Message content processing method, electronic equipment and vehicle
By using in-vehicle devices to perform semantic processing and optimization on the message content of communication applications, and then recognizing and converting it into voice signals for playback, the safety risks and inefficiency of drivers reviewing historical messages are resolved, achieving safe and efficient information acquisition.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-30
- Publication Date
- 2026-03-24
AI Technical Summary
When drivers review historical messages in communication applications while driving, existing technologies require visual viewing or voice broadcasting, causing them to look away from the road, increasing safety risks and making information acquisition inefficient, especially when dealing with lengthy or low-value content where it is difficult to quickly obtain core information.
The vehicle-mounted device performs semantic processing on the message content of communication applications, identifies key information, and converts it into voice signals for playback. This includes feature analysis, content importance assessment, and optimization processing. Representative sentences are extracted using a preset keyword library, sentence patterns, information entropy algorithm, and position weighting algorithm.
This allows drivers to safely access core message content without looking at the screen, reducing driving safety hazards and improving information acquisition efficiency and user experience.
Smart Images
Figure CN121728056A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present disclosure relates to the field of information processing technology, and in particular, to a message content processing method, an electronic device, and a vehicle. BACKGROUND
[0002] During driving, a driver often needs to review historical text messages in a communication application. The existing technology mainly realizes message review by visually checking a mobile phone or a vehicle screen, which causes the driver's line of sight to deviate from the road and increases the safety risk. In addition, when the number of historical messages is large and the content is lengthy, even if a voice broadcast method is used, the mechanical reading of each message is inefficient and cannot quickly extract core information. For example, when a group chat contains a large number of low-value contents such as "received" and "OK", the whole broadcast is time-consuming and lengthy, the information density is low, the driver is difficult to quickly obtain the core content, the information acquisition efficiency is low, and the user experience is poor. SUMMARY
[0003] To solve the above technical problems, the present disclosure provides a message content processing method, an electronic device, and a vehicle.
[0004] A first aspect of an embodiment of the present disclosure provides a message content processing method applied to a vehicle device, including: In response to an operation instruction, obtaining, based on the operation instruction, message content to be processed in a target communication application; Performing semantic processing on the message content to be processed to obtain target message content, the target message content being used to describe key information in the message content to be processed; Converting the target message content into a voice signal and controlling an audio device to play the voice signal.
[0005] In some embodiments of the present disclosure, the obtaining, based on the operation instruction, of the message content to be processed in the target communication application includes: Based on the operation instruction, obtaining, through an interconnection protocol of the vehicle device and a mobile terminal, historical message content in the target communication application; Based on the operation instruction, identifying an operation intention, the operation intention being used to represent a range of the message content to be processed in the historical message content; According to the operation intention, determining the message content to be processed from the historical message content.
[0006] In some embodiments of the present disclosure, the performing semantic processing on the message content to be processed to obtain the target message content includes: Performing feature analysis on the message content to be processed to obtain a feature parameter of the message content to be processed; According to a characteristic parameter of the to-be-processed message content, content importance of the to-be-processed message content is evaluated, and an importance evaluation result of the to-be-processed message content is obtained. According to a semantic processing path corresponding to the importance evaluation result of the to-be-processed message content, the to-be-processed message content is subjected to message optimization processing, and target message content is obtained.
[0007] In some embodiments of the present disclosure, the characteristic parameter includes a total number of messages, a total number of characters of messages, a proportion of key matters and low-information-content messages. The importance evaluation result of the to-be-processed message content is obtained by evaluating content importance of the to-be-processed message content according to the characteristic parameter of the to-be-processed message content, and includes: It is determined whether the to-be-processed message content contains content that meets a preset key matter; If the to-be-processed message content contains content that meets a preset key matter, it is determined that the importance evaluation result of the to-be-processed message content is first importance; If the to-be-processed message content does not contain content that meets a preset key matter, it is determined whether the total number of messages is greater than a first threshold value, and if the total number of messages is greater than the first threshold value, it is determined that the importance evaluation result of the to-be-processed message content is second importance; If the total number of messages is less than or equal to the first threshold value, it is determined whether the proportion of low-information-content messages is greater than a second threshold value, and if the proportion of low-information-content messages is greater than the second threshold value, it is determined that the importance evaluation result of the to-be-processed message content is third importance.
[0008] In some embodiments of the present disclosure, the to-be-processed message content is subjected to message optimization processing according to a semantic processing path corresponding to the importance evaluation result of the to-be-processed message content, and target message content is obtained, and includes: In the case where the importance evaluation result is first importance, the to-be-processed message content is matched with a preset keyword library and a preset sentence pattern mode, and key information items are identified and extracted from the to-be-processed message content; Based on the key information items, the target message content is obtained; The preset sentence pattern mode includes a time and event mode, a task assignment mode, and a number and measurement unit mode.
[0009] In some embodiments of the present disclosure, the to-be-processed message content is subjected to message optimization processing according to a semantic processing path corresponding to the importance evaluation result of the to-be-processed message content, and target message content is obtained, and includes: In a case where the importance evaluation result is second importance, a subject vocabulary and a key entity appearing in the to-be-processed message content are identified; A semantic association relationship between each piece of message content in the to-be-processed message content is analyzed to obtain a context semantic structure; At least one representative sentence is extracted from the to-be-processed message content based on an information entropy algorithm and a position weighting algorithm; The target message content is obtained according to the subject vocabulary, the key entity, the context semantic structure, and the at least one representative sentence.
[0010] In some embodiments of the present disclosure, the extraction of the at least one representative sentence from the to-be-processed message content based on the information entropy algorithm and the position weighting algorithm includes: For each sentence in the to-be-processed message content, an information entropy score of the each sentence is calculated based on the information entropy algorithm, and a position weighting score of the each sentence is calculated based on the position weighting algorithm; A representative score of the each sentence is obtained based on the information entropy score of the each sentence and the position weighting score of the each sentence; The at least one representative sentence is extracted from the to-be-processed message content based on the representative score of the each sentence.
