CN114999204B - Navigation information processing method, device, equipment and storage medium - Google Patents
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Abstract
The present disclosure provides a navigation information processing method, apparatus, device and storage medium, and relates to the field of computer technologies, in particular to the field of intelligent transportation and voice technologies. In some embodiments of the present disclosure, a first event voice uploaded by a first vehicle during a navigation process is received; identifying an event type corresponding to the first event voice; issuing an inquiry message corresponding to the event type to a first vehicle according to the first event voice, and uploading a second event voice through the first vehicle by a user based on the inquiry message to accurately acquire the event content; according to the first event voice and the second event voice uploaded by the first vehicle, the event content is automatically generated, the navigation event is automatically reported, and manual operation of a user is not needed.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method, an apparatus, a device, and a storage medium for processing navigation information.
Background
With the continuous improvement of automobile reserves, the self-driving trip is the trip choice of many users. In the driving process of the vehicle, the navigation application is used for line navigation, so that great convenience is brought to people.
Setting an event reporting entry of event information in the navigation application interface, wherein the event reporting entry is used for clicking the event reporting entry by a user in the navigation process, and submitting road condition-related or road-related event information to a server; the server verifies the information and then issues the information to help other users.
At present, a user reports event information through an event reporting inlet, the information processing efficiency is low, and the user needs to actively interact with a vehicle, so that the driving safety is influenced.
Disclosure of Invention
The disclosure provides a navigation information processing method, a navigation information processing device, a navigation information processing apparatus and a storage medium.
According to an aspect of the present disclosure, there is provided a navigation information processing method including:
receiving a first event voice uploaded by a first vehicle in a navigation process;
identifying an event type corresponding to the first event voice;
issuing an inquiry message corresponding to the event type to the first vehicle according to the first event voice so that the first vehicle can obtain a second event voice fed back by a user according to the inquiry message;
and generating event content according to the first event voice and the second event voice uploaded by the first vehicle.
According to another aspect of the present disclosure, there is provided a navigation information processing apparatus including:
the receiving module is used for receiving first event voice uploaded by a first vehicle in the navigation process;
the recognition module is used for recognizing the event type corresponding to the first event voice;
the issuing module is used for issuing an inquiry message corresponding to the event type to the first vehicle according to the first event voice so that the first vehicle can obtain a second event voice fed back by a user according to the inquiry message;
and the generation module is used for generating event content according to the first event voice and the second event voice uploaded by the first vehicle.
According to another aspect of the present disclosure, there is provided an electronic device including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method described above.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the above-described method.
According to another aspect of the present disclosure, there is provided a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps in the method described above.
In some embodiments of the present disclosure, a first event voice uploaded by a first vehicle during a navigation process is received; identifying an event type corresponding to the first event voice; issuing an inquiry message corresponding to the event type to the first vehicle according to the first event voice, and uploading second event voice by the first vehicle based on the inquiry message by a user to accurately acquire event content; and automatically generating event content according to the first event voice and the second event voice uploaded by the first vehicle, and automatically reporting the navigation event without manual operation of a user.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a schematic diagram of a navigation information processing system according to an exemplary embodiment of the present disclosure;
fig. 2 is a schematic flowchart of a navigation information processing method according to an embodiment of the disclosure;
fig. 3 is a schematic flowchart of a navigation information processing method according to a second embodiment of the disclosure;
fig. 4a is a schematic structural diagram of a navigation information processing apparatus according to an exemplary embodiment of the present disclosure;
fig. 4b is a schematic structural diagram of an identification module according to an exemplary embodiment of the present disclosure;
FIG. 5 illustrates a schematic block diagram of an example electronic device that can be used to implement embodiments of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Artificial intelligence is the subject of research that makes computers simulate some human mental processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), both at the hardware level and at the software level. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing, and the like; the artificial intelligence software technology mainly comprises a computer vision technology, a voice recognition technology, a natural language processing technology, machine learning/deep learning, a big data processing technology, a knowledge map technology and the like.
In the technical scheme of the disclosure, the collection, storage, use, processing, transmission, provision, disclosure and other processing of the personal information of the related user are all in accordance with the regulations of related laws and regulations and do not violate the good customs of the public order.
With the continuous improvement of automobile reserves, the self-driving trip is the trip choice of many users. In the driving process of the vehicle, the navigation application is used for line navigation, so that great convenience is brought to people.
