AI intelligent identification system and method for bird bathtub

Abstract

The invention discloses an AI intelligent identification system and method for a bird bathtub, and belongs to the technical field of bird observation. An AI intelligent recognition system for a bird bathtub comprises a machine box and a tub body, the tub body is located on one side of the bottom of the machine box, a metal bracket is arranged below the machine box, a water pipe connector is arranged at one end of the bottom of the metal bracket, a monitor is arranged on the outer surface of the machine box, and a light shield is arranged above the monitor. In order to solve the problems that an existing bathtub body is simple in structure and single in function, and people below the bathtub body cannot clearly watch the bathing process of birds in the bathing process, collected video information can be recorded into a cloud database to be stored, a user can search and watch through an APP, and the user experience is improved. Meanwhile, the system also compares the video information with an AI knowledge base, judges whether there is a user who pays attention to the birds through comparison and analysis, and carries out feedback prompt through an APP.

Description

Technical Field

[0001] The present invention relates to the technical field of bird observation, and specifically provides an AI intelligent recognition system and method for a bird bathing basin. Background Art

[0002] Once birds do not take a bath for a long time, their flying ability will weaken, and their feathers will no longer be beautiful. This is because in survival, the feathers of birds will also show a certain degree of wear. If they do not take a bath to wash off the more severely worn feathers, their flying ability will weaken. Moreover, under the feathers of birds is a paradise for parasites. Taking a bath can effectively clean the parasites hidden between the feathers, which is more beneficial to the health of birds. The most crucial thing is that in order to increase the waterproofness of their feathers, birds will peck some oily substances from the uropygial gland and smear them on their bodies. Over time, the oily substances will get stained with a lot of dust. Currently, many bird bathing basins are often set up in parks to help birds clean themselves;

[0003] The basin body structure of the existing bathing basins is relatively simple and the functions are single. During the bathing process of birds, the people below cannot clearly watch the bathing process of the birds. Summary of the Invention

[0004] The purpose of the present invention is to provide an AI intelligent recognition system and method for a bird bathing basin. Through the vision module, videos and photos of birds taking a bath can be taken, and the microphone module can collect and record the calls of birds. The collected video information will be stored in the cloud database. Users can search and watch through the APP. At the same time, the system will also compare the video information with the AI knowledge base, judge whether there are users who pay attention to such birds through comparison and analysis, and give feedback prompts through the APP, so as to solve the problems in the prior art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: an AI intelligent recognition system and method for a bird bathing basin, including a machine box and a basin body. The basin body is located on one side of the bottom of the machine box. A metal bracket is arranged below the machine box, and a water pipe interface is arranged at one end of the bottom of the metal bracket. A monitor is arranged on the outer surface of the machine box, and a light shield is arranged above the monitor. An antenna is arranged on the other side of the machine box. Among them, a power interface is arranged at the bottom of the monitor, and the monitor includes a vision module, a microphone module, a chip processor, and an infrared sensing module.

[0006] Further, the basin body includes a corrugated chassis and a basin edge. The corrugated chassis and the basin edge are set as an integrally formed structure. Among them, a water storage tank is arranged at the bottom of the corrugated chassis, a nozzle assembly is arranged above the corrugated chassis, a clamping plate is arranged on one side of the basin edge, and the clamping plate is connected to the machine box by bolts. A recyclable nozzle assembly is arranged inside the basin body. Cleverly, a petal-shaped water spray port is arranged in the center of the basin body to simulate the gushing of spring water, which is closer to nature. The basin body is made of a thin and environmentally friendly material, and an ultra-thin basin body with a certain strength is formed by the transformation and gradual change of the shape. The lower cover shields the fountain motor and forms a hierarchical sense of drop with the whole.

[0007] Further, the basin body includes a corrugated chassis and a basin edge. The corrugated chassis and the basin edge are set as an integrally formed structure. Among them, a water storage tank is arranged at the bottom of the corrugated chassis, a nozzle assembly is arranged above the corrugated chassis, a clamping plate is arranged on one side of the basin edge, and the clamping plate is connected to the machine box by bolts. A pressure sensing module capable of sensing gravity is also installed at the bottom of the basin body, so that it can sense whether there are birds staying above the basin body, and cooperating with the infrared sensing module can avoid the situation of accidentally touching the water spray switch.

[0008] Further, a water guide pipe is arranged on one side of the self-priming pump. The nozzle assembly includes a water outlet cover and a petal-shaped nozzle. Among them, the water outlet cover is connected to the water storage tank by a buckle. The water guide pipe extends to the inside of the petal-shaped nozzle, and the water pipe interface is connected to the external water supply pipeline structure. When the one-way water valve is opened, water can enter the inside of the water storage tank through the pipeline. At the same time, the one-way water valve can also play a role in current limiting to avoid the water inside the basin body overflowing due to a large amount of water entering the water storage tank.

