CN117793599A - Mosquito detection method and device - Google Patents
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Abstract
The invention provides a mosquito detection method and a device, which relate to the technical field of mosquito control, wherein the mosquito detection method is applied to a mobile phone and comprises the following steps: filtering the sound received by the microphones based on the central processing unit and the digital signal processor of the mobile phone to obtain mosquito sound which accords with specific frequency when mosquitoes fly; estimating a delay difference between the mosquito sound and a plurality of microphones of the mobile phone after the mosquito sound is recognized by an AI model which is deployed in the mobile phone and is pre-trained and monitors the mosquito sound; and the flight position and direction of the mosquitoes are calculated based on the time delay and a formula, when the AI model finds that the flight position of the mosquitoes is close to the preset alarm range of the mobile phone, the mobile phone shakes and prompts to alarm, and the screen of the mobile phone displays the flight position and direction of the mosquitoes, so that the mobile phone timely reminds a user to pay attention to the trails of the mosquitoes, and the biting of the mosquitoes is effectively avoided.
Description
Technical Field
The invention relates to the technical field of mosquito control, in particular to a mosquito detection method and device.
Background
At present, mosquito detection mainly detects mosquitoes through a camera or a red line transmitter and a receiver, and in the related art, infrared light is emitted through an infrared transmitter, and the camera detects reflection of the infrared light to form a background. When the mosquito passes through the infrared light field, interference is generated to infrared reflection, and the camera can detect the track of the mosquito through the interference. The scheme is mainly used for detecting and indicating indoor mosquitoes, equipment has high requirements on the environment and can only be installed indoors, indoor objects and people are relatively static, and frequent movement of the people and the objects can cause great interference to the equipment and influence the judgment of the equipment, so that a more reliable mosquito detection method is needed.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems in the related art to some extent.
Therefore, a first objective of the present invention is to provide a mosquito detection method, which can timely remind a user of paying attention to the trails of mosquitoes by moving a mobile phone, so as to effectively avoid the bites of the mosquitoes.
A second object of the present invention is to provide a mosquito detection device.
A third object of the present invention is to propose an electronic device.
A fourth object of the present invention is to propose a non-transitory computer readable storage medium storing computer instructions.
In order to achieve the above object, an embodiment of the present invention provides a mosquito detection method applied to a mobile phone, the method comprising:
filtering sounds received by a plurality of microphones of the mobile phone based on a central processor and a digital signal processor of the mobile phone to obtain mosquito sounds which accord with specific frequencies when mosquitoes fly;
after the AI model for monitoring the mosquito sound, which is pre-trained in the mobile phone, identifies the mosquito sound, estimating the time delay difference between the mosquito sound and a plurality of microphones of the mobile phone through a generalized cross-correlation function algorithm;
calculating the time delay difference among a plurality of microphones of the mobile phone based on the time delay and a formula so as to calculate the flying position and direction of the mosquitoes, and when the AI model finds that the flying position of the mosquitoes is close to the preset warning range of the mobile phone, warning is carried out through vibration and prompt tones of the mobile phone, and the flying position and direction of the mosquitoes are displayed through a screen of the mobile phone.
In order to achieve the above object, according to a second aspect of the present invention, there is provided a mosquito detection device for use in a mobile phone, the device comprising:
the filtering module is used for filtering the sounds received by the plurality of microphones of the mobile phone based on the central processor and the digital signal processor of the mobile phone so as to obtain mosquito sounds which accord with specific frequencies when mosquitoes fly;
the estimating module is used for estimating the time delay difference between the mosquito sound and a plurality of microphones of the mobile phone through a generalized cross correlation function algorithm after the AI model for monitoring the mosquito sound, which is pre-trained in the mobile phone, is used for recognizing the mosquito sound;
the calculating module is used for calculating the time delay difference among the plurality of microphones of the mobile phone based on the time delay and the formula so as to calculate the flying position and direction of the mosquitoes, and when the AI model finds that the flying position of the mosquitoes approaches to the preset warning range of the mobile phone, the mobile phone shakes and prompts the voice to warn, and the screen of the mobile phone displays the flying position and direction of the mosquitoes.
