Vehicle, control method and control device thereof and computer readable storage medium

Abstract

The invention discloses a vehicle, a control method and device thereof and a computer readable storage medium. The method comprises the steps that a natural language instruction sent by a user located outside the vehicle is received; acquiring state information of the vehicle, and inputting the natural language instruction and the state information into a pre-constructed large language model to output a target vehicle control instruction for controlling the vehicle; and verifying the target control instruction based on a preset verification mode, and issuing the target vehicle control instruction to the vehicle after the verification is passed, so that the vehicle executes the target vehicle control instruction. According to the method, complex and diversified user requirements can be processed, the generated vehicle control instruction is more accurate and personalized, the user experience is improved, the intelligent degree is enhanced, and the reliability and safety during vehicle control are improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a vehicle control method, a computer-readable storage medium, a vehicle, and a vehicle control device. Background Art

[0002] With the intelligent development of the automotive industry, remote vehicle control and intelligent interaction have become important ways to enhance the user experience. Currently, connected vehicle technology is widely used for various vehicle control and status monitoring functions. However, existing smart cockpit systems still have many shortcomings in terms of user experience, especially in terms of the intelligent and convenient interaction between users and vehicles.

[0003] Existing vehicle control systems rely heavily on pre-set command sets, failing to flexibly interpret personalized user commands. This lack of intelligence and flexibility negatively impacts the user experience. Furthermore, existing systems' security authentication is limited to basic authentication, lacking support for multi-level command verification and failing to effectively prevent potential misoperations or malicious commands. Summary of the Invention

[0004] The present application aims to solve at least one of the technical problems in the related art to a certain extent. To this end, the first purpose of the present application is to propose a vehicle control method, which receives natural language instructions issued by a user outside the vehicle, obtains the status information of the vehicle, and inputs the natural language instructions and the status information into a pre-built large language model to output a target vehicle control instruction for controlling the vehicle, verifies the target control instruction based on a preset verification method, and issues the target vehicle control instruction to the vehicle after the verification is passed, so that the vehicle executes the target vehicle control instruction. In this way, it can handle complex and diverse user needs, and the generated vehicle control instructions are more accurate and personalized, which improves the user experience, enhances the degree of intelligence, and improves the reliability and safety of controlling the vehicle.

[0005] A second object of the present application is to provide a computer-readable storage medium.

[0006] The third object of this application is to provide a vehicle.

[0007] A fourth objective of the present application is to provide a vehicle control device.

[0008] To achieve the above-mentioned objectives, the first aspect embodiment of the present application proposes a vehicle control method, which includes: receiving natural language instructions issued by a user outside the vehicle; obtaining vehicle status information, and inputting the natural language instructions and the status information into a pre-built large language model to output target vehicle control instructions for controlling the vehicle; verifying the target control instructions based on a preset verification method, and issuing the target vehicle control instructions to the vehicle after the verification is passed, so that the vehicle executes the target vehicle control instructions.

[0009] The vehicle control method according to an embodiment of the present application receives natural language commands issued by a user outside the vehicle, obtains vehicle status information, and inputs the natural language commands and status information into a pre-built large language model to output target vehicle control commands. The target control commands are then verified based on a preset verification method. Once verified, the target control commands are issued to the vehicle, causing it to execute the target control commands. This method can thus handle complex and diverse user needs, generating more accurate and personalized vehicle control commands, improving user experience, enhancing intelligence, and increasing reliability and safety when controlling the vehicle.

[0010] In addition, the vehicle control method according to the above embodiment of the present application may also have the following additional technical features:

[0011] According to one embodiment of the present application, the inputting of the natural language instructions and the status information into a pre-built large language model to output target vehicle control instructions for controlling the vehicle includes: parsing the natural language instructions based on the large language model to identify the user's target vehicle control requirements; and determining the target vehicle control instructions based on the large language model, the target vehicle control requirements, and the status information.

[0012] According to one embodiment of the present application, determining the target vehicle control instruction based on the large language model, the target vehicle control demand and the status information includes: judging whether the status information meets the target vehicle control demand based on the large language model; if it is determined that the target vehicle control demand is not met, determining the target vehicle control instruction in combination with target factors, wherein the target factors include one or more of the user's historical vehicle control demand parameters, preset safety and comfort parameters, and parameters that meet the vehicle's energy-saving performance.

[0013] According to one embodiment of the present application, the preset verification method includes at least one of the following verification methods: verifying whether the target control instruction is within the preset execution range of the vehicle; verifying whether the target control instruction meets the preset safety policy; verifying whether the target control instruction matches the status information.

