A vehicle remote control method and system

Abstract

The present disclosure provides a vehicle remote control method and system, relating to the field of automobile Internet of Vehicles, the method comprising: in response to a control instruction from a terminal, performing a control operation indicated by the control instruction on a target vehicle; parsing an instruction feature from the control instruction and obtaining current vehicle condition information of the target vehicle; simulating the vehicle condition of the target vehicle according to the current vehicle condition information and the instruction feature to obtain simulated vehicle condition information; performing information matching between the current vehicle condition information and the simulated vehicle condition information according to the instruction feature and a preset matching model configuration; intercepting vehicle condition information in the current vehicle condition information that does not match the simulated vehicle condition information within a preset time range to obtain target vehicle condition information, and sending the target vehicle condition information to the terminal, the target vehicle condition information being a collection of each item of vehicle condition information in the current vehicle condition information that matches the simulated vehicle condition information. Through the method of the present disclosure, the risk of misoperation caused by information jump is reduced.

Description

Technical Field

[0001] This disclosure relates to the field of vehicle networking, specifically to a method and system for remote vehicle control. Background Technology

[0002] With the rapid development of intelligent connected vehicle technology, remote vehicle control has gradually become an important part of modern automobiles. Users can remotely operate their vehicles through smart terminal devices, such as unlocking, starting, and adjusting the air conditioning, which provides great convenience. However, in practical applications, users often encounter the problem of fluctuating button states during remote control, mainly due to the synchronization delay between remote control commands and real-time vehicle status information. This instability not only affects the user experience but may also lead to misjudgments of the vehicle's status, thus causing unnecessary safety hazards.

[0003] While related technologies attempt to improve the vehicle's response speed to remote control commands, these solutions often only focus on vehicle status feedback after command execution, failing to effectively address the issue of fluctuating button states on smart terminal devices during the reporting of real-time vehicle status information. Furthermore, these technologies are relatively inadequate in handling abnormal situations, unable to provide timely and effective feedback on the actual vehicle status to users, resulting in a lack of necessary information support during operation. Summary of the Invention

[0004] This disclosure provides a vehicle remote control method and system, which aims to solve the problems existing in the background art.

[0005] To solve the above-mentioned technical problems, this disclosure is implemented as follows:

[0006] In a first aspect, embodiments of this disclosure provide a method for remotely controlling a vehicle, the method comprising:

[0007] In response to a control command from a terminal, the control operation indicated by the control command is executed on the target vehicle;

[0008] The command characteristics are parsed from the control commands, and the current vehicle status information of the target vehicle is obtained;

[0009] Based on the current vehicle condition information and the instruction characteristics, the vehicle condition of the target vehicle is simulated to obtain simulated vehicle condition information;

[0010] Based on the instruction features and the preset matching model configuration, the current vehicle condition information is matched with the simulated vehicle condition information;

[0011] Within a preset time range, intercept vehicle condition information in the current vehicle condition information that does not match the simulated vehicle condition information to obtain target vehicle condition information, and send the target vehicle condition information to the terminal. The target vehicle condition information is a collection of vehicle condition information in the current vehicle condition information that matches the simulated vehicle condition information.

[0012] Optionally, the method further includes:

[0013] According to the preset anomaly detection rules, the current vehicle condition information of the target vehicle is checked.

[0014] If an anomaly is detected in the current vehicle condition information, the matching of the current vehicle condition information with the simulated vehicle condition information is stopped.

[0015] The current vehicle condition information is sent to the terminal.

[0016] Optionally, based on the instruction features and a preset matching model configuration, the current vehicle condition information is matched with the simulated vehicle condition information, including:

[0017] Based on the instruction features and the preset matching model configuration, each vehicle condition information in the current vehicle condition information is compared with the corresponding vehicle condition information in the simulated vehicle condition information, and the degree of difference between each vehicle condition information is calculated.

[0018] The degree of difference is compared with a preset threshold.

[0019] For any item of vehicle condition information in the current vehicle condition information, if the difference is not greater than the preset threshold, then the vehicle condition information is determined to match the simulated vehicle condition information; if the difference is greater than the preset threshold, then the vehicle condition information is determined to not match the simulated vehicle condition information.

