AI-based smart mobility methods, devices, equipment, and storage media

Abstract

This application discloses an artificial intelligence-based smart mobility method, device, equipment, and storage medium, relating to the field of vehicle control technology. The AI-based smart mobility method includes: acquiring a first reference instruction and a vehicle operation history record; obtaining a second reference instruction based on the vehicle operation history record and the first reference instruction; sending the second reference instruction to a preset device, so that the preset device provides feedback on a correction instruction based on the second reference instruction; and obtaining a target instruction based on the correction instruction and the second reference instruction, so that vehicle control is completed according to the target instruction. This application monitors the vehicle's interior temperature, seat temperature, steering wheel temperature, etc., and combines this with the owner's driving habits and the vehicle's own capabilities to adjust the vehicle environment in advance, providing a more comfortable driving environment for the owner.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to intelligent mobility methods, devices, equipment and storage media based on artificial intelligence. Background Technology

[0002] As intelligentization, electrification, and connectivity become development trends in the automotive industry, car owners' demands for vehicle travel and experience are increasing. They expect vehicles to automatically adjust to their optimal state before use to provide a comfortable travel environment. This demand has driven the research and development of intelligent vehicle control systems, aiming to achieve automatic optimization of the vehicle environment through technological means.

[0003] Most vehicles on the market currently only support separate air conditioning control, which often fails to quickly create a comfortable in-car environment in inclement weather. Furthermore, these systems typically lack real-time monitoring and pre-processing capabilities for vehicle component status, making components prone to damage under extreme conditions or after prolonged use, thus affecting vehicle performance and lifespan.

[0004] Existing control methods suffer from two main problems: first, their ability to adjust the in-vehicle environment is limited, failing to meet drivers' personalized comfort needs; second, they lack real-time monitoring and preventative maintenance of vehicle components, leading to a high failure rate. These issues restrict the optimization of vehicle performance and the improvement of the driver's experience. Therefore, how to intelligently adjust the vehicle's travel environment based on the driver's habits has become an urgent problem to be solved. Summary of the Invention

[0005] The purpose of this application is to provide an artificial intelligence-based smart mobility method, device, equipment, and storage medium, aiming to solve the technical problem of how to intelligently adjust the vehicle's travel environment according to the driver's habits. To achieve the above objective, this application proposes an artificial intelligence-based smart mobility method, which includes:

[0006] Obtain the first reference command and vehicle operation history;

[0007] Based on the vehicle's operating history and the first reference instruction, a second reference instruction is obtained;

[0008] The second reference instruction is sent to a preset device, so that the preset device feeds back a correction instruction based on the second reference instruction;

[0009] Based on the correction instruction and the second reference instruction, a target instruction is obtained to enable vehicle control to be performed according to the target instruction.

[0010] In one embodiment, before obtaining the first reference command and vehicle operation history, the method further includes:

[0011] Acquire sensor data and preset combination commands;

[0012] Based on the sensor data, an initial command is obtained;

[0013] Based on the initial instruction and the preset combination instruction, a first reference instruction is obtained.

[0014] In one embodiment, a second reference instruction is obtained based on the vehicle's operating history and the first reference instruction, including:

[0015] The vehicle operation history records are cleaned to obtain reference vehicle operation history records;

[0016] Based on the preset algorithm and the reference vehicle's operating history, extended instructions are obtained;

[0017] Based on the extended instruction and the first reference instruction, a second reference instruction is obtained.

[0018] In one embodiment, after obtaining the second reference instruction based on the vehicle's operating history and the first reference instruction, the method further includes:

[0019] Acquire vehicle driving data and preset vehicle driving safety data;

[0020] If the vehicle driving data is less than or equal to the preset vehicle driving safety data, then the remaining safe driving data of the vehicle is obtained based on the vehicle driving data and the preset vehicle driving safety data.

[0021] If the vehicle driving data is greater than or equal to the preset vehicle driving safety data, an alarm message will be sent.

[0022] In one embodiment, if the vehicle driving data is greater than or equal to a preset vehicle driving safety data, an alarm message is sent, including:

[0023] Acquire and record device information and request signals;

[0024] Based on the request signal, the identification information of the terminal device is obtained;

[0025] If the identification information of the terminal device matches the information of the recording device, an alarm signal is sent.

[0026] In one embodiment, if the vehicle driving data is greater than or equal to a preset vehicle driving safety data, after sending the alarm information, the method further includes:

[0027] Obtain the current vehicle location, the location marked in the cloud, and map data;

[0028] Based on the current vehicle location, the location marked in the cloud, and map data, reference road information is obtained.

