Bird target monitoring method, device and system for ecological observation and medium

Abstract

The invention discloses an ecological observation-oriented bird target monitoring method, device, equipment, system and medium, and relates to the technical field of ecological monitoring, and the method comprises the steps: obtaining video stream data based on an imaging device; processing the video stream data based on a preset multi-target tracking model to obtain a bird target and a first motion track of the bird target in an image plane coordinate system; performing variety identification on the bird target based on a preset bird identification model to obtain variety information of the bird target; based on the variety information and the first movement track, reconstructing and obtaining a second movement track of the bird target in the space coordinate system; and associating the bird target, the variety information and the second movement track as multi-dimensional monitoring data. The problems that in the prior art, the monitoring data dimension is single, and the correlation degree between information is not high can be solved.

Description

Technical Field

[0001] This application relates to the field of ecological monitoring technology, and in particular to a method, device, equipment, system and medium for monitoring bird targets for ecological observation. Background Technology

[0002] Bird diversity is an important indicator of the health and integrity of an ecosystem, and monitoring bird activity is of great significance in ecological environment assessment and protection.

[0003] Existing bird monitoring technologies primarily focus on identifying and counting bird species, and recording their appearance times and numbers. However, bird ranges, migration routes, and behavioral patterns provide direct biological evidence of environmental change. Current technologies acquire monitoring data with limited dimensions, often consisting of isolated identification results or simple appearance records. They lack in-depth integration and correlation analysis of multi-dimensional information such as bird movement trajectories and behavioral physiological parameters, making it difficult to comprehensively and accurately reveal the intrinsic relationship between bird activity and the ecological environment. Summary of the Invention

[0004] The purpose of this application is to provide a method, device, equipment, system and medium for monitoring bird targets for ecological observation, so as to solve the problems of single-dimensional monitoring data and low correlation between information in the prior art.

[0005] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for monitoring bird targets for ecological observation, including: Based on video stream data acquired by the imaging device; The video stream data is processed based on a preset multi-target tracking model to obtain the bird target and the first motion trajectory of the bird target in the image plane coordinate system; Based on a preset bird recognition model, the species of the bird target is identified to obtain the species information of the bird target; Based on the species information and the first motion trajectory, the second motion trajectory of the bird target in the spatial coordinate system is reconstructed and obtained. The bird target, the species information, and the second movement trajectory are correlated to form multidimensional monitoring data.

[0006] Optionally, the step of reconstructing and obtaining the second motion trajectory of the bird target in a spatial coordinate system based on the species information and the first motion trajectory includes: Based on the species information, a preset mapping relationship is queried to obtain the typical actual area of ​​the bird target; Based on the typical actual area and the device imaging parameters, the actual distance information from the bird target to the imaging device at different preset time points is calculated, wherein the device imaging parameters include pixel size and focal length; Based on the actual distance information and the first motion trajectory, a second motion trajectory in the spatial coordinate system is determined through coordinate transformation.

[0007] Optionally, determining the second motion trajectory in the spatial coordinate system based on the actual distance information and the first motion trajectory through coordinate transformation includes: Obtain the first coordinate point of the bird target in the first movement trajectory at the i-th preset time node; Based on the actual distance at the i-th preset time node, the first coordinate point is converted into a second coordinate point in the spatial coordinate system; Based on the second coordinate points at all preset time points, curve fitting is performed to generate the second motion trajectory.

[0008] Optionally, calculating the actual distance information from the bird target to the imaging device at different preset time points based on the typical actual area and device imaging parameters includes: To acquire bird images simultaneously from multiple imaging devices at the same preset time point; For each imaging device, the pixel area occupied by the bird target in the bird image is obtained through image recognition and connected component analysis. Combined with the imaging parameters of the device and the typical actual area, the candidate distance information corresponding to the imaging device is calculated. The average value of the candidate distance information calculated by all imaging devices at the same preset time node is taken as the actual distance information at that preset time node.

