Bird image label automatic generation method for outdoor bird feeder based on AI decision and bird feeder

Abstract

The application discloses an outdoor bird feeder bird image label automatic generation method based on AI decision and a bird feeder, relates to the technical field of image processing, and comprises the following steps: collecting bird images; performing multi-parameter AI fusion analysis on the bird images to generate a standardized analysis data set; calling a label generation rule and a text style requirement matched with a target sharing platform from a preset platform label rule library; generating hierarchical labels based on the standardized analysis data set and the label generation rule; and outputting the generated hierarchical labels and the description text to a user interface. The application dynamically adapts to the differentiated needs of different platforms such as social platforms, science popularization platforms and short video platforms by combining a preset platform label rule library, automatically generates hierarchical labels and stylized description texts, and enables users to complete multi-platform synchronous sharing by only one key.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and in particular to an automatic bird image tag generation method and bird feeder based on AI decision-making for outdoor bird feeders. Background Technology

[0002] Existing outdoor bird feeders with camera functions suffer from fundamental technical flaws in image sharing. For example, after taking a picture of a bird, users must manually input tags, edit text, and adjust formatting, taking three to five minutes to share a single image, which is a heavy burden for frequent users. Furthermore, ordinary users lack professional ornithological knowledge, and manual labeling often results in errors in bird species identification, limited information dimensions, and non-standard tag formats, leading to low educational value of the images and making it difficult for platform recommendation algorithms to accurately match the target audience.

[0003] Furthermore, the tagging rules and content styles of different sharing platforms vary significantly, and existing devices cannot automatically adapt. Users need to manually edit for each platform, further increasing the operational burden. These shortcomings, combined, severely restrict the social attributes of devices and user engagement. Therefore, there is an urgent need for a solution that can automatically generate highly accurate tags and adapt to the needs of different platforms. Summary of the Invention

[0004] The main objective of this invention is to provide an AI-based method for automatically generating bird image tags for outdoor bird feeders and a bird feeder in general. This invention aims to solve the technical problems of existing outdoor bird feeders, which require users to manually label bird species, edit text, and adjust the format for different platforms after taking bird images. This process is cumbersome and time-consuming, and ordinary users often lack ornithological knowledge, resulting in high tag error rates and limited information dimensions.

[0005] To achieve the above objectives, this invention proposes an automatic bird image tag generation method based on AI decision-making for outdoor bird feeders, the method comprising the following steps: Collect bird images; Multi-parameter AI fusion analysis of bird images is performed to generate a standardized analytical dataset; Based on the target sharing platform selected by the user, retrieve the tag generation rules and text style requirements that match the target sharing platform from the preset platform tag rule library; Based on the standardized parsing dataset and tag generation rules, hierarchical tags are generated; and based on the standardized parsing dataset and text style requirements, descriptive text adapted to the style of the target sharing platform is generated. The generated layered labels and descriptive text are output to the user interface, allowing users to trigger a one-click sharing command to simultaneously send bird images, layered labels, and descriptive text to the target sharing platform, or trigger a manual modification command to modify the layered labels and / or descriptive text.

[0006] Furthermore, the methods also include: Synchronously record metadata associated with bird images and store it locally on the device or in the cloud; the metadata includes the shooting time, shooting location, device number, and environmental data collected by environmental sensors.

[0007] Furthermore, multi-parameter AI fusion analysis is performed on bird images to generate a standardized analytical dataset, specifically including: Image quality analysis is performed on bird images to assess image sharpness, brightness, and compositional dimensions, and a standardized image quality level is output. Using deep learning models, bird species identification and attribute extraction are performed on bird images, outputting at least the bird species category, body size, feather color, and behavioral status. The results are then linked to a cloud database to obtain professional science information, forming the bird species identification results. By combining the visual features and metadata of bird images, a comprehensive assessment of the season and environmental conditions at the time of shooting is conducted to form a seasonal environmental assessment result. The image quality level, bird species identification results, and seasonal environmental assessment results are integrated to generate a standardized analytical dataset.

