A vehicle real-time voice call processing method and system based on intranet browser

Abstract

The present invention relates to a method and system for processing real-time voice calls in a vehicle based on an intranet browser. The method comprises the following steps: Q1. When an autonomous vehicle is traveling on a preset road, it collects data information on the historical sound intensity levels and frequency values ​​of the ambient noise inside and outside the vehicle, and obtains data information on the voice inside and outside the vehicle in real time during a call; Q2. Based on the data information on the historical sound intensity levels and frequency values ​​of the ambient noise inside and outside the vehicle, a model for removing the ambient noise inside and outside the vehicle is constructed, and training and learning are performed to obtain a trained model for removing the ambient noise inside and outside the vehicle. The present invention not only solves the problems of high voice call delay, poor quality, and excessive bandwidth usage in the prior art, and ensures data security during calls, but also optimizes the quality of voice calls. Even when the vehicle is driving, the user can clearly hear the other party's voice, ensuring voice quality during calls.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle voice processing, and in particular to a vehicle real-time voice call processing method and system based on an intranet browser. Background Art

[0002] With the rapid development of autonomous driving technology, Level 4 autonomous vehicles are now capable of fully automated driving under certain conditions. However, real-time communication between the vehicle and the driver, passengers, or remote monitoring center in an autonomous driving environment has become a pressing technical challenge. Traditional communication methods (such as mobile phones and public networks) can suffer from high latency, insufficient bandwidth, and poor security, especially during autonomous vehicle operations. Furthermore, calls can be affected by ambient noise both inside and outside the vehicle, degrading the passenger experience. Summary of the Invention

[0003] In view of the above problems, the present invention provides a vehicle real-time voice call processing method and system based on an intranet browser, which not only solves the problems of high voice call delay, poor quality, excessive bandwidth occupation, etc. in the prior art, and ensures data security during the call, but also optimizes the voice call quality. Even when the vehicle is driving, the user can clearly hear the other party's voice, ensuring the voice quality during the call.

[0004] In order to achieve the above-mentioned and other related purposes, the present invention provides the following technical solutions:

[0005] A method for processing real-time vehicle voice calls based on an intranet browser, the method comprising:

[0006] Q1. An autonomous vehicle drives on a pre-set road, collecting historical sound intensity levels and frequencies of ambient noise inside and outside the vehicle, and acquiring real-time voice data during calls.

[0007] Q2. Based on the historical sound intensity level and frequency data of the ambient noise inside and outside the vehicle, a model for removing the ambient noise inside and outside the vehicle is constructed, and training and learning are performed to obtain a trained model for removing the ambient noise inside and outside the vehicle;

[0008] Q3. The data information of the voice inside and outside the car is input into the trained model for removing the ambient noise inside and outside the car, and the ambient noise in the voice inside and outside the car is removed to obtain the data information of the voice inside and outside the car after removal;

[0009] Q4. Based on the voice data information inside and outside the car after the removal, the multi-objective sparrow optimization algorithm based on chaotic mapping is used to optimize the voice data inside and outside the car to obtain the optimized voice data information inside and outside the car;

[0010] Q5. Based on the optimized voice data information inside and outside the vehicle, construct a vehicle voice call quality evaluation function W, evaluate the quality of the voice inside and outside the vehicle, and obtain evaluation data information of the voice quality inside and outside the vehicle.

[0011] Furthermore, in step Q2, the construction of a model for removing ambient noise inside and outside the vehicle, and the training and learning thereof include:

[0012] Q21. Based on the historical sound intensity level and frequency data of the ambient noise inside and outside the vehicle, establish a filter function R of the ambient noise inside and outside the vehicle.