[0020] Optionally, based on the instruction features and a preset matching model configuration, each item of vehicle condition information in the current vehicle condition information is compared with the corresponding item of vehicle condition information in the simulated vehicle condition information, and the degree of difference between each item of vehicle condition information is calculated, including:

[0021] Based on the instruction characteristics, all vehicle condition information items that need to be compared are determined;

[0022] Extract the corresponding vehicle condition information items from the current vehicle condition information and the simulated vehicle condition information respectively;

[0023] According to the difference calculation method configured in the matching model, the difference between each vehicle condition information item and the simulated vehicle condition information item is calculated, wherein the expression of the difference calculation method is:

[0024]

[0025] In the formula, D is the difference degree, X1 is the value of the current vehicle condition information, and X2 is the value of the simulated vehicle condition information.

[0026] Optionally, the step of simulating the vehicle condition of the target vehicle based on the current vehicle condition information and the instruction characteristics to obtain simulated vehicle condition information includes:

[0027] The current vehicle condition information and the command features are input into the simulation model, which is pre-trained using historical vehicle condition information and historical command features as training samples.

[0028] Obtain the simulated vehicle condition information output by the simulation model that corresponds to the current control command;

[0029] Also includes:

[0030] The instruction features are used as historical instruction features, and the current vehicle condition information is used as historical vehicle condition information. These are then input into the simulation model to perform iterative training on the simulation model.

[0031] Optionally, the method further includes:

[0032] In response to an update command from the target vehicle, the current vehicle condition information of the target vehicle is updated, wherein the update command is triggered when the target vehicle detects a change in the current vehicle condition information.

[0033] Secondly, this disclosure provides a vehicle remote control system, applied to a vehicle remote control method. The system includes: a terminal, a target vehicle, an instruction module, a simulation module, and a matching module, wherein the instruction module, simulation module, and matching module are deployed in the cloud.

[0034] The instruction module is used to parse instruction features from control instructions from the terminal and forward the control instructions to the simulation module and the matching module.

[0035] The simulation module is used to simulate the vehicle condition of the target vehicle based on the current vehicle condition information and the characteristics of the instruction, and obtain simulated vehicle condition information.

[0036] The matching module is used to match the current vehicle condition information with the simulated vehicle condition information according to the instruction features and the preset matching model configuration; and to intercept vehicle condition information in the current vehicle condition information that does not match the simulated vehicle condition information within a preset time range.

[0037] Optionally, the system further includes an exception module, which is deployed in the cloud;

[0038] The anomaly module is used to check the current vehicle condition information of the target vehicle according to the preset anomaly judgment rules; if the current vehicle condition information is found to be abnormal, the matching of the current vehicle condition information with the simulated vehicle condition information is stopped; and the current vehicle condition information is sent to the terminal.

[0039] Optionally, the target vehicle is equipped with a vehicle condition reporting module and an instruction execution module;

[0040] The vehicle condition reporting module is used to monitor the current vehicle condition information of the target vehicle; when a change in the current vehicle condition information is detected, an update command is triggered, which is used to instruct the cloud to update the current vehicle condition information of the target vehicle.

[0041] The instruction execution module is used to respond to control instructions from the terminal, call the controller to perform the control operation indicated by the control instruction on the target vehicle, and send the execution result to the vehicle condition reporting module.

[0042] Optionally, the instruction module forwards the control instructions to the simulation module and the matching module based on a publish-subscribe mechanism.

[0043] The technical solutions provided by the embodiments of this disclosure have at least the following beneficial effects:

[0044] This disclosure significantly improves the remote control performance of intelligent connected vehicles by responding to control commands from a terminal and executing corresponding control operations. First, by parsing the command characteristics in the control commands and obtaining the current vehicle condition information of the target vehicle, it accurately understands the user's intent and reflects the vehicle's status in real time. Second, it uses simulation technology to simulate the vehicle's condition, generating simulated vehicle condition information, allowing for prediction of the vehicle's response before actual operation. In particular, this disclosure uses a preset matching model configuration to match the current vehicle condition information with the simulated vehicle condition information, and intercepts vehicle condition information that does not match the simulated vehicle condition information within a preset time range.

