Driving scene determination method and system, electronic equipment and storage medium

Abstract

The invention provides a driving scene determination method and system, electronic equipment and a storage medium, and belongs to the technical field of vehicle-mounted data processing, and the method comprises the steps: recognizing the voice data of a user in a vehicle, and extracting a driving keyword; based on the driving keyword, an initial driving scene corresponding to the driving keyword is determined from a driving scene classification library, and the driving scene classification library comprises a plurality of driving scenes and keywords corresponding to the driving scenes; and adjusting the initial driving scene based on the driving environment information to obtain a target driving scene. According to the technical scheme provided by the embodiment of the invention, the driving scene suitable for the user is generated by identifying the voice data of the user and combining the actual driving environment information, so that the user experience is improved, and the driving safety of the vehicle is improved.

Description

TECHNICAL FIELD

[0001] The present disclosure belongs to the technical field of vehicle data processing, and particularly relates to a driving scene determination method and system, an electronic device, and a storage medium. BACKGROUND

[0002] With the continuous progress and in-depth development of intelligent technology of automobiles, the intelligent cockpit is the core interactive space in the vehicle interior, and how to improve the driving experience and riding experience of users in the intelligent cockpit is very important in the field.

[0003] In the related art, a pre-set fixed scene mode is mainly used to meet the diversified needs of users, and the switching of some scene modes needs to be manually operated by the user. This technical solution lacks flexibility, ignores the influence of the environment inside and outside the vehicle, cannot meet the various needs of users, and needs to be manually switched, which not only wastes time but also affects driving safety. SUMMARY

[0004] Embodiments of the present disclosure provide a solution to solve the problem of lack of flexibility, ignoring the influence of the environment inside and outside the vehicle, and being unable to meet the various needs of users in the related art, and the problem of wasting time and affecting driving safety by manually switching.

[0005] In a first aspect, the present disclosure provides a driving scene determination method, which comprises: recognizing voice data of a user in a vehicle and extracting a driving keyword; determining an initial driving scene corresponding to the driving keyword from a driving scene classification library based on the driving keyword, the driving scene classification library containing a plurality of driving scenes and keywords corresponding to each driving scene; adjusting the initial driving scene based on driving environment information to obtain a target driving scene.

[0006] In a second aspect, the present disclosure provides a driving scene determination system, which comprises: a voice recognition module configured to recognize voice data of a user in a vehicle and extract a driving keyword; a cloud large language module configured to determine an initial driving scene corresponding to the driving keyword from a driving scene classification library based on the driving keyword, the driving scene classification library containing a plurality of driving scenes and keywords corresponding to each driving scene; a scene configuration module configured to adjust the initial driving scene based on driving environment information to obtain a target driving scene.

[0007] In a third aspect, the present disclosure provides an electronic device, which comprises: a processor; and a memory for storing executable instructions of the processor; The processor is configured to execute the executable instructions to perform any method of the first aspect or the possible implementation manners of the first aspect.

[0008] In a fourth aspect, the embodiments of the present disclosure provide a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement any method of the first aspect or the possible implementation manners of the first aspect.

[0009] In a fifth aspect, the embodiments of the present disclosure provide a computer program product, which includes computer instructions. The computer instructions are executed by a processor to implement any method of the first aspect or the possible implementation manners of the first aspect.

[0010] The technical solution provided by the present disclosure can identify voice data of a user in a vehicle, extract a driving keyword, determine an initial driving scene corresponding to the driving keyword from a driving scene classification library based on the driving keyword, and adjust the initial driving scene based on driving environment information to obtain a target driving scene. The technical solution provided by the embodiments of the present disclosure can determine an initial driving scene through identification of voice data, and modify the initial driving scene in combination with driving environment information inside and outside the vehicle to obtain a target driving scene. This technical solution not only saves operation time for switching driving scenes, but also adapts the most suitable driving scene for the user in combination with driving environment information, meets the needs of the user, and improves the efficiency of scene determination. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the related art, the following will briefly introduce the drawings needed to be used in the embodiments or the related art description. Obviously, the drawings in the following description are some embodiments of the present disclosure, and other drawings can also be obtained by those skilled in the art without any creative effort. In the drawings: Figure 1 A flowchart of a driving scene determination method provided by an embodiment of the present disclosure; Figure 2 A structural diagram of a driving scene determination system provided by an embodiment of the present disclosure; Figure 3 A structural diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0012] Embodiments of the present disclosure are described below in detail, examples of which are shown in the accompanying drawings. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present disclosure, and cannot be understood as a limitation of the present disclosure.