[0011] In some embodiments of the present disclosure, the message optimization processing of the to-be-processed message content by using the semantic processing path corresponding to the importance evaluation result of the to-be-processed message content to obtain the target message content includes: In a case where the importance evaluation result is third importance, each piece of message content in the to-be-processed message content is matched with a preset low information quantity vocabulary library to obtain a message content that is matched successfully; The message content that is matched successfully is filtered to obtain the target message content.
[0012] A second aspect of an embodiment of the present disclosure provides a message content processing apparatus applied to a car machine device, and includes: An obtaining module is configured to, in response to an operation instruction, obtain to-be-processed message content in a target communication application based on the operation instruction; An obtaining module is configured to, in response to an operation instruction, obtain to-be-processed message content in a target communication application based on the operation instruction; A processing module is configured to convert the target message content into a voice signal and control an audio device to play the voice signal.
[0013] In some embodiments of the present disclosure, the obtaining module obtains the to-be-processed message content in the target communication application based on the operation instruction, and specifically is configured to: obtain historical message content in the target communication application based on the operation instruction through an interconnection protocol of the car machine device and the mobile terminal; identify an operation intention based on the operation instruction, the operation intention being used to represent a to-be-processed range of the historical message content; determine the to-be-processed message content from the historical message content according to the operation intention.
[0014] In some embodiments of the present disclosure, the obtaining module performs semantic processing on the to-be-processed message content to obtain target message content, and specifically is configured to: perform feature analysis on the to-be-processed message content to obtain a feature parameter of the to-be-processed message content; perform content importance evaluation on the to-be-processed message content according to the feature parameter of the to-be-processed message content to obtain an importance evaluation result of the to-be-processed message content; perform message optimization processing on the to-be-processed message content by using a semantic processing path corresponding to the importance evaluation result of the to-be-processed message content to obtain target message content.
[0015] In some embodiments of the present disclosure, the feature parameter includes a total number of messages, a total number of characters of messages, a proportion of key matters, and a proportion of low-information-content messages. The obtaining module performs content importance evaluation on the to-be-processed message content according to the feature parameter of the to-be-processed message content to obtain an importance evaluation result of the to-be-processed message content, and specifically is configured to: determine whether there is content conforming to a preset key matter in the to-be-processed message content; if there is content conforming to the preset key matter in the to-be-processed message content, determine that the importance evaluation result of the to-be-processed message content is a first importance; if there is no content conforming to the preset key matter in the to-be-processed message content, determine whether the total number of messages is greater than a first threshold value, and if the total number of messages is greater than the first threshold value, determine that the importance evaluation result of the to-be-processed message content is a second importance; if the total number of messages is less than or equal to the first threshold value, determine whether the proportion of low-information-content messages is greater than a second threshold value, and if the proportion of low-information-content messages is greater than the second threshold value, determine that the importance evaluation result of the to-be-processed message content is a third importance.
[0016] In some embodiments of the present disclosure, when the processing module adopts a semantic processing path corresponding to the importance evaluation result of the to-be-processed message content to perform message optimization processing on the to-be-processed message content to obtain target message content, the processing module is specifically configured to: In a case where the importance evaluation result is first importance, the to-be-processed message content is matched with a preset keyword library and a preset sentence pattern mode, and a key information item is identified and extracted from the to-be-processed message content; The target message content is obtained based on the key information item; The preset sentence pattern mode includes a time and event mode, a task assignment mode, and a number and measurement unit mode.
[0017] In some embodiments of the present disclosure, when the processing module adopts a semantic processing path corresponding to the importance evaluation result of the to-be-processed message content to perform message optimization processing on the to-be-processed message content to obtain target message content, the processing module is specifically configured to: In a case where the importance evaluation result is second importance, a subject word and a key entity appearing in the to-be-processed message content are identified; A semantic association relationship between each piece of message content in the to-be-processed message content is analyzed to obtain a context semantic structure; At least one representative sentence is extracted from the to-be-processed message content based on an information entropy algorithm and a position weighting algorithm; The target message content is obtained according to the subject word, the key entity, the context semantic structure, and the at least one representative sentence.
[0018] In some embodiments of the present disclosure, when the processing module extracts at least one representative sentence from the to-be-processed message content based on an information entropy algorithm and a position weighting algorithm, the processing module is specifically configured to: For each sentence in the to-be-processed message content, an information entropy score of the each sentence is calculated based on the information entropy algorithm, and a position weighting score of the each sentence is calculated based on the position weighting algorithm; A representative score of the each sentence is obtained based on the information entropy score of the each sentence and the position weighting score of the each sentence; At least one representative sentence is extracted from the to-be-processed message content based on the representative score of the each sentence.
[0019] In some embodiments of the present disclosure, when the processing module adopts a semantic processing path corresponding to the importance evaluation result of the to-be-processed message content to perform message optimization processing on the to-be-processed message content to obtain target message content, the processing module is specifically configured to: In a case where the importance evaluation result is third importance, each piece of message content in the to-be-processed message content is matched with a preset low information amount library to obtain message content that is matched successfully; The message content that is matched successfully is filtered to obtain the target message content.
[0020] A third aspect of the embodiments of the present disclosure provides an electronic device, comprising: a processor; a memory configured to store executable instructions; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the message content processing method provided in the first aspect.
[0021] A fourth aspect of the embodiments of the present disclosure provides a computer readable storage medium, which stores a computer program. When the computer program is executed by a processor, the processor implements the message content processing method provided in the first aspect.