[0045] This effectively filters out irrelevant or erroneous vehicle condition information, ensuring that the target vehicle condition information received by the end user is accurate and reliable. This reduces button malfunctions and improves the user experience and confidence. This disclosure not only improves the response speed and accuracy of remote vehicle control but also optimizes the user experience through an information interception mechanism, reducing the risk of misoperation due to information malfunctions. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a schematic diagram illustrating the steps of a vehicle remote control method according to an embodiment of this disclosure;

[0048] Figure 2 This is a schematic flowchart of a complete vehicle remote control method provided in one embodiment of the present disclosure;

[0049] Figure 3 This is a structural block diagram of a vehicle remote control system provided in one embodiment of the present disclosure. Detailed Implementation

[0050] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0051] This disclosure aims to address the problems of slow response speed, inaccurate information feedback, and repeated changes in the state of operation buttons in related technologies for remote vehicle control. To this end, this disclosure proposes an improved remote vehicle control method and system. By introducing command feature parsing, simulation, and information matching mechanisms, it effectively improves the performance of remote vehicle control and the user experience. This disclosure not only accurately parses user control commands and obtains real-time vehicle status information, but also uses simulation technology to predict vehicle response, ensuring that the vehicle status information received by the end user is accurate, thereby reducing instability during operation. The specific implementation methods of this disclosure and its advantages and effects in practical applications will be described in detail below.

[0052] Figure 1 This is a schematic diagram illustrating the steps of a vehicle remote control method according to an embodiment of this disclosure, as shown below. Figure 1 As shown, the method includes:

[0053] Step S101: In response to a control command from the terminal, perform the control operation indicated by the control command on the target vehicle;

[0054] Step S102: Parse the instruction features from the control instructions and obtain the current vehicle condition information of the target vehicle;

[0055] Step S103: Based on the current vehicle condition information and the instruction characteristics, simulate the vehicle condition of the target vehicle to obtain simulated vehicle condition information;

[0056] Step S104: Based on the instruction features and the preset matching model configuration, perform information matching between the current vehicle condition information and the simulated vehicle condition information;

[0057] Step S105: Within a preset time range, intercept vehicle condition information in the current vehicle condition information that does not match the simulated vehicle condition information to obtain target vehicle condition information, and send the target vehicle condition information to the terminal. The target vehicle condition information is a collection of various vehicle condition information in the current vehicle condition information that match the simulated vehicle condition information.

[0058] In step S101, in this embodiment of the disclosure, the terminal can be various types of intelligent mobile terminal devices, such as smartphones, tablets, or other intelligent devices. Users can conveniently remotely control and monitor the target vehicle by downloading a dedicated application, performing operations such as unlocking and starting the vehicle. The intelligent connected cloud first receives and responds to control commands issued by the user through the terminal device. The intelligent connected cloud is an integrated cloud computing platform mainly used to support remote control, data processing, and information interaction of intelligent connected vehicles. This disclosure considers using the intelligent connected cloud as a relay station for control commands to leverage its powerful data processing capabilities, computing power, and centralized management advantages to improve the remote control performance of intelligent connected vehicles. For ease of description, this disclosure will refer to the intelligent connected cloud as the cloud. Once a control command is received, the cloud will immediately parse the control command to obtain its command characteristics and call the corresponding control module to execute the operation specified in the command.

[0059] In step S102, in this embodiment of the disclosure, the instruction feature refers to the specific information and attributes contained in the remote control instruction issued by the user through the smart terminal device. The instruction feature helps the cloud understand the user's intent and perform corresponding processing. The instruction feature may include the instruction type, instruction parameters, instruction timeliness, and instruction context. For example, the control instruction is "Turn on the air conditioner and set the temperature to 22 degrees Celsius," the instruction type is "Turn on the air conditioner," the instruction parameter is "set the temperature to 22 degrees Celsius," and the instruction timeliness is high priority. After parsing the instruction feature, the cloud obtains the current vehicle condition information of the target vehicle through the communication interface with the target vehicle. The current vehicle condition information includes, but is not limited to, vehicle status, sensor data, environmental information, etc. By obtaining the current vehicle condition information, the user's control instruction is combined with the actual state of the vehicle to ensure the effectiveness and security of the instruction.

[0060] In step S103, after obtaining the current vehicle condition information and command characteristics, a pre-configured simulation model is used to simulate the vehicle condition. The simulation model can be an algorithm or program that generates a predicted target vehicle condition based on the input command characteristics and current vehicle condition information. The simulation model is based on machine learning, physical simulation, or other computational methods and aims to accurately reflect the expected state of the vehicle after executing the command. The vehicle condition that needs to be changed is determined according to the user's command. For example, if the command is "turn on the air conditioner and set the temperature to 22 degrees Celsius," the simulation will simulate the change in the vehicle's interior temperature after the air conditioner is turned on. The current vehicle condition information is used as a basis, taking into account the actual state of the vehicle. For example, if the interior temperature is already close to 22 degrees Celsius, the simulation results will indicate that the temperature change after the air conditioner is turned on is relatively small. Optionally, the influence of the external environment, such as external temperature and humidity, can also be considered, as these factors may affect the rate of change of the interior temperature. Simulated vehicle condition information is generated, including the expected state of the vehicle after executing the user command. For example, the simulated vehicle condition information indicates that after the air conditioner is turned on, the interior temperature is expected to drop to 22 degrees Celsius within a certain period of time.

