Bird behavior observation method, device and system, electronic equipment and medium

Abstract

The invention belongs to the technical field of animal observation, and aims to provide a bird behavior observation method, device and system, electronic equipment and a medium. The method comprises the following steps: receiving video data of a specified bird nest, and extracting image data from the video data; inputting the image data into a preset bird image detection model for bird image detection, detecting a bird image in the image data, and inputting the image data into a preset bird behavior recognition model so as to obtain bird behavior information matched with the image data; and obtaining bird behavior spectrum data according to the bird behavior information of the multiple pieces of image data and the image acquisition time. According to the method, users such as researchers can be assisted in large-scale bird behavior observation, the bird observation efficiency is high, meanwhile, bird behavior analysis is automatically carried out by adopting the bird behavior recognition model, the manual workload is reduced, and the observation precision and the result consistency are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of animal observation, and particularly relates to a method, device, system, electronic device and medium for observing bird behavior. Background Art

[0002] Birds are an important part of nature. Birds play multiple roles in the ecosystem, including seed dispersers and insect controllers, etc., and they participate in ecological processes such as seed dispersal, pollen transfer, pest control, and food chain regulation. In terms of environmental protection, since birds are highly sensitive to environmental changes, birds are one of the sensitive indicators in the ecosystem.

[0003] Based on this, bird observation has important value in aspects such as environmental protection, ecological balance and biodiversity. By observing the distribution, quantity and behavior of birds, it is convenient for people to understand the possible problems in the environment. At the same time, bird observation also provides people with opportunities to relax and immerse in nature, which can prompt people to understand the operation of the ecosystem more deeply and cultivate the love and protection awareness for nature.

[0004] In traditional technologies, bird observation is usually carried out by means of manual observation after video shooting, that is, a camera for shooting video is first set up around the bird's nest, and then researchers or volunteers manually check the video to analyze bird behavior.

[0005] However, in the process of using the existing technologies, the inventor found that there are at least the following problems in the existing technologies:

[0006] First of all, generally speaking, a researcher usually needs to observe 80 - 100 bird's nests in a year. Since there are a large number of birds, traditional bird breeding behavior observation requires a large amount of human participation, resulting in a large workload of human input and relatively low observation efficiency. Moreover, in the process of bird observation, the behavior data of birds in a large number of bird's nests are intertwined with a large amount of environmental data, and it is very difficult to effectively analyze bird behavior using the method of manual observation, and the difficulty of manual analysis is relatively large. In addition, due to the different subjective judgments and skill levels of observers, it may lead to inconsistencies in observation results. At the same time, for birds at a long distance or difficult to observe, the accuracy of observation is challenged, resulting in low observation accuracy. Summary of the Invention

[0007] The present invention aims to solve the above technical problems at least to a certain extent, and provides a method, device, system, electronic device and medium for observing bird behavior.

[0008] To achieve the above object, the present invention adopts the following technical solutions:

[0009] In a first aspect, the present invention provides a method for observing bird behavior, including:

[0010] Receive the video data of the specified bird's nest, and extract the image data from the video data;

[0011] Input the image data into a preset bird image detection model for bird image detection, and when a bird image is detected in the image data, proceed to the next step;

[0012] Input the image data into a preset bird behavior recognition model to obtain bird behavior information matching the image data;

[0013] Obtain bird behavior spectrum data according to the bird behavior information of multiple image data and the image acquisition time.