[0009] Optionally, the step of obtaining species information of the bird target by identifying the bird species based on a preset bird recognition model specifically includes: The video stream data is sampled at preset time intervals to obtain multiple sampled images; For each sampled image, a connected component analysis was performed on the bird targets to calculate their pixel count; the bird target region with the largest pixel count was selected as the keyframe image. The keyframe image is input into a preset bird recognition model, and the species information is output.

[0010] Optionally, the method further includes: Based on the first motion trajectory, calculate the displacement of the bird target at the preset time node; Based on the displacement and time interval, the flight speed of the bird target is calculated; Based on the flight speed at multiple preset time points, a speed-time fitting curve is generated, and the speed-time fitting curve is incorporated into the multidimensional monitoring data.

[0011] Optionally, the method further includes: Based on the location information of the bird target, the infrared imaging device is controlled to collect temperature data at preset time nodes; Based on temperature data at multiple preset time points, a temperature-time fitting curve is generated, and the temperature-time fitting curve is incorporated into the multidimensional monitoring data.

[0012] Secondly, this application provides a bird target monitoring device for ecological observation, comprising: The data acquisition module is used to acquire video stream data based on the imaging device; The trajectory and recognition module is used to process the video stream data based on a preset multi-target tracking model to obtain the bird target and its first motion trajectory in the image plane coordinate system. The bird species is identified based on a preset bird recognition model to obtain species information. The spatial trajectory reconstruction module is used to reconstruct and obtain the second motion trajectory of the bird target in the spatial coordinate system based on the species information and the first motion trajectory; The data fusion module is used to associate and store the bird target, the species information, and the second movement trajectory as multidimensional monitoring data.

[0013] Thirdly, this application provides a bird target monitoring system for ecological observation, comprising: Bionic eye camera device used to acquire video stream data; The data processing unit is communicatively connected to the bionic eye camera device and is used to execute the method as described in any one of the first aspects to generate the multidimensional monitoring data; A storage database is provided for storing the multidimensional monitoring data; The visual interaction unit, communicatively connected to the storage database and the data processing unit, is used for: Receive user instructions; Based on a preset large language model, the user command is parsed to generate at least one parsing command, which is used to query one or more items in the multidimensional monitoring data; In response to the user command, the multi-dimensional monitoring data corresponding to the parsing command is visualized on the screen.

[0014] Fourthly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method as described in any one of the first aspects.

[0015] Fifthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in any one of the first aspects.

[0016] This application includes the following technical effects: This application achieves accurate detection and species identification of bird targets through multi-target tracking and bird recognition technology. Its core innovation lies in utilizing the typical actual area corresponding to the identified species information, combined with imaging geometry principles, to convert the first motion trajectory on the two-dimensional plane of the image into a second motion trajectory in three-dimensional space in the real world, overcoming the limitation of traditional monitoring that can only provide two-dimensional information. Furthermore, this application integrates multi-dimensional behavioral and physiological data such as flight speed and body surface temperature, and provides natural language queries and visualizations through an intelligent interactive interface. Ultimately, it forms a comprehensive ecological observation solution with rich data dimensions, close information correlation, and intelligent and efficient interaction, providing strong technical support for in-depth research on bird behavior and its relationship with the environment. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 An application environment diagram of a bird target monitoring system for ecological observation provided in one embodiment of this application; Figure 2 A flowchart illustrating a bird target monitoring method for ecological observation provided in one embodiment of this application; Figure 3 for Figure 2 A detailed flowchart of step 203; Figure 4 for Figure 2 A detailed flowchart of step 204; Figure 5 for Figure 4 A detailed flowchart of step 402; Figure 6 for Figure 4 A detailed flowchart of step 403; Figure 7 This is a schematic diagram of the process for obtaining curvature fitting curves and velocity fitting curves according to one embodiment; Figure 8 A schematic diagram of the functional modules of a bird target monitoring device for ecological observation provided in an embodiment of this application; Figure 9 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0021] It should be noted that the terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented, for example, in orders other than those illustrated or described herein.