[0013] The terms "first" and "second" and the like in the specification of the embodiments of the present disclosure, claims, and drawings are used to distinguish similar objects, and do not have to be used to describe a particular order or chronological order. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present disclosure described herein can be implemented, for example, in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products, or apparatuses.

[0014] The driving scene determination method provided by the embodiments of the present disclosure can run on a terminal device or a server. The terminal device can be a local terminal device, including wearable devices such as VR (Virtual Reality), AR (Augmented Reality), and MR (Mixed Reality). The server can be a standalone physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, and basic cloud computing services such as big data and artificial intelligence platforms.

[0015] With the continuous progress and in-depth development of intelligent technology of automobiles, the intelligent cockpit is the core interaction space in the vehicle interior, and how to improve the driving experience and riding experience of users in the intelligent cockpit is very important in the field.

[0016] In the related art, a pre-set fixed scene mode is mainly used to meet the diversified needs of users, and manual operation is required for switching between some scene modes, so this technical solution lacks flexibility, ignores the influence of the environment inside and outside the vehicle, cannot meet the various needs of users, and manual switching not only wastes time but also affects driving safety.

[0017] Figure 1 A flowchart of a driving scene determination method provided by an exemplary embodiment of the present disclosure is shown, which scheme at least includes the following steps S101-S104: S101, recognizing voice data of a user in the vehicle and extracting driving keywords.

[0018] In some embodiments, the voice data refers to the natural language of the user.

[0019] Specifically, in the voice recognition process, the actual ASR recognition module is mainly used. Specifically, it can be set according to the actual situation.

[0020] In some embodiments, the voice data of the user in the vehicle is recognized, and the driving keywords are extracted, including steps S11-S12: S11, obtaining the voice data of the user in the vehicle, and converting the voice data into text data.

[0021] S12, extracting the driving keywords from the text data.

[0022] In some embodiments, after obtaining the voice data of the user in the vehicle according to the above step S11, the voice data can be retained in the local database. Specifically, the saved voice data can provide comprehensive context information for subsequent processing.

[0023] Specifically, for example, the user speaks the natural language (voice data) as: It is too hot in the car, help me generate a rapid cooling mode. After obtaining and recognizing the voice data and converting it into text data (It is too hot in the car, help me generate a rapid cooling mode), the keywords (too hot, cooling, rapid cooling) are extracted from the text data.

[0024] S102, determining the initial driving scene corresponding to the driving keyword from the driving scene classification library based on the driving keyword.

[0025] In some embodiments, the driving scene classification library contains a plurality of driving scenes and keywords corresponding to each driving scene.

[0026] Further, different keyword combinations correspond to different driving scenes. For example, the keyword (cooling) corresponds to driving scene a, and the keyword (cooling, rapid) corresponds to driving scene b.

[0027] In some embodiments, before determining the initial driving scene corresponding to the driving keyword from the driving scene classification library based on the driving keyword, the method further includes steps S21-S22: S21, detecting whether there is an unsafe keyword in the driving keyword.

[0028] S22, if there is an unsafe keyword in the driving keyword, ending the determination process of the driving scene.

[0029] In some embodiments, for S21 above, whether there is an unsafe keyword in the driving keyword refers to whether a single keyword or a combination of multiple keywords in the driving keyword is an unsafe keyword.

[0030] Specifically, the unsafe keyword refers to a word related to dangerous driving of a vehicle. For example, high speed, playing a video: this combination of keywords means playing a video in a vehicle driving at high speed, which will cause the user to drive dangerously. Through this technical solution, the safety of the user can be effectively protected, and potential safety risks can be avoided.