[0014] The present invention can assist users such as researchers in conducting large-scale bird behavior observations with high bird observation efficiency. At the same time, it automatically analyzes bird behaviors using a bird behavior recognition model, which helps reduce the manual workload and improves the accuracy and result consistency of observations. Specifically, in the implementation process of the present invention, video data of a specified bird's nest is collected to receive the video data of the specified bird's nest, and the image data is extracted from the video data; subsequently, the image data is input into a preset bird image detection model for bird image detection, and when a bird image is detected in the image data, the image data is input into a preset bird behavior recognition model to obtain bird behavior information matching the image data; finally, bird behavior spectrum data is obtained according to the bird behavior information of multiple image data and the image acquisition time. In this process, this embodiment can realize the intelligent extraction and analysis of bird behavior data, can detect bird behaviors such as in the hatching and brooding stages to analyze bird behavior information such as the participation time of males and females in different stages, the empty nest time, and the number of egg-turning times, and can finally output the behavior spectrum of birds. This method of obtaining the behavior spectrum without manual intervention greatly reduces the workload of researchers in obtaining behavior data, will provide more opportunities for scientific researchers to conduct in-depth research on bird breeding behaviors, and also facilitates scientific researchers to focus their energy more on deeper and more challenging research work.

[0015] In a possible design, extracting the image data from the video data includes:

[0016] Perform screen capture on the video data to obtain the captured video data including the bird's nest screen;

[0017] Extract the image data from the captured video data by means of image frame extraction.

[0018] In a possible design, after extracting the image data from the video data, the method further includes:

[0019] Preprocess the image data using the histogram equalization algorithm to obtain preprocessed image data, so as to input the preprocessed image data into a preset bird behavior recognition model.

[0020] In a possible design, the steps for obtaining the bird image detection model are as follows:

[0021] Construct an initial bird image detection model;

[0022] Obtain bird image sample data;

[0023] Perform bird image annotation on the bird image sample data to obtain annotated bird image sample data;

[0024] Divide the annotated bird image sample data into training sample data and test sample data;

[0025] Train the initial bird image detection model using the training sample data to obtain a trained bird image detection model;

[0026] Verify the trained bird image detection model using the test sample data, and use the trained bird image detection model as the final bird image detection model after passing the verification;

[0027] The steps for obtaining the bird behavior recognition model are as follows:

[0028] Construct an initial bird behavior recognition model;

[0029] Obtain bird image sample data;

[0030] Divide the annotated bird image sample data into training sample data and test sample data;

[0031] Train the initial bird behavior recognition model using the training sample data to obtain a trained bird behavior recognition model;

[0032] Verify the trained bird behavior recognition model using the test sample data, and use the trained bird behavior recognition model as the final bird behavior recognition model after passing the verification.

[0033] In a possible design, the bird behavior recognition model includes a bird hatching behavior model and a bird brooding behavior model. Among them, the bird behavior information obtained based on the bird hatching behavior model includes hatching behavior information, empty nest behavior information, and egg turning behavior information, and the bird behavior information obtained based on the bird brooding behavior model includes brooding behavior information, feeding behavior information, and adult bird leaving the nest behavior information.

[0034] In a possible design, the method further includes:

[0035] Receiving the monitoring data of the bird's nest environment;

[0036] Visualizing the monitoring data of the bird's nest environment, the video data, and the bird behavior spectrum data.

[0037] In a second aspect, the present invention provides a bird behavior observation device for implementing the bird behavior observation method as described in any one of the above; the bird behavior observation device includes:

[0038] A monitoring data receiving module, configured to receive video data of a specified bird's nest and extract image data from the video data;

[0039] A bird image detection module, communicatively connected to the monitoring data receiving module, configured to input the image data into a preset bird image detection model for bird image detection, and when a bird image is detected in the image data, proceed to the next step;

[0040] A bird behavior recognition module, communicatively connected to the bird image detection module, configured to input the image data into a preset bird behavior recognition model to obtain bird behavior information matching the image data;

[0041] A bird behavior spectrum generation module, communicatively connected to the bird behavior recognition module, configured to obtain bird behavior spectrum data according to the bird behavior information of multiple image data and the image acquisition time.

[0042] In a third aspect, the present invention provides a bird behavior observation device, including an environment detection module, an image acquisition module, a wireless communication gateway, and a cloud platform. Among them, the environment detection module and the image acquisition module are both communicatively connected to the cloud platform through the wireless communication gateway, and the cloud platform is used to implement the bird behavior observation method as described in any one of claims 1 to 7.