[0022] It should be noted that "at the time of..." in the embodiments of this application can be either at the instant when a certain situation occurs, or for a period of time after the occurrence of a certain situation. The embodiments of this application do not make specific limitations on this.

[0023] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] The bird target monitoring method for ecological observation provided in this application can be applied to, for example... Figure 1 The system shown includes a bionic eye camera device 101, a data processing unit 102, a storage database 103, and a visual interaction unit 104; wherein: Bionic eye camera device 101, used to acquire video stream data; The data processing unit 102 is communicatively connected to the bionic eye camera device and is used to execute the bird target monitoring method for ecological observation provided in the embodiments of this application to generate multi-dimensional monitoring data; Storage database 103 is used to store multidimensional monitoring data; The visual interaction unit, which communicates with the storage database and data processing unit, is used for: Receive user instructions; Based on a pre-defined large language model, the user command is parsed to generate at least one parsing command, which is used to query one or more items in the monitoring data. In response to user commands, the monitoring data corresponding to the commands is visualized and analyzed on the screen.

[0025] In one exemplary embodiment, such as Figure 2 As shown, a method for monitoring bird targets for ecological observation is provided. This method can be executed by computer equipment. In this embodiment, the method is applied to... Figure 1 The data processing unit 102 in the middle is used as an example for explanation, including the following steps 201 to 204. Wherein: Step 201: Based on the video stream data acquired by the imaging device; Specifically, the bionic eye camera device includes a wide-angle camera unit, which is used to acquire video stream data; Step 202: Process the video stream data based on the preset multi-target tracking model to obtain the bird target and the first motion trajectory of the bird target in the image plane coordinate system; Specifically, existing multi-target tracking models can be used to achieve target identification and tracking; Step 203: Based on the preset bird recognition model, identify the species of the bird target and obtain the species information of the bird target; Specifically, keyframe images of bird targets are obtained based on video stream data, and species information of bird targets is obtained by identifying the species of the keyframe images based on a bird recognition model; Step 204: Based on the species information and the first motion trajectory, reconstruct and obtain the second motion trajectory of the bird target in the spatial coordinate system, and associate the bird target, species information and the second motion trajectory as multi-dimensional monitoring data.

[0026] By implementing steps 201 to 204 above, multi-target tracking and bird identification technologies enable accurate detection and species identification of bird targets. Its core innovation lies in utilizing the typical actual area corresponding to the identified species information, combined with imaging geometry principles, to convert the first motion trajectory on the two-dimensional plane of the image into a second motion trajectory in three-dimensional space in the real world, overcoming the limitation of traditional monitoring that can only provide two-dimensional information. Furthermore, this application integrates multi-dimensional behavioral and physiological data such as flight speed and body surface temperature, and provides natural language queries and visualizations through an intelligent interactive interface, ultimately forming a comprehensive ecological observation solution with rich data dimensions, close information correlation, and intelligent and efficient interaction, providing strong technical support for in-depth research on bird behavior and its relationship with the environment.

[0027] In another exemplary embodiment of this application, in order to obtain a bird target region with complete features, thereby ensuring the accuracy of bird species identification, such as... Figure 3 As shown, step 203 above can be replaced by the following steps 301 to 303: Step 301: Sample the video stream data based on a preset time interval to obtain multiple sampled images; Specifically, the acquired sampled images also include corresponding timestamps; Step 302: Perform connected component analysis on the bird targets in the sampled images and calculate their pixel count; select the bird target region with the largest pixel count as the keyframe image; Specifically, the timestamp of the sampled image is obtained, and the target center point of the bird target at that timestamp can be obtained based on the first motion trajectory. Connectivity analysis is performed starting from the target center point to obtain the bird target region, and the number of target pixels (number of pixels) occupied by the bird target region is obtained. The bird target region with the largest number of target pixels is obtained as the keyframe image.

[0028] Step 303: Input the keyframe image into the preset bird recognition model and output the species information.

[0029] Specifically, pre-trained YOLO and R-CNN models can be used as preset multi-object tracking models.

[0030] Specifically, the above steps reduce the amount of data through temporal sampling, and then use connected component analysis to select bird targets with more features as keyframe images to improve recognition accuracy.