[0031] In some embodiments, based on the driving keyword, the initial driving scene corresponding to the driving keyword is determined from the driving scene classification library, and the method comprises: calculating the matching degree of the driving keyword and the keyword corresponding to each driving scene in the driving scene classification library, and if the matching degree is greater than a preset threshold, the driving scene corresponding to the keyword with the largest matching degree is taken as the initial driving scene.

[0032] In this embodiment, the keyword corresponding to each driving scene in the driving scene classification library can be a single keyword or a combination of multiple keywords, so it is necessary to calculate the matching degree of the driving keyword and the keyword corresponding to each driving scene in the driving scene classification library, and select the driving scene corresponding to the keyword with the largest matching degree. For example, the driving keyword is: a, b, the keyword corresponding to driving scene A in the driving scene classification library is a, b, c, d, the keyword corresponding to driving scene B is a, c, d, and the keyword corresponding to driving scene C is c, d. The matching degree of driving scene A is 50%, the matching degree of driving scene B is 30%, and the matching degree of driving scene C is 0%, so the driving scene A corresponding to the keyword with the largest matching degree is taken as the initial driving scene.

[0033] S103, adjusting the initial driving scene based on driving environment information to obtain a target driving scene.

[0034] In some embodiments, the initial driving scene mainly refers to a fixed driving scene or a historical driving scene (a driving scene used by the user), so in order to obtain a driving scene more suitable for the user's needs, the initial driving scene needs to be adjusted, which is as follows: Adjusting the initial driving scene based on the driving environment information to obtain a target driving scene comprises: adjusting at least one scene parameter contained in the initial driving scene based on the driving environment information, and then obtaining the target driving scene.

[0035] In some embodiments, the method further comprises: acquiring driving environment information in real time.

[0036] The driving environment information includes at least one of weather information, current road condition information, and passenger state information.

[0037] In some embodiments, to better meet the needs of the user, the driving environment information can include a historical adjustment record including historical voice data of the user during adjustment of the initial driving environment.

[0038] The initial driving scene includes at least one of the following scene parameters: a preset gear, a preset speed, a preset temperature, and a preset seat temperature.

[0039] Specifically, to better understand the present solution, the following example is given: the initial driving scene A includes the following scene parameters: gear a, vehicle speed b, vehicle temperature c, and seat temperature d. The driving environment information includes weather information: raining, traffic jam, and passenger state information: tired, and continuous driving for eight hours. The specific adjustment process is as follows: because the vehicle speed b is too fast, and the vehicle is in a traffic jam and it is raining and the road is slippery, the vehicle speed needs to be reduced, and the vehicle speed b is adjusted to vehicle speed f. Because it is hot and raining, the vehicle temperature c is too high, and the air conditioner needs to be turned on to start cooling.

[0040] Further, the above adjustment process uses intelligent adjustment, and AI is used to adjust preferentially. The actual situation can be set according to actual conditions.

[0041] In other embodiments, the above adjustment process can be intelligently adjusted by a cloud large language model, which deeply analyzes and understands the user's natural language request and the driving environment information, and adjusts the initial driving scene.

[0042] In this embodiment, to improve the accuracy of adjusting the driving scene, the user's historical voice record and the corresponding historical driving scene can be referred to, the user's habitual preference can be analyzed, and the initial driving scene can be adjusted.

[0043] In some embodiments, after the initial driving scene is adjusted to obtain a target driving scene based on the driving environment information, the method further includes generating a vehicle configuration parameter according to the target driving scene.

[0044] Further, the vehicle configuration parameter generated according to the target driving scene includes generating a vehicle configuration parameter according to the scene parameter of the target driving scene. The vehicle configuration parameter includes the scene parameter.

[0045] In some embodiments, the vehicle configuration parameter includes at least one of the following: gear, speed, temperature, atmosphere lamp, and seat heating.