[0029] In one embodiment, after obtaining a target instruction based on the correction instruction and the second reference instruction, so that vehicle control is completed according to the target instruction, the method further includes:

[0030] Get the preset time;

[0031] Based on the preset time, the vehicle operation history within the preset time period is obtained;

[0032] Based on the preset algorithm, the vehicle's historical operation history, and the vehicle's historical operation history within the preset time period, a correction instruction is obtained;

[0033] Update the correction instruction to the second reference instruction.

[0034] Furthermore, to achieve the above objectives, this application also proposes an artificial intelligence-based smart mobility device, the device comprising:

[0035] The acquisition module is used to acquire the first reference command and the vehicle's operating history.

[0036] The module is used to obtain a second reference instruction based on the vehicle's operating history and the first reference instruction;

[0037] The sending module is used to send the second reference instruction to a preset device, so that the preset device can feed back a correction instruction according to the second reference instruction;

[0038] The control module is used to obtain a target instruction based on the correction instruction and the second reference instruction, so as to complete vehicle control according to the target instruction.

[0039] In addition, to achieve the above objectives, this application also proposes an artificial intelligence-based smart mobility device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the artificial intelligence-based smart mobility method described above.

[0040] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the artificial intelligence-based smart mobility method described above.

[0041] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the artificial intelligence-based smart mobility method described above.

[0042] One or more technical solutions proposed in this application have at least the following technical effects:

[0043] This application first acquires a first reference command and the vehicle's historical operating data, comprehensively considering both historical data and current command requirements. Next, using this information, a second reference command is generated to more accurately guide vehicle operation. Then, the second reference command is sent to a preset device, which provides feedback correction commands based on this command, further optimizing the vehicle control strategy. Finally, the correction commands and the second reference command are combined to generate a target command, achieving precise vehicle control and improving vehicle operating efficiency and comfort. This application ensures intelligent and adaptable vehicle control, enabling the vehicle to intelligently adjust its operating state based on real-time data and historical experience to achieve optimal performance and safety standards. Attached Figure Description

[0044] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0045] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a flowchart illustrating an embodiment of the AI-based smart mobility method of this application.

[0047] Figure 2 This is a flowchart illustrating Embodiment 2 of the AI-based smart mobility method of this application.

[0048] Figure 3 This is a schematic diagram of the module structure of an artificial intelligence-based smart mobility device according to an embodiment of this application;

[0049] Figure 4 This is a schematic diagram of the device structure of the hardware operating environment involved in the AI-based smart mobility method in the embodiments of this application.

[0050] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0051] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0052] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0053] With intelligentization, electrification, and connectivity becoming development trends in the automotive industry, car owners' demands for vehicle travel and experience are increasing. They expect vehicles to automatically adjust to their optimal state before use to provide a comfortable travel environment. This demand has driven the research and development of intelligent vehicle control systems, aiming to achieve automatic optimization of the vehicle environment through technological means. Currently, most vehicles on the market only support separate air conditioning control, which often fails to quickly create a comfortable in-car environment in inclement weather. Furthermore, these systems typically lack real-time monitoring and pre-processing capabilities for the status of vehicle components, leading to damage to components under extreme conditions or after prolonged use, affecting vehicle performance and lifespan. Existing vehicle control methods mainly suffer from two problems: first, limited ability to adjust the in-car environment, failing to meet the personalized comfort needs of car owners; and second, a lack of real-time monitoring and preventative maintenance of vehicle components, resulting in a high failure rate. These problems limit the optimization of vehicle performance and the improvement of the car owner experience.

[0054] The main solution of this application embodiment is as follows: First, by acquiring a first reference instruction and the vehicle's historical operating data, this embodiment can comprehensively consider the vehicle's historical operating data and current instruction requirements. Next, using this information, a second reference instruction is obtained to more accurately guide the vehicle's operation. Then, the second reference instruction is sent to a preset device, which provides feedback correction instructions based on this instruction, further optimizing the vehicle control strategy. Finally, by combining the correction instructions and the second reference instruction, a target instruction is generated to achieve precise vehicle control, thereby improving the efficiency and comfort of vehicle operation. This embodiment ensures the intelligence and adaptability of vehicle control, enabling the vehicle to intelligently adjust its operating state based on real-time data and historical experience to achieve optimal performance and safety standards.

