A method and device for adjusting the cloud semantic waiting time based on location information

Abstract

The present invention discloses a method and apparatus for adjusting the cloud semantic waiting time based on location information. The method for adjusting the cloud semantic waiting time based on location information includes: obtaining audio stream information provided by a user in a vehicle; obtaining the current geographical location information of the vehicle; obtaining a preset response lookup table, which includes at least one preset route segment information and a response time, and one preset route segment information corresponds to one response time; obtaining the response time corresponding to the preset route segment information where the current geographical location information is located as the waiting time for waiting for the cloud to feedback cloud semantic information according to the audio stream information. This application dynamically adjusts the waiting time according to the current geographical location of the vehicle, so as to be able to give the most user-friendly waiting time. In areas where there is obviously no signal, the waiting time is reduced or the cloud is not directly used for processing, thus preventing wasting time and not getting results.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle voice interaction, and specifically relates to a method for adjusting the cloud semantic waiting time based on location information and a device for adjusting the cloud semantic waiting time based on location information. Background Art

[0002] In the prior art, a general semantic recognition system includes a cloud recognition engine and a local recognition engine. Among them, the advantage of the cloud recognition engine is that the recognition effect is good and it can support more generalized expressions. However, it depends on network communication and the time is uncontrollable. The terminal recognition engine, compared with the cloud engine, has a relatively poor effect and supports simple expressions. But it runs locally, quickly and stably.

[0003] In the actual use process, generally the cloud recognition engine is preferentially used. If the cloud recognition engine does not return a result within the specified time, then the local recognition engine is used.

[0004] However, in different places, the network stability is different. For example, in a tunnel, the cloud recognition result cannot be returned. At this time, waiting for the specified time is meaningless. Or in a remote suburb, although the result is returned slowly, it can still be used. At this time, extending the waiting time can improve the final accuracy.

[0005] Therefore, it is desirable to have a technical solution to solve or at least alleviate the above deficiencies of the prior art. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for adjusting the cloud semantic waiting time based on location information to overcome or at least alleviate at least one of the above defects of the prior art.

[0007] In one aspect of the present invention, there is provided a method for adjusting the cloud semantic waiting time based on location information. The method for adjusting the cloud semantic waiting time based on location information includes:

[0008] Obtain the audio stream information provided by the user in the vehicle and send it to the cloud;

[0009] Obtain the current geographical location information of the vehicle;

[0010] Obtain a preset response comparison table, where the preset response comparison table includes at least one preset route segment information and a response time, and one preset route segment information corresponds to one response time;

[0011] Obtain the response time corresponding to the preset route segment information where the current geographical location information is located as the waiting time for waiting for the cloud to feedback the cloud semantic information according to the audio stream information.

[0012] Optionally, before obtaining the preset response correspondence table, the method for adjusting the cloud semantic waiting time based on location information further includes:

[0013] Generating the preset response correspondence table.

[0014] Optionally, the generating the preset response correspondence table includes:

[0015] Obtaining the current navigation route of the vehicle;

[0016] Segmenting and analyzing the current navigation route to obtain multiple preset route segment information;

[0017] Identifying each preset route segment information respectively to set a response time for each preset route segment information.

[0018] Optionally, the preset response correspondence table further includes a preset route segment information type, and one preset route segment information corresponds to one preset route segment information type;

[0019] The method for adjusting the cloud semantic waiting time based on location information further includes:

[0020] Obtaining an image in front of the vehicle captured by a camera device of the vehicle;

[0021] Identifying the image in front of the vehicle to obtain an image recognition road segment type;

[0022] Obtaining the preset route segment information type corresponding to the preset route segment information corresponding to the currently used response time;

[0023] Determining whether the image recognition road segment type is the same as the preset route segment information type, if not, then

[0024] Obtaining an image recognition response time according to the image recognition road segment type and using the image recognition response time as the waiting time.