[0022] A fifth aspect of the embodiments of the present disclosure provides a computer program product, which comprises a computer program or instructions. When the computer program or instructions are executed by a processor, the message content processing method provided in the first aspect is implemented.
[0023] A sixth aspect of the embodiments of the present disclosure provides a vehicle, which comprises the electronic device provided in the third aspect.
[0024] The technical solutions provided by the embodiments of the present disclosure have the following advantages: The message content processing method, the electronic device and the vehicle provided by the embodiments of the present disclosure can acquire to-be-processed message content in a target communication application based on an operation instruction in response to the operation instruction. Further, the to-be-processed message content is subjected to semantic processing to obtain target message content, which is used to describe key information in the to-be-processed message content. Then, the target message content is converted into a voice signal, and an audio device is controlled to play the voice signal. In this way, by introducing intelligent semantic processing before voice broadcasting, key information in historical messages can be automatically extracted, so that a driver can safely acquire core content of the messages without looking at a screen, and driving safety risks are effectively reduced. Meanwhile, by means of semantic processing, information acquisition efficiency is greatly improved, and user experience is improved. BRIEF DESCRIPTION OF DRAWINGS
[0025] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and serve to explain the principles of the present disclosure together with the specification.
[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative effort.
[0027] Figure 1 is a flowchart of a message content processing method provided by an embodiment of the present disclosure; Figure 2 is a flowchart of another message content processing method provided by an embodiment of the present disclosure; Figure 3 is a flowchart of still another message content processing method provided by an embodiment of the present disclosure; Figure 4 is a schematic diagram of an overall flow of a message content processing method provided by an embodiment of the present disclosure; Figure 5 is a structural schematic diagram of a message content processing apparatus provided by an embodiment of the present disclosure; Figure 6 is a structural schematic diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION
[0028] In order to more clearly illustrate the above-mentioned purposes, features and advantages of the present disclosure, the following will further describe the solutions of the present disclosure. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.
[0029] In the following description, many specific details are set forth in order to fully understand the present disclosure, but the present disclosure can also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some of the embodiments of the present disclosure, not all the embodiments.
[0030] It should be understood that each step recorded in the method embodiments of the present disclosure can be executed in different order 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.
[0031] It should be noted that the modification of "one", "multiple" mentioned in the disclosure is illustrative but not restrictive, and those skilled in the art should understand that unless otherwise explicitly indicated in the context, it should be understood as "one or more".
[0032] It should be noted that the modification of "one", "multiple" mentioned in the disclosure is illustrative but not restrictive, and those skilled in the art should understand that unless otherwise explicitly indicated in the context, it should be understood as "one or more".
[0033] During driving, the driver often needs to review the historical text messages in the communication application. The existing technology mainly realizes message review through visual viewing of the mobile phone or vehicle screen, but this will cause the driver's line of sight to leave the road, increasing the safety risk. In addition, when the number of historical messages is large and the content is long, even if the voice broadcast method is used, the mechanical reading of each message will be inefficient and unable to quickly extract the core information. For example, when a group chat contains a large number of low-value content such as "received" and "good", the whole broadcast is time-consuming and lengthy, the information density is low, and the driver is difficult to quickly obtain the core content, resulting in low information acquisition efficiency and poor user experience. Therefore, the embodiment of the disclosure provides a message content processing method, which will be introduced below in combination with specific embodiments.
[0034] Figure 1 The flowchart of the message content processing method provided by the embodiment of the disclosure, the method can be executed by a message content processing device, the message content processing device can be realized by software and / or hardware, and the message content processing device can be configured in an electronic device, such as a server or a terminal. The terminal specifically includes a vehicle terminal, a computer, a tablet computer, etc.
[0035] As shown in Figure 1 The message content processing method provided by the embodiment of the disclosure can be applied to the field of information processing technology application, and can be applied to a vehicle device, for example, and can be used for processing message content. The message content processing method can include the following steps: S110, in response to an operation instruction, obtaining the message content to be processed in the target communication application based on the operation instruction.
[0036] In the embodiments of the present disclosure, the car machine device will respond to the operation instruction, and obtain the to-be-processed message content in the target communication application based on the operation instruction. Optionally, the operation instruction includes a voice instruction, a click control instruction, without limitation. For example, the user can trigger the operation instruction through voice (such as "play the chat of the work group last night") or click a specific control on the car machine screen. After receiving the operation instruction, the car machine device will obtain the to-be-processed message content in the target communication application based on the operation instruction.
[0037] In the embodiments of the present disclosure, after obtaining the to-be-processed message content in the target communication application, the car machine device will perform semantic processing on the to-be-processed message content, and obtain the target message content used to describe the key information in the to-be-processed message content.
[0038] In the embodiments of the present disclosure, after obtaining the to-be-processed message content in the target communication application, the car machine device will perform semantic processing on the to-be-processed message content, and obtain the target message content used to describe the key information in the to-be-processed message content.
[0039] In the embodiments of the present disclosure, the car machine device will convert the target message content into a voice signal, and control the audio device to play the voice signal. Specifically, the target message content can be converted into a voice signal by a voice synthesis engine (TextToSpeech, TTS), and the audio device is controlled to play the voice signal.
[0040] In the embodiments of the present disclosure, the car machine device will convert the target message content into a voice signal, and control the audio device to play the voice signal. Specifically, the target message content can be converted into a voice signal by a voice synthesis engine (TextToSpeech, TTS), and the audio device is controlled to play the voice signal.
[0041] In some embodiments, the method further includes: receiving a switching instruction of the user on the voice playing mode; and inputting the target message content or the original to-be-processed message content into the voice synthesis engine to convert into a corresponding voice signal for playing in response to the switching instruction. The embodiment provides a switchable mode of "optimized broadcasting" and "complete broadcasting", gives the user sufficient control, and takes into account different needs of intelligent efficiency and information integrity.