[0014] According to one embodiment of the present application, the method further includes: generating an error message when the target control instruction fails to be verified, and determining a new target vehicle control instruction based on a preset large language model, the natural language instruction and the status information.

[0015] According to one embodiment of the present application, the natural language instruction is input by the user based on an application of a terminal device, and the natural language instruction and the user's account information are encrypted. After receiving the natural language instruction issued by the user outside the vehicle, the method further includes: decrypting the natural language instruction and authenticating the natural language instruction based on a target verification mechanism; in the event of authentication failure, generating a failure message and sending it to the application to prompt the user that the operation was unsuccessful.

[0016] According to one embodiment of the present application, the authentication of the natural language instruction based on the target verification mechanism includes at least one of the following authentication methods: user identity authentication based on the account information; user identity authentication based on the user's biometric information; and permission management authentication based on the user's operating authority over the vehicle.

[0017] To achieve the above-mentioned purpose, a second embodiment of the present application proposes a computer-readable storage medium on which a program is stored, and when the program is executed by a processor, the above-mentioned vehicle control method is implemented.

[0018] According to the computer-readable storage medium of the embodiment of the present application, the above-mentioned vehicle control method is implemented during execution, which can handle complex and diverse user needs, and the generated vehicle control instructions are more accurate and personalized, thereby improving the user experience, enhancing the level of intelligence, and improving the reliability and safety of controlling the vehicle.

[0019] To achieve the above-mentioned purpose, a vehicle is proposed in an embodiment of the third aspect of the present application, comprising a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned vehicle control method is implemented.

[0020] According to the vehicle of the embodiment of the present application, by executing the above-mentioned vehicle control method, it is possible to handle complex and diverse user needs, and the generated vehicle control instructions are more accurate and personalized, thereby improving the user experience, enhancing the level of intelligence, and improving the reliability and safety when controlling the vehicle.

[0021] To achieve the above-mentioned purpose, the fourth aspect embodiment of the present application proposes a vehicle control device, which includes: a receiving module, which receives natural language instructions issued by a user outside the vehicle; an acquisition module, which is used to obtain the vehicle's status information, and input the natural language instructions and the status information into a pre-built large language model to output a target vehicle control instruction for controlling the vehicle; a control module, which is used to verify the target control instruction based on a preset verification method, and after the verification is passed, send the target vehicle control instruction to the vehicle, so that the vehicle executes the target vehicle control instruction.

[0022] According to the vehicle control device of the embodiment of the present application, the receiving module is used to receive natural language commands issued by a user outside the vehicle, the acquisition module is used to obtain the vehicle's status information and input the natural language commands and status information into a pre-built large language model to output target vehicle control commands. The control module is used to verify the target control commands based on a preset verification method and, after verification, issue the target vehicle control commands to the vehicle so that the vehicle executes the target vehicle control commands. As a result, the device can handle complex and diverse user needs, and the generated vehicle control commands are more accurate and personalized, improving the user experience, enhancing the level of intelligence, and increasing the reliability and safety of vehicle control.

[0023] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a flow chart of a vehicle control method according to an embodiment of the present application;

[0025] Figure 2 is a flowchart of a vehicle control method according to a specific example of the present application;

[0026] Figure 3 is a block diagram of a vehicle according to an embodiment of the present application;

[0027] Figure 4 4 is a block diagram of a control device for a vehicle according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0029] Currently, traditional smart cockpits rely on fixed commands and limited interaction logic. Users can only perform limited vehicle control operations through commands in a specific format. They lack support for natural language and diverse needs, which affects the user experience and is unable to provide personalized services based on the vehicle's real-time status (such as location, temperature, and battery / fuel levels). For example, a user may want to turn on the air conditioning in advance during hot weather, but traditional systems cannot proactively provide such services based on the environment and vehicle status, resulting in insufficient intelligence when handling complex situations. Furthermore, the security certification of existing systems is limited to basic certification, with insufficient support for multi-level verification of commands, failing to effectively prevent possible misoperations or malicious commands. Furthermore, existing systems fail to adequately address the "hallucination" phenomenon that can occur due to large language models, generating commands that do not meet user expectations or safety standards, thereby posing potential safety risks.

[0030] To this end, this application proposes a vehicle control method that can flexibly understand user commands based on natural language and make intelligent judgments based on real-time vehicle status information, thereby achieving richer vehicle control functions. At the same time, it adds a command verification layer and multi-layer encryption processing to improve the reliability and security of the vehicle control system.