Setting an event reporting entry of event information in the navigation application interface, wherein the event reporting entry is used for clicking the event reporting entry by a user in the navigation process, and submitting road condition-related or road-related event information to a server; the server verifies the information and then issues the information to help other users.
At present, a user reports event information through an event reporting inlet, the information processing efficiency is low, and the user needs to actively interact with a vehicle, so that the driving safety is influenced.
In view of the above technical problem, in some embodiments of the present disclosure, a first event voice uploaded by a first vehicle during navigation is received; identifying an event type corresponding to the first event voice; issuing an inquiry message corresponding to the event type to the first vehicle according to the first event voice, and uploading second event voice by the first vehicle based on the inquiry message by a user to accurately acquire event content; according to the first event voice and the second event voice uploaded by the first vehicle, the event content is automatically generated, the navigation event is automatically reported, and manual operation of a user is not needed.
The technical solutions provided by the embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic structural diagram of a navigation information processing system 10 according to an exemplary embodiment of the present disclosure. As shown in fig. 1, the navigation information processing system 10 includes a first vehicle 10a, a server 10b, and a second vehicle 10c. The first vehicle 10a and the second vehicle 10c establish communication connection with the server 10 b. The first vehicle 10a, the server 10b, and the second vehicle 10c shown in fig. 1 are exemplary illustrations, and are not limited to the implementation forms thereof.
The first vehicle 10a, the second vehicle 10c and the server 10b may be connected by wire or wirelessly. Optionally, communication connections between the first vehicle 10a and the second vehicle 10c and the server 10b may be established in a communication manner such as WIFI, bluetooth, infrared, or the first vehicle 10a and the second vehicle 10c and the server 10b may be established through a mobile network. The network standard of the mobile network may be any one of 2G (GSM), 2.5G (GPRS), 3G (WCDMA, TD-SCDMA, CDMA2000, UTMS), 4G (LTE), 4G + (LTE +), 5G-Advanced, wiMax, and the like.
In the present embodiment, the types of the first vehicle 10a and the second vehicle 10c are not limited, and the first vehicle 10a and the second vehicle 10c include, but are not limited to, any one of the following: electric vehicles, oil-powered vehicles, hybrid electric vehicles, hydrogen-powered vehicles, new energy and oil-powered hybrid vehicles, and the like.
In the present embodiment, the implementation form of the server 10b is not limited. For example, the server 10b may be a conventional server, a cloud host, a virtual center, or the like. The server 10b mainly includes a processor, a hard disk, a memory, a system bus, and the like, and is a general computer architecture type.
In the disclosed embodiment, the server 10b receives the first event speech uploaded by the first vehicle 10a during navigation; identifying an event type corresponding to the first event voice; issuing an inquiry message corresponding to the event type to the first vehicle 10a according to the first event voice, and uploading a second event voice by the first vehicle 10a based on the inquiry message by the user to accurately acquire the event content; according to the first event voice and the second event voice uploaded by the first vehicle, the event content is automatically generated, the navigation event is automatically reported, and manual operation of a user is not needed.
In one embodiment, the first event speech corresponds to an event type. In one implementation, the first event speech is subjected to speech recognition to obtain a first event text; performing word segmentation processing on the first event text to obtain a plurality of event words; and determining an event type corresponding to the first event voice according to the event segmentation words and the preset event keywords. It should be noted that, the preset event keywords are not limited in the present disclosure, and the preset event keywords may be adjusted according to actual situations. The word segmentation processing on the first event text can adopt the existing word segmentation algorithm to perform word segmentation processing.
Optionally, the event type corresponding to the first event voice is determined according to the event segmentation words and the preset event keywords. One way to implement this is to calculate the first similarity of each event participle and each preset event keyword respectively; selecting a target preset event keyword with a first similarity meeting a first set similarity condition from a plurality of preset event keywords; and taking the event type of the target preset event keyword as the event type corresponding to the first event voice. It should be noted that, the first set similarity condition is not limited in the present disclosure, and the first set similarity condition may be adjusted according to actual situations. The first setting is a similarity condition, for example, the similarity is maximum.