[0042] Therefore, in the embodiments of the present disclosure, the to-be-processed message content in the target communication application can be obtained based on the operation instruction in response to the operation instruction. Further, the target message content used to describe the key information in the to-be-processed message content is obtained by performing semantic processing on the to-be-processed message content. Furthermore, the target message content is converted into a voice signal, and the audio device is controlled to play the voice signal. Therefore, by introducing intelligent semantic processing before voice broadcasting, the key information of the historical message can be automatically extracted, so that the driver can safely obtain the core content of the message without looking at the screen, and the driving safety risk is effectively reduced. At the same time, through semantic processing, the information acquisition efficiency is greatly improved, and the user experience is improved.
[0043] Optionally, S110 can specifically include S1101, S1102, S1103: S1101, based on the operation instruction, obtaining the historical message content in the target communication application through the interconnection protocol between the vehicle machine device and the mobile terminal; In this step, the vehicle machine device receives the operation instruction, and will request to obtain the historical message content in the target communication application (such as WeChat) through the interconnection protocol (such as CarPlay, HiCar, etc.) established between the vehicle machine device and the mobile terminal such as a mobile phone. In some embodiments, the historical message content in the target communication application can also be obtained through an application interface. The historical message content can be historical message text, which is not limited.
[0044] S1102, based on the operation instruction, identifying the operation intention, the operation intention being used to represent the to-be-processed range of the historical message content; In this step, the vehicle machine device will analyze the operation instruction to identify the operation intention of the user, and the operation intention is used to represent the to-be-processed range of the historical message content, that is, which chat session and which time period of message the user wants to review.
[0045] S1103, determining the to-be-processed message content from the historical message content according to the operation intention.
[0046] In this step, the to-be-processed message content meeting the requirement is intercepted or screened from the obtained historical message content according to the operation intention. For example, the instruction is "play the chat of the family group last night", then the "family group" is located, and all messages from 19:00 to 24:00 of the previous day are screened as the to-be-processed message content.
[0047] Therefore, in the embodiments of the present disclosure, the vehicle machine device actively and accurately obtains the communication message, provides a data basis for subsequent intelligent processing, and greatly improves the operation convenience and safety in the driving scene through voice or simple touch triggering. It can accurately respond to complex instructions such as "play the chat of the family group last night" of the user, and realize more natural and convenient human-vehicle interaction.
[0048] Figure 2 is a flowchart of another message content processing method provided by the embodiments of the present disclosure.
[0049] As shown in Figure 2 , the message content processing method can include the following steps: S310, in response to an operation instruction, obtaining to-be-processed message content in a target communication application based on the operation instruction.
[0050] Specifically, the implementation process and principles of S310 and S110 are consistent, and will not be repeated here.
[0051] S320, performing feature analysis on the to-be-processed message content to obtain a feature parameter of the to-be-processed message content.
[0052] Specifically, the car machine device first performs word segmentation on the input to-be-processed message content, i.e., divides the sentence, removes meaningless characters such as mood words, and further performs feature analysis on the to-be-processed message content to obtain a feature parameter of the to-be-processed message content. In some embodiments, the feature parameter includes the total number of messages, the total number of characters of messages, the proportion of key matters and low information content messages. The key matters can contain the content of a specific user, or a sentence containing keywords such as “urgent” and “must today”. The proportion of low information content messages refers to the proportion of useless information.
[0053] S330, performing content importance evaluation on the to-be-processed message content according to the feature parameter of the to-be-processed message content to obtain an importance evaluation result of the to-be-processed message content.
[0054] In this step, the car machine device performs content importance evaluation on the to-be-processed message content according to the feature parameter of the to-be-processed message content to obtain an importance evaluation result of the to-be-processed message content.
[0055] In some embodiments, S330 can include S3301, S3302, S3303, and S3304: S3301, determining whether there is content meeting a preset key matter in the to-be-processed message content; First, it is determined whether there is content meeting a preset key matter in the to-be-processed message content. For example, it is determined whether there is “@name”, “meeting at 3 pm tomorrow”, “urgent report” and the like in the message content.
[0056] S3302, if there is content meeting a preset key matter in the to-be-processed message content, determining that the importance evaluation result of the to-be-processed message content is a first importance; If there is content meeting a preset key matter in the to-be-processed message content, it is determined that the importance evaluation result of the to-be-processed message content is a first importance, indicating that the to-be-processed message content contains important information that must be extracted and reminded first.
[0057] S3303, if there is no content meeting a preset key matter in the to-be-processed message content, determining whether the total number of messages is greater than a first threshold value, and if the total number of messages is greater than the first threshold value, determining that the importance evaluation result of the to-be-processed message content is a second importance; If there is no key matter, it is determined whether the total number of messages is greater than a first threshold value (for example, 20). If the total number of messages is greater than the first threshold value, it is determined that the importance evaluation result is a second importance, indicating that the dialogue is long and needs to be summarized.
[0058] S3304, if the total number of messages is less than or equal to the first threshold value, it is determined whether the proportion of low-information messages is greater than the second threshold value, and if the proportion of low-information messages is greater than the second threshold value, the importance evaluation result of the to-be-processed message content is determined as the third importance.
[0059] If the total number of messages does not exceed the first threshold value, it is further determined whether the proportion of low-information messages is greater than the second threshold value (for example, 40%). If the proportion of low-information messages is greater than the second threshold value, the importance evaluation result is determined as the third importance, indicating that the conversation is not long but has many useless messages and needs to be purified and filtered.
[0060] In some embodiments, if the proportion of low-information messages is less than or equal to the second threshold value, it can be considered unnecessary to optimize.
[0061] The importance evaluation logic of the present embodiment can simulate human thinking and intelligently identify conversation characteristics, i.e., whether it contains urgent matters, is lengthy, or has too much nonsense, thereby selecting the optimal path for subsequent processing.