[0025] Optionally, the identifying the image in front of the vehicle to obtain an image recognition road segment type includes:

[0026] Obtaining a trained road segment type classifier;

[0027] Extracting image features of the image in front of the vehicle;

[0028] Inputting the image features into the trained road segment type classifier to obtain a classification label;

[0029] Obtaining a preset road segment database, the preset road segment database includes at least one image recognition road segment type and a preset classification label, and one preset classification label corresponds to one image recognition road segment type;

[0030] Obtain the image recognition road segment type corresponding to the preset classification label corresponding to the classification label.

[0031] Optionally, the method for adjusting the cloud semantic waiting time based on the location information further includes:

[0032] Determine whether cloud semantic information fed back by the cloud according to the audio stream information is obtained within the waiting time. If so, then

[0033] Perform an action according to the cloud semantic information.

[0034] Optionally, the method for adjusting the cloud semantic waiting time based on the location information further includes:

[0035] Determine whether cloud semantic information fed back by the cloud according to the audio stream information is obtained within the waiting time. If not, then

[0036] Identify the audio stream information by a local recognition engine to obtain local semantic information.

[0037] Optionally, the preset road segment information includes at least one longitude and latitude interval information;

[0038] When identifying the audio stream information by a local recognition engine, the method for adjusting the cloud semantic waiting time based on the location information further includes:

[0039] Obtain the current vehicle information;

[0040] Obtain the time required for the vehicle to drive out of the current preset road segment according to the current vehicle information, the current geographical location information, and the longitude and latitude interval information.

[0041] Optionally, the method for adjusting the cloud semantic waiting time based on the location information further includes:

[0042] Generate voice broadcast information according to the time required for the vehicle to drive out of the current preset road segment.

[0043] This application also provides a device for adjusting the cloud semantic waiting time based on the location information. The device for adjusting the cloud semantic waiting time based on the location information includes:

[0044] An acquisition module, which is used to acquire the audio stream information provided by the user in the vehicle and send it to the cloud;

[0045] A geographical location acquisition module, which is used to acquire the current geographical location information of the vehicle;

[0046] A preset response comparison table acquisition module, which is used to acquire a preset response comparison table. The preset response comparison table includes at least one preset line segment information and a response time, and one preset line segment information corresponds to one response time;

[0047] A waiting time acquisition module, which is used to acquire the response time corresponding to the preset line segment information where the current geographical location information is located as the waiting time for waiting for the cloud to feedback cloud semantic information according to the audio stream information.

[0048] Beneficial effects

[0049] The present application has the following advantages:

[0050] The method for adjusting the cloud semantic waiting time based on location information in the present application dynamically adjusts the waiting time according to the current geographical location of the vehicle, so as to be able to give the user the most user-friendly waiting time. In areas where there is obviously no signal, the waiting time is reduced or the cloud is directly not used for processing, so as to prevent wasting time and not getting results. Brief description of the drawings

[0051] Figure 1 It is a schematic flowchart of the method for adjusting the cloud semantic waiting time based on location information in the first embodiment of the present application.

[0052] Figure 2 It is an electronic device for implementing Figure 1 The method for adjusting the cloud semantic waiting time based on location information shown.

[0053] Figure 3 It is a schematic diagram for signal strength judgment of the present application.

[0054] Figure 4 It is a schematic diagram of base station signal strength.

[0055] Figure 5 It is a schematic diagram of a navigation route of the present application. Detailed implementation manners

[0056] To make the objectives, technical solutions, and advantages of the present application more clear, the following will describe in more detail the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. In the drawings, the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The described embodiments are some, but not all, of the embodiments of the present application. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present application, and should not be construed as a limitation to the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts fall within the scope of protection of the present application. The following will explain the embodiments of the present application in detail with reference to the accompanying drawings.

[0057] See Figure 3 and Figure 4 , in existing mobile phones, such as Android mobile phones, the current method for judging signal strength is as follows:

[0058] In the settings of Android mobile phones, Google defines a unique signal unit called asu for Android mobile phones. Translated into Chinese, it means an independent signal unit. The conversion relationship between it and the signal strength dbm is: dbm = 2 * asu - 113. And for a mobile phone to have a relatively good call quality, asu must be greater than or equal to 10. Converted into dbm, it is -90 dbm.