[0008] Furthermore, the preset platform tag rule base includes: For different sharing platforms, the rules should at least define the maximum number of tags, the priority of core tags, the types of auxiliary tags, and the text style requirements; Among them, the target sharing platforms include at least social platforms, science popularization platforms, and short video platforms.

[0009] Furthermore, based on standardized parsing datasets and label generation rules, hierarchical labels are automatically generated, specifically including: The tags to be generated are divided into a core tag layer and an auxiliary tag layer; In the core label layer, bird species and shooting season information are extracted from the standardized parsed dataset and determined as mandatory labels; In the auxiliary label layer, the number of optional labels generated based on the label generation rules and other features in the standardized parsed dataset does not exceed the upper limit defined by the label generation rules.

[0010] Furthermore, when determining that the target sharing platform is a science popularization platform, the core tag automatically adds the Latin name of the bird in the bird species identification result.

[0011] Furthermore, the auxiliary labels include at least one of the following: image quality labels, environment labels, and behavior labels extracted from the standardized parsed dataset, as well as platform-specific labels added based on the characteristics of the target sharing platform.

[0012] Furthermore, based on standardized parsing datasets and text style requirements, descriptive text adapted to the style of the target sharing platform is automatically generated, specifically including: Based on the type of the target sharing platform, determine the corresponding text generation template and style parameter set, fill the template with the feature parameters from the standardized parsed dataset, and generate descriptive text; where: When the target sharing platform is a social platform, the first style parameter set is invoked. The first style parameter set is configured as follows: adopting a first-person narrative perspective, including words from a preset emotional vocabulary library, and the text length does not exceed a first threshold. In response to the target sharing platform being a science popularization platform, the second style parameter set is invoked. The second style parameter set is configured as follows: using objective declarative sentences, including at least the Latin name, protection level and shooting timestamp of the bird species identification results, and the text length does not exceed the second threshold, which is greater than the first threshold. In response to the target sharing platform being a short video platform, the third style parameter set is invoked. The third style parameter set is configured as follows: interactive guidance sentences in the form of questions or exclamations, including preset popular topic tags, and the text length does not exceed the third threshold.

[0013] Furthermore, the methods also include: According to the preset period, the user modification content recorded within the preset period is used as incremental training data to retrain or adjust the parameters of the AI ​​generation model that integrates and analyzes multiple parameters, so that the hierarchical labels and descriptive text subsequently generated by the AI ​​generation model are closer to the user's personal expression habits.

[0014] The present invention also proposes a bird feeder, comprising: Cameras are used to capture images of birds; Sensor components are used to synchronously record metadata associated with bird images; The processor is configured to perform the following operations: perform multi-parameter AI fusion analysis on bird images to generate a standardized analysis dataset; retrieve, based on the target sharing platform selected by the user, label generation rules and text style requirements matching the target sharing platform from a preset platform label rule library; automatically generate hierarchical labels using an AI generation model based on the standardized analysis dataset and label generation rules; automatically generate descriptive text adapted to the style of the target sharing platform based on the standardized analysis dataset and text style requirements; and record the user's modifications to the hierarchical labels and / or descriptive text, and input the modifications into the AI ​​generation model to iteratively optimize the AI ​​generation model. The human-computer interaction interface is used to present the generated hierarchical labels and descriptive text to the user, and to receive one-click sharing instructions or manual modification instructions from the user; wherein, in response to receiving a one-click sharing instruction, the processor synchronously sends the bird image, hierarchical labels and descriptive text to the target sharing platform; in response to receiving a manual modification instruction, the human-computer interaction interface receives the user's modification of the hierarchical labels and / or descriptive text.