[0062] S340, using a semantic processing path corresponding to the importance evaluation result of the to-be-processed message content, performing message optimization processing on the to-be-processed message content to obtain target message content.
[0063] In this step, after obtaining the importance evaluation result of the to-be-processed message content, the vehicle machine device can use a semantic processing path corresponding to the importance evaluation result of the to-be-processed message content to perform message optimization processing on the to-be-processed message content to obtain target message content.
[0064] In some embodiments, S340 includes S3401 and S3402: S3401, in the case where the importance evaluation result is the third importance, matching each message content in the to-be-processed message content with a preset low-information word library to obtain message content that matches successfully; S3402, filtering the message content that matches successfully to obtain target message content.
[0065] In the present embodiment, if it is the third importance, as shown in Figure 4 redundant information filtering (path A) is performed. Specifically, each message content is matched with a preset low-information word library (including "received", "good", "um", "1", "like", etc.). The message that matches successfully is determined as redundant information and is filtered out. The remaining unfiltered messages are spliced in the original order to form the refined target message text. It can effectively eliminate the screen brushing of social messages in group chats and retain substantive discussion content, making the voice broadcast content more compact and valuable.
[0066] S350, convert the target message content into a voice signal, and control the audio device to play the voice signal.
[0067] Specifically, the implementation process and principle of S350 and S130 are consistent, and details are not repeated here.
[0068] The embodiment of the disclosure responds to the operation instruction, obtains the to-be-processed message content in the target communication application based on the operation instruction, performs feature analysis on the to-be-processed message content, and obtains the feature parameter of the to-be-processed message content. Further, according to the feature parameter of the to-be-processed message content, the content importance of the to-be-processed message content is evaluated, and the importance evaluation result of the to-be-processed message content is obtained. The semantic processing path corresponding to the importance evaluation result of the to-be-processed message content is used to perform message optimization processing on the to-be-processed message content, and the target message content is obtained. Further, the target message content is converted into a voice signal, and the audio device is controlled to play the voice signal. Therefore, through the feature analysis and importance evaluation logic, the human thinking can be simulated, the dialogue features can be intelligently identified, that is, whether to contain urgent matters, whether to be long, and whether to be many nonsense, and the importance evaluation result is obtained, so that the optimal path is selected for subsequent processing.
[0069] Figure 3 is a flowchart of another message content processing method provided by the embodiment of the disclosure.
[0070] As shown in Figure 3 , the message content processing method can include the following steps: S410, in response to an operation instruction, obtaining to-be-processed message content in a target communication application based on the operation instruction.
[0071] Specifically, the implementation process and principle of S410 and S110 are consistent, and details are not repeated here.
[0072] S420, performing feature analysis on the to-be-processed message content to obtain a feature parameter of the to-be-processed message content.
[0073] Specifically, the implementation process and principle of S420 and S320 are consistent, and details are not repeated here.
[0074] S430, according to the feature parameter of the to-be-processed message content, the content importance of the to-be-processed message content is evaluated, and the importance evaluation result of the to-be-processed message content is obtained.
[0075] Specifically, the implementation process and principle of S430 and S330 are consistent, and details are not repeated here.
[0076] S440, in the case of the importance evaluation result being the first importance, matching the to-be-processed message content with a preset keyword library and a preset sentence pattern, identifying and extracting a key information item from the to-be-processed message content.
[0077] The preset sentence pattern includes a time and event mode, a task assignment mode, and a number and measurement unit mode.
[0078] Specifically, if the importance is the first importance, as shown in FIG. 4, key information extraction (path C) is performed. Specifically, the message content is matched with a preset keyword library (such as meeting, deadline, budget, responsible, etc.) and a preset sentence pattern, and a key information item is identified and extracted from the to-be-processed message content. The sentence pattern is, for example: Figure 4 The time and event mode matches a sentence such as "[today / tomorrow / this week] [time point] [meeting / submission / discussion]", and extracts "meeting at 3 pm tomorrow". The task assignment mode matches a sentence such as "[please / by] [name] [responsible / handle] [task content]", and extracts "please Zhang San to be responsible for the report part".
[0079] The number and measurement unit mode matches a sentence such as "[budget / amount] [number] [yuan / ten thousand]", and extracts "budget not more than 5000 yuan".
[0080] S450, based on the key information item, obtaining target message content.
[0081] Further, all the matched key information items are summarized to form the target message content. For example, "Remind you: there is an urgent matter. Zhang San asks you to be responsible for the report part. Meeting at 3 pm tomorrow. Budget not more than 5000 yuan".
[0082] The embodiment can quickly locate and highlight the most core task, time, and number information from massive information, so that the user can master the matters that must be acted upon in the first time.
[0083] S460, in the case of the importance evaluation result being the second importance, identifying a theme word and a key entity appearing in the to-be-processed message content.
[0084] In this step, if the importance is the second importance, as shown in FIG. 5, summary generation (path B) is performed. The car machine device identifies a theme word and a key entity appearing in the to-be-processed message content. Specifically, through word frequency statistics and named entity recognition technology, the most frequently appearing words (such as "project A" and "acceptance") and entities such as names and place names are found out.
[0085] Figure 4
[0086] S470. Analyze the semantic relationships between the messages in the message content to be processed to obtain the context semantic structure.
[0087] In this step, such as Figure 4 As shown, the semantic relationships between the messages in the message content to be processed are analyzed to obtain the contextual semantic structure. Specifically, by analyzing the temporal sequence and referencing relationships of the messages, a tree-like or contextual structure of the dialogue is constructed to understand "who replied to whom" and "which sub-topic they were discussing," thereby obtaining the contextual semantic structure.
[0088] S480, based on the information entropy algorithm and the position weighting algorithm, extracts at least one representative statement from the content of the message to be processed.