[0043] In a fourth aspect, the present invention provides an electronic device, including:

[0044] A memory, configured to store computer program instructions; and,

[0045] A processor, configured to execute the computer program instructions to complete the operations of the bird behavior observation method as described in any one of the above.

[0046] In a fifth aspect, the present invention provides a computer-readable storage medium, configured to store computer-readable computer program instructions, and the computer program instructions are configured to execute the operations of the bird behavior observation method as described in any one of the above when running. Description of the Drawings

[0047] Figure 1 is a flowchart of a method for observing bird behavior in an embodiment;

[0048] Figure 2 is a block diagram of a module of a bird behavior observation device in an embodiment;

[0049] Figure 3 is a block diagram of a module of an electronic device in an embodiment. Detailed implementation manners

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in combination with the accompanying drawings and the description of the embodiments or the prior art. Obviously, the following description of the structures of the accompanying drawings is only some embodiments of the present invention. For those of ordinary skill in the art, other accompanying drawings can be obtained based on these drawings without creative efforts. It should be noted here that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation to the present invention.

[0051] Embodiment 1:

[0052] This embodiment discloses a method for observing bird behavior, which can be but is not limited to being executed by a computer device or a virtual machine with certain computing resources, such as being executed by an electronic device such as a personal computer, a smart phone, a personal digital assistant, or a wearable device, or being executed by a virtual machine.

[0053] As Figure 1 shown, a method for observing bird behavior can be but is not limited to including the following steps:

[0054] S1. Receive video data of a specified bird's nest, and extract image data from the video data; it should be understood that the video data is video monitoring data obtained by real-time detection of the specified bird's nest. As an example, in this embodiment, when extracting image data from the video data, an image data is extracted from the video data at time intervals such as every 2 seconds and every 5 seconds. Based on this, multiple image data can be extracted from each video data.

[0055] Specifically, in this embodiment, extracting image data from the video data includes:

[0056] S101. Capture the video data to obtain captured video data including the bird's nest. It should be noted that, in this embodiment, the initial video data is 1080P video data, but in the actual picture, the bird's nest may only occupy a very small part. Based on this, this embodiment captures the video data in advance before extracting the image data for subsequent analysis, so that other pictures that do not include the bird's nest are not analyzed, thereby facilitating the improvement of the efficiency of subsequent image processing and saving CPU energy consumption.

[0057] S102. Extract image data from the captured video data by using an image frame extraction method. It should be noted that image frame extraction refers to extracting representative static image frames from the video at a certain time interval. In this embodiment, image data is selected by using an image frame extraction method, and subsequent bird behavior recognition is performed on the image data, which can help save data storage space and facilitate subsequent bird behavior recognition processing.

[0058] Since the bird's nest image is shot 24 hours a day, the brightness range of the image will change greatly with the change of illumination. In order to avoid the problem of reduced accuracy of subsequent image data analysis, the present embodiment further makes the following improvements: after extracting the image data from the video data, the method further includes:

[0059] S103. Preprocess the image data using a histogram equalization algorithm to obtain preprocessed image data, so as to input the preprocessed image data into a preset bird behavior recognition model.

[0060] It should be noted that histogram equalization is a histogram correction method based on the cumulative distribution function transformation method. It is a method of adjusting contrast using image histogram in the field of image processing. Through this method, brightness can be better distributed on the histogram. This can be used to enhance the local contrast of image data without affecting the overall contrast. Histogram equalization achieves this function by effectively expanding the commonly used brightness, which can improve the quality of subsequent image detection and analysis.

[0061] S2. Input the image data into a preset bird image detection model to perform bird image detection, and when a bird image is detected in the image data, proceed to the next step; otherwise, receive new image data; It should be noted that, in this embodiment, before performing bird behavior detection, this embodiment pre-performs bird image detection on the image data, so as to perform bird behavior detection on the image data when it is confirmed that there is an observed object in the image data, so as to improve the efficiency of subsequent bird behavior detection.