[0061] In step S104, a preset matching model configuration is used to perform information matching. The matching model can be an algorithm or a set of rules designed to compare current vehicle condition information with simulated vehicle condition information, determining their similarity and consistency. The matching model can be configured based on different vehicle condition characteristics (such as temperature, humidity, vehicle status, etc.) to ensure the accuracy and reliability of the matching. During the matching process, relevant features are extracted from the current and simulated vehicle condition information, such as in-vehicle temperature, air conditioning status, and lock status. Alternatively, the difference between the current and simulated vehicle condition information can be directly calculated to characterize their similarity. For example, if the simulated vehicle condition information shows an in-vehicle temperature of 22 degrees Celsius, while the current vehicle condition information shows 21 degrees Celsius, a 1-degree temperature difference within a preset threshold is considered a close match. Other matching criteria specified in the matching model configuration can also be used to determine whether the current vehicle condition information matches the simulated vehicle condition information. If they match, the vehicle's status is considered to conform to the user's instructions; if they do not match, further processing is required.

[0062] Step S105 involves an interception operation based on a preset time range. The core of the interception mechanism is to ensure that only vehicle condition information matching the simulated vehicle condition information is allowed to pass, while mismatched information is filtered out. If the current vehicle condition information displays a state inconsistent with the simulated vehicle condition information, this information is marked as mismatched. After interception and filtering, the vehicle condition information matching the simulated vehicle condition information is extracted from the current vehicle condition information and integrated into a target vehicle condition information set, representing the expected state of the vehicle after executing the user's command. Finally, the target vehicle condition information is sent to the terminal device. The target vehicle condition information includes various vehicle condition data consistent with the simulated vehicle condition information, used to update the display on the user's terminal device, ensuring that the user can obtain the latest vehicle status in real time.

[0063] This disclosure significantly improves the remote control performance of intelligent connected vehicles by responding to control commands from the terminal and executing corresponding control operations. First, by parsing the command characteristics in the control commands and obtaining the current vehicle condition information of the target vehicle, it accurately understands the user's intent and reflects the vehicle's status in real time. Second, it uses simulation technology to simulate the vehicle's condition, generating simulated vehicle condition information, allowing for anticipation of the vehicle's response before actual operation. In particular, this disclosure uses a preset matching model configuration to match the current vehicle condition information with the simulated vehicle condition information and intercepts vehicle condition information that does not match the simulated information within a preset time range. This effectively filters out irrelevant or erroneous vehicle condition information, ensuring that the target vehicle condition information received by the end user is accurate and reliable, thereby reducing button malfunctions and improving the user's operating experience and confidence. This disclosure not only improves the response speed and accuracy of remote vehicle control but also optimizes the user experience through an information interception mechanism, reducing the risk of misoperation caused by information malfunctions.

[0064] For example, the method further includes: checking the current vehicle condition information of the target vehicle according to a preset anomaly determination rule; stopping the information matching between the current vehicle condition information and the simulated vehicle condition information if an anomaly is detected in the current vehicle condition information; and sending the current vehicle condition information to the terminal.

[0065] The preset anomaly detection rules can define which vehicle condition information is considered abnormal based on the vehicle's normal operating parameters and historical data. For example, in-vehicle temperature exceeding the normal range, inconsistent sensor data, and vehicle status not meeting expectations may all be included in the anomaly detection scope. After obtaining the target vehicle's current condition information, the current condition information is checked according to the preset anomaly detection rules, specifically including data verification, logical judgment, and threshold comparison. If anomalies are found in the current condition information during the check, the matching process between the current condition information and the simulated condition information will be immediately stopped. This is because when anomalies occur, the simulation results become unreliable. If the simulated condition information continues to be used as a filtering reference to intercept and filter the already abnormal current condition information, incorrect or unreliable information will be propagated to the end user.

[0066] After information matching stops, the current vehicle status information is sent directly to the terminal device. This allows users to understand the vehicle's real-time status, especially in case of anomalies, enabling them to quickly obtain information and take appropriate action.