[0031] The following describes a vehicle control method, a computer-readable storage medium, a vehicle, and a vehicle control device proposed in embodiments of the present application with reference to the accompanying drawings.

[0032] Figure 1 Flowchart of a vehicle control method according to an embodiment of the present application.

[0033] like Figure 1 As shown, the vehicle control method of the embodiment of the present application may include the following steps:

[0034] S1, receiving natural language commands issued by the user outside the vehicle.

[0035] S2, obtains the vehicle's status information, and inputs the natural language instructions and status information into a pre-built large language model to output target vehicle control instructions.

[0036] S3: Verify the target control instruction based on a preset verification method, and send the target vehicle control instruction to the vehicle after the verification is passed, so that the vehicle executes the target vehicle control instruction.

[0037] Specifically, the system first receives natural language commands from a user outside the vehicle. For example, a user can send natural language commands through a mobile app or other remote device. The vehicle then receives and prepares to process these commands. The mobile app is the front-end interface for user-vehicle interaction. Users can use the mobile app to issue vehicle control commands in natural language, such as "help me cool down my car" or "navigate to the nearest gas station."

[0038] Then the vehicle status information is obtained. For example, the status information may include the vehicle's current location information, which can be obtained by the vehicle's positioning system. The status information may also include temperature information inside the vehicle and temperature information outside the vehicle, which can be obtained through corresponding temperature sensors. The status information may also include information such as power or oil level, which can be obtained based on the sensors configured for the vehicle model. The status information may also include other sensor data, such as tire pressure information and door and window status information.

[0039] After obtaining the natural language instructions and status information, the natural language instructions and status information can be input into a pre-built large language model to output the target vehicle control instructions. Pre-built large language models (such as GPT-4 (Generative Pre-trained Transformer 4) and BERT (Bidirectional Encoder Representations from Transformers)) can understand and generate natural language instructions. The integrated data packet is input into the large language model, for example, the integrated data is: "User instruction": "Help me cool the car down", "Vehicle status": {"Inside temperature": 25, "Outside temperature": 30, "Battery level": 80, "A / C status": "Off", "Window status": "Off", "Vehicle location": "GPS coordinates", "Fuel level": "Sufficient"}. Therefore, the large language model is used to parse the user's natural language commands and extract the core intent and key information. For example, the user command "Help me drive the car cooler" can be parsed as "Lower the temperature inside the car." The large language model also analyzes the vehicle's real-time status data, such as the current temperature inside the car is 25 degrees Celsius, the outside temperature is 30 degrees Celsius, the battery power is 80%, and the air conditioner is off, that is, the current power is sufficient, and the indoor temperature is high and the air conditioner is not turned on, so that the corresponding target vehicle control command can be generated, such as turning on the air conditioner and setting the air conditioner temperature to 22 degrees Celsius.

[0040] After determining the target control command, the target control command generated by the large language model can be verified to ensure its legality, security, and consistency. This means that the command can only be issued to the vehicle for execution after passing verification. This verification can be performed according to a pre-set verification method. For example, if the target control command contains data errors, such as setting the air conditioner temperature to 40 degrees Celsius, which exceeds the air conditioner's temperature adjustment range, the command is considered illegal. Alternatively, if the command is to "remotely unlock the vehicle" but the vehicle is located in a high-crime area, the command poses a safety hazard. If the command is deemed unreasonable or risky, it should not be issued to the vehicle for execution. If verification passes, such as in the example above, turning on the air conditioner and setting the temperature to 22 degrees Celsius, which is within the air conditioner's temperature adjustment range, the target control command can be issued to the vehicle for execution.

[0041] This allows the system to receive natural language commands from users outside the vehicle, combine them with the vehicle's real-time status data, generate targeted vehicle control commands, and verify the legitimacy, security, and consistency of these commands through a verification layer. Ultimately, verified commands are sent to the vehicle for execution, providing users with an intelligent, secure, and personalized vehicle control experience.

[0042] According to one embodiment of the present application, natural language instructions and status information are input into a pre-built large language model to output target vehicle control instructions for controlling the vehicle, including: parsing the natural language instructions based on the large language model to identify the user's target vehicle control requirements; determining the target vehicle control instructions based on the large language model, the target vehicle control requirements and the status information.

