Vehicle-based dialogue processing method and device, equipment and medium

Abstract

The embodiment of the invention relates to a conversation processing method and device based on a vehicle, equipment and a medium. The method comprises the steps that whether the vehicle meets a preset weak network access condition or not is determined; when a preset weak network access condition is satisfied, determining a current weak network type of a communication network between the vehicle and the server; obtaining a predetermined target network quality adjustment parameter corresponding to the current weak network type; the communication network is adjusted according to the target network quality adjustment parameter, and a target weak network index value of the adjusted communication network meets a preset weak network index gating condition; and performing conversation processing between the vehicle and the server according to the adjusted communication network. In the technical scheme, when the vehicle passes through the weak network, the communication network between the vehicle and the server is adjusted based on the predetermined network quality adjustment parameter, so that the signal quality of the communication network is improved, the smoothness of dialogue processing is ensured, and the dialogue interaction experience is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of communication technology, and in particular to a vehicle-based dialogue processing method, device, equipment and medium. Background Art

[0002] With the development of artificial intelligence model technology, artificial intelligence models are gradually being used in vehicle dialogue scenarios. Artificial intelligence models can deeply understand user needs, provide more accurate response information, and enhance the interactive experience.

[0003] In the related technology, the relevant artificial intelligence model is deployed in the server in advance. After the vehicle's on-board computer obtains the user's voice control command, the voice control command is sent to the server through the communication network between the vehicle and the server. The server conducts in-depth analysis of the voice control command based on the pre-deployed artificial intelligence model, generates response information corresponding to the voice control command, and then feeds back the response information to the vehicle through the communication network. The vehicle can send a voice conversion command to the server based on the response information. The server converts the response information into voice based on the text-to-speech conversion technology (Text To Speech, TTS) and sends it to the vehicle end. The vehicle end plays the voice to form an interactive mode of human-computer dialogue.

[0004] However, the interactive experience in the above-mentioned interactive mode is closely related to the quality of the communication network. During the driving process, the vehicle may enter a weak communication network due to frequent base station switching and complex roads (such as high mountains, tunnels, and remote villages). If the vehicle enters a weak network, the communication quality under the weak network is poor, which will lead to poor fluency of the conversation, affecting the conversation interaction experience. Summary of the invention

[0005] In order to solve the above technical problems or at least partially solve the above technical problems, the present disclosure provides a vehicle-based dialogue processing method, device, equipment and medium.

[0006] An embodiment of the present disclosure provides a vehicle-based dialogue processing method, the method comprising the following steps: determining whether the vehicle satisfies a preset weak network entry condition; when the preset weak network entry condition is met, determining the current weak network type of the communication network between the vehicle and the server; obtaining a predetermined target network quality adjustment parameter corresponding to the current weak network type; adjusting the communication network according to the target network quality adjustment parameter, wherein the target weak network index value of the adjusted communication network satisfies a preset weak network index gating condition; and performing dialogue processing between the vehicle and the server according to the adjusted communication network.

[0007] The disclosed embodiment also provides a vehicle-based dialogue processing device, the device comprising: a first determination module, used to determine whether the vehicle meets a preset weak network entry condition; a second determination module, used to determine the current weak network type of the communication network between the vehicle and the server when the preset weak network entry condition is met; an acquisition module, used to acquire a predetermined target network quality adjustment parameter corresponding to the current weak network type; an adjustment module, used to adjust the communication network according to the target network quality adjustment parameter, wherein the target weak network index value of the adjusted communication network meets the preset weak network index gating condition; and a dialogue processing module, used to perform dialogue processing between the vehicle and the server according to the adjusted communication network.

[0008] An embodiment of the present disclosure also provides an electronic device, which includes: a processor; a memory for storing executable instructions of the processor; the processor is used to read the executable instructions from the memory and execute the instructions to implement the vehicle-based dialogue processing method provided in the embodiment of the present disclosure.

[0009] The embodiment of the present disclosure also provides a computer-readable storage medium, which stores a computer program, and the computer program is used to execute the vehicle-based dialogue processing method provided by the embodiment of the present disclosure.