[0009] Further, the chip processor includes an image scanning unit and an audio analysis unit. Among them, the image scanning unit is used for:

[0010] Scanning the picture information taken by the vision module, and extracting the bird information in the picture. The bird information mainly includes the head area, the tail feather area, the wing area and the foot area;

[0011] The audio analysis unit is used for:

[0012] Analyzing the bird calls collected by the microphone module, judging the current bird calls of the birds taking a bath by referring to the sound frequency and size, and then removing the calls and noises of other birds in the environment according to the judgment result, so as to avoid the influence of the ambient noise on the analysis result and improve the analysis accuracy.

[0013] Further, the image scanning unit is also used for;

[0014] Extracting the picture information;

[0015] Perform region segmentation on the said image information to obtain the bird image region and the background image region;

[0016] Extract the gray values corresponding to the pixel points included in the bird image region;

[0017] Extract the gray values corresponding to the pixel points included in the background image region;

[0018] Use the gray values corresponding to the pixel points included in the bird image region in combination with the gray values corresponding to the pixel points included in the background image region to obtain the gray value threshold;

[0019] Among them, the gray value threshold is obtained through the following formula:

[0020]

[0021] Among them, S represents the gray value threshold; n represents the number of pixel points included in the bird image region; m represents the number of pixel points included in the said background image region; X 01i represents the gray value of the i-th pixel point included in the bird image region; X 02p represents the average gray value of the m pixel points included in the background image region; X 02i represents the gray value of the i-th pixel point included in the background image region; X 01p represents the average gray value of the n pixel points included in the bird image region;

[0022] Extract the pixel points with gray values lower than the gray value threshold from the pixel points included in the bird image region as the first pixel point set;

[0023] Extract the pixel points with gray values exceeding the gray value threshold from the pixel points included in the background image region as the second pixel point set;

[0024] Extract the average gray values corresponding to the first pixel point set and the second pixel point set;

[0025] Compare the difference between the average gray values corresponding to the first pixel point set and the second pixel point set with a preset gray difference threshold;

[0026] When the difference between the average gray values corresponding to the first pixel point set and the second pixel point set is lower than the preset gray difference threshold, adjust the contrast of the image information.

[0027] Furthermore, when the difference between the average gray values corresponding to the first pixel point set and the second pixel point set is lower than the preset gray difference threshold, adjusting the contrast of the image information includes:

[0028] Obtain a first gray coefficient by using the gray values corresponding to the pixel points included in the bird image area;

[0029] Among them, the first gray coefficient is obtained through the following formula:

[0030]

[0031] Among them, F 01 represents the first gray coefficient; n represents the number of pixel points included in the bird image area; X 01i represents the gray value of the i-th pixel point included in the bird image area; X 01p represents the average gray value of the n pixel points included in the bird image area; X 02p represents the average gray value of the m pixel points included in the background image area; S represents the gray value threshold;

[0032] Obtain a second gray coefficient by using the gray values corresponding to the pixel points included in the bird image area;

[0033] Among them, the second gray coefficient is obtained through the following formula:

[0034]

[0035] Among them, F 02 represents the second gray coefficient; m represents the number of pixel points included in the background image area; X 02i represents the gray value of the i-th pixel point included in the background image area; X 01p represents the average gray value of the n pixel points included in the bird image area; X 02p represents the average gray value of the m pixel points included in the background image area; S represents the gray value threshold;

[0036] Subtract the first gray coefficient from the second gray coefficient to obtain the coefficient difference between the first gray coefficient and the second gray coefficient;

[0037] Compare the coefficient difference with a preset coefficient difference;

[0038] When the coefficient difference is lower than the preset coefficient difference, then use the first gray coefficient and the second gray coefficient to reduce the contrast of the background image area, and the adjusted contrast value of the background image area is obtained through the following formula;

[0039]

[0040] Among them, D 02t represents the adjusted contrast value of the background image area; D 02 represents the contrast value of the background image area before adjustment; F01 represents the first gray scale coefficient; F 02 represents the second gray scale coefficient;

[0041] When the coefficient difference is not lower than a preset coefficient difference, the first gray scale coefficient and the second gray scale coefficient are used to improve the contrast of the bird image area, and the adjusted contrast value of the bird image area is obtained through the following formula;

[0042]

[0043] where D 01t represents the adjusted contrast value of the bird image area; D 01 represents the contrast value of the bird image area before adjustment; D 02 represents the contrast value of the background image area before adjustment; F 01 represents the first gray scale coefficient; F 02 represents the second gray scale coefficient.

[0044] Furthermore, the chip processor communicates with the AI knowledge base. Among them, the AI knowledge base includes a feature extraction module and a feature recognition module;

[0045] The AI knowledge base is used for:

[0046] Storing existing bird information features, automatically collecting network data and uploading and updating in real time while networking. The AI knowledge base will number the collected bird features;

[0047] The feature extraction module is used for:

[0048] Selecting the bird information collected by the image scanning unit, selecting the available information areas in the photo, judging whether the currently selected feature parts are available, and numbering and classifying the available feature information;

[0049] The feature recognition module is used for:

[0050] Quickly comparing the selected feature numbers with the information in the AI knowledge base, and obtaining the current bird name and race information through comparison and analysis.