[0031] In another exemplary embodiment of this application, in order to accurately obtain the actual distance between the target and the imaging device, such as... Figure 4 As shown, step 204 above may further include steps 401 to 403. Wherein: Step 401: Based on the species information, query the preset mapping relationship to obtain the typical actual area of ​​the bird target; Specifically, for common birds, there are already empirical values ​​available for reference, and the preset actual area can be set based on these empirical values.

[0032] Specifically, the empirical values ​​are as follows: small birds such as sparrows and tits: approximately 0.001 to 0.002 square meters; medium-sized birds such as pigeons and crows: approximately 0.005 to 0.01 square meters; large waterfowl such as ducks and geese: approximately 0.02 to 0.05 square meters; and large birds such as eagles and storks: greater than 0.05 square meters.

[0033] Step 402: Based on the typical actual area and the device imaging parameters, calculate the actual distance information from the bird target to the imaging device at different preset time nodes. The device imaging parameters include pixel size and focal length. Specifically, such as Figure 5 Step 402 includes the following steps 501 to 503: Step 501: Acquire bird images simultaneously captured by multiple imaging devices at a unified preset time point; Specifically, multiple preset time points are set, and bird images at the preset time points are obtained based on the time information in the video stream; Optionally, multiple preset time nodes can be sampled according to preset time intervals.

[0034] As one embodiment, the bionic eye camera device includes a wide-angle camera device, a medium-range camera device, and a telephoto camera device, and then acquires image information of the wide-angle camera device, the medium-range camera device, and the telephoto camera device at the same preset time node; Step 502: For each imaging device, based on image recognition and connected component analysis, obtain the pixel area occupied by the bird target in the bird image, and calculate the candidate distance information corresponding to the imaging device by combining the device imaging parameters and typical actual area. Specifically, the candidate distance for the j-th imaging device can be obtained using the following formula: Among them Candidate distance, This represents the typical actual area of ​​a bird target. Focal length For pixel size, Let be the area of ​​the target region under this imaging device.

[0035] Step 503: Calculate the average value of the candidate distance information obtained by all imaging devices at the same preset time node, and use the average value as the actual distance information at the preset time node.

[0036] Step 403: Based on the actual distance information and the first motion trajectory, determine the second motion trajectory in the spatial coordinate system through coordinate transformation.

[0037] Specifically, such as Figure 6 Step 403 also includes the following steps 601 to 603: Step 601: Obtain the first coordinate point of the bird target in the first motion trajectory at the i-th preset time node.

[0038] Step 602: Based on the actual distance at the i-th preset time node, convert the first coordinate point into a second coordinate point in the spatial coordinate system; Specifically, the conversion formula is as follows: ; Where (Xc,Yc,Zc) are the second coordinate points, and D is the actual distance information; Specifically, the first coordinate point is (x, y, ), then (x, y, 1) is the unit direction vector from the origin of the imaging device to the center of the target. We know that the target point is on this ray and the distance to the imaging device is D.

[0039] According to the principle of similar triangles: ; Here, D' is the Euclidean distance along the ray direction. Since our direction vector is a unit vector (x, y, 1), the actual distance D is related to D' as follows: ; Based on this, the above conversion formula can be determined.

[0040] Step 603: Based on the second coordinate points at all preset time nodes, perform curve fitting to generate the second motion trajectory.

[0041] In another exemplary embodiment of this application, in order to achieve deep correlation and fusion between motion trajectory observation and multi-dimensional behavioral physiological data (speed, body temperature), after step 202, the method of this application further includes the following steps 701 to 702: Step 701: Calculate the displacement of the bird target at a preset time node based on the first motion trajectory; calculate the flight speed of the bird target based on the displacement and time interval; generate a speed-time fitting curve based on the flight speed at multiple preset time nodes, and incorporate the speed-time fitting curve into the multidimensional monitoring data.