[0010] Compared with the prior art, the technical solution provided by the embodiments of the present disclosure has the following advantages:

[0011] The vehicle-based dialogue processing scheme provided by the disclosed embodiment determines whether the vehicle meets the preset weak network entry conditions. When the preset weak network entry conditions are met, the current weak network type of the communication network between the vehicle and the server is determined, and the predetermined target network quality adjustment parameters corresponding to the current weak network type are obtained. The communication network is adjusted according to the target network quality adjustment parameters, wherein the target weak network index value of the adjusted communication network meets the preset weak network index gating conditions, and then the dialogue processing between the vehicle and the server is performed according to the adjusted communication network. In this technical solution, when the vehicle passes through a weak network, the communication network between the vehicle and the server is adjusted based on the predetermined network quality adjustment parameters, which improves the signal quality of the communication network, ensures the fluency of dialogue processing, and improves the dialogue interaction experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale.

[0013] Figure 1A flow chart of a vehicle-based dialogue processing method provided in an embodiment of the present disclosure;

[0014] Figure 2 A flowchart of another vehicle-based dialogue processing method provided by an embodiment of the present disclosure;

[0015] Figure 3 A schematic diagram of the structure of a vehicle-based dialogue processing device provided in an embodiment of the present disclosure;

[0016] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0017] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0018] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0019] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0020] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0021] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0022] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0023] In order to solve the above problems, the embodiments of the present disclosure provide a vehicle-based dialogue processing method, which is introduced below in conjunction with specific embodiments.

[0024] Figure 1 The present invention provides a flow chart of a vehicle-based dialogue processing method provided by an embodiment of the present invention. The method can be executed by a vehicle-based dialogue processing device, wherein the device can be implemented by software and / or hardware, and can generally be integrated in an electronic device, which can be an electronic device built into the vehicle or other electronic devices arranged outside the vehicle. Figure 1 As shown, the method includes:

[0025] Step 101, determining whether the vehicle meets the preset weak network entry condition.

[0026] In one embodiment of the present disclosure, it is determined whether the vehicle meets a preset weak network entry condition to determine whether the vehicle is likely to enter a weak network.

[0027] It should be noted that in different application scenarios, the above preset weak network entry conditions are different, and the examples are as follows:

[0028] In some possible embodiments, the first current positioning information of the vehicle is identified. For example, the first current positioning information can be obtained through the Global Positioning System (GPS), and whether the vehicle enters a weak network area within a preset time is determined based on the first current positioning information, wherein the weak network area can be a high mountain, a tunnel, a remote mountain, and other areas whose geographical features meet the weak network geographical features, etc. For example, the current driving speed and driving direction of the vehicle can be obtained, and whether the vehicle passes through the weak network area within the preset time is determined based on the first current positioning information, the current driving speed, and the driving direction. For another example, the average time of multiple vehicles from the first current positioning information to the weak network area can be obtained in advance, and it is determined whether the average time is less than or equal to the preset time. If it is less than or equal to the preset time, it is determined that the vehicle meets the preset weak network entry condition.

[0029] In this example, based on the first current positioning information, it can be quickly determined whether the vehicle will enter a weak network, thereby improving the efficiency of subsequent adjustment of the performance of the communication network.

[0030] In this embodiment, when the vehicle does not enter the weak network area within the preset time, whether it enters the weak network is detected based on the weak network detection logic. In this embodiment, the sampled weak network index value of the communication network is collected according to the preset collection period, wherein, since the vehicle is driving at a high speed, the preset collection period can be shorter, wherein the sampled weak network index value includes at least one of the following:

[0031] Single conversation success rate: The single conversation success rate can be calculated by the ratio of the number of successful TTS synthesis messages to the total number of TTS request synthesis during the text-to-speech conversion process in a single conversation between the vehicle and the server.

[0032] TTS frame data reception jam rate: The TTS frame data reception jam rate can be the ratio of the TTS frame reception jam duration to the total TTS frame reception time during a single conversation;

[0033] httprtt: httprtt refers to the time difference from the first byte of the client request to the first byte of the received http header;

[0034] TCPRTT: TCPRTT refers to the time difference between the first byte sent from the client TCP channel and the first byte received;

[0035] Throughput: refers to the capacity of a data link, which can also be called throughput. Throughput is the ratio of the number of bytes obtained to the time consumed to obtain the bytes;

[0036] signal strength: wireless signal strength;

[0037] Bandwidth-delay product: refers to the product of a data link's throughput and the round-trip communication delay;

[0038] In this embodiment, based on the sampled weak network index value of the current collection period, it is determined whether the communication network meets the preset weak network index gating conditions. When the weak network index gating conditions are not met, the second current positioning information of the vehicle is identified. The second current positioning information can also be obtained based on the GPS system, etc., to determine whether the second current positioning information is located in a weak network area, that is, to determine whether the geographical features corresponding to the second current positioning information meet the weak network geographical features, etc. If it meets the weak network geographical features, it is determined that the second current positioning information is located in the weak network area. When it is located in the weak network area, it is determined that the preset weak network entry conditions are met.