[0062] Specifically, in this embodiment, the steps of acquiring the bird image detection model are as follows:

[0063] Build an initial bird image detection model;

[0064] Obtain bird image sample data; Specifically, in this embodiment, 1440 bird image sample data are extracted from the 2-hour video data obtained from historical observations. Among them, there are 1022 pictures of adult Chinese goshawks and 496 pictures of young Chinese goshawks.

[0065] Label the bird image sample data to obtain the labeled bird image sample data;

[0066] Divide the labeled bird image sample data into training sample data and test sample data; In this embodiment, for the pictures of adult Chinese goshawks, 500 pictures are randomly selected from them as the training sample data for building the neural network, and 50 pictures are selected as the test sample data. For the pictures of young Chinese goshawks, 400 pictures are randomly selected from them as the training sample data for building the neural network, and the remaining 96 pictures are used as the test sample data.

[0067] Train the initial bird image detection model with the training sample data to obtain the trained bird image detection model;

[0068] Verify the trained bird image detection model with the test sample data, and after passing the verification, use the trained bird image detection model as the final bird image detection model.

[0069] S3. Input the image data into a preset bird behavior recognition model to obtain bird behavior information matching the image data, thereby converting the image data into structured data that can be directly used for subsequent analysis; In the implementation process of this embodiment, to improve the recognition efficiency of bird behavior recognition, during the process of the bird behavior recognition model recognizing the image data, if in 12 consecutive (frame) judgments, there are 9 times when the obtained bird behavior information is egg-turning behavior information and only 1 time when the obtained bird behavior information is bird hatching behavior information, then the bird behavior information corresponding to this 1 frame of image data is regarded as egg-turning behavior information, thereby reducing the bird behavior recognition error and facilitating the improvement of the accuracy of bird behavior recognition.

[0070] Specifically, in this embodiment, the steps for obtaining the bird behavior recognition model are as follows:

[0071] Build an initial bird behavior recognition model;

[0072] Obtain bird image sample data;

[0073] Divide the labeled bird image sample data into training sample data and test sample data; in this embodiment, in the hatching mode, there are usually three behaviors: hatching behavior, empty nest behavior, and egg turning behavior. In this embodiment, 200 pieces of training sample data and 20 pieces of test sample data are prepared for each bird behavior in the hatching mode. In the chick-rearing mode, there are usually three behaviors: warming the chicks behavior, feeding behavior, and adult bird leaving the nest behavior. In this embodiment, 200 pieces of training sample data and 20 pieces of test sample data are prepared for each bird behavior in the chick-rearing mode.

[0074] Use the training sample data to train the initial bird behavior recognition model to obtain a trained bird behavior recognition model;

[0075] Use the test sample data to verify the trained bird behavior recognition model, and after passing the verification, use the trained bird behavior recognition model as the final bird behavior recognition model.

[0076] In this embodiment, the bird image detection model and the bird behavior recognition model can both but are not limited to using the YOLOv5 network model. Among them, the YOLO v5 network model is an object detection algorithm based on deep learning, with the characteristics of fast recognition speed and high accuracy. It can quickly process a large amount of image data, and while ensuring the accuracy, it reduces the amount of calculation. Compared with traditional object detection algorithms, the YOLO v5 network model can detect and recognize multiple objects in an image in a shorter time.

[0077] Specifically, in this embodiment, the bird observation items mainly focus on the hatching stage and the chick-rearing stage of birds. Therefore, the bird behavior recognition model includes a bird hatching behavior model and a bird chick-rearing behavior model. Among them, the bird behavior information obtained based on the bird hatching behavior model includes hatching behavior information, empty nest behavior information, and egg turning behavior information, and the bird behavior information obtained based on the bird chick-rearing behavior model includes warming the chicks behavior information, feeding behavior information, and adult bird leaving the nest behavior information.