[0013]

[0014] Where x1 is the historical sound intensity level data of the ambient noise inside and outside the vehicle, x2 is the historical frequency data of the ambient noise inside and outside the vehicle, α1, α2, and α3 are the weight coefficients of the ambient noise inside and outside the vehicle, and the filter value of the ambient noise inside and outside the vehicle is characterized to obtain the filter value data of the ambient noise inside and outside the vehicle;

[0015] Q22. Input the filter value data of the vehicle interior and exterior ambient noise into the vehicle interior and exterior ambient noise removal model for training and learning, and determine the vehicle interior and exterior ambient noise removal function U.

[0016]

[0017] Among them, y is the data information of the filtered value of the ambient noise inside and outside the car, β1, β2 and β3 are the penalty factors for removing the ambient noise inside and outside the car;

[0018] Q23. Based on the function U for removing the ambient noise inside and outside the vehicle, obtain the trained voice data information inside and outside the vehicle.

[0019] Furthermore, the penalty factors β1, β2 and β3 for removing the ambient noise inside and outside the vehicle are:

[0020]

[0021] Wherein, y is the data information of the filtered value of the ambient noise inside and outside the vehicle.

[0022] Furthermore, the constraints of the weight coefficients α1, α2, and α3 of the ambient noise inside and outside the vehicle are:

[0023]

[0024] Furthermore, in step Q4, the optimization of the voice data inside and outside the vehicle by using a multi-objective sparrow optimization algorithm based on chaotic mapping includes:

[0025] Q41. Based on the data information of the voice inside and outside the car after the removal, establish the chaotic mapping function P of the voice inside and outside the car,

[0026]

[0027] Wherein, z is the data information of the voice inside and outside the car after removal, δ1, δ2 and δ3 are the chaotic mapping parameters of the voice inside and outside the car, and the chaotic mapping sequence of the voice inside and outside the car is characterized to obtain the data information of the chaotic mapping sequence of the voice inside and outside the car;

[0028] Q42. Based on the data information of the chaotic mapping sequence of the voice inside and outside the car, the sparrow population is initialized, the population parameters and the maximum number of iterations are determined, and the data information of the sparrow population after the initialization is obtained;

[0029] Q43. Based on the data information of the initialized sparrow population, establish the fitness function S of the sparrow population individuals,

[0030]

[0031] Among them, r is the data information of the initialized sparrow population, γ1, γ2 and γ3 are the fitness determining factors of the individual sparrow population, and the fitness values ​​of the individual sparrow population are calculated to obtain the data information of the fitness values ​​of the individual sparrow population;

[0032] Q44. Based on the data information of the fitness value of the sparrow population individuals, establish a target optimization function G,

[0033]

[0034] Among them, h is the data information of the fitness value of the sparrow population individual, η1, η2 and η3 are the optimization factors of the sparrow population individual, and the voice data inside and outside the car are optimized to obtain the optimized voice data information inside and outside the car.

[0035] Furthermore, the constraint function f of the optimization factors η1, η2 and η3 of the sparrow population individuals is,

[0036]

[0037] Among them, the value range of the constraint function f is (1,2).

[0038] Furthermore, the vehicle's voice call quality evaluation function W is:

[0039]

[0040] Among them, g is the optimized data information of the voice inside and outside the car, and λ1, λ2 and λ3 are the evaluation factors of the voice inside and outside the car.

[0041] Furthermore, the method further comprises:

[0042] Q6. Based on the evaluation data information of the voice quality inside and outside the car, a preset threshold is set. If the evaluation data information of the voice quality inside and outside the car is less than the preset threshold, the requirement is not met, and the process returns to step Q3. If the evaluation data information of the voice quality inside and outside the car is greater than the preset threshold, the requirement is met, and a real-time voice call is conducted.

[0043] In order to achieve the above-mentioned objectives and other related objectives, the present invention also provides a vehicle real-time voice call processing system based on an intranet browser, including a computer device that is programmed or configured to execute any one of the steps of the vehicle real-time voice call processing method based on an intranet browser.

[0044] In order to achieve the above-mentioned purpose and other related purposes, the present invention also provides a computer-readable storage medium, which stores a computer program programmed or configured to execute any one of the above-mentioned vehicle real-time voice call processing methods based on an intranet browser.