[0043] Specifically, when natural language instructions and status information are input into a pre-built large language model to output target vehicle control instructions, the natural language instructions are first parsed based on the large language model to identify the user's target vehicle control needs. In other words, identifying the user's target vehicle control needs is to use the natural language processing technology of the large language model to convert the user's natural language instructions into clear and executable vehicle control needs. This involves not only understanding the semantics of the user's instructions, but also combining contextual information to ensure accurate identification of the user's intentions. For example, if the user inputs "help me drive the car cooler", the large language model will parse out that the user's core intention is "lower the temperature in the car". For another example, if the user inputs "navigate to the nearest gas station", the large language model will recognize that the user needs to refuel and needs a navigation route to the gas station.

[0044] After determining the target vehicle control requirement, the target vehicle control command can be determined based on the large language model, the target vehicle control requirement, and status information. For example, the large language model can analyze the vehicle's real-time status data to determine whether the current state meets the user's requirements. For example, if the current vehicle status is 25 degrees Celsius inside, 30 degrees Celsius outside, 80% battery charge, and the air conditioning is off, then specific vehicle control commands can be generated through logical reasoning based on the user's target vehicle control requirement and vehicle status. If the user's target requirement is to lower the temperature inside the vehicle, the reasoning result could be to turn on the air conditioning, set the temperature to 22 degrees Celsius, and set the wind speed to medium. This reasoning result can then be converted into specific target vehicle control commands and output as structured data.

[0045] This not only improves the user experience, but also ensures the safety and rationality of vehicle control commands.

[0046] According to one embodiment of the present application, target vehicle control instructions are determined based on a large language model, target vehicle control requirements, and status information, including: judging whether the status information meets the target vehicle control requirements based on the large language model; and when it is determined that the target vehicle control requirements are not met, determining the target vehicle control instructions in combination with target factors, wherein the target factors include one or more parameters of the user's historical vehicle control requirements, preset safety and comfort parameters, and parameters that meet the vehicle's energy-saving requirements.

[0047] Specifically, when determining the target vehicle control command based on the large language model, the target vehicle control requirement, and state information, the large language model first determines whether the state information meets the target vehicle control requirement. Specifically, the large language model first evaluates the vehicle's current state information to determine whether it meets the user's target vehicle control requirement. This step forms the basis for generating appropriate vehicle control commands. For example, if the target vehicle control requirement is to lower the interior temperature, the vehicle's real-time state data, including interior temperature, battery charge, air conditioning status, window status, and vehicle location, can be obtained from the vehicle management system. For example, if the interior temperature is 26 degrees Celsius, the battery charge is 100%, the air conditioning is off, the windows are closed, and the vehicle is traveling on the highway, the large language model combines the user's target vehicle control requirement with the vehicle's real-time state information to determine whether the current state meets the user's needs. In other words, if the current interior temperature is high, the current state does not meet the user's needs, and cooling measures are required. If the current temperature is 18 degrees Celsius, meaning the interior temperature is already low, then the current state already meets the user's needs and no further cooling is required.

[0048] Therefore, if it is determined that the target vehicle control demand is not met, target vehicle control instructions can be determined based on target factors. These target factors may include one or more of the user's historical vehicle control demand parameters, preset safety and comfort parameters, and vehicle energy efficiency parameters. The user's historical vehicle control demand parameters represent the user's past behavior and preferences. For example, if a user has repeatedly set the air conditioning temperature to 20 degrees Celsius, more personalized instructions can be generated based on the user's historical behavior. This preference will be prioritized. Preset safety and comfort parameters are preset safety and comfort standards. For example, the interior temperature should not fall below 18 degrees Celsius to prevent user discomfort, ensuring that the generated instructions are both safe and comfortable. Vehicle energy efficiency parameters refer to the vehicle's energy efficiency requirements. For example, avoiding high-energy-consuming operations when the battery is low ensures that the generated instructions meet the vehicle's energy efficiency requirements. For example, if the battery is low, the user may be advised to charge the vehicle first rather than turning on the high-energy-consuming air conditioning.

[0049] For example, after parsing the user's instructions, it can be determined that the user's intention is to lower the temperature inside the car. After evaluating the current vehicle status, it is determined that the temperature inside the car is 26 degrees Celsius, that is, the current temperature is high and does not meet the user's needs and needs to be cooled. Therefore, the vehicle control instructions can be generated in combination with the target factors. For example, the user has set the air-conditioning temperature to 20 degrees Celsius many times before, and the temperature inside the car should not be lower than 18 degrees Celsius, and the current battery power is 80%, which is sufficient to support the operation of the air-conditioning. The target vehicle control instructions can be determined, such as turning on the air-conditioning, setting the temperature to 20 degrees Celsius, and the wind speed to medium.

