Passive intelligent navigation system based on NLP technology

Abstract

The invention relates to the technical field of intelligent navigation, and provides a passive intelligent navigation system based on an NLP technology, which comprises a positioning module, a boundary early warning module, a voice conversion module, a voice recognition module, a prompt engineering module, a navigation module, a navigation text generation module, a voice generation module and a remote app management background, the positioning module acquires real-time position information of a user by using a positioning technology, the boundary early warning module is used for prompting the user to return a mode for the user to perform interaction selection through voice, and the voice conversion module and the voice recognition module convert received voice of the user into text information. The boundary early warning is triggered by adopting the geo-fencing technology, and the position of the user is monitored in real time and compared with the preset geo-fencing, so that the early warning can be given out when the user is about to leave the set area, the user is reminded to pay attention to safety, the user can be prevented from getting lost in an unfamiliar environment or entering a dangerous area, and the travel of the old is greatly facilitated.

Description

Technical Field

[0001] The present invention relates to the field of intelligent navigation technology, and in particular to a passive intelligent navigation system based on NLP technology. Background Art

[0002] China has entered an aging society, and urban changes are becoming more and more serious. A large number of elderly people do not know how to use smart terminals, especially those with Alzheimer's disease. It is common for them to not be able to find their way home when traveling, which brings a lot of inconvenience to the elderly themselves and their families. How to enable the elderly to travel passively and intelligently is a livelihood hotspot that needs to be paid close attention to.

[0003] Although smartphones can meet daily travel needs, many elderly people do not know how to use them. By embedding intelligent voice assistants based on NLP technology into speakers, they can passively provide navigation for the elderly by triggering warning events. At the same time, family members can also remotely observe the elderly's location in real time through the app and make remote calls, which greatly facilitates the elderly's travel and prevents them from being unable to return home. Therefore, a passive intelligent navigation system based on NLP technology is needed. Summary of the invention

[0004] In view of the deficiencies of the prior art, the present invention provides a passive intelligent navigation system based on NLP technology, which greatly facilitates the travel of the elderly and prevents them from getting lost when going out.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0006] A passive intelligent navigation system based on NLP technology includes a positioning module, a boundary warning module, a voice conversion module, a voice recognition module, a prompt engineering module, a navigation module, a navigation text generation module, a voice generation module and a remote app management background. The positioning module uses positioning technology to obtain user real-time location information. The boundary warning module is used to prompt the user to return to a certain way for the user to select through voice interaction. The voice conversion module and the voice recognition module convert the received user's voice into text information. The prompt engineering module is used to perform semantic understanding and reasoning on the text information of the voice and output it in a streaming manner. The navigation module is used to plan a navigation route. The navigation text generation module cooperates with the voice generation module to convert the navigation route into voice output. The remote app management background is used to locate the real-time position of the device and initiate a remote video call.

[0007] Preferably, the positioning technology uses any one of the device networks or wireless networks such as Beidou satellite navigation system, base station positioning, Wi-Fi positioning, etc. to obtain the real-time location of the user.

[0008] Preferably, the voice conversion module uses the Pyaud io library to generate a wav file from the user's voice information.

[0009] Preferably, the speech recognition module adopts the Tongyi SenseVoi ce model.

[0010] Preferably, the prompt engineering module adopts the Langchai n framework.

[0011] Preferably, the speech generation module adopts the common meaning CosyVoice.

[0012] A passive intelligent navigation method based on NLP technology comprises the following steps:

[0013] Step 1: Use the positioning module to obtain the user's real-time location information through positioning technology, provide a basis for the setting and monitoring of geographic fences, and simultaneously transmit it to the API of Gaode Map. Generate a restricted area through the boundary warning module, set the trigger conditions, monitor the user's location in real time and compare it with the boundary point coordinates of the restricted area. When the trigger condition is triggered, the speaker device will automatically broadcast the warning and prompt the user to return to the method for the user to choose through voice interaction. If the condition is not triggered, continue to monitor the user's location in real time;

[0014] Step 2: Receive the voice information of the user in step 1 through the voice conversion module, generate a wav file, and use the voice recognition module to convert the wav file into text information;

[0015] Step 3: Use the prompt engineering module to perform semantic understanding and reasoning on the text information in step 2, and output it in a streaming manner to make the interaction smoother;

[0016] Step 4: Use the navigation module to command the system to call the Amap API, pass in the current location information of the positioning system, and use the preset destination to plan the navigation route according to the travel mode obtained from the voice;

[0017] Step 5: Use the navigation text generation module in conjunction with the Tongyi Qianwen big model to convert the unstructured navigation route information returned by the AutoNavi API into a natural language description, and transmit it to the speech generation module to output the text returned by the big model in speech.