For example, the speech recognition is performed on the first event speech to obtain the first event text "so jammed was originally due to rear-end collision", and the word segmentation is performed on the first event text to obtain a plurality of event segmented words "so jammed", "originally", "yes", "because", "rear-end collision", and "rear-end collision". Wherein, the preset event keywords of the traffic accident event, such as ' traffic accident ', ' rear-end collision ' and ' car-turning ', respectively calculating the similarity of the ' this ' with the traffic accident ', ' this ' with the rear-end collision ', ' this ' with the turnover ', ' the traffic accident ', ' the rear-end collision ', ' the turnover ', ' the original ' with the traffic accident ', ' the original ' with the rear-end collision ', ' the original ' with the turnover ', ' the traffic accident ', ' the rear-end collision ' with the turnover ', ' the traffic accident ', ' the rear-end collision ' with the rear-end collision ', ' the rear-end collision ' with the turnover ', ' the traffic accident ', ' the rear-end collision ' and ' the turnover ', the method sequentially comprises 0.01, 0.05, 0.3, 0.2, 0.35, 0.02, 0.05, 0.01, 0.07, 0.05, 0.01, 0.99, 0.05, 0 and 0, the target preset event keyword ' rear-end ' with the largest similarity is selected from ' traffic accidents ', ' rear-end collision ' and ' rollover ', the type corresponding to the ' rear-end collision ' is determined, and the type corresponding to the event voice is the type of the traffic accidents.
In another embodiment, an event type corresponding to the first event speech is identified. One way to realize the method is to carry out vectorization processing on the first event voice and a plurality of preset event voices to respectively obtain a first voice vector and a plurality of second voice vectors; respectively calculating a second similarity of the first voice vector and each second voice vector; selecting target preset event voices with second similarity meeting a second set similarity condition from the preset event voices; and taking the event type of the target preset event voice as the event type corresponding to the first event voice. It should be noted that the existing vectorization algorithm may be used to perform vectorization processing on the first event speech and the multiple preset event speech. The present disclosure does not limit the second set similarity condition, which may be adjusted according to actual conditions, and the threshold value of the second similarity condition, for example, the similarity is the maximum.
For example, vectorizing a first event voice and a plurality of preset event voices to obtain a first voice vector and a plurality of second voice vectors respectively, and calculating a second similarity of the first voice vector and each second voice vector respectively; and selecting a target preset event voice with the maximum similarity from the preset event voices, and taking the event type of the target preset event voice as the event type corresponding to the first event voice.
In the embodiments described above and below, the event types include, but are not limited to, the following:
in the above embodiment, the server 10b issues the inquiry message corresponding to the event type to the first vehicle according to the first event voice. One way to achieve this is to determine a preset query template corresponding to the event type; and searching the inquiry message from a preset inquiry template according to the first event voice. Each event type corresponds to a corresponding query template, and each query template comprises query messages corresponding to the event type.
For example, the first vehicle 10a acquires the first event voice uploaded by the user in a voice manner "this is originally because of rear-end collision", the server 10b acquires the first event voice, determines a preset inquiry template of the traffic accident type, inquires a next inquiry message "which road the rear-end collision is on", "police arrived" or the like from the preset inquiry template.
In some embodiments, the server 10b may automatically analyze and understand the driving behavior of the first vehicle 10a, and complete the reporting of the navigation information, so as to improve the efficiency of reporting the navigation information. For example, if the user normally travels on a road to home, if the traveling speed is slower than that in the past, or if the user makes an urgent lane change or deceleration, the abnormal signal can be directly extracted and reported. The automatically analyzed reporting behavior can be actively inquired, for example, whether an accident exists on the left side of the place or not, and the report can be efficiently reported only by confirming the fact by a user or correcting the fact.
In some embodiments of the present disclosure, the user may send a query voice to the first vehicle 10a, the first vehicle 10a sends the query voice to the server 10b, and the server 10b collects the driving behavior of the second vehicle 10c or sends the query voice to the second vehicle 10c when the query voice cannot be answered. One way this can be accomplished is to receive a query voice uploaded by a first vehicle; identifying a target location in the query speech; determining a second vehicle with a distance to the target position smaller than a set distance threshold; issuing a second reply message to the first vehicle according to the driving behavior of the second vehicle and/or the first reply message returned by the second vehicle; wherein the first reply message is generated based on the query speech. It should be noted that, the set distance threshold is not limited in the embodiments of the present disclosure, and the set distance threshold may be adjusted according to actual situations. A distance threshold is set, for example, 1 km, 2 km, 3 km, and the like. The number of the second vehicles may be one or more.