[0050] For example, if a user enters a natural language command requesting fresh air, the large language model can interpret the user's core need as "open the windows." The system then determines the current state of the vehicle's windows. If the windows are already open, the current state meets the user's need, and no further action is required. If the windows are closed, the current state does not meet the user's need, and action is required to open the windows. This allows the system to generate vehicle control commands based on target factors. For example, if the user has no specific window operation preferences, the system can ensure that the vehicle speed is moderate when the windows are open to avoid strong winds causing discomfort to the user. Furthermore, if opening the windows may affect the vehicle's aerodynamic performance, but the battery charge is sufficient, this is acceptable. This allows the system to determine the target vehicle control command, such as opening the windows to a moderate degree.

[0051] This can improve the system's intelligence and response flexibility, ensure the rationality and safety of instructions, and provide users with an intelligent, safe and personalized vehicle control experience.

[0052] According to one embodiment of the present application, the preset verification method includes at least one of the following verification methods: verifying whether the target control instruction is within the vehicle's preset execution range; verifying whether the target control instruction satisfies the preset safety policy; and verifying whether the target control instruction matches the status information. The preset execution range can be determined based on actual conditions.

[0053] Specifically, preset verification methods are a key step in ensuring that the generated vehicle control commands are both reasonable and safe. These verification methods ensure that the commands are within the vehicle's execution range, comply with safety policies, and match the vehicle's current state. Preset verification methods may include verifying whether the target control command is within the vehicle's preset execution range, that is, ensuring that the generated vehicle control command is within the vehicle's execution capabilities to avoid generating unexecutable commands. For example, each vehicle has its own specific functions and limitations. For example, some vehicles may not have manual windows, and some electric vehicles may limit high-energy operations when the battery is low. Define the vehicle's preset execution range, including supported functions and operation limitations. The generated vehicle control command is checked to ensure that the command is within the vehicle's execution range. For example, if the command is "open the sunroof" but the vehicle does not have a sunroof function, the command will be marked as invalid.

[0054] The pre-set verification method can also include verifying whether the target control command meets the pre-set security policy. This ensures that the generated vehicle control command complies with the pre-set security policy to avoid potential security risks. For example, if a vehicle is parked on the side of the road in a crowded urban area and the generated vehicle control command is to open a window, the command will be marked as unsafe, indicating that opening the window may allow thieves to steal valuables inside the vehicle.

[0055] Pre-set verification methods can also include verifying that the target control command matches the vehicle's current state, ensuring that the generated vehicle control command matches the vehicle's current state to avoid conflicts or invalid operations. For example, if the command is to "turn on the air conditioning," but the vehicle's battery is low, the command may need to be adjusted or rejected to prevent the user from turning on the air conditioning and then driving the vehicle to a charging station with insufficient battery power.

[0056] Together, these verification methods ensure that generated vehicle control commands meet user needs, are safe and reliable, and take into account the vehicle's actual state and operational limitations. This intelligent vehicle control system not only enhances the user experience but also ensures the safety and rationality of vehicle control commands.

[0057] According to one embodiment of the present application, the vehicle control method further includes: generating an error message when the target control instruction verification fails, and determining a new target vehicle control instruction based on a preset large language model, natural language instructions and status information.

[0058] Specifically, when a generated target vehicle control command fails verification, the specific cause is identified and a corresponding error message is generated. These reasons for verification failure may include: the command being outside the vehicle's execution range, the command not complying with safety policies, or the command not matching the vehicle's current state. After generating the error message, the system reanalyzes the user's natural language command and the vehicle's current state information, generating a new target vehicle control command based on a pre-set large language model. This process ensures the system can flexibly respond to verification failures and generate more appropriate commands.

[0059] As a result, it can flexibly respond to various complex situations and provide users with an intelligent, safe and personalized vehicle control experience.

[0060] According to one embodiment of the present application, natural language instructions are input by a user based on an application on a terminal device, and the natural language instructions and the user's account information are encrypted. After receiving the natural language instructions issued by the user outside the vehicle, the vehicle control method also includes: decrypting the natural language instructions and authenticating the natural language instructions based on a target verification mechanism; in the event of authentication failure, generating a failure message and sending it to the application to prompt the user that the operation was unsuccessful.

