CN118190003B

CN118190003B - Speech recognition navigation method based on planning path data

Info

Publication number: CN118190003B
Application number: CN202410321592.0A
Authority: CN
Inventors: 彭郁森; 范化雷; 彭家亮
Original assignee: Ning Donggui
Current assignee: Ning Donggui
Priority date: 2024-03-20
Filing date: 2024-03-20
Publication date: 2025-04-04
Anticipated expiration: 2044-03-20
Also published as: CN118190003A

Abstract

The invention relates to the technical field of data processing, in particular to a voice recognition navigation method based on planned path data. The method comprises the steps of analyzing navigation requirements according to navigation request data to generate starting position data and navigation destination data, conducting navigation route planning based on the starting position data and the navigation destination data to generate planned path data, conducting real-time voice acquisition according to the planned path data to generate voice data to be processed, conducting optimization necessity assessment on the voice data to be processed and conducting path re-planning to generate path optimization data, transmitting the path optimization data to cloud equipment to generate optimization record summary data, conducting navigation optimization on navigator equipment based on the optimization record summary data to generate navigator update data. The invention analyzes the voice fragments during the user navigation to realize the real-time and high-efficiency processing of the changing requirement of the user navigation route.

Description

Speech recognition navigation method based on planning path data

Technical Field

The invention relates to the technical field of data processing, in particular to a voice recognition navigation method based on planned path data.

Background

Speech recognition allows a user to interact with the navigation system through natural language without relying on a keyboard or touch screen. The portable navigator greatly improves the convenience of using the portable navigator, and particularly under the situation that the driver needs to concentrate on driving or walking and the like. The voice navigation system can better meet the personalized needs of the user, because the voice navigation system can be adjusted according to the accent, the speed of speech and the word habit of the user, and provides more personalized navigation experience. However, the existing voice recognition navigation method based on the planned path data needs to control the system through specific voice instructions, the context of the voice instructions is weak, and the voice instructions cannot be accurately understood in time.

Disclosure of Invention

Based on this, it is necessary to provide a voice recognition navigation method based on planned path data to solve at least one of the above technical problems.

To achieve the above object, a voice recognition navigation method based on planned path data, the method comprising the steps of:

Step S1, performing navigation request voice detection based on voice acquisition equipment to generate navigation request data, performing equipment positioning according to the navigation request data to generate initial position data, performing travel mode and travel destination identification according to the navigation request data to generate travel mode data and destination information data, performing map position matching based on the travel mode data and the destination information data to generate navigation destination data;

Step S2, navigation route planning is carried out based on the initial position data and the navigation destination data to generate planned path data, turning stay state analysis is carried out according to the planned path data to generate turned stay data, and voice segment screening is carried out based on the turned stay data to generate voice data to be processed;

Step S3, carrying out optimization demand voice interception on voice data to be processed to generate fragment voice data, carrying out optimization necessity evaluation on the basis of the fragment voice data to generate necessity evaluation data, carrying out path re-planning on planning path data on the basis of the necessity evaluation data to generate path optimization data;

And S4, transmitting the path optimization data to cloud equipment to generate optimized record summary data, extracting navigation defect reasons based on the optimized record summary data to generate navigation defect feedback data, and optimizing the navigation equipment according to the navigation defect feedback data to generate navigator update data.

According to the invention, the navigation request is sent out in a natural voice mode through voice detection, so that the user experience and convenience are improved. The complexity of the user operation is reduced. And generating initial position data according to equipment positioning, providing accurate initial position information, and being beneficial to accurately planning a navigation route. And identifying and generating destination information data through a travel mode, knowing the travel mode and destination information of a user, and being beneficial to personalized navigation service and information pushing. And path planning is performed based on the starting position data and the navigation destination data, so that the generation of an optimal path is facilitated, time and resources are saved, and efficient navigation guidance is provided. Through turning stay state analysis and voice segment screening, key information is extracted, voice data processing amount is reduced, and processing efficiency is improved. And ensures real-time interaction with the user during navigation. The real-time performance and the accuracy of the navigation system are improved, the user experience is enhanced, and the user is ensured to obtain timely guidance and feedback in the navigation process. Through optimizing the required voice interception and the necessity evaluation, the voice data is more refined and targeted, redundant information is reduced, and the data quality is improved. And adjusting and optimizing the planned path according to the evaluation result of the voice data. The intelligent and individuation of the navigation system are improved, so that the path planning is more in line with the actual demands of users, and the accuracy and efficiency of the navigation path are optimized. Optimizing record summary data for recording optimization process. And optimizing the navigator equipment based on the optimized record summary data, and updating the navigation data. The performance and the adaptability of the navigation equipment are improved, so that the navigation equipment can better adapt to the continuously-changing road conditions and the user demands, and the stability and the accuracy of the navigation system are improved. Therefore, the voice recognition navigation method based on the planning path data filters the voice fragments during the user navigation by analyzing the traveling state of the user so as to reduce the workload of voice analysis, and performs optimization necessity evaluation on the filtered voice fragments so as to judge whether the user needs to optimize the navigation path, thereby timely processing the change requirement during the user navigation.

Preferably, step S1 comprises the steps of:

step S11, user voice collection is carried out based on voice collection equipment, and user voice data are generated;

Step S12, performing navigation request voice detection on user voice data to generate navigation request data;

step S13, equipment positioning is carried out based on the navigation request data, and initial position data is generated;

Step S14, classifying sentence keywords according to the navigation request data to generate classified keyword data, and identifying travel modes and travel destinations based on the classified keyword data to generate travel mode data and destination information data;

Step S15, extracting keywords from the destination information data to generate destination keyword data, presetting a current travel range according to travel mode data to generate current travel range data, and performing map position matching based on the current travel range data and the destination keyword data to generate navigation destination data.

By collecting the voice data of the user, the voice feature and accent of the user can be better known, and a foundation is provided for subsequent voice recognition and interaction. The accuracy and individuation of voice recognition are improved, the interaction effect between the system and the user is improved, and the system is more user-friendly.