[0015] This invention automatically identifies bird species, image quality, and seasonal environmental information in bird images through multi-parameter AI fusion analysis. Combined with a preset platform tag rule library, it dynamically adapts to the differentiated needs of different platforms such as social media, science popularization, and short videos, automatically generating layered tags and stylized descriptive text. Users can complete multi-platform synchronous sharing with just one click. At the same time, the AI ​​generation model is iteratively optimized based on user manual modification behavior, so that the generated content continuously approaches personal expression habits, greatly reducing the time required to share a single image. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the automatic generation method for bird image tags of outdoor bird feeders based on AI decision-making according to the present invention. Figure 2 This is a schematic diagram illustrating the multi-dimensional analysis of the original collected images by the AI ​​model of the AI-based decision-making method for automatically generating bird image tags for outdoor bird feeders according to the present invention. Figure 3 This is a schematic diagram of the bird feeder of the present invention; The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of the present invention and are not intended to limit the present invention.

[0020] To better understand the technical solution of the present invention, a detailed description will be provided below in conjunction with the accompanying drawings and specific embodiments.

[0021] like Figure 1 As shown, Figure 1 This is a flowchart illustrating the automatic generation method for bird image tags of outdoor bird feeders based on AI decision-making, as described in this invention.

[0022] A method for automatically generating bird image tags for outdoor bird feeders based on AI decision-making, the method includes the following steps: S10, capturing bird images; S20 performs multi-parameter AI fusion analysis on bird images to generate a standardized analytical dataset; S30: Based on the target sharing platform selected by the user, retrieve the tag generation rules and text style requirements that match the target sharing platform from the preset platform tag rule library; S40 generates hierarchical tags based on the standardized parsing dataset and tag generation rules; and generates descriptive text that adapts to the style of the target sharing platform based on the standardized parsing dataset and text style requirements. S50 outputs the generated layered labels and descriptive text to the user interface, allowing the user to trigger a one-click sharing command to simultaneously send the bird image, layered labels, and descriptive text to the target sharing platform, or trigger a manual modification command to modify the layered labels and / or descriptive text.

[0023] This invention proposes an AI-based method for automatically generating bird image tags for outdoor bird feeders. This method constructs a fully automated processing closed loop from image acquisition to multi-platform sharing through multi-parameter AI fusion analysis, intelligent platform adaptation, hierarchical content generation, and user feedback iteration. It completely solves the core pain points of traditional manual labeling, such as cumbersome operation, low label accuracy, and inability to adapt to the differentiated needs of multiple platforms.

[0024] S10, capturing bird images; In this embodiment, the built-in camera of the bird feeder continuously monitors the preset feeding area. When the AI ​​visual detection module recognizes a bird's arrival, it automatically triggers the high-definition camera to take pictures, avoiding false images of invalid events such as wind rustling through grass. It also supports continuous shooting mode, capturing 3 to 5 images per second to ensure clear capture of the bird's posture and key identification features. This automatic triggering shooting mechanism directly brings the following technical benefits: firstly, users can obtain high-quality bird images without manually operating the device, significantly lowering the barrier to entry; secondly, the continuous shooting mode ensures that clear, identifiable images are obtained even in scenarios where birds are moving rapidly, providing data quality assurance for the accuracy of bird species identification in subsequent steps.

[0025] Furthermore, while acquiring bird images, this method also includes simultaneously recording metadata associated with the bird images and storing it locally on the device or in the cloud. The metadata includes the capture time, accurate to the second; the capture location, obtained via GPS or base station positioning; the device ID, used to distinguish different bird feeder devices; and environmental data collected by environmental sensors, specifically temperature and humidity values ​​from temperature and humidity sensors, and light intensity from light sensors. This metadata is stored bound to the image data and automatically synchronized to the cloud server when network conditions are good, ensuring data security without occupying storage space on the user's mobile phone or other terminals. The synchronized recording of metadata provides crucial data support for subsequent seasonal environmental analysis, enabling the system to integrate image visual features with actual environmental data collected by sensors for cross-validation, thereby significantly improving the accuracy of seasonal and environmental determinations—a technical effect that cannot be achieved by relying solely on image visual analysis.