[0059] The current general method for identifying signal strength is: the device measures the signal strength at fixed intervals, and then updates the value of the signal strength. Before the next update of the signal strength value, this strength will be used as a sign for judging a strong network or a weak network.

[0060] However, in a vehicle scenario, the device moves very fast. Although the measurement time interval is very short, the corresponding moving distance of the vehicle device will be very long. Therefore, the signal may lag.

[0061] Figure 1 It is a schematic flowchart of the method for adjusting the cloud semantic waiting time based on location information according to the first embodiment of the present application.

[0062] As Figure 1 shown, the method for adjusting the cloud semantic waiting time based on location information includes:

[0063] Step 1: Obtain the audio stream information provided by the user in the vehicle and send it to the cloud;

[0064] Step 2: Obtain the current geographical location information of the vehicle;

[0065] Step 3: Obtain a preset response mapping table, where the preset response mapping table includes at least one preset route segment information and a response time, and one preset route segment information corresponds to one response time;

[0066] Step 4: Obtain the response time corresponding to the preset route segment information where the current geographical location information is located as the waiting time for waiting for the cloud to feedback the cloud semantic information according to the audio stream information.

[0067] The method for adjusting the cloud semantic waiting time based on location information in this application dynamically adjusts the waiting time according to the current geographical location of the vehicle, so as to be able to give the user the most user-friendly waiting time. In areas where there is obviously no signal, the waiting time is reduced or the cloud is not directly used for processing, so as to prevent wasting time without getting a result.

[0068] In this embodiment, before obtaining the preset response comparison table, the method for adjusting the cloud semantic waiting time based on location information further includes:

[0069] Generate the preset response comparison table.

[0070] In this embodiment, generating the preset response comparison table includes:

[0071] Obtain the current navigation route of the vehicle;

[0072] Segment and analyze the current navigation route to obtain multiple preset route segment information;

[0073] Identify each preset route segment information respectively, and set a response time for each preset route segment information.

[0074] This application can obtain the navigation route of the vehicle through high-precision map software (such as Amap, Baidu Map). By segmenting and analyzing the navigation route, multiple preset route segment information can be formed. For example, Figure 5 A schematic diagram of a navigation route is provided. It can be understood that the segmentation of the navigation route can be carried out based on a preset distance. For example, if a navigation route is 10 kilometers, then each 1 kilometer is segmented and corresponds to a response time. Another example is that through high-precision map software, it can be known where there are tunnels and where to enter remote small roads. At this time, segmentation can also be carried out according to the information provided by the high-precision map software. For example, in a 10-kilometer navigation route, there is a 2-kilometer tunnel, then this 2-kilometer tunnel is used as a preset route segment information and corresponds to a response time.

[0075] In this embodiment, the preset response comparison table further includes the type of preset route segment information, and one preset route segment information corresponds to one type of preset route segment information;

[0076] The method for adjusting the cloud semantic waiting time based on location information further includes:

[0077] Obtain an image in front of the vehicle captured by a camera device of the vehicle;

[0078] Identify the image in front of the vehicle to obtain an image recognition road segment type;

[0079] Obtain a preset road segment information type corresponding to the preset road segment information corresponding to the currently used response time;

[0080] Determine whether the image recognition road segment type is the same as the preset road segment information type. If not, then

[0081] Obtain an image recognition response time according to the image recognition road segment type and use the image recognition response time as the waiting time.

[0082] In some cases, it is possible that the position information of the navigation itself is inaccurate due to being too remote. At this time, through image recognition, some preset road segment categories can be recognized. For example, through image recognition, it can be recognized whether the vehicle is currently in a tunnel or in a mountainous area, etc., so as to provide a response time for the recognized preset road segment category, thereby preventing the situation where the navigation is not useful in certain cases.