[0055] It should be noted that the executing entity in this application embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions. The following uses an electronic control unit as an example to describe this embodiment and the subsequent embodiments.

[0056] Based on this, embodiments of this application provide an intelligent travel method based on artificial intelligence, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the AI-based smart mobility method of this application.

[0057] In this embodiment, the AI-based smart mobility method includes steps S10 to S40:

[0058] Step S10: Obtain the first reference command and vehicle operation history;

[0059] It should be noted that the first reference command can refer to a command based on the owner's driving habits, preset vehicle settings, or immediate driving needs. These commands can include specific operational requests from the owner, such as setting a target temperature, seat adjustment preferences, driving mode selection, etc., or starting parameters automatically generated by the vehicle according to preset programs. Vehicle operating history can be data records accumulated during the vehicle's past operation, including but not limited to mileage, fuel consumption, maintenance records, fault records, driving behavior patterns, etc. These historical records provide the system with background information on vehicle usage and performance, helping the system analyze vehicle usage patterns and potential problems.

[0060] Understandably, this step involves acquiring the operating commands set by the car owner, as well as the vehicle's past operating data records, including mileage, fuel consumption, maintenance and malfunction history, in order to integrate this real-time and historical information to provide comprehensive data support for the intelligent control of the vehicle.

[0061] As an example, before obtaining the first reference instruction and the vehicle's operating history, the process further includes: obtaining sensor data and a preset combination instruction; obtaining an initial instruction based on the sensor data; and obtaining the first reference instruction based on the initial instruction and the preset combination instruction.

[0062] Sensor data can be real-time data collected by various sensors on the vehicle, including but not limited to temperature sensors, pressure sensors, and speed sensors. This data reflects the vehicle's current physical state and environmental conditions, such as interior temperature, engine temperature, tire pressure, and vehicle speed. Initial commands are commands directly derived from sensor data, representing the system's immediate response to the current vehicle state. For example, if the temperature sensor indicates the interior temperature is too high, the initial command might be to lower the air conditioning temperature. Preset combination commands can be a set of commands based on conditions and rules preset by the owner. These commands may include default settings upon vehicle startup, safety checklists, and energy-saving mode settings. Preset combination commands reflect the vehicle's regular operating mode and the owner's personal preferences. For example, by combining steering wheel temperature sensor data, driver's seat sensor data, and air conditioning adjustment mode, initial data is obtained showing the steering wheel and driver's seat temperatures 10 minutes after adjusting the air conditioning to level 4. Given the owner's preset combination of a steering wheel temperature of 23 degrees Celsius and a driver's seat temperature of 25 degrees Celsius, this first reference command can be achieved by adjusting the air conditioning to level 4 recirculation for 11 minutes.

[0063] Specifically, the system first collects real-time data from various vehicle sensors, such as temperature, pressure, and speed. Then, it generates initial commands based on this sensor data for the current vehicle state. Next, these initial commands are combined with pre-set combination commands to obtain the first reference command, which forms the basis for subsequent intelligent control processes. This first reference command combines real-time physical conditions with preset operational logic, providing a starting point for intelligent vehicle control.

[0064] Step S20: Obtain a second reference instruction based on the vehicle operation history and the first reference instruction;

[0065] It should be noted that the second reference command refers to a further refined and optimized command generated after comprehensively analyzing the vehicle's historical operating data and the first reference command generated based on current sensor data and preset combination commands. This command takes into account the vehicle's past operating modes, performance trends, and maintenance records, as well as the current vehicle status and the owner's immediate needs, to more accurately guide the vehicle's operation and control, thereby achieving more efficient, safe, and personalized vehicle operation.

[0066] Understandably, by analyzing the vehicle's historical operating records and the first reference command generated based on sensor data and preset combination commands, a second reference command is generated by combining this information. This command can more accurately reflect the vehicle's actual operating needs and the owner's driving habits, providing more refined guidance for the vehicle's intelligent control.

[0067] As an example, after obtaining the second reference instruction based on the vehicle's historical operation record and the first reference instruction, the method further includes: acquiring vehicle driving data and preset vehicle driving safety data; if the vehicle driving data is less than or equal to the preset vehicle driving safety data, obtaining the vehicle's remaining safe driving data based on the vehicle driving data and the preset vehicle driving safety data; if the vehicle driving data is greater than or equal to the preset vehicle driving safety data, sending an alarm message.