[0026] S20 performs multi-parameter AI fusion analysis on bird images to generate a standardized analytical dataset; like Figure 2 As shown, Figure 2 This is a schematic diagram illustrating the multi-dimensional analysis of the original collected images by the AI ​​model of the AI-based decision-making method for automatically generating bird image tags for outdoor bird feeders according to the present invention. refer to Figure 2 In this embodiment, after bird images are acquired, multi-parameter AI fusion analysis is performed on the bird images to generate a standardized analytical dataset. This step is the core foundation for achieving high-accuracy label generation in this invention. Its specific implementation involves the collaborative work of three sub-modules. The first sub-module performs image quality analysis on the bird images to assess image sharpness, specifically by calculating edge sharpness and noise levels; assess brightness, specifically by analyzing exposure and contrast; and assess composition dimensions, including calculating the area ratio of the bird subject in the image, determining whether the subject is centered, and detecting whether the subject is obscured by branches or bird feeder structures. Based on the above assessment results, a standardized image quality level is output, classifying the images into three levels: excellent, good, and poor. This quality level not only serves as a basis for auxiliary label generation but also provides sharing suggestions for users, preventing them from sharing blurry or overexposed low-quality images, thereby improving the overall quality of shared content.

[0027] The second submodule utilizes a deep learning model for bird species identification and attribute extraction from bird images. The deep learning model employs a convolutional neural network-based target detection and classification architecture, trained on image samples of over 200 common wild bird species in China, achieving an accuracy rate of nearly 100%. Furthermore, the model not only outputs the bird species category but also simultaneously identifies multiple different birds in the image and counts the number of each species. It further extracts bird size characteristics, classifying them into small, medium, and large types; feather color, described using preset color labels; and behavioral states, including typical behaviors such as feeding, perching, and flying. The identification results are automatically linked to a cloud-based bird database to obtain professional scientific information such as the bird's Latin name, protection level, and habits, forming the bird species identification result. This design solves the problem of low accuracy and limited information dimensions in manual labeling caused by ordinary users' lack of professional ornithological knowledge. It expands the label information dimensions from only one bird species name when manually labeled to more than five dimensions, including bird species, number, size, feather color, behavioral state, Latin name, and protection level, while ensuring the professionalism and accuracy of the information.

[0028] The third submodule combines visual features and metadata from bird images to comprehensively assess the season and environmental conditions at the time of shooting, forming a seasonal environmental assessment result. Specifically, the season determination integrates two data sources: first, the month information corresponding to the shooting time extracted from metadata; and second, visual features extracted from the image background, including vegetation color, the density or withering of leaves, and whether there is snow cover on the ground. Cross-validating the month information with visual features can accurately determine whether the shooting season is spring, summer, autumn, or winter, and can further identify special periods such as the breeding season and migration season. The environmental assessment also integrates two data sources: first, visual features extracted from the image, such as sky color, ground wetness, and the presence of visible raindrops or snowflakes; and second, environmental sensor data extracted from metadata, including temperature, humidity, and light intensity. Integrating and analyzing these two data sources can accurately determine whether the outdoor environmental condition is sunny, cloudy, rainy, or snowy, and whether the vegetation condition is lush or withered. This multi-source data fusion approach significantly improves the robustness and accuracy of seasonal and environmental determination in complex outdoor scenarios compared to single methods that rely solely on image analysis or sensor data, providing a reliable basis for generating accurate seasonal and environmental labels.

[0029] Furthermore, the outputs of the three sub-modules are integrated: image quality level, bird species identification results, and seasonal environmental assessment results are combined to generate a standardized analytical dataset. This dataset serves as the unified input for all subsequent label generation and text generation steps, ensuring consistency of data sources and standardization of the processing flow.