[0046] Specifically, continuing to take the above-mentioned embodiment as an example, after adjusting the initial driving scene A, a target driving scene is obtained, and the scene parameters of the target driving scene are: gear r, vehicle speed j, vehicle temperature n, seat temperature s. Based on the scene parameters of the target scene, the vehicle configuration parameters are generated: gear r, speed j, temperature n, ambient light remains in the default state, seat heating s.

[0047] In some embodiments, the method further comprises: modifying the current state of each control object on the vehicle side according to the vehicle configuration parameters.

[0048] In some embodiments, based on the driving keyword, the initial driving scene corresponding to the driving keyword is determined from the driving scene classification library, comprising: if the driving keyword is a scene mode name, the initial driving scene corresponding to the driving keyword is determined.

[0049] Specifically, if the driving keyword is a rapid cooling mode, the initial driving scene corresponding to the rapid cooling mode is determined, and the initial driving scene is taken as the target driving scene. In this way, the efficiency of scene switching can be improved.

[0050] In some embodiments, when the vehicle ends the target driving scene, each control object on the vehicle side returns to the initial state.

[0051] The technical solution provided by the present disclosure identifies the voice data of the user in the vehicle, extracts the driving keyword, determines the initial driving scene corresponding to the driving keyword from the driving scene classification library based on the driving keyword, and the driving scene classification library contains a plurality of driving scenes and the keywords corresponding to each driving scene. Based on the driving environment information, the initial driving scene is adjusted to obtain a target driving scene. The technical solution provided by each embodiment of the present disclosure can determine the initial driving scene through the recognition of voice data, and modify the initial driving scene in combination with the driving environment information inside and outside the vehicle, and then obtain the target driving scene. This technical solution not only improves the operation time of driving scene switching, but also combines the driving environment information to adapt the most suitable driving scene for the user, meets the user's needs, and improves the efficiency of scene determination.

[0052] Figure 2 A structural schematic diagram of a driving scene determination system provided by an exemplary embodiment of the present disclosure is provided. The system comprises: a voice recognition module 201, a cloud-side large language module 202, and a scene configuration module 203. The voice recognition module 201 is configured to recognize the voice data of the user in the vehicle and extract the driving keyword. The cloud large language module 202 is configured to determine an initial driving scene corresponding to the driving keyword from a driving scene classification library based on the driving keyword, the driving scene classification library containing a plurality of driving scenes and keywords corresponding to the driving scenes. The scene configuration module 203 is configured to adjust the initial driving scene based on driving environment information to obtain a target driving scene.

[0053] In some embodiments, the voice recognition module 201 is further configured to: obtain voice data of a user in the vehicle and convert the voice data into text data; extract the driving keyword from the text data.

[0054] In some embodiments, the system further comprises a detection unit configured to: detect whether there is an unsafe keyword in the driving keyword; if there is an unsafe keyword in the driving keyword, end the determination process of the driving scene.

[0055] In some embodiments, the cloud large language module 202 is further configured to: calculate a matching degree of the driving keyword and the keywords corresponding to the driving scenes in the driving scene classification library, and if the matching degree is greater than a preset threshold, the driving scene corresponding to the keyword with the greatest matching degree is taken as the initial driving scene.

[0056] In some embodiments, the system further comprises an acquisition unit configured to: acquire driving environment information in real time, the driving environment information including at least one of the following: weather information, current road condition information, passenger state information; The initial driving scene includes at least one of the following scene parameters: a preset gear, a preset speed, a preset temperature, and a preset seat temperature. In some embodiments, the scene configuration module 203 is further configured to: adjust at least one scene parameter contained in the initial driving scene based on the driving environment information, and further obtain the target driving scene.

[0057] In some embodiments, the scene configuration module 203 is further configured to: generate vehicle configuration parameters according to the target driving scene, the vehicle configuration parameters including at least one of the following: gear, speed, temperature, atmosphere lamp, and seat heating.

[0058] In some embodiments, the scene configuration module 203 is further configured to: modify a current state of each control object on the vehicle side according to the vehicle configuration parameters.