[0042] Step 702: Based on the location information of the bird target, control the infrared imaging device to collect temperature data at preset time nodes; generate a temperature-time fitting curve based on the temperature data at multiple preset time nodes, and incorporate the temperature-time fitting curve into the multidimensional monitoring data.

[0043] This application provides a method for monitoring bird targets for ecological observation, which has the following technical effects; By reducing the amount of data through temporal sampling, and then using connected component analysis to select bird targets with more features as keyframe images, the recognition accuracy can be improved.

[0044] By obtaining the typical actual area of ​​bird species through information on bird species, and then combining the imaging parameters and imaging principles of the equipment, the actual distance of the bird target relative to the imaging device at different times is calculated. Finally, the first motion trajectory of the image plane is fused with the distance information to reconstruct the motion trajectory in the real spatial coordinate system.

[0045] Based on the curves of temperature and flight speed obtained from the first motion trajectory, the location, temperature, and flight speed of bird targets are correlated with time information. This enables deep correlation and fusion of motion trajectory observation with multi-dimensional behavioral and physiological data (speed, body temperature), greatly enhancing the comprehensiveness and relevance of monitoring information and providing richer quantitative evidence for understanding the interaction between bird activity patterns and the ecological environment.

[0046] Based on the same inventive concept, this application also provides a bird target monitoring system for ecological observation. The solution provided by this system is similar to the implementation scheme described above, and will not be repeated here. In addition, this system deeply integrates complex multi-dimensional monitoring data with human-computer interaction based on a large language model, enabling the retrieval of monitoring data through natural language queries, or further processing and visualization of the monitoring data, greatly improving data interactivity and interpretation efficiency. Figure 1 A bird target monitoring system for ecological observation includes a bionic eye camera device 101, a data processing unit 102, a storage database 103, and a visualization interaction unit 104; wherein The bionic eye camera device 101, as a hardware device for basic data acquisition, includes a wide-angle camera and an auxiliary camera, both of which are used to acquire video stream data. Alternatively, the bionic eye camera device includes a wide-angle camera device, a medium-range camera device, and a telephoto camera device.

[0047] The data processing unit 102 is used to acquire multidimensional monitoring data according to the above-mentioned bird target monitoring method for ecological observation.

[0048] Database 103 is used to store monitoring data; The visualization interaction unit 104 is used to display information such as the bird target and its corresponding species, as well as the second motion trajectory in the spatial coordinate system to the user. Specifically, based on user commands and a large language model, it can display one or more of the following: bird target, corresponding species information, and the second motion trajectory in the spatial coordinate system.

[0049] Furthermore, the visual interaction unit 104 is specifically used for: Obtain user instructions; The system parses user commands based on a pre-defined large language model to obtain at least one parsing command. These parsing commands include: obtaining the flight trajectory of a specific bird target; obtaining one of the following: the location, flight speed, and temperature of a specific bird target at a given timestamp; obtaining multiple of these parameters; and obtaining the species information of a specific bird target. In response, the corresponding monitoring data is displayed on the screen according to the parsing instructions.

[0050] As an optional embodiment, the monitoring data also includes a temperature fitting curve for the bird target, and the parsing instructions further include: obtaining the temperature curve of a certain bird target, obtaining the temperature information of a certain bird target at a certain time stamp, obtaining the flight speed curve of a certain bird target, obtaining the flight speed of a certain bird target at a certain time stamp, etc. The visualization interaction unit 104 is also specifically used to display the corresponding monitoring data.

[0051] Furthermore, the parsing instructions also include: obtaining the number of bird targets at a specific timestamp. The visualization interaction unit 104 is also specifically used for: Image information at that timestamp is obtained from video stream data, and the number of birds is obtained based on a preset text and image recognition model and displayed to the user.

[0052] Based on the same inventive concept, this application also provides an ecological observation-oriented bird target monitoring device for implementing the above-mentioned ecological observation-oriented bird target monitoring method. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more ecological observation-oriented bird target monitoring device embodiments provided below can be found in the limitations of the ecological observation-oriented bird target monitoring method described above, and will not be repeated here.