[0068] Vehicle driving data can be data collected during actual vehicle operation, which may include real-time information such as current speed, distance traveled, remaining fuel, and tire wear. Preset vehicle driving safety data can be safety thresholds preset based on vehicle manufacturer recommendations, regulatory requirements, or owner settings, such as maximum speed limits, maximum driving distances, and fuel level warning thresholds. Remaining safe driving data can be the distance or time the vehicle can safely travel based on current driving data and preset safety data. For example, if the vehicle's fuel level is below a preset threshold, the system will calculate the remaining distance the vehicle can travel based on the current fuel level. Alarm information can be sent when vehicle driving data reaches or exceeds preset safety data to remind the driver or vehicle management system to take necessary safety measures. Alarm information may be visual, auditory, or tactile warnings, such as warning lights on the dashboard, audible alarms, or seat vibrations, or it may be sent to the central control screen or user-linked terminal devices, with the aim of ensuring vehicle safety and preventing accidents.

[0069] Specifically, the system first collects actual vehicle driving data and preset safety thresholds, then compares these two sets of data: if the actual driving data (such as speed or mileage) is within the safety threshold range, the system calculates the remaining safe distance or time the vehicle can continue driving; if the actual driving data exceeds the safety threshold, the system automatically sends an alarm message to remind the driver or takes other safety actions to ensure vehicle safety. This process aims to monitor vehicle status in real time and prevent potential safety risks.

[0070] As an example, if the vehicle driving data is greater than or equal to a preset vehicle driving safety data, an alarm message is sent, including: obtaining recording device information and a request signal; obtaining the terminal device's identification information based on the request signal; and sending an alarm signal if the terminal device's identification information matches the recording device information.

[0071] The recorded device information can be device-related information stored within the system, possibly including the device's model, serial number, registration information, etc., which is used to uniquely identify and verify the device. The request signal can be a signal or request issued by the terminal device, possibly to obtain services, transmit data, or perform an operation. This request signal may contain specific instructions or queries that require processing. The identification information can be the unique identification information of the terminal device, such as a device ID, MAC address, or other unique identifiers, used to identify and distinguish different terminal devices in a network or system.

[0072] Specifically, the system first collects request signals from terminal devices and records device information. Then, it extracts the terminal device's identification information, such as device ID or MAC address, from the request signals. If this identification information matches the device information recorded by the system, indicating that the request comes from a known and trusted device, the system will send an alarm signal. This could be to confirm that a security check has passed, trigger an early warning mechanism, or execute other predetermined security response measures. This process ensures that only verified devices can trigger specific system responses, enhancing the system's security and responsiveness.

[0073] As an example, if the vehicle driving data is greater than or equal to the preset vehicle driving safety data, after sending the alarm information, the method further includes: obtaining the current vehicle location, the location marked in the cloud, and map data; and obtaining reference road information based on the current vehicle location, the location marked in the cloud, and map data.

[0074] The current vehicle location can be the vehicle's actual geographical location at a specific moment, typically obtained through GPS or other positioning systems, providing the vehicle's latitude and longitude coordinates. The cloud-marked location can be preset location information related to the vehicle stored on a cloud server; these locations may include vehicle repair shops, gas stations, and other places that can address the alarm signal issue. Map data can be detailed maps containing information such as roads, traffic rules, and geographical features; this data can come from digital map service providers and is used to assist navigation and route planning. Reference road information can be road information obtained through a comprehensive analysis of the current vehicle location, the cloud-marked location, and map data, used to assist driving decisions. This may include optimal driving routes, traffic conditions, estimated arrival times, road construction information, etc., helping the vehicle plan routes and make corresponding driving adjustments.

[0075] Specifically, the system first obtains the vehicle's real-time geographic location, i.e., its current location, using GPS or other positioning technologies. Simultaneously, it retrieves preset location information related to the vehicle from a cloud server, such as frequently visited repair shops or gas stations. These cloud-marked locations contribute to personalized navigation services. Furthermore, it integrates the latest map data, including road networks, traffic rules, and real-time traffic conditions. Combining these three elements—accurate current vehicle location, personalized cloud location markings, and detailed map data—the system calculates reference road information for the vehicle. This includes recommended optimal routes, estimated travel times, and potential traffic conditions, providing the vehicle with real-time, accurate navigation services and assisting driving decisions to ensure driving safety and efficiency.