[0051] Furthermore, the AI knowledge base communicates with the user information base and the cloud database respectively. Among them, the user information base is used for:

[0052] Storing APP registered user information, analyzing the user's preference tendency during the registration process and daily use, and recording the bird information concerned by the user;

[0053] After the AI knowledge base analyzes the information of the bird taking a bath currently, the data integration unit searches for users who are interested in this bird according to the bird information, and sends a prompt notification through the APP to remind users to watch the live broadcast;

[0054] The cloud database is used for:

[0055] Storing and recording videos.

[0056] An AI intelligent recognition method for a bird bath tub includes the following steps:

[0057] Step 1: After the infrared sensing module cooperates with the pressure sensing module at the bottom of the tub to detect a bird, the self-priming pump and the one-way water valve will be opened, and the self-priming pump is used to spray water through the nozzle assembly to help the bird clean itself;

[0058] Step 2: At the same time, the vision module and the microphone module will start working. The vision module can take videos and photos of the bird taking a bath, while the microphone module can collect and record the calls of the bird;

[0059] Step 3: The collected video information will be stored in the cloud database. Users can search for and watch it through the APP. At the same time, the system will also compare the video information with the AI knowledge base;

[0060] Step 4: By comparing and analyzing, it is judged whether there are users who are interested in this type of bird, and feedback prompts are sent through the APP to allow users to directly watch the live broadcast.

[0061] Compared with the prior art, the beneficial effects of the present invention are:

[0062] 1. In the present invention, a recyclable nozzle assembly is provided inside the tub. A petal-shaped water spray port is cleverly arranged in the center of the tub to simulate the gushing of spring water, which is closer to nature. The tub is made of a thin and environmentally friendly material, and the shape is used to form a thin and ultra-thin tub with a certain strength through the transition and gradient of the shape. The lower cover blocks the fountain motor and forms a hierarchical sense of drop with the whole. A pressure sensing module that can sense gravity is also installed at the bottom of the tub, so that it can sense whether there is a bird staying above the tub, and cooperate with the infrared sensing module to avoid mis-touching the water spray switch;

[0063] 2. In the present invention, the vision module can take videos and photos of the bird taking a bath, while the microphone module can collect and record the calls of the bird. The collected video information will be stored in the cloud database. Users can search for and watch it through the APP. At the same time, the system will also compare the video information with the AI knowledge base, and judge whether there are users who are interested in this type of bird through comparison and analysis, and send feedback prompts through the APP;

[0064] 3. In the present invention, the bird calls collected by the microphone module are analyzed. Based on the sound frequency and amplitude, the bird calls of the birds taking a bath are judged. Then, according to the judgment result, other bird calls and noises in the environment are excluded, which can avoid the influence of environmental noise on the analysis result and improve the analysis accuracy. The AI knowledge base stores the existing bird information characteristics. At the same time, it automatically collects network data and uploads and updates it in real time. The AI knowledge base numbers the collected bird characteristics, and faster searching can be achieved through the comparison of the numbers.

[0065] 4. The feature extraction module is used to extract the bird information collected by the image scanning unit, select the available information areas in the photo, and judge whether the currently extracted feature parts are available. The available feature information is numbered and divided. During the extraction process, for the head area, tail feather area, wing area, and foot area, more than two available areas need to be ensured to perform the extraction and comparison operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 is the overall front view of the present invention;

[0067] Figure 2 is the overall side view of the present invention;

[0068] Figure 3 is the overall sectional structure schematic diagram of the present invention;

[0069] Figure 4 is the schematic diagram of the nozzle assembly structure of the present invention;

[0070] Figure 5 is the schematic diagram of the water storage tank structure of the present invention;

[0071] Figure 6 is the bird recognition flow chart of the present invention.

[0072] In the figure: 1. Machine box; 2. Basin body; 3. Metal bracket; 4. Nozzle assembly; 101. Monitor; 102. Light shield; 103. Antenna; 104. Power interface; 105. Vision module; 106. Microphone module; 107. Chip processor; 108. Infrared sensing module; 109. AI knowledge base; 1071. Image scanning unit; 1072. Audio analysis unit; 1091. Feature extraction module; 1092. Feature recognition module; 1093. User information base; 1094. Data integration unit; 1095. Cloud database; 1096. APP; 201. Waveform chassis; 202. Basin edge; 203. Water storage tank; 204. Self-priming pump; 205. Check valve; 2021. Clamping plate; 2031. Limit card slot; 2032. Water inlet; 2041. Water conduit; 301. Water pipe interface; 401. Outlet cover; 402. Flap nozzle. Detailed implementation manners