[0030] S30: Based on the target sharing platform selected by the user, retrieve the tag generation rules and text style requirements that match the target sharing platform from the preset platform tag rule library; After generating a standardized parsed dataset, the system retrieves tag generation rules and text style requirements matching the target sharing platform from a pre-defined platform tag rule library, based on the user's selected target sharing platform. The pre-defined platform tag rule library is a set of rules pre-established for different sharing platforms. Each rule defines at least the maximum number of tags, the priority of core tags, auxiliary tag types, and text style requirements. Target sharing platforms include at least three categories: social media platforms, science popularization platforms, and short video platforms. Specifically, for social media platforms such as WeChat Moments and Xiaohongshu, the corresponding rule configuration is: a preference for 3 to 5 concise, relatable, and emotionally resonant tags, with a short and lively text style; for science popularization platforms such as Zhihu, Guokr, and the China Bird Observation Network, the corresponding rule configuration is: a preference for 5 to 8 professional, accurate, and structured tags, which must include information such as the Latin name of the bird species and its protection level, with a rigorous and objective text style; for short video platforms such as Douyin and Kuaishou, the corresponding rule configuration is: a preference for 5 to 10 popular, traffic-driven tags, which must include industry-standard topic tags such as "daily outdoor bird feeding" and "real footage of wild birds," with a concise, attractive text style that encourages interaction. The platform's tag rule library supports online updates in the cloud, allowing it to adapt to the latest algorithm rules and trending topic changes across various platforms in a timely manner. This platform adaptation assessment step technically solves the pain point of users having to manually adjust tags and text one by one due to a lack of understanding of the differentiated requirements of each platform. This enables a single content generation system to intelligently adapt to multiple platforms, significantly improving sharing efficiency.

[0031] S40 generates hierarchical tags based on the standardized parsing dataset and tag generation rules; and generates descriptive text that adapts to the style of the target sharing platform based on the standardized parsing dataset and text style requirements. Specifically, after completing the platform compatibility assessment, content generation begins. Through standardized parsing of the dataset and tag generation rules, hierarchical tags are automatically generated. The process involves dividing the tags to be generated into two levels: a core tag layer and an auxiliary tag layer. In the core tag layer, bird species and shooting season information are extracted from the standardized parsing dataset and identified as mandatory tags. Core tags have the highest priority and must be generated for all platforms, forming the basic framework of the tag system. In the auxiliary tag layer, optional tags are dynamically generated based on the tag generation rules and other features in the standardized parsing dataset, with the number not exceeding the upper limit defined by the tag generation rules. Auxiliary tags are used to supplement multi-dimensional information or meet platform-specific requirements.

[0032] Building upon this foundation, when determining that the target sharing platform is a science popularization platform, the core tag automatically adds the Latin name of the bird from the bird species identification result to meet the mandatory requirements of science popularization platforms for professional information. This adaptive adjustment ensures that the same set of hierarchical tag generation logic can cover different information depth needs from general social media to professional science popularization. Auxiliary tags include image quality tags extracted from standardized parsed datasets, such as "HD" and "real shot"; environmental tags, such as "sunny day," "outdoor," and "snow scene"; behavioral tags, such as at least one of "feeding" and "flock"; and platform-specific tags added based on the characteristics of the target sharing platform, such as "healing" and "daily" for social media platforms, and "hot" and "recommended" for short video platforms. The generated tags are sorted and optimized according to the rule of core tags first, auxiliary tags second, and popular tags first, ensuring that the final tag order conforms to the reading habits of users on the target platform and the algorithm recommendation logic. The hierarchical tag generation mechanism, while ensuring information integrity, achieves precise matching of the number of tags and content preferences of different platforms, avoiding both information loss due to too few tags and obscuring the key points due to too many tags.

[0033] In this embodiment, descriptive text adapted to the style of the target sharing platform is generated synchronously with the hierarchical tags. Based on the standardized parsing dataset and text style requirements, the specific process of automatically generating the descriptive text is as follows: According to the type of the target sharing platform, the corresponding text generation template and style parameter set are determined; the feature parameters from the standardized parsing dataset are filled into the template to generate the descriptive text. Different style parameter sets are configured for different platforms. When the target sharing platform is a social platform, the first style parameter set is invoked. The first style parameter set is configured to: adopt a first-person narrative perspective, such as "My bird feeder has welcomed another little guest"; include words from a preset emotional vocabulary library, such as "healing," "cute," and "punctual"; and have a text length not exceeding a first threshold, which is set to 50 characters, to suit the lightweight reading characteristics of social platforms. Responding to the target sharing platform being a science popularization platform, a second style parameter set is invoked. This second style parameter set is configured to: use objective declarative sentences, begin with time markers such as "shot on," and include at least the Latin name, protection level, and shooting timestamp from the bird identification results; the text length should not exceed a second threshold of 200 characters. This second threshold is greater than the first threshold to accommodate more professional information. Responding to the target sharing platform being a short video platform, a third style parameter set is invoked. This third style parameter set is configured to: use interactive guiding sentences in the form of questions or exclamations, such as "This is so cute!" or "Does anyone know what kind of bird this is?", include preset popular hashtags such as "#outdoorbirdfeedingdays" and "#wildbirdsrealshots," and the text length should not exceed a third threshold of 100 characters to match the fast-paced, highly interactive characteristics of short video platforms. This stylized text generation mechanism automates the descriptive text writing process that previously required users to conceive and edit each piece of text, and the generated text style accurately matches the context and habits of the target platform, making the shared content more widely disseminated.