To achieve the above object, an embodiment of a third aspect of the present invention provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect.
To achieve the above object, an embodiment of a fourth aspect of the present invention proposes a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the method according to the first aspect.
The mosquito detection method, the device, the electronic equipment and the storage medium provided by the embodiment of the invention are applied to a mobile phone, and the method comprises the following steps: filtering the sound received by the microphones based on the central processing unit and the digital signal processor of the mobile phone to obtain mosquito sound which accords with specific frequency when mosquitoes fly; estimating a delay difference between the mosquito sound and a plurality of microphones of the mobile phone after the mosquito sound is recognized by an AI model which is deployed in the mobile phone and is pre-trained and monitors the mosquito sound; and the flight position and direction of the mosquitoes are calculated based on the time delay and a formula, when the AI model finds that the flight position of the mosquitoes is close to the preset alarm range of the mobile phone, the mobile phone shakes and prompts to alarm, and the screen of the mobile phone displays the flight position and direction of the mosquitoes, so that the mobile phone timely reminds a user to pay attention to the trails of the mosquitoes, and the biting of the mosquitoes is effectively avoided.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The foregoing and/or additional aspects and advantages of the invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
fig. 1 is a schematic flow chart of a mosquito detection method according to an embodiment of the invention;
fig. 2 is a schematic flow chart of another mosquito detection method according to an embodiment of the invention;
fig. 3 is a schematic layout diagram of a mosquito sound and a microphone according to an embodiment of the invention;
FIG. 4 is a flow chart showing a method for detecting mosquitoes according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a mosquito detection device according to an embodiment of the invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
The technical scheme of the invention is to acquire, store, use, process and the like data, which all meet the relevant regulations of national laws and regulations.
The mosquito detection method and apparatus according to the embodiments of the present invention are described below with reference to the accompanying drawings.
Fig. 1 is a flow chart of a mosquito detection method according to an embodiment of the invention.
As shown in fig. 1, the method comprises the steps of:
step 101, filtering sounds received by a plurality of microphones of the mobile phone based on a central processor and a digital signal processor of the mobile phone to obtain mosquito sounds conforming to a specific frequency when mosquitoes fly.
Alternatively, the central processor and the digital signal processor of the mobile phone may be CPU (Central Processing Unit) and DSP (Digital Signal Processing), respectively, and the microphone of the mobile phone may be a microphone array or the like.
Alternatively, mosquitoes may emit sounds at specific frequencies, typically between 250HZ and 1000HZ, and primarily between 500HZ and 600HZ.
Further, an embodiment of filtering the sounds received by the plurality of microphones of the mobile phone based on the central processor and the digital signal processor of the mobile phone to obtain the mosquito sounds conforming to the specific frequency when the mosquitoes fly may be that the central processor and the digital signal processor of the mobile phone based on the mobile phone filter out the environmental noise which is received by the microphone of the mobile phone and is beyond 250hz to 1000hz, thereby realizing accurate selection of the mosquito sounds conforming to the specific frequency when the mosquitoes fly.
Step 102, estimating the delay difference between the mosquito sound and the plurality of microphones of the mobile phone through a generalized cross-correlation function algorithm after the AI model for monitoring the mosquito sound, which is pre-trained in the mobile phone, recognizes the mosquito sound.
Alternatively, after the AI model for monitoring mosquito sounds pre-trained in the mobile phone recognizes the mosquito sounds, one embodiment of estimating the delay differences between the mosquito sounds and the plurality of microphones of the mobile phone through the generalized cross correlation function algorithm may be to deploy an initial AI model in an application software on the mobile phone; the method comprises the steps of obtaining a mosquito sound library, pre-training an initial AI model based on AI software and the mosquito sound library to obtain an AI model for monitoring mosquito sound, and estimating time delay from the mosquito sound to a plurality of microphones of a mobile phone through a generalized cross-correlation function algorithm after the AI model identifies the mosquito sound.
