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

Abstract

Embodiments of the present disclosure relate to a vehicle-based dialogue processing method, device, equipment and medium, wherein the method comprises: determining whether the vehicle meets a preset weak network entering condition; when the preset weak network entering condition is met, determining a current weak network type of a communication network between the vehicle and a server; obtaining a target network quality adjustment parameter corresponding to the current weak network type determined in advance; adjusting the communication network according to the target network quality adjustment parameter, wherein a target weak network index value of the adjusted communication network meets a preset weak network index gating condition; and performing 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 parameter, the signal quality of the communication network is improved, the fluency of dialogue processing is ensured, and the dialogue interaction experience is improved.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of communication, and particularly relates to a vehicle-based dialogue processing method and device, equipment and medium. BACKGROUND

[0002] With the development of artificial intelligence model technology, artificial intelligence models are gradually applied in vehicle dialogue scenarios. Through artificial intelligence models, user needs can be deeply understood, more accurate response information can be provided, and interactive experience can be improved.

[0003] In related technologies, relevant artificial intelligence models are pre-deployed in a server. After a user's voice control instruction is obtained at a vehicle machine end, the voice control instruction is sent to the server through a communication network between the vehicle and the server. The server performs deep analysis on the voice control instruction based on the pre-deployed artificial intelligence model, generates response information corresponding to the voice control instruction, and then feeds back the response information to the vehicle through the communication network. The vehicle can send a voice conversion instruction to the server based on the response information. The server converts the response information into voice based on text-to-speech (TTS) technology and sends the voice to the vehicle end. The vehicle end plays the voice, forming a human-computer dialogue interaction mode.

[0004] However, in the above interaction mode, the interactive experience is closely related to the quality of the communication network. During driving, the vehicle may frequently switch base stations and the road may be complex (such as high mountains, tunnels, and remote villages), which may result in a weak network. If the vehicle enters a weak network, the communication quality in the weak network is poor, which may result in low dialogue fluency and affect the dialogue interactive experience. SUMMARY

[0005] 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] The present disclosure provides a vehicle-based dialogue processing method, which includes the following steps: determining whether a vehicle meets a preset weak network entering condition; when the preset weak network entering condition is met, determining a current weak network type of a communication network between the vehicle and a server; obtaining a target network quality adjustment parameter corresponding to the current weak network type that is determined in advance; adjusting the communication network according to the target network quality adjustment parameter, wherein a target weak network index value of the adjusted communication network meets 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 embodiment of the present disclosure further provides a vehicle-based dialogue processing device, the device comprising: a first determination module configured to determine whether a vehicle meets a preset weak network entering condition; a second determination module configured to determine a current weak network type of a communication network between the vehicle and a server when the preset weak network entering condition is met; an acquisition module configured to acquire a 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 a target weak network index value of the adjusted communication network meets a preset weak network index gating condition; and a dialogue processing module configured to perform dialogue processing between the vehicle and the server according to the adjusted communication network.

[0008] The embodiment of the present disclosure further provides an electronic device, comprising: a processor; a memory for storing executable instructions of the processor; and the processor is configured to read the executable instructions from the memory and execute the instructions to implement the vehicle-based dialogue processing method provided by the embodiment of the present disclosure.

[0009] The embodiment of the present disclosure further provides a computer-readable storage medium, the storage medium storing a computer program, the computer program being used to execute the vehicle-based dialogue processing method provided by the embodiment of the present disclosure.

[0010] The technical solution provided by the embodiment of the present disclosure has the following advantages compared with the prior art.

[0011] The vehicle-based dialogue processing solution provided by the embodiment of the present disclosure determines whether a vehicle meets a preset weak network entering condition, determines a current weak network type of a communication network between the vehicle and a server when the preset weak network entering condition is met, acquires a target network quality adjustment parameter corresponding to the current weak network type, adjusts the communication network according to the target network quality adjustment parameter, wherein a target weak network index value of the adjusted communication network meets a preset weak network index gating condition, and then performs dialogue processing between the vehicle and the server according to the adjusted communication network. In the technical solution, when the vehicle passes through a weak network, the communication network between the vehicle and the server is adjusted based on a pre-determined network quality adjustment parameter, the signal quality of the communication network is improved, the fluency of dialogue processing is ensured, and the dialogue interaction experience is improved. BRIEF DESCRIPTION OF DRAWINGS

[0012] The above and other features, advantages, and aspects of the embodiments of the present disclosure will become more apparent upon reading the following detailed description in conjunction with the accompanying drawings, in which like reference numerals refer to like elements. It is to be understood that the drawings are diagrammatic and schematic representations of the preferred embodiments of the present disclosure, and are not limiting of the scope of the present disclosure.