[0076] Step S30: Send the second reference instruction to the preset device so that the preset device can feed back a correction instruction based on the second reference instruction;

[0077] It should be noted that preset devices can be devices or system components that are pre-set or designated in the vehicle system to receive and process instructions. These devices can be control units inside the vehicle, such as the engine control unit (ECU), body control module (BCM), or other sensors and actuators, or they can be devices outside the vehicle, such as smartphone applications, telematics systems, or cloud servers, capable of receiving instructions from the vehicle and responding. Correction instructions refer to the adjusted or optimized instructions returned by the preset devices after processing the received second reference instructions. These correction instructions may include fine-tuning of the original instructions to ensure that vehicle control is more precise and adaptable to actual operating conditions. For example, based on the preset combination of instructions, such as getting into the car at 8:00 AM every day, a first reference instruction is obtained that the car should control the vehicle components to reach a comfortable temperature (which can be set by the owner) at 8:00 AM. Based on the vehicle's operating history, if getting into the car at 7:50 AM every day, a second reference instruction is obtained that the car should control the vehicle components to reach a comfortable temperature at 7:50 AM. However, if the owner receives the second reference instruction and believes that the actual departure time should be 7:55 AM, then a correction instruction is issued that the car should control the vehicle components to reach a comfortable temperature at 7:55 AM.

[0078] Understandably, a second reference command, generated based on the vehicle's historical operating data and the first reference command, is sent to pre-set devices, such as the vehicle's control unit or an external monitoring system. These pre-set devices then provide feedback correction commands based on the received second reference command. These correction commands may include adjustments or supplements to the original commands to ensure more precise vehicle control and adaptation to actual operating conditions, thereby optimizing vehicle performance and safety. Providing correction commands based on the commands and the vehicle's own real-time data to optimize vehicle control and response helps improve the intelligence level of the vehicle system, ensuring safer, more efficient, and more personalized vehicle operation.

[0079] Step S40: Based on the correction instruction and the second reference instruction, a target instruction is obtained to enable vehicle control to be completed according to the target instruction.

[0080] It should be noted that the target instruction can be the final control instruction formed by combining the correction instruction and the second reference instruction. This target instruction is the direct basis for the vehicle control system to execute specific operations. It integrates the correction instruction fed back from preset equipment (which may include responses to environmental changes or adjustments to equipment status) and the previously generated second reference instruction (derived based on the vehicle's operating history and the first reference instruction). The target instruction ensures that the vehicle's control actions can accurately meet the current driving conditions, safety requirements, and the owner's personalized needs, achieving intelligent vehicle control, including but not limited to adjusting the vehicle's power output, braking system, steering system, and in-vehicle environmental control, to achieve a safe, efficient, and comfortable driving experience. For example, in the example above, when the owner receives the second reference instruction and believes that the actual departure time should be 7:55, the feedback would be a correction instruction for the car to control the vehicle components to reach a comfortable temperature at 7:55. Combining this with the second reference instruction, and through intelligent data analysis of the vehicle's operating history data, a more precise time is predicted, resulting in the target instruction for the car to control the vehicle components to reach a comfortable temperature at 7:53.

[0081] Understandably, the corrective instructions and the second reference instructions are analyzed together to generate a precise target instruction. This instruction will guide the vehicle's control system to perform specific operations, such as adjusting the vehicle's power system, suspension system, or interior environment, to ensure that the vehicle's operation conforms to the driver's intentions and adapts to the current road and environmental conditions, thereby achieving safe and efficient driving.

[0082] As an example, based on the correction instruction and the second reference instruction, a target instruction is obtained so that after vehicle control is completed according to the target instruction, the method further includes: obtaining a preset time; obtaining a vehicle operation history record within the preset time based on the preset time; obtaining a correction instruction based on a preset algorithm, the vehicle operation history record, and the vehicle operation history record within the preset time; and updating the correction instruction to the second reference instruction.

[0083] The preset time period can be a pre-defined time frame set by the system. This period can be of any length, such as a day, a week, or a month, specifying the timeframe for collecting and analyzing vehicle operating data. The vehicle's historical operating data within the preset time period is a collection of all operating data within that preset time frame, including historical information such as mileage, speed, acceleration, fuel consumption, and maintenance records. This data can be used to analyze vehicle usage patterns and performance trends. The preset algorithm is a pre-defined calculation or analysis method used to process and analyze the vehicle's historical operating data. These algorithms may include statistical analysis, pattern recognition, and predictive models, aiming to extract useful information and insights from historical data. Corrective instructions are instructions generated based on the preset algorithm's analysis of the vehicle's historical operating data within the preset time period, used to adjust or optimize vehicle performance and operation. These instructions may involve adjustments to vehicle maintenance plans, suggestions for improving driving behavior, or adjustments to vehicle settings, with the aim of improving vehicle efficiency, safety, and reliability. For example, after the target instruction is obtained that the vehicle components reach a comfortable temperature at 7:53, after a week (preset time), a correction instruction is obtained based on the operating data within the week (the vehicle's operating history within the preset time) indicating that the vehicle actually entered the vehicle at 8:30. This is then updated to the second reference instruction, which can be sent to the user terminal to obtain the user's correction instruction. Finally, the updated target instruction is obtained to cope with changes in vehicle usage time.