[0067] This disclosure, through the inspection and processing of anomalies in the current vehicle condition information of the target vehicle, promptly identifies potential problems and takes measures to avoid transmitting erroneous information to the user. This ensures the accuracy and reliability of the information, further enhancing the security and user experience of the intelligent connected vehicle remote control system.

[0068] For example, according to the instruction features and the preset matching model configuration, matching the current vehicle condition information with the simulated vehicle condition information includes: comparing each item of vehicle condition information in the current vehicle condition information with the corresponding item of vehicle condition information in the simulated vehicle condition information item by item according to the instruction features and the preset matching model configuration, and calculating the difference degree between each item of vehicle condition information; comparing the difference degree with a preset threshold; for any item of vehicle condition information in the current vehicle condition information, if the difference degree is not greater than the preset threshold, then it is determined that the item of vehicle condition information matches the simulated vehicle condition information; if the difference degree is greater than the preset threshold, then it is determined that the item of vehicle condition information does not match the simulated vehicle condition information.

[0069] The main purpose of information matching is to ensure consistency between current vehicle condition information and simulated vehicle condition information, thereby determining whether the actual vehicle state matches the user's control commands. During the matching process, each data point in the current vehicle condition information (such as temperature, humidity, vehicle status, etc.) is compared item by item with the corresponding item in the simulated vehicle condition information. Each vehicle condition feature is analyzed individually to ensure a meticulous matching process. For example, if the current vehicle condition information shows an interior temperature of 21 degrees Celsius, while the simulated vehicle condition information shows 22 degrees Celsius, these two values ​​will be compared. For each comparison item, the degree of difference between them is calculated. The degree of difference can be calculated using absolute difference or relative difference. The calculated degree of difference is then compared with a preset threshold, which is used to determine the degree of matching between the current and simulated vehicle condition information. For example, if the preset threshold is 2 degrees, then in the above example, a difference of 1 degree is less than the threshold of 2 degrees, indicating that the two values ​​match. Based on the comparison results, each piece of vehicle condition information will be evaluated. If the difference between any piece of vehicle condition information and the simulated vehicle condition information is not greater than a preset threshold, then the vehicle condition information is considered to match the simulated vehicle condition information, meaning that the vehicle condition information can be considered reliable and conforms to the user's control commands. Conversely, if the difference is greater than the preset threshold, then the vehicle condition information is considered to not match the simulated vehicle condition information, indicating that the vehicle's condition does not match expectations.

[0070] This disclosure effectively identifies inconsistent vehicle condition information through precise item-by-item comparison and difference calculation, thereby improving the user's operating experience and reducing the risk of misoperation.

[0071] For example, based on the instruction features and a preset matching model configuration, each item of vehicle condition information in the current vehicle condition information is compared with the corresponding item of vehicle condition information in the simulated vehicle condition information, and the degree of difference between each item of vehicle condition information is calculated, including:

[0072] Based on the instruction characteristics, all vehicle condition information items that need to be compared are determined;

[0073] Extract the corresponding vehicle condition information items from the current vehicle condition information and the simulated vehicle condition information respectively;

[0074] According to the difference calculation method configured in the matching model, the difference between each vehicle condition information item and the simulated vehicle condition information item is calculated, wherein the expression of the difference calculation method is:

[0075]

[0076] In the formula, D is the difference degree, X1 is the value of the current vehicle condition information, and X2 is the value of the simulated vehicle condition information.

[0077] Based on the command characteristics, the vehicle condition information items to be compared are first identified. Assume the user issues the command "Turn on the air conditioning and set the temperature to 22 degrees Celsius." Based on this command, the vehicle condition information items to be compared include: current interior temperature, air conditioning status (on or off), and vehicle lock status. Data corresponding to the identified vehicle condition information items is extracted from the current vehicle condition information and simulated vehicle condition information. The difference between the current vehicle condition information items and the simulated vehicle condition information items is calculated according to the difference calculation method configured in the preset matching model. In this embodiment, the similarity is calculated using a relative difference method.

[0078] For each extracted vehicle condition information item, a difference calculation is performed. Assuming the current in-vehicle temperature and the simulated in-vehicle temperature are X1 = 21 and X2 = 22 respectively, the difference is:

[0079]

[0080] It should be noted that the vehicle interior temperature is a typical numerical vehicle condition information, but most vehicle condition information is non-numerical, such as the air conditioning status, which only has two representations: on and off. It needs to be converted into numerical values ​​for comparison. You can pre-assign a first value to the "on" state and a second value to the "off" state, and then substitute them into the above formula for calculation.