The application software may use AI software such as pyrach to deploy an initial AI model in the AI software.
The mosquito voice library can be established based on the voice frequency and intensity of various mosquitoes in various mosquito samples.
Alternatively, the mobile handset microphones may be at least 2 (2 or more).
Specifically, in the case of a mobile phone with two microphones, the delay difference between the mosquito sound and the two microphones of the mobile phone can be estimated through a generalized cross correlation function (GCC) algorithm, in the case of a mobile phone with at least three microphones, the flying position and direction of the mosquito can be directly calculated through a three-point positioning method, and the delay difference between the mosquito sound and the multiple microphones of the mobile phone can also be estimated through the generalized cross correlation function (GCC) algorithm.
And 103, calculating the time delay difference among the plurality of microphones of the mobile phone based on the time delay and the formula to calculate the flying position and direction of the mosquitoes, and when the AI model finds that the flying position of the mosquitoes is close to the preset warning range of the mobile phone, warning through vibration and prompt tone of the mobile phone, and displaying the flying position and direction of the mosquitoes through the screen of the mobile phone.
Alternatively, the frequency and intensity of mosquito sounds are relatively stable over a period of time, and the TDOA moveout localization method is applicable.
Specifically, in the case that the time difference positioning method is TDOA, the time delay and the formula in the TDOA method can be used to calculate the time delay difference between the plurality of microphones of the mobile phone, determine the relative position of the mosquito, and calculate the flying position and direction of the mosquito.
In some embodiments, the vibration and the alert tone of the mobile phone can be classified based on the distance between mosquitoes, for example, the mobile phone can be alert through indirect vibration when the distance between mosquitoes is 10m, the mobile phone can be alert through continuous vibration when the distance between mosquitoes is 7m, the mobile phone can be alert through indirect vibration and the alert tone when the distance between mosquitoes is 5m, and the mobile phone can be alert through continuous vibration and the alert tone when the distance between mosquitoes is 2m, so that a user can be reminded of paying attention to the trails of the mosquitoes in time, and the biting of the mosquitoes can be effectively avoided.
According to the mosquito detection method, the voice received by the microphones is filtered based on the central processor and the digital signal processor of the mobile phone, so that mosquito sounds which accord with specific frequencies can be emitted when mosquitoes fly; estimating a delay difference between the mosquito sound and a plurality of microphones of the mobile phone after the mosquito sound is recognized by an AI model which is deployed in the mobile phone and is pre-trained and monitors the mosquito sound; and the flight position and direction of the mosquitoes are calculated based on the time delay and a formula, when the AI model finds that the flight position of the mosquitoes is close to the preset alarm range of the mobile phone, the mobile phone shakes and prompts to alarm, and the screen of the mobile phone displays the flight position and direction of the mosquitoes, so that the mobile phone timely reminds a user to pay attention to the trails of the mosquitoes, and the biting of the mosquitoes is effectively avoided.
In order to clearly illustrate the above embodiment, the present embodiment also provides a mosquito detection method, and fig. 2 is a schematic flow chart of another mosquito detection method according to the embodiment of the present invention.
As shown in fig. 2, the method may include the steps of:
in step 201, the mobile phone-based central processing unit and the digital signal processor filter the sounds received by the plurality of microphones of the mobile phone to obtain mosquito sounds conforming to a specific frequency when the mosquitoes fly.
Step 202, estimating the delay difference between the mosquito sound and a plurality of microphones of the mobile phone through a generalized cross-correlation function algorithm after the AI model for monitoring the mosquito sound, which is pre-trained in the mobile phone, recognizes the mosquito sound.
It should be noted that, regarding the specific implementation of steps 201 to 202, reference may be made to the related description in the above embodiments.
In step 203, in the case that the mobile phone has two microphones and the mosquito sound is used as the far-field sound source, the delay difference between the arrival of the mosquito sound at the first microphone and the second microphone is obtained.