[0084] Specifically, a preset time range is first determined, and then historical vehicle operation data within that time range is collected. Next, the historical operation data is compared with previously obtained historical operation data, and a preset algorithm is used to analyze this historical data to identify potential performance problems or optimization opportunities. Based on these analysis results, correction instructions are generated, and finally, these correction instructions are integrated into a second reference instruction so that the vehicle control strategy can be updated and optimized in real time, thereby improving the efficiency and safety of vehicle operation.

[0085] This embodiment provides an AI-based smart mobility method. First, by acquiring a first reference instruction and the vehicle's historical operating data, it comprehensively considers the vehicle's historical operating data and current instruction requirements. Next, using this information, a second reference instruction is obtained to more accurately guide vehicle operation. Then, the second reference instruction is sent to a preset device, which provides feedback correction instructions based on this instruction, further optimizing the vehicle control strategy. Finally, by combining the correction instructions and the second reference instruction, a target instruction is generated to achieve precise vehicle control, thereby improving vehicle operating efficiency and comfort. This embodiment ensures the intelligence and adaptability of vehicle control, enabling the vehicle to intelligently adjust its operating state based on real-time data and historical experience to achieve optimal performance and safety standards.

[0086] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 , Figure 2 This is a flowchart illustrating the second embodiment of the AI-based smart mobility method of this application. Step S20 of the AI-based smart mobility method includes steps S21 to S23:

[0087] Step S21: Clean the vehicle operation history data to obtain a reference vehicle operation history.

[0088] It should be noted that the reference vehicle operating history can be a dataset of vehicle operating history obtained after a data cleaning process, used for further analysis and decision-making. Data cleaning refers to processing the raw vehicle operating history to eliminate erroneous, duplicate, incomplete, or inconsistent data, ensuring data quality and accuracy. By extracting clean and reliable information from a large amount of raw data, this information can be used for vehicle performance analysis, fault diagnosis, maintenance plan development, etc., providing accurate data support for intelligent vehicle control and health management.

[0089] Understandably, data cleaning is performed on the collected vehicle operation history records to remove erroneous and redundant information, resulting in a clean, accurate, and reliable reference vehicle operation history record. This cleaned dataset will be used for subsequent analysis and decision-making, helping to more accurately understand and predict vehicle performance, failure trends, and maintenance needs, providing high-quality data support for intelligent vehicle control and health management.

[0090] Step S22: Based on the preset algorithm and the reference vehicle operation history, obtain extended instructions;

[0091] It should be noted that extended instructions can be a series of instructions or suggestions generated by using preset algorithms to conduct in-depth analysis of the cleaned reference vehicle's operating history. These instructions, based on trends, patterns, and insights from historical data, aim to provide more specific guidance for vehicle operation and maintenance. Extended instructions may include performance optimization suggestions, predictive maintenance instructions, and driving behavior adjustments.

[0092] Understandably, pre-defined algorithms are used to conduct in-depth analysis of the cleaned historical operating records of reference vehicles, thereby generating extended instructions. These extended instructions are based on trends, patterns, and anomalies identified in the historical data, aiming to provide more specific operational guidance and maintenance suggestions, such as performance optimization, predictive maintenance, and driving behavior adjustments, to optimize vehicle operating efficiency, safety, and reliability. This guides future vehicle operation and maintenance, improving the level of intelligence in vehicle management.

[0093] Step S23: Based on the extended instruction and the first reference instruction, obtain the second reference instruction.

[0094] Understandably, the extended instructions obtained from analyzing the vehicle's historical operating records are combined with the initially collected first reference instructions. Taking into account the vehicle's historical performance data, current status, and preset operating parameters, a second reference instruction is generated. This second reference instruction is more comprehensive and refined, integrating real-time data and historical analysis results to more accurately guide vehicle control and adjustments. This ensures that vehicle operation meets immediate needs while also considering long-term performance and maintenance strategies, thereby optimizing vehicle control.