[0078] S4. Obtain the avian behavior spectrum data based on the avian behavior information and the image acquisition time of multiple image data. It should be noted that the animal behavior spectrum refers to the comprehensive recorded data of the behaviors shown by animals within a certain period of time, usually including various daily activities, social interactions, food acquisition, reproductive behaviors, etc. Such recorded data aims to deeply understand the characteristics of animals in aspects such as ecology, ethology, and physiology, so as to reveal the lifestyle and survival strategies of animals in their natural environment. In this embodiment, by obtaining the avian behavior spectrum data, it is of great significance for understanding aspects such as the avian ecosystem, population ecology, and adaptive evolution. By analyzing the avian behavior spectrum data, researchers can reveal information such as the adaptive behaviors, social structures, and reproductive strategies of birds in different environments, providing in-depth insight basis for avian biology and ecology research.

[0079] The method further includes:

[0080] S5. Receive the nest environment monitoring data; specifically, in this embodiment, environmental information detection devices such as temperature sensors, humidity sensors, and illuminance sensors are used to collect environmental parameters, so as to early identify some potential problems based on the environmental monitoring data in the future; during the implementation process, the environmental information detection devices can be installed around or inside the bird nest to achieve real-time collection of the environmental parameters of the birds' living environment and subsequent environmental analysis. In this embodiment, the nest environment monitoring data can be received through wireless transmission methods such as 4G wireless transmission and WiFi transmission, but is not limited thereto.

[0081] S6. Visualize the nest environment monitoring data, the video data, and the avian behavior spectrum data.

[0082] Specifically, in this embodiment, the Grafana platform is used to visualize the nest environment monitoring data, the video data, and the avian behavior spectrum data. The Grafana platform is a cross-platform and open-source data visualization web application platform that can display relevant data charts in a web browser, facilitating users such as scientific researchers to view in a timely manner. Specifically, in this embodiment, when the Grafana platform visualizes the nest environment monitoring data, the "Time series" component can be used to achieve this, thereby showing the change of the environmental monitoring data over time. The "Stat" component can also be used to display the current nest environment monitoring data in real time. The specific display situation can be determined according to the user's needs. In this embodiment, the "Video" component of the Grafana platform can be used to display real-time video data or historical video data, and the "pie chart" component of the Grafana platform can be used to visualize the avian behavior spectrum data in the form of a behavior state distribution.

[0083] It should be noted that in this embodiment, the Chinese goshawk is used as an observation sample. Specifically, since there is a close relationship between the survival of the Chinese goshawk and the environment in the northern part of Beijing, this relationship is reflected in aspects such as ecological balance, habitat selection, and food chain. The Chinese goshawk plays an important role in its ecosystem. As predators, they help control the populations of birds and small mammals, maintaining ecological balance. For habitats, Chinese goshawks usually choose to nest in open areas and forest edges for better foraging, breeding, and protecting offspring. In addition, the Chinese goshawk is a top predator in the food chain, mainly feeding on small mammals, birds, and reptiles. By controlling the number of prey, they play a regulatory role in the food chain, affecting the relative abundance of other biological groups. Therefore, the relationship between the Chinese goshawk and the environment is a dynamic balance of interdependence and mutual influence. They play an important ecological role in their ecosystem and are suitable for using the Chinese goshawk as an observation sample.

[0084] Birds can exhibit complex and variable behaviors, which are closely related to their survival, reproduction, social interactions, etc. Common bird behaviors include: foraging (food delivery) behavior, breeding care behavior, social behavior, vigilance behavior, etc. Through research with scientific researchers, in this embodiment, the state of Chinese goshawk nests is divided into the incubation state and the chick-rearing state. Specifically, parameters such as the participation time, empty nest time, and number of egg-turning times of male and female Chinese goshawks during the incubation stage, as well as behaviors such as warming the chicks, delivering food, and adult birds leaving the nest during the chick-rearing stage are analyzed. These behaviors are common in birds and are applicable to the analysis of most birds including the Chinese goshawk.