By detecting the voice data of the user, the navigation request information is identified, and the interaction efficiency and convenience of the user and the navigation system are improved. The navigation requirements of the user can be accurately identified from the voice data, so that the user can respond to the requirements of the user more accurately and customized services can be provided. The equipment is used for positioning, so that the initial position information of the user is acquired, and the navigation system can provide the most accurate and proper route planning. After the initial position data is acquired, an optimal navigation route can be planned for the user more accurately, and time and resources are saved. The sentences in the navigation request data are extracted and classified by the keywords, so that the specific requirements of the user can be understood, for example, the keywords in the 'I want to go to the hospital' can be classified as 'hospital', and the user requirements can be analyzed more accurately. Through keyword classification, the travel mode and destination information of the user can be identified, and a more accurate information basis is provided for subsequent navigation services. Extracting keywords from destination information helps to more accurately understand the specific location the user wants to travel to, e.g., extracting the keyword "mall" from "buy something to market". By combining the current travel range of the user and the extracted destination keywords, the possible destination range can be preset better and matched with the map position, so that support is provided for accurate determination of the navigation destination.

Preferably, step S14 comprises the steps of:

step S141, performing word segmentation annotation on the navigation request data to generate word segmentation request data;

step S142, carrying out semantic analysis on the navigation request data to generate word association data, and carrying out keyword classification on the initial keyword data by utilizing the word association data to generate classified keyword data;

Step S143, carrying out travel mode identification based on the classified keyword data to generate travel mode data;

Step S144, performing location entity recognition based on the classified keyword data to generate location recognition result data;

Step S145, carrying out identification accuracy assessment based on the mapping result data to generate identification accuracy data;

And step S146, carrying out data connection processing on the place identification result data and the identification accuracy data to generate destination information data.

The invention extracts the key information in the request by marking the words and extracting the key words of the navigation request data. The understanding and accuracy of the navigation intention of the user are improved, and a foundation is provided for subsequent semantic analysis and keyword extraction. Semantic analysis and keyword classification are carried out, and word association in the request data is processed. The deep understanding of navigation request data is improved, the classification and classification of keywords are optimized, and the intellectualization and individuation of a navigation system are enhanced. And carrying out travel mode identification based on the classified keyword data, and identifying the travel mode selected by the user. The travel mode of the user is determined, and the system is beneficial to planning a navigation path suitable for the travel mode for the user more accurately. Location information in the navigation request is identified and a geographic location map is performed. Accurate location information is provided, and the accuracy and the effectiveness of navigation are enhanced. And (3) carrying out accuracy evaluation on the recognition result data, and evaluating the accuracy of the place recognition. The accuracy of the location recognition result is ensured, and the accurate interpretation and response capability of the navigation system to the user requirements is improved. Combining the location identification and accuracy assessment data, accurate destination information is generated. Complete and accurate navigation destination information is provided, and accuracy of navigation path planning and optimization of user experience are ensured.

Preferably, step S15 comprises the steps of:

step S151, acquiring navigation history data and preset common address data;

Step S152, dividing the destination information data based on a preset recognition accuracy threshold, and generating destination keyword data when the destination information data is larger than the preset recognition accuracy threshold;

Step 153, when the destination information data is smaller than or equal to a preset recognition accuracy threshold, fuzzy address matching is performed based on preset common address data to generate destination keyword data;

Step S154, travel tool division is carried out on the navigation history data to generate tool division history data, travel distance range formulation is carried out according to the tool division history data to generate travel range data;

Step S155, performing current travel range presetting on travel range data based on travel mode data to generate current travel range data, and performing map position matching on destination keyword data based on the current travel range data to generate navigation destination data.

The invention acquires the navigation history data of the user and the preset common address data. Important data sources for analysis by the navigation system are provided to assist in understanding the user's usual destinations and navigation preferences. And performing accuracy processing on the destination information data based on a preset threshold value. The reliability of destination information is ensured, and the accuracy and reliability of navigation are improved for data higher than a threshold value. When the destination information is insufficient, fuzzy matching is performed by using the common address data. The destination information is supplemented, the understanding capability of the system to the intention of the user is enhanced, and the integrity and accuracy of navigation are improved. And carrying out tool division on the navigation history data, and determining the travel distance range. And the travel range data is subjected to the travel range presetting based on the travel mode data, the current travel range data is generated, a more accurate reference range is provided for the generation of the follow-up destination data, and the adaptability and individuation of the navigation path are improved. And performing destination map position matching based on the current travel range data. Accurate destination data is provided, accurate interpretation and response of the navigation system to user requirements are ensured, and effectiveness and user experience of navigation path planning are enhanced.

Preferably, step S153 includes the steps of:

step S1531, when the destination information data is less than or equal to a preset recognition accuracy threshold, generating fuzzy location entity data;

Step S1532, performing entity identification matching on the fuzzy place entity data based on preset common address data to generate matching result data;

step S1533, when the matching result data is failed, performing accurate address consultation based on the mapping result data to generate address consultation result data;

Step S1534, performing location data association on the fuzzy location entity data according to the address consultation result data, and performing supplementary correction on preset common address data to generate common address data;

And step S1535, extracting the destination address according to the common address data to generate destination key data.

The present invention provides for generating ambiguous location entity data. Incomplete destination information is supplemented, and basic data is provided for subsequent matching and query. And matching and identifying the fuzzy place entity based on preset common address data. The accuracy of fuzzy place entities is improved, more reliable destination keyword data is generated when matching is successful, and the accuracy of navigation is enhanced. And when the matching fails, performing accurate address consultation to generate consultation result data. Under the condition of failure in matching, more accurate address information is acquired through consultation, and the integrity and accuracy of destination information are improved. And associating and correcting the fuzzy place entity and the common address data according to the consultation result. The accuracy and the integrity of the navigation system are improved, and a more reliable data base is provided for the navigation system. And extracting destination information based on the corrected common address data. More reliable destination keyword data is generated, and accuracy of navigation path planning and optimization of user experience are ensured.

Preferably, step S2 comprises the steps of:

step S21, navigation route planning is carried out based on the initial position data and the navigation destination data, and planning path data is generated;

step S22, path navigation is carried out according to the planned path data, stay state analysis is carried out, and stay state data are generated;

s23, utilizing voice acquisition equipment, and carrying out real-time voice acquisition based on stay state data to generate stay period voice data;

Step S24, acquiring a movement direction based on the stay state data to generate stay direction data, and extracting turning directions of the stay direction data by using the planned path data to generate turned stay data;

and S25, screening the voice fragments of the voice data in the stay period based on the turned stay data to generate voice data to be processed.