[0013] Figure 1A flowchart of a vehicle-based dialogue processing method provided by an embodiment of the present disclosure is shown in FIG. 1.

[0014] Figure 2 A flowchart of another vehicle-based dialogue processing method provided by an embodiment of the present disclosure is shown in FIG. 2.

[0015] Figure 3 A structural diagram of a vehicle-based dialogue processing apparatus provided by an embodiment of the present disclosure is shown in FIG. 3.

[0016] Figure 4 A structural diagram of an electronic device provided by an embodiment of the present disclosure is shown in FIG. 4. DETAILED DESCRIPTION

[0017] Embodiments of the present disclosure will be described in more detail by referring to the drawings. Although certain embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein, but rather the embodiments are provided to make the present disclosure more thorough and complete. It is understood that the drawings and embodiments of the present disclosure are for exemplary purposes only and are not intended to limit the scope of the present disclosure.

[0018] It is understood that each of the steps recited in the method embodiments of the present disclosure can be executed in different orders and / or in parallel. In addition, the method embodiments can include additional steps and / or omit the execution of the steps shown. The scope of the present disclosure is not limited in this respect.

[0019] The term “comprising” and variations thereof as used herein are open-ended, that is, “including but not limited to”. The term “based on” is “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”. Related definitions are given throughout the description.

[0020] It is noted that the terms “first”, “second”, and the like in the present disclosure are merely used to distinguish different devices, modules or units, and do not imply the order or interdependence of the functions performed by these devices, modules or units.

[0021] It is noted that the terms “one”, “multiple” in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that “one or more” should be understood unless otherwise explicitly stated in the context.

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

[0023] To solve the above problems, the embodiment of the disclosure provides a vehicle-based dialogue processing method, which will be introduced below in combination with specific embodiments.

[0024] Figure 1 A flowchart of a vehicle-based dialogue processing method provided by the embodiment of the disclosure is shown in the figure. The method can be executed by a vehicle-based dialogue processing device. The device can be implemented by software and / or hardware, and can be integrated in an electronic device. The electronic device can be an electronic device built in a vehicle, or an electronic device arranged outside the vehicle, etc. As shown in the figure, the method comprises the following steps. Figure 1

[0025] Step 101: Determine whether the vehicle meets the preset weak network entering condition.

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

[0027] It should be noted that the above-mentioned preset weak network entering condition is different in different application scenarios, for example:

[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 by a global positioning system (GPS), and it is determined whether the vehicle enters a weak network area within a preset time according to the first current positioning information. The weak network area can be an area with geographical features such as high mountains, tunnels, remote mountains, etc. For example, the current driving speed, driving direction, etc. of the vehicle can be obtained, and it is determined whether the vehicle passes through a weak network area within a preset time according to the first current positioning information, the current driving speed, and the driving direction. For another example, the average time of a plurality of vehicles from the first current positioning information to the weak network area can also 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 entering condition.

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

[0030] In this embodiment, when the vehicle does not enter a weak network area within a preset time, it is detected whether to enter a weak network based on a weak network detection logic. In this embodiment, a sampling weak network index value of the communication network is collected according to a preset collection period. Since the vehicle drives at a high speed, the preset collection period can be short. The sampling 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 measuring the ratio of the number of successful TTS synthesis messages to the total number of TTS synthesis requests during the text-to-speech conversion process 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 acquired to the time it takes to acquire 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 acquisition 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., wherein, 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. 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 value for each type of weak network indicator value is preset.

[0041] In the present example, the preset weak network indicator gating condition is determined to be met when any of the weak network indicator values is less than the corresponding weak network indicator threshold value; or,

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

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

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

[0045] Step 103, the target network quality adjustment parameter corresponding to the current weak network type is obtained.

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

[0047] In the present embodiment, when the preset weak network entering condition is met, the current weak network type of the communication network between the vehicle and the server is determined. That is, in an embodiment of the present disclosure, the standard range of the network quality parameter corresponding to each weak network type can be defined in advance, the current actual network quality parameter of the communication network is obtained, and the current weak network type is determined based on the comparison between the actual network parameter and the standard range of the network quality parameter.