[0061] Specifically, users can enter natural language commands through a terminal device (such as a mobile app). These commands and the user's account information are encrypted to ensure data security and privacy during transmission. Specifically, users enter natural language commands through the mobile app, such as "Help me cool my car" or "Navigate to the nearest gas station." At the same time, the user enters their account information for identity authentication. The mobile app encrypts the user's commands and account information using algorithms such as AES and RSA to prevent data theft or tampering during transmission. After receiving the encrypted data, it first decrypts the data. Then, the natural language commands are authenticated using a target verification mechanism to ensure they originate from a legitimate user. The target verification mechanism uses an account and password authentication method. For example, a user enters the command "Help me cool my car" and enters the username "user123" and password "password123" through their mobile phone. The decrypted data is then extracted to reveal the user's command and account information. The correct username "user123" and password "password123" can be verified. If verification fails, a failure message, such as "Incorrect username or password, please re-enter," can be generated and encrypted and sent back to the user's mobile app. This allows the user to see a prompt on the mobile app: "Incorrect username or password, please re-enter."

[0062] This ensures the security and privacy of natural language commands and account information entered by users during transmission. Furthermore, through decryption and authentication mechanisms, the legitimacy of the commands and the user's identity can be verified. If authentication fails, a failure message is generated and sent back to the user, indicating that the operation was unsuccessful. This mechanism not only enhances system security but also ensures the legitimacy and reliability of user operations.

[0063] According to one embodiment of the present application, natural language instructions are authenticated based on a target verification mechanism, including at least one of the following authentication methods: user identity authentication based on account information; user identity authentication based on user biometric information; and permission management authentication based on the user's vehicle operating authority.

[0064] Specifically, natural language commands are authenticated based on a target verification mechanism to ensure that only legitimate users can issue valid vehicle control commands. This process verifies the user's identity and permissions through multiple authentication methods, including account information authentication, biometric information authentication, and permission management authentication. User identity authentication based on account information uses the user's account information (such as username and password) to verify the user's identity and ensure that the user is legitimate. For example, if a user enters the username user123 and password password123 through a mobile application, the user's username and password are compared with the username and password stored in the database. If the verification passes, subsequent operations are executed; if the verification fails, a failure message is generated and sent back to the mobile application.

[0065] User authentication is performed based on biometric information, using biometric information (such as fingerprint, facial recognition, voice recognition, and iris recognition) to enhance security. If the fingerprint in the pre-selected and stored database differs from the currently entered fingerprint, the authentication fails and a failure message is generated and sent back to the mobile application. If the authentication succeeds, subsequent operations continue.

[0066] Permission management authentication is performed based on the user's vehicle operation permissions. This verifies whether the user is authorized to operate a specific vehicle, ensuring that the user can only control the vehicle they authorize. This is done by checking whether the user account has permission to remotely start the vehicle. If the user has permission, the process continues to the next step. If the user does not have sufficient permission, a failure message is generated and fed back to the mobile app.

[0067] Therefore, through multi-factor authentication mechanisms (such as username and password, biometrics, device verification, etc.) and permission management, we ensure that only authorized users can access vehicle control functions. This process not only improves system security but also ensures the legitimacy and reliability of user operations. If authentication or authorization fails, a failure message will be immediately fed back to the mobile app, notifying the user that the operation was unsuccessful, effectively preventing unauthorized access.

[0068] The following combination Figure 2 To describe the method of this application.

[0069] As a specific example, the vehicle control method of the present application may include the following steps:

[0070] S101, receiving a natural language command issued by a user outside a vehicle.

[0071] S102, decrypting the natural language instruction and performing user identity authentication based on account information, user biometric information, and authority management authentication based on the user's vehicle operation authority to authenticate the natural language instruction.

[0072] S103: Determine whether the natural language instruction fails authentication. If yes, go to step S104; if not, go to step S111.

[0073] S104: Acquire vehicle status information and parse natural language instructions based on a large language model to identify the user's target vehicle control needs.

[0074] S105: Determine whether the state information meets the target vehicle control requirement based on the large language model. If yes, execute step S106; if not, execute step S107.

[0075] S106, keep the original status information of the vehicle unchanged and go to step S101.

[0076] S107 , determining a target vehicle control instruction in combination with target factors, wherein the target factors include one or more of a user's historical vehicle control demand parameters, preset safety and comfort parameters, and parameters satisfying vehicle energy saving.

[0077] S108: Determine whether the target control instruction is within the vehicle's preset execution range, whether the target control instruction satisfies the preset safety policy, and whether the target control instruction matches the status information. If yes, proceed to step S109; if not, proceed to step S110.