[0081] Through the above steps, the current vehicle condition information and the simulated vehicle condition information can be effectively compared item by item, the difference can be calculated and a matching judgment can be made, thus ensuring the accuracy and reliability of intelligent connected vehicles when remotely controlled.

[0082] For example, the step of simulating the vehicle condition of the target vehicle based on the current vehicle condition information and the command features to obtain simulated vehicle condition information includes: inputting the current vehicle condition information and the command features into a simulation model, wherein the simulation model is pre-trained using historical vehicle condition information and historical command features as training samples; and obtaining the simulated vehicle condition information output by the simulation model corresponding to the current control command.

[0083] Also includes:

[0084] The instruction features are used as historical instruction features, and the current vehicle condition information is used as historical vehicle condition information. These are then input into the simulation model to perform iterative training on the simulation model.

[0085] Simulation aims to predict the state of a target vehicle under a specific command by analyzing current vehicle condition information and command characteristics. The simulation model is a pre-trained algorithm model, optionally built using machine learning or deep learning techniques. Its training samples include historical vehicle condition information and historical command characteristics. By using historical data for pre-training, the simulation model can learn the vehicle's response patterns under different commands, thus generating corresponding simulated vehicle condition information when receiving new commands.

[0086] The current vehicle condition information and command characteristics are input into the simulation model. Based on its internal learning and training, the simulation model outputs simulated vehicle condition information corresponding to the current control command. For example, if the current vehicle condition information shows that the interior temperature is 21 degrees Celsius, and the user command is "set the temperature to 22 degrees Celsius", the simulation model can output "simulated interior temperature is 22 degrees Celsius, air conditioning is on".

[0087] To continuously improve the accuracy of the simulation model, current command characteristics and current vehicle condition information are input as new historical data. By continuously updating the model's training data, it can better adapt to new situations and changes. The simulation model will adjust and optimize based on the new data to improve its future predictive capabilities. For example, if in a certain operation, the user command is "turn on the air conditioning," but the actual vehicle condition shows that the air conditioning fails to turn on properly, the simulation model records this situation and considers this feedback in future training to better simulate similar scenarios.

[0088] This disclosure utilizes current vehicle condition information and command characteristics to perform simulations using a simulation model, generating accurate simulated vehicle condition information. Simultaneously, the iterative training process ensures continuous optimization and adaptability of the model, thereby improving the performance and user experience of remote control for intelligent connected vehicles.

[0089] For example, the method further includes: updating the current vehicle condition information of the target vehicle in response to an update instruction from the target vehicle, wherein the update instruction is triggered when the target vehicle detects a change in the current vehicle condition information.

[0090] When the vehicle's internal systems or sensors detect changes in vehicle condition information, they automatically generate and send update commands to enable the cloud to obtain the latest status of the target vehicle in a timely manner. The target vehicle's monitoring system tracks and records various vehicle condition information in real time (such as fuel level, speed, temperature, fault status, etc.). Once a change in the current vehicle condition information is detected, an update command is generated through the vehicle condition reporting module. For example, if the vehicle's fuel level drops from 50 liters to 45 liters, or the vehicle speed increases from 0 to 10 km / h, these changes will trigger the generation of an update command. Once an update command is triggered, it is sent to the target vehicle's cloud platform for further processing and recording. Upon receiving the update command, the cloud updates the current vehicle condition information according to the information in the command.

[0091] This disclosure updates current vehicle status information in a timely manner to ensure the accuracy and real-time nature of information during the operation of intelligent connected vehicles, thereby improving the reliability of remote control.

[0092] Figure 2 This is a schematic flowchart illustrating a complete vehicle remote control method according to an embodiment of this disclosure, as shown below. Figure 2 As shown in this embodiment, the user first sends a control command through the terminal. After receiving the control command, the cloud first parses and caches the command. The parsed command features are then forwarded to different processing modules. In the left branch, based on the parsed command features, simulation model, and the actual vehicle condition information, the target vehicle condition is simulated to generate simulated vehicle condition information, which is then returned to the mobile terminal so that the user can understand the expected state of the vehicle. In the right branch, based on the command features and interception model configuration, the actual vehicle condition is intercepted, filtering out vehicle condition information that does not match the simulated vehicle condition. Simultaneously, an anomaly monitoring mechanism is activated to monitor for any abnormalities in remote control operations in real time. Once an anomaly is detected, the vehicle condition interception process is immediately interrupted, and the real vehicle condition information is fed back to the mobile terminal to ensure that the user can obtain the actual state of the vehicle in a timely manner. This not only provides accurate simulated vehicle condition information but also effectively intercepts mismatched vehicle conditions, improving the user's operating experience and security.