[0059] The technical solution provided by the present disclosure includes: recognizing voice data of a user in a vehicle, extracting a driving keyword, determining an initial driving scene corresponding to the driving keyword from a driving scene classification library based on the driving keyword, the driving scene classification library including a plurality of driving scenes and keywords corresponding to each driving scene, and adjusting the initial driving scene based on driving environment information to obtain a target driving scene. The technical solution provided by each embodiment of the present disclosure can determine an initial driving scene through recognition of voice data, and modify the initial driving scene in combination with driving environment information inside and outside the vehicle to obtain a target driving scene. This technical solution not only improves the operation time of driving scene switching, but also combines driving environment information to adapt the most suitable driving scene for the user, meet the needs of the user, and improve the efficiency of scene determination.

[0060] It should be understood that the device embodiments and the method embodiments can correspond to each other, and similar descriptions can be referred to the method embodiments. To avoid repetition, no further description is given here. Specifically, the device can perform the above-mentioned method embodiments, and the foregoing and other operations and / or functions of each module in the device are respectively for the corresponding processes in each method in the above-mentioned method embodiments, and for the sake of brevity, no further description is given here.

[0061] The device of the embodiments of the present disclosure is described above in combination with the drawings from the perspective of functional modules. It should be understood that the functional modules can be realized in the form of hardware, or in the form of instructions of software, or in the form of a combination of hardware and software modules. Specifically, each step of the method embodiments in the embodiments of the present disclosure can be completed by integrated logic circuits of hardware and / or instructions of software in the processor, and the steps of the method disclosed in combination with the embodiments of the present disclosure can be directly embodied as hardware code processing performed and completed by the processor, or executed and completed by a combination of hardware and software modules in the code processing processor. Alternatively, the software module can be located in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and combines the hardware to complete the steps in the above-mentioned method embodiments.

[0062] Figure 3 is a schematic block diagram of an electronic device provided by the embodiments of the present disclosure, which can include: The memory 301 is used to store computer programs and transmit the program codes to the processor 302. In other words, the processor 302 can call and run the computer programs from the memory 301 to implement the method in the embodiments of the present disclosure.

[0063] For example, the processor 302 can be configured to perform the above-described method embodiments according to instructions in the computer program.

[0064] In some embodiments of the present disclosure, the processor 302 can include but is not limited to: A general processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic component, a discrete hardware component, and the like.

[0065] In some embodiments of the present disclosure, the memory 301 includes but is not limited to: A volatile memory and / or a non-volatile memory. The non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically EPROM (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM) used as an external cache. By way of example, and not limitation, many forms of RAM can be used, such as a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate SDRAM (DDR SDRAM), an enhanced SDRAM (ESDRAM), a synch link DRAM (SLDRAM), and a direct Rambus RAM (DR RAM).

[0066] In some embodiments of the present disclosure, the computer program can be divided into one or more modules, which are stored in the memory 301 and executed by the processor 302 to complete the method provided by the present disclosure. The one or more modules can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the electronic device.

[0067] As shown in Figure 3 The electronic device can further include: The transceiver 303 can be connected to the processor 302 or the memory 301.

[0068] The processor 302 can control the transceiver 303 to communicate with other devices, specifically, can send information or data to other devices, or receive information or data sent by other devices. The transceiver 303 can include a transmitter and a receiver. The transceiver 303 can further include an antenna, and the number of antennas can be one or more.

[0069] It should be understood that various components in the electronic device are connected through a bus system, wherein the bus system includes a data bus, a power supply bus, a control bus and a state signal bus in addition to a data bus.

[0070] The present disclosure also provides a computer storage medium having a computer program stored thereon, which, when executed by a computer, enables the computer to perform the method of the above-mentioned method embodiments. Alternatively, the present disclosure also provides a computer program product containing instructions, which, when executed by a computer, enables the computer to perform the method of the above-mentioned method embodiments.

