Remote monitoring system based on bird species identification and population number estimation

Abstract

The invention discloses a remote monitoring system based on bird species identification and population number estimation, which comprises a front-end monitoring subsystem, a data transmission subsystem, a data analysis and processing subsystem and a user interaction subsystem, and is characterized in that the front-end monitoring subsystem is composed of a plurality of monitoring nodes; each monitoring node is provided with a high-definition camera, a sound sensor, a temperature and humidity sensor and an illumination sensor, the devices work cooperatively, and data of birds and the environment where the birds are located are collected in an omnibearing mode. According to the remote monitoring system based on bird type identification and population number estimation, manual intervention is greatly reduced through an automatic monitoring and analysis process, the monitoring efficiency is improved, the accuracy of bird type identification and population number estimation is remarkably improved based on an algorithm of deep learning and multi-modal data fusion, and the accuracy of bird type identification and population number estimation is improved. And real-time transmission and processing of data are realized through a remote monitoring system.

Description

Technical Field

[0001] This invention relates to the field of biodiversity monitoring and information technology, specifically a remote monitoring system based on bird species identification and population estimation. Background Technology

[0002] Key aspects of existing technologies: Current bird monitoring methods primarily include manual field observation and monitoring based on fixed cameras. Manual field observation relies on professionals who periodically observe and record information such as bird species, numbers, and behaviors in specific areas. Monitoring based on fixed cameras involves setting up cameras in bird habitats, recording videos, and then manually reviewing and analyzing them. Meanwhile, some existing technologies attempt to use image recognition technology for bird identification, but these are mostly based on simple image feature comparisons and perform poorly in complex environments (such as changes in lighting, bird obstruction, and complex backgrounds).

[0003] 2. Problems and shortcomings of existing technologies:

[0004] (1) Manual observation is inefficient and has large errors: Manual field observation requires a lot of manpower, material resources and time, and the observation range is limited. It is easily affected by the subjective factors of the observers (such as professional level, fatigue level, etc.), resulting in insufficient data accuracy and reliability.

[0005] (2) Fixed camera monitoring lacks real-time and automatic analysis capabilities: Videos recorded by traditional fixed cameras need to be reviewed manually afterward, making it impossible to obtain bird information in real time. Moreover, the workload of manually analyzing videos is huge and inefficient, making it difficult to meet the need for timely monitoring of bird population dynamics.

[0006] (3) Existing image recognition technology has poor adaptability: Existing image recognition-based bird monitoring technology cannot accurately extract bird features when faced with complex and ever-changing natural environments, and has a weak ability to distinguish similar bird species, resulting in low recognition accuracy.

[0007] (4) Inability to accurately estimate population size: Most existing technologies can only simply record the occurrence of birds, making it difficult to accurately estimate the population size of birds and thus unable to provide comprehensive and accurate data support for bird conservation and ecological research. Summary of the Invention

[0008] (a) Technical problems to be solved

[0009] To address the shortcomings of existing technologies, this invention provides a remote monitoring system based on bird species identification and population estimation. This system improves the accuracy of bird species identification, especially in complex natural environments, reducing misjudgments and omissions, and enabling precise estimation of bird populations. It fully considers factors such as bird activity patterns and distribution characteristics, providing reliable data for bird conservation and ecological research. The remote monitoring system enables real-time transmission and processing of monitoring data, allowing researchers and conservation agencies to obtain bird information promptly and take appropriate protective measures. The system also enhances stability and adaptability, enabling it to operate normally in different geographical environments (such as mountains, wetlands, and forests) and climatic conditions.

[0010] (II) Technical Solution

[0011] To achieve the above objectives, the present invention is implemented through the following technical solution: a remote monitoring system based on bird species identification and population estimation, comprising a front-end monitoring subsystem, a data transmission subsystem, a data analysis and processing subsystem, and a user interaction subsystem. The front-end monitoring subsystem consists of multiple monitoring nodes, each equipped with a high-definition camera, a sound sensor, a temperature and humidity sensor, and a light sensor. These devices work together to collect data on birds and their environment from all angles.

[0012] The data transmission subsystem utilizes wireless communication technology to transmit data collected by the front-end monitoring subsystem to a cloud server. In remote areas with poor network signals, satellite communication is used as a supplementary transmission method to ensure stable and timely data transmission. Simultaneously, the transmitted data is encrypted to guarantee its security and integrity.