[0093] Figure 3 This is a structural block diagram of a vehicle remote control system provided in one embodiment of the present disclosure, such as... Figure 3 As shown, the system is applied to a vehicle remote control method as described above. The system includes: a terminal 201, a target vehicle 202, an instruction module 203, a simulation module 204, and a matching module 205, wherein the instruction module 203, the simulation module 204, and the matching module 205 are deployed in the cloud.

[0094] The instruction module 203 is used to parse instruction features from control instructions from the terminal 201 and forward the control instructions to the simulation module 204 and the matching module 205.

[0095] The simulation module 204 is used to simulate the vehicle condition of the target vehicle based on the current vehicle condition information and the instruction characteristics to obtain simulated vehicle condition information.

[0096] The matching module 205 is used to match the current vehicle condition information with the simulated vehicle condition information according to the instruction features and the preset matching model configuration; and to intercept vehicle condition information in the current vehicle condition information that does not match the simulated vehicle condition information within a preset time range.

[0097] For example, the system further includes an exception module 206, which is deployed in the cloud;

[0098] The anomaly module 206 is used to check the current vehicle condition information of the target vehicle according to the preset anomaly judgment rules; if the current vehicle condition information is found to be abnormal, stop the information matching between the current vehicle condition information and the simulated vehicle condition information; and send the current vehicle condition information to the terminal.

[0099] For example, the target vehicle is equipped with a vehicle condition reporting module 207 and an instruction execution module 208;

[0100] The vehicle condition reporting module 207 is used to monitor the current vehicle condition information of the target vehicle 202; when the current vehicle condition information is detected to have changed, an update command is triggered, which is used to instruct the cloud to update the current vehicle condition information of the target vehicle.

[0101] The instruction execution module is used to respond to the control instruction from the terminal 201, call the controller to perform the control operation indicated by the control instruction on the target vehicle 202, and send the execution result to the vehicle condition reporting module 207.

[0102] For example, the instruction module forwards the control instructions to the simulation module and the matching module based on a publish-subscribe mechanism.

[0103] The instruction module acts as the publisher, distributing the parsed control instructions to a specific message channel.

[0104] This message channel can be a topic, and modules subscribed to this topic can receive messages from the instruction module. The simulation and matching modules, as subscribers, have already subscribed to the message channel published by the instruction module. When the instruction module publishes a control command, the simulation and matching modules automatically receive the control command and process it accordingly based on the command's characteristics.

[0105] This disclosure considers a publish-subscribe mechanism as the forwarding rule, taking into account that the main advantage of the publish-subscribe mechanism is the decoupling between modules. The instruction module does not need to know the specific implementation or state of the simulation module and the matching module; it only needs to publish the message to the channel, thereby improving the overall response speed and processing capacity.

[0106] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods and systems. Therefore, embodiments of this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, embodiments of this disclosure can take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0107] This disclosure describes embodiments of methods, terminal devices (systems), and computer program products according to embodiments of this disclosure with reference to flowchart illustrations and / or block diagrams. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing terminal equipment to cause a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0108] While preferred embodiments of the present disclosure have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the present disclosure.

[0109] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" or any other variations thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element. The above provides a detailed description of a vehicle remote control method and system. Specific examples have been used to illustrate the principles and implementation methods of this disclosure. The descriptions of the above embodiments are only for the purpose of helping to understand the method and its core ideas; at the same time, for those skilled in the art, based on the ideas of this disclosure, there will be changes in specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this disclosure.