[0039] Among them, in some possible embodiments, when a type of weak network indicator value is included, a weak network indicator threshold corresponding to the weak network indicator value is pre-set, and when the weak network indicator value is less than the corresponding weak network indicator threshold, it is determined that the preset weak network indicator gating condition is met.

[0040] In some possible embodiments, when multiple types of weak network indicator values ​​are included, a weak network indicator threshold for each type of weak network indicator value is preset.

[0041] In this example, when any type of weak network indicator value is less than the corresponding weak network indicator threshold, it is determined that the preset weak network indicator gating condition is met; or,

[0042] In this example, the number of weak network indicator categories whose weak network indicator values ​​are less than the corresponding weak network indicator threshold is counted, and the ratio of the number of weak network indicator categories to the total number of weak network indicator categories is calculated. When the ratio is greater than the preset ratio threshold, it is determined that the preset weak network indicator gating condition is met.

[0043] For example, when the sampled weak network indicator values ​​include a single conversation success rate and a TTS frame data reception jamming rate, the weak network indicator threshold corresponding to the single conversation success rate is a, and the weak network indicator threshold corresponding to the TTS frame data reception jamming rate is b. If the single conversation success rate corresponding to the current sampling period is lower than a, and / or the TTS frame data reception jamming rate is greater than b, it is considered that the preset weak network indicator gating conditions are met.

[0044] Step 102, when the preset weak network entry condition is met, determine the current weak network type of the communication network between the vehicle and the server.

[0045] Step 103: Obtain a predetermined target network quality adjustment parameter corresponding to the current weak network type.

[0046] In one embodiment of the present disclosure, a network quality adjustment parameter corresponding to a weak network type is predetermined. The network quality adjustment parameter may be any parameter related to the communication quality when the vehicle terminal and the server communicate. For example, the network parameter may include the number of open router service ports, etc.; for example, the network quality adjustment parameter may also include network function configuration items, etc., wherein the function configuration items include a data retransmission rate, a redundancy level based on forward error correction (FEC) technology (wherein the redundancy level may be determined according to network quality parameters such as a packet loss rate, and the redundancy level is used to determine the magnitude of redundant information added), etc.

[0047] In this implementation, when the preset weak network entry condition is met, the current weak network type of the communication network between the vehicle and the server is determined. That is, in one embodiment of the present disclosure, the standard range of network quality parameters corresponding to each weak network type can be predefined, the current actual network quality parameters of the communication network are obtained, and the current weak network type is determined based on the comparison between the actual network parameters and the standard range of network quality parameters.

[0048] Then, after the weak network type is determined, a predetermined target network quality adjustment parameter corresponding to the weak network type is obtained. Since the target network quality adjustment parameter is predetermined, it is helpful to improve the adjustment efficiency of the weak network performance.

[0049] Of course, in one embodiment of the present disclosure, when the preset weak network entry conditions are met, the service mode of related functions based on the conversation mode can also be changed. For example, the TTS synthesis method can be adjusted from synthesis in the server to local synthesis in the vehicle to improve the single conversation success rate of TTS.

[0050] In some possible embodiments, at least one correspondence between a weak network type and a corresponding network quality adjustment parameter is pre-stored, so that the preset correspondence is queried based on the current weak network type to determine the target network quality adjustment parameter that matches successfully. The preset correspondence includes at least one candidate weak network type and a candidate network quality adjustment parameter corresponding to each candidate weak network type.

[0051] In some possible embodiments, in order to further ensure the adaptability of the determined target network quality adjustment parameters to the weak network, the correspondence between the environment type and the candidate network quality adjustment parameters can also be pre-stored, wherein the environment type includes but is not limited to one of rainy days, sunny days, cloudy days, etc., and the weak network type includes but is not limited to one of high latency, jitter, congestion, packet loss, low bandwidth, etc., so that the current environment type when the vehicle is driving can be identified, and the current weak network type can be determined. Then, according to the current environment type and the current weak network type, the correspondence is queried to determine the predetermined target network quality adjustment parameters.