The invention generates the optimal path planning through the starting position and the navigation destination data, provides the optimal navigation scheme and saves time and resources. According to the starting position and the destination data, a personalized navigation route can be provided, and the factors such as the preference of a user, traffic conditions and the like are considered. Real-time navigation guidance is provided for a user through path navigation, the stay condition of the user in the navigation process is known through stay state analysis, path planning is optimized, and the accuracy and the practicability of a navigation route are ensured. Analysis of the data of the stay state helps to improve the navigation system and provide more intelligent, real-time navigation services. By means of the voice acquisition device, voice data are acquired during stay of the user, so that the user can know that the user needs change, feedback or other relevant information in the navigation process is helpful to improve the response capability of the navigation system, and service more close to the user needs is provided. The movement direction of the user is acquired through the stay state data, so that the relationship between the current position of the user and the navigation path is understood, and more accurate navigation guidance is provided. And the planning path data is combined, the relevant information of the turned stay is extracted, basic information can be provided for turning reminding, crossing guiding and the like of a navigation system, and the instantaneity and the accuracy of navigation are improved. The voice data during stay is screened based on the turned stay data, so that voice fragments related to turning can be extracted, and the unnecessary voice information quantity is reduced. The screened voice data to be processed is more likely to contain the reason information of stay with the turning, so that the quality and the relevance of the voice data are improved, and the subsequent voice recognition and analysis are convenient.

Preferably, step S3 comprises the steps of:

s31, carrying out data preprocessing on voice data to be processed to generate noise reduction voice data;

step S32, irrelevant voice screening is carried out on the noise reduction voice data to generate navigation association voice data;

Step S33, carrying out demand keyword recognition on text voice data to generate demand keyword data;

step S34, selecting relevant voice fragments of text voice data according to the required keyword data to generate fragment voice data;

step S35, high-necessity data screening is carried out on text voice data according to the necessity evaluation data, and analysis data to be optimized is generated;

and step S36, carrying out optimization strategy formulation based on the analysis data to be optimized to generate path optimization strategy data, and carrying out path re-planning on the planned path data according to the path optimization strategy data to generate path optimization data.

The invention carries out pretreatment and noise reduction treatment on voice data to be processed. The quality of voice data is improved, and the influence of noise on voice recognition is reduced. And filtering out irrelevant voice and converting the processed voice data into text data. The voice information related to navigation is extracted, and the text form conversion type facilitates the subsequent extraction of key information and semantic analysis. And carrying out required keyword recognition on the text voice data. The key words of the navigation requirements, which are proposed by the user in the voice, are determined, and important navigation guidance is provided for subsequent processing. The necessity of navigation optimization is evaluated, and the effectiveness and rationality of navigation path optimization are ensured. Text voice data is screened according to the necessity evaluation data, and the data which needs to be optimized most are screened, so that the pertinence and the effectiveness of path optimization are improved. And according to the data to be optimized obtained by screening, researching and analyzing, formulating a targeted optimization strategy, and generating path optimization strategy data. And planning the original planning path data again according to the path optimization strategy data to generate path optimization data. The optimization and the effectiveness of the navigation path are ensured, and the navigation accuracy and the user experience are improved.

Preferably, step S34 includes the steps of:

Step S341, extracting characteristic information of the text voice data to generate characteristic text data;

step S342, carrying out context association on the characteristic text data according to the required keyword data to generate associated characteristic text data;

step S343, selecting characteristic voice fragments of navigation associated voice data based on the associated characteristic text data to generate fragment voice data;

Step S344, performing voice quality analysis on the segment voice data to generate voice quality data;

Step S345, optimizing the necessity evaluation of the voice quality data by utilizing the optimizing necessity evaluation formula to generate necessity evaluation data.

The invention generates the characteristic text data by extracting the characteristic information of the text voice data. The key features of the voice data are extracted, and the information quantity and accuracy of the data are improved. And carrying out context association on the feature text data according to the demand keyword data to generate associated feature text data. The voice data is associated with the demand keywords, so that the association of the voice data is increased, and more comprehensive information is provided for subsequent analysis. And feature voice fragments of navigation associated voice data are selected based on the associated feature text data, and voice fragments related to the required keywords are screened out, so that the data volume is reduced, and the pertinence and the efficiency of subsequent analysis are improved. And carrying out voice quality analysis on the segment voice data to generate voice quality data. And obtaining evaluation data associated with the voice quality, and providing a basis for subsequent optimization necessity evaluation. And evaluating the voice quality data by using an optimization necessity evaluation formula. According to the evaluation result, the optimization requirement of the voice data is determined, and the importance and the necessity of the voice data on navigation optimization are ensured.

Preferably, the optimization necessity evaluation formula in step S344 is as follows:

Where E is the necessity evaluation value, t ₁ is the start time of evaluation, t ₂ is the end time of evaluation, n is the total number of voice segments at time t, a _i is the quality score of voice segment i, b _i is the length of voice segment i, c _i is the voice recognition accuracy of voice segment i, d _i is the ambient sound recognition accuracy of voice segment i, E _i is the ambient noise level of voice segment i, f _i is the incoherence level value of voice segment i, α is the navigation route length, h _t is the congestion level value of route at time t, g is the correlation value of route congestion, v is the frequency of use of route, θ is the history optimization frequency, E is the base of natural logarithm, k is the complexity of route, ω is the deviation correction value of the necessity evaluation value.

The invention constructs an optimized necessity evaluation formula for optimizing the necessity evaluation of voice quality data to generate necessity evaluation data. The formula fully considers the estimated starting time t ₁, the estimated ending time t ₂, the total number of voice fragments n at time t, the quality score a _i of the voice fragment i, the length b _i of the voice fragment i, the voice recognition accuracy c _i of the voice fragment i, the ambient sound recognition accuracy d _i of the voice fragment i, the ambient noise level e _i of the voice fragment i, the incoherence degree value f _i of the voice fragment i, the navigation route length alpha, the congestion degree value h _t of the route at time t, the association value g of route congestion, the use frequency v of the route, the history optimization frequency theta, the base e of natural logarithms, the complexity k of the route, the deviation correction value omega of the necessity evaluation value and the interaction relation among variables, and forms the following functional relation:

The overall quality of the sound clip is represented by a _i. A higher quality score means a clearer, more accurate sound. b _i influence the weight of the sound clip quality score. Longer sound clips may have more information but may also contain more unwanted information. The recognition accuracy of sound clips and the accuracy of ambient sounds directly affect the clarity and effectiveness of the sound. The ambient noise level and the level of discontinuity affect the purity and consistency of the sound. The logarithmic processing is performed so that the relative effects of the influencing factors are more balanced. When some factors are relatively large, the logarithmic function may slow down its effect, preventing one of the factors from excessively affecting the overall evaluation value.And evaluating the actual road condition of the navigation path by taking the length of the navigation path as a reference. The actual state of the navigation route is evaluated by the association value of the congestion degree and the route congestion, and different travel modes have different route congestion association values, for example, in a city, the route congestion association value of a walking mode is close to zero. The negative reference indicates that the contribution of this portion is to reduce the overall evaluation value.The influence of the frequency of use of the route and the historical optimization frequency on the overall evaluation is considered. The higher the usage frequency and the history optimization frequency, the greater the contribution to the overall necessity evaluation value. The integral formula comprehensively evaluates the navigation voice fragments in a period of time through the integral term, and the dynamic variability of the voice fragments of the user is considered.Reflecting the impact of path complexity and length on the evaluation. When the complexity of the route is relatively high or the route length is long,Will approach 0. Meaning that paths of greater complexity and length will have a greater negative impact on the overall evaluation. The functional relation can accurately and quickly evaluate the necessity evaluation value to determine whether the navigation route is optimized according to the voice segment information of the user. And the reliability of route optimization judgment is improved. And the deviation correction value omega of the necessity evaluation value is utilized to adjust and correct the functional relation, so that the error influence caused by parameter error items is reduced, the necessity evaluation value E is generated more accurately, and the accuracy and reliability of the navigation route optimization necessity evaluation are improved. Meanwhile, the deviation correction value in the formula is adjusted according to actual conditions, for example, the acoustic characteristics, the route characteristics and the like are considered, omega is adjusted based on knowledge in the fields, so that the optimization necessity evaluation is carried out by being applied to different voice quality data, and the flexibility and the applicability of the algorithm are improved.

Preferably, step S4 comprises the steps of:

Step S41, transmitting path optimization data to cloud equipment to generate optimized record summary data;

step S42, clustering the optimized record summary data by a similar optimization method to generate optimized content division data;

step S43, optimizing reason analysis is carried out according to the optimizing content dividing data, and optimizing reason data are generated;

Step S44, extracting navigation defect reasons based on the optimized reason data, and generating navigation defect feedback data;

And step S45, carrying out equipment optimization strategy formulation according to the navigation defect feedback data to generate equipment optimization strategy data, and carrying out navigation equipment optimization according to the equipment optimization strategy data to generate navigator update data.

The invention allows for centralized storage and processing of data by transmitting path optimized data to the cloud device. The method is beneficial to forming comprehensive optimized records and provides a data basis for subsequent analysis and decision making. The optimization methods can be categorized by clustering the optimization records by similar methods. The method is helpful for identifying and understanding the similarity and the difference between different optimization methods, and provides a basis for deeper analysis. And (3) specific analysis of the focus optimization content, and determining the reason for the optimization. By deeply analyzing the optimization content of each category, specific reasons for the optimization can be found, and guidance is provided for subsequent optimization. And extracting the equipment defect information from the optimization reasons. Through analysis of the optimization reasons, potential defects or problems of the device can be identified. This information helps to formulate targeted improvements. And based on the navigation defect feedback data, formulating a corresponding equipment optimization strategy. According to the formulated equipment optimization strategy, the navigation equipment is correspondingly optimized, and the optimization results in the generation of new navigator update data, so that the performance and functions of the navigation equipment are improved, and the navigation equipment is more suitable for actual demands.

The application has the beneficial effects that through voice detection, the user can send out the navigation request in a natural voice mode, and the user experience and the interactivity are improved. The starting position data is generated through equipment positioning, accurate starting position information is provided, and accurate planning of a navigation route is facilitated. And identifying and generating destination information data through a travel mode so as to know the travel mode and destination information of a user, and providing a basis for personalized navigation service and information pushing. And the optimal path planning is generated through the navigation route planning, so that time and resources are saved, and efficient navigation guidance is provided. Through turning stay state analysis and voice segment screening, key information is extracted, voice data processing amount is reduced, and processing efficiency is improved. By intercepting the voice data to be processed, the fragment voice data related to navigation is extracted, the unnecessary information quantity is reduced, and the effectiveness and the efficiency of data processing are improved. The navigation requirements are evaluated based on the segment voice data, the necessity of optimization is determined, and whether the planned path needs to be re-planned can be accurately judged. And re-planning the planned path according to the necessity evaluation data to generate path optimization data, which is helpful for providing a navigation route which is more accurate and meets the requirements of users. Transmitting the path optimization data to the cloud equipment and generating optimized record summary data, and providing detailed record and data support for the improvement of the navigation system. And analyzing the defect reasons existing in the navigation system based on the optimized record summary data, thereby being beneficial to finding and solving the system problem and extracting the navigation defect feedback data. And performing targeted equipment optimization according to the navigation defect feedback data, and generating navigator update data to improve the performance and functions of the navigation equipment. Therefore, the voice recognition navigation method based on the planning path data filters the voice fragments during the user navigation by analyzing the traveling state of the user so as to reduce the workload of voice analysis, and performs optimization necessity evaluation on the filtered voice fragments so as to judge whether the user needs to optimize the navigation path, thereby timely processing the change requirement during the user navigation.

Drawings

FIG. 1 is a flow chart of steps of a voice recognition navigation method based on planned path data;

FIG. 2 is a flowchart illustrating the detailed implementation of step S2 in FIG. 1;

FIG. 3 is a flowchart illustrating the detailed implementation of step S3 in FIG. 1;

FIG. 4 is a flowchart illustrating the detailed implementation of step S4 in FIG. 1;

The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

The following is a clear and complete description of the technical method of the present patent in conjunction with the accompanying drawings, and it is evident that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.

Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.

It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.

To achieve the above objective, referring to fig. 1 to 4, a voice recognition navigation method based on planned path data, the method includes the following steps:

In the embodiment of the present invention, as described with reference to fig. 1, a step flow diagram of a voice recognition navigation method based on planned path data according to the present invention is provided, and in this example, the voice recognition navigation method based on planned path data includes the following steps:

in the embodiment of the invention, the voice data of the user is collected through the voice collecting equipment. Speech preprocessing, such as noise cancellation, speech signal enhancement, etc., is performed to ensure speech quality. And recognizing key information in the navigation request voice by using a voice recognition technology or natural language processing. And extracting key data such as navigation destination, travel mode and the like in the user request. And acquiring the current position information of the user by using a positioning technology. And converting the data obtained by positioning into initial position information, and ensuring accuracy and usability. And identifying the travel mode and destination information of the user by analyzing the navigation request data. And combining the map data, matching the travel mode with the destination information, and generating final navigation destination data.

