Remote control processing method and related device of vehicle-mounted terminal

Abstract

The present invention relates to the technical field of data processing, and discloses a remote control processing method for a vehicle-mounted terminal and a related device. The remote control processing method for the vehicle-mounted terminal comprises: receiving a remote control instruction sent by a remote control device; inputting the remote control instruction into a trained instruction authentication and verification model, performing authentication and verification on the received remote control instruction, and obtaining an authentication result; determining the obtained authentication result, and if the authentication result indicates that the authentication is passed, parsing the remote control instruction and extracting the controller number and remote control content contained therein; the present invention verifies the remote control instruction using the trained instruction authentication and verification model, and can effectively identify and filter out unauthorized or forged instructions, thereby enhancing the security of the vehicle control system.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a remote control processing method of a vehicle-mounted terminal and related devices. Background Art

[0002] With the development of the modern transportation industry, in-vehicle terminals have become a key technology for enhancing the capabilities of intelligent transportation systems. In-vehicle terminals can integrate multiple communication technologies to provide users with services such as in-vehicle and out-of-vehicle information exchange, remote monitoring, and control. Remote control is one of the core applications of in-vehicle terminals, allowing users to control and configure the vehicle through remote control devices, such as remote starting, door locking, and air conditioning adjustment.

[0003] In existing technologies, remote control of vehicle-mounted terminals typically relies on wireless communication technologies and established network protocols. The vehicle-mounted terminal receives and interprets control commands from the user's remote control device, then operates the vehicle systems accordingly. These methods typically involve command reception, authentication, and command execution.

[0004] However, the main technical drawback of existing technologies lies in the inadequate security and accuracy of their authentication mechanisms. Traditional authentication methods are subject to the risk of impersonation or replay attacks, and their efficiency and accuracy are limited when processing complex or ambiguous commands. Furthermore, when remotely controlling an in-vehicle terminal, ensuring the security and accuracy of command transmission and execution is crucial for its stable operation. Therefore, a more efficient and secure remote control processing method is needed to ensure that the in-vehicle terminal can accurately execute control commands from legitimate users. Summary of the Invention

[0005] The present invention provides a remote control processing method and related devices for a vehicle-mounted terminal, which are used to solve the above-mentioned technical problems.

[0006] A first aspect of the present invention provides a remote control processing method for a vehicle-mounted terminal, the remote control processing method for the vehicle-mounted terminal comprising:

[0007] Receive a remote control instruction sent by a remote control device; wherein the remote control instruction includes at least instruction metadata, a sending source, a controller number, and remote control content;

[0008] Inputting the remote control command into the trained command authentication and verification model, performing authentication on the received remote control command, and obtaining an authentication result;

[0009] Determine the authentication result obtained. If the authentication result shows that the authentication is passed, parse the remote control instruction to extract the controller number and remote control content contained in the remote control instruction;

[0010] According to the controller number obtained by the analysis, the corresponding target controller is mapped and located in the vehicle terminal;

[0011] The remote control content obtained by parsing is sent to the located target controller, and the vehicle-mounted terminal is remotely controlled based on the remote control content.

[0012] Optionally, in a first implementation of the first aspect of the present invention, after determining the authentication result, if the authentication result indicates that the authentication is passed, the method further includes:

[0013] If the authentication result shows that the authentication fails, the remote control instruction will be refused to be executed.

[0014] Optionally, in a second implementation of the first aspect of the present invention, the training process of the instruction authentication and verification model includes:

[0015] Collecting training data and inputting the training data into a pre-built deep learning network; wherein the training data includes at least instruction metadata, a sending source, a controller number, and remote control content; the pre-built deep learning network has multiple sub-models, wherein the multiple sub-models include an instruction metadata analysis sub-model, a sending source verification sub-model, a controller number identification sub-model, and a remote control content security detection sub-model;

[0016] Based on the instruction metadata analysis sub-model, receiving metadata including a controller number and remote control content, analyzing the validity of the controller number and the format and structure of the remote control content, and outputting metadata verification indicators;

[0017] Based on the sending source verification sub-model, using the sending source data, verifying the legitimacy of the remote control device that initiates the request, ensuring that the control instruction comes from an authorized device, and outputting a sending source legitimacy indicator;

[0018] Analyze the controller number based on the controller number identification sub-model, extract features of the controller number to obtain number feature information, update parameters of the controller number identification sub-model based on the number feature information, identify the controller number based on the controller coding sub-model after parameter update, and output a controller number identification detection index;

[0019] Based on the remote control content security detection sub-model, analyze the remote control content, identify potential malicious instructions and unexpected operations, evaluate the security risks of the content, and output content security indicators;

[0020] Extracting preset authentication and certification standards from the collected remote control command data, and using the output of each sub-model as an authentication and certification indicator; wherein the authentication and certification standards represent the characteristic performance of the remote control command under legal and expected control operations, and serve as a reference standard in the training process;

[0021] Calculate an error value based on the deviation between the authentication indicators output by each sub-model in the deep learning network and the preset authentication standard value; wherein the error value indicates the gap between the predicted result and the actual result during the authentication process;

[0022] An adaptive learning rate adjustment strategy is applied to gradually optimize the parameters of each sub-model in the deep learning network to minimize the error value until the training of the deep learning network is completed and a trained instruction authentication and detection model is obtained.