[0053] In one exemplary embodiment, such as Figure 8 As shown, a bird target monitoring device for ecological observation is provided, comprising: The data acquisition module is used to acquire video stream data based on the imaging device; The trajectory and recognition module is used to process video stream data based on a preset multi-target tracking model to obtain bird targets and their first motion trajectory in the image plane coordinate system. Based on a pre-defined bird identification model, bird species are identified to obtain species information; The spatial trajectory reconstruction module is used to reconstruct and obtain the second motion trajectory of the bird target in the spatial coordinate system based on the species information and the first motion trajectory; The data fusion module is used to associate and store bird target, species information and secondary movement trajectory as multidimensional monitoring data.

[0054] As an optional implementation, the spatial trajectory reconstruction module is specifically used for: Based on the species information, query the preset mapping relationship to obtain the typical actual area of ​​the bird target; Based on typical actual area and equipment imaging parameters, the actual distance information from bird targets to the imaging device at different preset time points is calculated. Among them, the equipment imaging parameters include pixel size and focal length. Based on the actual distance information and the first motion trajectory, the second motion trajectory in the spatial coordinate system is determined through coordinate transformation.

[0055] As an optional implementation method, the spatial trajectory reconstruction module is further used for: Obtain the first coordinate point of the bird target in the first motion trajectory at the i-th preset time node; Based on the actual distance at the i-th preset time node, the first coordinate point is converted into the second coordinate point in the spatial coordinate system; Based on the second coordinate points at all preset time points, curve fitting is performed to generate the second motion trajectory.

[0056] As an optional implementation, the spatial trajectory reconstruction module is further used for: To acquire bird images simultaneously from multiple imaging devices at a unified preset time point; For each imaging device, based on image recognition and connected component analysis, the pixel area occupied by the bird target in the bird image is obtained, and combined with the device imaging parameters and typical actual area, the candidate distance information corresponding to the imaging device is calculated. The average value of the candidate distance information calculated by all imaging devices at the same preset time node is taken as the actual distance information at that preset time node.

[0057] As an optional implementation, the trajectory and recognition module is specifically used for: Video stream data is sampled at preset time intervals to obtain multiple sampled images; Connectivity analysis was performed on bird targets in the sampled images to calculate their pixel count; the bird target region with the largest pixel count was selected as the keyframe image. Input the keyframe images into a pre-set bird recognition model and output species information.

[0058] As an optional implementation, the spatial trajectory reconstruction module is specifically used for: Based on the first motion trajectory, calculate the displacement of the bird target at the preset time node; The flight speed of bird targets is calculated based on displacement and time interval; Based on the flight speed at multiple preset time points, a speed-time fitting curve is generated, and the speed-time fitting curve is incorporated into the monitoring data.

[0059] As an optional implementation, the spatial trajectory reconstruction module is specifically used for: Based on the location information of bird targets, control the infrared imaging device to collect temperature data at preset time nodes; Based on temperature data at multiple preset time points, a temperature-time fitting curve is generated and incorporated into the monitoring data.

[0060] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 9 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a bird target monitoring method for ecological observation.

[0061] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0062] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0063] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0064] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0065] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0066] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0067] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0068] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. In summary, the content of this specification should not be construed as a limitation of this application.

Claims

1. A bird target monitoring method for ecological observation, characterized by, The bird target monitoring method for ecological observation comprises the following steps: acquiring video stream data based on an imaging device; processing the video stream data based on a preset multi-target tracking model to acquire a bird target and a first motion trajectory of the bird target in an image plane coordinate system; identifying the species of the bird target based on a preset bird identification model to acquire species information of the bird target; reconstructing and acquiring a second motion trajectory of the bird target in a spatial coordinate system based on the species information and the first motion trajectory; associating the bird target, the species information and the second motion trajectory as multi-dimensional monitoring data.