[0073] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0074] In order to solve the problems that the basin structure of the existing bird bath is relatively simple and the functions are single, and people below cannot clearly watch the bathing process of the birds during the bathing process of the birds; please refer to Figure 1-6 , the following technical solutions are provided in this embodiment:

[0075] An AI intelligent recognition system for a bird bath includes a machine box 1 and a basin body 2. In addition to the machine box 1 and the basin body 2, a photovoltaic structure can also be used to supply electric energy. The basin body 2 is located on one side of the bottom of the machine box 1. A metal bracket 3 is arranged below the machine box 1. A water pipe interface 301 is arranged at one end of the bottom of the metal bracket 3. A monitor 101 is arranged on the outer surface of the machine box 1. A light-shielding cover 102 is arranged above the monitor 101. An antenna 103 is arranged on the other side of the machine box 1. Among them, a power interface 104 is arranged at the bottom of the monitor 101. The monitor 101 includes a vision module 105, a microphone module 106, a chip processor 107 and an infrared sensing module 108;

[0076] The self-priming pump 204 is used to spray water through the nozzle assembly 4 to help the birds clean. At the same time, the vision module 105 and the microphone module 106 will start to work. The vision module 105 can take videos and photos of the birds bathing, while the microphone module 106 can collect and record the calls of the birds. The collected video information will be stored in the cloud database 1095. Users can search and watch through the APP 1096. At the same time, the system will also compare the video information with the AI knowledge base 109, judge whether there are users who are concerned about such birds through comparison and analysis, and give feedback prompts through the APP 1096, allowing users to directly watch the live broadcast;

[0077] The basin body 2 includes a corrugated chassis 201 and a basin rim 202. The corrugated chassis 201 and the basin rim 202 are integrally formed. Among them, a water storage tank 203 is provided at the bottom of the corrugated chassis 201, a nozzle assembly 4 is provided above the corrugated chassis 201, a clamping plate 2021 is provided on one side of the basin rim 202, and the clamping plate 2021 is bolted to the machine box 1. A pressure sensing module capable of sensing gravity is also installed at the bottom of the basin body 2, so as to sense whether there are birds staying above the basin body 2, and cooperate with the infrared sensing module 108 to avoid mis-touching the water spraying switch;

[0078] A recyclable nozzle assembly 4 is provided inside the basin body 2. Cleverly, a petal-shaped water spraying port is provided in the center of the basin body 2 to simulate the gushing of spring water, which is closer to nature. The basin body 2 is made of a thin and environmentally friendly material, and an ultra-thin basin body 2 with a certain strength is formed by the transformation and gradual change of the shape. The lower cover shields the fountain motor and forms a hierarchical sense of drop with the whole;

[0079] A one-way water valve 205 is provided inside the water storage tank 203. An inlet 2032 is provided at the bottom of the one-way water valve 205. Among them, the water pipe interface 301 is connected to the one-way water valve 205 through the inlet 2032, and the water pipe interface 301 is connected to the external water supply pipeline structure. When the one-way water valve 205 is opened, water can enter the inside of the water storage tank 203 through the pipeline. At the same time, the one-way water valve 205 can also play a role in current limiting to avoid the water inside the basin body 2 overflowing due to a large amount of water entering the water storage tank 203. A self-priming pump 204 is provided on one side of the one-way water valve 205. The self-priming pump 204 is connected to the water storage tank 203 through a limit card slot 2031. A water guide pipe 2041 is provided on one side of the self-priming pump 204. The nozzle assembly 4 includes an outlet cover 401 and a petal-shaped nozzle 402. Among them, the outlet cover 401 is connected to the water storage tank 203 by a buckle, and the water guide pipe 2041 extends into the inside of the petal-shaped nozzle 402;

[0080] The self-priming pump 204 can pump out the water inside the water storage tank 203, transport it to the petal-shaped nozzle 402 through the water guide pipe 2041, and then spray the water out through the petal-shaped nozzle 402;

[0081] The chip processor 107 includes an image scanning unit 1071 and an audio analysis unit 1072. Among them, the image scanning unit 1071 is used for:

[0082] Scanning the picture information taken by the vision module 105 and extracting the bird information in the picture. The bird information mainly includes the head area, the tail feather area, the wing area and the foot area;

[0083] The audio analysis unit 1072 is used for:

[0084] Analyze the bird calls collected by the microphone module 106, determine the current bird calls during bathing by referring to the sound frequency and magnitude, and then eliminate other bird calls and noises in the environment according to the judgment result, so as to avoid the influence of background noises on the analysis result and improve the analysis accuracy;

[0085] The chip processor 107 communicates with the AI knowledge base 109. Among them, the AI knowledge base 109 includes a feature extraction module 1091 and a feature recognition module 1092;

[0086] Specifically, the image scanning unit is also used for;

[0087] Extract the picture information;

[0088] Perform region segmentation on the image information to obtain a bird image region and a background image region;

[0089] Extract the gray values corresponding to the pixel points included in the bird image region;

[0090] Extract the gray values corresponding to the pixel points included in the background image region;