[0048] Further, after determining the weak network type, the target network quality adjustment parameter corresponding to the weak network type is obtained in advance. Since the target network quality adjustment parameter is determined in advance, it helps to improve the adjustment efficiency of weak network performance.

[0049] Of course, in one embodiment of the present disclosure, the service mode of the related function based on the dialogue mode can also be changed when the preset weak network entry condition is met, for example, the TTS synthesis mode can be adjusted from synthesis in the server to local synthesis in the vehicle to improve the single dialogue success rate of TTS, etc.

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

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

[0052] Step 104, adjusting 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.

[0053] After obtaining the target network quality adjustment parameter, the communication network between the vehicle and the server is adjusted according to the target network quality adjustment parameter, 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 condition, for example, the adjusted single dialogue success rate is not less than a, the TTS frame data receiving jitter rate is not greater than b, etc.

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

[0055] After adjusting the communication network, dialogue processing between the vehicle and the server is performed 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, such as a ChatGPT model. After obtaining the voice control instruction of the user at the vehicle machine end, the voice control instruction is sent to the server through the adjusted communication network. The server analyzes the voice control instruction in depth based on the pre-deployed artificial intelligence model, generates response information corresponding to the voice control instruction, and then feeds back the response information to the vehicle through the communication network. The vehicle can send a voice conversion instruction 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 end. The vehicle end plays the voice.

[0056] In the dialogue processing process, the network quality adjustment parameter can also 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 parameter, the real-time weak network index value of the communication network is obtained. The real-time weak network index value and the target network quality parameter are input into a pre-constructed deep learning model. The fine-tuned network quality adjustment parameter is 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 adjustment parameter. 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. In this way, the iterative optimization of the target network quality adjustment parameter is realized. In this embodiment, since the vehicle passes through the weak network area, the corresponding passing position may be different. Therefore, the network quality adjustment parameter corresponding to each historical passing position in the weak network area can also be stored by the passing position as a granularity. In this embodiment, the network quality adjustment parameter is iteratively optimized based on each historical passing position. Therefore, when the vehicle passes through the same weak network area next time, the network quality adjustment parameter corresponding to the matching historical passing position is queried according to the actual passing position of the vehicle to further improve the weak network adjustment quality. If the distance between the actual passing position information and the historical passing position is less than a preset distance threshold, it is determined that the actual passing position information matches the historical passing position. Alternatively, in multiple historical passing positions, the historical passing position closest to the actual passing position is determined as the matching historical passing position.

[0057] To sum up, the vehicle-based dialogue processing method in the embodiment of the present disclosure determines whether the vehicle meets the preset weak network entering condition. When the preset weak network entering condition is met, the current weak network type of the communication network between the vehicle and the server is determined, the target network quality adjustment parameter corresponding to the current weak network type is acquired, and the communication network is adjusted 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 then the dialogue processing between the vehicle and the server is performed according to the adjusted communication network. In the technical solution, when the vehicle passes through the weak network, the communication network between the vehicle and the server is adjusted based on the pre-determined network quality adjustment parameter, the signal quality of the communication network is improved, the fluency of the dialogue processing is ensured, and the dialogue interaction experience is improved.

[0058] The following refers to an embodiment to illustrate how to determine the candidate network quality adjustment parameter corresponding to the candidate weak network type.

[0059] In one embodiment of the present disclosure, as shown in Figure 2 Before querying the preset correspondence relationship, the following steps are further included:

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

[0061] In the embodiment, the test network between the vehicle and the server is built in advance, wherein the building manner of the test network is different in different application scenarios, and examples are as follows:

[0062] In some possible examples, the test network can be built using a wireless network card, wherein the test network is connected with a preset network quality adjustment device, and the network quality adjustment device includes any one of a programmable router, a network loss instrument, etc.

[0063] In some possible examples, the test network can be built using a third-party tool, such as an electronic device with TrafficControl (TC) software, a double network card, a router or a network loss instrument, wherein TC is a network traffic management and queue scheduling tool set provided by a Linux kernel, which is used to simulate and control network conditions, such as bandwidth limitation, delay, packet loss, etc. TC allows relevant personnel to test the performance of the vehicle and the cloud server under different network conditions in an experimental environment.

[0064] Step 202, a network transmission parameter corresponding to each candidate weak network type is determined.