[0013] The data analysis and processing subsystem is deployed on a cloud server. After receiving the data transmitted from the front end, it first performs preprocessing on the data, including image denoising and sound signal enhancement. Then, it uses deep learning algorithms to extract and identify features of bird images and combines sound features to help determine the bird species.

[0014] The user interaction subsystem provides users with a convenient operating platform, including web and mobile applications. Through this platform, users can view monitoring data in real time, query historical data and statistical analysis results, and set monitoring tasks and early warning thresholds. When abnormal monitoring data occurs, the system automatically sends early warning information to users, making it convenient for users to keep abreast of bird movements.

[0015] Preferably, the high-definition camera is used to acquire image information of birds, and the sound sensor is used to capture bird calls, while the temperature and humidity sensor and the light sensor are used to monitor environmental parameters.

[0016] Preferably, in terms of population size estimation, a population size estimation model is established using machine learning algorithms based on the distribution density, activity range, and environmental parameters of birds in the image, so as to achieve accurate estimation of bird population size.

[0017] Preferably, the wireless communication technology is 4G, 5G or LoRa.

[0018] Preferably, the abnormalities in the monitoring data include the appearance of endangered birds and sudden changes in their population size.

[0019] Preferably, the bird species identification algorithm specifically includes the following steps:

[0020] S1. Image recognition using convolutional neural networks: An improved convolutional neural network model is used to identify key image features of birds.

[0021] S2. Sound Feature-Assisted Recognition: Using sound signal processing technology, features are extracted from the collected bird calls, and sound features are fused with image features through comprehensive analysis of multimodal data.

[0022] Preferably, the method for estimating bird population size includes the following steps:

[0023] T1. Image density-based estimation: Based on the distribution of bird targets in the image, calculate the bird distribution density. Combine the camera's shooting angle, field of view, and actual area of ​​the monitoring area to establish a mathematical model of image density and actual population size, thereby estimating the bird population size in the monitoring area.

[0024] T2. Dynamically Adjusted Estimation Model: Considering the activity patterns of birds and the impact of environmental factors on population size, a dynamically adjusted population size estimation model is established using machine learning algorithms. By continuously learning from historical data and real-time monitoring data, the model can automatically adjust parameters to improve the accuracy of the estimation.

[0025] Preferably, the bird activity patterns in step T2 include diurnal activity differences and seasonal migration, and the environmental factors include temperature, humidity and food resources. The machine learning algorithm is either random forest or support vector machine.

[0026] (III) Beneficial Effects

[0027] This invention provides a remote monitoring system based on bird species identification and population estimation. Compared with existing technologies, it has the following advantages:

[0028] (1) The remote monitoring system based on bird species identification and population estimation greatly reduces human intervention and improves monitoring efficiency through automated monitoring and analysis processes. Furthermore, the algorithm based on deep learning and multimodal data fusion significantly improves the accuracy of bird species identification and population estimation.

[0029] (2) The remote monitoring system based on bird species identification and population estimation realizes real-time data transmission and processing through the remote monitoring system. Users can obtain bird information in a timely manner and receive early warnings when abnormal situations occur, so as to take timely protective measures.

[0030] (3) The remote monitoring system based on bird species identification and population estimation can work stably under various complex geographical and climatic conditions by adopting multiple communication methods and sensors adapted to different environments, thus expanding the monitoring range.

[0031] (4) This remote monitoring system based on bird species identification and population estimation provides important data support for bird conservation, ecological research, and biodiversity assessment through accurate bird species identification and population estimation data, which helps to formulate scientific and reasonable conservation strategies and research plans. Attached Figure Description

[0032] Figure 1 This is a block diagram illustrating the system principle of the present invention. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] Please see Figure 1 This invention provides two technical solutions: a remote monitoring system based on bird species identification and population estimation, specifically including the following embodiments:

[0035] Example 1: A remote monitoring system based on bird species identification and population estimation includes a front-end monitoring subsystem, a data transmission subsystem, a data analysis and processing subsystem, and a user interaction subsystem. The front-end monitoring subsystem consists of multiple monitoring nodes, each equipped with a high-definition camera, a sound sensor, a temperature and humidity sensor, and a light sensor. These devices work together to collect data on birds and their environment from all angles.