[0085] Among them, the behavior of warming the chicks by birds refers to the behavior of adult birds providing warmth, protection, and care for the chicks during incubation. Generally, adult birds will continue to keep the chicks warm with their body temperature. They will wrap the chicks with their feathers under their wings or beside their bodies, forming a warm protective layer to regulate the body temperature of the chicks through their own body temperature. In a lower-temperature environment, they will cover the chicks more tightly to provide more warmth. The food delivery behavior of birds is the behavior of adult birds being responsible for feeding the chicks during the chick-rearing period. Adult birds will transfer food beak-to-beak to the chicks after eating. This behavior can not only provide nutrition but also provide additional warmth for the chicks. Generally speaking, the behavior of adult birds leaving the nest is the behavior of adult birds leaving the nest to find food for the chicks. Some scholars also believe that the behavior of adult birds leaving the nest may also be affected by factors such as external environmental changes and food supply.

[0086] This embodiment can assist users such as researchers in conducting large-scale bird behavior observations. It has a high bird observation efficiency. At the same time, it uses a bird behavior recognition model to automatically analyze bird behaviors, which helps reduce the manual workload and improves the accuracy and result consistency of observations. Specifically, during the implementation of this embodiment, video data of a specified bird's nest is collected to receive the video data of the specified bird's nest, and image data is extracted from the video data. Subsequently, the image data is input into a preset bird image detection model for bird image detection. When a bird image is detected in the image data, the image data is input into a preset bird behavior recognition model to obtain bird behavior information matching the image data. Finally, bird behavior spectrum data is obtained based on the bird behavior information of multiple image data and the image acquisition time. During this process, this embodiment can realize the intelligent extraction and analysis of bird behavior data, can detect bird behaviors such as during the hatching and brooding stages to analyze bird behavior information such as the participation time of males and females in different stages, the time of the empty nest, and the number of egg-turning times, and can finally output the behavior spectrum of birds. This method of obtaining the behavior spectrum without manual intervention greatly reduces the workload of researchers in obtaining behavior data, will provide more opportunities for scientific researchers to conduct in-depth research on bird breeding behaviors, and also facilitates scientific researchers to focus their energy more on deeper and more challenging research work.

[0087] Embodiment 2:

[0088] This embodiment discloses a bird behavior observation device for implementing the bird behavior observation method in Embodiment 1; as Figure 2 shown, the bird behavior observation device includes:

[0089] A monitoring data receiving module, configured to receive video data of a specified bird's nest and extract image data from the video data;

[0090] A bird image detection module, communicatively connected to the monitoring data receiving module, configured to input the image data into a preset bird image detection model for bird image detection, and when a bird image is detected in the image data, proceed to the next step;

[0091] A bird behavior recognition module, communicatively connected to the bird image detection module, configured to input the image data into a preset bird behavior recognition model to obtain bird behavior information matching the image data;

[0092] A bird behavior spectrum generation module, communicatively connected to the bird behavior recognition module, configured to obtain bird behavior spectrum data based on the bird behavior information of multiple image data and the image acquisition time.

[0093] It should be noted that for the working process, working details and technical effects of the bird behavior observation device provided in Embodiment 2, reference can be made to Embodiment 1, which will not be elaborated here.

[0094] Embodiment 3:

[0095] Based on Embodiment 1 or 2, this embodiment discloses a bird behavior observation system, including an environment detection module, an image acquisition module, a wireless communication gateway and a cloud platform. Among them, the environment detection module and the image acquisition module are both communicatively connected to the cloud platform through the wireless communication gateway, and the cloud platform is used to implement the bird behavior observation method described in any one of Claims 1 to 7.

[0096] In this embodiment, the environment detection module includes but is not limited to temperature and humidity sensors and light sensors, and is used to collect nest environment monitoring data. The environment detection module may also include environmental sensors such as carbon dioxide sensors, PM2.5 sensors, PM10 sensors, wind direction sensors, wind sensors, rainfall sensors, etc. The more types of sensors there are, the more environmental parameters can be collected, and the more accurate the subsequent description of the bird's living environment will be. The environmental monitoring data collected by the environment detection module can be sent to the cloud platform through the wireless communication gateway. Different environmental sensors can communicate with the wireless communication gateway using different communication protocols. For example, the temperature and humidity sensor can communicate with the wireless communication gateway using a custom bus protocol, the carbon dioxide sensor can communicate with the wireless communication gateway using a serial port protocol, and the light sensor can communicate with the wireless communication gateway using the I2C protocol, which will not be elaborated here.