[0023] Optionally, in a third implementation of the first aspect of the present invention, the controller number is analyzed based on the controller number identification sub-model, features are extracted for the controller number to obtain number feature information, parameters of the controller number identification sub-model are updated based on the number feature information, the controller number is identified based on the controller coding sub-model after parameter update, and a controller number identification detection index is output, including:

[0024] Based on the controller number identification sub-model, the controller number is analyzed and features of the controller number are extracted to obtain number feature information, wherein the number feature information includes at least a first number feature and a second number feature; the first number feature and the second number feature are both combinations of multiple characters;

[0025] Matching a corresponding target data table from a database based on the first numbering feature; wherein the database stores a plurality of data tables and a matching rule for matching a corresponding target data table from the database based on the first numbering feature;

[0026] Based on the second numbering feature, filtering out corresponding serial numbers from the target data table;

[0027] According to the serial number, a corresponding model parameter set is matched from a database to obtain a model parameter set of a controller number identification sub-model;

[0028] The controller number identification sub-model is parameter-updated based on the model parameter set of the controller number identification sub-model, the controller number is identified based on the controller coding sub-model after parameter update, and a controller number identification detection index is output.

[0029] Optionally, in the fourth implementation method of the first aspect of the present invention, the preset authentication standard value includes at least an actual metadata verification indicator, an actual sending source legitimacy indicator, an actual controller number identification detection indicator, and an actual content security indicator.

[0030] A second aspect of the present invention provides a remote control processing device for a vehicle-mounted terminal, the remote control processing device for the vehicle-mounted terminal comprising:

[0031] A receiving module, configured to receive a remote control instruction sent by a remote control device; wherein the remote control instruction at least includes instruction metadata, a sending source, a controller number, and remote control content;

[0032] An authentication module is used to input the remote control command into the trained command authentication detection model, authenticate the received remote control command, and obtain an authentication result;

[0033] A parsing module is used to determine the authentication result obtained. If the authentication result shows that the authentication is passed, the remote control instruction is parsed to extract the controller number and remote control content contained in the remote control instruction;

[0034] A mapping module is used to map and locate the corresponding target controller in the vehicle terminal according to the controller number obtained by parsing;

[0035] The control module is used to send the remote control content obtained by parsing to the located target controller, and remotely control the vehicle-mounted terminal based on the remote control content.

[0036] The third aspect of the present invention provides a remote control processing device for a vehicle-mounted terminal, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the remote control processing device of the vehicle-mounted terminal executes the above-mentioned remote control processing method of the vehicle-mounted terminal.

[0037] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned remote control processing method for a vehicle-mounted terminal.

[0038] The technical solution provided by the present invention has the following beneficial effects: the present invention provides a remote control processing method and related device for an on-board terminal, which receives a remote control instruction sent by a remote control device; inputs the remote control instruction into a trained instruction authentication and verification model, authenticates and verifies the received remote control instruction, and obtains an authentication result; determines the authentication result obtained, and if the authentication result shows that the authentication is passed, parses the remote control instruction, extracts the controller number and remote control content contained therein; maps and locates the corresponding target controller in the on-board terminal according to the controller number obtained by the analysis; sends the remote control content obtained by the analysis to the located target controller, and remotely controls the on-board terminal based on the remote control content. The present invention verifies the remote control instruction by using the trained instruction authentication and verification model, which can effectively identify and filter out unauthorized or forged instructions, thereby enhancing the security of the vehicle control system. After receiving the remote control instruction, authentication and parsing are automatically performed to extract the required control information, reducing manual intervention and improving the automation level and response speed of the processing flow. By accurately parsing the controller number and remote control content in the control command, the correct command is ensured to be sent to the correct controller in the vehicle terminal, achieving precise control. If the command is recognized as authorized, the system responds quickly and accurately transmits the control command to the target controller, making remote operation more efficient and effective. Proper authentication and verification can reduce the risks caused by incorrect control command execution or unauthorized operation, making vehicle control more reliable and accurate. Using models for authentication and verification facilitates updating and maintaining authentication algorithms in an ever-changing security threat environment to adapt to new security challenges. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 Schematic diagram of an embodiment of a remote control processing method of a vehicle-mounted terminal in an embodiment of the present invention;

[0040] Figure 2 FIG. 1 is a schematic diagram of an embodiment of a remote control processing device of a vehicle-mounted terminal in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] An embodiment of the present invention provides a remote control processing method for a vehicle-mounted terminal and a related device. The terms "first", "second", "third", "fourth", etc. (if any) in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or devices.

[0042] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 An embodiment of a remote control processing method of a vehicle-mounted terminal in an embodiment of the present invention includes:

[0043] Step 101: Receive a remote control instruction sent by a remote control device; wherein the remote control instruction at least includes instruction metadata, sending source, controller number, and remote control content;

[0044] It is understandable that the execution subject of the present invention may be a remote control processing device of a vehicle-mounted terminal, or may be a terminal or a server, which is not specifically limited here. The embodiment of the present invention is described by taking a server as the execution subject as an example.