[0071] When implemented in software, the functions can be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media can be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired computer program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, or a twisted pair, as examples, then the coaxial cable, fiber optic cable, or twisted pair are included in the definition of medium. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), and Blu-Ray® disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0072] In one embodiment, the techniques described herein can be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the techniques can be realized in whole or in part in a computer-readable medium during execution of software by a computer-based system, a processing device based system, or other system(s). Computer-readable media can include computer storage media and communication media. Storage media can be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, semiconductor memory, such as erasable programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), or flash memory, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, functional modules can comprise components of an integrated circuit, programmable logic device, a field programmable gate array (FPGA), or other logic device implemented in a hardware device. In one embodiment, a functional module can comprise a computer program product that can be traded as a product. In another embodiment, a functional module can comprise a computer program product that can be downloaded or transferred via a network, such as the Internet, Intranet, Extranet, or a local area network.

[0073] In several embodiments provided in the present disclosure, it should be understood that the disclosed system, device, and method can be implemented in other manners. For example, the described device embodiments are merely illustrative. For example, the division of the modules is merely logical function division. There can be another division manner for the actual implementation, for example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interface, device, or module, and can be in electrical, mechanical, or other forms.

[0074] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed to multiple network elements. Part or all of the modules can be selected as needed to achieve the purpose of the embodiments. For example, the functional modules in various embodiments of the disclosure can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0075] The above is only a specific embodiment of the disclosure, but the protection scope of the disclosure is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the disclosure, which should be covered within the protection scope of the disclosure. Therefore, the protection scope of the disclosure should be subject to the protection scope of the claims.

Claims

1. A method of determining a driving scenario, characterized in that, The method comprises: recognizing voice data of a user in the vehicle and extracting a driving keyword; based on the driving keyword, determining an initial driving scene corresponding to the driving keyword from a driving scene classification library, the driving scene classification library containing a plurality of driving scenes and keywords corresponding to each driving scene; adjusting the initial driving scene based on driving environment information to obtain a target driving scene.

2. The method of claim 1, wherein, The method further comprises: obtaining voice data of a user in the vehicle and converting the voice data into text data; extracting the driving keyword from the text data.

3. The method of claim 1, wherein, Before the step of determining the initial driving scene corresponding to the driving keyword from the driving scene classification library based on the driving keyword, the method further comprises: detecting whether there is an unsafe keyword in the driving keyword; if there is an unsafe keyword in the driving keyword, ending the determination process of the driving scene.

4. The method of claim 1, wherein, The method further comprises: calculating the matching degree of the driving keyword and the keywords corresponding to each driving scene in the driving scene classification library, and if the matching degree is greater than a preset threshold, taking the driving scene corresponding to the keyword with the largest matching degree as the initial driving scene.

5. The method of claim 1, wherein, The method further comprises: obtaining driving environment information in real time, the driving environment information comprising at least one of the following: weather information, current road condition information, and passenger state information. The initial driving scene comprises at least one of the following scene parameters: a preset gear, a preset speed, a preset temperature, and a preset seat temperature. The method further comprises: adjusting at least one scene parameter contained in the initial driving scene based on the driving environment information to obtain the target driving scene.

6. The method of claim 1, wherein, After the step of adjusting the initial driving scene based on the driving environment information to obtain the target driving scene, the method further comprises: generating vehicle configuration parameters according to the target driving scene, the vehicle configuration parameters comprising at least one of the following: gear, speed, temperature, atmosphere lamp, and seat heating.

7. The method of claim 6, wherein, The method further comprises: modifying the current state of each control object on the vehicle side according to the vehicle configuration parameters.

8. A system for determining a driving scenario, characterized in that The system comprises: a voice recognition module configured to recognize voice data of a user in the vehicle and extract a driving keyword; a cloud large language module configured to determine an initial driving scene corresponding to the driving keyword from a driving scene classification library based on the driving keyword, the driving scene classification library containing a plurality of driving scenes and keywords corresponding to each driving scene; a scene configuration module configured to adjust the initial driving scene based on driving environment information to obtain a target driving scene.

9. An electronic device, comprising: The system comprises: a processor; and a memory configured to store executable instructions of the processor; wherein the processor is configured to execute the method of any one of claims 1-7 by executing the executable instructions.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the method of any one of claims 1-7.

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