[0036] The data transmission subsystem utilizes wireless communication technology to transmit data collected by the front-end monitoring subsystem to a cloud server. In remote areas with poor network signals, satellite communication is used as a supplementary transmission method to ensure stable and timely data transmission. Simultaneously, the transmitted data is encrypted to guarantee its security and integrity.

[0037] The data analysis and processing subsystem is deployed on a cloud server. After receiving the data transmitted from the front end, it first performs preprocessing on the data, including image denoising and sound signal enhancement. Then, it uses deep learning algorithms to extract and identify features of bird images and combines sound features to help determine the bird species.

[0038] The user interaction subsystem provides users with a convenient operating platform, including web and mobile applications. Through this platform, users can view monitoring data in real time, query historical data and statistical analysis results, and set monitoring tasks and early warning thresholds. When abnormal monitoring data occurs, the system automatically sends early warning information to users, making it convenient for users to keep abreast of bird movements.

[0039] In this embodiment of the invention, a high-definition camera is used to collect image information of birds, and a sound sensor is used to capture the calls of birds. The temperature and humidity sensor and the light sensor are used to monitor environmental parameters.

[0040] In this embodiment of the invention, in terms of population size estimation, a population size estimation model is established using machine learning algorithms based on the distribution density, activity range, and environmental parameters of birds in the image, so as to achieve accurate estimation of bird population size.

[0041] In this embodiment of the invention, the wireless communication technology is 4G, 5G, or LoRa.

[0042] In this embodiment of the invention, abnormal monitoring data includes the appearance of endangered birds and sudden changes in their population size.

[0043] In this embodiment of the invention, the bird species identification algorithm specifically includes the following steps:

[0044] S1. Image recognition using Convolutional Neural Networks (CNNs): An improved CNN model is used to identify key image features of birds. For example, an attention mechanism is introduced on the basis of classic models such as ResNet and VGG to improve the ability to distinguish between different bird species. The model is trained with a large amount of labeled bird image data, and the model parameters are continuously optimized to enhance the model's generalization ability and enable it to adapt to complex natural environments.

[0045] S2. Sound Feature-Assisted Recognition: Utilizing sound signal processing technology, features such as frequency and temporal characteristics are extracted from the collected bird calls. These sound features are then fused with image features, and through comprehensive analysis of multimodal data, the accuracy of bird species identification is further improved. For example, birds with similar appearances but unique calls can be effectively distinguished through sound features.

[0046] Example 2: The technical solution of this embodiment of the invention differs from that of Example 1 in that the bird population estimation method specifically includes the following steps:

[0047] T1. Image density-based estimation: Based on the distribution of bird targets in the image, calculate the bird distribution density. Combine the camera's shooting angle, field of view, and actual area of ​​the monitoring area to establish a mathematical model of image density and actual population size, thereby estimating the bird population size in the monitoring area.

[0048] T2. Dynamically Adjusted Estimation Model: Considering the activity patterns of birds and the impact of environmental factors on population size, a dynamically adjusted population size estimation model is established using machine learning algorithms. By continuously learning from historical data and real-time monitoring data, the model can automatically adjust parameters to improve the accuracy of the estimation.

[0049] In this embodiment of the invention, the bird activity patterns in step T2 include diurnal activity differences and seasonal migration, and the environmental factors include temperature, humidity and food resources. The machine learning algorithm is either random forest or support vector machine.

[0050] (1) Monitoring node deployment: Based on the characteristics of bird habitats and research needs, select representative monitoring areas and distribute monitoring nodes reasonably. Ensure that the equipment installation location of each monitoring node can fully collect information on birds and their environment, while also considering the power supply and communication needs of the equipment, such as using solar power and optimizing the layout of communication antennas.

[0051] (2) System Initialization and Model Training: After the system deployment is completed, the front-end monitoring equipment is initialized to ensure normal operation. At the same time, a large amount of bird image and sound data, as well as corresponding environmental data, are collected to train and optimize the bird species identification model and population estimation model. During the training process, the model parameters are continuously adjusted to improve the model performance.

[0052] (3) System Operation and Maintenance: After the system is put into operation, the front-end monitoring equipment continuously collects data and transmits it to the cloud server. The data analysis and processing subsystem processes and analyzes the data in real time. The system is regularly maintained, including equipment inspection, data backup, model update and other tasks, to ensure the stable operation of the system.

[0053] (4) User usage and feedback: Users can log in to the system via the web or mobile application to view monitoring data and analysis results. According to actual needs, users can set monitoring tasks and early warning thresholds, and provide feedback on problems and suggestions found during use to the system developers so that the system can be further optimized and improved.