[0078] S109: Sending a target vehicle control instruction to the vehicle so that the vehicle executes the target vehicle control instruction.

[0079] S110, generate error information and go to step S101.

[0080] S111, generate a failure message and send it to the application to prompt the user that the operation is unsuccessful, and go to step S101.

[0081] In summary, the vehicle control method according to the embodiments of the present application receives natural language commands issued by a user outside the vehicle, obtains the vehicle's status information, and inputs the natural language commands and status information into a pre-built large language model to output target vehicle control commands. The target control commands are then verified based on a preset verification method, and after verification, the target control commands are issued to the vehicle, causing the vehicle to execute the target control commands. As a result, this method can handle complex and diverse user needs, generating more accurate and personalized vehicle control commands, improving the user experience, enhancing the level of intelligence, and increasing the reliability and safety of vehicle control.

[0082] Corresponding to the above embodiment, the present application also proposes a computer-readable storage medium.

[0083] The computer-readable storage medium of the embodiment of the present application stores a program thereon, and when the program is executed by a processor, the above-mentioned vehicle control method is implemented.

[0084] According to the computer-readable storage medium of the embodiment of the present application, by executing the above-mentioned vehicle control method, it is possible to handle complex and diverse user needs, and the generated vehicle control instructions are more accurate and personalized, thereby improving the user experience, enhancing the level of intelligence, and improving the reliability and safety of controlling the vehicle.

[0085] Corresponding to the above embodiments, the present application also proposes a vehicle.

[0086] like Figure 3 As shown, the vehicle 200 of the embodiment of the present application may include: a memory 210, a processor 220, and a program stored in the memory 210 and executable on the processor 220. When the processor 220 executes the program, the above-mentioned vehicle control method is implemented.

[0087] According to the vehicle of the embodiment of the present application, by executing the above-mentioned vehicle control method, it is possible to handle complex and diverse user needs, and the generated vehicle control instructions are more accurate and personalized, thereby improving the user experience, enhancing the level of intelligence, and improving the reliability and safety when controlling the vehicle.

[0088] Corresponding to the above embodiments, the present application also proposes a vehicle control device.

[0089] like Figure 4As shown, the vehicle control device 100 of the embodiment of the present application includes: a receiving module 110, an acquisition module 120 and a control module 130.

[0090] The receiving module 110 is used to receive natural language commands issued by a user outside the vehicle. The acquisition module 120 is used to obtain vehicle status information and input the natural language commands and status information into a pre-built large language model to output target vehicle control commands. The control module 130 is used to verify the target control commands based on a preset verification method and, if verified, issue the target control commands to the vehicle for execution.

[0091] According to one embodiment of the present application, the acquisition module 120 inputs natural language instructions and status information into a pre-built large language model to output target vehicle control instructions for controlling the vehicle, specifically for: parsing natural language instructions based on the large language model to identify the user's target vehicle control requirements; determining the target vehicle control instructions based on the large language model, the target vehicle control requirements and the status information.

[0092] According to one embodiment of the present application, the acquisition module 120 determines the target vehicle control instructions based on the large language model, the target vehicle control demand and the status information, and is specifically used to: judge whether the status information meets the target vehicle control demand based on the large language model; when it is determined that the target vehicle control demand is met, determine the target vehicle control instructions in combination with the target factors, wherein the target factors include one or more of the user's historical vehicle control demand parameters, preset safety and comfort parameters, and parameters that meet the vehicle's energy-saving performance.

[0093] According to one embodiment of the present application, the preset verification method includes at least one of the following verification methods: verifying whether the target control instruction is within the preset execution range of the vehicle; verifying whether the target control instruction meets the preset safety policy; verifying whether the target control instruction matches the status information.

[0094] According to one embodiment of the present application, the acquisition module 120 is further used to: generate an error message when the target control instruction verification fails, and determine a new target vehicle control instruction based on a preset large language model, natural language instructions and status information.

[0095] According to one embodiment of the present application, the natural language instructions are input by the user based on the application of the terminal device, and the natural language instructions and the user's account information are encrypted. After the receiving module 110 receives the natural language instructions issued by the user outside the vehicle, it is also used to: decrypt the natural language instructions and authenticate the natural language instructions based on the target verification mechanism; in the case of authentication failure, a failure message is generated and sent to the application to prompt the user that the operation was unsuccessful.

[0096] According to one embodiment of the present application, the receiving module 110 authenticates natural language instructions based on a target verification mechanism, and is also used to: perform user identity authentication based on account information; perform user identity authentication based on user biometric information; and perform permission management authentication based on the user's vehicle operating authority.