[0089] In this step, the vehicle-mounted device will extract at least one representative statement from the message content to be processed based on the information entropy algorithm and the location weighting algorithm.
[0090] In some embodiments, S480 includes, but is not limited to, S4801, S4802, and S4803: S4801. For each statement in the message content to be processed, calculate the information entropy score of each statement based on the information entropy algorithm and calculate the position weighting score of each statement based on the position weighting algorithm. S4802. Based on the information entropy score of each statement and the position-weighted score of each statement, obtain the representative score of each statement; S4803. Based on the representative score of each statement, extract at least one representative statement from the message content to be processed.
[0091] In this embodiment, for each statement in the message content to be processed, its information entropy score and position-weighted score are calculated. These two scores are combined to form a representative score. The sentences with the highest scores are selected as representative statements. The information entropy score characterizes the uniqueness and information content of each statement relative to the entire message content; the position-weighted score characterizes the position of each statement within the entire message content. Statements at the beginning and end of a dialogue are generally more important and given higher weight. This embodiment, based on the information entropy and position-weighted algorithm, can extract representative statements from lengthy dialogues, improving message retrieval efficiency and saving user time.
[0092] S490. Obtain the target message content based on the subject vocabulary, key entities, contextual semantic structure, and at least one representative statement.
[0093] In this step, the car machine device will obtain the target message content according to the theme vocabulary, the key entity, the context semantic structure and the at least one representative sentence. Specifically, the theme vocabulary, the key entity and the representative sentence are organized into a coherent summary text according to the context semantic structure through the pre-set text generation template or the lightweight generation model. For example, "everyone discussed the acceptance of project A last night. Li Si raised the progress problem, and Wang Wu suggested submitting the report before Friday. Finally, it was decided that Zhang San would summarize the opinions".
[0094] By optimizing the summary generation path, the core context can be automatically summarized from the lengthy dialogue, and the lengthy original text reading can be replaced by short message content, saving the user's time.
[0095] It should be noted that the semantic processing paths corresponding to the first importance, the second importance and the third importance are parallel, and any one of them can be used, any two of them can be used, or all of them can be used, which is not limited here. When it is determined that multiple semantic processing paths need to be used for processing, the message content obtained after processing each semantic processing path is assembled in time sequence or logical sequence to obtain the target message content.
[0096] In the embodiments of the present disclosure, in response to an operation instruction, the feature parameters of the to-be-processed message content in the target communication application are obtained based on the operation instruction. Then, the content importance of the to-be-processed message content is evaluated according to the feature parameters of the to-be-processed message content, and the importance evaluation result of the to-be-processed message content is obtained. In the case that the importance evaluation result is the first importance, the to-be-processed message content is matched with the preset keyword library and the preset sentence pattern mode, the key information items are identified and extracted from the to-be-processed message content, and the target message content is obtained based on the key information items. At the same time, in the case that the importance evaluation result is the second importance, the theme vocabulary and the key entity appearing in the to-be-processed message content are identified, the semantic association relationship between each message content in the to-be-processed message content is analyzed, the context semantic structure is obtained, at least one representative sentence is extracted from the to-be-processed message content based on the information entropy algorithm and the position weighting algorithm, and the target message content is obtained according to the theme vocabulary, the key entity, the context semantic structure and the at least one representative sentence. Therefore, by using the key information extraction path and the preset sentence pattern matching, important information such as meeting time, task allocation and budget amount can be accurately captured and preferentially reported, so that the user will not miss the most important matters. At the same time, by optimizing the summary generation path, the core context can be automatically summarized from the lengthy dialogue, and the lengthy original text reading can be replaced by short message content, saving the user's time.
[0097] Figure 5This is a schematic diagram of the structure of a message content processing device provided in an embodiment of this disclosure.
[0098] In this embodiment of the disclosure, the message content processing device can be located within an electronic device and is understood as a functional module within the aforementioned electronic device. Specifically, the electronic device can be a server or a terminal, wherein the terminal specifically includes an in-vehicle terminal, a computer, or a tablet computer, etc., and is not limited thereto.
[0099] like Figure 5 As shown, the message content processing device 700 can be applied to vehicle-mounted equipment and may include an acquisition module 710, an acquisition module 720, and a processing module 730.
[0100] The acquisition module 710 is used to respond to an operation instruction and acquire the message content to be processed in the target communication application based on the operation instruction; The module 720 is used to perform semantic processing on the message content to be processed to obtain target message content, which is used to describe the key information in the message content to be processed. The processing module 730 is used to convert the target message content into a voice signal and control the audio device to play the voice signal.
[0101] In this embodiment, in response to an operation command, the system can acquire the content of a message to be processed in a target communication application based on the operation command. Further, semantic processing is performed on the content of the message to be processed to obtain target message content, which describes key information in the content of the message to be processed. Then, the target message content is converted into a speech signal, and an audio device is controlled to play the speech signal. Thus, by introducing intelligent semantic processing before voice playback, key information can be automatically extracted from historical messages, allowing drivers to safely obtain the core content of messages without looking at the screen, effectively reducing driving safety hazards. Simultaneously, semantic processing greatly improves information acquisition efficiency and enhances the user experience.
[0102] In some embodiments of this disclosure, when the acquisition module 710 acquires the message content to be processed in the target communication application based on the operation instruction, it is specifically used for: Based on the operation instructions, the historical message content of the target communication application is obtained through the interconnection protocol between the vehicle-mounted device and the mobile terminal. Based on the operation instructions, the operation intent is identified, and the operation intent is used to characterize the scope of the historical message content to be processed; The message content to be processed is determined from the historical message content according to the stated operational intent.