[0052] Step 104, adjusting the communication network according to the target network quality adjustment parameter, wherein the target weak network index value of the communication network after adjustment satisfies a preset weak network index gating condition.

[0053] After obtaining the target network quality adjustment parameters, the communication network between the vehicle and the server is adjusted according to the target network quality adjustment parameters, for example, the corresponding FEC redundancy level is adjusted, etc., wherein the target weak network index value of the adjusted communication network meets the preset weak network index gating conditions, for example, the adjusted single conversation success rate is not less than a, the TTS frame data reception jamming rate is not greater than b, etc.

[0054] Step 105 , performing a dialogue process between the vehicle and the server according to the adjusted communication network.

[0055] After adjusting the communication network, the conversation between the vehicle and the server is processed according to the adjusted communication network. For example, a TTS conversion model and an artificial intelligence model are pre-deployed in the communication server, where the artificial intelligence model can be any artificial intelligence model, for example, a ChatGPT model, etc. After the vehicle's on-board computer obtains the user's voice control command, the voice control command is sent to the server through the adjusted communication network. The server performs an in-depth analysis of the voice control command based on the pre-deployed artificial intelligence model, generates response information corresponding to the voice control command, and then feeds back the response information to the vehicle through the communication network. The vehicle can send a voice conversion command to the server based on the response information. The server converts the response information into voice based on the TTS conversion model and sends it to the vehicle, which plays the voice.

[0056] Among them, during the dialogue processing process, the network quality adjustment parameters can be further fine-tuned according to the real-time weak network index value between the vehicle and the cloud server. For example, after adjusting the communication network according to the target network quality parameters, the real-time weak network index value of the communication network is obtained, and the real-time weak network index value and the target network quality parameters are input into a pre-built deep learning model, and the fine-tuned network quality adjustment parameters are determined based on the deep learning model. The communication network between the vehicle and the server is adjusted based on the fine-tuned network quality parameters. If the weak network index value corresponding to the fine-tuned network quality adjustment parameter is better than the weak network index value adjusted according to the target network quality parameter, the target network quality adjustment parameter is updated according to the fine-tuned network quality adjustment parameter, thereby achieving iterative optimization of the target network quality adjustment parameter. Among them, in this embodiment, since the corresponding passing position may be different when the vehicle passes through the weak network area, the network quality adjustment parameters corresponding to each historical passing position in the weak network area can also be stored with the passing position as the granularity, that is, in this embodiment, the network quality adjustment parameters are iteratively optimized based on each historical passing position, so that the next time a vehicle passes through the same weak network area, the network quality adjustment parameters corresponding to the matching historical passing position are queried according to the actual passing position of the vehicle, so as to further improve the weak network adjustment quality, wherein, if the distance between the actual passing position information and the historical passing position is less than the preset distance threshold, it is determined that the actual passing position information matches the historical passing position, or, among multiple historical passing positions, the historical passing position closest to the actual passing position is determined as the matching historical passing position.

[0057] In summary, the vehicle-based dialogue processing method of the disclosed embodiment determines whether the vehicle meets the preset weak network entry conditions. When the preset weak network entry conditions are met, the current weak network type of the communication network between the vehicle and the server is determined, and the predetermined target network quality adjustment parameters corresponding to the current weak network type are obtained. The communication network is adjusted according to the target network quality adjustment parameters, wherein the target weak network index value of the adjusted communication network meets the preset weak network index gating conditions, and then, the dialogue processing between the vehicle and the server is performed according to the adjusted communication network. In this technical solution, when the vehicle passes through a weak network, the communication network between the vehicle and the server is adjusted based on the predetermined network quality adjustment parameters, which improves the signal quality of the communication network, ensures the fluency of dialogue processing, and improves the dialogue interaction experience.

[0058] The following describes how to determine the candidate network quality adjustment parameters corresponding to the candidate weak network type with reference to the embodiments.

[0059] In one embodiment of the present disclosure, Figure 2 As shown, before querying the preset corresponding relationship, it also includes:

[0060] Step 201, building a test network between the vehicle and the server.