[0097] It should be noted that for details not disclosed in the control device of the vehicle in the embodiment of the present application, please refer to the details disclosed in the control method of the vehicle in the embodiment of the present application, and the details will not be repeated here.

[0098] According to the vehicle control device of the embodiment of the present application, the receiving module is used to receive natural language commands issued by a user outside the vehicle, the acquisition module is used to obtain the vehicle's status information and input the natural language commands and status information into a pre-built large language model to output target vehicle control commands. The control module is used to verify the target control commands based on a preset verification method and, after verification, issue the target vehicle control commands to the vehicle so that the vehicle executes the target vehicle control commands. As a result, the device can handle complex and diverse user needs, and the generated vehicle control commands are more accurate and personalized, improving the user experience, enhancing the level of intelligence, and increasing the reliability and safety of vehicle control.

[0099] It should be noted that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0100] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0101] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0102] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0103] In this application, unless otherwise specified or limited, the terms "installed," "connected," "connect," "fixed," etc. should be understood in a broad sense. For example, they can refer to fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two elements or interaction between two elements, unless otherwise specified. Those skilled in the art will understand the specific meanings of the above terms in this application based on specific circumstances.

[0104] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A vehicle control method, characterized in that: The method comprises: Receiving natural language commands from a user outside the vehicle; Acquiring vehicle status information, and inputting the natural language instruction and the status information into a pre-built large language model to output a target vehicle control instruction for controlling the vehicle; The target control instruction is verified based on a preset verification method, and after the verification is passed, the target vehicle control instruction is issued to the vehicle, so that the vehicle executes the target vehicle control instruction.

2. The vehicle control method according to claim 1, characterized in that: Inputting the natural language instruction and the state information into a pre-built large language model to output a target vehicle control instruction for controlling the vehicle includes: Parsing the natural language instruction based on the large language model to identify the user's target vehicle control needs; The target vehicle control instruction is determined based on the large language model, the target vehicle control requirement and the state information.

3. The vehicle control method according to claim 2, characterized in that: The determining the target vehicle control instruction based on the large language model, the target vehicle control requirement and the state information includes: Determining whether the state information meets the target vehicle control requirement based on the large language model; When it is determined that the target vehicle control demand is not met, the target vehicle control instruction is determined in combination with target factors, wherein the target factors include one or more of the user's historical vehicle control demand parameters, preset safety and comfort parameters, and parameters that meet vehicle energy saving.

4. The vehicle control method according to claim 1, wherein: The preset verification method includes at least one of the following verification methods: Verifying whether the target control instruction is within a preset execution range of the vehicle; Verifying whether the target control instruction satisfies a preset security policy; Verify whether the target control instruction matches the status information.

5. The vehicle control method according to claim 4, characterized in that: The method further comprises: If the target control instruction fails to be verified, an error message is generated, and a new target vehicle control instruction is determined based on a preset large language model, the natural language instruction, and the state information.

6. The vehicle control method according to claim 1, characterized in that: The natural language instruction is input by a user based on an application of a terminal device, and the natural language instruction and the user's account information are encrypted. After receiving the natural language instruction from the user outside the vehicle, the method further includes: decrypting the natural language instruction and authenticating the natural language instruction based on a target verification mechanism; In the event of authentication failure, a failure message is generated and sent to the application to prompt the user that the operation was unsuccessful.

7. The vehicle control method according to claim 6, characterized in that: The authenticating the natural language instruction based on the target verification mechanism includes at least one of the following authentication methods: Performing user identity authentication based on the account information; User identity authentication based on user biometric information; Perform authority management authentication based on the user's authority to operate the vehicle.

8. A computer-readable storage medium, characterized in that A program is stored thereon, and when the program is executed by a processor, the vehicle control method according to any one of claims 1 to 7 is implemented.

9. A vehicle, characterized in that: include: A memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the vehicle control method according to any one of claims 1 to 7 is implemented.

10. A vehicle control device, characterized in that: The device comprises: A receiving module, configured to receive a natural language command issued by a user outside the vehicle; an acquisition module, configured to acquire status information of a vehicle and input the natural language instruction and the status information into a pre-built large language model to output a target vehicle control instruction for controlling the vehicle; The control module is used to verify the target control instruction based on a preset verification method, and issue the target vehicle control instruction to the vehicle after the verification is passed, so that the vehicle executes the target vehicle control instruction.

Images