[0045] The present invention has the following positive effects:

[0046] 1. The present invention removes the environmental noise from the voice inside and outside the vehicle by constructing a model to remove the environmental noise from the voice inside and outside the vehicle, obtaining the data information of the voice inside and outside the vehicle after the removal. The multi-objective sparrow optimization algorithm based on chaotic mapping is then used to optimize the voice data inside and outside the vehicle. This not only improves the voice call quality, allowing users to clearly hear the other party's voice even when the vehicle is in motion, ensuring voice quality during the call, but also ensures the stability of the call process, avoiding the delay fluctuations and bandwidth limitations brought by the public network, and is particularly suitable for use in autonomous driving environments.

[0047] 2. The present invention evaluates the quality of voice inside and outside the vehicle by constructing a vehicle voice call quality evaluation function W. This not only solves the problems of high voice call delay, poor quality, and excessive bandwidth usage in the existing technology, and ensures data security during the call, but also ensures that real-time voice calls between the vehicle and browser sides have high quality and low latency, adapting to communication needs in different network environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Schematic diagram of the method flow of the present invention;

[0049] Figure 2A schematic diagram of the process of constructing a model for removing ambient noise inside and outside a vehicle according to the present invention;

[0050] Figure 3 The figure is a flow chart of the multi-objective sparrow optimization algorithm based on chaotic mapping of the present invention. DETAILED DESCRIPTION

[0051] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0052] Example 1: Figure 1 As shown, a method for processing real-time vehicle voice calls based on an intranet browser includes:

[0053] Q1. An autonomous vehicle drives on a pre-set road, collecting historical sound intensity levels and frequencies of ambient noise inside and outside the vehicle, and acquiring real-time voice data during calls.

[0054] Q2. Based on the historical sound intensity level and frequency data of the ambient noise inside and outside the vehicle, a model for removing the ambient noise inside and outside the vehicle is constructed, and training and learning are performed to obtain a trained model for removing the ambient noise inside and outside the vehicle;

[0055] Q3. The data information of the voice inside and outside the car is input into the trained model for removing the ambient noise inside and outside the car, and the ambient noise in the voice inside and outside the car is removed to obtain the data information of the voice inside and outside the car after removal;

[0056] Q4. Based on the voice data information inside and outside the car after the removal, the multi-objective sparrow optimization algorithm based on chaotic mapping is used to optimize the voice data inside and outside the car to obtain the optimized voice data information inside and outside the car;

[0057] Q5. Based on the optimized voice data information inside and outside the vehicle, construct a vehicle voice call quality evaluation function W, evaluate the quality of the voice inside and outside the vehicle, and obtain evaluation data information of the voice quality inside and outside the vehicle.

[0058] In this embodiment, if Figure 2 As shown, in step Q2, the construction of the environmental noise removal model inside and outside the vehicle, and the training and learning include:

[0059] Q21. Based on the historical sound intensity level and frequency data of the ambient noise inside and outside the vehicle, establish a filter function R of the ambient noise inside and outside the vehicle.

[0060]

[0061] Where x1 is the historical sound intensity level data of the ambient noise inside and outside the vehicle, x2 is the historical frequency data of the ambient noise inside and outside the vehicle, α1, α2, and α3 are the weight coefficients of the ambient noise inside and outside the vehicle, and the filter value of the ambient noise inside and outside the vehicle is characterized to obtain the filter value data of the ambient noise inside and outside the vehicle;

[0062] Q22. Input the filter value data of the vehicle interior and exterior ambient noise into the vehicle interior and exterior ambient noise removal model for training and learning, and determine the vehicle interior and exterior ambient noise removal function U.

[0063]

[0064] Among them, y is the data information of the filtered value of the ambient noise inside and outside the car, β1, β2 and β3 are the penalty factors for removing the ambient noise inside and outside the car;

[0065] Q23. Based on the function U for removing the ambient noise inside and outside the vehicle, obtain the trained voice data information inside and outside the vehicle.