[0061] In this embodiment, a test network between the vehicle and the server is pre-built, wherein the test network is built in different ways in different application scenarios, as shown below:

[0062] In some possible examples, a test network may be constructed using a wireless network card, wherein the test network is connected to a preset network quality adjustment device, and the network quality adjustment device includes any one of a programmable router, a network loss meter, and the like.

[0063] In some possible examples, a test network can be built using third-party tools, such as electronic devices with TrafficControl (TC) software, dual network cards, routers, or network loss meters. TC is a network traffic management and queue scheduling tool set provided by the Linux kernel to simulate and control network conditions, such as bandwidth limitations, delays, packet loss, etc. TC allows relevant personnel to test the performance of the car extreme and cloud servers under different network conditions in an experimental environment.

[0064] Step 202: determine the network transmission parameters corresponding to each candidate weak network type.

[0065] The network transmission parameters include packet loss rate, throughput and other parameters related to the quality of network transmission data.

[0066] Step 203: Control a preset network quality adjustment device to adjust the test network according to the network transmission parameter, wherein the candidate weak network indicator value of the adjusted test network does not meet the preset weak network indicator gating condition.

[0067] In this embodiment, the preset network quality adjustment device is controlled to adjust the test network according to each weak network indicator gating condition, wherein the test weak network indicator value of the adjusted test network does not meet the corresponding weak network indicator gating condition, that is, the communication network under the weak network is simulated by the network quality adjustment device.

[0068] Among them, the network quality adjustment device can adjust the test network by existing technology, which will not be described in detail here.

[0069] It should be noted that in different application scenarios, the methods of controlling the preset network quality adjustment device to adjust the test network according to the network transmission parameters are different, and examples are described as follows:

[0070] In some possible examples, a test network adjustment instruction for a first candidate weak network type is obtained through a first preset artificial intelligence model, wherein, in order to ensure the adjustment efficiency, the first preset artificial model can be set on the vehicle side to control the first preset artificial intelligence model to call a first preset test execution event corresponding to the first candidate weak network type according to the test network adjustment instruction, wherein the first preset test execution event is used to control the network quality adjustment device to adjust the test network according to the network transmission parameters corresponding to the first candidate weak network type, wherein the first preset test execution event includes but is not limited to a switching program execution event corresponding to the first candidate weak network type (for example, including an interface call event of the switching program, a command line execution event, etc.).

[0071] In some possible examples, a second preset artificial intelligence model can be deployed in advance on the vehicle side, and a preset network page can be obtained through the second preset artificial intelligence model, wherein the preset network page contains at least one candidate adjustment control corresponding to at least one candidate weak network type. For example, a preset network page containing at least one candidate adjustment control is generated in advance. After the second preset artificial intelligence model obtains the user's call instruction for the preset network page, the second preset artificial intelligence model can call a browser to access and obtain the preset network page. The second preset artificial intelligence model can scan the preset network page to obtain at least one candidate adjustment control contained in the preset network page, and then send a voice notification message corresponding to at least one candidate adjustment control, and prompt the user through the voice notification message The weak network type corresponding to at least one candidate adjustment control contained in the preset network page, etc., and the target adjustment control selected by the user from at least one candidate adjustment control according to the voice notification message is obtained through the second preset artificial intelligence model.

[0072] Among them, the target adjustment control corresponds to the second candidate weak network type, and the user can select the target adjustment control by manual adjustment or voice selection, etc., to control the second preset artificial intelligence model to call the second preset test execution event corresponding to the second candidate weak network type, wherein the second preset test execution event is used to control the network quality adjustment device to adjust the test network according to the network transmission parameters corresponding to the second candidate weak network type, wherein the second preset test execution event includes but is not limited to the switching program execution event corresponding to the second candidate weak network type (for example, including the interface call event of the switching program, the command line execution event, etc.).

[0073] Therefore, users can switch relevant candidate weak network types by interacting with relevant preset artificial intelligence models.

[0074] Step 204, determining a candidate network quality adjustment parameter according to the adjusted test network, wherein the candidate network quality adjustment parameter is used to adjust the corresponding test network, and the candidate weak network index value of the adjusted test network meets a preset weak network index gating condition.

[0075] In this embodiment, the network quality adjustment parameters are determined according to the adjusted test network, wherein, after the corresponding test network is adjusted according to the network quality adjustment parameters, the candidate weak network indicator value of the corresponding test network meets the preset weak network indicator gating conditions, and the network quality adjustment parameters can be obtained based on deep learning, etc.