2. The bird target monitoring method for ecological observation according to claim 1, characterized in that, The method further comprises the following steps: querying a preset mapping relationship according to the species information to acquire a typical actual area of the bird target; calculating actual distance information of the bird target to the imaging device at different preset time nodes based on the typical actual area and device imaging parameters, wherein the device imaging parameters include a pixel size and a focal length; determining the second motion trajectory in the spatial coordinate system through coordinate conversion based on the actual distance information and the first motion trajectory.

3. The bird target monitoring method for ecological observation according to claim 2, characterized in that, The method further comprises the following steps: acquiring a first coordinate point of the bird target in the first motion trajectory at an i-th preset time node; converting the first coordinate point to a second coordinate point in the spatial coordinate system based on the actual distance at the i-th preset time node; performing curve fitting based on the second coordinate points at all the preset time nodes to generate the second motion trajectory.

4. The bird target monitoring method for ecological observation according to claim 2, characterized in that, The method further comprises the following steps: acquiring bird pictures synchronously collected by multiple imaging devices for a unified preset time node; acquiring a pixel area occupied by a bird target in the bird pictures based on image recognition and connected domain analysis for each imaging device, and calculating candidate distance information corresponding to the imaging device by combining the device imaging parameters and the typical actual area; calculating an average value of the candidate distance information calculated by all the imaging devices at the same preset time node, and taking the average value as the actual distance information at the preset time node.

5. The bird target monitoring method for ecological observation according to claim 1, characterized in that, The method further comprises the following steps: sampling the video stream data based on a preset time interval to acquire multiple sample pictures; performing connected domain analysis on the bird target in the sample pictures to calculate a pixel number thereof; screening a bird target region with the largest pixel number as a key frame picture; inputting the key frame picture into the preset bird identification model to output the species information.

6. The bird target monitoring method for ecological observation according to any one of claims 1-5, characterized in that, The bird target monitoring method for ecological observation further comprises the following steps: According to the first motion trajectory, the displacement of the bird target at a preset time node is calculated; Based on the displacement and time interval, the flight speed of the bird target is calculated; According to the flight speed at multiple preset time nodes, a speed-time fitting curve is generated, and the speed-time fitting curve is included in the multi-dimensional monitoring data.

7. The bird target monitoring method for ecological observation according to any one of claims 1-5, characterized in that, The bird target monitoring method for ecological observation further includes: Based on the position information of the bird target, an infrared imaging device is controlled to collect temperature data at a preset time node; According to the temperature data at multiple preset time nodes, a temperature-time fitting curve is generated, and the temperature-time fitting curve is included in the multi-dimensional monitoring data.

8. An ecological observation-oriented bird target monitoring device, characterized by, The bird target monitoring device for ecological observation includes: A data acquisition module is configured to acquire video stream data based on an imaging device; A trajectory and recognition module is configured to process the video stream data based on a preset multi-target tracking model to obtain a bird target and a first motion trajectory of the bird target in an image plane coordinate system; A variety recognition is performed on the bird target based on a preset bird recognition model to obtain variety information; A spatial trajectory reconstruction module is configured to reconstruct and obtain a second motion trajectory of the bird target in a spatial coordinate system based on the variety information and the first motion trajectory; A data fusion module is configured to store the bird target, the variety information, and the second motion trajectory as multi-dimensional monitoring data.

9. An ecological observation-oriented bird target monitoring system characterized by, It includes: A bionic eye camera device is configured to acquire video stream data; A data processing unit is communicatively connected to the bionic eye camera device and configured to execute the bird target monitoring method for ecological observation according to any one of claims 1-7 to generate multi-dimensional monitoring data; A storage database is configured to store the multi-dimensional monitoring data; The visualization interaction unit is communicatively connected to the storage database and the data processing unit and is configured to: Receive a user instruction; Parse the user instruction based on a preset large language model to generate at least one parsed instruction, the parsed instruction being used to query one or more items of the monitoring data; In response to the user instruction, the monitoring data corresponding to the parsed instruction is visually displayed on a screen.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by a processor, implements the steps of the bird target monitoring method for ecological observation according to any one of claims 1-7. The computer program, when executed by a processor, implements the steps of the bird target monitoring method for ecological observation according to any one of claims 1-7.

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