[0066] In this embodiment, the penalty factors β1, β2 and β3 for removing the ambient noise inside and outside the vehicle are:

[0067]

[0068] Wherein, y is the data information of the filtered value of the ambient noise inside and outside the vehicle.

[0069] In this embodiment, the constraints of the weight coefficients α1, α2, and α3 of the ambient noise inside and outside the vehicle are:

[0070]

[0071] In this embodiment, the browser-side device includes a browser that supports the WebRTC protocol, an audio acquisition device (such as a microphone), an audio playback device (such as a speaker) and an intranet communication module (such as a Wi-Fi module), which is used to initiate voice call requests, receive voice data, and play voice.

[0072] Vehicle-side equipment: This includes an onboard audio processing unit, microphone array, speakers, audio encoding and decoding modules, intranet communication modules, and an autonomous driving control system. This system is used to collect voices from inside and outside the vehicle, encode and compress the data, transmit the voice data, and play the voices.

[0073] Intranet communication equipment: including high-bandwidth, low-latency routers, switches, and bandwidth management equipment to ensure efficient and stable communication quality for real-time voice calls between the browser and the vehicle.

[0074] Data storage device: used to store voice call data, call logs, communication protocols, and system status information. Data storage uses encryption to ensure the security of communication data.

[0075] Real-time Example 2: Based on the vehicle real-time voice call processing method based on the intranet browser in Example 1, the present invention is further illustrated and described below.

[0076] like Figure 1 As shown, a method for processing real-time vehicle voice calls based on an intranet browser includes:

[0077] Q1. An autonomous vehicle drives on a pre-set road, collecting historical sound intensity levels and frequencies of ambient noise inside and outside the vehicle, and acquiring real-time voice data during calls.

[0078] Q2. Based on the historical sound intensity level and frequency data of the ambient noise inside and outside the vehicle, a model for removing the ambient noise inside and outside the vehicle is constructed, and training and learning are performed to obtain a trained model for removing the ambient noise inside and outside the vehicle;

[0079] Q3. The data information of the voice inside and outside the car is input into the trained model for removing the ambient noise inside and outside the car, and the ambient noise in the voice inside and outside the car is removed to obtain the data information of the voice inside and outside the car after removal;

[0080] Q4. Based on the voice data information inside and outside the car after the removal, the multi-objective sparrow optimization algorithm based on chaotic mapping is used to optimize the voice data inside and outside the car to obtain the optimized voice data information inside and outside the car;

[0081] Q5. Based on the optimized voice data information inside and outside the vehicle, construct a vehicle voice call quality evaluation function W, evaluate the quality of the voice inside and outside the vehicle, and obtain evaluation data information of the voice quality inside and outside the vehicle.

[0082] In this embodiment, if Figure 3 As shown, in step Q4, the optimization of the voice data inside and outside the vehicle using the multi-objective sparrow optimization algorithm based on chaotic mapping includes:

[0083] Q41. Based on the data information of the voice inside and outside the car after the removal, establish the chaotic mapping function P of the voice inside and outside the car,

[0084]

[0085] Wherein, z is the data information of the voice inside and outside the car after removal, δ1, δ2 and δ3 are the chaotic mapping parameters of the voice inside and outside the car, and the chaotic mapping sequence of the voice inside and outside the car is characterized to obtain the data information of the chaotic mapping sequence of the voice inside and outside the car;

[0086] Q42. Based on the data information of the chaotic mapping sequence of the voice inside and outside the car, the sparrow population is initialized, the population parameters and the maximum number of iterations are determined, and the data information of the sparrow population after the initialization is obtained;

[0087] Q43. Based on the data information of the initialized sparrow population, establish the fitness function S of the sparrow population individuals,

[0088]

[0089] Among them, r is the data information of the initialized sparrow population, γ1, γ2 and γ3 are the fitness determining factors of the individual sparrow population, and the fitness values ​​of the individual sparrow population are calculated to obtain the data information of the fitness values ​​of the individual sparrow population;

[0090] Q44. Based on the data information of the fitness value of the sparrow population individuals, establish a target optimization function G,

[0091]

[0092] Among them, h is the data information of the fitness value of the sparrow population individual, η1, η2 and η3 are the optimization factors of the sparrow population individual, and the voice data inside and outside the car are optimized to obtain the optimized voice data information inside and outside the car.