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

[0066] In step 203, the preset network quality adjustment device is controlled to adjust the test network according to the network transmission parameters, wherein the candidate weak network indicator value of the adjusted test network does not satisfy 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 satisfy 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] Wherein, the network quality adjustment device adjusts the test network, which can be realized by the prior art, and will not be repeated here.

[0069] It should be noted that in different application scenarios, the way of controlling the preset network quality adjustment device to adjust the test network according to the network transmission parameters is different, which is illustrated as follows:

[0070] In some possible examples, the first preset artificial intelligence model is used to obtain the test network adjustment instruction for the first candidate weak network type, wherein in order to ensure the adjustment efficiency, the first preset artificial intelligence model can be set at the vehicle terminal, and the first preset artificial intelligence model is controlled 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, and the first preset test execution event includes but is not limited to the switching program execution event (such as interface calling event, command line execution event, etc.) corresponding to the first candidate weak network type.

[0071] In some possible examples, a second preset artificial intelligence model can be deployed in advance at the vehicle terminal, and the second preset artificial intelligence model is used to obtain a preset network page, 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 calling instruction of the user for the preset network page, the browser can 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 the at least one candidate adjustment control, prompt the user through the voice notification message that at least one candidate adjustment control contained in the preset network page corresponds to the weak network type, etc., and the second preset artificial intelligence model is used to obtain the target adjustment control selected by the user from the at least one candidate adjustment control according to the voice notification message.

[0072] The target adjustment control corresponds to the second candidate weak network type, and the user can select the target adjustment control in a manual adjustment manner or a voice selection manner. 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 parameter corresponding to the second candidate weak network type. The second preset test execution event includes, but is not limited to, a switching program execution event (for example, an interface call event, a command line execution event, etc.) corresponding to the second candidate weak network type.

[0073] In this way, the user can realize switching of the related candidate weak network type through interaction with the related preset artificial intelligence model.

[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 satisfies the preset weak network index gating condition.

[0075] In this embodiment, the network quality adjustment parameter is determined according to the adjusted test network. After the corresponding test network is adjusted according to the network quality adjustment parameter, the candidate weak network index value of the corresponding test network satisfies the preset weak network index gating condition. The network quality adjustment parameter can be obtained based on deep learning.

[0076] It can be understood that in the embodiments of the present disclosure, the candidate network quality adjustment parameter is determined in advance for the adjusted test network. The candidate network quality adjustment parameter is used to adjust the corresponding test network from not satisfying the preset weak network index gating condition to satisfying the corresponding preset weak network index gating condition. Since the candidate network quality adjustment parameter is determined in advance in the test stage, in the actual application scenario, for vehicles that have passed through the same weak network type, it is not necessary to determine the corresponding network quality adjustment parameter in real time, but to directly reuse the pre-determined candidate network quality adjustment parameter, so as to realize rapid adjustment of the weak network and improve the communication service quality of the communication network on the vehicle.

[0077] Step 205: storing the correspondence between each candidate weak network type and the candidate network quality adjustment parameter.

[0078] After the candidate network quality adjustment parameter is obtained, the correspondence between the candidate network quality adjustment parameter and the corresponding candidate test weak network type is directly stored, so as to reuse the corresponding candidate network quality adjustment parameter in subsequent actual applications. In the actual execution process, the communication network of the vehicle that has passed through the same weak network type can be rapidly adjusted according to the corresponding candidate network quality adjustment parameter.

[0079] In summary, the vehicle-based dialogue processing method of the embodiment of the present disclosure 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 pre-determined 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 This is a structural diagram of a vehicle-based dialogue processing device provided by an embodiment of the present disclosure. The device 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 configured to determine whether the vehicle meets a preset weak network entry condition;

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

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

[0085] An adjustment module 340 is configured to adjust the communication network according to the target network quality adjustment parameter, wherein the target weak network indicator value of the communication network after adjustment satisfies a preset weak network indicator 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 if 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 sampling weak network index value of the current collection period meets a preset weak network index gating condition;

[0093] identify second current positioning information of the vehicle when the preset weak network index gating condition is not met;

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

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

[0096] query a preset correspondence relationship to determine a target network quality adjustment parameter corresponding to the current weak network type, wherein the preset correspondence 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 an embodiment of the present disclosure, further comprising: a test module configured to:

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

[0099] determine a network transmission parameter corresponding to each candidate weak network type;

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

[0102] store a correspondence relationship between each candidate weak network type and the candidate network quality adjustment parameter.