In the embodiment of the invention, navigation route planning is performed through the initial position data and the navigation destination data, and the planned route data is generated by considering factors such as road network, traffic conditions and the like. A navigation algorithm (such as Dijkstra algorithm or a-algorithm) is used to calculate the optimal path, ensuring the accuracy and effectiveness of the path. Based on the planned path data, turn and stay states in the path are analyzed, turn points and stay points are identified, and turned stay data is generated. Such as the identification and recording of intersections, junctions, parking areas, etc. And screening the voice data during stay based on the turned stay data, extracting voice fragments related to turning, and generating voice data to be processed.

In the embodiment of the invention, the noise-reduced voice data is generated by preprocessing the voice data to be processed, including noise reduction, filtering and the like. And screening irrelevant voices on the basis of the noise reduction voice data, and extracting navigation-related voice data. And carrying out text conversion on the navigation-related voice data to generate text voice data. Keyword information of navigation requirements is identified and extracted from text voice data. And selecting the voice fragments related to the navigation requirements according to the requirement keyword data to generate fragment voice data. And carrying out optimization necessity evaluation on the basis of the segment voice data, determining high-value data which is helpful for navigation optimization, and generating the data to be optimized for analysis. And formulating a path optimization strategy based on the high-necessity data to be optimized, and generating path optimization strategy data. And re-planning the planned path data according to the path optimization strategy data to generate path optimization data.

In the embodiment of the invention, the path optimization data is uploaded to the cloud for storage and analysis through a network or other transmission modes, and the optimization record summary data is generated. And classifying the optimized records by using a clustering or similar method according to the optimized records, and generating optimized content division data so as to analyze and identify the optimized reasons. Analyzing the optimized content division data, identifying specific reasons causing optimization, extracting navigation defect data, and guiding the subsequent navigator to optimize. Based on the feedback data of the navigation defects, the navigator equipment is optimized and updated, the defects are repaired, the navigation performance is improved, and the navigator update data is generated.

Preferably, step S1 comprises the steps of:

step S14, carrying out navigation keyword recognition according to the navigation request data to generate navigation keyword information data, wherein the navigation keyword information data comprises travel mode data and destination information data;

Step S15, destination positioning is carried out according to the navigation key information data, and navigation destination data are generated;

and S16, carrying out data association on the starting position data and the navigation destination data to generate navigation demand data.

By collecting the voice data of the user, the voice feature and accent of the user can be better known, and a foundation is provided for subsequent voice recognition and interaction. The accuracy and individuation of voice recognition are improved, the interaction effect between the system and the user is improved, and the system is more user-friendly. By performing navigation request voice detection on user voice data, a user's navigation request can be accurately captured. The accurate understanding of the navigation system to the user intention is improved, and the correctness and pertinence of the subsequent navigation process are ensured. By means of device positioning, starting point information of navigation is provided. And identifying key information in the navigation request, including travel mode and destination information. To better understand the travel needs and destinations of the user. Through destination location, the system is able to obtain location data of navigation destinations. The navigation terminal information is provided, and key data support is provided for path planning and navigation processes. The data association obtains navigation demand data, which contains key information such as a starting point, an ending point and the like. The method provides complete navigation demand information, is beneficial to path planning and optimization of a subsequent navigation system, and ensures the accuracy and efficiency of the navigation process.

In the embodiment of the invention, the voice data of the user is recorded and collected by using the voice collecting equipment preloaded by the navigator, so that the voice quality and the voice definition are ensured. The collected voice data is stored on a safe and reliable medium or server, and is properly managed and backed up. A suitable speech detection algorithm or speech recognition model is selected, such as a deep learning based speech recognition model (e.g., CNN, RNN, transformer, etc.). Preprocessing is performed on user voice data, including denoising, audio format conversion and the like, so as to improve the accuracy of voice recognition. And recognizing and extracting voice fragments or keywords related to the navigation request by using a voice detection algorithm to generate navigation request data. Or by guiding the user to use preset sentences to get navigation request data, such as "navigate, me want to go to mall". "and the like. And selecting a proper positioning mode by utilizing technologies such as GPS, base station positioning or Wi-Fi positioning. And acquiring the initial position information of the user through the selected positioning technology, and generating initial position data. And performing word segmentation labeling, semantic analysis and keyword extraction on the navigation request data to obtain initial keyword data. And classifying the keywords by utilizing semantic analysis and associated data, and identifying the key information about the travel mode. Location information of a destination is obtained by identifying a location entity and a geographic location map. The recognition result is evaluated for accuracy, and detailed information data of the destination, such as a location, a name, etc., is generated based on the evaluation result. And acquiring navigation history data of the user and preset common address data, and related travel tool and distance range data. And screening and preprocessing the destination information data according to the recognition accuracy threshold value to generate destination keyword data. Map position matching is performed based on the current travel range data, destination information is identified and located, and navigation destination data is generated.

Preferably, step S14 comprises the steps of:

In the embodiment of the invention, the navigation request data is divided into vocabulary sequences by using Natural Language Processing (NLP) technology, such as word segmentation tools (e.g. jieba, NLTK, etc.), for example. Based on the word segmentation result, keywords related to navigation are extracted from the word segmentation request data through a keyword extraction algorithm (such as TF-IDF, textRank and the like), and initial keyword data are generated. And generating relevance data among words by using semantic analysis methods in NLP, such as Word vector models (Word 2Vec, gloVe), BERT and the like. Based on semantic association data, the initial keyword data are classified by adopting methods such as clustering and classification to form classified keyword data, and keywords with higher association are classified and summarized. Based on the categorized keyword data, information representing the travel pattern such as "walking", "bicycle", "driving" and the like in the keyword is identified using rule matching or machine learning models, and travel pattern data is generated. Using NLP technology or entity recognition model, entity information representing the location, such as location name, address, etc., is identified from the categorized keyword data, generating location recognition result data. And matching the identified place information with geographic position data or map service, mapping the geographic position information into specific geographic position information, and generating mapping result data. And verifying and evaluating the mapping result data, evaluating the accuracy of the location identification by using indexes such as relativity, accuracy and the like, and generating identification accuracy data. Combining the place recognition result and the recognition accuracy data, carrying out data connection and filtering, and extracting destination information data with high accuracy for specifying a navigation destination.