[0018] Preferably, the step of generating a restricted area in step one is: using geo-fencing technology to define an area on a map, and setting either leaving the area or staying for more than a certain period of time as a trigger condition.

[0019] Preferably, the steps of semantic understanding and reasoning in step three are: passing the recognized speech text data to the PromptTemp l ate prompt template, passing the input text to the Tongyi Qianwen large language model by creating an LLM chain, and processing it using the Tongyi Qianwen large language model.

[0020] Preferably, the preset destination in step 4 is the user's residential area, relatives' location, etc.

[0021] The present invention provides a passive intelligent navigation system based on NLP technology. It has the following beneficial effects:

[0022] 1. The present invention uses geo-fence technology to trigger boundary warnings. By monitoring the user's location in real time and comparing it with the preset geo-fence, it can issue a warning when the user is about to leave the set area, reminding the user to pay attention to safety, helping to prevent the user from getting lost or entering dangerous areas in unfamiliar environments, greatly facilitating the travel of the elderly.

[0023] 2. The present invention allows users to passively initiate voice interaction, which combines the navigation capabilities of a large language model and a map to provide users with more real-time, natural, and intelligent navigation, reducing the complexity of user operations and allowing the elderly to conveniently use the navigation function, making navigation more comfortable. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A diagram of a passive intelligent navigation system based on NLP technology of the present invention;

[0025] Figure 2 It is a schematic diagram of the intelligent navigation process of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Example:

[0028] As one aspect of this application, please see the attached Figure 1The embodiment of the present invention provides a passive intelligent navigation system based on NLP technology, including a positioning module, a boundary warning module, a voice conversion module, a voice recognition module, a prompt engineering module, a navigation module, a navigation text generation module, a voice generation module and a remote app management background. The positioning module uses positioning technology to obtain user real-time location information. The boundary warning module is used to prompt the user to return to a certain way for the user to select through voice interaction. The voice conversion module and the voice recognition module convert the received user's voice into text information. The prompt engineering module is used to perform semantic understanding and reasoning on the text information of the voice and output it in a streaming manner. The navigation module is used to plan a navigation route. The navigation text generation module cooperates with the voice generation module to convert the navigation route into voice output. The remote app management background is used to locate the real-time location of the device and initiate a remote video call.

[0029] The positioning technology uses any one of the Beidou satellite navigation system, base station positioning, Wi-Fi positioning and other device networks or wireless networks to obtain the user's real-time location.

[0030] The voice conversion module uses the Pyaud io library to generate a wav file for the user's voice information, the speech recognition module uses the Tongyi SenseVoi ce model, the prompt engineering module uses the Langchai n framework, and the speech generation module uses the Tongyi CosyVoi ce.

[0031] By allowing users to passively initiate voice interaction, combined with the navigation capabilities of a large language model and maps, it provides users with more real-time, natural, and intelligent navigation, reduces the complexity of user operations, and enables the elderly to use the navigation function conveniently, making navigation guidance more comfortable.

[0032] Based on the above-mentioned passive intelligent navigation system based on NLP technology, as another aspect of this application, please refer to the attached Figure 2 , a passive intelligent navigation method based on NLP technology, comprising the following steps:

[0033] Step 1: Use the positioning module to obtain the user's real-time location information through positioning technology, provide a basis for the setting and monitoring of geographic fences, and simultaneously transmit it to the API of Gaode Map. Use the geographic fence technology through the boundary warning module to define the area on the map, and set any one of leaving the area and staying for more than a certain period of time as the trigger condition, and set the trigger condition to generate a restricted area, monitor the user's location in real time and compare it with the boundary point coordinates of the restricted area. When the trigger condition is triggered, the speaker device will automatically broadcast the warning and prompt the user to return to the method for the user to choose through voice interaction. If the condition is not triggered, the user's location will continue to be monitored in real time;

[0034] Step 2: Receive the voice information of the user in step 1 through the voice conversion module, generate a wav file, and use the voice recognition module to convert the wav file into text information;