[0091] Use the gray values corresponding to the pixel points included in the bird image region in combination with the gray values corresponding to the pixel points included in the background image region to obtain a gray value threshold;

[0092] Among them, the gray value threshold is obtained through the following formula:

[0093]

[0094] Among them, S represents the gray value threshold; n represents the number of pixel points included in the bird image region; m represents the number of pixel points included in the background image region; X 01i represents the gray value of the i-th pixel point included in the bird image region; X 02p represents the average gray value of the m pixel points included in the background image region; X 02i represents the gray value of the i-th pixel point included in the background image region; X 01p represents the average gray value of the n pixel points included in the bird image region;

[0095] Extract the pixel points with gray values lower than the gray value threshold from the pixel points included in the bird image region as the first pixel point set;

[0096] Extract the pixel points with gray values exceeding the gray value threshold from the pixel points included in the background image region as the second pixel point set;

[0097] Extract the average gray values corresponding to the first pixel point set and the second pixel point set;

[0098] Compare the difference between the average gray values corresponding to the first pixel point set and the second pixel point set with a preset gray difference threshold;

[0099] When the difference between the average gray values corresponding to the first pixel point set and the second pixel point set is lower than the preset gray difference threshold, adjust the contrast of the image information.

[0100] The technical effects of the above technical solution are as follows: By performing regional segmentation on the image information, the bird image region and the background image region can be accurately obtained, providing a basis for subsequent processing. Respectively extract the gray values corresponding to the pixel points included in the bird image region and the background image region, providing accurate data for subsequent calculation of the gray value threshold. Use the gray values of the bird image region and the background image region to calculate the gray value threshold through a certain formula. This method comprehensively considers the gray value distributions of the two regions, making the setting of the threshold more reasonable. Since the calculation of the threshold depends on the gray value distribution of the image itself, this method can dynamically adjust the threshold according to different image contents, improving the flexibility and accuracy of processing. According to the gray value threshold, classify the pixel points of the bird image region and the background image region respectively to form the first pixel point set and the second pixel point set. By comparing the difference between the average gray values corresponding to the first pixel point set and the second pixel point set with the preset gray difference threshold, it can be judged whether the contrast of the image information needs to be adjusted. When the difference between the average gray values is lower than the preset gray difference threshold, adjust the contrast of the image information in a timely manner to improve the clarity and visual effect of the image. Through the adjustment of the contrast, the quality of the image can be optimized, making the details in the image clearer, thereby improving the accuracy of image recognition and analysis. This technical solution is not only applicable to simple image scenarios, but also can handle image recognition and analysis tasks under complex scenarios, and has a wide range of application prospects.

[0101] In summary, through the technical effects such as accurately extracting image information, reasonably setting the gray value threshold, timely and effectively adjusting the contrast, and improving the accuracy of image recognition and analysis, the above technical solution provides a new solution for the field of image processing and recognition.

[0102] Specifically, when the difference between the average gray values corresponding to the first pixel point set and the second pixel point set is lower than the preset gray difference threshold, adjust the contrast of the image information, including:

[0103] Obtain a first gray coefficient using the gray values corresponding to the pixel points included in the bird image region;

[0104] Among them, the first gray coefficient is obtained through the following formula:

[0105]

[0106] Among them, F 01 represents the first gray coefficient; n represents the number of pixel points included in the bird image area; X 01i represents the gray value of the i-th pixel point included in the bird image area; X 01p represents the average gray value of the n pixel points included in the bird image area; X 02p represents the average gray value of the m pixel points included in the background image area; S represents the gray value threshold;

[0107] Obtain the second gray coefficient by using the gray values corresponding to the pixel points included in the bird image area;

[0108] Among them, the second gray coefficient is obtained through the following formula:

[0109]

[0110] Among them, F 02 represents the second gray coefficient; m represents the number of pixel points included in the background image area; X 02i represents the gray value of the i-th pixel point included in the background image area; X 01p represents the average gray value of the n pixel points included in the bird image area; X 02p represents the average gray value of the m pixel points included in the background image area; S represents the gray value threshold;

[0111] Subtract the first gray coefficient from the second gray coefficient to obtain the coefficient difference between the first gray coefficient and the second gray coefficient;

[0112] Compare the coefficient difference with a preset coefficient difference;

[0113] When the coefficient difference is lower than the preset coefficient difference, then use the first gray coefficient and the second gray coefficient to reduce the contrast of the background image area, and the adjusted contrast value of the background image area is obtained through the following formula;

[0114]

[0115] Among them, D 02t represents the adjusted contrast value of the background image area; D 02 represents the contrast value of the background image area before adjustment; F 01 represents the first gray coefficient; F 02 represents the second gray coefficient;

[0116] When the coefficient difference is not lower than a preset coefficient difference, the first gray coefficient and the second gray coefficient are used to increase the contrast of the bird image region, and the adjusted contrast value of the bird image region is obtained through the following formula;

[0117]

[0118] where D 01t represents the adjusted contrast value of the bird image region; D 01 represents the contrast value of the bird image region before adjustment; D 02 represents the contrast value of the background image region before adjustment; F 01 represents the first gray coefficient; F 02 represents the second gray coefficient.