Preferably, step S15 comprises the steps of:

step S151, acquiring navigation history data and preset common address data;

In the embodiment of the invention, related data including past navigation records, frequently-used places, frequently-used destinations and the like of a user are extracted and arranged from the existing navigation records or the user navigation history. The user can set some common addresses in advance, such as home, formula, frequent shops and the like, and preset storage is performed in the system. An accuracy threshold of the identification destination information is set so as to distinguish the accuracy of the identification. The threshold value can be preset through the expected matching result, the higher accuracy threshold value represents that the matching result is fewer, but the higher accuracy threshold value can also miss the correct matching result, the destination information data provided by the user is processed, and if the identification accuracy of the destination information exceeds the preset threshold value, the destination information is identified as the credible destination information. When the identification accuracy of the destination information reaches or exceeds a preset threshold, key information such as an address, a place name, and the like is extracted therefrom, and destination key data is generated. These key data may be digests or identifications of identifying accurate destination information. If the accuracy is lower than the threshold value, a fuzzy matching flow is entered. And carrying out fuzzy matching on destination information which fails to meet the accuracy requirement based on preset common address data, and identifying and generating fuzzy location entity data. And performing entity identification matching by using the fuzzy place entity data and preset common address data, for example, if the mode place entity data is 'home', performing associated inquiry with the data in the preset common address data, if the identifier of 'home' exists, considering successful matching, taking the address corresponding to the 'home' as destination keyword data, and if the matching fails, executing an address consultation flow. And under the condition of failure in matching, address consultation is carried out, and address consultation result data are obtained. And (3) associating the address consultation result data with the common address data, correcting the common address data, and ensuring the accuracy and the integrity of the data. And extracting destination address information based on the corrected common address data, and finally generating destination keyword data. The travel tools in different navigation records are identified and divided, and can be walking, bicycles, automobiles and the like. This process can be accomplished by recording the vehicles used in navigation. Tool division history data is formed, and the use condition of each tool in the navigation history is recorded. A common travel distance range may be preset, such as 1 to 3 km walk-out, 5 to 10 km city public transportation, etc. And correcting typical travel distance ranges of different travel tools according to the tool division history data, and formulating proper ranges of various travel tools of different users. For example, walking distance is corrected to 1 to 5 km, urban public transportation is corrected to 8 to 15 km, and so on. Thereby generating travel range data. And carrying out current travel range presetting on the travel range data based on the travel mode data, and generating current travel range data. And comparing and screening the destination keyword data with the map position according to the current travel range data, so as to ensure that the destination is in a proper travel range. According to the map position matching result, determining destination data conforming to the travel tool range, and directly generating navigation destination data when the matching result only contains one result; when the matching result only contains one result, accurate address question-answer confirmation is needed until navigation destination data is generated.

Preferably, step S153 includes the steps of:

In the embodiment of the invention, the collected destination information data is evaluated, and if the accuracy is smaller than or equal to the preset recognition accuracy threshold value, the collected destination information data is identified as fuzzy data. These destination information data with accuracy below the threshold are marked as ambiguous location entity data, which may include ambiguous location names, incomplete addresses, etc. And matching the fuzzy place entity data by using preset common address data. If a match is successful, it is considered valid destination information. And matching the fuzzy place entity data successfully matched with the common address data to generate matching result data. And when the matching is successful, extracting key information from the matching result to generate destination key data. And when the fuzzy place entity data matching fails, performing accurate address consultation of the mapping result data. Based on the mapping result data, obtaining accurate address consultation result and generating corresponding address consultation result data. And associating the address consultation result data with the fuzzy place entity data, and associating the accurate address information with the fuzzy place entity data so as to improve the accuracy of the data. And correcting and supplementing preset common address data according to the associated accurate address information. It may involve updating and revising information such as addresses, names, landmarks, etc. And extracting final destination address information based on the corrected common address data. Key information, such as place names, address details, etc., is extracted from the destination address information, generating final destination key data.

Preferably, step S2 comprises the steps of:

As an example of the present invention, referring to fig. 2, the step S2 in this example includes:

In the embodiment of the invention, the optimal or most suitable navigation route is calculated by using a suitable navigation algorithm (such as Dijkstra, a, etc.) based on the map data, the starting position data and the navigation destination data. And converting the navigation route calculated by the algorithm into a data format, wherein the data comprises information such as route nodes, directions, distances, predicted time and the like.

In embodiments of the present invention, the user is guided to the destination according to the planned path data by using a navigation engine or algorithm (e.g., a GPS navigation system or a map application). Navigation instructions, steering cues, etc. are provided by map data and real-time location information. Based on the planned path data and the real-time location information, the state of the current location of the user is analyzed, and a stay point or a region with longer stay time is identified. Parking areas, traffic congestion points or other possible stay states are identified, relevant information is recorded and stay state data is generated.

In the embodiment of the invention, the voice collection is started when the stay state is recognized by using special voice collection equipment (such as a microphone, a voice recognizer and the like). And carrying out voice acquisition in real time based on the stay state data, and capturing voice information during stay.

in the embodiment of the invention, the movement direction during stay is calculated by using the position information or the movement track in the stay state data. The direction of movement at rest is inferred by a change in position or direction, or by a gyroscopic device, the resting direction data is generated. Based on the navigation route information in the planned path data, the stay direction data is analyzed to determine whether a turn has occurred. Identifying whether a stay period occurs after a turn, and recording the type, angle or other relevant information of the turn to generate turned stay data.

In the embodiment of the invention, the voice data during stay is screened based on the data of the turned stay, so that the voice fragments related to turning are extracted. And selecting voice fragments related to the turning according to the time, the position or other characteristic information of the turning, and generating voice data to be processed.

Preferably, step S3 comprises the steps of:

As an example of the present invention, referring to fig. 3, the step S3 in this example includes:

In the embodiment of the invention, the voice data to be processed is preprocessed by applying audio processing technology such as filtering, downsampling and the like. Including removing background noise, eliminating interference, smoothing audio, etc., to reduce interference from subsequent processing. Noise reduction algorithm or software such as Fourier transform, wavelet transform or machine learning model is used to perform noise reduction processing on the preprocessed voice data, so as to improve the quality of the voice signal.

In the embodiment of the invention, the voice fragments which are irrelevant to navigation, such as background noise, nonsensical dialogue and the like, are screened out by utilizing the technologies of voice recognition, acoustic feature analysis and the like, so that voice data relevant to navigation are generated. And performing voice-to-text processing on the screened navigation related voice data, and converting the voice into text data by using a voice recognition technology, so that the subsequent keyword recognition or analysis is convenient.