In the above embodiment, the driving behavior includes a travel path, and the generation module issues the second reply message to the first vehicle according to the driving behavior of the second vehicle. One way to achieve this is to collect the driving path of the second vehicle in a set period; determining the associated information of the target position according to the driving path; generating a second reply message according to the correlation information of the target position; and issuing the second reply message to the first vehicle.
For example, a query voice "xx parking lots still open" uploaded by the first vehicle is received, and a target position "xx spot" in "xx parking lots still open" is identified; all the second vehicles whose distance from "xx place" is less than 1 km are searched. The method comprises the steps of collecting a driving path of a second vehicle within 5 minutes, recognizing that the second vehicle drives into the XX parking lot, determining the opening of the XX parking lot, generating a second reply message of the opening of the XX parking lot, sending the second reply message of the opening of the XX parking lot to the first vehicle, and accurately answering a query voice uploaded by the first vehicle, which indicates how the XX parking lot is still opened.
In the above embodiment, when the generating module issues the second reply message to the first vehicle according to the first reply message returned by the second vehicle, an implementation manner is that the generating module generates the second reply message according to the first reply message; and issuing the second reply message to the first vehicle.
For example, a query voice "xx parking lots still open" uploaded by the first vehicle is received, and a target position "xx spot" in "xx parking lots still open" is identified; all the second vehicles whose distance from "xx place" is less than 1 km are searched. The server sends the 'X & ltx & gt place parking lot leaving' to the second vehicle, the user of the second vehicle replies questions and returns a first reply message to the server, the server performs operations such as semantic recognition and the like on the first reply message according to the first reply message returned by the second vehicle to obtain a second reply message of 'X & ltx & gt place parking lot leaving', the second reply message of 'X & ltx & gt place parking lot leaving' is sent to the first vehicle, and the accurate answer is made to the 'X & ltx & gt place parking lot leaving' inquiry voice uploaded by the first vehicle.
In other embodiments of the present disclosure, the server 10b recommends the corresponding information to the first vehicle 10a based on the driving behavior of the first vehicle 10a, historical operating records, vehicle information, and the like. For example, the server 10b recommends information such as a parking lot, a toll booth, and a gas station to the first vehicle 10 a.
In addition to the navigation information processing system 10 provided above, the present embodiment also provides a navigation information processing method, which may depend on the navigation information processing system 10, but is not limited to the navigation information processing system 10 provided above.
Fig. 2 is a schematic flowchart of a navigation information processing method according to an embodiment of the present disclosure. As shown in fig. 2, the method includes:
s201: receiving first event voice uploaded by a first vehicle in a navigation process;
s202: identifying an event type corresponding to the first event voice;
s203: issuing an inquiry message corresponding to the event type to the first vehicle according to the first event voice so that the first vehicle can obtain a second event voice fed back by a user according to the inquiry message;
s204: and generating event content according to the first event voice and a second event voice uploaded by the first vehicle.
In this embodiment, the main execution body of the method is a server. The implementation form of the server is not limited. For example, the server may be a conventional server, a cloud host, a virtual center, or the like server device. The server mainly comprises a processor, a hard disk, a memory, a system bus and the like, and a general computer architecture type.
In the embodiment of the disclosure, the server receives first event voice uploaded by a first vehicle in a navigation process; identifying an event type corresponding to the first event voice; issuing an inquiry message corresponding to the event type to the first vehicle according to the first event voice, and uploading second event voice by the first vehicle based on the inquiry message by a user to accurately acquire event content; according to the first event voice and the second event voice uploaded by the first vehicle, the event content is automatically generated, the navigation event is automatically reported, and manual operation of a user is not needed.
In one embodiment, the first event speech corresponds to an event type. In one implementation, the first event speech is subjected to speech recognition to obtain a first event text; performing word segmentation processing on the first event text to obtain a plurality of event words; and determining an event type corresponding to the first event voice according to the event segmentation words and the preset event keywords. It should be noted that, the preset event keywords are not limited in the present disclosure, and the preset event keywords may be adjusted according to actual situations. The word segmentation processing on the first event text can adopt the existing word segmentation algorithm to perform word segmentation processing.