[0076] It can be understood that in the embodiments of the present disclosure, candidate network quality adjustment parameters are determined in advance for the adjusted test network, and the candidate network quality adjustment parameters are used to adjust the corresponding test network from not satisfying the preset weak network indicator gating conditions to satisfying the corresponding preset weak network indicator gating conditions. Since the candidate network quality adjustment parameters are predetermined during the testing phase, in actual application scenarios, for vehicles passing through the same weak network type, there is no need to determine the corresponding network quality adjustment parameters in real time. Instead, the predetermined candidate network quality adjustment parameters can be directly reused to achieve rapid adjustment of the weak network, thereby improving the communication service quality of the communication network on the vehicle.

[0077] Step 205: Store the corresponding relationship between each candidate weak network type and the candidate network quality adjustment parameter.

[0078] After obtaining the candidate network quality adjustment parameters, the corresponding relationship between the candidate network quality adjustment parameters and the corresponding candidate test weak network type is directly stored, so as to reuse the corresponding candidate network quality adjustment parameters in subsequent practical applications. In the actual execution process, the communication network of vehicles passing through the same weak network type can be quickly adjusted according to the corresponding candidate network quality adjustment parameters.

[0079] In summary, the vehicle-based dialogue processing method of the disclosed embodiment predetermines the network quality adjustment parameters corresponding to the weak network type based on the weak network simulation method. When the vehicle passes through the same type of weak network type, the communication network between the vehicle and the server can be adjusted according to the predetermined network quality adjustment parameters, thereby realizing rapid adjustment of network quality and improving dialogue fluency.

[0080] In order to implement the above embodiments, the present disclosure also proposes a vehicle-based dialogue processing device.

[0081] Figure 3 The structure diagram of a vehicle-based dialogue processing device provided by an embodiment of the present disclosure is shown in FIG. 1 , which can be implemented by software and / or hardware and can generally be integrated into an electronic device. Figure 3 As shown, the device includes: a first determination module 310, a second determination module 320, an acquisition module 330, an adjustment module 340 and a dialogue processing module 350, wherein:

[0082] A first determination module 310 is used to determine whether the vehicle meets a preset weak network entry condition;

[0083] A second determination module 320 is used to determine the current weak network type of the communication network between the vehicle and the server when the preset weak network entry condition is met;

[0084] The acquisition module 330 is used to acquire a predetermined target network quality adjustment parameter corresponding to the current weak network type;

[0085] An adjustment module 340, configured to adjust the communication network according to the target network quality adjustment parameter, wherein the target weak network index value of the communication network after adjustment satisfies a preset weak network index gating condition;

[0086] The dialogue processing module 350 is used to perform dialogue processing between the vehicle and the server according to the adjusted communication network.

[0087] In one embodiment of the present disclosure, the first determining module 310 is specifically configured to:

[0088] Identifying first current location information of the vehicle;

[0089] Determine whether the vehicle enters a weak network area within a preset time according to the first current positioning information, wherein when the vehicle enters the weak network area within the preset time, it is determined that a preset weak network entry condition is met.

[0090] In one embodiment of the present disclosure, the first determining module 310 is specifically configured to:

[0091] When the vehicle does not enter the weak network area within the preset time, the sampled weak network index value of the communication network is collected according to the preset collection period;

[0092] Determine whether the sampled weak network indicator value of the current collection period meets the preset weak network indicator gating condition;

[0093] When the preset weak network indicator gating condition is not met, identifying the second current positioning information of the vehicle;

[0094] Determine whether the second current positioning information is located in a weak network area, wherein when located in the weak network area, determine that a preset weak network entry condition is met.

[0095] In one embodiment of the present disclosure, the acquisition module 330 is specifically configured to:

[0096] A preset corresponding relationship is queried to determine a target network quality adjustment parameter corresponding to the current weak network type, wherein the preset corresponding relationship includes at least one candidate weak network type and a candidate network quality adjustment parameter corresponding to each candidate weak network type.