[0034] S50 outputs the generated layered labels and descriptive text to the user interface, allowing the user to trigger a one-click sharing command to simultaneously send the bird image, layered labels, and descriptive text to the target sharing platform, or trigger a manual modification command to modify the layered labels and / or descriptive text.

[0035] Once the content is generated, the generated layered tags and descriptive text are output to the user interface. This allows users to trigger a one-click sharing command to simultaneously send the bird image, layered tags, and descriptive text to the target sharing platform, or trigger a manual editing command to modify the layered tags and / or descriptive text. This design achieves extremely convenient sharing while fully respecting users' personalized expression needs. Users do not need to edit from scratch; sharing can be completed with a single click. The time from capturing to sharing a single image is reduced from 3 to 5 minutes, traditionally required for manual annotation, to less than 10 seconds. At the same time, for users who wish to modify the content, the system provides a flexible editing entry point, without locking the generated result, maintaining user control.

[0036] Building upon this foundation, the method also incorporates a closed-loop iterative optimization mechanism. Specifically, according to a preset period, user modifications recorded within that period are used as incremental training data to retrain or adjust the parameters of the AI ​​generation model in the multi-parameter AI fusion analysis. This ensures that the hierarchical tags and descriptive text subsequently generated by the AI ​​generation model closely resemble the user's personal expression habits. The preset period can be set to 7 days. Within each period, user behavior data, such as adding, deleting, and modifying tags, editing text content, and adjusting formatting, are collected and used for model fine-tuning after anonymization. As usage time increases, the AI ​​generation model will increasingly understand the specific user's expression preferences. For example, some users prefer more emotional tags, some prefer more concise text, and some are accustomed to specific topic tags. The system can automatically adapt to all of these, achieving personalized content generation. This iterative optimization mechanism transforms repeated manual modifications by users from "repetitive labor" into "training data," making the system's intelligence positively correlated with user usage time, forming a virtuous cycle where users become more comfortable using the system and the system becomes smarter with use.

[0037] like Figure 3 As shown, Figure 3 This is a schematic diagram of the bird feeder of the present invention. refer to Figure 3 The present invention also proposes a bird feeder, which is a hardware carrier for performing the above-described method. The bird feeder includes a camera 10 for capturing images of birds. The camera is mounted on the main body of the bird feeder, facing the feeding area, supports high-definition shooting and continuous shooting modes, and can be automatically triggered when birds visit, ensuring that clear images are captured without disturbing the birds.

[0038] The bird feeder also includes a sensor assembly 20 for synchronously recording metadata associated with bird images. The sensor assembly includes a temperature and humidity sensor, a light intensity sensor, and a GPS or base station positioning module integrated into the motherboard. Each sensor collects environmental data at the corresponding moment when the camera triggers the shooting, and then binds and stores the data with the image file after aligning the data with the image file with the timestamp.