In the embodiment of the invention, the keywords or phrases related to the navigation requirement are extracted from the text-to-speech data by applying natural language processing technology such as text word segmentation, keyword extraction algorithm and the like. Keywords expressing navigation requirements or indications are identified and extracted by using machine learning or rule matching and other methods to generate requirement keyword data.

In the embodiment of the invention, the relevant voice fragments are screened out through the requirement keywords, and the fragment voice data related to the navigation requirement can be generated by utilizing technologies such as time stamp, semantic matching or keyword matching. And further analyzing the voice quality of the selected voice data associated with navigation to judge whether the associated text information is reliable or not, and generating the optimized necessity evaluation data of the associated segment.

in the embodiment of the invention, the voice text data with high optimization necessity is selected by using the evaluation value in the necessity evaluation data as a basis to generate the analysis data to be optimized. The data most needed to be optimized can be selected by setting a threshold or by employing a sorting algorithm. The high priority phonetic text data is marked or classified in preparation for analysis for path optimization.

In the embodiment of the invention, problems or opportunities for improvement in navigation are identified by carrying out deep analysis on the data to be optimized. Areas in the path plan that need improvement, such as traffic congestion, inaccurate routes, etc., are determined. And formulating an optimization strategy according to the data analysis result, and determining a targeted path improvement scheme. Possible optimization strategies include adjusting route algorithms, considering real-time traffic information, selecting better roads, avoiding congested areas, etc. And converting the formulated path optimization strategy into a specific path planning adjustment scheme. And (5) using the optimized algorithm or rule to adjust and reprogram the original planning path data. Generating path optimization data including updated navigation path information according to the adjusted path plan. Ensuring that the optimization data can be effectively identified and applied by the navigation system.

Preferably, step S34 includes the steps of:

In the embodiment of the invention, the characteristic text data in the voice text is extracted by using natural language processing technologies such as word segmentation, part-of-speech tagging and the like of the text content, and the characteristic text data is associated with keywords based on the required keyword data. I.e., associating a particular keyword or phrase to a particular acoustic or semantic feature. Associated feature text data is generated, and the voice fragments related to the navigation requirements are associated with key information. A feature speech segment associated with a particular navigation need is selected based on the associated feature text data. Algorithms or rules may be employed to select speech segments that are closely related to demand, where considerations of semantic matching, speech quality, duration, etc. may be involved. The quality of the segmented speech data is assessed using acoustic analysis techniques such as signal-to-noise ratio, spectral smoothness, distortion, etc. Features of the segment-related speech data, such as clarity, speech rate, pronunciation accuracy, etc., are extracted to quantify the speech quality. Taking voice quality data as input, applying a preset optimizing necessity evaluation formula, wherein the formula fully considers parameters such as an evaluated time interval, total number of voice fragments, voice fragment quality score and the like, so as to accurately and rapidly evaluate and obtain a necessity evaluation value, and determining whether navigation route optimization is to be performed according to voice fragment information of a user. I.e. taking into account the influence of speech quality in the navigation optimization requirements. The calculated optimized necessity evaluation value is used to quantify the degree of influence of speech quality on path optimization. Or predicting influence of voice quality on path optimization based on historical data and a feature engineering construction model by a machine learning model, and carrying out statistical analysis and regression methods, namely carrying out optimization necessity evaluation by the methods of finding out correlation between voice quality and path optimization and the like, and generating necessity evaluation data.

Preferably, step S4 comprises the steps of:

As an example of the present invention, referring to fig. 4, the step S4 includes, in this example:

in the embodiment of the invention, the integrity and the safety of the data are ensured by transmitting the path optimization data to the cloud server through the safe communication protocol. And integrating and summarizing the collected path optimization data at the cloud end, wherein a database or a data warehouse mode can be adopted to summarize the data for subsequent analysis and processing.

In the embodiment of the invention, key features, such as information of a path optimization method, optimization parameters, effect evaluation and the like, are extracted from the optimized record summary data. Records are grouped using a similarity metric algorithm (e.g., a clustering algorithm), typically using K-means, hierarchical clustering, etc., with similar optimized records clustered into the same category. And analyzing the clustering result, identifying the types of different optimization modes or methods, and knowing the distribution and performance of similar optimization methods.

In the embodiment of the invention, the optimized content classification data which is clustered or categorized is prepared, wherein other data (such as effect evaluation data) related to the optimized content classification data is included. Analysis methods, such as root cause analysis, flowcharts, causal analysis, or statistical methods, are selected to find the root cause that leads to the optimization requirement. Analyzing the data, identifying specific reasons causing path optimization requirements, and sorting and recording the specific reasons.

In the embodiment of the invention, the defects or problems in the navigation system are identified by performing deep analysis through optimizing the reason data. Possible defect types are identified, such as navigation errors, inaccurate information, path deviations, etc. On the basis of the identified defects, the specific causes causing these defects are further extracted. Possible reasons are found from the data, such as data source problems, algorithmic logic, real-time updates, etc.

In the embodiment of the invention, a specific strategy for optimizing equipment is formulated through the navigation defect feedback data. Determining a solution to each defect may include improving algorithms, adding data sources, optimizing user interfaces, and so forth. And carrying out actual improvement or updating on the navigation equipment according to the formulated optimization strategy. Developing, testing and deploying an improved scheme aiming at defects, and ensuring that the defects after updating the navigation equipment are solved.