[0097] In one embodiment of the present disclosure, it further includes: a testing module, which is used to:

[0098] Build a test network between the vehicle and the server;

[0099] Determine network transmission parameters corresponding to each candidate weak network type;

[0100] Controlling a preset network quality adjustment device to adjust the test network according to the network transmission parameter, wherein the candidate weak network indicator value of the adjusted test network does not meet the preset weak network indicator gating condition;

[0101] Determine a candidate network quality adjustment parameter according to the adjusted test network, wherein the candidate network quality adjustment parameter is used to adjust the corresponding test network, and the candidate weak network indicator value of the adjusted test network meets the preset weak network indicator gating condition;

[0102] The correspondence between each candidate weak network type and the candidate network quality adjustment parameter is stored.

[0103] In one embodiment of the present disclosure, the test module is specifically used to:

[0104] Acquire a test network adjustment instruction for a first candidate weak network type through a first preset artificial intelligence model;

[0105] Control the first preset artificial intelligence model to call the first preset test execution event corresponding to the first candidate weak network type according to the test network adjustment instruction, wherein the first preset test execution event is used to control the network quality adjustment device to adjust the test network according to the network transmission parameters corresponding to the first candidate weak network type.

[0106] In one embodiment of the present disclosure, the test module is specifically used to:

[0107] Acquire a preset network page through a second preset artificial intelligence model, wherein the preset network page includes at least one candidate adjustment control corresponding to at least one candidate weak network type;

[0108] Sending a voice notification message corresponding to at least one candidate adjustment control through a second preset artificial intelligence model;

[0109] Acquire, by a second preset artificial intelligence model, a target adjustment control selected by the user from at least one candidate adjustment control according to the voice notification message, wherein the target adjustment control corresponds to the second candidate weak network type;

[0110] Control the second preset artificial intelligence model to call the second preset test execution event corresponding to the second candidate weak network type, wherein the second preset test execution event is used to control the network quality adjustment device to adjust the test network according to the network transmission parameters corresponding to the second candidate weak network type.

[0111] The vehicle-based dialogue processing device provided in the embodiments of the present disclosure can execute the vehicle-based dialogue processing method provided in any embodiment of the present disclosure, and has the corresponding functional modules and beneficial effects of the execution method.

[0112] In order to implement the above embodiments, the present disclosure also proposes a computer program product, including a computer program / instruction, which implements the vehicle-based dialogue processing method in the above embodiments when executed by a processor.

[0113] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure.

[0114] The following specific reference Figure 4 , which shows a schematic diagram of the structure of an electronic device 400 suitable for implementing the embodiment of the present disclosure. The electronic device 400 in the embodiment of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0115] like Figure 4As shown, the electronic device 400 may include a processor (e.g., a central processing unit, a graphics processing unit, etc.) 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a memory 408 to a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 are also stored. The processor 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0116] Typically, the following devices may be connected to the I / O interface 405: an input device 406 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 407 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a memory 408 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 409. The communication device 409 may allow the electronic device 400 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 4 The electronic device 400 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0117] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device 409, or installed from a memory 408, or installed from a ROM 402. When the computer program is executed by the processor 401, the above-mentioned functions defined in the vehicle-based dialogue processing method of the embodiment of the present disclosure are executed.

[0118] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0119] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0120] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0121] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device:

[0122] Determine whether the vehicle meets the preset weak network entry conditions. When the preset weak network entry conditions are met, determine the current weak network type of the communication network between the vehicle and the server, obtain the predetermined target network quality adjustment parameters corresponding to the current weak network type, and adjust the communication network according to the target network quality adjustment parameters, wherein the target weak network index value of the adjusted communication network meets the preset weak network index gating conditions, and then, perform the dialogue processing between the vehicle and the server according to the adjusted communication network. In this technical solution, when the vehicle passes through a weak network, the communication network between the vehicle and the server is adjusted based on the predetermined network quality adjustment parameters, which improves the signal quality of the communication network, ensures the smoothness of the dialogue processing, and improves the dialogue interaction experience.

[0123] The electronic device may be written in one or more programming languages ​​or a combination thereof to write computer program code for performing the operations of the present disclosure, including but not limited to object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may 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 may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0124] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0125] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0126] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0127] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0128] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[0129] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0130] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A vehicle-based dialogue processing method, characterized in that: The following steps are involved: Determine whether the vehicle meets the preset weak network entry conditions; When the preset weak network entry condition is met, determining a current weak network type of the communication network between the vehicle and the server; Acquire a predetermined target network quality adjustment parameter corresponding to the current weak network type; Adjusting the communication network according to the target network quality adjustment parameter, wherein the target weak network index value of the adjusted communication network satisfies a preset weak network index gating condition; The dialog process between the vehicle and the server is performed according to the adjusted communication network.