[0054] In summary, this invention significantly reduces human intervention and improves monitoring efficiency through automated monitoring and analysis processes. Furthermore, the algorithm based on deep learning and multimodal data fusion significantly enhances the accuracy of bird species identification and population estimation. Real-time data transmission and processing are achieved through a remote monitoring system, allowing users to obtain bird information promptly and receive early warnings in case of anomalies, facilitating timely protective measures. By employing multiple communication methods and sensors adapted to different environments, it can operate stably under various complex geographical and climatic conditions, expanding the monitoring range. Accurate bird species identification and population estimation data provide crucial data support for bird conservation, ecological research, and biodiversity assessment, contributing to the development of scientifically sound conservation strategies and research plans.

[0055] Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0056] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0057] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A remote monitoring system based on bird species identification and population estimation, characterized in that: It includes a front-end monitoring subsystem, a data transmission subsystem, a data analysis and processing subsystem, and a user interaction subsystem. The front-end monitoring subsystem consists of multiple monitoring nodes, each equipped with a high-definition camera, a sound sensor, a temperature and humidity sensor, and a light sensor. These devices work together to collect data on birds and their environment from all angles. The data transmission subsystem utilizes wireless communication technology to transmit data collected by the front-end monitoring subsystem to a cloud server. In remote areas with poor network signals, satellite communication is used as a supplementary transmission method to ensure stable and timely data transmission. Simultaneously, the transmitted data is encrypted to guarantee its security and integrity. The data analysis and processing subsystem is deployed on a cloud server. After receiving the data transmitted from the front end, it first performs preprocessing on the data, including image denoising and sound signal enhancement. Then, it uses deep learning algorithms to extract and identify features of bird images and combines sound features to help determine the bird species. The user interaction subsystem provides users with a convenient operating platform, including web and mobile applications. Through this platform, users can view monitoring data in real time, query historical data and statistical analysis results, and set monitoring tasks and early warning thresholds. When abnormal monitoring data occurs, the system automatically sends early warning information to users, making it convenient for users to keep abreast of bird movements.

2. The remote monitoring system based on bird species identification and population estimation according to claim 1, characterized in that: The high-definition camera is used to collect image information of birds, and the sound sensor is used to capture bird calls. The temperature and humidity sensor and the light sensor are used to monitor environmental parameters.

3. The remote monitoring system based on bird species identification and population estimation according to claim 1, characterized in that: In terms of population estimation, a population estimation model is established using machine learning algorithms based on the distribution density, activity range, and environmental parameters of birds in the images, so as to achieve accurate estimation of bird population size.

4. The remote monitoring system based on bird species identification and population estimation according to claim 1, characterized in that: The wireless communication technology is 4G, 5G, or LoRa.

5. The remote monitoring system based on bird species identification and population estimation according to claim 1, characterized in that: The anomalies in the monitoring data include the appearance of endangered birds and sudden changes in their population size.

6. The remote monitoring system based on bird species identification and population estimation according to claim 1, characterized in that: The bird species identification algorithm specifically includes the following steps: S1. Image recognition using convolutional neural networks: An improved convolutional neural network model is used to identify key image features of birds; S2. Sound Feature-Assisted Recognition: Using sound signal processing technology, features are extracted from the collected bird calls, and sound features are fused with image features through comprehensive analysis of multimodal data.

7. The remote monitoring system based on bird species identification and population estimation according to claim 1, characterized in that: The methods for estimating bird population sizes specifically include the following steps: T1. Image density-based estimation: Based on the distribution of bird targets in the image, calculate the bird distribution density. Combine the camera's shooting angle, field of view, and actual area of ​​the monitoring area to establish a mathematical model of image density and actual population size, thereby estimating the bird population size in the monitoring area. T2. Dynamically Adjusted Estimation Model: Considering the activity patterns of birds and the impact of environmental factors on population size, a dynamically adjusted population size estimation model is established using machine learning algorithms. By continuously learning from historical data and real-time monitoring data, the model can automatically adjust parameters to improve the accuracy of the estimation.

8. A remote monitoring system based on bird species identification and population estimation according to claim 7, characterized in that: The bird activity patterns in step T2 include diurnal activity differences and seasonal migration, and the environmental factors include temperature, humidity and food resources. The machine learning algorithm is either random forest or support vector machine.

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