Optionally, the mobile phone is set to have two microphones m 1 (first microphone) and m 2 (second microphone), m 1 And m 2 The distance was D (0.1 m). The mosquito sound velocity is c (340 m/s), the frequency is f (500 hz), and the mosquito sound and microphone have an angle θ, as shown in fig. 3.
Further, according to the wavelength formulaFar-field sound decision formula->The wavelength of the mosquito sound can be calculated to be 0.68m, and the far-field sound is calculated to be 0.059m from the boundary distance, so that the mosquito sound can be considered as a far-field sound source in general.
As shown in fig. 3: s is the sound source (mosquito sound), m 1 ,m 2 Respectively microphones on the mobile phone, D is the distance between the microphones, and D is the distance difference between the sound reaching the microphones.
Setting x 1 And x 2 Respectively, sound signals received by microphones. τ 1 ，τ 2 Is the time at which the sound arrives at the microphone. n is n 1 ，n 2 Is environmental noise outside 250hz-1000 hz.
Is the sound reaching the microphone m 1 ,m 2 Time delay difference between:
x 1 (t)＝s(t-τ 1 )+n 1 (t)
x 2 (t)＝s(t-τ 2 )+n 2 (t)
according to the generalized interrelationship function and the characteristics thereof, whenτ is the signal x 1 Sum signal x 2 Time delay of->In the time-course of which the first and second contact surfaces,when the extreme value is reached.
Is the cross-power spectral density function ψ 12 Is a PHAT weighting function, generally taking +.>
By means of the signals collected by the two microphones we can calculateMaximum value of (2), and corresponding +.>
Step 204, calculating the time delay difference between the two microphones of the mobile phone based on the time delay and the formula, and calculating the included angle between the corresponding propagation path of the mosquito sound and the first microphone and the second microphone so as to position the flying position and the direction of the mosquito sound.
To sum up, the time delay difference between the mosquito sound and the microphone is calculatedThe time delay and formula can be used:
and calculating the included angle between the corresponding propagation path of the mosquito sound and the first microphone and the second microphone so as to position the flying position and the flying direction of the mosquito sound.
According to the mosquito detection method, the voice received by the microphones is filtered based on the central processor and the digital signal processor of the mobile phone, so that mosquito sounds which accord with specific frequencies can be emitted when mosquitoes fly; estimating a delay difference between the mosquito sound and a plurality of microphones of the mobile phone after the mosquito sound is recognized by an AI model which is deployed in the mobile phone and is pre-trained and monitors the mosquito sound; under the condition that the mobile phone is provided with two microphones and mosquito sounds are used as far-field sound sources, acquiring a time delay difference between the mosquito sounds reaching the first microphone and the second microphone; calculating the time delay difference between the two microphones of the mobile phone based on the time delay and a formula, and calculating the included angle between the corresponding propagation path of the mosquito sound and the first microphone and the second microphone so as to position the flying position and the direction of the mosquito sound. If the range is exceeded, the user can be reminded and trace information thereof can be provided.
In addition, in order to better understand the present invention, the present invention also proposes an implementation flowchart of a mosquito detection method, as shown in fig. 4, in which an original sound is collected through a plurality of microphones (microphone 1, & gt, microphone n) on a mobile phone, then an environmental noise filtering is performed on the original sound based on a central processor and a digital signal processor of the mobile phone, and the microphone 1, & gt, microphone n corresponding to a specific frequency of the mosquito sound is identified through an AI model for monitoring the mosquito sound, so that a flying position and a direction of the mosquito are positioned through a TODA method, specifically, the TODA method includes estimating a time delay difference between the mosquito sound and the plurality of microphones of the mobile phone through a generalized cross correlation function algorithm, and calculating the time delay difference between the plurality of microphones of the mobile phone based on the time delay and a formula, so as to estimate a flying position and a direction of the mosquito.
In order to achieve the above embodiments, the present invention further provides a mosquito detection device.
Fig. 5 is a schematic structural diagram of a mosquito detection device according to an embodiment of the invention.
As shown in fig. 5, the mosquito detection device 50 includes: the filtering module 51, the estimating module 52, the calculating module 53.