[0035] Step 3: Use the prompt engineering module to perform semantic understanding and reasoning on the text information in step 2, pass the recognized speech text data to the PromptTemp l ate prompt template, create an LLM chain to pass the input text to the Tongyi Qianwen large language model, use the Tongyi Qianwen large language model to process it, and perform streaming output to make the interaction smoother;

[0036] Step 4: Call the Amap API through the navigation module command system, input the current location information of the positioning system, and use the preset destination to plan the navigation route according to the travel mode obtained from the voice. The preset destination is the user's residential area, relatives' location and other areas;

[0037] Step 5: Use the navigation text generation module in conjunction with the Tongyi Qianwen big model to convert the unstructured navigation route information returned by the AutoNavi API into a natural language description, and transmit it to the speech generation module to output the text returned by the big model in speech.

[0038] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A passive intelligent navigation system based on NLP technology, characterized in that: It includes a positioning module, a boundary warning module, a voice conversion module, a voice recognition module, a prompt engineering module, a navigation module, a navigation text generation module, a voice generation module and a remote app management background. The positioning module uses positioning technology to obtain the user's real-time location information. The boundary warning module is used to prompt the user to return to the method for the user to select through voice interaction. The voice conversion module and the voice recognition module convert the received user's voice into text information. The prompt engineering module is used to perform semantic understanding and reasoning on the text information of the voice and output it in a streaming manner. The navigation module is used to plan the navigation route. The navigation text generation module cooperates with the voice generation module to convert the navigation route into voice output. The remote app management background is used to locate the real-time position of the device and initiate a remote video call.

2. According to claim 1, a passive intelligent navigation system based on NLP technology is characterized in that: The positioning technology uses any one of the device networks or wireless networks such as the Beidou satellite navigation system, base station positioning, and Wi-Fi positioning to obtain the user's real-time location.

3. According to claim 1, a passive intelligent navigation system based on NLP technology is characterized in that: The voice conversion module uses the Pyaudio library to generate a wav file from the user's voice information.

4. According to claim 1, a passive intelligent navigation system based on NLP technology is characterized in that: The speech recognition module uses the Tongyi SenseVoice model.

5. The passive intelligent navigation system based on NLP technology according to claim 1, characterized in that: The prompt engineering module adopts the Langchain framework.

6. The passive intelligent navigation system based on NLP technology according to claim 1, characterized in that: The voice generation module adopts CosyVoice.

7. A passive intelligent navigation method based on NLP technology, using a passive intelligent navigation system based on NLP technology as claimed in any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Use the positioning module to obtain the user's real-time location information through positioning technology, provide a basis for the setting and monitoring of geographic fences, and simultaneously transmit it to the API of Gaode Map. Generate a restricted area through the boundary warning module, set the trigger conditions, monitor the user's location in real time and compare it with the boundary point coordinates of the restricted area. When the trigger condition is triggered, the speaker device will automatically broadcast the warning and prompt the user to return to the method for the user to choose through voice interaction. If the condition is not triggered, continue to monitor the user's location in real time; Step 2: Receive the voice information of the user in step 1 through the voice conversion module, generate a wav file, and use the voice recognition module to convert the wav file into text information; Step 3: Use the prompt engineering module to perform semantic understanding and reasoning on the text information in step 2, and output it in a streaming manner to make the interaction smoother; Step 4: Use the navigation module to command the system to call the Amap API, pass in the current location information of the positioning system, and use the preset destination to plan the navigation route according to the travel mode obtained from the voice; Step 5: Use the navigation text generation module in conjunction with the Tongyi Qianwen big model to convert the unstructured navigation route information returned by the AutoNavi API into a natural language description, and transmit it to the speech generation module to output the text returned by the big model in speech.

8. The passive intelligent navigation system based on NLP technology according to claim 7, characterized in that: The step of generating the restricted area in step 1 is: using geo-fencing technology to define the area on the map, and setting any one of leaving the area and staying for more than a certain period of time as a trigger condition.

9. The passive intelligent navigation system based on NLP technology according to claim 7, characterized in that: The steps of semantic understanding and reasoning in step three are: passing the recognized speech text data to the PromptTemplate prompt template, passing the input text to the Tongyi Qianwen large language model by creating an LLM chain, and processing it using the Tongyi Qianwen large language model.

10. The passive intelligent navigation system based on NLP technology according to claim 7, characterized in that: The preset destination in step 4 is the user's residential area, relatives' location, etc.

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