[0045] Specifically, the detailed implementation steps of step 101 are as follows:

[0046] Activate the communication interface of the vehicle terminal:

[0047] Make sure the vehicle terminal's communication interface (such as Wi-Fi, LTE, Bluetooth, etc.) is turned on and ready to receive data.

[0048] Initialize the interface and ensure that the security protocol is in place to encrypt received remote control commands.

[0049] To monitor the signal from the remote control:

[0050] The vehicle-mounted terminal continuously monitors the instructions sent by the remote control device.

[0051] For example: If the remote control device is the user's smartphone, the vehicle terminal needs to monitor the command signal sent from the smartphone.

[0052] Receive remote control commands:

[0053] Once the communication interface detects an incoming signal, it immediately captures the signal and extracts the remote control command data from it.

[0054] The command data packet usually contains key information elements for subsequent processing and verification, including but not limited to command metadata (such as timestamp, command type, etc.), source information (user ID or device ID), controller number (identifying which vehicle system or device should respond to the command) and remote control content (specific operation commands or change settings).

[0055] Verify instruction integrity:

[0056] Confirm that the received remote control command has not been damaged during transmission, for example by checking the integrity of the data packet or calculating a checksum.

[0057] Record instructions to the receiving log:

[0058] The details of the received remote control commands are recorded in the log system of the vehicle terminal to facilitate subsequent tracking and problem diagnosis.

[0059] For example, when receiving a command, the vehicle-mounted terminal stores information related to the command in a log file in the local memory, and the record includes a timestamp, command content, and sending source.

[0060] Step 102: Input the remote control command into the trained command authentication and verification model, perform authentication on the received remote control command, and obtain an authentication result;

[0061] Specifically, the detailed implementation steps of step 102 are as follows:

[0062] Prepare the command authentication and verification model:

[0063] Ensure that a trained command authentication model is pre-installed and configured in the vehicle terminal. This model is based on machine learning or deep learning and is trained on a large amount of data to identify and verify the legitimacy of remote control commands.

[0064] Load the parameters and weights required by the model and prepare the model for real-time remote control command authentication and verification processing.

[0065] Extract instruction content:

[0066] Key information is extracted from the remote control instruction received in step 101, including instruction metadata, sending source, controller number and remote control content.

[0067] For authentication, special attention is paid to information such as security credentials of the source of the instruction, digital signatures, etc. that can prove the legitimacy of the control instruction.

[0068] Processing control instruction information:

[0069] Format the extracted control command content to meet the input format requirements of the command authentication and verification model. This involves steps such as decoding, decryption, or standardizing data structures.

[0070] Input control instructions to the authentication model:

[0071] The formatted remote control command data is input into the command authentication and verification model running on the vehicle terminal.

[0072] By calling the model's interface or service, real-time calculations are performed and evaluation results on the legality of the input instructions are obtained.

[0073] Get the authentication result:

[0074] After the authentication and verification model is processed, the authentication result will be output. The authentication result is binary (i.e. authentication passed or failed), and more detailed information such as the score or trust level is also provided.

[0075] For example, the model may return a security score indicating the probability that the remote control command is considered legitimate, or provide a precise error message indicating where authentication failed or a problem existed.

[0076] Record authentication details:

[0077] The detailed results of the authentication are recorded in the security log of the vehicle terminal, including whether the authentication is successful or failed, and related authentication information.

[0078] The relevant authentication information can be used for subsequent security audits, behavioral analysis, or forensics in the event of a security incident.

[0079] Step 103: Determine the authentication result obtained. If the authentication result shows that the authentication is passed, parse the remote control instruction to extract the controller number and remote control content contained in the remote control instruction;

[0080] Specifically, the detailed implementation steps of step 103 are as follows:

[0081] Review the authentication results:

[0082] The authentication status is checked from the authentication result obtained in step 102. This step involves reading the data returned by the authentication detection model to determine whether the legitimacy of the remote control instruction has been successfully verified.

[0083] Determine whether the instruction is authenticated:

[0084] Set conditional logic to evaluate whether the authentication result is equal to "authentication passed". If the embedded authentication model provides more complex outputs (such as confidence scores), you need to set a threshold. Only when the confidence score is higher than the threshold, the instruction is considered to have passed the authentication.

[0085] Parsing remote control commands:

[0086] If the result indicates that the command authentication is passed, the vehicle terminal will perform the analysis of the remote control command data.

[0087] Parsing involves decoding (if the instructions are sent encrypted), splitting the packet to extract key fields, and converting the data into an actionable internal format.

[0088] Extract controller number and control content:

[0089] After successfully parsing the remote control command, special attention is paid to extracting the controller number and remote control content.

[0090] For example, if a remote control command contains information such as "controller number: HVAC system, control content: set temperature to 22°C", this step will identify and extract "HVAC system" as the controller number and "set temperature to 22°C" as the remote control content.

[0091] Verify parsing correctness:

[0092] Confirm that the parsing process is performed correctly and the controller number and remote control content are accurate.