The filtering module 51 is configured to filter sounds received by a plurality of microphones of the mobile phone based on the central processor and the digital signal processor of the mobile phone, so as to obtain mosquito sounds that meet a specific frequency when mosquitoes fly;
an estimation module 52 for estimating a delay difference between the mosquito sound and the plurality of microphones of the mobile phone through a generalized cross correlation function algorithm after the AI model for monitoring the mosquito sound, which is pre-trained in the mobile phone, recognizes the mosquito sound;
the estimating module 53 is configured to estimate a time delay difference between a plurality of microphones of the mobile phone based on a time delay and a formula, so as to estimate a mosquito flight position and a mosquito direction, and when the AI model finds that the mosquito flight position is close to a preset warning range of the mobile phone, warn through vibration and a warning tone of the mobile phone, and display the mosquito flight position and the mosquito flight direction through a screen of the mobile phone.
Further, in one possible implementation of the embodiment of the present invention, the frequency of the mosquito sound is between 250hz and 1000 hz.
Further, in a possible implementation of the embodiment of the present invention, the estimation module 52 is configured to:
deploying an initial AI model in an application software on the mobile handset;
and acquiring a mosquito sound library, pre-training the initial AI model based on AI software and the mosquito sound library to obtain an AI model for monitoring the mosquito sound, and estimating the time delay difference between the mosquito sound and a plurality of microphones of the mobile phone through a generalized cross-correlation function algorithm after the AI model identifies the mosquito sound.
Further, in one possible implementation manner of the embodiment of the present invention, the calculating module 53 is specifically configured to:
under the condition that the mobile phone is provided with two microphones and mosquito sounds are used as far-field sound sources, acquiring a time delay difference between the mosquito sounds reaching the first microphone and the second microphone;
and calculating the time delay difference between the two microphones of the mobile phone based on the time delay and the formula, and calculating the included angle between the corresponding propagation path of the mosquito sound and the first microphone and the second microphone so as to position the flying position and the flying direction of the mosquito sound.
Further, in one possible implementation manner of the embodiment of the present invention, the calculating module 53 is further specifically configured to:
under the condition that the mobile phone is provided with at least three microphones, the flying position and the flying direction of the mosquitoes are calculated through the three-point positioning device.
According to the mosquito detection device, the voice received by the microphones is filtered based on the central processor and the digital signal processor of the mobile phone, so that mosquito sounds which accord with specific frequencies can be emitted when mosquitoes fly; estimating a delay difference between the mosquito sound and a plurality of microphones of the mobile phone after the mosquito sound is recognized by an AI model which is deployed in the mobile phone and is pre-trained and monitors the mosquito sound; and the flight position and direction of the mosquitoes are calculated based on the time delay and a formula, when the AI model finds that the flight position of the mosquitoes is close to the preset alarm range of the mobile phone, the mobile phone shakes and prompts to alarm, and the screen of the mobile phone displays the flight position and direction of the mosquitoes, so that the mobile phone timely reminds a user to pay attention to the trails of the mosquitoes, and the biting of the mosquitoes is effectively avoided.
In order to achieve the above embodiment, the present invention further provides an electronic device, including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the aforementioned method.
To achieve the above embodiments, the present invention also proposes a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the aforementioned method.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order from that shown or discussed, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present invention.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in a hardware manner or in a software functional module manner. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (12)
1. A method for detecting mosquitoes, wherein the method is applied to a mobile phone, and the method comprises the following steps:
filtering sounds received by a plurality of microphones of the mobile phone based on a central processor and a digital signal processor of the mobile phone to obtain mosquito sounds which accord with specific frequencies when mosquitoes fly;
after the AI model for monitoring the mosquito sound, which is pre-trained in the mobile phone, identifies the mosquito sound, estimating the time delay difference between the mosquito sound and a plurality of microphones of the mobile phone through a generalized cross-correlation function algorithm;
calculating the time delay difference among a plurality of microphones of the mobile phone based on the time delay and a formula so as to calculate the flying position and direction of the mosquitoes, and when the AI model finds that the flying position of the mosquitoes is close to the preset warning range of the mobile phone, warning is carried out through vibration and prompt tones of the mobile phone, and the flying position and direction of the mosquitoes are displayed through a screen of the mobile phone.