[0093] In this embodiment, the constraint function f of the optimization factors η1, η2 and η3 of the sparrow population individuals is,

[0094]

[0095] Among them, the value range of the constraint function f is (1,2).

[0096] In this embodiment, the vehicle's voice call quality evaluation function W is:

[0097]

[0098] Among them, g is the optimized data information of the voice inside and outside the car, and λ1, λ2 and λ3 are the evaluation factors of the voice inside and outside the car.

[0099] In this embodiment, the method further includes:

[0100] Q6. Based on the evaluation data information of the voice quality inside and outside the car, a preset threshold is set. If the evaluation data information of the voice quality inside and outside the car is less than the preset threshold, the requirement is not met, and the process returns to step Q3. If the evaluation data information of the voice quality inside and outside the car is greater than the preset threshold, the requirement is met, and a real-time voice call is conducted.

[0101] In this embodiment, the present invention also provides a vehicle real-time voice call processing system based on an intranet browser, including a computer device that is programmed or configured to execute any one of the steps of the vehicle real-time voice call processing method based on an intranet browser.

[0102] In this embodiment, the present invention further provides a computer-readable storage medium, which stores a computer program programmed or configured to execute any one of the methods for processing real-time vehicle voice calls based on an intranet browser.

[0103] Any reference to memory, storage, database or other media used in the embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0104] In summary, the present invention not only solves the problems of high voice call delay, poor quality, and excessive bandwidth occupation in the prior art, and ensures data security during the call, but also optimizes the quality of voice calls. Even when the vehicle is driving, the user can clearly hear the other party's voice, ensuring the voice quality during the call.

[0105] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A vehicle real-time voice call processing method based on an intranet browser, characterized in that: The method comprises: Q1. An autonomous vehicle drives on a pre-set road, collecting historical sound intensity levels and frequencies of ambient noise inside and outside the vehicle, and acquiring real-time voice data during calls. Q2. Based on the historical sound intensity level and frequency data of the ambient noise inside and outside the vehicle, a model for removing the ambient noise inside and outside the vehicle is constructed, and training and learning are performed to obtain a trained model for removing the ambient noise inside and outside the vehicle; Q3. The data information of the voice inside and outside the car is input into the trained model for removing the ambient noise inside and outside the car, and the ambient noise in the voice inside and outside the car is removed to obtain the data information of the voice inside and outside the car after removal; Q4. Based on the voice data information inside and outside the car after the removal, the multi-objective sparrow optimization algorithm based on chaotic mapping is used to optimize the voice data inside and outside the car to obtain the optimized voice data information inside and outside the car; Q5. Based on the optimized voice data information inside and outside the vehicle, construct a vehicle voice call quality evaluation function W, evaluate the quality of the voice inside and outside the vehicle, and obtain evaluation data information of the voice quality inside and outside the vehicle.

2. The method for processing real-time vehicle voice calls based on an intranet browser according to claim 1, characterized in that: In step Q2, the construction of a model for removing ambient noise inside and outside the vehicle, and the training and learning thereof include: Q21. Based on the historical sound intensity level and frequency data of the ambient noise inside and outside the vehicle, establish a filter function R of the ambient noise inside and outside the vehicle. Where x1 is the historical sound intensity level data of the ambient noise inside and outside the vehicle, x2 is the historical frequency data of the ambient noise inside and outside the vehicle, α1, α2, and α3 are the weight coefficients of the ambient noise inside and outside the vehicle, and the filter value of the ambient noise inside and outside the vehicle is characterized to obtain the filter value data of the ambient noise inside and outside the vehicle; Q22. Input the filter value data of the vehicle interior and exterior ambient noise into the vehicle interior and exterior ambient noise removal model for training and learning, and determine the vehicle interior and exterior ambient noise removal function U. Among them, y is the data information of the filtered value of the ambient noise inside and outside the car, β1, β2 and β3 are the penalty factors for removing the ambient noise inside and outside the car; Q23. Based on the function U for removing the ambient noise inside and outside the vehicle, obtain the trained voice data information inside and outside the vehicle.