[0097] The image acquisition module can collect data such as bird videos and images, which is convenient for users such as scientific researchers to observe the dynamics of birds in real time. In this embodiment, the image acquisition module uses the AX620A development board, which integrates a camera, a processor and an LCD (Liquid Crystal Display), and provides complete example codes, enabling rapid development.

[0098] In this embodiment, the wireless communication gateway uses the MQTT (Message Queuing Telemetry Transport) protocol, which is a lightweight Internet of Things communication protocol based on the publish / subscribe mode. With the characteristics of being simple to implement, supporting QoS (Quality of Service, a security mechanism of the network, a technology used to solve problems such as network latency and congestion), and having small message sizes, it is suitable for the scenario of field bird observation in this embodiment where a small code footprint and / or very precious network bandwidth are required for remote connections.

[0099] It should be understood that this embodiment also includes a power supply module for providing power support to devices such as wireless communication gateways. In this embodiment, the power supply module is powered by solar energy and includes modules such as solar photovoltaic panels and energy storage batteries, which are suitable for outdoor scenarios. In this embodiment, the selected solar photovoltaic panel has a maximum power of 5 watts, and the output voltage ranges from 5V to 6V, with a variation of up to 1V as the intensity of external sunlight changes.

[0100] Embodiment 4

[0101] Based on Embodiment 1 or 2, this embodiment discloses an electronic device, which can be a smart phone, a tablet computer, a notebook computer, a desktop computer, etc. The electronic device may be referred to as a user terminal, a portable terminal, a desktop terminal, etc. As Figure 3 shown, the electronic device includes:

[0102] A memory for storing computer program instructions; and,

[0103] A processor for executing the computer program instructions to complete the operations of the bird behavior observation method described in any one of Embodiment 1.

[0104] Specifically, the processor 301 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 301 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 301 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the wake state, also known as the CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 301 may be integrated with a GPU (Graphics Processing Unit), and the GPU is responsible for rendering and drawing the content to be displayed on the display screen.

[0105] The memory 302 may include one or more computer-readable storage media, which may be non-transitory. The memory 302 may further include high-speed random access memory, as well as non-volatile memory, such as one or more disk storage devices and flash storage devices. In some embodiments, the non-transitory computer-readable storage media in the memory 302 is used to store at least one instruction, which is used to be executed by the processor 301 to implement the bird behavior observation method provided in Embodiment 1 of the present application.

[0106] In some embodiments, the terminal may optionally further include: a communication interface 303 and at least one peripheral device. The processor 301, the memory 302, and the communication interface 303 may be connected through a bus or signal lines. Each peripheral device may be connected to the communication interface 303 through a bus, signal lines, or a circuit board. Specifically, the peripheral device includes at least one of a radio frequency circuit 304, a display screen 305, and a power supply 306.

[0107] The communication interface 303 can be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 301 and the memory 302. In some embodiments, the processor 301, the memory 302, and the communication interface 303 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 301, the memory 302, and the communication interface 303 can be implemented on a separate chip or circuit board, and this embodiment does not limit this.

[0108] The radio frequency circuit 304 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 304 communicates with a communication network and other communication devices through electromagnetic signals.

[0109] The display screen 305 is used to display a UI (User Interface). The UI may include graphics, text, icons, videos, and any combination thereof.

[0110] The power supply 306 is used to supply power to each component in the electronic device.

[0111] Embodiment 5:

[0112] Based on any one of Embodiments 1 to 4, this embodiment discloses a computer-readable storage medium for storing computer-readable computer program instructions, and the computer program instructions are configured to perform the operations of the bird behavior observation method as described in Embodiment 1 when running.