2. The method of claim 1, wherein the mosquito sound has a frequency between 250hz and 1000 hz.
3. The method of claim 1, wherein estimating the delay difference between the mosquito sound and the plurality of microphones of the mobile handset by a generalized cross-correlation function algorithm after the AI model for monitoring the mosquito sound pre-trained in the mobile handset recognizes the mosquito sound comprises:
deploying an initial AI model in an application software on the mobile handset;
and acquiring a mosquito sound library, pre-training the initial AI model based on AI software and the mosquito sound library to obtain an AI model for monitoring the mosquito sound, and estimating the time delay difference between the mosquito sound and a plurality of microphones of the mobile phone through a generalized cross-correlation function algorithm after the AI model identifies the mosquito sound.
4. The method of claim 1, wherein calculating the time delay difference between the plurality of microphones of the mobile phone based on the time delay and the formula to calculate the mosquito's flying position and direction comprises:
under the condition that the mobile phone is provided with two microphones and mosquito sounds are used as far-field sound sources, acquiring a time delay difference between the mosquito sounds reaching the first microphone and the second microphone;
and calculating the time delay difference between the two microphones of the mobile phone based on the time delay and the formula, and calculating the included angle between the corresponding propagation path of the mosquito sound and the first microphone and the second microphone so as to position the flying position and the flying direction of the mosquito sound.
5. The method according to claim 4, further comprising:
under the condition that the mobile phone is provided with at least three microphones, the flying position and the flying direction of the mosquitoes are calculated through a three-point positioning method.
6. A mosquito detection device for use with a mobile handset, the device comprising:
the filtering module is used for filtering the sounds received by the plurality of microphones of the mobile phone based on the central processor and the digital signal processor of the mobile phone so as to obtain mosquito sounds which accord with specific frequencies when mosquitoes fly;
the estimating module is used for estimating the time delay difference between the mosquito sound and a plurality of microphones of the mobile phone through a generalized cross correlation function algorithm after the AI model for monitoring the mosquito sound, which is pre-trained in the mobile phone, is used for recognizing the mosquito sound;
the calculating module is used for calculating the time delay difference among the plurality of microphones of the mobile phone based on the time delay and the formula so as to calculate the flying position and direction of the mosquitoes, and when the AI model finds that the flying position of the mosquitoes approaches to the preset warning range of the mobile phone, the mobile phone shakes and prompts the voice to warn, and the screen of the mobile phone displays the flying position and direction of the mosquitoes.
7. The apparatus of claim 6, wherein the mosquito sound has a frequency between 250hz and 1000 hz.
8. The apparatus of claim 6, wherein the estimation module is configured to:
deploying an initial AI model in an application software on the mobile handset;
and acquiring a mosquito sound library, pre-training the initial AI model based on AI software and the mosquito sound library to obtain an AI model for monitoring the mosquito sound, and estimating the time delay difference between the mosquito sound and a plurality of microphones of the mobile phone through a generalized cross-correlation function algorithm after the AI model identifies the mosquito sound.
9. The apparatus of claim 6, wherein the calculation module is specifically configured to:
under the condition that the mobile phone is provided with two microphones and mosquito sounds are used as far-field sound sources, acquiring a time delay difference between the mosquito sounds reaching the first microphone and the second microphone;
and calculating the time delay difference between the two microphones of the mobile phone based on the time delay and the formula, and calculating the included angle between the corresponding propagation path of the mosquito sound and the first microphone and the second microphone so as to position the flying position and the flying direction of the mosquito sound.
10. The apparatus of claim 9, wherein the calculation module is further specifically configured to:
under the condition that the mobile phone is provided with at least three microphones, the flying position and the flying direction of the mosquitoes are calculated through the three-point positioning device.
11. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.
12. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-5.
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