[0095] This embodiment first cleanses the vehicle's historical operating data to remove invalid or erroneous data points, obtaining accurate and reliable reference historical operating data. Next, using this cleaned data and a preset algorithm, the historical data is analyzed to identify patterns and trends, thereby generating extended instructions. These instructions may include performance optimization suggestions and predictive maintenance instructions. Finally, combining these extended instructions with the initial first reference instruction, and comprehensively considering real-time needs and historical analysis results, a second reference instruction is derived. This instruction provides a more comprehensive and refined guidance for vehicle control. This embodiment ensures that vehicle operation meets both immediate needs and long-term performance and maintenance strategies, achieving optimized and intelligent management of vehicle control. This enables the vehicle control system to make more accurate and forward-looking decisions based on detailed historical data and real-time feedback.

[0096] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the intelligent mobility method based on artificial intelligence in this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0097] This application also provides an artificial intelligence-based smart mobility device, please refer to... Figure 3 The AI-based smart mobility device includes:

[0098] Module 10 is used to acquire the first reference command and vehicle operation history.

[0099] The module 20 is used to obtain a second reference instruction based on the vehicle operation history and the first reference instruction;

[0100] The sending module 30 is used to send the second reference instruction to a preset device, so that the preset device can feed back a correction instruction according to the second reference instruction;

[0101] The control module 40 is used to obtain a target instruction based on the correction instruction and the second reference instruction, so as to complete vehicle control according to the target instruction.

[0102] The AI-based smart mobility device provided in this application, employing the AI-based smart mobility method described in the above embodiments, can solve the technical problem of how to intelligently adjust the vehicle's travel environment according to the driver's habits. Compared with the prior art, the beneficial effects of the AI-based smart mobility device provided in this application are the same as those of the AI-based smart mobility method provided in the above embodiments, and other technical features in the AI-based smart mobility device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0103] This application provides an AI-based smart mobility device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the AI-based smart mobility method described in Embodiment 1 above.

[0104] The following is for reference. Figure 4 This document illustrates a structural schematic diagram of an AI-based smart mobility device suitable for implementing embodiments of this application. The AI-based smart mobility device in these embodiments may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 4 The AI-based smart mobility device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments in this application.

[0105] like Figure 4As shown, an AI-based smart mobility device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to programs stored in read-only memory (ROM) 1002 or programs loaded from storage device 1003 into random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the AI-based smart mobility device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the AI-based smart mobility device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows an AI-based smart mobility device with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems may be implemented alternatively.

[0106] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0107] The AI-based smart mobility device provided in this application, employing the AI-based smart mobility method described in the above embodiments, can solve the technical problem of how to intelligently adjust the vehicle's travel environment according to the driver's habits. Compared with the prior art, the beneficial effects of the AI-based smart mobility device provided in this application are the same as those of the AI-based smart mobility method provided in the above embodiments, and other technical features of this AI-based smart mobility device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0108] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0109] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0110] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the artificial intelligence-based smart mobility method in the above embodiments.

[0111] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0112] The aforementioned computer-readable storage medium may be included in an AI-based smart mobility device; or it may exist independently and not be incorporated into an AI-based smart mobility device.

[0113] The aforementioned computer-readable storage medium carries one or more programs that, when executed by an AI-based smart mobility device, cause the AI-based smart mobility device to: acquire a first reference instruction and a vehicle operation history record; obtain a second reference instruction based on the vehicle operation history record and the first reference instruction; send the second reference instruction to a preset device, so that the preset device can provide feedback on a correction instruction based on the second reference instruction; and obtain a target instruction based on the correction instruction and the second reference instruction, so that vehicle control can be completed according to the target instruction.

[0114] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0115] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0116] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0117] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., computer programs) for executing the aforementioned AI-based smart mobility method, thereby solving the technical problem of how to intelligently adjust the vehicle's travel environment according to the driver's habits. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the AI-based smart mobility method provided in the above embodiments, and will not be repeated here.

[0118] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the artificial intelligence-based smart mobility method described above.

[0119] The computer program product provided in this application can solve the technical problem of how to intelligently adjust the vehicle's travel environment according to the driver's habits. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the artificial intelligence-based smart travel method provided in the above embodiments, and will not be repeated here.