[0093] Auxiliary logging can be performed for subsequent auditing or troubleshooting.

[0094] Step 104: Map and locate the corresponding target controller in the vehicle terminal according to the controller number obtained by the analysis;

[0095] Specifically, the detailed implementation steps of step 104 are as follows:

[0096] Create a controller mapping table:

[0097] Create or pre-configure a controller mapping table in the vehicle terminal system. This table maps each available controller number to the actual controller hardware or software instance in the vehicle terminal. For example, the mapping table contains entries such as {"HVAC system": controller instance 1, "Audio system": controller instance 2}.

[0098] Read the controller number:

[0099] The controller number field is read from the data extracted in step 103. This number specifies which vehicle system or device should respond to the received remote control command.

[0100] Query the mapping table:

[0101] Using the extracted controller number as a key, the controller mapping table within the vehicle terminal is searched to locate the instance or object of the target controller. This step involves searching in a data structure or calling a specific service through a software interface.

[0102] Positioning target controller:

[0103] Based on the query result, the corresponding controller instance is located. For example, if the controller number parsed in step 103 is "audio system", this step will locate the control module or interface that controls the vehicle audio system.

[0104] Confirm controller reachability:

[0105] During the mapping process, it is confirmed that the target controller instance is currently reachable, that is, the controller hardware or software module is online and ready to receive and execute control commands.

[0106] Record positioning operations:

[0107] The controller's positioning operations and results are recorded in the vehicle terminal's system log so that any problems can be traced and debugged if they occur.

[0108] Step 105: Send the parsed remote control content to the located target controller, and remotely control the vehicle-mounted terminal based on the remote control content.

[0109] Specifically, the detailed implementation steps of step 105 are as follows:

[0110] Prepare remote control commands:

[0111] The remote control content parsed in step 103 is formatted so that it conforms to the command format accepted by the target controller. For example, if the remote control content instructs to adjust the temperature in the car, then the instruction needs to be converted into a specific command protocol understood by the target controller.

[0112] Send control commands:

[0113] The formatted control command is sent via the communication mechanism within the vehicle terminal to the target controller located in step 104. This involves sending data to the internal bus or calling a defined API interface.

[0114] To perform remote control operations:

[0115] After receiving the control command from the vehicle terminal, the target controller performs the corresponding operation. For example, if the remote control content is to turn on the interior lighting, the target controller will accept the command and activate the interior lighting system.

[0116] Confirm operation feedback:

[0117] After the target controller completes the operation, it usually provides execution feedback. The vehicle terminal should receive and confirm this feedback to ensure that the remote control operation has been executed correctly.

[0118] Error handling:

[0119] If an error occurs while sending a control command or when the controller is executing, the vehicle terminal should perform error handling, including retrying the transmission, recording the error information, notifying the user, or taking recovery measures.

[0120] Update system status:

[0121] After a remote control operation, the vehicle computer updates its internal state to reflect the changed vehicle status. For example, if a remote command changes the vehicle's temperature setting, the system state needs to be updated to reflect the new temperature setting.

[0122] Record operation logs:

[0123] To ensure that operations are traceable, all successful or failed remote control operations are recorded in the operation log of the vehicle terminal.

[0124] Another embodiment of the remote control processing method of the vehicle terminal in the embodiment of the present invention includes:

[0125] After determining the authentication result obtained, if the authentication result indicates that the authentication is passed, the method further includes:

[0126] If the authentication result shows that the authentication fails, the remote control instruction will be refused to be executed.

[0127] Another embodiment of the remote control processing method of the vehicle terminal in the embodiment of the present invention includes:

[0128] The training process of the instruction authentication and verification model includes:

[0129] Collecting training data and inputting the training data into a pre-built deep learning network; wherein the training data includes at least instruction metadata, a sending source, a controller number, and remote control content; the pre-built deep learning network has multiple sub-models, wherein the multiple sub-models include an instruction metadata analysis sub-model, a sending source verification sub-model, a controller number identification sub-model, and a remote control content security detection sub-model;

[0130] Based on the instruction metadata analysis sub-model, receiving metadata including a controller number and remote control content, analyzing the validity of the controller number and the format and structure of the remote control content, and outputting metadata verification indicators;

[0131] Based on the sending source verification sub-model, using the sending source data, verifying the legitimacy of the remote control device that initiates the request, ensuring that the control instruction comes from an authorized device, and outputting a sending source legitimacy indicator;

[0132] Analyze the controller number based on the controller number identification sub-model, extract features of the controller number to obtain number feature information, update parameters of the controller number identification sub-model based on the number feature information, identify the controller number based on the controller coding sub-model after parameter update, and output a controller number identification detection index;

[0133] Based on the remote control content security detection sub-model, analyze the remote control content, identify potential malicious instructions and unexpected operations, evaluate the security risks of the content, and output content security indicators;

[0134] Extracting preset authentication and verification criteria from the collected remote control command data, and using the output of each sub-model as an authentication and verification indicator; wherein the authentication and verification criteria represent the characteristic performance of the remote control command under legal and expected control operations, and serve as a reference standard during the training process;

[0135] Calculate an error value based on the deviation between the authentication indicators output by each sub-model in the deep learning network and the preset authentication standard value; wherein the error value indicates the gap between the predicted result and the actual result during the authentication process;

[0136] An adaptive learning rate adjustment strategy is applied to gradually optimize the parameters of each sub-model in the deep learning network to minimize the error value until the training of the deep learning network is completed and a trained instruction authentication and detection model is obtained.