[0083] In this embodiment, identifying the image in front of the vehicle to obtain an image recognition road segment type includes:

[0084] Obtain a trained road segment type classifier;

[0085] Extract image features of the image in front of the vehicle;

[0086] Input the image features into the trained road segment type classifier to obtain a classification label;

[0087] Obtain a preset road segment database, where the preset road segment database includes at least one image recognition road segment type and a preset classification label, and one preset classification label corresponds to one image recognition road segment type;

[0088] Obtain the image recognition road segment type corresponding to the preset classification label corresponding to the classification label.

[0089] In this embodiment, the method for adjusting the cloud semantic waiting time based on location information further includes:

[0090] Determine whether cloud semantic information fed back by the cloud according to the audio stream information is obtained within the waiting time. If so, then

[0091] Perform an action according to the cloud semantic information.

[0092] In this embodiment, the waiting time generally corresponding to the preset route segment information can be 2s, 0s, 4s, etc., which can be set according to needs.

[0093] In this embodiment, the types of image recognition road segments generally include mountain road segments, rural road segments, tunnel road segments, urban road segments, etc. Each road segment corresponds to a waiting time, for example, 2s, 0s, etc.

[0094] In this embodiment, the method for adjusting the cloud semantic waiting time based on the location information further includes:

[0095] Determine whether cloud semantic information feedback by the cloud according to the audio stream information is obtained within the waiting time. If not, then

[0096] Identify the audio stream information according to the local recognition engine to obtain local semantic information.

[0097] In this embodiment, the preset route segment information includes at least one longitude and latitude interval information;

[0098] When identifying the audio stream information according to the local recognition engine, the method for adjusting the cloud semantic waiting time based on the location information further includes:

[0099] Obtain the current vehicle information;

[0100] Obtain the time required for the vehicle to leave the current preset route segment according to the current vehicle information, current geographical location information, and longitude and latitude interval information.

[0101] In this embodiment, the method for adjusting the cloud semantic waiting time based on the location information further includes:

[0102] Generate voice broadcast information according to the time required for the vehicle to leave the current preset route segment.

[0103] When entering some road segments with poor signals, such as inside a tunnel, perhaps at this time the user wants to know how long it will take to drive out of this tunnel, or perhaps the user himself doesn't know that the voice recognition effect of this road segment is poor. At this time, through the voice broadcast method, the user can be made to understand how much longer it will take to drive out of the road segment with poor voice recognition effect, and in addition, the user can also know that the poor voice recognition effect at this time is due to poor signal, rather than the cloud being unable to recognize.

[0104] The following further elaborates on this application by way of examples. It can be understood that these examples do not constitute any limitation to this application.

[0105] See Figure 5In the illustrated implementation, in this embodiment, through the high-precision map, it is obtained that: the first road section 1 is in the urban area, the second road section 2 is in the suburban area, and the third road section 3 is in the tunnel.

[0106] In this embodiment, being in the urban area means there are many network devices and excellent communication quality. Being in the suburban area means there are few network devices and poor communication quality; being in the tunnel means there are no network devices and normal communication cannot be carried out.

[0107] When the vehicle is in different areas, different waiting times are used:

[0108] 1. Urban area: The waiting duration is set to 2S.

[0109] 2. Tunnel: The waiting duration is set to 0S.

[0110] 3. Suburban area: The waiting duration is set to 4S.

[0111] In this way, it is ensured that the device can feedback to the user at the fastest speed.

[0112] This application also provides a device for adjusting the cloud semantic waiting time based on location information. The device for adjusting the cloud semantic waiting time based on location information includes an acquisition module, a geographical location acquisition module, a preset response comparison table acquisition module, and a waiting time acquisition module.