Optionally, the event type corresponding to the first event voice is determined according to the event segmentation words and the preset event keywords. One way to implement is to calculate the first similarity between each event word and each preset event keyword; selecting a target preset event keyword with a first similarity meeting a first set similarity condition from a plurality of preset event keywords; and taking the event type of the target preset event keyword as the event type corresponding to the first event voice. It should be noted that, the first set similarity condition is not limited in the present disclosure, and the first set similarity condition may be adjusted according to actual situations. The first setting is a similarity condition, for example, the similarity is maximum.
For example, the speech recognition is performed on the first event speech to obtain the first event text "so jammed was originally due to rear-end collision", and the word segmentation is performed on the first event text to obtain a plurality of event segmented words "so jammed", "originally", "yes", "because", "rear-end collision", and "rear-end collision". Wherein, the preset event keywords of the traffic accident event, such as ' traffic accident ', ' rear-end collision ' and ' car-turning ', respectively calculating the similarity of the ' this ' with the traffic accident ', ' this ' with the rear-end collision ', ' this ' with the turnover ', ' the traffic accident ', ' the rear-end collision ', ' the turnover ', ' the original ' with the traffic accident ', ' the original ' with the rear-end collision ', ' the original ' with the turnover ', ' the traffic accident ', ' the rear-end collision ' with the turnover ', ' the traffic accident ', ' the rear-end collision ' with the rear-end collision ', ' the rear-end collision ' with the turnover ', ' the traffic accident ', ' the rear-end collision ' and ' the turnover ', the method sequentially comprises 0.01, 0.05, 0.3, 0.2, 0.35, 0.02, 0.05, 0.01, 0.07, 0.05, 0.01, 0.99, 0.05, 0 and 0, the target preset event keyword ' rear-end ' with the largest similarity is selected from ' traffic accidents ', ' rear-end collision ' and ' rollover ', the type corresponding to the ' rear-end collision ' is determined, and the type corresponding to the event voice is the type of the traffic accidents.
In another embodiment, an event type corresponding to the first event speech is identified. One way to implement the method is to carry out vectorization processing on the first event voice and a plurality of preset event voices to respectively obtain a first voice vector and a plurality of second voice vectors; respectively calculating a second similarity of the first voice vector and each second voice vector; selecting target preset event voices with second similarity meeting a second set similarity condition from the preset event voices; and taking the event type of the target preset event voice as the event type corresponding to the first event voice. It should be noted that the existing vectorization algorithm may be used to perform vectorization processing on the first event speech and the multiple preset event speech. The second set similarity condition is not limited in the present disclosure, and may be adjusted according to actual conditions, and the second similarity condition threshold, for example, is the maximum similarity.
For example, vectorization processing is performed on the first event voice and a plurality of preset event voices to obtain a first voice vector and a plurality of second voice vectors respectively, and second similarities of the first voice vector and each second voice vector are calculated respectively; and selecting a target preset event voice with the maximum similarity from the preset event voices, and taking the event type of the target preset event voice as the event type corresponding to the first event voice.
In the embodiments described above and below, the event types include, but are not limited to, the following:
in the above embodiment, the server issues the inquiry message corresponding to the event type to the first vehicle according to the first event voice. One way to implement this is to determine a preset query template corresponding to the event type; and searching the inquiry message from a preset inquiry template according to the first event voice. Each event type corresponds to a corresponding query template, and each query template comprises query messages corresponding to the event type.
For example, the first vehicle acquires the first event voice uploaded by the user in a voice manner, that is, the voice is originally blocked because of rear-end collision, the server acquires the first event voice, determines a preset inquiry template of the traffic accident type, and inquires a next inquiry message of which road the rear-end collision is on, police' the arrival of the police and the like from the preset inquiry template.
In some embodiments, the server may automatically analyze and understand the driving behavior of the first vehicle, and complete the reporting of the navigation information, so as to improve the efficiency of reporting the navigation information. For example, if the user normally travels on a road to home, if the traveling speed is slower than that in the past, or if the user makes an urgent lane change or deceleration, the abnormal signal can be directly extracted and reported. The automatically analyzed reporting behavior can be actively inquired, for example, whether an accident exists on the left side of the place or not, and the report can be efficiently reported only by confirming the fact by a user or correcting the fact.