[0039] The bird feeder also includes a processor 30, which is configured to perform the following operations: perform multi-parameter AI fusion analysis on bird images to generate a standardized analysis dataset; retrieve tag generation rules and text style requirements matching the target sharing platform from a preset platform tag rule library based on the user-selected target sharing platform; automatically generate hierarchical tags using an AI generation model based on the standardized analysis dataset and tag generation rules; automatically generate descriptive text adapted to the style of the target sharing platform based on the standardized analysis dataset and text style requirements; and record user modifications to the hierarchical tags and / or descriptive text, and input the modifications into the AI ​​generation model for iterative optimization. The processor is the core computing unit of the bird feeder and can be an embedded neural network processor or a general-purpose application processor, running a deep learning inference model and a content generation model, responsible for all computational tasks from image analysis to content generation.

[0040] The bird feeder also includes a human-computer interaction interface 40, which presents the generated layered tags and descriptive text to the user and receives user input for one-click sharing or manual modification. This interface is a graphical user interface within a mobile terminal application paired with the bird feeder. The interface displays a list of generated tags and a preview of the descriptive text, and includes "One-Click Share" and "Edit" buttons. Upon receiving a one-click share command, the processor simultaneously sends the bird image, layered tags, and descriptive text to the target sharing platform. Upon receiving a manual modification command, the interface receives user modifications to the layered tags and / or descriptive text; after modification, the user can confirm sharing or save. Thus, the bird feeder hardware and its accompanying software work together to fully implement all steps of the method of this invention, forming a complete technical closed loop from the image acquisition front-end to the content sharing back-end.

[0041] This invention automatically identifies bird species, image quality, and seasonal environmental information in bird images through multi-parameter AI fusion analysis. Combined with a preset platform tag rule library, it dynamically adapts to the differentiated needs of different platforms such as social media, science popularization, and short videos, automatically generating layered tags and stylized descriptive text. Users can complete multi-platform synchronous sharing with just one click. At the same time, the AI ​​generation model is iteratively optimized based on user manual modification behavior, so that the generated content continuously approaches personal expression habits, greatly reducing the time required to share a single image.

[0042] The above description is only a part of the embodiments of the present invention and does not limit the patent scope of the present invention. All equivalent structural transformations made under the technical concept of the present invention using the contents of the present invention specification and drawings, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A method for automatically generating bird image tags for outdoor bird feeders based on AI decision-making, characterized in that, The method includes the following steps: Collect bird images; The bird images are subjected to multi-parameter AI fusion analysis to generate a standardized analytical dataset; Based on the target sharing platform selected by the user, retrieve the tag generation rules and text style requirements that match the target sharing platform from the preset platform tag rule library; Based on the standardized parsing dataset and the tag generation rules, hierarchical tags are generated; and based on the standardized parsing dataset and the text style requirements, descriptive text adapted to the style of the target sharing platform is generated. The generated layered labels and descriptive text are output to the user interface so that the user can trigger a one-click sharing command to simultaneously send the bird image, the layered labels, and the descriptive text to the target sharing platform, or trigger a manual modification command to modify the layered labels and / or the descriptive text.

2. The method for automatically generating bird image tags for outdoor bird feeders based on AI decision-making according to claim 1, characterized in that, The method further includes: The metadata associated with the bird images is recorded synchronously and stored locally on the device or in the cloud; the metadata includes the shooting time, shooting location, device number, and environmental data collected by environmental sensors.

3. The method for automatically generating bird image tags for outdoor bird feeders based on AI decision-making according to claim 1, characterized in that, The process of performing multi-parameter AI fusion analysis on the bird images to generate a standardized analytical dataset specifically includes: The bird images are analyzed for image quality to assess their sharpness, brightness, and compositional dimensions, and a standardized image quality level is output. The bird images are used to identify bird species and extract attributes using a deep learning model, outputting at least the bird species category, body size, feather color, and behavioral status. The model is also linked to a cloud database to obtain professional science information, thus forming the bird species identification result. By combining the visual features of the bird images with the metadata, the season and environmental conditions at the time of shooting are comprehensively assessed to form the seasonal environmental assessment result; The image quality level, the bird species identification result, and the seasonal environment assessment result are integrated to generate the standardized analytical dataset.

