobile information protection method based on blockchain and artificial intelligence is characterized by: The method comprises: Collect target operation data of target vehicles; Inputting an operation characteristic parameter set obtained by traversing the target operation data based on predetermined operation characteristics into a security threat identification machine model, and obtaining real-time security threats through the security threat identification machine model; Generating a target data block of the target vehicle based on the real-time security threat, and storing the target data block in a preset blockchain; Performing security monitoring on a target electronic control unit of the target vehicle to obtain target security monitoring information; analyzing the target safety monitoring information and determining whether the target electronic control unit complies with predetermined safety constraints; If it meets the requirements, the smart contract is activated to perform security protection processing on the real-time security threat of the target vehicle.

9. An electronic device, characterized in that: include: at least one processor; a memory communicatively coupled to the at least one processor; The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the device performs the steps of the method as claimed in claim 8.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which implements the steps of the method according to claim 8 when executed.

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