In some embodiments of the present disclosure, a user may send a query voice to a first vehicle, which sends the query voice to a server, which collects driving behavior of a second vehicle or sends the query voice to the second vehicle when the query voice cannot be answered. One way of doing this is to receive a query voice uploaded by the first vehicle; identifying a target location in the query speech; determining a second vehicle with a distance to the target position smaller than a set distance threshold; issuing a second reply message to the first vehicle according to the driving behavior of the second vehicle and/or the first reply message returned by the second vehicle; wherein the first reply message is generated based on the query speech. It should be noted that, the set distance threshold is not limited in the embodiments of the present disclosure, and the set distance threshold may be adjusted according to actual situations. A distance threshold is set, for example, 1 km, 2 km, 3 km, and the like. The number of the second vehicles may be one or more.
In the above embodiment, the driving behavior includes a travel path, and the generation module issues the second reply message to the first vehicle according to the driving behavior of the second vehicle. One way to achieve this is to collect the driving path of the second vehicle in a set period; determining the associated information of the target position according to the driving path; generating a second reply message according to the correlation information of the target position; and issuing the second reply message to the first vehicle.
For example, a query voice "xx parking lots still open" uploaded by the first vehicle is received, and a target position "xx spot" in "xx parking lots still open" is identified; all the second vehicles whose distance from "xx place" is less than 1 km are searched. The method comprises the steps of collecting a driving path of a second vehicle within 5 minutes, recognizing that the second vehicle drives into the XX parking lot, determining the opening of the XX parking lot, generating a second reply message of the opening of the XX parking lot, sending the second reply message of the opening of the XX parking lot to the first vehicle, and accurately answering a query voice uploaded by the first vehicle, which indicates how the XX parking lot is still opened.
In the above embodiment, when the generating module issues the second reply message to the first vehicle according to the first reply message returned by the second vehicle, an implementation manner is that the generating module generates the second reply message according to the first reply message; and issuing the second reply message to the first vehicle.
For example, a query voice "xx parking lots still open" uploaded by the first vehicle is received, and a target position "xx spot" in "xx parking lots still open" is identified; all the second vehicles whose distance from "xx place" is less than 1 km are searched. The server sends the 'X & ltx & gt place parking lot leaving' to the second vehicle, the user of the second vehicle replies questions and returns a first reply message to the server, the server performs operations such as semantic recognition and the like on the first reply message according to the first reply message returned by the second vehicle to obtain a second reply message of 'X & ltx & gt place parking lot leaving', the second reply message of 'X & ltx & gt place parking lot leaving' is sent to the first vehicle, and the accurate answer is made to the 'X & ltx & gt place parking lot leaving' inquiry voice uploaded by the first vehicle.
In other embodiments of the present disclosure, the server recommends the corresponding information to the first vehicle based on the driving behavior of the first vehicle, the historical operating records, the vehicle information, and the like. For example, the server recommends information to the first vehicle such as parking lots, toll booths, and gas stations.
With reference to the descriptions of the above embodiments, fig. 3 is a schematic flow chart of a navigation information processing method according to a second embodiment of the disclosure. As shown in fig. 3, the method includes:
s301: receiving inquiry voice uploaded by a first vehicle;
s302: identifying a target location in the query speech;
s303: determining a second vehicle with a distance to the target position smaller than a set distance threshold;
s304: issuing a second reply message to the first vehicle according to the driving behavior of the second vehicle and/or the first reply message returned by the second vehicle;
wherein the first reply message is generated based on the query voice.
In this embodiment, the implementation form of executing the main body server by the method is not limited. For example, the server may be a conventional server, a cloud host, a virtual center, or the like. The server mainly comprises a processor, a hard disk, a memory, a system bus and the like, and is a general computer architecture type.
The implementation manner of each step in this embodiment can refer to the description of each embodiment, which is not described again in this embodiment, and meanwhile, this embodiment can achieve the beneficial effects of the corresponding parts in each embodiment.
Fig. 4a is a schematic structural diagram of a navigation information processing apparatus 40 according to an exemplary embodiment of the present disclosure. The navigation information processing apparatus 40 includes a receiving module 41, an identifying module 42, a distributing module 43, and a generating module 44.
The receiving module 41 is used for receiving first event voice uploaded by a first vehicle in the navigation process;
the recognition module 42 is used for recognizing the event type corresponding to the first event voice;
the issuing module 43 is configured to issue an inquiry message corresponding to the event type to the first vehicle according to the first event voice, so that the first vehicle obtains a second event voice fed back by the user according to the inquiry message;
and the generating module 44 is configured to generate the event content according to the first event voice and the second event voice uploaded by the first vehicle.