4. The method for automatically generating bird image tags for outdoor bird feeders based on AI decision-making according to claim 1, characterized in that, The preset platform tag rule base includes: For different sharing platforms, the rules pre-established shall at least define the upper limit of the number of tags, the priority of core tags, the types of auxiliary tags, and the text style requirements; Among them, the target sharing platforms include at least social platforms, science popularization platforms, and short video platforms.

5. The method for automatically generating bird image tags for outdoor bird feeders based on AI decision-making according to claim 1, characterized in that, The automatic generation of hierarchical labels based on the standardized parsed dataset and the label generation rules specifically includes: The tags to be generated are divided into a core tag layer and an auxiliary tag layer; In the core label layer, bird species and shooting season information are extracted from the standardized parsed dataset and determined as mandatory labels; In the auxiliary label layer, the number of optional labels generated according to the label generation rules and other features in the standardized parsed dataset does not exceed the upper limit defined by the label generation rules.

6. The method for automatically generating bird image tags for outdoor bird feeders based on AI decision-making according to claim 5, characterized in that, When determining that the target sharing platform is a science popularization platform, the core tag also automatically adds the Latin name of the bird in the bird species identification result.

7. The method for automatically generating bird image tags for outdoor bird feeders based on AI decision-making according to claim 5, characterized in that, The auxiliary labels include at least one of image quality labels, environment labels, and behavior labels extracted from the standardized parsed dataset, as well as platform-specific labels added based on the characteristics of the target sharing platform.

8. The method for automatically generating bird image tags for outdoor bird feeders based on AI decision-making according to claim 1, characterized in that, The automatic generation of descriptive text adapted to the style of the target sharing platform, based on the standardized parsed dataset and the text style requirements, specifically includes: Based on the type of the target sharing platform, determine the corresponding text generation template and style parameter set, fill the template with the feature parameters from the standardized parsing dataset, and generate the descriptive text; wherein: When the target sharing platform is a social platform, the first style parameter set is invoked. The first style parameter set is configured as follows: adopting a first-person narrative perspective, including words from a preset emotional vocabulary library, and the text length does not exceed a first threshold. In response to the target sharing platform being a science popularization platform, the second style parameter set is invoked. The second style parameter set is configured as follows: using objective declarative sentence structure, including at least the Latin name, protection level and shooting timestamp of the bird species identification result, and the text length does not exceed the second threshold, which is greater than the first threshold. In response to the target sharing platform being a short video platform, a third style parameter set is invoked. The third style parameter set is configured as follows: interactive guidance sentences in the form of questions or exclamations, including preset popular topic tags, and the text length does not exceed a third threshold.

9. The method for automatically generating bird image tags for outdoor bird feeders based on AI decision-making according to claim 1, characterized in that, The method further includes: According to a preset period, the user modification content recorded within the preset period is used as incremental training data to retrain or adjust the parameters of the AI ​​generation model that performs multi-parameter AI fusion analysis, so that the hierarchical labels and descriptive text subsequently generated by the AI ​​generation model are closer to the user's personal expression habits.

10. A bird feeder, characterized in that, include: Cameras are used to capture images of birds; Sensor components for synchronously recording metadata associated with the bird images; The processor is configured to perform the following operations: perform multi-parameter AI fusion analysis on the bird images to generate a standardized analysis dataset; retrieve, from a preset platform tag rule library, tag generation rules and text style requirements matching the target sharing platform according to the target sharing platform selected by the user; automatically generate hierarchical tags based on the standardized analysis dataset and the tag generation rules using an AI generation model; and automatically generate descriptive text adapted to the style of the target sharing platform based on the standardized analysis dataset and the text style requirements. In addition, the system records the user's modifications to the hierarchical tags and / or the descriptive text, and inputs the modifications into the AI ​​generation model to iteratively optimize the AI ​​generation model. A human-computer interaction interface is used to present the generated hierarchical labels and descriptive text to the user, and to receive a one-click sharing command or a manual modification command input by the user; wherein, in response to receiving the one-click sharing command, the processor synchronously sends the bird image, the hierarchical labels and the descriptive text to the target sharing platform; in response to receiving the manual modification command, the human-computer interaction interface receives the user's modification of the hierarchical labels and / or the descriptive text.

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