[0137] Specific, important term explanations:

[0138] Command metadata: Descriptive information related to the control command, such as the controller number and details of the remote control content.

[0139] Source Verification Sub-Model: This is the deep learning network part responsible for confirming that the remote control request is initiated by a legitimate authorized device.

[0140] Controller number identification sub-model: A model component used to identify and verify the correctness of the controller number in the remote control command.

[0141] Remote control content security: Determines the risk level of whether remote operations and instructions contain malicious or inappropriate behavior.

[0142] Adaptive learning rate adjustment strategy: A strategy that automatically adjusts the learning rate for different features or parameters during machine learning training to improve learning performance and accuracy.

[0143] Application scenarios:

[0144] This technical solution is suitable for in-vehicle terminal systems, especially in the era of increasing smart cars and remote control functions, where ensuring the correct execution of instructions and system security is crucial.

[0145] Furthermore, a smart car manufacturer is developing a new in-vehicle terminal system and needs to ensure the security and correctness of remote control commands. The following steps are performed:

[0146] Training data collection:

[0147] A large amount of sample data containing command metadata, sending source, controller number, and remote control content is collected as training material.

[0148] Initial network construction:

[0149] Build a deep learning network CarControlNet that contains necessary sub-models, including command metadata analysis, source verification, controller number identification, and remote control content security detection sub-models.

[0150] Model training:

[0151] The collected training data is fed into CarControlNet to train the network. The sub-models parse metadata, verify the source, identify the controller number, and verify the security of remote control content, calculating and outputting their respective verification metrics.

[0152] Performance standard value setting:

[0153] Set authentication and certification standard values ​​corresponding to legal and correct remote control operations for reference during training.

[0154] Model optimization:

[0155] The training effect is measured by calculating the error between the output of each sub-model and the preset authentication standard value, and an adaptive learning rate adjustment strategy is used to tune the parameters of each part of the network to reduce the error value.

[0156] Model testing and deployment:

[0157] After training is completed, the model performance is evaluated using a test dataset to verify its accuracy in judging remote control requests, and then it is deployed to the in-vehicle terminal system.

[0158] Beneficial effects:

[0159] Improved security: Unauthorized or malicious remote control requests can be effectively identified and rejected, protecting the vehicle from manipulation.

[0160] Reduce erroneous operations: Prevent the execution of incorrect instructions through accurate controller number identification and content security detection.

[0161] Improved system reliability: Ensures that all valid instructions are correctly understood and executed by the system, improving the reliability of the vehicle terminal system.

[0162] Supporting intelligent development: As the smart car market grows, this technology has the potential to support more advanced remote control functions, keeping pace with the development of future automotive technology.

[0163] Another embodiment of the remote control processing method of the vehicle terminal in the embodiment of the present invention includes:

[0164] The controller number identification sub-model is based on the controller number, and the controller number is analyzed and features are extracted for the controller number to obtain number feature information. The controller number identification sub-model is updated based on the number feature information. The controller number is identified based on the controller coding sub-model after the parameter update, and a controller number identification detection index is output, including:

[0165] Based on the controller number identification sub-model, the controller number is analyzed and features of the controller number are extracted to obtain number feature information, wherein the number feature information includes at least a first number feature and a second number feature; the first number feature and the second number feature are both combinations of multiple characters;

[0166] Matching a corresponding target data table from a database based on the first numbering feature; wherein the database stores a plurality of data tables and a matching rule for matching a corresponding target data table from the database based on the first numbering feature;

[0167] Based on the second numbering feature, filtering out corresponding serial numbers from the target data table;

[0168] According to the serial number, a corresponding model parameter set is matched from a database to obtain a model parameter set of a controller number identification sub-model;

[0169] The controller number identification sub-model is parameter-updated based on the model parameter set of the controller number identification sub-model, the controller number is identified based on the controller coding sub-model after parameter update, and a controller number identification detection index is output.

[0170] Specific, important term explanations:

[0171] Controller ID Recognition Sub-Model: A component of a deep learning model that is responsible for extracting and verifying the unique identification code of the controller.

[0172] Number characteristic information: The different character combinations that make up the controller number represent the distinctive controller characteristics.

[0173] Target data table: A set of tables pre-stored in the database for retrieving and matching related data based on specific features.

[0174] Model parameter set: A set of parameters such as weights and biases in a deep learning model that determines the performance and output of the model.

[0175] Sequence number: A unique identifier for a data item in a database, used to retrieve a specific set of parameters or other related information.

[0176] Application scenarios:

[0177] This technical solution can be applied to vehicle networks, smart homes, industrial control systems, and any occasion where the legitimacy of equipment needs to be authenticated and authorized.