3. The method for processing real-time vehicle voice calls based on an intranet browser according to claim 2, characterized in that: The penalty factors β1, β2 and β3 for removing the ambient noise inside and outside the vehicle are: Wherein, y is the data information of the filtered value of the ambient noise inside and outside the vehicle.

4. The method for processing real-time vehicle voice calls based on an intranet browser according to claim 2, characterized in that: The constraints of the weight coefficients α1, α2 and α3 of the ambient noise inside and outside the vehicle are:

5. The method for processing real-time vehicle voice calls based on an intranet browser according to claim 1, characterized in that: In step Q4, the optimization of the voice data inside and outside the vehicle using a multi-objective sparrow optimization algorithm based on chaotic mapping includes: Q41. Based on the data information of the voice inside and outside the car after the removal, establish the chaotic mapping function P of the voice inside and outside the car, Wherein, z is the data information of the voice inside and outside the car after removal, δ1, δ2 and δ3 are the chaotic mapping parameters of the voice inside and outside the car, and the chaotic mapping sequence of the voice inside and outside the car is characterized to obtain the data information of the chaotic mapping sequence of the voice inside and outside the car; Q42. Based on the data information of the chaotic mapping sequence of the voice inside and outside the car, the sparrow population is initialized, the population parameters and the maximum number of iterations are determined, and the data information of the sparrow population after the initialization is obtained; Q43. Based on the data information of the initialized sparrow population, establish the fitness function S of the sparrow population individuals, Among them, r is the data information of the initialized sparrow population, γ1, γ2 and γ3 are the fitness determining factors of the individual sparrow population, and the fitness values ​​of the individual sparrow population are calculated to obtain the data information of the fitness values ​​of the individual sparrow population; Q44. Based on the data information of the fitness value of the sparrow population individuals, establish a target optimization function G, Among them, h is the data information of the fitness value of the sparrow population individual, η1, η2 and η3 are the optimization factors of the sparrow population individual, and the voice data inside and outside the car are optimized to obtain the optimized voice data information inside and outside the car.

6. The method for processing real-time vehicle voice calls based on an intranet browser according to claim 5, characterized in that: The constraint function f of the optimization factors η1, η2 and η3 of the sparrow population individuals is, Among them, the value range of the constraint function f is (1,2).

7. The method for processing real-time vehicle voice calls based on an intranet browser according to claim 1, characterized in that: The vehicle's voice call quality evaluation function W is: Among them, g is the optimized data information of the voice inside and outside the car, and λ1, λ2 and λ3 are the evaluation factors of the voice inside and outside the car.

8. The method for processing real-time vehicle voice calls based on an intranet browser according to claim 1, characterized in that: The method further comprises: Q6. Based on the evaluation data information of the voice quality inside and outside the car, a preset threshold is set. If the evaluation data information of the voice quality inside and outside the car is less than the preset threshold, the requirement is not met, and the process returns to step Q3. If the evaluation data information of the voice quality inside and outside the car is greater than the preset threshold, the requirement is met, and a real-time voice call is conducted.

9. A vehicle real-time voice call processing system based on an intranet browser, comprising a computer device, characterized in that: The computer device is programmed or configured to execute the steps of the vehicle real-time voice call processing method based on an intranet browser as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program programmed or configured to execute the vehicle real-time voice call processing method based on an intranet browser as described in any one of claims 1 to 8.

Images