[0120] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. An artificial intelligence-based smart travel method, characterized in that, The method comprises: acquiring a first reference instruction and a vehicle operation history record; obtaining a second reference instruction according to the vehicle operation history record and the first reference instruction; sending the second reference instruction to a preset device to make the preset device feed back a correction instruction according to the second reference instruction; obtaining a target instruction based on the correction instruction and the second reference instruction to make vehicle control completed according to the target instruction; before the acquiring a first reference instruction and a vehicle operation history record, the method further comprises: acquiring sensor data and a preset combined instruction, wherein the preset combined instruction is an instruction set combined according to conditions and rules preset by a vehicle owner; obtaining an initial instruction based on the sensor data; obtaining a first reference instruction based on the initial instruction and the preset combined instruction; after the obtaining a second reference instruction according to the vehicle operation history record and the first reference instruction, the method further comprises: acquiring vehicle driving data and preset vehicle driving safety data; if the vehicle driving data is less than or equal to the preset vehicle driving safety data, obtaining vehicle remaining safety driving data based on the vehicle driving data and the preset vehicle driving safety data; if the vehicle driving data is greater than or equal to the preset vehicle driving safety data, sending an alarm information; if the vehicle driving data is greater than or equal to the preset vehicle driving safety data, the sending an alarm information comprises: acquiring recording device information and a request signal; obtaining identification information of a terminal device according to the request signal; if the identification information of the terminal device matches the recording device information, sending an alarm signal.

2. The method of claim 1, wherein, The obtaining a second reference instruction according to the vehicle operation history record and the first reference instruction comprises: performing data cleaning on the vehicle operation history record to obtain a reference vehicle operation history record; obtaining an extended instruction based on a preset algorithm and the reference vehicle operation history record; obtaining a second reference instruction based on the extended instruction and the first reference instruction.

3. The method as claimed in claim 1, wherein, after the sending an alarm information if the vehicle driving data is greater than or equal to the preset vehicle driving safety data, the method further comprises: acquiring a current vehicle position, a cloud-labeled position and map data; obtaining reference road information based on the current vehicle position, the cloud-labeled position and the map data.

4. The method as claimed in claim 1, wherein, after the obtaining a target instruction based on the correction instruction and the second reference instruction to make vehicle control completed according to the target instruction, the method further comprises: acquiring a preset time; obtaining a vehicle operation history record within the preset time based on the preset time; obtaining a correction instruction based on a preset algorithm, the vehicle operation history record and the vehicle operation history record within the preset time; updating the correction instruction to the second reference instruction.

5. An intelligent travel device based on artificial intelligence, characterized in that, The device is used to implement steps of the intelligent travel method based on artificial intelligence according to any one of claims 1 to 4, and the device comprises: an acquiring module, configured to acquire a first reference instruction and a vehicle operation history record; an obtaining module, configured to obtain a second reference instruction according to the vehicle operation history record and the first reference instruction; The sending module is configured to send the second reference instruction to a preset device, so that the preset device feeds back a correction instruction according to the second reference instruction. The control module is configured to obtain a target instruction based on the correction instruction and the second reference instruction, so that vehicle control is completed according to the target instruction. The obtaining module is further configured to obtain sensor data and a preset combination instruction before the first reference instruction and the vehicle operation history record are obtained, wherein the preset combination instruction is an instruction set combined according to conditions and rules preset by a vehicle owner; an initial instruction is obtained based on the sensor data; and the first reference instruction is obtained based on the initial instruction and the preset combination instruction. The obtaining module is further configured to obtain vehicle driving data and preset vehicle driving safety data after the second reference instruction is obtained based on the vehicle operation history record and the first reference instruction; if the vehicle driving data is less than or equal to the preset vehicle driving safety data, vehicle remaining safety driving data is obtained based on the vehicle driving data and the preset vehicle driving safety data; and if the vehicle driving data is greater than or equal to the preset vehicle driving safety data, an alarm information is sent. The obtaining module is further configured to obtain record device information and a request signal; to obtain identification information of a terminal device according to the request signal; and to send an alarm signal if the identification information of the terminal device matches the record device information.

6. An intelligent travel device based on artificial intelligence, characterized in that, The device comprises a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the intelligent travel method based on artificial intelligence according to any one of claims 1 to 4.

7. A storage medium, characterized by The storage medium is a computer readable storage medium, and the storage medium stores a computer program. When the computer program is executed by the processor, the steps of the intelligent travel method based on artificial intelligence according to any one of claims 1 to 4 are implemented.

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