Claims

1. A method for remote vehicle control, characterized in that, The method includes: In response to a control command from a terminal, the system parses the command features from the control command and obtains the current vehicle status information of the target vehicle; then it executes the control operation indicated by the control command on the target vehicle. Based on the current vehicle condition information and the instruction characteristics, the vehicle condition of the target vehicle is simulated to obtain simulated vehicle condition information; Based on the instruction features and the preset matching model configuration, the current vehicle condition information is matched with the simulated vehicle condition information; Within a preset time range, intercept vehicle condition information in the current vehicle condition information that does not match the simulated vehicle condition information to obtain target vehicle condition information, and send the target vehicle condition information to the terminal. The target vehicle condition information is a collection of vehicle condition information in the current vehicle condition information that matches the simulated vehicle condition information. Based on the instruction characteristics and the preset matching model configuration, the current vehicle condition information is matched with the simulated vehicle condition information, including: Based on the instruction features and the preset matching model configuration, each item of vehicle condition information in the current vehicle condition information is compared with the corresponding item of vehicle condition information in the simulated vehicle condition information, and the degree of difference between each item of vehicle condition information is calculated. The degree of difference is compared with a preset threshold. For any item of vehicle condition information in the current vehicle condition information, if the difference is not greater than the preset threshold, then the vehicle condition information is determined to match the simulated vehicle condition information; if the difference is greater than the preset threshold, then the vehicle condition information is determined to not match the simulated vehicle condition information. Based on the instruction features and the preset matching model configuration, each item of vehicle condition information in the current vehicle condition information is compared with the corresponding item of vehicle condition information in the simulated vehicle condition information, and the degree of difference between each item of vehicle condition information is calculated, including: Based on the instruction characteristics, all vehicle condition information items that need to be compared are determined; Extract the corresponding vehicle condition information items from the current vehicle condition information and the simulated vehicle condition information respectively; According to the difference calculation method configured in the matching model, the difference between each vehicle condition information item and the simulated vehicle condition information item is calculated, wherein the expression of the difference calculation method is: In the formula, D represents the degree of difference. The value of the current vehicle condition information. The value is the simulated vehicle condition information.

2. The method according to claim 1, characterized in that, The method further includes: According to the preset anomaly detection rules, the current vehicle condition information of the target vehicle is checked. If an anomaly is detected in the current vehicle condition information, the matching of the current vehicle condition information with the simulated vehicle condition information is stopped. The current vehicle condition information is sent to the terminal.

3. The method according to claim 1, characterized in that, The step of simulating the vehicle condition of the target vehicle based on the current vehicle condition information and the instruction characteristics to obtain simulated vehicle condition information includes: The current vehicle condition information and the command features are input into the simulation model, which is pre-trained using historical vehicle condition information and historical command features as training samples. Obtain the simulated vehicle condition information output by the simulation model that corresponds to the current vehicle condition information and command characteristics; Also includes: The instruction features are used as historical instruction features, and the current vehicle condition information is used as historical vehicle condition information. These are then input into the simulation model to perform iterative training on the simulation model.

4. The method according to claim 1, characterized in that, The method further includes: In response to an update command from the target vehicle, the current vehicle condition information of the target vehicle is updated, wherein the update command is triggered when the target vehicle detects a change in the current vehicle condition information.

5. A vehicle remote control system, characterized in that, The system, applicable to the method as described in any one of claims 1-4, comprises: a terminal, a target vehicle, an instruction module, a simulation module, and a matching module, wherein the instruction module, simulation module, and matching module are deployed in the cloud; The instruction module is used to parse instruction features from control instructions from the terminal and forward the control instructions to the simulation module and the matching module. The simulation module is used to simulate the vehicle condition of the target vehicle based on the current vehicle condition information and the characteristics of the instruction, and obtain simulated vehicle condition information. The matching module is used to match the current vehicle condition information with the simulated vehicle condition information according to the instruction features and the preset matching model configuration; and to intercept vehicle condition information in the current vehicle condition information that does not match the simulated vehicle condition information within a preset time range.

6. The system according to claim 5, characterized in that, The system also includes an exception module, which is deployed in the cloud; The anomaly module is used to check the current vehicle condition information of the target vehicle according to the preset anomaly judgment rules; if the current vehicle condition information is found to be abnormal, the matching of the current vehicle condition information with the simulated vehicle condition information is stopped; and the current vehicle condition information is sent to the terminal.

7. The system according to claim 5, characterized in that, The target vehicle is equipped with a vehicle status reporting module and an instruction execution module; The vehicle condition reporting module is used to monitor the current vehicle condition information of the target vehicle; when a change in the current vehicle condition information is detected, an update command is triggered, which is used to instruct the cloud to update the current vehicle condition information of the target vehicle. The instruction execution module is used to respond to control instructions from the terminal, call the controller to perform the control operation indicated by the control instruction on the target vehicle, and send the execution result to the vehicle condition reporting module.

8. The system according to claim 5, characterized in that, The instruction module forwards the control instructions to the simulation module and the matching module based on a publish-subscribe mechanism.

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