[0119] The technical effects of the above technical solution are as follows: By calculating the first gray coefficient and the second gray coefficient, this technical solution can more finely analyze the gray distribution characteristics of the bird image region and the background image region. These two coefficients not only consider the gray values within their respective regions but also combine the gray value threshold S, making the analysis more comprehensive. By comparing the coefficient difference between the first gray coefficient and the second gray coefficient with the preset coefficient difference, this technical solution can determine whether contrast adjustment is needed and the direction of adjustment (whether to increase or decrease). This strategy avoids blind adjustment and improves the accuracy and effectiveness of adjustment. When the coefficient difference is lower than the preset value, the first gray coefficient and the second gray coefficient are used to reduce the contrast of the background image region. This method can reduce the interference of the background on the bird image and make the bird image more prominent. When the coefficient difference is not lower than the preset value, the first gray coefficient and the second gray coefficient are used to increase the contrast of the bird image region. This method can enhance the details and clarity of the bird image and improve the accuracy of image recognition and analysis. The formula takes into account the contrast value D02 of the background image region before adjustment, the first gray coefficient F01, and the second gray coefficient F02, and obtains the adjusted contrast value D02t through reasonable calculation. This adjustment method not only considers the original contrast but also combines the change of the gray coefficient, making the adjustment result more reasonable. The formula also takes into account the contrast value D01 of the bird image region before adjustment, the contrast value D02 of the background image region before adjustment, the first gray coefficient F01, and the second gray coefficient F02. By comprehensively considering these factors, the formula can more accurately calculate the adjusted contrast value D01t to meet the requirements of image processing. Through the fine contrast adjustment strategy and method, this technical solution can optimize the visual effect of the image, making the image clearer, more prominent, and easier to identify. The adjustment of contrast can enhance the key information in the image (such as the bird image) and reduce the interference information (such as the background image), thereby improving the accuracy and reliability of image recognition.

[0120] In summary, through a fine contrast adjustment strategy, scientific methods, reasonable formulas, and technical effects such as improving image quality and recognition accuracy, this technical solution provides a new solution for the field of image processing and analysis.

[0121] The AI knowledge base 109 is used for:

[0122] Storing existing bird information features, automatically collecting network data and uploading updates in real time while connected to the network. The AI knowledge base 109 will number the collected bird features, and more rapid searching can be achieved through the comparison of the numbers.

[0123] The feature extraction module 1091 is used for:

[0124] Extracting the bird information collected by the image scanning unit 1071, extracting the available information areas in the photo, and judging whether the currently extracted feature parts are available. Number and classify the available feature information. During the extraction process, for the head area, tail feather area, wing area, and foot area, two or more available areas need to be ensured to perform the extraction and comparison operation.

[0125] The feature recognition module 1092 is used for:

[0126] Quickly comparing the extracted feature numbers with the information in the AI knowledge base 109, and obtaining the current bird name and species information through comparison and analysis.

[0127] The AI knowledge base 109 communicates with the user information base 1093 and the cloud database 1095 respectively. Among them, the user information base 1093 is used for:

[0128] Storing the registered user information of the APP1096, analyzing the user's preference tendencies during the registration process and daily use, and recording the bird information that the user is interested in.

[0129] After the AI knowledge base 109 analyzes the current bird information of the bath, the data integration unit 1094 searches for users who are interested in this bird based on this bird information, and sends a prompt notification through the APP1096 to remind users to watch the live broadcast.

[0130] The cloud database 1095 is used for:

[0131] Used to store recorded videos.

[0132] Working principle: after the infrared sensor module 108 cooperates with the pressure sensor module at the bottom of the basin body 2 to detect the bird, the self-priming water pump 204 and the one-way water valve 205 will be turned on, and the self-priming water pump 204 will spray water through the nozzle assembly 4 to help the bird to wash. At the same time, the visual module 105 and the microphone module 106 will start working. The visual module 105 can be used to take videos and photos of the bird bathing, and then the image scanning unit 1071 scans the picture information taken by the visual module 105 to extract the bird information in the picture. The bird information mainly includes The head area, tail feather area, wing area and foot area, and the microphone module 106 can collect and record the bird calls, the audio analysis unit 1072 can analyze the bird calls collected by the microphone module 106, and judge the bird calls currently bathing by referring to the sound frequency and size, and then eliminate other bird calls and noise in the environment according to the judgment result, so as to avoid the influence of environmental noise on the analysis result and improve the analysis accuracy. The collected video information will be collected and stored in the cloud database 1095, and in the AI ​​knowledge The existing bird information features are stored in the knowledge base 109, and the network automatically collects network data and uploads and updates in real time. The AI ​​knowledge base 109 will number the collected bird features, and faster search can be achieved by comparing the numbers. The feature extraction module 1091 is first used to extract the bird information collected by the image scanning unit 1071, extract the available information area in the photo, and determine whether the currently extracted feature parts are available, and divide the available feature information by number. In the process of extraction, the head area, tail feather area, wing area and foot area need to ensure more than two available areas to achieve the extraction and comparison operation. Then, the feature identification module 1092 will quickly compare the extracted feature number with the information in the AI ​​knowledge base 109, and the current bird name and race information can be obtained through comparison analysis. Users can search and watch through APP1096. At the same time, the system will also compare the video information with the AI ​​knowledge base 109, and determine whether there are users who pay attention to such birds through comparison analysis, and provide feedback prompts through APP1096, so that users can watch the live broadcast directly.