[0113] The acquisition module is used to acquire the audio stream information provided by the user in the vehicle and send it to the cloud;

[0114] The geographical location acquisition module is used to acquire the current geographical location information of the vehicle;

[0115] The preset response comparison table acquisition module is used to acquire a preset response comparison table. The preset response comparison table includes at least one preset road section information and a response time, and one preset road section information corresponds to one response time;

[0116] The waiting time acquisition module is used to acquire the response time corresponding to the preset road section information where the current geographical location information is located as the waiting time for waiting for the cloud to feedback the cloud semantic information according to the audio stream information.

[0117] It should be noted that the foregoing explanations of the method embodiments also apply to the system of this embodiment, and will not be repeated here.

[0118] This application also provides an electronic device. In this embodiment, the electronic device is an edge server, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, it implements the method for adjusting the cloud semantic waiting time based on location information as described above.

[0119] The present application also provides a computer-readable storage medium storing a computer program, which when executed by a processor can implement the method for adjusting the cloud semantic waiting time based on location information as described above.

[0120] Figure 2 It is an exemplary structural diagram of an electronic device capable of implementing the method for adjusting the cloud semantic waiting time based on location information provided by an embodiment of the present application.

[0121] As Figure 2 shown, the electronic device includes an input device 501, an input interface 502, a central processing unit 503, a memory 504, an output interface 505, and an output device 506. Among them, the input interface 502, the central processing unit 503, the memory 504, and the output interface 505 are interconnected through a bus 507. The input device 501 and the output device 506 are respectively connected to the bus 507 through the input interface 502 and the output interface 505, and then connected to other components of the electronic device. Specifically, the input device 504 receives external input information and transmits the input information to the central processing unit 503 through the input interface 502; the central processing unit 503 processes the input information based on computer-executable instructions stored in the memory 504 to generate output information, temporarily or permanently stores the output information in the memory 504, and then transmits the output information to the output device 506 through the output interface 505; the output device 506 outputs the output information to the outside of the electronic device for user use.

[0122] That is to say, Figure 2 the electronic device shown can also be implemented as including: a memory storing computer-executable instructions; and one or more processors, which can implement the method for adjusting the cloud semantic waiting time based on location information in combination with Figure 1 the description when executing the computer-executable instructions.

[0123] In one embodiment, Figure 2 the electronic device shown can be implemented as including: a memory 504 configured to store executable program code; one or more processors 503 configured to run the executable program code stored in the memory 504 to execute the method for adjusting the cloud semantic waiting time based on location information in the above embodiment.

[0124] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and a memory.

[0125] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash RAM. The memory is an example of computer-readable media.

[0126] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can store information by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transitory media that can be used to store information that can be accessed by a computing device.

[0127] Those skilled in the art will appreciate that the embodiments of the present application may be provided as a method, system or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0128] Furthermore, it is obvious that the term "comprising" does not exclude other elements or steps. The multiple elements, modules or devices recited in the apparatus claims may also be implemented by one element or a general device through software or hardware. The terms first, second, etc. are used to identify names and do not denote any particular order.

[0129] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a portion of code that includes one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks marked may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or overall flowchart, may be implemented by a dedicated hardware-based system that performs the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0130] In this embodiment, the so-called processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may also be any conventional processor, etc.

[0131] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the device / terminal device by running or executing the computer programs and / or modules stored in the memory, and by calling the data stored in the memory. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0132] In this embodiment, if the modules / units integrated in the device / terminal device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by hardware related to computer program instructions. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0133] It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. Although this application is disclosed above with preferred embodiments, it is not actually used to limit this application. Any person skilled in the art can make possible changes and modifications without departing from the spirit and scope of this application. Therefore, the protection scope of this application should be determined by the scope defined by the claims of this application.

[0134] Although the present invention has been described in detail above with general descriptions and specific implementation examples, based on the present invention, some modifications or improvements can be made, which are obvious to those skilled in the art. Therefore, these modifications or improvements made without departing from the spirit of the present invention all fall within the scope of protection required by the present invention.