Optionally, fig. 4b is a schematic structural diagram of an identification module 42 according to an exemplary embodiment of the present disclosure; the identification module 42 includes: a recognition submodule 421, a word segmentation submodule 422 and a determination submodule 423;
the recognition sub-module 421 is configured to perform voice recognition on the first event voice to obtain a first event text;
the participle sub-module 422 is configured to perform participle processing on the first event text to obtain a plurality of event participles;
the determining sub-module 423 is configured to determine an event type corresponding to the first event voice according to the multiple event segments and the multiple preset event keywords.
Optionally, when determining the event type corresponding to the first event voice according to the plurality of event segments and the plurality of preset event keywords, the determining sub-module 423 is configured to:
respectively calculating first similarity of each event segmentation word and each preset event keyword;
selecting a target preset event keyword with a first similarity meeting a first set similarity condition from a plurality of preset event keywords;
and taking the event type of the target preset event keyword as the event type corresponding to the first event voice.
Optionally, the recognition module 42, when recognizing the event type corresponding to the first event voice, is configured to:
vectorizing the first event voice and a plurality of preset event voices to respectively obtain a first voice vector and a plurality of second voice vectors;
respectively calculating a second similarity of the first voice vector and each second voice vector;
selecting target preset event voices with second similarity meeting a second set similarity condition from the preset event voices;
and taking the event type of the target preset event voice as the event type corresponding to the first event voice.
Optionally, when issuing the query message corresponding to the event type to the first vehicle according to the first event voice, the issuing module 43 is configured to:
determining a preset inquiry template corresponding to the event type;
and searching the inquiry message from a preset inquiry template according to the first event voice.
Optionally, the generating module 44 is further configured to:
receiving a query voice uploaded by a first vehicle;
identifying a target location in the query speech;
determining a second vehicle with a distance to the target position smaller than a set distance threshold;
issuing a second reply message to the first vehicle according to the driving behavior of the second vehicle and/or a first reply message returned by the second vehicle;
wherein the first reply message is generated based on the query speech.
Optionally, the driving behavior includes a driving path, and the generating module 44, when issuing the second reply message to the first vehicle according to the driving behavior of the second vehicle, is configured to:
collecting a driving path of a second vehicle in a set period;
determining the associated information of the target position according to the driving path;
generating a second reply message according to the correlation information of the target position;
and issuing the second reply message to the first vehicle.
Optionally, the generating module 44 is configured to, when issuing the second reply message to the first vehicle according to the first reply message returned by the second vehicle:
receiving a first reply message returned by a second vehicle;
generating a second reply message according to the first reply message;
and issuing the second reply message to the first vehicle.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
FIG. 5 illustrates a schematic block diagram of an example electronic device 500 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 5, the apparatus 500 comprises a computing unit 501 which may perform various appropriate actions and processes in accordance with a computer program stored in a Read Only Memory (ROM) 502 or a computer program loaded from a storage unit 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
A number of components in the device 500 are connected to the I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, or the like; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 508, such as a magnetic disk, optical disk, or the like; and a communication unit 509 such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The computing unit 501 may be a variety of general-purpose and/or special-purpose processing components having processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The computing unit 501 performs the various methods and processes described above. For example, in some embodiments, the navigation information processing method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 500 via the ROM 502 and/or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the navigation information processing method described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the navigation information processing method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The 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, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on 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 compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), the internet, and blockchain networks.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server can be a cloud Server, also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service ("Virtual Private Server", or simply "VPS"). The server may also be a server of a distributed system, or a server incorporating a blockchain.
It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (12)
1. A navigation information processing method, comprising:
receiving first event voice uploaded by a first vehicle in a navigation process;
identifying an event type corresponding to the first event voice;
issuing an inquiry message corresponding to the event type to the first vehicle according to the first event voice so that the first vehicle can obtain a second event voice fed back by a user according to the inquiry message;
generating event content according to the first event voice and the second event voice uploaded by the first vehicle;
the method further comprises the following steps:
automatically resolving the driving behavior of the first vehicle;
the reporting behavior obtained through automatic analysis is sent to the first vehicle for active inquiry, so that a user can confirm or correct the active inquiry through the first vehicle;
wherein the method further comprises:
receiving inquiry voice uploaded by the first vehicle;
identifying a target location in the query voice;
determining a second vehicle with a distance to the target position smaller than a set distance threshold;
collecting a driving path of a second vehicle in a set period;
determining the associated information of the target position according to the driving path;
generating a second reply message according to the correlation information of the target position;
and sending the second reply message to the first vehicle.