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

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

[0105] 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 parameter corresponding to the first candidate weak network type.

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

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

[0108] sending, by the second preset artificial intelligence model, a voice notification message corresponding to the at least one candidate adjustment control;

[0109] obtaining, 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;

[0110] controlling the second preset artificial intelligence model to call a 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 parameter corresponding to the second candidate weak network type.

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

[0112] To achieve the above-mentioned embodiments, the present disclosure further proposes a computer program product, comprising computer programs / instructions, which are executed by a processor to realize the vehicle-based dialogue processing method in the above-mentioned embodiments.

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

[0114] The following will be specifically referred to Figure 4 which shows a structural schematic diagram of an electronic device 400 suitable for being used to implement the electronic device 400 in the embodiments of the present disclosure. The electronic device 400 in the embodiments of the present disclosure can include but is not limited to mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablets), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. Figure 4 The electronic device shown is only an example, and should not bring any limitation to the functions and use range of the embodiments of the present disclosure.

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

[0116] In general, the following devices can be connected to the I / O interface 405: input devices 406 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 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 can allow the electronic device 400 to communicate wirelessly or wired with other devices to exchange data. Although Figure 4 The electronic device 400 is shown with various devices, but it is understood that all of the shown devices are not required to be implemented or present. More or fewer devices can alternatively be implemented or present.

[0117] In particular, the processes described above with reference to the flowcharts can be implemented as a computer software program according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product including a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network through the communication device 409, or installed from the memory 408, or installed from the ROM 402. When the computer program is executed by the processor 401, the above-described functions defined in the vehicle-based dialogue processing method of embodiments of the present disclosure are performed.

[0118] It should be noted that the computer-readable medium described above can be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable storage medium, for example, can be, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any suitable combination of the foregoing. More specific examples of the computer-readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program used by or in connection with an instruction execution system, apparatus or device. In the disclosure, the computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, in which the computer-readable program code is contained. Such a propagated data signal can take any of a variety of forms, including, but not limited to, an electromagnetic signal, an optical signal, or any suitable combination of the foregoing. The computer-readable signal medium can also be any computer-readable medium that is not a storage medium and that can communicate, propagate or transport a program for use by or in connection with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including, but not limited to, wire, cable, RF (radio frequency), etc., or any suitable combination of the foregoing.

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

[0120] The computer-readable medium described above can be included in the electronic device described above; or can exist separately from the electronic device described above.

[0121] The computer-readable medium described above carries one or more programs, when the one or more programs are executed by the electronic device, cause the electronic device to:

[0122] The vehicle is determined whether to meet the preset weak network entering condition, when the preset weak network entering condition is met, the current weak network type of the communication network between the vehicle and the server is determined, the target network quality adjustment parameter corresponding to the current weak network type is acquired, the communication network is adjusted 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 then the dialogue processing between the vehicle and the server is carried out according to the adjusted communication network. In the technical solution, when the vehicle passes through the weak network, the communication network between the vehicle and the server is adjusted based on the pre-determined network quality adjustment parameter, the signal quality of the communication network is improved, the fluency of the dialogue processing is ensured, and the dialogue interaction experience is improved.

[0123] Electronic devices can be written in one or more programming languages or combinations thereof including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0124] The flow and block diagrams in the drawings show architectural, functional, and operational representations of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flow and block diagrams can represent a module, a segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks may

[0125] The units described in the embodiments of the present disclosure can be implemented by means of software, or by hardware. In some cases, the name of the unit does not constitute a limitation on the unit itself.

[0126] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (ASICs), Application-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

[0127] In the context of the present disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium will include one or more lines of a program of a processor, a portable computer diskette, 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 disc 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 merely exemplary of the present disclosure and the application of the principles thereof. It is not intended to limit the disclosed concepts to the precise forms disclosed. Rather, it is intended to cover such departures from the present disclosure as come within the scope of the concepts disclosed herein and the patentable scope of the present disclosure. For example, the features described above can be interchanged with similar features of the disclosure (but not limited to) having similar functions, or the features described above can be combined with other features of the disclosure (but not limited to) having similar functions.