[0178] Furthermore, an automobile manufacturer needs to ensure that each controller device in its vehicle system can be correctly identified and authenticated to prevent unauthorized devices from accessing the system. Therefore, it implements the following solution:

[0179] Feature extraction:

[0180] The manufacturer analyzes the serial number of each controller device in the vehicle and extracts the first and second coding features from the serial number. For example, the first coding feature contains the manufacturing date and batch of the controller, while the second coding feature contains the device model and manufacturing location information.

[0181] Database Matching:

[0182] The manufacturer has a database containing multiple data tables, each containing relevant controller information based on the first coding feature. The system automatically matches the corresponding data table based on the controller's first coding feature transmitted by the vehicle terminal.

[0183] Filter serial number:

[0184] The serial number corresponding to the second coding feature is screened out from the matched target data table. For example, the specific controller model and production line are located through the second coding feature.

[0185] Parameter collection acquisition:

[0186] Based on the filtered serial numbers, the corresponding controller number identification sub-model parameter set is obtained from the database, and the latest model weights and biases are pulled for model update.

[0187] Model update and testing:

[0188] The obtained parameter set is used to update the parameters of the controller number recognition sub-model, and the updated model is used to carefully identify the controller number of the vehicle terminal, and finally the recognition detection index is output.

[0189] Beneficial effects:

[0190] Improve identification accuracy: Ensure that each controller is correctly identified by accurately matching the controller's feature information and sequence number.

[0191] Enhance system security: Ensure the system only responds to commands from legitimate controllers, preventing unauthorized manipulation.

[0192] Optimized performance updates: As controller models are updated, manufacturers can quickly deploy the latest model parameters to the system to maintain the timeliness of recognition capabilities.

[0193] Reduce maintenance costs: The automatic matching mechanism of the database reduces the workload and error rate of manually updating the parameters of each controller sub-model.

[0194] Furthermore, the remote control instruction is parsed to extract the controller number and remote control content contained in the remote control instruction, including:

[0195] Receiving a remote control instruction containing instruction metadata from a sending source; wherein the remote control instruction includes at least first instruction metadata, second instruction metadata, and third instruction metadata;

[0196] Decrypting the first instruction metadata using a unique decryption algorithm stored in the vehicle terminal to obtain a controller number in plain text format and verification information corresponding to the controller number in plain text format; wherein the encryption algorithm of the first instruction metadata corresponds to the unique decryption algorithm;

[0197] Performing feature recognition on the controller number in plain text format and the verification information to confirm the validity of the controller number and obtain an extracted feature value;

[0198] Decrypt the second instruction metadata based on the obtained characteristic value to obtain the remote control content; if the decryption fails, the instruction is deemed illegal, subsequent operations are terminated, and security processing is performed;

[0199] Performing a hash operation on the remote control content obtained after decrypting the second instruction metadata to verify the integrity of the message and obtain a second hash value;

[0200] Decrypting the third instruction metadata using the second hash value to confirm the security proof of the sending source and the legitimacy of the instruction; if the instruction verification fails, terminating the operation and deleting all previously received instruction metadata;

[0201] Specific, important term explanations:

[0202] 1. Remote Control Command: A command sent from a source to a vehicle terminal for remotely controlling various vehicle functions. It typically includes a controller number and specific control content.

[0203] 2. Command Metadata: The metadata contained in the command is divided into first command metadata, second command metadata, and third command metadata, which are used to store the controller number, control content, and security verification information respectively.

[0204] 3. Controller ID: A unique number used to identify the vehicle controller, ensuring that remote control commands can be sent to the correct controller.

[0205] 4. Verification Information: Characteristic information used to verify the validity of the controller number to ensure the security and legality of the control instructions.

[0206] 5. Hash Operation: An algorithm that maps input data into a fixed-length hash value to verify data integrity and security.

[0207] Technical solution application scenarios:

[0208] This technical solution is applicable to various application scenarios that require remote control and management of vehicles, including but not limited to the following:

[0209] 1. Internet of Vehicles (IoV): In an IoV environment, remote management and control of vehicle functions are achieved through remote control commands, improving the vehicle's intelligence and connectivity.

[0210] 2. Fleet management: In the fleet management system, each vehicle in the fleet is remotely dispatched and controlled through remote control commands to optimize fleet operating efficiency.

[0211] 3. Remote diagnosis and maintenance: In the vehicle remote diagnosis and maintenance scenario, vehicles can be remotely inspected and repaired through remote control commands to improve maintenance efficiency and reduce costs.

[0212] 4. Intelligent Transportation System: In an intelligent transportation system, vehicles are controlled and managed in real time through remote control commands to optimize traffic flow and improve road safety.

[0213] Further, the implementation steps:

[0214] 1. Receive remote control commands

[0215] A remote control instruction containing instruction metadata is received from a transmission source, where the instruction includes at least first instruction metadata, second instruction metadata, and third instruction metadata.

[0216] 2. Decrypt the first instruction metadata

[0217] The first instruction metadata is decrypted using a unique decryption algorithm stored in the vehicle-mounted terminal to obtain the controller number and verification information in plain text format.

[0218] 3. Verify the validity of the controller number

[0219] Feature recognition is performed on the controller number and verification information in plain text format to confirm the validity of the controller number and obtain the extracted feature value.