[0133] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0134] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention.

Claims

1. An AI intelligent recognition system for bird bathing basins, characterized in that: The invention comprises a machine box (1) and a basin body (2), wherein the basin body (2) is located at one side of the bottom of the machine box (1), a metal bracket (3) is arranged below the machine box (1), a water pipe interface (301) is arranged at one end of the bottom of the metal bracket (3), a monitor (101) is arranged on the outer surface of the machine box (1), a light shield (102) is arranged above the monitor (101), and an antenna (103) is arranged on the other side of the machine box (1), wherein a power interface (104) is arranged at the bottom of the monitor (101), and the monitor (101) comprises a visual module (105), a microphone module (106), a chip processor (107) and an infrared sensor module (108).

2. The AI ​​intelligent identification system for bird bathing basins according to claim 1 is characterized by: The basin body (2) comprises a corrugated base (201) and a basin edge (202), wherein the corrugated base (201) and the basin edge (202) are arranged as an integrally formed structure, wherein a water storage tank (203) is arranged at the bottom of the corrugated base (201), a nozzle assembly (4) is arranged above the corrugated base (201), and a clamping plate (2021) is arranged on one side of the basin edge (202), and the clamping plate (2021) is connected to the machine box (1) by bolts.

3. The AI ​​intelligent identification system for bird bathing basins according to claim 2 is characterized in that: A one-way water valve (205) is arranged inside the water storage tank (203), and a water inlet (2032) is arranged at the bottom of the one-way water valve (205), wherein the water pipe interface (301) is connected to the one-way water valve (205) via the water inlet (2032), and a self-priming water pump (204) is arranged on one side of the one-way water valve (205), and the self-priming water pump (204) is connected to the water storage tank (203) via a limit slot (2031).

4. The AI ​​intelligent identification system for bird bathing basins according to claim 3 is characterized by: A water guide pipe (2041) is provided on one side of the self-priming water pump (204), and the nozzle assembly (4) comprises a water outlet cover (401) and a flap-type nozzle (402), wherein the water outlet cover (401) is connected to the water storage tank (203) by means of a buckle, and the water guide pipe (2041) extends to the interior of the flap-type nozzle (402).

5. The AI ​​intelligent identification system for bird bathing basins according to claim 1 is characterized by: The chip processor (107) comprises an image scanning unit (1071) and an audio parsing unit (1072), wherein the image scanning unit (1071) is used to: Scanning the image information taken by the visual module (105) to extract bird information in the image, the bird information mainly including the head area, tail feather area, wing area and foot area; The audio parsing unit (1072) is used to: The bird calls collected by the microphone module (106) are analyzed, and the bird calls currently bathing are determined by referring to the sound frequency and volume, and other bird calls and noises in the environment are eliminated based on the determination result.

6. The AI ​​intelligent identification system for bird bathing basins according to claim 5, characterized in that: The image scanning unit (1071) is further used for: Extracting the picture information; Performing regional segmentation on the image information to obtain a bird image region and a background image region; Extracting grayscale values ​​corresponding to pixels in the bird image area; Extracting the grayscale values ​​corresponding to the pixels contained in the background image area; Obtaining a gray value threshold by combining the gray values ​​corresponding to the pixels in the bird image area with the gray values ​​corresponding to the pixels in the background image area; The gray value threshold is obtained by the following formula: Where S represents the gray value threshold; n represents the number of pixels contained in the bird image area; m represents the number of pixels contained in the background image area; X 01i represents the gray value of the i-th pixel contained in the bird image area; X 02p Represents the average gray value of the m pixels contained in the background image area; X 02i Represents the gray value of the i-th pixel contained in the background image area; X 01p Represents the average gray value of n pixels contained in the bird image area; Extracting pixel points with a gray value lower than a gray value threshold from the pixel points included in the bird image area as a first pixel point set; Extracting pixel points exceeding a grayscale value threshold from the pixel points included in the background image area as a second pixel point set; Extracting average grayscale values ​​corresponding to the first pixel point set and the second pixel point set; Compare the difference between the average grayscale values ​​corresponding to the first pixel point set and the second pixel point set with a preset grayscale difference threshold; When the difference between the average grayscale values ​​corresponding to the first pixel point set and the second pixel point set is lower than a preset grayscale difference threshold, the contrast of the image information is adjusted.