2. The method of claim 1, wherein the identifying an event type to which the first event speech corresponds comprises:
performing voice recognition on the first event voice to obtain a first event text;
performing word segmentation processing on the first event text to obtain a plurality of event words;
and determining the event type corresponding to the first event voice according to the event participles and the preset event keywords.
3. The method of claim 2, wherein the determining an event type corresponding to the first event speech according to the event segmentations and the preset event keywords comprises:
respectively calculating a first similarity of each event segmentation word and each preset event keyword;
selecting a target preset event keyword with the first similarity meeting a first set similarity condition from the preset event keywords;
and taking the event type of the target preset event keyword as the event type corresponding to the first event voice.
4. The method of claim 1, wherein the identifying an event type to which the first event speech corresponds comprises:
vectorizing the first event voice and the preset event voices to respectively obtain a first voice vector and a plurality of second voice vectors;
respectively calculating a second similarity of the first voice vector and each second voice vector;
selecting target preset event voices with the second similarity meeting a second set similarity condition from the preset event voices;
and taking the event type of the target preset event voice as the event type corresponding to the first event voice.
5. The method of claim 1, wherein said issuing a query message corresponding to the event type to the first vehicle according to the first event voice comprises:
determining a preset inquiry template corresponding to the event type;
and searching the inquiry message from the preset inquiry template according to the first event voice.
6. A navigation information processing apparatus comprising:
the receiving module is used for receiving first event voice uploaded by a first vehicle in the navigation process;
the recognition module is used for recognizing the event type corresponding to the first event voice;
the issuing module is used for issuing an inquiry message corresponding to the event type to the first vehicle according to the first event voice so that the first vehicle can obtain a second event voice fed back by a user according to the inquiry message;
the generating module is used for generating event content according to the first event voice and the second event voice uploaded by the first vehicle;
the analysis module is used for automatically analyzing the driving behavior of the first vehicle;
the issuing module is further configured to issue the automatically analyzed reporting behavior to the first vehicle for active inquiry, so that a user can confirm or correct the active inquiry through the first vehicle;
wherein the generating module is further configured to:
receiving inquiry voice uploaded by the first vehicle;
identifying a target location in the query voice;
determining a second vehicle with a distance to the target position smaller than a set distance threshold;
collecting a running path of the second vehicle in a set period;
determining the associated information of the target position according to the driving path;
generating a second reply message according to the correlation information of the target position;
and sending the second reply message to the first vehicle.
7. The apparatus of claim 6, wherein the identification module comprises: the recognition submodule, the word segmentation submodule and the determination submodule;
the recognition submodule is used for carrying out voice recognition on the first event voice to obtain a first event text;
the word segmentation sub-module is used for carrying out word segmentation on the first event text to obtain a plurality of event word segmentations;
and the determining submodule is used for determining the event type corresponding to the first event voice according to the event participles and the preset event keywords.
8. The apparatus according to claim 7, wherein the determining sub-module, when determining the event type corresponding to the first event voice according to the event segmentation words and preset event keywords, is configured to:
respectively calculating first similarity of each event segmentation word and each preset event keyword;
selecting a target preset event keyword with the first similarity meeting a first set similarity condition from the preset event keywords;
and taking the event type of the target preset event keyword as the event type corresponding to the first event voice.
9. The apparatus of claim 6, wherein the recognition module, when recognizing the event type corresponding to the first event speech, is configured to:
vectorizing the first event voice and a plurality of preset event voices to respectively obtain a first voice vector and a plurality of second voice vectors;
respectively calculating a second similarity of the first voice vector and each second voice vector;
selecting target preset event voices with the second similarity meeting a second set similarity condition from the preset event voices;
and taking the event type of the target preset event voice as the event type corresponding to the first event voice.
10. The apparatus of claim 6, wherein the issuing module, when issuing the query message corresponding to the event type to the first vehicle according to the first event voice, is configured to:
determining a preset inquiry template corresponding to the event type;
and searching the inquiry message from the preset inquiry template according to the first event voice.
11. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.
12. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-5.
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