[0220] 4. Decrypt the second instruction metadata

[0221] Based on the extracted feature value, the second instruction metadata is decrypted to obtain the remote control content. If the decryption fails, the instruction is deemed illegal, the operation is terminated and security processing is performed.

[0222] 5. Verify message integrity

[0223] A hash operation is performed on the remote control content obtained after decrypting the second instruction metadata to obtain a second hash value to verify the integrity of the message.

[0224] 6. Decrypting the third instruction metadata

[0225] The third instruction metadata is decrypted using the second hash value to confirm the security proof of the sending source and the legitimacy of the instruction. If the verification fails, the operation is terminated and all previously received instruction metadata is deleted.

[0226] Beneficial effects:

[0227] 1. Improve the security of remote control: Through a step-by-step decryption and verification mechanism, the legitimacy and security of remote control commands are ensured, preventing illegal commands from affecting the vehicle.

[0228] 2. Enhance the reliability of instruction execution: Through hash operations and feature recognition technology, the integrity and reliability of instruction content are ensured to prevent instructions from being tampered with during transmission.

[0229] 3. Optimize the remote control process: A step-by-step decryption and verification method makes remote control command processing more efficient and systematic, ensuring that commands can be accurately sent to the target controller and executed.

[0230] 4. Wide applicability: This solution is suitable for multiple application scenarios such as vehicle networking, fleet management, remote diagnosis and intelligent transportation systems, and has wide application value and practical effects.

[0231] 5. Improve system intelligence: Realize intelligent management of vehicle functions through remote control commands, improve the system's automation level and user experience.

[0232] The above describes the remote control processing method of the vehicle terminal in the embodiment of the present invention. The following describes the remote control processing device of the vehicle terminal in the embodiment of the present invention. Figure 2 In one embodiment of the present invention, a remote control processing device for a vehicle-mounted terminal includes:

[0233] A receiving module, configured to receive a remote control instruction sent by a remote control device; wherein the remote control instruction at least includes instruction metadata, a sending source, a controller number, and remote control content;

[0234] An authentication module is used to input the remote control command into the trained command authentication detection model, authenticate the received remote control command, and obtain an authentication result;

[0235] The parsing module is used to determine the authentication result obtained. If the authentication result shows that the authentication is passed, the remote control instruction is parsed to extract the controller number and remote control content contained therein;

[0236] A mapping module is used to map and locate the corresponding target controller in the vehicle terminal according to the controller number obtained by parsing;

[0237] The control module is used to send the remote control content obtained by parsing to the located target controller, and remotely control the vehicle-mounted terminal based on the remote control content.

[0238] The present invention also provides a remote control processing device for a vehicle-mounted terminal, which includes a memory and a processor. The memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor executes the steps of the remote control processing method for the vehicle-mounted terminal in the above-mentioned embodiments.

[0239] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions, and when the instructions are run on a computer, the computer executes the steps of the remote control processing method of the vehicle-mounted terminal.

[0240] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0241] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0242] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A remote control processing method for a vehicle-mounted terminal, characterized in that: The following steps are involved: Receive a remote control instruction sent by a remote control device; wherein the remote control instruction includes at least instruction metadata, a sending source, a controller number, and remote control content; The remote control instruction is input into the trained instruction authentication and verification model, and the received remote control instruction is authenticated to obtain an authentication result; the training process of the instruction authentication and verification model includes: collecting training data, and inputting the training data into a pre-built deep learning network; wherein, the training data at least includes instruction metadata, sending source, controller number, and remote control content; the pre-built deep learning network includes multiple sub-models, and the multiple sub-models include an instruction metadata analysis sub-model, a sending source verification sub-model, a controller number identification sub-model, and a remote control content security detection sub-model; based on the instruction metadata analysis sub-model, metadata containing the controller number and remote control content is received, the validity of the controller number, and the format and structure of the remote control content are analyzed, and a metadata verification index is output; based on the sending source verification sub-model, the sending source data is used to verify the legitimacy of the remote control device that initiates the request, to ensure that the control instruction comes from an authorized device, and a sending source legitimacy index is output; based on the controller number identification sub-model, the controller number is analyzed, and feature extraction is performed on the controller number Take, obtain number feature information, update the parameters of the controller number identification sub-model based on the number feature information, identify the controller number based on the controller encoding sub-model after the parameter update, and output the controller number identification detection index; based on the remote control content security detection sub-model, analyze the remote control content, identify potential malicious instructions and unexpected operations, evaluate the security risks of the content, and output the content security index; extract the preset authentication and certification standard value from the collected remote control instruction data, and use the output of each sub-model as the authentication and certification index; wherein, the authentication and certification standard value represents the characteristic performance of the remote control instruction under legal and expected control operations, and is used as a reference standard in the training process; calculate the error value based on the deviation between the authentication and certification index output by each sub-model in the deep learning network and the preset authentication and certification standard value; wherein, the error value shows the gap between the predicted result and the actual result in the authentication and certification process; apply the adaptive learning rate adjustment strategy to gradually optimize the parameters of each sub-model in the deep learning network to minimize the error value until the training of the deep learning network is completed, and obtain the trained instruction authentication and certification detection model; Determine the authentication result obtained. If the authentication result shows that the authentication is passed, parse the remote control instruction and extract the controller number and remote control content contained therein; According to the controller number obtained by the analysis, the corresponding target controller is mapped and located in the vehicle terminal; The remote control content obtained by parsing is sent to the located target controller, and the vehicle-mounted terminal is remotely controlled based on the remote control content.