The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A speech recognition navigation method based on planned path data, characterized in that it comprises the following steps:

Step S1: performing navigation request voice detection based on a voice acquisition device to generate navigation request data; performing device positioning based on the navigation request data to generate starting position data; performing travel mode and travel destination identification based on the navigation request data to generate travel mode data and destination information data; performing map position matching based on the travel mode data and destination information data to generate navigation destination data;

Step S2: performing navigation route planning based on the starting position data and the navigation destination data to generate planned path data; performing turn and stop state analysis based on the planned path data to generate turned and stopped data; performing voice segment screening based on the turned and stopped data to generate voice data to be processed;

Step S3: intercepting the voice data to be processed to meet the optimization requirements and generating segment voice data; evaluating the necessity of optimization based on the segment voice data and generating necessity evaluation data; replanning the planned path data based on the necessity evaluation data and generating path optimization data; Step S3 includes the following steps:

Step S31: preprocessing the speech data to be processed to generate noise-reduced speech data;

Step S32: filtering out irrelevant voices from the noise reduction voice data to generate navigation-related voice data; converting the navigation-related voice data into text to generate text voice data;

Step S33: performing demand keyword recognition on the text and voice data to generate demand keyword data;

Step S34: selecting voice segments associated with the text voice data according to the required keyword data to generate segment voice data; performing optimization necessity evaluation according to the segment voice data to generate necessity evaluation data; Step S34 includes the following steps:

Step S341: extracting feature information from the text and speech data to generate feature text data;

Step S342: context-associate the feature text data according to the demand keyword data to generate associated feature text data;

Step S343: selecting characteristic voice segments from the navigation-related voice data based on the associated characteristic text data to generate segment voice data;

Step S344: performing voice quality assessment on the segment voice data to generate voice quality data;

Step S345: Perform optimization necessity evaluation on the voice quality data using the optimization necessity evaluation formula to generate necessity evaluation data; the optimization necessity evaluation formula in step S345 is as follows:

;

In the formula, is the necessity assessment value, is the start time of the evaluation, The end time of the evaluation. For time The total number of speech segments at For voice clips The quality rating of For voice clips Length, For voice clips The speech recognition accuracy of For voice clips The accuracy of environmental sound recognition is For voice clips The ambient noise level, For voice clips The incoherence value of is the length of the navigation route, For time The congestion level of the route at that time, is the irrelevance value of the route, is the frequency of route usage, Optimize frequency for history, is the base of natural logarithms, is the complexity of the route, It is the deviation correction value of the necessity assessment value;

Step S35: screening the text and speech data for high necessity data according to the necessity evaluation data to generate data to be optimized and analyzed;

Step S36: Formulate an optimization strategy based on the analyzed data to be optimized to generate path optimization strategy data; replan the planned path data according to the path optimization strategy data to generate path optimization data;

Step S4: Transmit the path optimization data to the cloud device to generate optimization record summary data; extract the causes of navigation defects based on the optimization record summary data to generate navigation defect feedback data; optimize the navigation equipment according to the navigation defect feedback data to generate navigator update data.

2. The speech recognition navigation method based on planned path data according to claim 1, characterized in that step S1 comprises the following steps:

Step S11: collecting user voice based on the voice collection device to generate user voice data;

Step S12: performing navigation request voice detection on the user voice data to generate navigation request data;

Step S13: positioning the device based on the navigation request data to generate starting position data;

Step S14: classifying the sentence keywords according to the navigation request data to generate classified keyword data; identifying the travel mode and travel destination based on the classified keyword data to generate travel mode data and destination information data;

Step S15: extract keywords from the destination information data to generate destination keyword data; preset the current travel range according to the travel mode data to generate current travel range data; match the map position based on the current travel range data and the destination keyword data to generate navigation destination data.

3. The speech recognition navigation method based on planned path data according to claim 2, characterized in that step S14 comprises the following steps:

Step S141: performing word segmentation annotation on the navigation request data to generate word segmentation request data; performing keyword extraction on the word segmentation request data to generate initial keyword data;

Step S142: semantically analyzing the navigation request data to generate word association data; using the word association data to classify the initial keyword data to generate classified keyword data;

Step S143: Identify the travel mode based on the classified keyword data and generate travel mode data;

Step S144: performing location entity recognition based on the classified keyword data to generate location recognition result data; performing geographic location mapping based on the location recognition result data to generate mapping result data;

Step S145: performing recognition accuracy evaluation based on the mapping result data to generate recognition accuracy data;

Step S146: Perform data concatenation processing on the location recognition result data and the recognition accuracy data to generate destination information data.

4. The speech recognition navigation method based on planned path data according to claim 2, characterized in that step S15 comprises the following steps:

Step S151: Acquire preset frequently used address data and navigation history data;

Step S152: processing the destination information data based on a preset recognition accuracy threshold, and generating destination keyword data when the destination information data is greater than the preset recognition accuracy threshold;

Step S153: when the destination information data is less than or equal to the preset recognition accuracy threshold, fuzzy address matching is performed based on the preset common address data to generate destination keyword data;

Step S154: dividing the navigation history data into travel tools to generate tool division history data; formulating the travel distance range according to the tool division history data to generate travel range data;

Step S155: Preset the current travel range of the travel range data based on the travel mode data to generate the current travel range data; match the map position based on the current travel range data and the destination keyword data to generate the navigation destination data.

5. The speech recognition navigation method based on planned path data according to claim 4, characterized in that step S153 comprises the following steps:

Step S1531: when the destination information data is less than or equal to a preset recognition accuracy threshold, generating fuzzy place entity data;

Step S1532: performing entity identification matching on the fuzzy location entity data based on the preset common address data to generate matching result data; when the matching result data is a successful match, generating destination keyword data;

Step S1533: when the matching result data indicates a matching failure, accurate address consultation is performed based on the mapping result data to generate address consultation result data;

Step S1534: performing location data association on the fuzzy location entity data according to the address consultation result data, and supplementing and correcting the preset common address data to generate common address data;

Step S1535: extract the destination address based on the commonly used address data and generate destination keyword data.

6. The speech recognition navigation method based on planned path data according to claim 2, characterized in that step S2 comprises the following steps:

Step S21: performing navigation route planning based on the starting position data and the navigation destination data to generate planned path data;

Step S22: performing path navigation according to the planned path data, and performing stay status analysis to generate stay status data;

Step S23: using a voice collection device and performing real-time voice collection based on the stay status data to generate voice data during the stay period;

Step S24: acquiring the movement direction based on the stay state data to generate stay direction data; extracting the turning direction from the stay direction data using the planned path data to generate turned stay data;

Step S25: filtering the voice data during the stop period based on the turn stop data to generate voice data to be processed.

7. The speech recognition navigation method based on planned path data according to claim 1, characterized in that step S4 comprises the following steps:

Step S41: Transmitting the path optimization data to the cloud device to generate optimization record summary data;

Step S42: clustering the optimization record summary data according to similar optimization methods to generate optimization content division data;

Step S43: performing optimization reason analysis based on the optimization content division data to generate optimization reason data;

Step S44: extracting the cause of the navigation defect based on the optimized cause data, and generating navigation defect feedback data;

Step S45: Formulate equipment optimization strategy based on navigation defect feedback data and generate equipment optimization strategy data; optimize navigation equipment based on the equipment optimization strategy data and generate navigator update data.