2. The method according to claim 1, characterized in that The determining whether the vehicle meets the preset weak network entry condition includes: Identifying first current location information of the vehicle; Determine whether the vehicle enters a weak network area within a preset time according to the first current positioning information, wherein when the vehicle enters the weak network area within the preset time, it is determined that the preset weak network entry condition is met.

3. The method according to claim 2, characterized in that After determining whether the vehicle enters a weak network area within a preset time according to the first current positioning information, the method further includes: When the vehicle does not enter the weak network area within the preset time, collecting the sampled weak network index value of the communication network according to a preset collection period; Determine whether the sampled weak network indicator value of the current acquisition period meets the preset weak network indicator gating condition; When the preset weak network indicator gating condition is not met, identifying second current positioning information of the vehicle; Determine whether the second current positioning information is located in a weak network area, wherein when located in the weak network area, determine that the preset weak network entry condition is met.

4. The method according to claim 1, characterized in that The obtaining of a predetermined target network quality adjustment parameter corresponding to the current weak network type includes: A preset corresponding relationship is queried to determine a target network quality adjustment parameter corresponding to the current weak network type, wherein the preset corresponding relationship includes at least one candidate weak network type and a candidate network quality adjustment parameter corresponding to each candidate weak network type.

5. The method according to claim 4, characterized in that Before querying the preset corresponding relationship, the method further includes: Building a test network between the vehicle and the server; Determining network transmission parameters corresponding to each of the candidate weak network types; Controlling a preset network quality adjustment device to adjust the test network according to the network transmission parameter, wherein the candidate weak network indicator value of the adjusted test network does not meet the preset weak network indicator gating condition; Determine a candidate network quality adjustment parameter according to the adjusted test network, wherein the candidate network quality adjustment parameter is used to adjust the corresponding test network, and the candidate weak network index value of the adjusted test network satisfies the preset weak network index gating condition; The corresponding relationship between each candidate weak network type and the candidate network quality adjustment parameter is stored.

6. The method according to claim 5, characterized in that The controlling the preset network quality adjustment device to adjust the test network according to the network transmission parameter includes: Acquire a test network adjustment instruction for a first candidate weak network type through a first preset artificial intelligence model; Control the first preset artificial intelligence model to call the first preset test execution event corresponding to the first candidate weak network type according to the test network adjustment instruction, wherein the first preset test execution event is used to control the network quality adjustment device to adjust the test network according to the network transmission parameters corresponding to the first candidate weak network type.

7. The method according to claim 5, characterized in that The controlling the preset network quality adjustment device to adjust the test network according to the network transmission parameter includes: Acquire a preset network page through a second preset artificial intelligence model, wherein the preset network page includes at least one candidate adjustment control corresponding to the at least one candidate weak network type; Sending a voice notification message corresponding to the at least one candidate adjustment control through the second preset artificial intelligence model; Acquire, by the second preset artificial intelligence model, a target adjustment control selected by the user from the at least one candidate adjustment control according to the voice notification message, wherein the target adjustment control corresponds to the second candidate weak network type; Control the second preset artificial intelligence model to call the second preset test execution event corresponding to the second candidate weak network type, wherein the second preset test execution event is used to control the network quality adjustment device to adjust the test network according to the network transmission parameters corresponding to the second candidate weak network type.

8. A vehicle-based dialogue processing device, characterized in that: include: A first determination module is used to determine whether the vehicle meets the preset weak network entry condition; A second determination module, configured to determine a current weak network type of the communication network between the vehicle and the server when the preset weak network entry condition is met; An acquisition module, used to acquire a predetermined target network quality adjustment parameter corresponding to the current weak network type; An adjustment module, configured to adjust the communication network according to the target network quality adjustment parameter, wherein the target weak network index value of the communication network after adjustment satisfies a preset weak network index gating condition; The dialogue processing module is used to perform dialogue processing between the vehicle and the server according to the adjusted communication network.

9. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is used to read the executable instructions from the memory and execute the executable instructions to implement the vehicle-based dialogue processing method described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is used to execute the vehicle-based dialogue processing method described in any one of claims 1-7.

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