[0103] In some embodiments of the present disclosure, the obtaining module 720 performs semantic processing on the to-be-processed message content to obtain target message content, and specifically for: performing feature analysis on the to-be-processed message content to obtain a feature parameter of the to-be-processed message content; performing content importance evaluation on the to-be-processed message content according to the feature parameter of the to-be-processed message content to obtain an importance evaluation result of the to-be-processed message content; performing message optimization processing on the to-be-processed message content by using a semantic processing path corresponding to the importance evaluation result of the to-be-processed message content to obtain target message content.
[0104] In some embodiments of the present disclosure, the feature parameter includes a total number of messages, a total number of characters of messages, a proportion of key matters, and a proportion of low-information-content messages. When the obtaining module 720 performs content importance evaluation on the to-be-processed message content according to the feature parameter of the to-be-processed message content to obtain an importance evaluation result of the to-be-processed message content, specifically for: determining whether the to-be-processed message content contains content that meets a preset key matter; if the to-be-processed message content contains content that meets a preset key matter, determining that the importance evaluation result of the to-be-processed message content is first importance; if the to-be-processed message content does not contain content that meets a preset key matter, determining whether the total number of messages is greater than a first threshold value, and if the total number of messages is greater than the first threshold value, determining that the importance evaluation result of the to-be-processed message content is second importance; if the total number of messages is less than or equal to the first threshold value, determining whether the proportion of low-information-content messages is greater than a second threshold value, and if the proportion of low-information-content messages is greater than the second threshold value, determining that the importance evaluation result of the to-be-processed message content is third importance.
[0105] When the processing module 730 performs message optimization processing on the to-be-processed message content by using a semantic processing path corresponding to the importance evaluation result of the to-be-processed message content to obtain target message content, specifically for: in a case where the importance evaluation result is first importance, matching the to-be-processed message content with a preset keyword library and a preset sentence pattern, identifying and extracting a key information item from the to-be-processed message content; obtaining the target message content based on the key information item; wherein the preset sentence pattern includes a time and event mode, a task assignment mode, and a number and measurement unit mode.
[0106] In some embodiments of the present disclosure, the processing module 730 adopts a semantic processing path corresponding to the importance evaluation result of the to-be-processed message content to perform message optimization processing on the to-be-processed message content to obtain target message content, and specifically is configured to: In the case where the importance evaluation result is second importance, identify a theme vocabulary and a key entity appearing in the to-be-processed message content; analyze semantic association relationships between each piece of message content in the to-be-processed message content to obtain a context semantic structure; extract at least one representative sentence from the to-be-processed message content based on an information entropy algorithm and a position weighting algorithm; obtain the target message content according to the theme vocabulary, the key entity, the context semantic structure, and the at least one representative sentence.
[0107] In some embodiments of the present disclosure, when the processing module 730 extracts at least one representative sentence from the to-be-processed message content based on an information entropy algorithm and a position weighting algorithm, it is specifically configured to: for each sentence in the to-be-processed message content, calculate an information entropy score of the each sentence based on the information entropy algorithm and calculate a position weighting score of the each sentence based on the position weighting algorithm; obtain a representative score of the each sentence based on the information entropy score of the each sentence and the position weighting score of the each sentence; extract at least one representative sentence from the to-be-processed message content based on the representative score of the each sentence.
[0108] In some embodiments of the present disclosure, when the processing module 730 adopts a semantic processing path corresponding to the importance evaluation result of the to-be-processed message content to perform message optimization processing on the to-be-processed message content to obtain target message content, it is specifically configured to: in the case where the importance evaluation result is third importance, match each piece of message content in the to-be-processed message content with a preset low information quantity vocabulary library to obtain message content that matches successfully; filter the message content that matches successfully to obtain the target message content.
[0109] It should be noted that, Figure 5 The message content processing apparatus 700 shown can perform each step in the above method embodiments and achieve each process and effect in the above method embodiments, and thus is not described here in detail.
[0110] Figure 6is a structural schematic diagram of an electronic device provided by an embodiment of the present disclosure.
[0111] In an embodiment of the present disclosure, Figure 6 The electronic device shown can be a server or a terminal, where the terminal specifically includes a vehicle-mounted terminal, a computer, a tablet computer, and the like, which are not limited herein.
[0112] As Figure 6 The electronic device can include a processor 810 and a memory 820 storing computer program instructions, as shown.
[0113] Specifically, the processor 810 described above can include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or can be configured as one or more integrated circuits that implement an embodiment of the present disclosure.
[0114] The memory 820 can include a mass storage for information or instructions. By way of example and not limitation, the memory 820 can include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 820 can include removable or non-removable (or fixed) media. Where appropriate, the memory 820 can be internal or external to the integrated gateway device. In a particular embodiment, the memory 820 is a non-volatile solid-state memory. In a particular embodiment, the memory 820 includes read-only memory (ROM). Where appropriate, the ROM can be mask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM), or flash memory, or a combination of two or more of these.
[0115] The processor 810 reads and executes the computer program instructions stored in the memory 820 to perform the steps of the message content processing method provided by an embodiment of the present disclosure.
[0116] In one example, the electronic device can further include a transceiver 830 and a bus 840. Wherein, as Figure 6As shown, the processor 810, the memory 820 and the transceiver 830 are connected and accomplish communication with each other through the bus 840.
[0117] The bus 840 includes hardware, software or both. By way of example and not limitation, the bus can include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side BUS (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or another suitable bus or a combination of two or more of these. Where appropriate, the bus 840 can include one or more buses.
[0118] The embodiment of the present disclosure further provides a computer readable storage medium, which can store a computer program. When the computer program is executed by a processor, the processor implements the message content processing method provided by the embodiment of the present disclosure.
[0119] The computer program is executed by the processor to implement the following steps: in response to an operation instruction, obtaining target message content to be processed in a target communication application based on the operation instruction; performing semantic processing on the target message content to be processed to obtain target message content, the target message content being used to describe key information in the target message content to be processed; converting the target message content into a voice signal, and controlling an audio device to play the voice signal.