[0113] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the present invention can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. Optionally, they can be implemented by program codes executable by the computing device. Thus, they can be stored in a storage device and executed by the computing device, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module for implementation. In this way, the present invention is not limited to any specific combination of hardware and software.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for observing bird behavior, characterized in that: Including: Receiving video data of a specified bird's nest and extracting image data from the video data; Inputting the image data into a preset bird image detection model for bird image detection, and when a bird image is detected in the image data, proceeding to the next step; Inputting the image data into a preset bird behavior recognition model to obtain bird behavior information matching the image data; Obtaining bird behavior spectrum data based on the bird behavior information of multiple image data and the image acquisition time.

2. The method for observing bird behavior according to claim 1, wherein: Extracting image data from the video data includes: Performing frame capture on the video data to obtain captured video data including the bird's nest image; Extracting image data from the captured video data by means of image frame extraction.

3. A method for observing bird behavior according to claim 1, characterized in that: After extracting image data from the video data, the method further includes: Preprocessing the image data using a histogram equalization algorithm to obtain preprocessed image data for inputting the preprocessed image data into a preset bird behavior recognition model.

4. A method for observing bird behavior according to claim 1, characterized in that: The steps for obtaining the bird image detection model are as follows: Constructing an initial bird image detection model; Obtaining bird image sample data; Performing bird image annotation on the bird image sample data to obtain annotated bird image sample data; Dividing the annotated bird image sample data into training sample data and test sample data; Training the initial bird image detection model using the training sample data to obtain a trained bird image detection model; Validating the trained bird image detection model using the test sample data, and taking the trained bird image detection model as the final bird image detection model after passing the validation; The steps for obtaining the bird behavior recognition model are as follows: Constructing an initial bird behavior recognition model; Obtaining bird image sample data; Dividing the annotated bird image sample data into training sample data and test sample data; Training the initial bird behavior recognition model using the training sample data to obtain a trained bird behavior recognition model; Validating the trained bird behavior recognition model using the test sample data, and taking the trained bird behavior recognition model as the final bird behavior recognition model after passing the validation.

5. A method for observing bird behavior according to claim 1, characterized in that: The bird behavior recognition model includes a bird hatching behavior model and a bird brooding behavior model. Among them, the bird behavior information obtained based on the bird hatching behavior model includes hatching behavior information, empty nest behavior information, and egg turning behavior information, and the bird behavior information obtained based on the bird brooding behavior model includes brooding behavior information, food delivery behavior information, and adult bird leaving the nest behavior information.

6. A method for observing bird behavior according to claim 1, characterized in that: The method further includes: Receiving bird's nest environment monitoring data; Visually displaying the bird's nest environment monitoring data, the video data, and the bird behavior spectrum data.

7. A bird behavior observation device, characterized in that: For implementing the bird behavior observation method according to any one of claims 1 to 6; the bird behavior observation device includes: A monitoring data receiving module for receiving video data of a specified bird's nest and extracting image data from the video data; A bird image detection module, communicatively connected to the monitoring data receiving module, for inputting the image data into a preset bird image detection model to perform bird image detection, and when a bird image is detected in the image data, proceeding to the next step; A bird behavior recognition module, communicatively connected to the bird image detection module, for inputting the image data into a preset bird behavior recognition model to obtain bird behavior information matching the image data; A bird behavior spectrum generation module, communicatively connected to the bird behavior recognition module, for obtaining bird behavior spectrum data according to the bird behavior information of multiple image data and the image acquisition time.

8. A bird behavior observation system, characterized in that: It includes an environment detection module, an image acquisition module, a wireless communication gateway and a cloud platform. Among them, the environment detection module and the image acquisition module are both communicatively connected to the cloud platform through the wireless communication gateway, and the cloud platform is used to implement the bird behavior observation method according to any one of claims 1 to 6.

9. An electronic device, characterized in that: Comprising: A memory for storing computer program instructions; And, A processor for executing the computer program instructions to complete the operations of the bird behavior observation method according to any one of claims 1 to 6.

10. A computer-readable storage medium for storing computer-readable computer program instructions, characterized in that: The computer program instructions are configured to perform the operations of the bird behavior observation method according to any one of claims 1 to 6 when running.

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