[0129] Furthermore, while operations are depicted in a particular, sequential order, this should not be understood as requiring or implying that the operations are performed in the order depicted and described. Many of the operations can in fact be performed in parallel or in any suitably order. In addition, while a particular implementation has been shown and described, modifications can be made by those skilled in the art without departing from the scope of the present disclosure. For example, the various features of the disclosure described above can be combined with other features of the disclosure (but not limited to) having similar functions, or the features described above can be combined with other features of the disclosure (but not limited to) having similar functions.

[0130] Although the subject matter has been described in language specific to structural features and / or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

1. A vehicle-based dialog processing method, characterized by, The method comprises the following steps: determining whether the vehicle meets a preset weak network entering condition; when the preset weak network entering condition is met, determining a current weak network type of a communication network between the vehicle and a server; querying a preset correspondence relationship to determine a target network quality adjustment parameter corresponding to the current weak network type, wherein the preset correspondence relationship comprises at least one candidate weak network type and a candidate network quality adjustment parameter corresponding to each candidate weak network type, and the preset correspondence relationship is constructed by the following steps: building a test network between the vehicle and the server, determining a network transmission parameter corresponding to each candidate weak network type, controlling a preset network quality adjustment device to adjust the test network according to the network transmission parameter, wherein a candidate weak network index value of the adjusted test network does not meet a preset weak network index gating condition, 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, a candidate weak network index value of the adjusted test network meets the preset weak network index gating condition, and storing the correspondence relationship between each candidate weak network type and the candidate network quality adjustment parameter; adjusting the communication network according to the target network quality adjustment parameter, wherein a target weak network index value of the adjusted communication network meets a preset weak network index gating condition; performing a dialogue process between the vehicle and the server according to the adjusted communication network.

2. The method of claim 1, wherein, The determination of whether the vehicle meets the preset weak network entering condition comprises: identifying first current positioning information of the vehicle; determining 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 entering condition is met.

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

4. The method of claim 1, wherein, The control of the preset network quality adjustment device to adjust the test network according to the network transmission parameter comprises: obtaining a test network adjustment instruction for a first candidate weak network type through a first preset artificial intelligence model; controlling 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 a network transmission parameter corresponding to the first candidate weak network type.

5. The method of claim 1, wherein, The control preset network quality adjustment device adjusts the test network according to the network transmission parameter, comprising: obtaining a preset network page through a second preset artificial intelligence model, wherein the preset network page contains at least one candidate adjustment control corresponding to the at least one candidate weak network type one by one; sending a voice notification message corresponding to the at least one candidate adjustment control through the second preset artificial intelligence model; obtaining a target adjustment control selected by a user from the at least one candidate adjustment control according to the voice notification message through the second preset artificial intelligence model, wherein the target adjustment control corresponds to a second candidate weak network type; controlling the second preset artificial intelligence model to call a 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 parameter corresponding to the second candidate weak network type.

6. A vehicle-based dialog processing apparatus characterized by comprising: Comprising: The first determination module is used for determining whether the vehicle meets a preset weak network entering condition; The second determination module is used for determining a current weak network type of a communication network between the vehicle and a server when the preset weak network entering condition is met; The acquisition module is used for querying a preset correspondence relationship to determine a target network quality adjustment parameter corresponding to the current weak network type, wherein the preset correspondence relationship includes at least one candidate weak network type and a candidate network quality adjustment parameter corresponding to each candidate weak network type; the test module is used for constructing the preset correspondence relationship, and the test module is specifically used for constructing a test network between the vehicle and the server, determining a network transmission parameter corresponding to each candidate weak network type, and controlling a preset network quality adjustment device to adjust the test network according to the network transmission parameter, wherein a candidate weak network index value of the adjusted test network does not meet a preset weak network index gating condition, a candidate network quality adjustment parameter is determined according to the adjusted test network, the candidate network quality adjustment parameter is used to adjust a corresponding test network, a candidate weak network index value of the adjusted test network meets the preset weak network index gating condition, and a correspondence relationship between each candidate weak network type and the candidate network quality adjustment parameter is stored; The adjustment module is used for adjusting the communication network according to the target network quality adjustment parameter, wherein a target weak network index value of the adjusted communication network meets a preset weak network index gating condition; The dialogue processing module is used for performing dialogue processing between the vehicle and the server according to the adjusted communication network.

7. An electronic device, comprising: The electronic device comprises: 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 executable instructions to realize the dialogue processing method based on the vehicle in any one of claims 1-5.

8. 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 dialogue processing method based on the vehicle in any one of claims 1-5.

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