7. The AI ​​intelligent identification system for bird bathing basins according to claim 6, characterized in that: When the difference between the average grayscale values ​​corresponding to the first pixel point set and the second pixel point set is lower than a preset grayscale difference threshold, the contrast of the image information is adjusted, including: Obtaining a first grayscale coefficient using grayscale values ​​corresponding to pixels included in the bird image area; Wherein, the first grayscale coefficient is obtained by the following formula: Among them, F 01 represents the first grayscale coefficient; n represents the number of pixels contained in the bird image area; X 01i represents the gray value of the i-th pixel contained in the bird image area; X 01p represents the average gray value of n pixels contained in the bird image area; X 02p represents the average gray value of m pixels contained in the background image area; S represents the gray value threshold; Obtaining a second grayscale coefficient using grayscale values ​​corresponding to pixels included in the bird image area; The second grayscale coefficient is obtained by the following formula: Among them, F 02 represents the second grayscale coefficient; m represents the number of pixels contained in the background image area; X 02i Represents the gray value of the i-th pixel contained in the background image area; X 01p represents the average gray value of n pixels contained in the bird image area; X 02p represents the average gray value of m pixels contained in the background image area; S represents the gray value threshold; Subtracting the first grayscale coefficient from the second grayscale coefficient to obtain a coefficient difference between the first grayscale coefficient and the second grayscale coefficient; Comparing the coefficient difference with a preset coefficient difference; When the coefficient difference is lower than the preset coefficient difference, the contrast of the background image area is reduced by using the first grayscale coefficient and the second grayscale coefficient, and the adjusted contrast value of the background image area is obtained by the following formula; Among them, D 02t Indicates the contrast value of the background image area after adjustment; D 02 Indicates the contrast value of the background image area before adjustment; F 01 Indicates the first grayscale coefficient; F 02 represents the second grayscale coefficient; When the coefficient difference is not less than the preset coefficient difference, the contrast of the bird image area is improved by using the first grayscale coefficient and the second grayscale coefficient, and the adjusted contrast value of the bird image area is obtained by the following formula; Among them, D 01t Indicates the contrast value of the bird image area after adjustment; D 01 Indicates the contrast value of the bird image area before adjustment; D 02 Indicates the contrast value of the background image area before adjustment; F 01 Indicates the first grayscale coefficient; F 02 Indicates the second grayscale coefficient.

8. The AI ​​intelligent identification system for bird bathing basins according to claim 5, characterized in that: The chip processor (107) and the AI ​​knowledge base (109) are interconnected, wherein the AI ​​knowledge base (109) includes a feature extraction module (1091) and a feature recognition module (1092); AI Knowledge Base (109) for: The existing bird information features are stored, and network data is automatically collected and uploaded and updated in real time. The AI ​​knowledge base (109) numbers the collected bird features; The feature extraction module (1091) is used to: Extracting the bird information collected by the image scanning unit (1071), extracting the available information area in the photo, and determining whether the currently extracted feature part is available, and dividing the available feature information into numbers; The feature recognition module (1092) is used to: The extracted feature numbers are quickly compared with the information in the AI ​​knowledge base (109), and the current bird name and race information are obtained through comparison analysis.

9. The AI ​​intelligent identification system for bird bathing basins according to claim 8, characterized in that: The AI ​​knowledge base (109) is interoperable with the user information base (1093) and the cloud database (1095), wherein the user information base (1093) is used to: storing the user information registered by the APP (1096), analyzing the user's preferences during the registration process and daily use, and recording the bird information that the user is interested in; After the AI ​​knowledge base (109) analyzes the information of the bird currently bathing, the data search unit (1094) searches for users who are interested in the bird according to the bird information, and sends a prompt notification through the APP (1096) to remind the user to watch the live broadcast; Cloud database (1095), used for: Used to store recorded video.

10. A method for AI intelligent identification of bird bathing basins, implemented based on the AI ​​intelligent identification system for bird bathing basins according to claim 9, characterized in that: The steps include: Step 1: After the infrared sensor module (108) cooperates with the pressure sensor module at the bottom of the basin body (2) to detect the bird, the self-priming water pump (204) and the one-way water valve (205) are turned on, and the self-priming water pump (204) is used to spray water through the nozzle assembly (4) to help the bird to wash; Step 2: The visual module (105) and the microphone module (106) are started at the same time, the visual module (105) takes a video and a photo of the bird bathing, and the microphone module (106) collects and records the bird's calls; Step 3: The collected video information will be collected and stored in the cloud database (1095), and the user can search and watch it through the APP (1096). At the same time, the system will also compare the video information with the AI ​​knowledge base (109); Step 4: Through comparison and analysis, determine whether there are users who are interested in this type of bird, and provide feedback through the APP (1096) to allow users to watch the live broadcast directly.

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