Claims

1. A method for adjusting the cloud semantic waiting time based on location information, characterized in that, The method for adjusting the cloud semantic waiting time based on location information includes: Obtaining the audio stream information provided by the user in the vehicle and sending it to the cloud; Obtaining the current geographical location information of the vehicle; Obtaining a preset response correspondence table, where the preset response correspondence table includes at least one preset route segment information and a response time, and one preset route segment information corresponds to one response time; among them, generating the preset response correspondence table includes: obtaining the current navigation route of the vehicle; segmenting and analyzing the current navigation route to obtain multiple preset route segment information; respectively identifying each preset route segment information to set a response time for each preset route segment information; Obtaining the response time corresponding to the preset route segment information where the current geographical location information is located as the waiting time for waiting for the cloud to feedback cloud semantic information according to the audio stream information; Judging whether the cloud semantic information feedback by the cloud according to the audio stream information is obtained within the waiting time. If so, performing an action according to the cloud semantic information; if not, identifying the audio stream information according to the local recognition engine to obtain local semantic information.

2. The method for adjusting the cloud semantic waiting time based on location information according to claim 1, characterized in that, The preset response correspondence table further includes a preset route segment information type, and one preset route segment information corresponds to one preset route segment information type; The method for adjusting the cloud semantic waiting time based on location information further includes: Obtaining the image in front of the vehicle captured by the vehicle's camera device; Identifying the image in front of the vehicle to obtain the image recognition road segment type; Obtaining the preset route segment information type corresponding to the preset route segment information corresponding to the currently used response time; Judging whether the image recognition road segment type is the same as the preset route segment information type. If not, then Obtaining the image recognition response time according to the image recognition road segment type and using the image recognition response time as the waiting time.

3. The method for adjusting the cloud semantic waiting time based on location information according to claim 2, wherein The identifying the image in front of the vehicle to obtain the image recognition road segment type includes: Obtaining a trained road segment type classifier; Extracting the image features of the image in front of the vehicle; Inputting the image features into the trained road segment type classifier to obtain a classification label; Obtaining a preset road segment database, where the preset road segment database includes at least one image recognition road segment type and a preset classification label, and one preset classification label corresponds to one image recognition road segment type; Obtaining the image recognition road segment type corresponding to the preset classification label corresponding to the classification label.

4. The method for adjusting the cloud semantic waiting time based on location information according to claim 3, wherein The preset route segment information includes at least one longitude and latitude interval information; When identifying the audio stream information according to the local recognition engine, the method for adjusting the cloud semantic waiting time based on location information further includes: Obtaining the current vehicle information; Obtaining the time required for the vehicle to drive out of the current preset route segment according to the current vehicle information, the current geographical location information, and the longitude and latitude interval information.

5. The method for adjusting the cloud semantic waiting time based on location information according to claim 4, characterized in that, The method for adjusting the cloud semantic waiting time based on location information further includes: Generating voice broadcast information according to the time required for the vehicle to drive out of the current preset route segment.

6. A device for adjusting the cloud semantic waiting time based on location information, characterized in that, The device for adjusting the cloud semantic waiting time based on location information includes: An acquisition module, which is used to acquire the audio stream information provided by the user in the vehicle and send it to the cloud; A geographical location acquisition module, which is used to acquire the current geographical location information of the vehicle; A preset response correspondence table acquisition module, which is used to acquire a preset response correspondence table. The preset response correspondence table includes at least one preset route segment information and a response time, and one preset route segment information corresponds to one response time. Wherein, generating the preset response correspondence table includes: acquiring the current navigation route of the vehicle; segmenting and analyzing the current navigation route to obtain multiple preset route segment information; respectively identifying each preset route segment information, so as to set a response time for each preset route segment information; A waiting time acquisition module, which is used to acquire the response time corresponding to the preset route segment information where the current geographical location information is located as the waiting time for waiting for the cloud to feedback the cloud semantic information according to the audio stream information; Judge whether the cloud semantic information feedback by the cloud according to the audio stream information is obtained within the waiting time. If so, perform an action according to the cloud semantic information; if not, identify the audio stream information according to the local recognition engine to obtain the local semantic information.

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