[0120] The storage medium can include, for example, a memory 820 storing computer program instructions, which can be executed by the processor 810 of the electronic device to complete the message content processing method provided by the embodiments of the present disclosure. Alternatively, the storage medium can be a non-transitory computer-readable storage medium, for example, a non-transitory computer-readable storage medium can be a Read-Only Memory (ROM), a Random Access Memory (RAM), an external cache memory, an optical disc read-only memory (Compact Disc ROM, CD-ROM), a magnetic tape, a floppy disk, a flash memory, and an optical data storage device, etc. As an illustration but not limitation, RAM is available in various forms, such as Static Random Access Memory (SRAM) and Dynamic Random Access Memory (DRAM), etc.
[0121] The embodiments of the present disclosure further provide a vehicle including the electronic device, and each of the processes and effects in the above-mentioned embodiments of the present disclosure can be achieved, which will not be repeated here.
[0122] The embodiments of the present disclosure further provide a computer program product including a computer program or instructions, which, when executed by a processor, implement the message content processing method provided by the embodiments of the present disclosure, and each of the processes and effects in the above-mentioned embodiments of the present disclosure can be achieved, which will not be repeated here.
[0123] The above is only a specific implementation of the present disclosure, enabling those skilled in the art to understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to these embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A message content processing method, characterized in that, Applied to in-vehicle infotainment systems, the method includes: In response to an operation command, the content of the message to be processed in the target communication application is obtained based on the operation command. Semantic processing is performed on the message content to be processed to obtain target message content, which is used to describe the key information in the message content to be processed. The target message content is converted into a voice signal, and the audio device is controlled to play the voice signal.
2. The method according to claim 1, characterized in that, The step of obtaining the message content to be processed in the target communication application based on the operation instruction includes: Based on the operation instructions, the historical message content of the target communication application is obtained through the interconnection protocol between the vehicle-mounted device and the mobile terminal. Based on the operation instructions, the operation intent is identified, and the operation intent is used to characterize the scope of the historical message content to be processed; The message content to be processed is determined from the historical message content according to the stated operational intent.
3. The method according to claim 1, characterized in that, The step of performing semantic processing on the message content to be processed to obtain the target message content includes: Perform feature analysis on the message content to be processed to obtain the feature parameters of the message content to be processed; Based on the feature parameters of the message content to be processed, the importance of the message content to be processed is evaluated to obtain the importance evaluation result of the message content to be processed. Using the semantic processing path corresponding to the importance assessment result of the message content to be processed, message optimization processing is performed on the message content to be processed to obtain the target message content.
4. The method according to claim 3, characterized in that, The feature parameters include the total number of messages, the total number of characters in the messages, and the proportion of key items and low-information messages; The step of evaluating the importance of the message content to be processed based on its characteristic parameters to obtain the importance evaluation result of the message content to be processed includes: Determine whether the message content to be processed contains any content that meets preset key criteria; If the message content to be processed contains content that meets the preset key requirements, then the importance assessment result of the message content to be processed is determined to be of the highest importance. If there is no content in the message content to be processed that meets the preset key items, then it is determined whether the total number of messages is greater than the first threshold. If the total number of messages is greater than the first threshold, then the importance assessment result of the message content to be processed is determined to be the second importance. If the total number of messages is less than or equal to the first threshold, then it is determined whether the proportion of low-information-content messages is greater than the second threshold. If the proportion of low-information-content messages is greater than the second threshold, then the importance assessment result of the message content to be processed is determined to be third importance.
5. The method according to claim 3, characterized in that, The step of using a semantic processing path corresponding to the importance assessment result of the message content to be processed to perform message optimization processing on the message content to be processed, and obtaining the target message content, includes: If the importance assessment result is first importance, the message content to be processed is matched with a preset keyword library and a preset sentence pattern to identify and extract key information items from the message content to be processed; Based on the key information items, the target message content is obtained; The preset sentence patterns include time and event patterns, task assignment patterns, and number and unit of measurement patterns.
6. The method according to claim 3, characterized in that, The step of using a semantic processing path corresponding to the importance assessment result of the message content to be processed to perform message optimization processing on the message content to be processed, and obtaining the target message content, includes: If the importance assessment result is second most important, identify the keywords and key entities appearing in the message content to be processed; Analyze the semantic relationships between the messages in the message content to be processed to obtain the contextual semantic structure; Based on the information entropy algorithm and the position weighting algorithm, at least one representative sentence is extracted from the content of the message to be processed. The target message content is obtained based on the subject vocabulary, the key entities, the contextual semantic structure, and the at least one representative statement.
7. The method according to claim 6, characterized in that, The method based on information entropy algorithm and position weighting algorithm extracts at least one representative statement from the message content to be processed, including: For each statement in the message content to be processed, calculate the information entropy score of each statement based on the information entropy algorithm and calculate the position weighting score of each statement based on the position weighting algorithm; Based on the information entropy score of each statement and the position-weighted score of each statement, a representative score for each statement is obtained; Based on the representative score of each statement, at least one representative statement is extracted from the message content to be processed.
8. The method according to claim 3, characterized in that, The step of using a semantic processing path corresponding to the importance assessment result of the message content to be processed to perform message optimization processing on the message content to be processed, and obtaining the target message content, includes: If the importance assessment result is third importance, each message in the message content to be processed is matched with a preset low information content word library to obtain the successfully matched message content; The successfully matched message content is filtered to obtain the target message content.
9. An electronic device, characterized in that, include: Memory; processor; as well as Computer programs; The computer program is stored in the memory and configured to be executed by the processor to implement the method as described in any one of claims 1-8.
10. A vehicle, characterized in that, Including the electronic device as described in claim 9.