2. The remote control processing method of the vehicle terminal according to claim 1, characterized in that: After determining the authentication result obtained, if the authentication result indicates that the authentication is passed, the method further includes: If the authentication result shows that the authentication fails, the remote control instruction is refused to be executed.

3. The remote control processing method of the vehicle terminal according to claim 1, characterized in that: The controller number identification sub-model is based on the controller number, and the controller number is analyzed and features are extracted for the controller number to obtain number feature information. The controller number identification sub-model is updated based on the number feature information. The controller number is identified based on the controller coding sub-model after the parameter update, and a controller number identification detection index is output, including: Based on the controller number identification sub-model, the controller number is analyzed and features of the controller number are extracted to obtain number feature information, wherein the number feature information includes at least a first number feature and a second number feature; the first number feature and the second number feature are both combinations of multiple characters; Matching a corresponding target data table from a database based on the first numbering feature; wherein the database stores a plurality of data tables and a matching rule for matching a corresponding target data table from the database based on the first numbering feature; Based on the second numbering feature, filtering out corresponding serial numbers from the target data table; According to the serial number, a corresponding model parameter set is matched from a database to obtain a model parameter set of a controller number identification sub-model; The controller number identification sub-model is parameter-updated based on the model parameter set of the controller number identification sub-model, the controller number is identified based on the controller coding sub-model after parameter update, and a controller number identification detection index is output.

4. The remote control processing method of the vehicle terminal according to claim 1, characterized in that: The preset authentication standard values ​​include at least an actual metadata verification index, an actual transmission source legitimacy index, an actual controller number identification detection index, and an actual content security index.

5. A remote control processing device for a vehicle-mounted terminal, characterized in that: The remote control processing device of the vehicle terminal includes: A receiving module, configured to receive a remote control instruction sent by a remote control device; wherein the remote control instruction at least includes instruction metadata, a sending source, a controller number, and remote control content; An authentication module is used to input the remote control instruction into the trained instruction authentication and verification model, authenticate the received remote control instruction, and obtain an authentication result; the training process of the instruction authentication and verification model includes: collecting training data and inputting the training data into a pre-built deep learning network; wherein the training data at least includes instruction metadata, sending source, controller number, and remote control content; the pre-built deep learning network includes multiple sub-models, and the multiple sub-models include an instruction metadata analysis sub-model, a sending source verification sub-model, a controller number identification sub-model, and a remote control content security detection sub-model; based on the instruction metadata analysis sub-model, metadata containing the controller number and remote control content is received, the validity of the controller number, and the format and structure of the remote control content are analyzed, and a metadata verification index is output; based on the sending source verification sub-model, the sending source data is used to verify the legitimacy of the remote control device that initiates the request, to ensure that the control instruction comes from an authorized device, and a sending source legitimacy index is output; based on the controller number identification sub-model, the controller number is analyzed, feature extraction is performed on the controller number, and number feature information is obtained, and the controller number is verified based on the number feature information. The identification sub-model performs parameter update, identifies the controller number based on the controller encoding sub-model after parameter update, and outputs the controller number identification detection index; based on the remote control content security detection sub-model, analyzes the remote control content, identifies potential malicious instructions and unexpected operations, evaluates the security risks of the content, and outputs the content security index; extracts the preset authentication and certification standard value from the collected remote control instruction data, and uses the output of each sub-model as the authentication and certification index; wherein, the authentication and certification standard value represents the characteristic performance of the remote control instruction under legal and expected control operations, and is used as a reference standard in the training process; calculates the error value based on the deviation between the authentication and certification index output by each sub-model in the deep learning network and the preset authentication and certification standard value; wherein, the error value shows the gap between the predicted result and the actual result in the authentication and certification process; applies the adaptive learning rate adjustment strategy to gradually optimize the parameters of each sub-model in the deep learning network to minimize the error value until the training of the deep learning network is completed to obtain the trained instruction authentication and certification detection model; The parsing module is used to determine the authentication result obtained. If the authentication result shows that the authentication is passed, the remote control instruction is parsed to extract the controller number and remote control content contained therein; A mapping module is used to map and locate the corresponding target controller in the vehicle terminal according to the controller number obtained by the analysis; The control module is used to send the remote control content obtained by parsing to the located target controller, and remotely control the vehicle terminal based on the remote control content.

6. A remote control processing device for a vehicle terminal, characterized in that: The remote control processing device of the vehicle terminal includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor calls the instructions in the memory to enable the remote control processing device of the vehicle terminal to execute the remote control processing method of the vehicle terminal according to any one of claims 1 to 4.

7. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the remote control processing method of the vehicle terminal according to any one of claims 1 to 4 is implemented.

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